Intelligent Decision Support System for IoT-Enabling Technologies. 9781685078652, 9781685079437, 9781685077860, 9798886970746, 9798891130159, 9798891130562

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Internet of Things and Machine Learning Engineering and Sustainable Development

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Internet of Things and Machine Learning Internet of Everything: Smart Sensing Technologies T. Kavitha (Editor) V. Ajantha Devi (Editor) S. Neelavathy Pari (Editor) Sakkaravarthi Ramanathan (Editor) 2022. ISBN: 978-1-68507-865-2 (Hardcover) 2022. ISBN: 978-1-68507-943-7 (eBook) Artificial Intelligence and Digital Diversity Inclusiveness in Corporate Restructuring Ramamurthy Venkatesh, PhD (Editor) Richa Goel, PhD (Editor) S. K. Baral, PhD (Editor) 2022. ISBN: 978-1-68507-786-0 (Hardcover) 2022. ISBN: 979-8-88697-074-6 (eBook)

More information about this series can be found at https://novapublishers.com/product-category/series/internet-of-things-andmachine-learning/

Engineering and Sustainable Development Modeling for Sustainable Development: A Multidisciplinary Approach Rajendra Kumar, PhD (Editor) R. C. Singh, PhD (Editor) Rohit Khokher, PhD (Editor) Vishal Jain, PhD (Editor) 2022. ISBN: 979-8-89113-015-9 (Softcover) 2021. ISBN: 979-8-89113-056-2 (eBook) More information about this series can be found at https://novapublishers.com/product-category/series/engineering-and-sustainabledevelopment/

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Subrata Sahana Anil Kumar Sagar Sanjoy Das and Vishal Jain Editors

Intelligent Decision Support System for IoT Enabling Technologies Opportunities, Challenges and Applications

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Copyright © 2024 by Nova Science Publishers, Inc. All rights reserved. No part of this book may be reproduced, stored in a retrieval system or transmitted in any form or by any means: electronic, electrostatic, magnetic, tape, mechanical photocopying, recording or otherwise without the written permission of the Publisher. We have partnered with Copyright Clearance Center to make it easy for you to obtain permissions to reuse content from this publication. Please visit copyright.com and search by Title, ISBN, or ISSN. For further questions about using the service on copyright.com, please contact:

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NOTICE TO THE READER The Publisher has taken reasonable care in the preparation of this book but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of information contained in this book. The Publisher shall not be liable for any special, consequential, or exemplary damages resulting, in whole or in part, from the readers’ use of, or reliance upon, this material. Any parts of this book based on government reports are so indicated and copyright is claimed for those parts to the extent applicable to compilations of such works. Independent verification should be sought for any data, advice or recommendations contained in this book. In addition, no responsibility is assumed by the Publisher for any injury and/or damage to persons or property arising from any methods, products, instructions, ideas or otherwise contained in this publication. This publication is designed to provide accurate and authoritative information with regards to the subject matter covered herein. It is sold with the clear understanding that the Publisher is not engaged in rendering legal or any other professional services. If legal or any other expert assistance is required, the services of a competent person should be sought. FROM A DECLARATION OF PARTICIPANTS JOINTLY ADOPTED BY A COMMITTEE OF THE AMERICAN BAR ASSOCIATION AND A COMMITTEE OF PUBLISHERS.

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Contents

Preface ………………………………………………….………………… vii Chapter 1

IoT-Based Smart Agriculture: Enhancing Agricultural Practices with Robotics and Cloud Computing Applications …… 1 N. A. Prashanth and Madhu Palati

Chapter 2

The Impact of IoT on the Environment: A Descriptive Study in India ………..………………… 35 Archan Mitra and Sayani Das

Chapter 3

A Smart Internet of Things (IoT) Enabled Agricultural Farming System ………………………… 59 Justin Joy, V. L. Helen Josephine, A. Angel, B. Abisheakkumar, Monica Seles E.S. and Rajan John

Chapter 4

IoT and Machine Learning Applications for Industrial Reliability Frameworks ……………..… 73 Suneel Kumar Rath, Madhusmita Sahu and Shom Prasad Das

Chapter 5

An IoT-Enabled Model for COVID-19 Patient Healthcare …………….………………………. 95 Sandeep Mathur, Rajbala Simon Vinayak Vashistha and Yazdani Hasan

Chapter 6

A Healthcare Revolution in Cross Domain Applications Using Advanced Computational Techniques ………………………….. 123 Ajay Sudhir Bale, N. Vinay, Asma Zabi, E. Eshwar and Suhaas Veera Raghavan Reddy

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Contents

Chapter 7

Predictive Analytics and Deep Learning Models for the Prediction of the Length of Stay, Diabetes, Colorectal Cancer and Cardiovascular Diseases in Patients …………………………………… 151 Arshpreet Kaur and Jagdeep Kaur

Chapter 8

Wireless Sensor Network Based Crop-Growth Monitoring Using Derived-Parameters in an Intelligent Greenhouse …………….………...… 183 Suman Lata and H. K. Verma

Chapter 9

Machine Learning-Based Google App Store Cataloging Using a Naive Bayes Algorithm ….…….. 211 Jyothi Chinna Babu, Nuka Mallikharjuna Rao, Potala Venkata Subbaiah, Khalaf Osamah Ibrahim and Ghaida Muttashar Abdulsahib

Chapter 10

Barriers to Digital Transformation: A Case Study of the Insurance Industry …….…….... 237 Vijay Anant Athavale and Himanshu Jain

Chapter 11

An IoT-Based Intelligent Healthcare System for Diabetes Prediction ………………………..…..…. 275 Navneet Verma and Sumit Kumar Rana

Chapter 12

Technological Scrutiny on Energy-Harvested Wireless Sensors for IoMT Healthcare Systems …… 299 Bhanu Priyanka Valluri and Nitin Sharma

About the Editors ………………………………………………………. 329 Index

…………………………………………………………. 331

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Preface

This book focuses on the uses of the Internet of Things (IoT) and Decision Support Systems. The major areas covered in this book are IoT challenges and opportunities, IoT Enabling Technologies, Decision Support Systems, Smart Applications, and Intelligent Systems to help in addressing various societal and economic issues. This book includes various specific issues related to the healthcare, insurance, and agricultural sectors. Intelligent and IoT-based applications like Healthcare Systems, Intelligent Transportation Systems, Business intelligence, and Artificial Intelligence in Sustainable Agriculture are extensively discussed. IoT and Machine Learning applications for the Industrial sector are also discussed. This book is most suitable for data scientists, doctors, engineers, economists, and specialists in the agricultural sector. This book will benefit the UG/PG and research scholars to find interesting technically sound facts on various domains like agriculture, insurance, environment and healthcare sectors with applications of IoT, machine learning, artificial intelligence, etc.

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Chapter 1

IoT-Based Smart Agriculture: Enhancing Agricultural Practices with Robotics and Cloud Computing Applications N. A. Prashanth1, and Madhu Palati Department of Electrical and Electronics Engineering, BMS Institute of Technology and Management, Bengaluru, India

Abstract People may have a different perception towards agricultural process, but the fact is that modern agriculture is precise, smarter, and more datacentered than ever. The “Internet of Things (IoT)” is emerging constantly to redesign virtually every industry and moved agriculture to quantitative approaches from statistical. These revolutionary changes are revolutionizing the current agricultural processes and making new opportunities and challenges. This study focuses on the potential of IoT and wireless technologies in agriculture, along with challenges that may come out when this technology will be integrated with traditional methods of farming. This study analyzes network and IoT devices in detail for agricultural applications. It will explain the way technology is helping farmers in all stages, i.e., from sowing to harvesting. In addition, using UAVs for crop monitoring, cloud computing, and other applications will be discussed. This study helps identifying future and existing trends in IoT in agriculture while focusing on research challenges.



Corresponding Author’s Email: [email protected]; [email protected].

In: Intelligent Decision Support System for IoT Enabling Technologies Editors: Subrata Sahana, Anil Kumar Sagar, Sanjoy Das et al. ISBN: 979-8-89113-249-8 © 2024 Nova Science Publishers, Inc.

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Keywords: Unmanned Aerial Vehicles, Robotics, Wireless Technologies, Agricultural Practices, Internet of Things

Introduction A lot of innovations have been made to improve the agricultural production with limited workforce and resources in the history of mankind. Nevertheless, the supply and demand have never matched due to rise in population. The global population has been forecasted to cross 9.8 billion mark, i.e., 25% growth as compared to the current figure (UN.org, 2019a). This rise in population will be majorly seen in developing nations (Our World in Data, 2019). Meanwhile, urbanization is the trend which is expected to continue rapidly, with over 70% of population in the world predicted to be moved into cities by 2050 (UN.org, 2019b). Food demand will grow further with the rise in income levels and purchasing power in developing economies.

Figure 1. Key technology drivers in agriculture.

Hence, these countries must be more precautious on their food quality and diet, as customer choices may shift from grains and wheat to legumes and

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meat. Food production must be doubled by 2050 to meet such urban, richer, and larger population (UN.org, 2019d; Zhang & Davidson, 2018). Especially, the existing 2.1 billion tons of cereal which are produced must be 3 billion tons and meat which is produced every year must rise up by 20 million tons to meet the increasing need of over 470 million tons. Figure 1 illustrates Key Technology Drivers in Agriculture.

Figure 2. Challenges in implementing technology in Agriculture.

Background Apart from food, crop production is also important as economy of several countries depends upon crops like rubber, gum, and cotton. In addition, bioenergy market which relies on food crops started growing constantly. Over 110 million tons of coarse grains were used to produce ethanol only (i.e., around 10% of global production). Because of increasing dependence on food crops for producing bio-energy, biofuel, and other industrial purposes, there are stakes on food security. These demands have led to a further rise in pressure on agricultural resources which are already limited. Sadly, just a small part of the land area is arable on earth because of various constraints like climate, temperature, soil quality, and topography and areas which are most suitable are not homogenous. A lot of new changes come

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out that can be hard to quantify when looking at the diversity of plants and landscapes. Economic and political factors further shaped the agricultural land like density of population, climate, and land, while constant urbanization is a serious threat to arable land. There has been a decline in total land available for food production over the past decades (Bruinsma, 2017). For food production, a total of 19.5 million square miles (39.47% of land area in the world) was available in 1991, which was declined to around 18.6 million square miles (37.73% of land area in the world) in the year 2013 (Zhang et al., 2018). There has been a significant gap between supply and demand of food and getting alarming over time. With further examination, it has been found that characteristics of every field vary and measured differently in both quantity and quality. Important characteristics like presence of vital nutrients, soil type, pest control, and irrigation flow define the capability and suitability for a particular crop. Characteristics differ in the majority of situations in an individual crop field, even though the whole farm cultivates the same crop. Hence, optimal production of yield needs site-specific analysis. Certain crops rotate every season in the same field and reach various stages of biological cycle in a year where temporal and geographical changes have specific growth needs to boost crop production. To respond to such demands with different issues, novel technology-oriented approaches are needed by farmers for production with limited hands and less land. Farmers should frequently visit farmlands over the crop lifespan for better insights into crop conditions as part of the typical farming process. Since 70% of time for farming is spent on understanding and monitoring of crop condition rather than field work, smart agriculture is the need of the hour (Navulur& Prasad, 2017). Given the vast agriculture sector, it significantly calls for precise and technology solutions to achieve sustainability while reducing carbon footprint. Modern communication technologies and sensors provide completely remote eye for farmers to monitor field status without actually going there. Wireless sensors constantly monitor the crops accurately and detect illnesses at early stages. This is why smart tools are used in modern agriculture for applications like crop harvesting, sowing, transportation and storage. Different sensors are used for timely reporting to make the whole operation cost-effective and smart with accurate monitoring facilities. Different robotic weeders, harvesters, autonomous tractors, satellites and drones are used for agriculture. It is easy to set up the sensors and start gathering data quickly. It will be available online to farmers to conduct further

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analyses. Sensors help in accurate data collection in every area when providing site- and crop-specific agriculture. IoT has just started making its presence felt in different industries and sectors, including healthcare, manufacturing, energy, communications, and agriculture to boost performance and minimize inefficiencies in all markets (Sisinni et al., 2018; Ayaz et al., 2017; Lin et al., 2017; Shi et al., 2019; Elijah et al., 2018). It goes without saying that existing applications are merely the beginning and actual impact is still not observed. Given the significant growth in agriculture in recent years, It is important to note that IoT will play an essential role in many agricultural applications. IoT provides immense capabilities, such as communication to connect smart sensors, vehicles, etc. to mobile devices online, and several services like cloud computing, remote or local data acquisition, agriculture automation, and user interfacing. These capabilities can make a revolution in the agriculture sector. Figure 1 illustrates key technology drivers and Figure 2 illustrates key challenges in implementation of technology in agriculture sector.

Applications in Agriculture Sector Latest IOT and sensor technologies have immense potential to fundamentally replace traditional farming practices, such as

Soil Sampling Soil is the belly of plants and soil sampling is probably the initial step for soil testing to gather field data, which is further analyzed for several important decisions at various levels. Soil testing is primarily used to analyze the nutritional status of a field to address nutrient deficiencies. Complete soil tests are needed every year, especially in Spring. It may be required in Winter or Fall as per weather and soil condition (Dinkins & Jones, 2013). Factors which are vital for soil nutrient quality are cropping history, application of fertilizer, soil type, topography and irrigation level. These factors give information about the physical, chemical and biological properties of soil to determine the constraints. Soil mapping gives opportunity to sow various types of crops in a field to match the properties of soil well, such as sowing time, seed suitability, and depth of planting as some of them are deeply rooted. In addition, agriculture can be used well to grow several crops together to make the most of resources.

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Irrigation Oceans hold over 97% of water on earth, i.e., in the form of salt water, and only the rest of 3% is held as fresh water. Over 2/3rd of freshwater is frozen as polar ice caps and glaciers (USGS, 2019). Only around 0.5% of freshwater which is not frozen is in the atmosphere or off the ground as the rest of the water is found underground (USBR, 2019). This 0.5% fresh water fulfills all the needs of humanity and manages ecosystem as enough freshwater should be sustained in lakes, rivers, and other water bodies. It goes without saying that over 70% of this fresh water is used solely by agriculture sector (FAO, 2018; Motoshita et al., 2018), which rises to 75% in a lot of countries like Brazil and 80% in some poor countries (de Oliveira et al., 2017). The monitoring process is the main cause of such demand as decision-making was very common with visual inspection in 2013 (Zhang et al., 2018; USDA, 2022). Desertification has affected over 168 countries in 2013 and around 50% of global population is expected to be living in areas where there is a lack of water supply by 2030, as estimated by the “UN Convention to Combat Desertification (UNCCD).” Given the water crises worldwide, there is a rise in demand in various sectors like agriculture and it should be available in places where it is required in specific amount. There has been a rise in awareness to save the water resources with more efficient irrigation technologies. A lot of methods for controlled irrigation like sprinkler and drip are widely used to deal with the problems of water shortage. These were also used in conventional approaches like furrow and flood irrigation. When it comes to shortage of water, both crop quantity and quality are affected at its worst as excess or poor irrigation affects soil quality and causes various microbial diseases. It is not easy to evaluate water demand of crops accurately, which involve different factors like irrigation, crop type, precipitation, soil type, soil moisture, and crop requirements. An accurate air and soil moisture control technique is needed with wireless sensors to improve crop health and save water. The fastest-growing IoT technologies will be adopted to change the existing situation of irrigation practices. The use of IoT technologies will improve crop efficiency greatly, for example, irrigation management based on “crop water stress index (CWSI)” (Zhang et al., 2018).

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Fertilizer Fertilizer is a chemical or natural element that supplies vital nutrients to the plants for fertility and growth. There are three vital macronutrients which are mainly needed in plants like “phosphorous (P)” for flowers, root, and fruit growth, “nitrogen (N)” for growth of leaves, “potassium (K)” for the supply of water and growth of stems (Dittmar et al., 2000). Modern IoT technologies can analyze the spatial patterns of nutrient needs with utmost precision and limited workforce (Lavanya et al., 2020). For example, satellite/aerial images are used for the surveillance of nutrition status of crops by the “Normalized Difference Vegetation Index (NDVI).” With smart agriculture, fertilization can accurately determine the needed dose of vital nutrients and reduce their adverse impact on the environment. Figure 3 illustrates key processes, inputs and outputs used in smart agriculture.

Figure 3. Some major processes and inputs and possible smart farming outputs.

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Disease and Pest Control The potato blight outbreak was caused by the loss of yield and crop failure due to the erstwhile Irish Potato Famine or the Great Famine, which caused the death toll of 1 million Irish residents in 1950s (Mokyr, 2022). The “southern corn leaf blight” is another disease which is still causing financial loss of over $1 billion to corn farmers in America (Bruns, 2017). Over 20% to 40% of crop yields across the world are lost every year to diseases and pests as estimated by the Food and Agriculture Organization (FAO, 2022). Hence, farming industry depends heavily on agrochemicals like pesticides and fertilizers to prevent the losses of production. Every year, over 0.5 million tons of agrochemicals are used every year in the US alone and 2 million tons across the world (Pohanish, 2014). Majority of pesticides used for farming are hazardous to health for both cattle and humans and leave permanent, the extreme effect on earth, while contaminating the whole environment (Carvalho, 2017; Waskom et al., 2017). Modern IoT devices like robots, drones, and wireless sensors are helping farmers to reduce the dependence on pesticides drastically by detecting pests and other harmful elements. In comparison to conventional prescription or calendar-based pest control measures, recent IoT devices provide real-time pest management, modeling, monitoring, and forecasting of diseases (Kim et al., 2018; Venkatesan et al., 2018).

Crop Yield Monitoring Crop yield monitoring analyzes several relevant aspects like amount of moisture, flow of grain mass, and amount of harvested grain. It records the yield of the crop and level of moisture accurately, the overall yield of the crop, and further steps. Yield monitoring is well regarded as an important step of farming during harvesting and before that. It is very important to track the yield quality as it relies on various factors like proper pollination with best pollen to predict yields of the seed during changing conditions of the environment (Wietzke et al., 2018; Gholami et al., 2014). Buyers globally have become more specific towards the quality of the fruit in open markets. So, proper production relies on the appropriate fruit size for the right market (Ayaz et al., 2017). Crop yield forecasting refers to the prediction of the production (i.e., in tons per hectare) and yield, before harvesting. This forecasting is very

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important for the growers to make right decisions and plan for the future. Another important factor is determining the maturity and quality of yield to pick the best harvest time. It consists of several stages of development and conditions like size, color, etc. Prediction of the right time to harvest improves production and quality of the crop and also gives time to handle the strategy. Efficient scheduling can make a great difference, even though harvesting is the final stage. To make the most of crops, growers should know the best time for harvesting such crops. Figure 4 illustrates a farm area network (FAN) mechanism that can give insights to the whole farm in real time for the farmer.

Figure 4. A FAN System powered by IoT.

Communication Technologies in Farming Cloud Computing There is a great potential of smart agriculture with the rise in agricultural practices thanks to better data-oriented decision-making. Smart agriculture needs better tools and technology for better data processing to keep up with this success at a reasonable cost, so that the data can be helpful to make field decisions with due diligence. To serve this purpose, cloud services can be used

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by farmers to access data for predictive analysis in a way that they can pick the right product provided as per their specific needs. Cloud computing enables farmers to use knowledge-based resources with treasure of data and experiences on farming practices and tools in the market with required details. All such things come with expert advice from different sources like processing and farming of agricultural yields. This scenario can be further extended to provide access to supply chains, databases, and billing systems. Figure 5 shows potential fluid computing infrastructure and relationship like Mist, Fog, and Edge computing for smart agriculture.

Figure 5. Infrastructure of cloud computing in agricultural IoT

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Smart Phones Cellular communication plays a very important role in rural areas as a key technology driver due to its concerns of availability. These days, smart phones are primary and very common source of communication whenever needed to update and contact farming community. Smartphone industry has seen great advancements with steep price declines, making it more effective, especially for small-scale farmers. Smartphone sensors which are widely used in agricultural activities are listed in Table 1. Table 1. Sensors used in several agricultural fields Sensors Camera

Applications For capturing any object

Microphone

To detect common/uncommon sound and conversion of the same into digital signals Location tracking (longitude and latitude)

GPS

Accelerometer

To calculate acceleration to track orientation and tilting of object

Gyroscope

To sense the angular velocity of an object twisting or rotating

Inertial sensor

Depends on Gyro and Accelerometer to track the altitude of an object in inertial system

Common uses Checking status of chlorophyll, detecting diseases, harvest preparedness, “Leaf Area Index (LAI),” fruit ripeness, soil erosion and other purposes. Bug detection, maintenance, etc. to make audio queries.

Sources (Chung et al., 2018; Parisi, 2018; Camacho &Arguello, 2018; Prosdocimi et al., 2017; Han et al., 2016; Moonrungsee et al., 2015) (Kou & Wu, 2018; Frommberger et al., 2013)

To generate alerts. Widely used for tracking and driving machines, crop mapping, and land management To ensure accurate camera rotation or movement and detect machine and worker activities Measuring canopy structure and device movement

(Wan et al., 2018; Stiglitz et al., 2017; Xie et al., 2016)

Accurate distance of leaf, plant, or any object using image sensor

(Debauche et al., 2019; Orlando et al., 2016; Camacho & Arguello, 2018) (Wan et al., 2018; Kou & Wu,2018; Andriamandroso et al., 2017) (Frommberger et al., 2013; Andriamandroso et al., 2017)

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It goes without saying that there are plenty of opportunities and advancements associated with cloud services, but there are some challenges too. First of all, plenty of sensors are being designed and used in smart agriculture, and each has its semantics and data format. In addition, a lot of decision-support solutions are based on application, while a farmer may need to use different systems for a particular application like soil mapping. Hence, cloud system needs to manage different data and formats and it is also important to set such formats for various applications.

Cellular From 2G to 4G, all modes of cellular network can be used as per the bandwidth demand and purpose. However, a major concern here is the availability and reliability of a mobile network in villages. To deal with this problem, satellite data transmission can be an alternative but this mode of communication is very expensive. So, it is not feasible for medium- and small-sized farmlands. Choosing the mode of communication is also based on the demand for applications. Sensors are needed in some farms which can work with low frequency but should work for the long term, which needs long battery backup. This way, the ideal solution for network coverage is Low Power Wide Area Network (LPWAN) for both larger connectivity at a low price and long battery life (Beecham Research, 2016). Pasture and crop management are some of the key applications which demand LPWAN and it can be suitable for various applications related to farming due to its success rate.

Zigbee It is especially built for different applications like as an alternative to current non-standard tools. As per the application needs, Router, Coordinator, and End User are the three devices working on this protocol. Cluster Tree, Start, and Mesh are the topologies that Zigbee supports (de Oliveira et al., 2017). On the basis of these features and future needs of agriculture, Zigbee can be helpful for greenhouse which calls for communications in short range. When it comes to track several parameters, Zigbee transfers the real-time data to end users from the sensor node. Zigbee can be used for fertilization and irrigation applications as its modules are linked to communicate, for example, monitoring moisture and other soil contents in drip irrigation. Later on, a

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notification is sent to the farmer regarding field data update where GSM communication is needed at long range or shorter distances are covered by Bluetooth version.

Bluetooth Bluetooth is a wireless standard that connects small-head devices for shorter range for close-range work. Since it doesn’t need much power, cost and learning curve, this technology is very successful in several smart farming purposes. In addition, this technology advances in several IoT systems with the introduction of Bluetooth Smart or Bluetooth Low Energy (BLE). Hong & Hsieh (2016) conducted a study on a programmable logic controller (PLC) and Bluetooth with timer control, integrated control strategy (ICS) and the soil moisture control method in order to perform smart irrigation. They were aimed to look for the best use of energy and water for several field or greenhouse purposes. Bjarnason (2017) also developed a BLE-based temperature and moisture sensor for weather and agriculture environments of fields. Intrinsic support for the availability of the smartphone is the main cause of referring to the BLE. In addition, Taşkın et al. (2018) chose a similar approach by designing a novel sensor node to track ambient temperature and lighting with BLE protocol designed for applications related to agriculture based on IoT. In order to meet the need for LAN communications, Wi-Fi is also used in smart agriculture in short range. Mendez et al. (2012) investigated a Wi-Fi system for remote monitoring where sensor relies on WSN802G model. Those sensor nodes were deployed to link with a central server wirelessly to store and gather the data which is monitored for further giving information after proper analysis.

UAVS and Robotics IOT has made significant growth in a lot of industries recently, such as farming industry like fishing, poultry, etc., but there are limited communication facilities like Wi-Fi or base stations in agriculture, which restrict the growth of IoT. The communication and related infrastructure are not good enough in rural areas and developing countries. It is one of the major challenges to introduce IoT in agriculture. Hence, UAVs or “Unmanned Aerial Vehicles” or simply “drones” are widely used in agriculture and they usually come in two

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types – “multi-rotor and fixed-wing drones” as illustrated in Figure 6. In terms of payload capacity and cost, both of these drones come in different ranges and they have various hardware changes. For instances, fixed-wing drones are suitable to cover a large field because they can fly in long ranges and they can also avoid crashes.

Figure 6. Types of drones in agriculture.

Along with UAVs, robotics had also seen great improvements in agricultural productivity and improved yields. Robots like weeding and spraying robots are widely used to reduce the use of agrochemical. These machines are camera and laser guided to detect and extract weed automatically. They move along the crops themselves and ultimately improve the yield with less human intervention. Fruit-picking and plant-transplanting robots are widely used to replace traditional approaches in agriculture.

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Crop Harvesting Robots To automate the process of harvesting and ensure its accuracy, robots have been playing a very important role over the past few decades. Given the robot services, a lot of intensive studies have been done to enhance the sensitivity of shape, color, size, localization, and shape of fruits (Zhao et al., 2016; Zujevs et al., 2015; Bargoti& Underwood, 2017; Bac et al., 2017). It takes intense research on automatic fruit harvesting with smart sensors which can gather explicit and precise details of a specific fruit and crop. It is not easy to detect the accurate target in natural scenario as a lot of fruits are sealed partly or completely under the branches and leaves or overlaid with other fruits (Feng et al., 2019). A lot of important studies are intensely based on image processing, ML and computer vision techniques considering for this purpose. Very sophisticated and specialized techniques are needed in this process to differentiate the conditions of fruit as there are plenty of colors, sizes, and shapes of a pepper alone for harvesting. Due to the complex nature of agriculture, a lot of robots are designed for particular crops. There are several prominent robots widely used for harvesting are Octinion (Peters, 2018) and SW 6010 (PEPPERL+FUCHS, 2022) for harvesting strawberries; FFRobot (FFRobotics) for apples and other fruits grown on trees; and SWEEPER bot (Hemming, 2018) for growing peppers. These robots are capable to collect more than 10,000 fruits in an hour.

IOT-Based Tractors As the resources related to rural labor are being focused because of the growth of the agriculture industry, tractors and various heavy machines started gaining prominence in this sector. A regular tractor can deliver 40 times faster performance with very less cost as compared to farm workers (Otufodunrin, 2018). To meet this constantly rising demand, agricultural equipment makers like Hello Tractors, John Deere, etc. started offering smart solutions to meet the needs of farmers. With technological advancements, a lot of manufacturers have made autonomous tractors and also Cloud services. This is not a new phenomenon, as autonomous tractors were already in the market, even in the absence of semi-automatic cars. The best part of self-driving tractors is that they can easily skip the place they visited earlier or cut down on the overlap even below an inch. Additionally, they don’t even need a driver to make very

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accurate turns. It provides better accuracy with less errors, especially when it comes to shred weeds or sprinkle pesticides. When a human operates the machinery, this level of accuracy is not possible. Currently, there is no completely automatic tractor available. A lot of manufacturers and researchers are working tirelessly to level up this technology. As per the future demands and existing process, around 700,000 tractors are predicted to be loaded with features like tractor guidance or autosteer by 2028 (Ghaffarzadeh, 2020), and around 40,000 level-5 fully autonomous, unmanned tractors are predicted to be sold in the market by 2038 (GIM International, 2018).

Wireless Sensors Nowadays collecting the data related to crop condition and elegant farming, wireless sensors are used. Wireless sensors are widely used individually when needed. They are also combined with heavy machinery and modern agricultural tools as per the application needs. Here are some of the major types of sensors as per their purpose, working process, and benefits provided by them.

FPGA Sensors These types of sensors are widely used in agriculture as they can be reconfigured easily. Measuring humidity, transpiration and irrigation in realtime are some of their applications where they can be useful (Millan-Almaraz et al., 2010; Husni et al., 2018). Their real-world utilization is still in nascent stage because of challenges like cost, size, and power demand. They need more electricity and not ideal for constant monitoring as they further increase the cost and compromise performance (De La Piedra et al., 2012). This way, “Field-Programmable Gate Array” sensors can provide the best results as per the specific application needs. Acoustic Sensors These sensors provide various applications in agriculture, such as weeding, soil cultivation, harvesting fruits, etc. The cost-effective solutions and quick response are the best part of this technology, especially when it comes to portability. It measures changes in noise as it comes in contact with soil particles (Kong et al., 2017). They are best suited for pest detection and control

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(Srivastava et al., 2013) and they categorize types of seeds as per their sound absorption range (Gasso-Tortajada et al., 2010).

Optical Sensors They utilize the spectacles of light reflection and measure organic matter, color, moisture, minerals, clay, and other elements of the soil (Murray, 2018; Povh et al., 2014). They test the potential of the soil to reflect light as per various ranges of the electromagnetic spectrum. In wave reflections, the changes which took place help in indicating the soil density changes and various parameters. For a basic assessment of the plant, fluorescent-based optical sensors are widely used to manage the maturation of fruits (Pajares et al., 2011). In addition, these sensors can classify olives, grove canopies and other crops when combined with a microwave sprinkling (Molina et al., 2011).

Ultrasonic Sensors These types of sensors are the best choice as they can operate in different applications, are cost-effective, easy to use, adjustable, and can work in different applications. Some of the common applications are measuring spray distance (like width control and boom height for object detection, uniform spray coverage, and avoiding collision), tracking crop canopy, and tank monitoring (Dvorak et al., 2016; Gómez Álvarez-Arenas et al., 2016). These sensors can be integrated with camera to detect weed (Pajares et al., 2013), especially where they can identify plant height and camera identifies crop and weed canopies.

Airflow Sensors These sensors can measure permeability of soil, air, moisture percent and soil structure to differentiate various kinds of soils. It is possible to make measurements at individual locations or can be used in mobile mode or fixed position. The pressure needed to push specified quantity of air at a specific depth into the ground to achieve the desired output. It goes with the process of different properties of soil like structure, compaction, and moisture for generating different signatures (García-Ramos et al., 2012).

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Optoelectronic Sensors They can classify different types of plants to detect herbicide, weed, and other wild plants in wider rows (Andújar et al., 2011). They can combine with location details to map the spread of weed and resolution (Andújar et al., 2009). They can also differentiate between soil and vegetation from reflection.

Electrochemical Sensors They are widely used to determine important soil quality to evaluate the pH level of soil (Yew et al., 2014). These sensors can easily replace the traditional procedure for chemical soil analysis, which is usually time consuming and costly. These sensors can measure micro and macro nutrients in soil, pH and salinity (Cocovi-Solberg et al., 2014).

Mechanical Sensors For determining the soil compaction or the resistance of the soil, these sensors are used. These are made to go through the soil for recording the force generated in the soil using strain gauge or load cells (Hemmat et al., 2013). A pressure unit determines mechanical resistance of the soil, i.e., the ratio of force required to get through the soil medium with frontal part.

Electromagnetic Sensors They can record electromagnetic response, electrical conductivity, adjust applications of variable rate in the real situation, and identify electrical response. This sensor can measure soil particle capability with electric circuits to accumulate or conduct electric charge which can be possible with noncontact and contact methods (Folnović, 2017). Organic matter and residual nitrates can be determined with these sensors in the soil (Yunus & Mukhopadhyay, 2010).

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Eddy Covariance-based Sensors Such types of sensors could be useful to quantify exchanges of water vapor, Co2, methane, etc. and energy between the atmosphere and the earth’s surface. This method accurately determines the surface environment and traces fluxes of gas over different ecosystems for agricultural purposes (Moureaux et al., 2012). These sensors are widely known to be used in a close chamber because of high accuracy as they can calculate constant flux over large landscapes (Kumar et al., 2017).

Mass Flow Sensors These types of sensors are widely useful for monitoring crop yield as it gives yield details by determining the grain flow, for example, when going through the harvester. Detecting mass grain flow to evaluate yield is nothing new (Schuster et al., 2017). These are the most important component, but yield monitoring combines various modules like data storage, grain moisture sensor, and internal program for data analysis.

LiDAR The “Light Detection and Ranging or LiDAR” sensor is widely applicable in segmentation and land mapping, 3D modelling of the farmland, soil type analysis, tracking soil loss and erosion, and forecasting crop yield (Weiss & Biber, 2011). It is also applicable for gathering dynamic measurement of the leaf area of a fruit tree. It is used for 3D mapping with GPS (del-MoralMartínez et al., 2016). It is also used to determine the biomass of several trees and crops (Crabit et al., 2011).

SWLB Sensors The “Soft Water Level-Based (SWLB)” sensors are widely used in catchments to determine hydrological patterns like water flow and level. It measures stream flows, rainfall, and other water activities (Crabit et al., 2011).

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Remote Sensors These types of sensors can store and capture geographical data, manipulate, analyze, present, and manage all kinds of geographical or spatial information. Like LiDAR, these sensors are also used in yield forecasting, crop monitoring, yield date forecasting, yield modelling, plants and pest identification, degradation mapping, and land cover mapping (Jaafar & Woertz, 2016; Yalew et al., 2016; Hegazy et al., 2015). A satellite-controlled sensor, Argos is the best example which processes, collects, and disseminates data from the environment from mobile and fixed platforms across the world (Rose & Welsh, 2010). Additionally, “automatic packet reporting system (APRS)” is widely used for reporting telemetry data using satellite network (Patmasari et al., 2018).

Telematics These sensors enable telecommunications between two vehicles for agricultural applications. They can gather data from blind spots or remote areas, machine operations reporting on component status, record travel routes and location to avoid repeating the patch (Mark & Griffin, 2016). These sensors are helping hand for farm managers to store and record all the data associated with farm operations on their own. They can also reduce threats like theft of farm machinery, etc. (Mohamed, 2013).

Current Challenges and Future Scope The 2030 Agenda for Sustainable Development plan was announced in the year 2015 in which global community and the UN have made an aim to eradicate hunger by 2030. However, WHO has revealed recent figures which don’t seem convincing enough in favor of the agenda as over 800 million people are suffering from food deprivation across the world (WHO, 2018). Even though such figures are alarming, quality of food is more shocking. Apart from the availability of food, the quality is yet another concern which is critical. In response, crop production should improve not just for food quality and cash crops are also needed to cultivate to meet industry demands, like rubber, cotton, etc. and rising bioenergy demand like ethanol.

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Figure 7 illustrates some of the key challenges that agriculture industry might face in the far future, i.e., 2050. Basically, this diagram shows three key challenges, i.e., feeding over 10 billion people, saving land, and cutting down on greenhouse gas emissions by over 60%. However, when looking closely, these challenges result in several other challenges like constantly shrinking fertile land, lack of rural labor, scarcity of water, and extreme weather conditions.

Figure 7. Key Challenges for Sustainable Growth of Agriculture.

Wireless Sensors for Agricultural Application Wireless sensors are placed around the fields smartly to provide detailed and updated insights to the farmers in real-time, so that they can implement appropriate measures to secure the crops to improve food production and reduce waste. Wireless sensor networks are widely used to keep farmers informed about almost every stage of their crop production and help them to prepare their machinery to avoid the loss of crop and improve cultivation. GPS and WSN can be used with tractors to deal with uneven terrain and improve the preparation of land to grow crops. Digital signal processing and image

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recognition technologies have enabled WSNs to track crop health and quality accurately. For sustainable use of agriculture, IoT should be the core of farming approaches. It should cover everything like crop transportation, power and water usage, operating farm equipment, market research, and maintenance alerts. The IoT has potential to streamline these tasks and make them more predictable by determining the needs of the crop at each level. It has already become a breakthrough, and it will further change the way agricultural activities are perceived as it will enable farmers to control their assets and lands in a different way to enhance their efficiency. In addition, significant advances in 5G network and WSNs can further shape the future of IOT and provide real-time information to the farmers on the go. Over 75 million IoT devices are going to be operating in agriculture sector given the recent success. In addition, an average farm might generate over 4.1 million data points on a regular basis by the year 2050 (Afshin et al., 2019).

Machine Learning Data analytics and machine learning are widely used for data mining as per the trends. For example, machine learning can be helpful in agriculture by predicting the best genes to produce crops. It can give the best seed to the growers worldwide that is best suitable to their respective areas and climate. On the other hand, ML models can show the most demanded products and which are not available yet. This way, farmers can get the most important information to make farming decisions. Recent advancements in analytics and ML can make it easy to weed out unnecessary crops and classify the produce before they reach their customers.

UAVs and Robots Farmers widely use drones to monitor crop growth and as a mode to deal with hunger and various extreme effects on the environment. In addition, UAVs can sprinkle pesticides and water properly in tough terrains and when the crops have uneven heights. Drones have been a valuable asset for farmers not just for quick spraying, but also for precision as compared to traditional techniques. With mission control and recent advancements in swarm

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technology, several drones with sensors like 3D cameras can provide important information to manage the fields. Farmers can keep their eye on the sky by using agricultural drones. But there are several challenges they need to overcome to make the best use of this advancement, especially when using them in extreme weather and integrating other technologies. Apart from drones, robots have also improved yields and productivity in agriculture. Weeding and spraying robots, for example, have reduced the use of chemicals. Camera and laser guided robots can remove and detect weeds without farmers. They can pass through the rows of crops themselves and improve the yield quickly with less human intervention. Robots that can pick fruits and transfer plants have added greater efficiency to conventional approaches (A. Ananda, et al. 2021).

Renewable and Smart Grids Despite having a lot of potential, there are some limitations smart agriculture is facing in the growth of IOT. A power outage is a serious issue because smart farming needs a lot of power. Long term deployment of sensors, constant use of GPS, and transmission of identified data through GPRS are some of the major causes behind huge power demand. In remote areas, farmers used to utilize and bring renewable sources of energy for heavy prices and randomly, which restrained them from using those resources in farming. Deep analysis is needed to deal with power issues in the long run related to sources of power like data transmission in remote locations. However, microgrids and smart grids can integrate “distributed energy sources (DERs)” smoothly to be adopted by farmers. DERs have gained a lot of prominence among farmers with smart meters as they can sell extra power to the power company. Integrated heat and energy systems and advanced energy storage solutions can make DER better for farmers as energy can be stored and heat can be used by heating and cooling when required. Other two barriers to these solutions are public perceptions and needs for healthy investment (S. Sahana, et al. 2016).

Vertical Farming (VF) Apart from modern technologies, recent agricultural practices can play a vital role in dealing with challenges related to resource and geographic limitations.

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While arable land is shrinking eventually, over 3 million people globally are estimated to migrate to cities, adding up more pressure to the current urban resources which are already limited (World Migration Report, 2015). Due to the constant migration of the rural population, 60% of the global population will rely on cities by 2030 and it might further rise to 68 percent by 2050 (UN, 2018). Both issues could be devastating for food production if existing agricultural practices are continued for a few more years. Vertical Farming could be the solution to all these challenges as it solves the challenges of water and land shortage, and it seems to be best suited near the cities. It is shown as a solution to shrinking arable land and lack of food in some places in the world. Hydroponics is another solution to control the demand for space and water to a huge extent.

Communication If IoT has to achieve true success, it needs advancement in connectivity. In terms of the telecom sector, there is a great potential in connectivity and valueadded solutions to influence the whole chain (Huawei, 2017). A lot of telecom companies globally provide connectivity solutions, but they cover only a fraction of the whole smart agriculture segment. The network operators should provide an exclusive range of services for growers. A lot of agricultural people are not technically well-educated and need end-to-end solutions from the operators rather than just connectivity. It will definitely improve the market share of telecom and cellular operators. These operators should be partnered with investors for end-to-end services, which need heavy investment. The nature of partnership and bodies involved are very important for the outcome of success like solution providers, device makers, system integrators, and others. This alliance would be helpful to operators to get deeper insights into the industry and improve market share. It can also build strong relations between the farmers and organizations to spread awareness of the pros of smart agriculture. Cellular technology can only be successful when service providers can make the most of its flexibility, potability and privilege of 2-way communication to provide tailored and cost-effective solutions. They should be able to provide exactly what farmers demand at the right place. In addition, policy changes are important for quick penetration of agriculture to have access to quality and reliable inputs. Peters (2018) conducted research on 23

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studies in developing nations and found that smartphone and cellular technologies have a great future for small farmers to boost their yields. In addition, licensed “low-power wide-area (LPWA)” technology might be a breakthrough in smart farming. Because of the devices it supports, and its features like wide coverage and low energy needs, it is best suited to the economics and geography of agriculture and it can be a game changer to smart farming in the future. Meanwhile, NB-IoT or Narrowband IoT has great support from the industry as the benchmark for LPWA. It can also deliver great changes in connectivity in the agriculture sector by revolutionizing the perception on the internet. Given its potential, there are expectations that prominent network operators can make great revenues with robust IoT ambitions by giving smart agriculture to LPWA tech companies. Important measures and infrastructure development are needed to achieve success in the long term.

Conclusion It has become more important than ever to focus on more efficient and smarter technologies to meet the ever-rising food demand of the global population ahead of the fertile land which is constantly shrinking. Developing the latest approaches to improve crop yield has become the need of the hour. These days, an innovative, tech-enthusiast younger population can be seen choosing agriculture as their profession. They focus on agriculture as a mode to get freedom from fossil fuels, labeling nutrition and safety, monitoring crop yield, and partnering with suppliers, growers, buyers, and retailers. This study has focused on all such aspects and the role of IoT and other technologies to make agricultural practices smarter to meet future demand. UAVs, sensors, cloud computing, and various communication and smartphone technologies are discussed well. In addition, this study provides a deeper insight to recent efforts in the research fraternity. Additionally, a lot of IoTbased platforms and architectures are available for agricultural practices. In a nutshell, every inch of agricultural land is important to boost crop yield. Sustainable IoT technologies and sensors must be used to make the most of every inch of farmlands.

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Lin, Jie, Wei Yu, Nan Zhang, Xinyu Yang, Hanlin Zhang, and Wei Zhao. "A survey on internet of things: Architecture, enabling technologies, security and privacy, and applications." IEEE internet of things journal 4, no. 5 (2017): 1125-1142. Liu, Hongli, Xi Wang, and Jin Bing-kun. "Study on NDVI optimization of corn variable fertilizer applicator." INMATEH-Agricultural Engineering 56, no. 3 (2018). MaamarFerkoun, “Cloud computing helps agriculture industry grow,” IBM, (2015), https://www.ibm.com/blogs/cloud-computing/2015/01/23/cloud-computing-helpsagriculture-industry-grow/. Mark, T., & Griffin, T. (2016). Defining the barriers to telematics for precision agriculture: Connectivity supply and demand (No. 1376-2016-109815). McGonigle, Andrew JS, Thomas C. Wilkes, Tom D. Pering, Jon R. Willmott, Joseph M. Cook, Forrest M. Mims III, and Alfio V. Parisi. "Smartphone spectrometers." Sensors 18, no. 1 (2018): 223. Mendez, Gerard Rudolph, MohdAmriMdYunus, and Subhas Chandra Mukhopadhyay. "A WiFi based smart wireless sensor network for monitoring an agricultural environment." In 2012 IEEE International Instrumentation and Measurement Technology Conference Proceedings, pp. 2640-2645. IEEE, 2012. Millan-Almaraz, Jesus Roberto, Rene de Jesus Romero-Troncoso, Ramon Gerardo Guevara-Gonzalez, Luis Miguel Contreras-Medina, Roberto Valentin CarrilloSerrano, Roque Alfredo Osornio-Rios, Carlos Duarte-Galvan, Miguel Angel RiosAlcaraz, and Irineo Torres-Pacheco. "FPGA-based fused smart sensor for real-time plant-transpiration dynamic estimation." Sensors 10, no. 9 (2010): 8316-8331. Mohamed, A. K. E. "Analysis of telematics systems in agriculture." theses Master of Science, Department of Machinery, Utilization, CULS, Prague (2013). Mokyr, J. (2022). Great Famine | Definition, Causes, Significance, & Deaths. Britannica. Retrieved 10 October 2022, from https://www.britannica.com/event/Great-FamineIrish-history. Molina, Iñigo, Carmen Morillo, Eduardo García-Meléndez, Rafael Guadalupe, and Maria Isabel Roman. "Characterizing olive grove canopies by means of ground-based hemispherical photography and spaceborne RADAR data." Sensors 11, no. 8 (2011): 7476-7501. Moonrungsee, Nuntaporn, SomkidPencharee, and JaroonJakmunee. "Colorimetric analyzer based on mobile phone camera for determination of available phosphorus in soil." Talanta 136 (2015): 204-209. Motoshita, Masaharu, Yuya Ono, Stephan Pfister, Anne-Marie Boulay, Markus Berger, Keisuke Nansai, KiyotakaTahara, NorihiroItsubo, and Atsushi Inaba. "Consistent characterisation factors at midpoint and endpoint relevant to agricultural water scarcity arising from freshwater consumption." The International Journal of Life Cycle Assessment 23, no. 12 (2018): 2276-2287. Moureaux, Christine, Eric Ceschia, Nicolas Arriga, Pierre Béziat, Werner Eugster, Werner L. Kutsch, and Elizabeth Pattey. "Eddy Covariance: A Practical Guide to Measurement and Data Analysis." Springer Atmospheric Sciences (2012). Murray, Seth C. "Optical sensors advancing precision in agricultural production." Photon. Spectra 51, no. 6 (2018): 48.

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Navulur, Sridevi, and MN Giri Prasad. "Agricultural management through wireless sensors and internet of things." International Journal of Electrical and Computer Engineering 7, no. 6 (2017): 3492. Orlando, Francesca, ErmesMovedi, DavideCoduto, Simone Parisi, Lucio Brancadoro, Valentina Pagani, Tommaso Guarneri, and Roberto Confalonieri. "Estimating leaf area index (LAI) in vineyards using the PocketLAI smart-app." Sensors 16, no. 12 (2016): 2004. Otufodunrin, L. (2018).Hello Tractor | Impact Journalism Day 2018. Retrieved 10 October 2022, from http://impactjournalismday.com/story/hello-tractor/. Our World in Data (2019). How is the Global Population Distributed Across the World? Accessed:[Online]. Available: https://ourworldindata.org/world-population-growth. Pajares, Gonzalo, Andrea Peruzzi, and Pablo Gonzalez-de-Santos. "Sensors in agriculture and forestry." Sensors 13, no. 9 (2013): 12132-12139. Pajares, Gonzalo. "Advances in sensors applied to agriculture and forestry." Sensors 11, no. 9 (2011): 8930-8932. Patmasari, Raditiana, InungWijayanto, R. S. Deanto, Y. P. Gautama, and HuriantiVidyaningtyas. "Design and realization of automatic packet reporting system (APRS) for sending telemetry data in Nano satellite communication system." JMECS (Journal of Measurements, Electronics, Communications, and Systems) 4, no. 1 (2018): 1-7. PEPPERL+FUCHS (2022). AGROBOT Strawberry Harvester with Industrial Sensors. Retrieved 10 October 2022, from https://www.pepperl-fuchs.com/global/en/27566. htm. Peters, A. (2018). This strawberry-picking robot gently picks the ripest berries with its robo-hand. Fast Company. Available at https://www.fastcompany.com/40473583/ this-strawberry-picking-robot-gently-picks-the-ripest-berries-with-its-robo-hand. Pohanish, R. P. (2014). Sittig's handbook of pesticides and agricultural chemicals. William Andrew. Povh, FabrícioPinheiro, W. de Paula Gusmao dos Anjos, M. Yasin, S. W. Harun, and H. Arof. "Optical sensors applied in agricultural crops." Optical sensors-New developments and practical applications (2014): 141-163. Prosdocimi, Massimo, Maria Burguet, Simone Di Prima, Giulia Sofia, EnricTerol, Jesús Rodrigo Comino, ArtemiCerdà, and Paolo Tarolli. "Rainfall simulation and Structurefrom-Motion photogrammetry for the analysis of soil water erosion in Mediterranean vineyards." Science of the Total Environment 574 (2017): 204-215. Rose, Ian, and Matt Welsh. "Mapping the urban wireless landscape with Argos." In Proceedings of the 8th ACM conference on embedded networked sensor systems, pp. 323-336. 2010. Sahana, S., K. Singh, S. Das, and R. Kumar, “Energy efficient shortest path routing protocol in underwater sensor networks,” in Computing, Communication and Automation (ICCCA), 2016 International Conference on, 2016, pp. 546–550. Schuster, Jason N., Matthew J. Darr, and Robert P. McNaull. "Performance benchmark of yield monitors for mechanical and environmental influences." In 2017 ASABE Annual International Meeting, p. 1. American Society of Agricultural and Biological Engineers, (2017).

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Wan, Xuefen, Jian Cui, Xueqin Jiang, Jingwen Zhang, Yi Yang, and Tao Zheng. "Smartphone based hemispherical photography for canopy structure measurement." In 2017 International Conference on Optical Instruments and Technology: Optoelectronic Measurement Technology and Systems, vol. 10621, pp. 200-205. SPIE, 2018. Waskom, R. M., Bauder, T., & Pearson, R. (2017). Best management practices for agricultural pesticide use.Bulletin #XCM-177. Weiss, Ulrich, and Peter Biber. "Plant detection and mapping for agricultural robots using a 3D LIDAR sensor." Robotics and autonomous systems 59, no. 5 (2011): 265-273. WHO (2018). Global hunger continues to rise. Retrieved 10 October 2022, from https://www.who.int/news/item/11-09-2018-global-hunger-continues-to-rise---newun-report-says. Wietzke, A., Westphal, C., Gras, P., Kraft, M., Pfohl, K., Karlovsky, P., & Smit, I. (2018). Insect pollination as a key factor for strawberry physiology and marketable fruit quality. Agriculture, ecosystems & environment, 258, 197-204. World Migration Report (2015). Retrieved 10 October 2022, from https://worldmigrationreport.iom.int/world-migration-report-2015. Xie, Xinhua, Xiangqian Zhang, Bing He, Dong Liang, Dongyang Zhang, and Linsheng Huang. "A system for diagnosis of wheat leaf diseases based on Android smartphone." In Optical Measurement Technology and Instrumentation, vol. 10155, pp. 572-580. SPIE, 2016. Yalew, Seleshi G., Ann Van Griensven, Marlous L. Mul, and Pieter van der Zaag. "Land suitability analysis for agriculture in the Abbay basin using remote sensing, GIS and AHP techniques." Modeling Earth Systems and Environment 2, no. 2 (2016): 1-14. Yew, Tan Kong, YuzmanYusoff, Lam KienSieng, HanifCheLah, Hasmayadi Majid, and Noor Shelida. "An electrochemical sensor ASIC for agriculture applications." In 2014 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp. 85-90. IEEE, 2014. Yunus, MohdAmriMd, and Subhas Chandra Mukhopadhyay. "Novel planar electromagnetic sensors for detection of nitrates and contamination in natural water sources." IEEE Sensors Journal 11, no. 6 (2010): 1440-1447. Zhang, Lei, Ibibia K. Dabipi, and Willie L. Brown Jr. "Internet of Things applications for agriculture." Internet of things A to Z: technologies and applications (2018): 507-528. Zhang, Lei, Ibibia K. Dabipi, and Willie L. Brown Jr. "Internet of Things applications for agriculture." Internet of things A to Z: technologies and applications (2018): 507-528. Zhang, Xin, and Eric A. Davidson. "Improving nitrogen and water management in crop production on a national scale." In AGU Fall Meeting Abstracts, vol. 2018, pp. B22B01. 2018. Zhao, Y., Gong, L., Huang, Y., & Liu, C. (2016). A review of key techniques of visionbased control for harvesting robot. Computers and Electronics in Agriculture, 127, 311-323. Zujevs, A., Osadcuks, V., &Ahrendt, P. (2015). Trends in robotic sensor technologies for fruit harvesting: 2010-2015. Procedia Computer Science, 77, 227-233.

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Chapter 2

The Impact of IoT on the Environment: A Descriptive Study in India Archan Mitra1,2,* and Sayani Das2 1School

of Media Studies, Presidency University, Bangalore, India of Mass Communication, Film and Television Studies, Kolkata, India

2Institute

Abstract The emergence of IOT as the future of cognitive culture and environmental sustainability is not surprising. The progression of our global civilization toward better stewardship of the environment may be facilitated by innovations in technology, which have the potential to help in this transition. According to a study, data centres are to blame for 2% to 5% of all greenhouse gas emissions worldwide. However, while choosing data centre technology, just 28% of worldwide IT decisionmakers take environmental considerations into account. These issues have made it clearer than ever before how important a sustainable economic paradigm is. The authors of this study examine how the Internet of Things (IoT) might benefit the environment and predict that it will pave the way for a circular economy and a reduction in global emissions. The implementation of the concept of circular economies has emerged as being driven mostly by the Internet of Things. Businesses are making use of the Internet of Things (IoT) to speed up the shift to a circular economy and lower global carbon emissions. This can be accomplished by automating self-monitoring equipment or by improving recycling design. All of these factors help to enhance the circular economy. Waste can be reduced by extending the lifespan of products *

Corresponding Author’s Email: [email protected].

In: Intelligent Decision Support System for IoT Enabling Technologies Editors: Subrata Sahana, Anil Kumar Sagar, Sanjoy Das et al. ISBN: 979-8-89113-249-8 © 2024 Nova Science Publishers, Inc.

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Archan Mitra and Sayani Das and repairing them for subsequent uses. Systems made possible by the Internet of Things have the ability to optimise every step of the product cycle, from acquiring raw materials to disassembling and recycling products, with the goal of reducing waste. Through the use of intelligent devices and management systems that facilitate the recovery, restoration, and recycling of objects while also minimising trash, the Internet of Things (IoT) can contribute to the reduction of waste. IoT helps businesses transition to a circular economy and lessen their carbon footprint. These all support the circular economy. The study methodology used by the researcher is descriptive in nature, establishing the groundwork for what is rather than inferring causes or connections to advance a comparative understanding of the same. The technique used to obtain the data is auxiliary in nature and advances the objective. The need of the hour may be to comprehend the role of IoT in environmental sustainability from a social science perspective, and these demands align with those of IT experts who must better understand the target audience before creating our IoT world.

Keywords: IoT, Environmental Sustainability, SDG, Circular Economy

Introduction Since the dawn of the 21st century, researchers in several fields have been raising concerns about the impending ecological disaster we are in the midst of. Changes are necessary in the behaviours that have brought us to this point, such as dumping unprecedented amounts of greenhouse emissions into the sky, overexploiting the Earth's limited resources, and continuously destroying the natural world and its surroundings. Additionally, the COVID-19 pandemic, issues post-Brexit, and changes in corporate social responsibility have brought to light the significance of putting sustainable development at the forefront of both present and future advancement and expansion (Cf, O. D. D. S. 2015). The loss of biodiversity, climate change, and the impending crisis caused by a lack of clean water are some of the most significant environmental concerns, but they are not the only ones. In today's day and age, governments and nations all over the world are attempting to develop strategies and solutions that are sustainable. The environmental impact of Internet of Things (IoT) applications can be positive (Rani, et al. 2020), Araral, et al. 2020). Recent research (Laine, et al. 2014), (Lopez-Vargas, et al. 2020) has focused on the ways in which the Internet of Things might contribute to environmental

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preservation. This book chapter discusses the impact that the Internet of Things (IoT) has had on sustainable urban development and urbanisation. A sustainable agenda is currently permeating all sectors of the economy; it was only a matter of time until it made its way into the technological arena as well. Sustainable technology development practises may also play a key role in lowering global emissions. This section of the book delves into one of the hottest subjects in tech right now—the Internet of Things (IoT)—and how it may be used to better the natural world. The Internet of Things, commonly abbreviated as IoT, is a framework of principles that supports the concept of device communication that takes place via the internet. The Internet of Things (IoT) is currently applicable to all elements of the economy and is one of the areas within the information technology industry that is increasing at one of the fastest rates. From its 2021 worth of $300.3 billion, it is expected to climb to $650.5 billion by 2026 (globenewswire, n.d.). During the time that Internet of Things devices were at the height of their popularity, people believed that they were good for the environment. However, as the number of Internet of Things devices in use throughout the world has risen, it has been seen that, just like any other device, they increase the quantity of energy that is consumed. This is a concern since there are not enough resources to match the demand (Huh et al. 2017). The Internet of Things, on the other hand, has the potential to offer results that are gentler to the environment than other modern things. For instance, due to their diminutive stature, Internet of Things devices have a lower baseline requirement for the amount of plastic and other materials, and as a consequence, they generate a reduced volume of electronic waste. The Internet of Things is able to carry out a rising number of duties despite the fact that the industry as a whole is continuing to get more complex. This requires making use of resources that have less power in order to do the same tasks that larger machines would typically complete. This helps conserve some energy. Hardware for the Internet of Things is frequently extremely small, which indicates that it has a constrained amount of storage space for data. As a consequence of this, the software that runs on IoT hardware needs to be built in a particular way in order for it to perform properly while having a restricted number of resources. The rise of the market for internet-connected objects (IoT) also resulted in the creation of new specialised tools that were aimed at making operations and communications inside the IoT more streamlined and cutting down on energy usage. These tools were meant to lower the quantity of energy that was

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utilised (Sundmeaker, et al. 2010). Some examples of such products include those that allow for the optimisation of devices connected to the internet of things as well as the cloud (AWS). The so-called "green" IoT is another way the Internet of Things (IoT) adds to environmental problems (Debasis, et al. 2011). Optimisation of the "green" Internet of Things (IoT) should have as its primary goal the decrease of energy use. It is a set of power-saving methods for both the hardware and the software, with an emphasis on alternative or low-power energy sources. These power-saving measures can be combined. The manner in which Green IoT handles data additionally indicates how the procedure is handled. The circular economy is an emerging economic idea that does away with garbage and landfills as a method of offering a solution to the world's most pressing environmental problems by means of the application of environmentally responsible business practises. This would provide a way by which the world's most critical environmental problems may be solved (Lieder, et al. 2016). This is the core principle behind something that is regarded as a "circular economy" (Lieder, et al. 2016). In the paradigm of the circular economy, best practises are integrated to reduce the use of disposables and to keep materials and resources in productive use for as long as possible across all consumer, industrial, and manufacturing processes that involve the use of renewable and non-renewable resources. This is done in order to maintain materials and resources in productive use for as long as possible. This is done in order to maximise the amount of time that supplies and resources are put to productive use. In the wider endeavour to repair the damage done by a linear economy that is not sustainable, in which industries and consumers quickly use up goods and materials and then trash them away, these projects are key components of the answer (Lieder, et al. 2016). The Internet of Things has emerged as the fundamental drive behind the implementation of the concept of circular economies. Circular economies aim to minimise waste and maximise resource utilisation. Businesses are increasingly turning to the Internet of Things (IoT) in order to aid in the transition to a circular economy and the reduction of our collective carbon footprint (Nobre, et al. 2017). This can take the form of automating devices for the purpose of self-monitoring or upgrading designs for recycling. Both of these options are good for the environment. The Internet of Things (IoT) is a network that is made up of many different types of linked gear, such as sensors, radios, and routers, that are able to wirelessly collect data and communicate it with one another. Organisations across a wide variety of vertical industries apply internet of things technology to optimise their

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operations, with the goals of lowering their impact on the environment and making more environmentally responsible decisions. The goals of these organisations include: Insights and automation provided by smart technology contribute to environmental sustainability in a number of ways, including the maximisation of the use of natural resources, the minimization of waste, the escalation of the use of renewable energy sources such as solar and wind, and the improvement of the ecological friendliness of urban areas. All of these factors contribute to the further strengthening of the circular economy (Mohanty, et al. 2016). Around 85 percent of Internet of Things installations, according to a report by the World Economic Forum on the Internet of Things Guidelines for Sustainability, are contributing to the accomplishment of sustainable development goals (Vermesan, et al. 2014). When it comes to making the world a more sustainable place, the innovation that was made possible by the Internet of Things is truly game-changing. Internet of Things (IoT) devices that have connected sensors are able to identify industrial pollutants, which contributes to the automation of manufacturing and the proactive management of renewable and non-renewable resources. These methods make it feasible for businesses to eliminate unplanned equipment failures, reduce the number of truck rolls, enhance operational efficiency, and extend the useful life of smart equipment.

Literature Review Climate change and the importance of environmental monitoring, particularly air quality monitoring, are now essential issues. There are now a number of ongoing real-world projects that are focusing on "air quality." These systems use the IoT as their primary means of communication infrastructure. An Internet of Things (IoT)-based weather monitoring system is being developed for agricultural use (Math, et al. 2018). As a result, the indices that were tracked on a consistent basis were the following: temperature, air pressure, humidity, light intensity, and dew point. Some of the survey articles that have been published in the Internet of Things domain over the past few years include ones by (Atzori, et al. 2010) (Agrawal, et al. 2013) (Gubbi, et al. 2013) (Said, et al. 2013) (Perera, et al. 2013) (Madakam, et al. 2015) (Al-Fuqaha, et al. 2015) (Andrew, et al. 2015). Related surveys have also been conducted by Atzori et al. which have addressed the enabling technologies as well as applications and open concerns that are present in the realm of the Internet of Things (IoT). A study titled "Enabling the Factors for Integration in Various

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Technologies" was given by Agrawal and colleagues within the broader context of the Internet of Things (IoT). In addition, an examination of the fundamental technologies that will be implemented in the creation of the Internet of Things as well as the Internet of Things' most essential application domain has been carried out. Gubbi et al. offered the vision of the Internet of Things (IoT) by emphasising how important it is for WSN, distributed computing, and the Internet to converge. The community of people involved in technological research was this presentation's target audience. In their article, Said et al. explored the architectures and applications of the present age, as well as the issues caused by the Internet of Things. The authors, Perera et al. discussed how there has been a significant growth in the number of sensor deployments related to the Internet of Things over the course of the preceding ten years. In the article by Madakam et al. the essential conditions, characteristics, and nicknames of the Internet of Things were brought to light. In addition to this, the study shed light on the various ways in which the Internet of Things (IoT) could be utilised in our day-to-day lives. In the field of the Internet of Things (IoT), Fuqaha et al. placed a significant amount of emphasis on protocols, supporting technologies, and a wide variety of application issues. Their research paper presents the architecture of the Internet of Things (IoT), along with its numerous components and the various ways in which they might connect to one another. In conclusion, the research sheds light on the challenges that are endemic to the Internet of Things (IoT) business. Whitmore et al. recognised a number of characteristics of the Internet of Things, some of which include identifying methods, sensing technologies, networking capabilities, and processing power.

Objectives The objectives of this book chapter are as follows: ● ●

To examine the function and implications of Internet of Things (IoT) enabled systems from a socioscientific perspective. To analyse how these systems contribute to and affect the establishment of sustainable lifestyles.

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Methodology For this book chapter, the descriptive style was chosen in order to achieve the requirements outlined by the chapter's objectives. Descriptive research designs' primary goal is to convey research topics in depth, going beyond the level of superficiality, and, as a result, to provide a thorough account of the topic under study (Sandelowaski, et al. 2000). As a result, it may make future research on the issue, including the use of new research techniques, simpler. As a result, researchers have access to a wide range of methodologies that make the research process easier to carry out when performing descriptive research. In order to complete the book chapter, secondary data had to be acquired.

Results and Discussion Optimization of IoT Devices for Environmental Sustainability The presence of advanced technologies that are able to carry out individual evaluations of a wide range of environmental conditions, such as the pH of the soil, the water temperature, the relative humidity, and so on, is one of the distinguishing characteristics of an ideal setting. These technologies are one of the reasons why an ideal setting can be distinguished from other types of settings. It is justified to replace humans with computers in agricultural production because artificial intelligence has led to measurable cost reductions for commercial farms as well as improved agricultural yields. These benefits can be attributed to AI's increased efficiency (Eslava, et al. 2015). IoT infrastructure and data-driven decision support systems present a feasible method for avoiding the additional expenses that are associated with the acquisition of pesticides. This is due to the fact that plants that are grown in greenhouses are less likely to be infested by pests than those that are grown in open fields. This is due to the fact that plants cultivated inside greenhouses are less likely to be infested by pests than plants grown outside in open fields (European Parliament, 2021). There is compelling reason for the installation of devices that are connected to the Internet of Things in a manner that is sustainable, given the disruptions that can be caused to agricultural systems by both weather and population increase.

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The outcomes of pilot projects in agriculture that make use of the Internet of Things have been highly encouraging and have demonstrated promising results, as can be seen from the aforementioned results. The researchers Zamora Izquierdo et al. developed a system for the Internet of Things (IoT) that relied not only on cloud computing but also on computation at the edge of the network (Skarmeta, et al. 2019). This technique was developed so that it would be possible to adjust the microclimate of a greenhouse by enhancing ventilation through the use of motorised windows. This would make it possible for the environment within the greenhouse to be more favourable for plant growth. A thermal-shade screen system was installed on the building's roof in order to improve the structure's energy efficiency and cut down on the building's overall carbon footprint. The use of an electromechanical traction system made it possible for the operation of the system to be carried out in an automated manner. This was made possible due to the fact that it made it feasible for the operation to be carried out automatically. Internet of Things (IoT) devices are often deployed by customers as part of an effort to increase their environmental responsibility. These interconnected gadgets gather data from commercial buildings, businesses, residences, automobiles, and other areas in order to examine, grasp, and ultimately enhance the efficiency of operating procedures. This information may have originated from any number of places. By the year 2030, there will reportedly be a total of approximately 24.1 billion Internet of Things devices that are active, according to research that was carried out by Transforma Insights (Fernando, et al. 2020). Utilising equipment that is linked to the internet of things confers a variety of benefits upon its users. In order to make use of them, one must, however, take into account the impact that they have on the environment around them. The production, delivery, and installation of devices are all activities that take a significant amount of time. Additionally, devices themselves consume energy while they are functioning. Not the least of their problems is the fact that they will, at some point, have to be thrown away (Christidis, et al. 2016). They are also difficult to maintain because a technician may need direct access to the device in order to diagnose any faults and carry out any necessary software updates. This makes it tough to keep up with any essential changes. Because of this, dealing with them is difficult in more ways than one. This is especially true for devices that are smaller and less expensive, as prolonged device maintenance and ongoing advancements are frequently not economically feasible for these types of devices, resulting in more frequent device replacements. This is especially true for devices that are less expensive. This is especially true for smaller, less expensive gadgets in comparison to

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larger ones with the same functionality. This is especially true for devices that are more compact and affordable, as additional innovations for these kinds of items are not always economically feasible.

Architect for Devices to Reduce Anthropocene Lean methods ensure that every available resource is utilised to its full potential so that the desired outcomes can be achieved. They are designed, constructed, and outfitted to use fewer resources, which reduces the negative impact on the environment that their manufacturing and eventual disposal have, in addition to reducing the amount of energy that is necessary to run them (Iryna, et al. 2019). Rare-earth metals are used in many of the components that make up electronic devices, such as mobile phones, for example. These metals are used in electronic devices such as cell phones (Haque, et al. 2014). These materials have an impact on the environment both during and after their extraction and disposal. The overall quantity of these materials that are used in the design can be cut down, which is one step that can be taken towards the creation of a layout that is friendlier to the environment. Devices that are efficient lessen their operational impact by employing software that is both up-to-date and secure, in addition to utilising breakthroughs that have been made in the management of both code and data. Even after prolonged deployment in the field, devices with durable hardware can continue to deliver the functionality and value for which they were originally conceived. They are able to adapt to changing company requirements and rapidly recover from any operational setbacks that may occur. If it can function for a longer period of time, then the carbon footprint it leaves behind will be smaller. This is because the amount of labour required to manufacture the item, ship it, put it in place, and remove it after use will be substantially lower (Oztemel, et al. 2020). As a result, it is essential to implement systems that not only make efficient use of available resources but also continue to be of benefit to the company for the longest period of time possible. Finding the best possible compromise for one's needs enables a person to not only improve the operational efficiency of their business but also maximise the positive impact that their actions can have on the environmental sustainability of their operations.

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IOT and Small Business in Maintaining Sustainability Standards Small and medium-sized firms (SMEs) are responsible for the creation of more than half of the world's employment opportunities. They are indispensable to the economy of the entire world and have the potential to contribute to the fight against the climate crisis. Small and medium-sized enterprises (SMEs) can benefit from the technologies of the Fourth Industrial Revolution (4IR), such as the Internet of Things (IoT), by reducing the amount of waste they produce, boosting their efficiency, and improving the way they monitor their processes (Lacy, et al. 2020).

Sustainability-Related Internet of Things Applications The average cost of an IoT sensor is predicted to drop from $1.30 in 2004 to $0.38 in 2020 (Shen, et al. 2020). Reasons for the price decline include an uptick in the number of vendors offering their wares, improvements in technology's optimisation and efficiency, and a decrease in the price of connectivity, storage, and processing. With the lowering of entry barriers, small and medium-sized enterprises (SMEs) can more easily adopt the tried and true IoT applications that will increase their bottom line and ensure their company's long-term viability.

IOT Applications for Environmental Sustainability The applications of Internet of Things (IoT) for environmental sustainability are as follows:

Water Leak Management The Internet of Things (IoT) has the potential to offer immediate protection against water damage due to leaks or plumbing problems. Sensors allow for the detection of both leaks and moisture. An optical metre reader is able to deliver readings in a timeframe that is very close to being considered realtime. These readings include water, gas, electricity, temperature, and pressure (Sadeghi, et al. 2015). The effects of leaks and water damage could be lessened

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by employing a strategy that is integrated with the Internet of Things and makes use of data analysis. This has the potential to save the economy several billion dollars each year while also reducing the amount of water that is in limited supply.

Predictive Servicing In the manufacturing industry and elsewhere, the data collected by IoT devices installed on industrial machinery can be used to develop models that forecast the occurrence of events of interest, such as the need for replacement components or the failure of equipment. Businesses have a better chance of reducing the possibility of a breakdown, properly planning for maintenance, and operating at their maximum capacity if they do this. It has been demonstrated that predictive maintenance may cut down on breakdowns by 70 percent, cut down on maintenance expenditures by 25 percent, and increase productivity by 25 percent (Selcuk, et al. 2017).

Environmental Surveillance With the help of sensors, it is possible to detect pollutants in the air and water, in addition to the temperature and humidity. The installation of sensors and other networked devices in pipes, machines, and rooms has the potential to make any area more hygienic, productive, and safe (Rashid, et al. 2016). This information may be utilised by businesses in order to monitor their processes, reduce the amount of trash they produce, and guarantee that they are in compliance with environmental standards.

Case Study 1: Smart Energy Management Present-day buyers place a premium on eco-friendly products. Internet of Things devices aid in the administration of various electrical supply networks. Everything associated with electric utilities, from generators to consumers, is included here. In addition to saving money, these intelligent energy management systems also cut down on harmful pollutants. Wireless utility metres provide data on energy consumption at building areas, individual assets, and industrial assets, and this can be examined in the context of energy

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usage monitoring (Saleem, et al. 2019). To better manage energy use and promote environmental sustainability, businesses and individuals can benefit from these data-driven insights. Power quality monitoring helps achieve many energy management objectives: ● ● ●

Use of fossil fuels in buildings, both residential and commercial, is decreased. Restoring Grid Stability. Avoiding power surges that can damage equipment and interrupt service.

Case Study 2: Air Quality Monitoring One of the new problems facing the world today is air pollution. The World Health Organisation estimates that 7 billion people's lives have been cut short due to environmental and household air pollution. Air pollution is also dangerous to human health. Food and vegetation, renewable energy, weather, and water are all profoundly impacted by poor air quality (Bruce, et al. 2015). Nonetheless, cities are now able to keep tabs on the AQI because of the novel and inexpensive IoT devices. The underlying source of air pollution may then be monitored in real time, and cities can take preventative action to clean the air their residents breathe. The following are a few real-world applications of air quality monitoring: ● ● ●

Measurements for detecting carbon monoxide gas in residential and commercial spaces. Controlling agricultural and municipal waste methane emissions requires careful monitoring. Toxic metals, lead, and particle matter in the air are being tracked as part of ambient air quality.

Case Study 3: Water Quality Monitoring Both the management and conservation of clean water are essential to the health of our planet and the people who live on it. Modern technology may be able to assist with both of these endeavours. Monitoring the quality of water with Internet-of-Things-based sensors is an effective method for reducing

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pollution and improving the administration of water resources (Islam, et al. 2015). Water analysis using Internet of Things technology can be beneficial to a variety of building types, as well as water and wastewater treatment facilities, irrigation systems, and industrial operations. The Internet of Things (IoT) is utilised by state-of-the-art smart water monitoring systems, which enable accurate evaluations of contaminants, oxygen levels, additional variables, and pH. (Nie, et al. 2020). It is now possible, with the assistance of the Internet of Things, to track the spread of potentially harmful substances from the general public to private dwellings as well as business establishments. The cutting-edge equipment aids in our continued good health. Here are a few examples: ● ● ● ●

Citywide Water Treatment Plant Supervision. Groundwater and stormwater levels are being monitored. Control and monitoring of irrigation systems in agriculture. Quality control of municipal water supplies and drinking water.

Case Study 4: Toxic Gas Detection In order to discover toxins in the air before they can cause damage to the environment or human health, hazardous gas detection systems conduct air quality analyses in a variety of industrial operations. The air concentration of a target gas is quantified by these systems. Connectivity to the Internet of Things allows these systems to swiftly send important alerts and initiate operations like turning off valves, shutting down systems, and launching fire alarms and chemical mitigation systems (Srinivas, et al. 2017). Several realworld applications of harmful gas monitoring include: ● ● ● ●

Safe management of poisonous, combustible, or pyrophoric (suddenly combustible in air) gases requires constant gas monitoring. In parking garages, enclosed spaces, and industrial settings (warehouses, for example), H2S and CO monitoring are essential. Protection against poisonous and corrosive gases in the workplace through detection of their presence. Testing for Toxic Gases in Mining Operations.

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Case Study 5: Smart Waste Management Along with the increase in the human population comes a corresponding increase in the amount of waste that individuals produce. The inconsistent and inefficient collection of waste has made an already terrible situation much worse (Elmustafa, et al. 2019). Waste and rubbish collection issues can be resolved with the use of the Internet of Things and its network of wireless sensors by providing building managers with access to real-time information on trash cans. This will allow for more efficient waste and rubbish collection (Rose, et al. 2014). Facility managers are able to prioritise which garbage cans need to be emptied based on the current levels of rubbish that have accumulated in those cans. When waste management companies get access to this data, they may be able to optimise their collection schedules and lower their carbon footprints by reducing the number of times garbage trucks are required to travel between collection sites.

Case Study 6: Smart Agriculture Natural resources, such as freshwater and arable land, are becoming increasingly difficult to come by as a direct result of the rise in the global population. The perennially low yields of staple crops have contributed significantly to the deterioration of the situation. The concept of sustainable food production through the use of intelligent agriculture, which can also reduce the environmental footprint and waste of resources, holds the key to satisfying the need for food around the world. Intelligent farming systems that are enabled by the internet of things can help achieve environmental sustainability. The factors, such as soil conditions, that can affect crop development are analysed by intelligent devices that collect data on these factors (Ayaz, et al. 2019). The study of the data that was acquired provides useful information regarding a variety of agricultural practises, such as fertilisation, fumigation, irrigation, and planting, to name just a few of these practises. Because of the knowledge that is driven by data, farmers are able to steer clear of circumstances that may have an impact on the wellbeing of their crops. Additionally, smart agriculture reduces the amount of water, chemicals, and other resources that are used, in addition to the number of human interventions that are both error-prone and ineffective (Mavridou, et al. 2019). The final results of this will be increased production rates and a reduced negative impact on the environment.

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Case Study 7: Cold-Chain Management Waste accounts for close to one-third of all food produced around the globe. As a consequence of this, 1.6 billion metric tonnes of food are wasted every year, resulting in a revenue loss of 1.2 trillion dollars. There is typically a lot of waste when it comes to the amount of energy and resources that are consumed in the process of cultivating, harvesting, and transporting food, which is another environmental concern that needs to be taken into mind (Cribb, et al. 2010). Inaccurate temperature recording occurred at multiple points along the food supply chain, which resulted in this considerable loss. Temperature is by far the most important variable in the supply chain that can have an effect on the quality of the food. A temperature that is not maintained properly might lead to the spoilage and waste of food. Wireless Internet of Things sensors make it possible for smart cold chains to monitor environmental conditions that have an effect on food. These environmental factors include temperature, humidity, and the intensity of the light. The integrity of the product's purity and quality are never put at risk thanks to the use of sophisticated cold chains (Ali, et al. 2021).

Discussion from Case Studies After reviewing the case study, the researchers are able to reach the conclusion that Internet of Things devices play a role in the preservation of natural resources as a whole. One can use the case studies that are given at the beginning of the chapter to figure out what will be talked about in the next dialogues. By integrating Internet of Things devices into our standard way of life, we can cut down on our own carbon footprints as well as the footprints of entire businesses. It has come to our attention that prior to the establishment of any enterprise in India, an EIA (environmental impact assessment) is undertaken for that industry. This demonstrates that green industrialization may be achieved through the utilisation of smart IoT technology within the company. This makes it easier to comprehend the impact that industry has on the surrounding environment. The application of technology related to the Internet of Things (IoT) may help reduce the amount of damage caused by this factor.

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Decreased Costs Associated with Operations An Internet of Things device fleet, which has the potential to deliver data in real time, may assist businesses in streamlining their workflows and reducing the costs of their operations. It is possible for devices to actively warn workers of their state, allowing for maintenance to be scheduled ahead of time before it has an impact on productivity. They can be incorporated into larger systems, which will help improve overall operational effectiveness and contribute to cost savings. For example, heating, ventilation, and air conditioning (HVAC) systems can be tracked, monitored, and controlled by smart building systems. This allows for the tracking of building usage and the adaptation of HVAC systems to take advantage of lower time-of-use expenses, which results in cost savings.

Increased Productivity While Maintaining a Secure Working Environment It is possible for Internet of Things devices to manage, monitor, and notify staff of changes in workflows or productivity. This can help staff members make decisions about employment that are better informed. Ford is implementing specialised Internet of Things (IoT) technologies as well as body tracking sensor technology in order to protect its workers from overexertion and improve their overall performance. Engineers and ergonomists will utilise the information to enhance each workstation, making it possible for employees to move more efficiently and providing them with assistance in preventing injuries. Because of Ford's innovative use of IoT, the company has been able to reduce the number of injuries that occur on assembly lines by 70 percent.

Better Client Experiences IoT tools make it far simpler than ever before for organisations to monitor, track, discover, and analyse data relating to their customers. It is possible for businesses to forecast shifts in customer behaviour as well as patterns of that behaviour. Innovative Internet of Things technologies have the potential to enhance the customer experience by making it more personalised based on

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previous interactions. Think about providing personalised coupons to customers via a mobile app on their smart devices as they enter a store or place of business, or installing tracking sensors on cars carrying shipments. Devices connected to the internet of things have the potential to assist businesses in gathering, transferring, and analysing the personal data they already possess about their customers. This can be of assistance to businesses in the development of outstanding customer experiences that engage customers on a deeper level and improve customer loyalty.

Additional Information Regarding the Company Devices connected to the Internet of Things can help businesses collect data to help them get insights into their operations, both internally and externally. Using beacon technology and other Internet of Things sensors, retail businesses redesign their stores based on the traffic patterns that are currently in place. Through the use of internet-connected IoT devices, logistics organisations are able to coordinate delivery locations and schedules, which allows them to make more efficient use of both personnel and vehicles. Return on investment (ROI) for brand-new goods and services can be increased rapidly by companies that use the internet of things (IoT) to speed up the process of modernising their organisations. They will be able to provide value to the organisation in a quicker and more efficient manner since there will be more data from the devices that can be acted upon that is freely available.

Confidentiality and Safety It will become ever more difficult to protect the privacy of the information that is gathered and transmitted by IoT devices as these technologies continue to develop and find more widespread application. Internet of Things devices aren't often included in the plan for ensuring their cybersecurity, despite the fact that ensuring cybersecurity is of the utmost importance. Physical tampering, network-based assaults, software attacks on the internet, and hardware attacks are the types of threats that the devices need to be safeguarded against in order to be effective. Another issue of concern is data privacy, which is especially pertinent in light of the fact that devices connected to the Internet of Things are rapidly

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being employed in highly sensitive areas like banking and healthcare. Securing data is not only prudent from a commercial perspective, but it is also mandated by law in many places around the world. This is due to the fact that worldwide information privacy legislation is becoming increasingly effective. It is possible that the integration of encryption and other security processes will be challenging if there are many devices involved. Some businesses may opt to use less capable platforms because they are more costeffective rather than investing the time, effort, and money necessary to integrate them across all devices. This may be the case when the integration process is costly. One single security hole is all that is required for a company to gain knowledge that is extremely valuable in this field.

Problem with the Technology It may appear like Internet of Things (IoT) devices are doing simple operations, such as counting swipes at a safe door, but in reality, these activities involve a significant amount of technology that is quite complicated. In addition, if they are contributing crucial data to a different workflow or system, they might have a negative impact on all of the processes that are related to that workflow or system. If there is a mistake in computing the number of swipes at the entrance, it is not a major concern; however, if another piece of equipment mixes up temperature information with entry swipe information, it can have devastating implications. In addition, it is not always simple to discover a solution to the issue. When deploying devices connected to the Internet of Things, there is frequently a high learning curve involved. It is vital to establish a plan explaining how and why one will use the products in question before making the purchase. This should be done before making the actual purchase. One will be in a position to provide assistance for them in this manner, and at the same time, one will have the satisfaction of knowing that they are operating as intended.

Reliance upon Availability of Power and Connectivity Connectivity to the internet as well as a steady supply of power are prerequisites for the effective operation of a wide variety of electronic devices. When one piece of machinery fails to function properly, the others, as well as

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anything else that is connected to it, are unable to do their jobs. IoT devices are now so deeply ingrained in the operations of today's organisations that if even a single one of them stops functioning, it is possible that the entire operation may come to a halt. Because power outages will inevitably occur at some point in time, it is the obligation of businesses to find out how the outages will affect their machinery and take the required precautions in advance. If the staff members are made aware of the processes for troubleshooting and incident management in addition to what to do in the event that a device breaks, then it will be somewhat simpler for them to deal with this problem (F. Chauhan, et al. 2022). It is likely that different devices manufactured by different manufacturers will not be compatible with the hardware that is already in use because the protocols and standards for the integration of the Internet of Things have not yet been finalised. It may be challenging to correctly deploy them due to the fact that each one may require a distinct connection and setup of the hardware (A. K. Ray, et al. 2023). Before implementing any necessary adjustments, the organisation needs to have a grasp of the requirements of the network. During the process of deploying a device, it is vital to account for additional time in order to address any potential issues with troubleshooting or associated tasks.

Price Increases Overall (Time and Money) IoT device deployment frequently requires significant time and financial investment. There are a large number of devices that need to be purchased and set up, in addition to staff members who need to install them, other staff members who need to integrate them into the network, and support requests to the vendor. If all of the companies migrate to the same location, it will be much easier for them to repay their investment. If the corporation decides to distribute them, one can anticipate a significant hike in the cost. Before making a purchase, it is important for organisations to organise the deployment budget and strategy in order to avoid a variety of potential complications.

Conclusion Researchers have a comprehensive understanding of the IoT-based system since a descriptive study was conducted using a social science point of view

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as the vantage point. The Internet of Things (IoT) is a crucial stage in the process of deploying environmentally friendly technology. This is due to the fact that problems that are caused by it can be corrected in the future, bringing us closer to a comprehensive system. Because of this, it is a key step in the process of deploying environmentally friendly technologies. Because this is a descriptive study, there are no related or scored variables included in it. This is because the nature of the study is descriptive. It relies primarily on qualitative research methodologies. A study that is written in a descriptive fashion can have additional support for its findings by conducting research on major parts of the topic.

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Chapter 3

A Smart Internet of Things (IoT) Enabled Agricultural Farming System Justin Joy1,*, PhD V. L. Helen Josephine1, PhD A. Angel1 B. Abisheakkumar1 Monica Seles E.S.1 and Rajan John2, PhD 1MBA

Department, School of Business and Management, Kengeri Campus, Christ University, Bangalore, India 2College of Computer Science & Information Technology, Department of Computer Science, Jazan University, Jazan, Kingdom of Saudi Arabia

Abstract Industry 4.0 has brought about a profound revolution in recent times. This advancement profoundly impacted technology usage in every aspect and has significantly improved businesses. Agriculture is one of the evergreen economic contributors to India’s GDP. With improvements in adaptability in this sector, the time is ripe for instituting IoT (Internet of Things)-based smart agriculture. Water scarcity and drastic climate change are real issues affecting crop yields, leading to the failure in the timely fulfillment of market demand (Nawandar 2019). The authors have collaborated to address these concerns by creating a system comprising a functional hardware prototype and an android application for regulating irrigation and temperature. The introduction of IoT (Internet of Things) *

Corresponding Author’s Email: [email protected].

In: Intelligent Decision Support System for IoT Enabling Technologies Editors: Subrata Sahana, Anil Kumar Sagar, Sanjoy Das et al. ISBN: 979-8-89113-249-8 © 2024 Nova Science Publishers, Inc.

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Justin Joy, V. L. Helen Josephine, A. Angel et al. automates crop monitoring and reduces labor costs. By using IoT, (Internet of Things) an earmarked agricultural field is covered with sensors. The sensors are concealed so as not to be affected by the bleakness of the external environment. These sensors work in tandem with drip irrigation following the sensed climatic conditions. The water is pumped directly to the root zone in an optimally sensed manner. The authors developed and tested the system successfully in a greenhouse system. The process initially aims to extract the values of soil parameters by using IoT (Internet of Things) sensors and appropriately control the watering of crops, thus enabling the cultivation of crops even in a hot and dry climate. Crops can be irrigated from a remote location and their temperature can be meticulously regulated to ensure they remain within an optimal range. Water utilization for agricultural crops is optimized with the use of automated irrigation systems that use W.S.N (Wireless Sensor-Networks) and G.P.R.S (General-Packet-Radio-Service) modules. The algorithm employed in the system to control water usage is based on the needs of the crop and the terrain. The entire system is powered by photovoltaic panels, which are useful in rural and isolated areas without electricity (Raut and Shere 2014). A cellular network is used for duplex communication. Continuous monitoring and irrigation schedule programming are used by web apps to manage irrigation. This is also possible using a browser and web pages. A system with three identical automatic irrigation systems can save water use by up to 90%.

Keywords: IoT, Industry 4.0, Smart Farming System, Greenhouse System, Automated Irrigation System, Soil Moisture Monitoring, Digital Humidity, Temperature Sensing

Introduction New technologies can increase agricultural productivity from irrigated land, even in arid and sub-humid zones. Agriculture is now vying with other industries and sectors for water use. It is now vital to implement new technology for adequate water management due to the rising demand for water and significant increases in energy expenditure. Understanding evapotranspiration processes and effective irrigation techniques are necessary for the wise use of water for crops. Regrettably, an inadequate amount of the water applied for crop irrigation is utilized efficiently, resulting in wastage of a substantial amount of water out of drainage. Excess water is a strain on the energy constraints and wastage of energy may also lead to unnecessary environmental pollution (Oommen, Kurian and Joy 2022). Injurious soil

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erosion may result from and be triggered by this loss. Irrigation’s primary goal is to give plants the amount of water they require to yield their best yield. Greenhouse systems provide a controlled environment for crops to grow by regulating temperature, moisture, and humidity levels, resulting in healthier plants and increased productivity. The system offers a suitable temperaturecontrolled environment necessary for crop growth, while also protecting them from environmental and atmospheric agents through the use of appropriate equipment. With the simultaneous control of the crops’ climate, greenhouse agriculture improves product quality and allows for higher yields. By maintaining a stable and optimal environment for crop growth, greenhouse systems offer a promising solution for sustainable agriculture and food production. The objective of the proposed project is to enable farmers or gardeners to adopt greenhouse systems and integrate the latest IoT (Internet of Things) technology into agriculture practices, thus promoting modern and efficient methods of crop cultivation. This system has been developed to shine a pathway for the farmers to reduce the need for continuous monitoring of soil parameters and to reduce the time, labor, and water consumed in agriculture. This IoT (Internet of Things) solution helps to monitor and irrigate crops, relieving the farmers of the need to regularly monitor and water the crops manually.

Problem Statement Excessive water usage for agricultural farming purposes is a common practice. This leads to wastage of water – a resource increasingly becoming scarce. There is a need to reduce this wastage and to reduce the time and efforts for crops and soil monitoring. The article presents a concept of an IoT (Internet of Things)-enabled automated system designed to streamline the involvement of farmers or gardeners in greenhouse systems. The present discourse advocates a comprehensive system comprising a functional hardware prototype and an android application, intended for the regulation of irrigation and temperature. The implementation of this system would lead to a significant reduction in laborious monitoring of soil and weather parameters for agricultural operations, while simultaneously limiting the excessive consumption of water.

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Technical Objectives The technical objectives of the proposed work include illustrating the knowhow of extracting the estimates of ground factors by using sensors to regulate the watering of crops and cultivate crops even in hot and arid climatic regions.

The Process and Elements in the System The proposed method makes use of IoT (Internet of Things). IoT (Internet of Things), often known as connected devices, is the networking of physical objects. It permits data communication between machines, structures, and other electronic and software-enabled objects. These items’ ability to gather and communicate data is facilitated by a sensor, actuator, and network connectivity. A sensor is a tool that recognizes or quantifies a physical characteristic and then records, indicates, or reacts to it in some other way. Soil moisture sensors are utilized to determine the volumetric water content of the soil, while a highly cost-effective primary temperature and humidity sensor, known as the DHT11 (Digital Humidity and Temperature) sensor, is employed to measure atmospheric humidity. This sensor utilizes a thermistor and a capacitive humidity sensor, and it produces a digital signal on the data pin. The proposed system consists of DHT11 (Digital Humidity and Temperature) and a hygrometer sensor. IoT (Internet of Things) promotes better control over the internal processes and provides a large amount of data to be collected from the sensor. This results in lower production risks. Efficient monitoring of the farming environment is ensured by using IoT (Internet of Things) platforms (Chen 2012). It also helps the farmers monitor and control the field from multiple locations by enabling remote monitoring and increasing crop production by tracking planting, watering, pesticide application, and harvesting. It measures the moisture, temperature, and humidity content for one crop, but this design could enable the user to select multiple crop choices. Thus, a particular crop’s moisture, temperature, and humidity content can be viewed and then controlled based on the moisture and temperature threshold value. These sensors are used in a greenhouse system with an advanced irrigation system. An automatic irrigation system typically uses just two soil factors, such as temperature and soil moisture. Other factors including light, air moisture, humidity, and soil PH value are not typically

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considered while making decisions. This system operates using sensor threshold values that are influenced by soil moisture and temperature. It indicates the state of the soil, and the system selects a cutoff point. Sometimes this system irrigates the farm with more or less water and may not be as per crop requirements. But even then, the water surplus or deficiency is limited to a great extent. The maximum profit scenario is also not considered per crop type and water availability. An IoT (Internet of Things) based solution, namely Smart Irrigation and Temperature Control, has been proposed for greenhouse systems to regulate irrigation and monitor crops. This cutting-edge system effectively alleviates the demanding task of frequent crop monitoring and watering. If the network is dispersed over a vast area, maintenance takes a lot of time and is difficult. Decisions about irrigation will need to be made from much further locations, so it will be necessary to note, record, and track spatial data. Additionally, geospatial data can be mapped to websites and computer programs, making it simple for users to make decisions remotely.

Technical Roll Out The present study outlines an innovative irrigation system that comprises a DHT11 (Digital Humidity and Temperature) sensor and a hygrometer sensor, enabling the precise measurement of soil temperature, humidity, and moisture. Additionally, it incorporates the Nodemcu device with an inbuilt Wi-Fi shield that facilitates the storage of data in the firebase. An Android application has been developed that displays these values and controls the motor and cooling fan. Initially, the DHT11 and hygrometer sensors gather temperature, humidity, and moisture values from the soil, which are then stored in the cloud firebase using the Nodemcu with the inbuilt Wi-Fi shield, thereby enabling seamless transfer of data from Arduino to the cloud firebase. The Android application retrieves these values and allows the user to view soil parameters while providing the option to control the motor. An alert is generated when the moisture value surpasses the threshold value, thereby prompting the user to turn on the motor. Similarly, when the temperature value exceeds the threshold value, the cooling fan turns on, and if the temperature falls below a certain threshold, the cooling system is turned off. Overall, this system presents a unique solution to address irrigation and temperature control challenges, facilitating efficient crop management while optimizing resource utilization.

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The advantages of this work are that they reduce water usage and store data in a database in the Cloud, thereby enabling the possibility of remote access. Crops can be cultivated even in hot arid regions by the usage of a cooling fan. Drip irrigation is used to optimize water usage. This work involves the acquisition of crucial soil parameters, namely temperature, humidity, and moisture, through a systematic collection process. The gathered data is subsequently integrated into the Firebase platform, which facilitates real-time remote access. To enhance user experience, Android Studio is utilized to display the essential parameters such as temperature, humidity, and moisture to the end-user. Thus, an Android application is developed. When the moisture value exceeds or falls below the threshold value, an alert is sent via the application. As a result, the farmers have the ability to turn on and off the motor. Farmers can turn on or off the cooling fan after receiving an alert through the application when the temperature reading is more or lower than the threshold value. Figure 1 below illustrates the architectural system design described in this section.

Figure 1. System Architectural design for Smart (IoT) enabled Agricultural Farming System.

Figure 1 shows the hardware interface design of our system. In this process, the soil moisture sensor detects the level of moisture present in the soil, while the DHT11 (Digital Humidity and Temperature) sensor accurately measures the temperature and humidity. The collected sensor data is then transmitted to the cloud-based Firebase platform using the ESP8266 (ESPRESSIF) Wi-Fi module embedded within Nodemcu. To enable prompt action by farmers, notifications are sent to control the motor and cooling fans.

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Sensors Used The modules identified in the application for the analysis of the data collected and recorded involve data acquisition, data sent to the firebase, view data through the mobile application, control of watering based on moisture value, and control of temperature based on DHT11 (Digital Humidity and Temperature Sensor) temperature value. A soil moisture sensor (hygrometer), temperature-humidity sensor (DHT11) (Digital Humidity and Temperature Sensor), and Wi-Fi module are used here in the project for Data Acquisition. The real-time data are measured and communicated to cloud (Firebase). ESP8266 Wi-Fi module is used to transfer the data through the cloud in Table 1. Table 1. Sensor Specifications Sl. No 1 2

Sensor DHT11 (Temperature and Humidity sensor) Hygrometer (Moisture)

Measured in Temperature in ͦ C (0 to 50) Humidity in % (20 to 90) Moisture in integer (1 to 1024)

Soil Moisture Sensor This sensor measures the soil’s volumetric content and provides a moisture metric. It has two output modes: analog mode and digital mode. Dry soil conducts poor electricity that shows lower water levels.

Digital Humidity and Temperature Sensor (DHT11) This sensor measures the temperature and humidity level of the soil. Its output is calibrated to a digital signal. Its sensing element is connected to an 8-bit single-chip computer. The range coverage for temperature and humidity is 20 meters per sensor. Data Sent to the Firebase: Data acquired is sent to the Cloud Firebase, and the values are updated every 30 minutes to enable access to this data from remote locations.

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Modules Used Wi-Fi Module (ESP8266) (ESPRESSIF) Real-time data is detected by multiple sensors and transmitted to the cloud database, specifically Firebase, by means of the ESP8266 module developed by ESPRESSIF. In the ESP8266 (ESPRESSIF) module, the essential pins to be connected are the transmitter, receiver, and power supply on the ground. This WiFi module is connected to the access points which receive the transmitted data. ESP8266 (ESPRESSIF) plays a significant role in sending the real-time data collected from the hardware components. The real-time data contains sensors for soil moisture and temperature and humidity. The only disadvantage of using the ESP8266 (ESPRESSIF) Wi-Fi module is that it should be connected to an open IP to promote good data transmission to the cloud firebase (Saha, et al., 2018).

Firebase One of the main advantages of using firebase is a real-time database, and it promotes a perfect platform for beginners. All the data is stored in JSON format. It also provides fast and secure hosting to the developers from any remote location. The data to be imported or exported should be in JSON format. It also promotes better efficiency when we sync data with any realtime application. All the data is processed and stored in the Cloud. Firebase is also used for cloud storage, remote configuration, real-time database, etc.

View Data through Mobile Application To have the temperature, humidity, and moisture data displayed to the user, Android studio is employed. Android Studio consists of all the API (Application Programming Interface) which is required to create an application. The developed applications can be tested using the android studio emulators. The language used mainly in the android studio is Java. Android Studio uses the PhoneGap framework. This open-source framework uses languages like CSS3 (Cascading Style Sheet level 3), HTML5 (Hypertext Markup Language), etc.

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Control of Watering Based on Moisture Value The management of watering and irrigation is facilitated through the use of a mobile application that permits control over the activation and deactivation of the water pumping motor. This mechanism effectively optimizes water consumption, thereby reducing overall water usage. Additionally, the application displays the moisture level automatically each time it is opened on the mobile device, enabling the user to monitor the real-time status of soil moisture from any remote location. In the event that the moisture level falls below the lower threshold, the user receives an alert message prompting the activation of the pumping motor. Upon receiving the alert, the user can assess the necessity of watering the crops and decide whether to activate the pumping motor accordingly. Similarly, when the moisture level exceeds the upper threshold, the user is alerted to turn off the pumping motor. Based on the moisture level displayed, the user can decide whether to turn off the motor. It is important to note that water requirements may differ from one plant to another, leading to variations in the moisture levels across different plant types.

Water Pumping Motor The water pumping motor used is brushless and maintenance-free. It consists of a stator and circuit board sealed by epoxy resin. This mechanism works based on amphibious design. It also helps to avoid leaking problems. This water pump consumes low energy and is quiet (less than 35db). It also has a long working life of more than 30,000 hours. The water pump motor ensures a wide temperature resistance range from 0 – 60 degrees Celsius. This pump is used widely in aquariums, car cooling, humidifier, air conditioner, and circulations systems (Ndzi, Harun and Ramli 2014).

Control of Temperature Based on DHT11 Temperature Value The user controls the temperature by switching on and off a DC (Direct Current) cooling fan using the mobile application. This process facilitates the growth of moderate climate crops in exceedingly hot and parched regions

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through the regulation of temperature using cooling fans. Such temperature manipulation results in higher crop yield and better quality. The user can obtain real-time soil temperature readings from any remote location through the application, which automatically displays the temperature values upon launch. Once the temperature falls below a certain threshold, an alert is triggered to prompt the user to activate the cooling fan. Subsequently, the user can evaluate the temperature requirements of the crops before deciding to operate the cooling system. Additionally, an alert message is sent to the user to switch off the cooling fan if the moisture level surpasses the upper threshold value. By monitoring the temperature levels and responding to alerts promptly, the user can optimize crop growth and conserve energy by regulating the cooling arrangement (K. Negi, et al., 2022).

Figure 2. Switching On the cooling fan.

DC Cooling Fan DC (Direct Current) cooling systems are slimmer, lighter, and more compact than AC (Alternating Current) cooling fans. The DC fan is also used to transmit power across a grid. It will also be convenient to change the voltage up and down rather than changing the positive and negative values in the AC fans. The current travels on the skin layer of the conductor for DC (Direct Current) fans, whereas the current flows throughout the entire conductor for AC (Alternating Current) fans. So, DC (Direct Current) cooling fans are more

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efficient than AC (Alternating Current) cooling fans. It consists of a DC (Direct Current) motor used to transform electrical energy into mechanical energy by using direct current.

Figure 3. Switching Off the water pump.

The output of the Arduino Uno displays the soil parameter values like temperature, humidity, and moisture. It’s displayed on the output screen (A Ali, et al., 2021). When the user clicks the pump off, the output shown on their phone or web browser is presented in Figure 2 and Figure 3. These figures also display the real-time values of humidity, temperature, and moisture after being retrieved. As a result, users are kept informed of all essential soil parameters in real-time in Table 2. Table 2. Day 1 Hardware Results Timestamp 9:15:45 9:30:34 14:00:23 14.10:34 20.00:12 20:20:56

Moisture (1 -1024) 570 583 950 887 338 400

Temperature (in ˚C)

Humidity (in %)

26 26 35 34 24 24

72 71 40 45 69 67

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Implications Social Impact We hold the view that our article, when appropriately implemented, has the potential to significantly contribute to society by mitigating water wastage through the prudent reduction of excessive water consumption in agricultural practices. This will result in an increase in the production of crops. The time involved in the overall monitoring process and execution of timely corrective processes will also be significantly reduced.

Economic Aspect This article provides a pathway and an opportunity to promote this knowledge of technology for the betterment of farmers who are subject to various constraints of water shortage and lack of labor. This proposal, we authors believe, will also encourage the ideation of sustainable and smart selfcultivation in any desired location.

Conclusion The present study outlines a system consisting of a functional hardware prototype and an Android application for controlling temperature and irrigation. Wi-Fi is utilized for wireless transmission of data, which is successfully retrieved from the Cloud Firebase for monitoring purposes. The mobile application provides an intuitive and user-friendly interface and enables users to view the real-time values of soil parameters from remote locations. Implementation of this system can aid farmers in transitioning from traditional agricultural practices and establishing a highly productive greenhouse system in diverse regions, not only in India but also worldwide. The microcontroller in the proposed system and method promises an increase in system life by reducing power consumption (Oruganti, Khosla and Thundat 2020) (Van Neste, et al., 2020). It is shown to be practical and affordable to apply Modern irrigation and temperature supervision for a greenhouse system in order to maximize water resources for agricultural produce. Even in hot and arid temperature regions, this technique enables the production of plants that

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prefer a mild climate. IoT (Internet of Things) encourages improved internal process control, offers a wealth of data to be collected from the sensor, and ultimately reduces production risks. The wealth of data that IoT (Internet of Things) generates can be used to gain further insights for future research and opportunities to optimize production (Joy and Nambirajan, Learning analytics for academic management system enhancement: A participatory action research in an Indian context 2021). Efficient monitoring of the farming environment is ensured by using an IoT (Internet of Things) platform. It also helps the farmers to monitor and control the field from distant locations by enabling remote monitoring. It also has enormous potential for increased crop production by tracking planting, watering, pesticide application, and harvesting. The roster of authors encompasses individuals who have authored a patent for an IoT (Internet of Things) enabled automated agricultural farming system, a comprehensive solution conceived to tackle water scarcity, production constraints, and soil erosion challenges prevalent in specific agricultural regions (Joy, A and Josephine V.L., Smart Internet of Things (IOT) Enabled Agricultural Farming System 2022).

References Ali A., A. Chaudhary, and S. Sahana, “A Review of Defense against Distributed DoS attack based on Artificial Intelligence Approaches,” in 2021 IEEE 6th International Conference on Computing, Communication and Automation (ICCCA), 2021, pp. 32– 38. Chen, Y.-K. (2012). Challenges and opportunities of internet of things. In Proceedings of the 17th Asia and South Pacific Design Automation Conference (ASP-DAC) (pp. 383388). Joy, Justin, and T Nambirajan. 2021. “Learning analytics for academic management system enhancement: A participatory action research in an Indian context.” Management in Education (Sage, British Educational Leadership, Management & Administration Society (BELMAS)) 1-16. doi:10.1177/08920206211037689. Joy, Justin, Angel A, and Helen Josephine V. L. 2022. Smart Internet of Things (IOT) Enabled Agricultural Farming System. India Patent 202241047525. August 26. https://ipindiaservices.gov.in/PublicSearch/PublicationSearch/Eregister. Negi K., G. P. Kumar, G. Raj, S. Sahana, and V. Jain, “Degree of Accuracy in Credit Card Fraud Detection Using Local Outlier Factor and Isolation Forest Algorithm,” in 2022 12th International Conference on Cloud Computing, Data Science \& Engineering (Confluence), 2022, pp. 240–245.

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Nawandar, Neha K. 2019. “IoT based low cost and intelligent module for smart irrigation system.” Elsevier. Ndzi, David L., Azizi Harun, and Fitri M Ramli. 2014. “Wireless sensor network coverage measurement and planning in mixed crop farming.” Elseiver. Oommen, Koshy P, Sijin Kurian, and Justin Joy. 2022. “Carbon Dioxide Neutralization Across the Global Supply Chain.” ECS Transactions. The Electrochemical Society. 12131-12142. doi:https://doi.org/10.1149/10701.12131ecst. Oruganti, K, A Khosla, and T G Thundat. 2020. “Wireless Power-Data Transmission for Industrial Internet of Things: Simulations and Experiments.” IEEE Access. 187965187974. doi:10.1109/ACCESS.2020.3030658. Raut, Jyotsna, and B Shere. 2014. “Automatic Drip Irrigation System using Wireless Sensor Network and Data Mining Algorithm Ms.” Semantic Scholar. Saha, Tonmoy Kumar, Tyler Nathan Knaus, Ajit Khosla, and Praveen Kumar Sekhar. 2018. “A CPW-fed flexible UWB antenna for IoT applications.” Microsystem Technologies (Springer) 28: 5-11. doi:https://doi.org/10.1007/s00542-018-4260-0. Van Neste, C W, Thomas Thundat, Ajit Khosla, Sarah Szanton, and Larry A Nagahara. 2020. “Perspective—Maintaining the Quality of Life in Depopulating Communities: Expanding Smart Sensing via a Novel Power Supply.” Journal of The Electrochemical Society 167 (3). doi:https://doi.org/10.1149/1945-7111/ab729d.

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Chapter 4

IoT and Machine Learning Applications for Industrial Reliability Frameworks Suneel Kumar Rath1,* Madhusmita Sahu1,† and Shom Prasad Das2,‡ 1Department

of Computer Science and Engineering, C. V. Raman Global University, Bhubaneswar, Odisha, India 2Department of Computer Science and Engineering, Birla Global University, Bhubaneswar, Odisha, India

Abstract Algorithms that can learn on their own from data are the subject of artificial intelligence (AI) and machine learning (ML) research. Deep Learning (DL) principles utilized in video games and self-driving cars are evidence that machine learning techniques have evolved greatly over the past ten years. As a result, researchers have started to look at machine learning’s potential for use in the industry. According to various studies, machine learning is one of the key enabling technologies that will enable the transition from an old-school production system to Industry 4.0. Contrarily, industrial applications remain uncommon and exclusive to a select few global businesses. To clarify both the genuine potential of machine learning algorithms in operation management and their potential drawbacks, this chapter tackles these problems. Production organizations must constantly advance, which calls for flat, adaptable organizations as *

Corresponding Author’s Email: [email protected]. Corresponding Author’s Email: [email protected]. ‡ Corresponding Author’s Email: [email protected]. †

In: Intelligent Decision Support System for IoT Enabling Technologies Editors: Subrata Sahana, Anil Kumar Sagar, Sanjoy Das et al. ISBN: 979-8-89113-249-8 © 2024 Nova Science Publishers, Inc.

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Suneel Kumar Rath, Madhusmita Sahu and Shom Prasad Das well as versatile data and material management frameworks. The study looks at ML approaches that seem to be used to build intelligent behavior-producing systems. It is contingent on two workshops on learning the intelligent manufacturing system, a comprehensive literature review, and several obligations. In addition, symbolic, hybrids, and subsymbolic approaches, together with their uses in manufacturing, are examined. Hybrid solutions aim to combine the benefits of numerous methodologies. The best can be chosen by course of action for a particular set of circumstances; several methods of production are compared and contrasted.

Keywords: Operation Management, Machine Learning, Industrial Applications, Intelligent Manufacturing, Deep Learning

Introduction The next wave of virtual disruption will be unleashed thanks to machine learning. We anticipate that businesses will be ready. Businesses that used it early on have benefited from it. Computer vision, Deep learning, natural language processing, robots, and other technologies are examples of the technology of the future. The digitization of the world provides the foundation for the next generation of ML software. ML is frequently at the vanguard of industries that have adopted digital technology. Machines are learning and getting better at doing things on their own, so businesses need to adapt or else they will lose business to their competitors. These new changes are called “Industry 4.0” and it includes different technologies (like the Physical Cyber System and the Internet of Things (IoT). This means that machines are becoming more intelligent and can do things on their own, like making products and services. However, some businesses are not ready for this new world and may struggle to keep up (Kunst, et al. 2019) (Lee, et al. 2014). Although predictive maintenance has a lot of benefits, it also has a lot of disadvantages. Product data management has many advantages, including better use of human resources, financial (Schmidt, et al. 2018) and financial (Adhikari, et al. 2018) resources, increased productivity, system fault reduction (Balogh, et al. 2018), and less unexpected downtime. ML can serve as a prognostic tool and failure prediction tool, including calculating a machine’s lifespan using a lot of information to train an ML machine (Chukwuekwe, et al. 2016) (Zhou, et al. 2018) and identify defects (Bousdekis, et al. 2019) (Ansari, et al. 2020). However, there hasn’t been any

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ML research done in areas connected to design and production yet. However, the field has a very very long history of both successes and failures, similar to other areas of AI (Artificial Intelligence) (Lu, et al. 1990). The most prevalent aspect of these Research and Development projects has been a single-word adoption of ML concepts or techniques (Rath, et al. 2022) into an existing modeling and adaptive system for a stated development and designing issue. However, more research is needed to fully understand the details of ML in the context of engineering (Rath, et al. 2022). With this essay, we aim to describe and define “mutual human-machine learning (Rath, et al. 2022)” in the workplace of the future. When people and machines are working together on a project, the main challenge is figuring out how to learn from each other. To successfully collaborate, we consider scenarios where AI and people (Rath, et al. 2022) are present as well as the capacities of humans and machines (Figure 1) in existing frameworks (Rath, et al. 2021).

Figure 1. Collaboration between humans and machines.

Research Techniques and Their Impact Using the approach outlined by Tranfield et al. (Tranfield, et al. 2003) in 2003, which changed the focus of research methods from the clinical to the control sciences, an effective survey was carried out to meet the study’s two objectives. With remarkable success, other authors have utilized this technique to retrieve data from scientific publications (Garengo, et al. 2005). This assessment only considers ML applications in control over production planning within the framework of version 4.0. Although (Zhong et al. 2016).’s emphasis on assembly grew constrained, they did provide a bibliometric evaluation of huge records packages in a variety of areas, including banking,

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healthcare, supply chain, and others. In their review of data-driven smart manufacturing, (Jiang, et al. 2018) highlighted pertinent references. The reviewers found that there are many interesting gaps and insights to be found in the scientific literature about machine learning, but they didn’t review all of the chapters. Instead, they used a pre-defined approach to review 3000 different chapters. They found that Natural Language Processing (NLP) can help spot important trends, but it can’t show all of the interesting information in the chapters. A systematic review, on the other hand, is a stricter review that follows a specific process while also looking at each paper in detail. The supply chain domain is related to PPC, but it’s not always included in the scope of this assessment. This means that readers should contact the authors of (Sharp, et al. 2018) for more information about quantitative approaches, technologies, terminology, and significant adaptability of the supply chain elements. There is a growing area of research called supply chain flexibility, which is focused on adapting to disruptions. This was studied by Hosseini et al. (Hosseini, et al. 2016), who utilized Bayesian network techniques to select providers based on flexible criteria. Finally, Ivanov and Hossein (2016) offered a tool for evaluating, while selecting important supply network links, considering supplier resilience. Industry 4.0 is a way to create digital copies of real-world objects and processes so that they can be more easily managed and improved. It aims to create a network of interconnected value chains that can share information and resources more efficiently. The aim is to shorten the development cycle for products, by integrating key manufacturing, operation functions, and design into a single digital process. To create an entirely connected piece of equipment, several input channels from operation to product engineering must be established. No matter how it is set up in the assembly system, the related hardware makes it possible to maintain vision. The main goal is to compile a variety of hardware-generated online and offline signals to support models that can instantly detect an abnormality or problem. The chapter’s reminder is broken out as follows: The auditing writing procedure that was utilized to choose the sample of a scientific chapter will be explained in the section titled “Research techniques and contributions”. The main goal of this study will be clearly outlined, and a brief bibliometric calculation will be presented to examine the important concepts used in the study. The part of the four axes that make up the logical system will be discussed under “Analytical framework” incorporates.

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Several Machine Learning Paradigms ML is a field of study that tries to improve performance on a specific task by using data that is similar to the task. This is done by looking for patterns in the data and then using that information to improve performance on the task. There are a few different types of machine learning, but the most common is supervised learning. supervised learning is when the machine learning system is given a set of training data (data that is used to teach the machine learning system how to do the task) and then the machine learning system is asked to do the task on a new set of data. Unsupervised learning is when the ML system is not given a set of training data. Unsupervised learning is when the ML system is given a set of data, but the particular type of system is not told what task to do with the data. One common type of supervised learning is gradient descent. Gradient descent is when the ML system is given a set of training data, and then this type of particular system is asked to find the lowest point on a curve. The machine learning system is given a set of parameters (like the slope of the curve), and then the machine learning system is asked to find the lowest point on the curve. The machine learning system is then given a new set of training data, and it is asked to find the lowest point on the curve. This process is repeated until the machine learning system no longer improves on the training data. The third approach to machine learning is genetic algorithms. Genetic algorithms are used to learn from data. They work by breaking data down into binary or Boolean pieces and then creating rules based on those pieces. The most well-known way to resolve conflicts in this way is with the go big or go home matching methodology. Rules are applied sequentially, depending on the design of the production system. Some genetic algorithms use crossover and mutation, which are undedicated from real Genetic Algo techniques. These methods produce new rival rules from high-quality guardians. Rule enlistment also uses Condition-action rules, Decision Trees, and other informational frameworks. The Exhibition-related part organizes cases further down the Decision Tree branches using an all-or-none match mechanism or chooses the main rule whose criteria are appropriate for the situation. The final way that we learn new information is by using a different type of algorithm called analytical learning. This algorithm takes information and breaks it down into different logical principles. It then uses a performance framework to solve multi-step issues. One way that this is done is by using a statistical function to help decide which pieces of information are important. This function is then used to help create the information structure for the learning system. The way

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that we learn new information is by using different algorithms, like a rule induction engine. This engine will go through all the different decision trees and look for ways to include information about what is being learned in the system. One common strategy is to first present data as Horn clauses. This is similar to how Prolog stores information. Then, the learning elements use historical data to build up more complex rules. They use nearby search-control rules to help find evidence that can help support these more complex rules.

Industrial Applications The sections that follow, arranged according to the theme, describe different types of ML and IoT applications that are used in manufacturing. Following a general introduction to each theme, there is a description of the important research questions our contributors have suggested and have dispersed in gathering materials and noteworthy distributions.

Design Using machine learning methods, numerous research teams have independently overcome numerous design issues. The talk covers design models, which are a way of thinking about how things work. First, a specific design model is assumed, which then continues with a technique for integrated system generation and different methods of optimization. The latter two cases address particular system and product design issues (Srinivasan, et al. 1997). By completing the total idea and convening intricacies of new, somewhat identical components using current part knowledge, one particular design retrieval method attempts to reduce the repetitive design effort. Approaches design retrieval as a concept of problem in clustering, where bits are individually displayed and assigned to an existing group or family based on the likelihood of correctly anticipating the value of the relevant characteristic. An important aspect of the algorithm has proven to be its straightforward hierarchical structure. In a small example, technique beat the extensive look, according to a (Anand, et al. 1995) report on tolerance allocation using ANN. The method has the benefit of requiring no quantifiable assumptions and the capacity to accept adaptive mean shifts, making it appropriate for utilization as a component of a system for dynamic quality assurance and learning.

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Models must be created in the first phase, model generation, to meet aims for current modeling (such as execution speed or outcomes readability) (Figure 2).

Figure 2. The two steps of the interactivity-based adaptive modeling system are model creation and model application.

In the earliest stages, a huge number of models with distinct modeling properties are built using a variety of inductive learning techniques (A model can divide the qualities of something and keep track of how often each one is repeated. Then, it can use that information to decide how much respect to give to each quality.).

Organizing Processes Cycle arrangement expertise is one of the most successful fields for integrating numerous learning methodologies because of its extreme complexity and the intricate relationships between knowledge from many sources. Several applications use pre-made tools and procedures in addition to experiments that produce data that is helpful to those in addition to the technical community. The experimentation on this subject is arranged below by application area.

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The Identification and Handling of Machining Features An explanation of the recognition of a predetermined set of artificial features using a rule-based technique is given in (Chan, et al. 1994). The user must be capable of altering the identification due to the possibility of new components, and processes in real-time revealing unanticipated component interactions. In this case, this is done by adding new rules as necessary. New rules added that are tailored to the situation increase identification skills, and factory-specific issues improve the interface arranging component. We are still learning what boundaries work best for each person, so we are changing the way we acknowledge people. This change might take some time, but it will help us get the right answers in the future. On the other hand, step-by-step learning can be extremely inefficient for both humans and machines since it cannot generalize.

Group Technologies and Machining Cells Part families are a new type of family, and GT (group technology) problems can happen when people are working together in groups. So, some special solutions need to be used because the traditional ways of coding and classifying things don’t always work well with part families. Previous attempts at numerical programming or syntactic example recognition failed, mostly because they were imprecisely proportionate to changing models and sections of machines. This area of investigation is crucial because, in these, a link between plan and assembly is being established. The most popular methods are feature-based ones. By using the present CAM cycle to help produce the output of the decomposition process, we could make it easier for people who design things (CAD) to work with the same tools and methods (CAM) that are used to decompose (Subrahmanyam, et al. 1995) things.

Organization and Decision-Making Activity planning is the process of creating a scheme from the required component-specific (Operation Area) to the appropriate functional bounds (Area of choice). Learning helps us decide how to proceed with this planning by using the manuals and modeling tools that are already available (for example, simulations). It has been shown that there are significant variations

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(Kim, et al. 1996) in activity planning techniques, and this is something that Learning is working on improving.

Planning the Order of Operations There are two main limiting factors when it comes to using AI in CAPP: the lack of successful business AI advancements and the information procurement bottleneck (Eversheim, et al. 1993). There have been many advances in CAPP (computer-aided process planning), which is a way of planning the steps that a process will take. One of the latest advances is the use of AI and ML techniques to automate some of the tasks involved in CAPP. What is an organizing problem? An organizing problem is a problem that has two characteristics: the fundamental structure of the world and an objective declaration. One approach is to use an integrated, hybrid approach (Veloso, et al. 1995), just as is done in other fields. PRODIGY is a learning and planning system that can be used for this purpose. A series of operators, beginning with the fundamental condition, convert reality into one that achieves the desired result statement to address the organizing problem. When all of an operator’s prerequisites are satisfied before it is executed, the operator arrangement (also known as an absolute request plan) is valid. This means that it is reasonable to expect that all of the prerequisites are satisfied before the operator arrangement is executed.

Breach during Development of ML Solution An Archive of Attacks That Have Been Carefully Selected The MITRE ATT&CK framework groups together the same “strategies and methodology” that attacks are broken down into in traditional software security. The database includes information on attacks by nation-state adversaries as well as researchers. Each attack is described, with information on the superior perseverance threat that is familiar to use, suggestions for detection, and citations to published works that provide more contexts. The two most important aspects of the rules are the Axes of Quality and the Technical Catalog Assessment. Here we propose that different types of similar systematic records of attacks be created, ideally by expanding the popular

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Miter Framework. We encourage adversarial ML experts to submit new attacks to the Miter system when they are released so that detectives for security can view multiple legitimate and malicious ML assaults in one place.

Machine Learning with Adversarial Security Code In addition to enabling more developers to check the source code in a conventional software setting, secure coding practices enable engineers to remove exploitable vulnerabilities from their applications. For example, there are well-defined secure coding principles in Python, C, Java, and C++ (Sharif, et al. 2017) that protect against typical software flaws such as memory deterioration. There is a paucity of antagonistic machine learning- particularly security guidance. TensorFlow is the major system that delivers thorough guidelines surrounding dangers from programs in the past (Carlini, et al. 2017) as well as access to instruments for evaluating hostile attacks (Nigam, et al. 1998), even though other toolkits also include suggested practices (Tensor Flow, Pytorch , Keras [Carlini, et al. 2017)]). Providing best practices for removing ambiguous program behavior and exploitable vulnerabilities should be the primary goal of future adversarial ML research, in our opinion. Due to the intricacy of the field, we are aware that making specific recommendations is difficult (Hall, et al. 2009).

Machine Learning Systems: Dynamic and Static Analysis Static analysis tools are used to find potential defects in existing code without having to write the code or check for coding standard violations. The tools convert the source code into a hypothetical tree of language structures, which produces a control flow graph. The graph of a control flow is examined for unusual coding conventions and for quality assurance procedures that have been converted into logic. If an illogical procedure is found, it is raised as an error. With Python tools like Pyt , for example, one can find security flaws in conventional software. Before application code is published to the code repository, static analysis tools are predicted to eventually integrate with IDEs to deliver a different or similar scientific understanding of the linguistics and semantics, preventing the introduction of security vulnerabilities.

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Systems Using Machine Learning for Logging and Auditing ML systems are like computers, which can be used to do things like understand text or pictures. A lot of things happen on ML systems, like when someone types something in a text box, or when a picture is downloaded from the internet. Sometimes, these things can happen accidentally. But sometimes people do things on ML systems intending to harm, like when someone tries to hack into a computer. ML systems can be used to help security responders (Twycross, et al. 2010) (Van der Aalst, et al. 2005) figure out if something bad is happening on a computer. For example, if someone types in a string of gibberish, or if a picture of a bomb is downloaded from the internet, ML systems can help security responders figure out what happened. ML systems can be used to help security responders (Papernot, et al. 2018) figure out if something bad is happening on a computer, but sometimes people try to harm ML systems. This means that security responders need to have some other way of knowing if something bad is happening on a computer, like by looking at logs or screenshots. But sometimes people try to harm ML systems. For example, if someone tries to hack into a computer, they might try to use the computer’s ML system to do something bad.

Monitoring and Detection of ML Systems ML environments are becoming more complex and difficult to understand because security analysts don’t have the necessary skills. A lot of exciting investigation has been done on the shortcomings of the present ineffective location frameworks and how to improve (Gilmer, et al. 2018) them. Additionally, we advise that detection algorithms be created in a way that is easy for security professionals to understand. The identification of logic, for example, is expressed in a particular standard form known as Sigma in traditional software security. By enabling self-documentation, Sigma can transform a single analyst’s insights into a method of thinking for many people’s protection for others. The function of Sigma may translate the findings of one expert into preventive activity for some by allowing the selfdocumentation of substantial justification for detecting an attacker’s methods, in contrast to MITRE and ATT&CK which offer a vast library of awareness of antagonistic strategies.

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Analytics, Machine Learning, and Data Data has grown rapidly in the past few years, so new industries and technologies have been created that rely on machine learning and analytics. These industries are doing well, but different types of traditional industries like healthcare, manufacturing, and retail are struggling. Meanwhile, ML and IoT are the foundation of some new industries, such as cloud-based customer support. Yet, enterprises, sectors, or organizations are not the only ones that can use analytics and machine learning. It has a border-level impact on both domestic and international political policies. Governments must carry out thorough data analysis and technology assessments based on data before building a “smart city,” for instance. In recent years, the amount of data has increased tremendously. This led to the development of technologies, and industry services based on machine learning and also in analytics. ML is a technology that can be used to help support new sectors, like customer support. However, this technology is also useful for many other things, like analyzing political policies. Before implementing a “smart city,” the government needs to do a lot of data analysis and study different technologies. Analytics for manufacturing using machine learning.

Analytics for Manufacturing Using Machine Learning Manufacturing or Construction is renowned for its thorough description, quality management systems, and risk-specific report. There has been development in the area of technology, especially if you find the different areas of industrial establishment and contemporary digitalization, also in the production of auxiliary and technological records with detailed information described from sensors, other machine-generated data also from telemetry; they utilized ML or AI techniques, Data Science, CC (Cloud Computing), IoT (Internet of Things) methods to assist their clients. A subtle revolution is taking a special place using data to create a useful instrument for a range of goals, including productivity and profit. Among its uses are those in the creation of chemicals, vehicles, meteorology, and a wide range of other things. Manufacturing has historically been slower to adopt ML and AI analytics and technologies than other sectors like Finance, Healthcare, and Customer service. On the other hand, widespread success across industries has encouraged industrial leaders to use it.

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Machine Learning Analytics for Banking and Finance The financial markets are always changing and adapting, so the market behavior of financial professionals is constantly being studied. One type of study that is being done is called ‘quantitative analysis’. This type of analysis is based on the idea that psychological traits, such as mood and attitude, can be used to predict how the markets will behave. Recently, a new financial concept has been developed that combines these psychological traits with quantitative analysis. This new concept is called ‘behavioral finance’. Behavioral finance is being used more and more in the banking and finance world because it is important to keep track of client data, keep accurate previous records, and understand the economic or financial world’s quantitative side.

Machine Learning-Based Healthcare Analytics All of us are affected by the healthcare sector. As a result of cutting-edge technologies, traditional healthcare institutions are transforming. A few years ago, the medical industry was dominated by doctors. However, in recent years, technology has become a big part of managing the health of patients, and analytics play a big role in this. Improve treatment outcomes and results while reducing expenses with ML analytics. DL, ML, and AI are transforming the healthcare industry (Figure 3) and giving it new purpose and direction.

Figure 3. Utilising healthcare analytics.

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By combining an imaging library with computations and models that ultimately identify and categorize medical disorders, deep learning techniques are used to teach PCs how to analyze medical images. Accurate imaging diagnosis enables the prompt delivery of medication to millions of people. Millions of lives are subsequently saved as a result.

Analytics for Marketing Using Machine Learning Success in marketing depends on adaptation. Modern marketers employ social technology to evaluate and examine factors that might have an impact on consumers. Using web analytics technologies, organizations may monitor users’ online activity and learn more about their browsing patterns. Understanding and tracking technology advancements have led to the development of intelligent machine learning technologies. Marketing can benefit from machine learning analytics in several different ways. Customized product recommendations and messaging, for instance, hold tremendous promise. Consumers change from inactive site visitors into active clients for that company or website as a result. Marketing firms can use machine learning-powered analytics to collect, manage, and analyze large amounts of data from a variety of sources (such as the flow of website visits, purchasing patterns, responses to earlier advertisements, and mobile app usage). In the area of marketing, machine learning analytics can help aqueduct the informational and analytical divide. Businesses can gain a competitive edge by using ML-based systems to extract insights from data. Text analytics, smart statistics, scenario-based simulation, predictive modeling, “what if” research, scenario-based “what if” research and Data Mining are all components of data science. These methods are used to find significant analyzing data for patterns and correlations.

Artificial Intelligence in the Retail Industry The retail sector is experiencing positive change as a result of artificial intelligence. Retailers can use it to locate key activity spaces that are concealed beneath a sea of opportunities and useless data. They are now capable of taking in and evaluating data that was once thought to be beyond the capabilities of humans—at least when done manually. With data on consumer turnover,

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merchants can anticipate the future and make educated judgments. Because of advancements in data capture and storage technology, there is now an abundance of information available regarding items, prices, sales, performance, consumer behavior, and logistical details.

Consumer Analytics Using Machine Learning Using customer analytics, businesses can anticipate and predict what they will do for their customers. They can also figure out which clients are in danger, how to attract new ones, and who is committed to their brand. It also assists businesses in segmenting their customer base to implement the best business strategy for client acquisition and retention. In addition, it determines whether High-value clients are at risk and what proposal to make to that group of customers. When there is a lot of information available for free, it is essential to use AI or ML-based analysis to analyze customer data to make good business decisions. On the other hand, the AI-based examination’s success depends on using the appropriate system, good information, well-studied plans, and extensive arrangements in place.

Research on Predictive Maintenance Many Predictive Maintenance approaches have been presented in the literature. This research often uses the IoT idea to obtain sensor data, which is then evaluated using ML and DL models. It is possible to create a model of the entire system using the sensor data that has been obtained, such as temperature, vibration, and humidity. By spotting anomalies in the Big Data generated by the model, it is possible to alert the system and, more importantly, maintain it. This enables intervention at the machine’s primary stop before it breaks down. Unexpected downtimes, staff costs, and maintenance expenses can all be avoided with such planned maintenance interventions. This will increase the effectiveness of production (I. Aggarwal, et al. 2022). An ML model was used to evaluate the data that was gathered in a different experiment by controlling inexpensive IoT sensors. Data was transferred to ERP frameworks and stored in the cloud thanks to Azure IoT, which made data collection simpler. The purpose of neural networks was to

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provide rapid and precise prediction. Feed-forward layer logic is used to set up the neural organization geography, and integrated circuit bundles were used to direct a significant evaluation of the unshakable quality of the Electro Migration process. The framework’s MTTF (mean time to failure) features and failure type predictions were calculated using these integrated circuit packages. The FB100 package of functions from the machine learning library was used in this case to organize the providing failure forecasts using machine data, and with the introduction of Industry 4.0, a specific application focused on analyzing the Big Data generated by Industrial IoT and distributed control systems. In another industrial activity, IoT technology was used to manage equipment, resulting in the production of big data. Additionally, information on operator error interventions and failed alarms was gathered. Maintenance 4.0 is a brand-new platform that combines cloud technologies, ML, and Data Mining to perform data analytics on the data. The study leads to the system being dynamically monitored, and the maintenance of the application was fully naturally incorporated into the support specialist’s work area plan. In contrast to other studies, ours does not include the installation of additional equipment or technologies for IoT sensors. Instead, only prior failure alerts from the ERP system are used to create a subset of Big Data. The ability of an ML and AI algorithm to anticipate errors is the subject of this investigation (I. Aggarwal, et al. 2022).

The Proposed Approach In this study, production line downtimes were predicted using data from the facility’s prior three years of downtimes. Unplanned downtime is primarily brought on by hardware issues and environmental factors. Expert investigations have found certain similarities between the locations where the industrial packing robots are shown, including working hours and seasonal concerns. An AI system is built to handle data during business hours from the system. This framework is based on professional knowledge, and failure data from the training process is used to teach the AI. A grouping strategy is used to organize the data that will be delivered to the AI. After a process of weight refreshes, the AI gets to the point where it can predict failure. The hardware of the system is also subject to a component reliability study. Considering the ongoing system outages, the most MTTF values for every component are computed. By showing accessible information in the AI and performing reliability information, theoretical and practical tests are conducted.

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Possibilities for Further Research This innovative study examined several research articles that were selected based on a thorough literature review. These chapters were examined using a four-dimensional analytical framework. To study ML in Control Systems, Production Planning and, researchers first looked at the different components of the technique. Then, they located and assessed the data sources needed to build a model. The various applications of data-driven models in Industry 4.0 were finally discovered through an analysis of use cases. Their application led to the discovery and analysis of 4.0’s properties. Eleven recurrent tasks were found as a result of the activities, and they were then utilized to construct an ML-PPC model. Depending on how frequently they were utilized, four groups were created: OUAs (Often Used Activities), SUAs (Sustained Use Activities), CUAs (Commonly Used Activities), and MUAs (Medium Use Activities). The activities of the CUAs and OUAs clusters are extensively documented based on these clusters in this literature. MUAs perform a significant amount of data pre-processing work, but academics haven’t done a good job of describing it. After a careful analysis of the approaches, the most often used families in the scientific literature were identified. Q-Learning, Regression, clustering, ensemble learning, and other techniques were shown to be the most successful ones. Based on these conclusions, the top six most often used families have undergone a temporary formation inquiry. The findings revealed a greater interest in ensemble learning, which led to a more in-depth examination of each family’s strategies. The most common method for neural networks was the multi-layer perceptron. However, applications of deep learning methods like LSTM, CNN, and Deep Neural Networks are just beginning. There are a few things to bear in mind when it comes to ensemble learning. The study found that Rapid Miner, MATLAB, and Python were the tools that were utilized the most frequently when creating ML-PPC models. However, one flaw of the review is that the majority of authors did not mention the tools they used. The fact that all the results are based on a small number of different scientific literature suggests that it can be utilized in most classrooms in large part. The Scalability and software cost also the availability of market-labor skills, integration with existing information systems, and other factors are additional considerations. Artificial created and managerial the most common right now is data frequently used information sources. The first option suggests that businesses should place a value on the data that is stored in data systems, while the final option argues that it is difficult to collect all of the data needed to build ML-PPC models.

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The sparing use of data from Internet of Things (IoT) sources like Equipment and Product data demonstrates the strong interest in these data collection techniques. In the end, MLPPC models were unable to include User data because of the difficulties in acquiring it and the significant responsibility it carries for data security. Regarding 4.0 features, the findings indicate that selfactualization: the management of resources occupies a significant portion of the scientific literature in ML-PPC. This makes sense given that managing resources to implement the business strategy is one of all the PPC’s primary goals. The production method for self-direction, self-education, and knowledge revelation at the subsequent level, as well as age, all suggest that they will be dealt with more frequently. According to the proposed crossmatrix, 76% of the prospective research areas include either not studied at all or only sparingly. The ML-PPC is still an important topic because of this. To implement 4.0, offering numerous research opportunities. There are following three essential points could be used to summarize all three primary perspectives for future research: ●

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In ML-PPC, we recognize the importance of IoT, which helps us update our model to better address the problem of idea drift, and also improve the design of our data-gathering system. To do this, it is necessary to switch from a linear to a circular machine learning method and process, taking into consideration the requirement for frequent retraining using fresh data. With this method of thinking, it would be possible to identify the necessary elements and the retraining strategy early on and to measure them again at a cheap cost. Improvements to the PPC’s design, logistics, and integration are reportedly possible thanks to several use cases. On the other hand, recent literature seems to overlook methods as well as PPC-related applications in product and process design. The objectives of this research are to take into account environmental factors and the interaction of humans to improve the growth of moral Industry 4.0 manufacturing. This involves understanding what and how people communicate with the finest models of ML-PPC and taking into account both the immediate and long-term effects of the MLPPC system on the working circumstances of employees. If the system deteriorates the working conditions of employees, a rebuild is necessary.

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The following areas will receive further attention: ●

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The recommended order for choosing activities will be examined once ML-PPC models have been applied. Which of these needs to be finished to produce a plan that will help the shift from a linear to a circular workflow? Utilizing sector-specific data, the most appropriate methods and technology will be connected to each activity: To create a useful utility that is helpful to new practitioners in both research and industry, you need to connect the methodology, tools, and activities that are important to both fields. An evaluation of the data availability solutions and workarounds that are currently available will be presented to deal with the issue of class imbalance and make use of dependability learning in the context of PPC. A major concern has been identified as the availability of data.

Conclusion Machine-learning algorithms have demonstrated their usefulness in a wide range of practical manufacturing applications. Effective links to commercial databases, commercial implementations of these strategies, and well-designed user interfaces are now offered by several businesses worldwide. Many of these algorithms, though, do not come to their flaws. It is possible to swiftly mine tens of thousands of training instances from datasets using all the methods outlined in this article. On the other hand, numerous significant data sets are significantly larger. To develop effective AI or ML techniques for such high-class datasets, additional research is required. The primary goal of this in-depth literature review was to investigate the most pressing issues about machine learning and reasoning within the context of Industry 4.0. This industry’s principles and technologies were examined. Additionally, we discussed how difficult it would be to put it into practice. This research sought out ontology-using architectures and frameworks or ML models to find reasoning. In favor of focusing on predictive maintenance of the cyber-physical system and similar research that applied predictive maintenance in different scenarios, such as forecasting software breakdown was disregarded.

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References Adhikari, P., Rao, H. G., & Buderath, M. (2018, October). Machine learning based data driven diagnostics & prognostics framework for aircraft predictive maintenance. In Proceedings of the 10th International Symposium on NDT in Aerospace, Dresden, Germany (pp. 24-26). Ansari, F., Glawar, R., & Sihn, W. (2020). Prescriptive maintenance of CPPS by integrating multimodal data with dynamic Bayesian networks. In Machine Learning for Cyber Physical Systems: Selected papers from the International Conference ML4CPS 2017 (pp. 1-8). Springer Berlin Heidelberg. Balogh, Z., Gatial, E., Barbosa, J., Leitão, P., & Matejka, T. (2018, June). Reference architecture for a collaborative predictive platform for smart maintenance in manufacturing. In 2018 IEEE 22nd international conference on intelligent engineering systems (INES) (pp. 000299-000304). IEEE. Bousdekis, A., Mentzas, G., Hribernik, K., Lewandowski, M., von Stietencron, M., & Thoben, K. D. (2019). A unified architecture for proactive maintenance in manufacturing enterprises. In Enterprise Interoperability VIII: Smart Services and Business Impact of Enterprise Interoperability (pp. 307-317). Springer International Publishing. Carlini, N., & Wagner, D. (2017, November). Adversarial examples are not easily detected: Bypassing ten detection methods. In Proceedings of the 10th ACM workshop on artificial intelligence and security (pp. 3-14). Chan, A. K. W., & Case, K. (1994). Process planning by recognizing and learning machining features. International Journal of Computer Integrated Manufacturing, 7(2), 77-99. Chukwuekwe, D. O., Glesnes, T., & Schjølberg, P. (2016). Condition Monitoring for Predictive Maintenance–Towards Systems Prognosis within the Industrial Internet of Things. Eversheim, W., & Schneewind, J. (1993). Computer-aided process planning—State of the art and future development. Robotics and Computer-Integrated Manufacturing, 10(12), 65-70. Garengo, P., Biazzo, S., & Bititci, U. S. (2005). Performance measurement systems in SMEs: A review for a research agenda. International journal of management reviews, 7(1), 25-47. Gilmer, J., Adams, R. P., Goodfellow, I., Andersen, D., & Dahl, G. E. (2018). Motivating the rules of the game for adversarial example research. arXiv preprint arXiv:1807.06732. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., & Witten, I. H. (2009). The WEKA data mining software: an update. ACM SIGKDD explorations newsletter, 11(1), 10-18. Hosseini, S., & Barker, K. (2016). A Bayesian network model for resilience-based supplier selection. International Journal of Production Economics, 180, 68-87. I. Aggarwal, S. Sahana, S. Das, and I. Das, “AI Based Interactive System-HOMIE,” in Advanced Communication and Intelligent Systems: First International Conference,

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ICACIS 2022, Virtual Event, October 20-21, 2022, Revised Selected Papers, 2023, pp. 339–347. Jiang, S. L., Liu, M., Lin, J. H., & Zhong, H. X. (2016). A prediction-based online soft scheduling algorithm for the real-world steelmaking-continuous casting production. Knowledge-Based Systems, 111, 159-172. Jiang, S. L., Zheng, Z., & Liu, M. (2018). A preference-inspired multi-objective soft scheduling algorithm for the practical steelmaking-continuous casting production. Computers & Industrial Engineering, 115, 582-594. Kim, S. H., & Lee, C. M. (1996). Advanced manufacturing systems through explicit and implicit learning. Work paper, Graduate School of Management, KAIST, Sooul, Korea. Kopardekar, P., & Anand, S. (1995). Tolerance allocation using neural networks. The International Journal of Advanced Manufacturing Technology, 10, 269-276. Kunst, R., Avila, L., Binotto, A., Pignaton, E., Bampi, S., & Rochol, J. (2019). Improving devices communication in Industry 4.0 wireless networks. Engineering Applications of Artificial Intelligence, 83, 1-12. Lee, J., Kao, H. A., & Yang, S. (2014). Service innovation and smart analytics for industry 4.0 and big data environment. Procedia cirp, 16, 3-8. Lu, S. C. (1990). Machine learning approaches to knowledge synthesis and integration tasks for advanced engineering automation. Computers in Industry, 15(1-2), 105-120. McCallum, A., & Nigam, K. (1998, July). A comparison of event models for naive bayes text classification. In AAAI-98 workshop on learning for text categorization (Vol. 752, No. 1, pp. 41-48). Papernot, N. (2018). A marauder’s map of security and privacy in machine learning. arXiv preprint arXiv:1811.01134. Rath, S. K., Sahu, M., Das, S. P., & Mohapatra, S. K. (2022, March). Hybrid Software Reliability Prediction Model Using Feature Selection and Support Vector Classifier. In 2022 International Conference on Emerging Smart Computing and Informatics (ESCI) (pp. 1-4). IEEE. Rath, S. K., Sahu, M., Das, S. P., & Pradhan, J. (2022). An Improved Software Reliability Prediction Model by Using Feature Selection and Extreme Learning Machine. In Meta Heuristic Techniques in Software Engineering and Its Applications: METASOFT 2022 (pp. 219-231). Cham: Springer International Publishing. Rath, S. K., Sahu, M., Das, S. P., & Pradhan, J. (2022). Survey on Machine Learning Techniques for Software Reliability Accuracy Prediction. In Meta Heuristic Techniques in Software Engineering and Its Applications: METASOFT 2022 (pp. 4355). Cham: Springer International Publishing. Rath, S. K., Sahu, M., Das, S. P., Bisoy, S. K., & Sain, M. (2022). A Comparative Analysis of SVM and ELM Classification on Software Reliability Prediction Model. Electronics, 11(17), 2707. Schmidt, B., & Wang, L. (2018). Predictive maintenance of machine tool linear axes: A case from manufacturing industry. Procedia manufacturing, 17, 118-125. Sharif, M., Bhagavatula, S., Bauer, L., & Reiter, M. K. (2017). Adversarial generative nets: Neural network attacks on state-of-the-art face recognition. arXiv preprint arXiv:1801.00349, 2(3), 139.

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Sharp, M., Ak, R., & Hedberg Jr, T. (2018). A survey of the advancing use and development of machine learning in smart manufacturing. Journal of manufacturing systems, 48, 170-179. Srinivasan, M., & Moon, Y. B. (1997). Aframework fora goal-drivenapproachto grouptechnology applications using conceptual clustering. International journal of production research, 35(3), 847-866. Subrahmanyam, S., & Wozny, M. (1995). An overview of automatic feature recognition techniques for computer-aided process planning. Computers in industry, 26(1), 1-21. Suneel Kumar Rath, Madhusmita Sahu, Shom Prasad Das,2021 “Software Reliability Prediction: A Review”. International Journal of Engineering Research & Technology (ISSN: 2278-0181). Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a methodology for developing evidence‐informed management knowledge by means of systematic review. British journal of management, 14(3), 207-222. Twycross, J., Aickelin, U., & Whitbrook, A. (2010). Detecting anomalous process behaviour using second generation artificial immune systems. arXiv preprint arXiv:1006.3654. V. Maheshwari, S. Sahana, S. Das, I. Das, and A. Ghosh, “Factors Influencing Security Issues in Cloud Computing,” in Advanced Communication and Intelligent Systems: First International Conference, ICACIS 2022, Virtual Event, October 20-21, 2022, Revised Selected Papers, 2023, pp. 348–358. Van der Aalst, W. M., & de Medeiros, A. K. A. (2005). Process mining and security: Detecting anomalous process executions and checking process conformance. Electronic Notes in Theoretical Computer Science, 121, 3-21. Veloso, M., Carbonell, J., Perez, A., Borrajo, D., Fink, E., & Blythe, J. (1995). Integrating planning and learning: The PRODIGY architecture. Journal of Experimental & Theoretical Artificial Intelligence, 7(1), 81-120. Zhou, C., & Tham, C. K. (2018, December). Graphel: A graph-based ensemble learning method for distributed diagnostics and prognostics in the industrial internet of things. In 2018 IEEE 24th international conference on parallel and distributed systems (ICPADS) (pp. 903-909). IEEE.

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Chapter 5

An IoT-Enabled Model for COVID-19 Patient Healthcare Sandeep Mathur1,* Rajbala Simon2,† Vinayak Vashistha2,‡ and Yazdani Hasan3,¶ 1School

of Science, Noida International University, Greater Noida, Uttar Pradesh, India Institute of Information Technology, Amity University, Noida, Uttar Pradesh, India 3Manav Rachna University, School of Engineering and Technology, Haryana, India 2Amity

Abstract The definition of the “Internet of Things (IoT)” has caused another advancement for IoT union in Artificial Intelligence, products sensor, and implementation frameworks. IoT innovations are the perfect synonym for an item related to these ideas in this technological revolution of covered devices and machines. For example, lighting apparatuses, indoor regulators, house securities framework and camera, and rest of house apparatuses help the most solo regular biologic system and could be controllable through devices adjoined with environmental surroundings examples being mobiles and brilliant speakers. This ‘Savvy Healthcare,’ likewise known, prompts the formation of digital medicinal service frameworks, partnering feasible restorative asset and social *

Corresponding Author’s Email: [email protected]. Corresponding Author’s Email: [email protected]. ‡ Corresponding Author’s Email: [email protected]. ¶ Corresponding Author’s Email: [email protected]. †

In: Intelligent Decision Support System for IoT Enabling Technologies Editors: Subrata Sahana, Anil Kumar Sagar, Sanjoy Das et al. ISBN: 979-8-89113-249-8 © 2024 Nova Science Publishers, Inc.

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Sandeep Mathur, Rajbala Simon, Vinayak Vashistha et al. insurance administrations. IoT devices could be used for empowering remote well-being checkers and crisis notice frameworks. Goldman Sachs, 2015 reports showed that the social insurance IoT devices “could spare the health care in the United States for more than $300 billion in yearly human services uses through expanding income and diminishing expense.” Far over, this usage of mobiles for helping medical follow-ups prompts the making of ‘m-wellbeing,’ usage “to investigate, catch, transmit and store well-being dimensions from numerous assets, with sensors and other biomedical finding outlines.” In this chapter, we will propose a patient care model in which we will explain the mechanism and modeling of the Healthcare system using the IoT through which we can help COVID-19-suffering patients and their family members effectively, based on the Internet of Things.

Keywords: Artificial Intelligence, IoT, Healthcare, Biologic System, Cloud Computing.

Introduction It is the use of Internetwork in physical devices, setup with devices, Internet networks, and other types of gadgets, (e.g., sensors) that enabled the devices to convey and cooperate with others about the Internet and could be remote checked and controllable. The definition of the IoT had developed cause of this union in other advancements, constant examinations, Artificial Intelligence, product sensors, and implant frameworks. At the buyer showcase, Internet of Things innovations are needed for item contributing to this idea of the “savvy house,” covering devices and machines, (e.g., lighting apparatuses, indoor regulators, house securities frameworks and cameras, and other house apparatuses) which help in almost one regular biological system and could be controlled through devices categorized with these environments: cell phone and brilliant speaker. These IoT ideas accepted noticeable analyses, particularly along the field of protections and securities concerns noticed through these devices and the aim of inescapable nearness (Mathur, 2021).

Therapeutic and Medicinal Services In the given Figure 1 the vast range of applications of IoT Healthcare has been showcased.

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Figure 1. Application of IoT Healthcare (Mathur, 2022)

The IoT in the field of Medics (likewise known as webs of well-being’s thing) are used in the Internet of Things for therapeutics and well beings regarding the purposeof info accumulation and examinations in research, and observing. The ‘Savvy Healthcare,’ prompts these formations in the digitalized medicinal service frameworks, partnering usable restorative asset and socials insurances administrations. Internet of Things devices could be used for empowering remote well beings of checked and crisis noticed framework. This could likewise change it for guaranteed suitable weights and backing joined by patient’s w/o this manuals cooperation by the nurse. Goldman Sachs 2015 reports showed which social insurance Internet of Things devices “could spare this United States more than $300 billion in yearly human services uses thru expanding income and diminishing expense.” However, the usage of mobiles for helping therapeutics followed-ups prompted the making of ‘m-wellbeing,’ used “to investigate, catch, transmit and store wellbeing measurements from sources.” The sensor makes the systems savvy sensor that could gather, procedure, move, and dissect profitable info in more conditions, e.g., association in-house checking devices for clinical base frameworks. Start to finish well beings checked that the Internet of Things stage is an add on to accessibility about antennas and perpetual patient, helping solo oversees for wellbeing of vital and repeated medicine prerequisites. Advancement in plastics and textures device manufacturer strategies has empowered ultra-minimal efforts, used-andtossed IoMT sensors. The sensor, along with the required RFID devices, could be manufactured through papers or e-materials as a remote-control expending detection device. Application has been settled up about purposes of-cares restorative diagnostic, where versatilities and low frameworks multifaceted nature in Figure 1. Application of IoT Healthcare (Mathur, 2022).

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Figure 2. Diversification of IoT Healthcare (Sandeep, 2021).

In the given Figure 2, the Diversification of IoT Healthcare is explained. Initiating in 2018 IoMT wasn’t exclusively being joined by the clinic research centers industries, yet additionally in this medicine service and health care coverings ventures. IoMT during human services industries has currently allowed specialists, patients, and others (e.g., gatekeepers of patients, attendants, and family, and as follows.) as a piece of the framework, where patient records are introduced in the databases, allowing specialists and the reminder for medicinal staffs for approaching this persons’ data. Parallel, IoTbased framework is quiet a focus, which includes adapting to these patient ailments. IoMT aims at protection of businesses and gives access to the betterment and newest kind of dynamic data. These utilizations of Internet of Things for human service assume the central job as overseeing endless sicknesses and malady counter of action and control. Remotely observing is created through conceivable association in ground-breaking which is remotely arranged. The network empowers well-being experts to catch patient’s information and apply complex calculation in well-being information for the investigation.

Literature Review ●

In the given Figure 3, the various types of IoT Healthcare have been defined. To Turn Data into Actions: Quantified well- beings are eventually the fate of human services as well as beings which quantifiable could be improved. Consequently, it’s insightful for exploiting evaluated well-being innovation Figure 3. Types of IoT Healthcare (Taiwo, 2020).

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Figure 3. Types of IoT Healthcare (Taiwo, 2020)

Figure 4. Cloud Computing through IoT Healthcare (Shah, 2019).

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We additionally realize which information influences execution in this way, the item estimation and following of well-being for better results is the reason we need IoT. In the Figure 4, the access of Cloud Computing through IoT Healthcare. To Improve Patient Health: What if this wearable device associated with a patient discloses to you when his pulse is going haywire or in this event which he has fallen behind in taking great consideration of himself and shared which data on other devices which you utilized while working? By refreshing the individual wellbeing information of patients on this cloud and dispensing with this need to nourish it into these EMRs, IoT guarantees that each minor detail is thought about to settle on these most worthwhile choices for patients. Additionally, it very well can be utilized as a therapeutic adherence and house observing instrument.

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To Promote Preventive Care: Prevention had converted into the essential regions of centers as social insurance costs are anticipated for becoming unmanageable afterward. This among boards accesses for continuing, greater constancy info on each well beings would change social insurance through helping the individual lives more beneficial live and counteracts ailment. To Enhance Patient Satisfaction and Engagement: IoT could expand tolerant fulfillment through advancing careful work processes; e.g., illuminating patients’ release from medical procedures to their families. It could build quiet commitment by enabling patients to invest more energy in cooperating with their doctors as it diminishes this requirement for direct patient-doctor communication as devices associated with this web are conveying significant information. To Advance Care Management: It could empower care groups to gather and associate much information focuses on close-to-house wellness from wearables like pulse, rest, sweat, temperature, and movement. Thus, sensor-bolstered data could convey alarms to patients and parental figures progressively, so they get occasionactivated information like cautions and triggers for raised pulse and so forth. This won’t simply greatly improve work process streamlining yet in addition, guarantee that all consideration is overseen from this solace of house. In the given Figure 5, Advance Care management through IoT has been showcased. To Advance Population Health Management: IoT empowers suppliers to coordinate devices to watch this development of wearables as information caught through this device will fill in this information which is generally passed up as a great opportunity in EHR.

Figure 5. Advance Care management through IoT (Mathur, 2021).

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Care groups could get knowledge-driven prioritization and use IoT for house checking of constant sicknesses. This is another way in which parental figures could make their quality felt in the everyday lives of these patients.

IoT Healthcare Uses Diminishing Emergency Room Wait Times Hardly anything is as dull and exhausting as the visits to the crisis rooms. Besides the subsequent restorative costs, crisis rooms visit could take hours to finish Because of some ongoing resourcefulness and the IoT, in any event— “Mt. Sinai Medical Center in New York City” — adequately cut sit-tight occasions for half of these crisis rooms patients which is needing inpatient care. The organization along with “GE Healthcare and newest, IoT-driven programming, known as AutoBed,” track inhabitance amongst Twelve hundred units and factor in fifteen distinct measurements to survey these requirements of individual patients. Which is an exceedingly powerful framework that features a portion of the more imaginative and energizing employments of the IoT.

Figure 6. Market size of IoT in healthcare (Parajuli, 2020).

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Remote Health and Monitoring The given Figure 6 shows the significant Market size of IoT in healthcare. At times, the patient doesn’t need to visit the crisis rooms or emergency clinics. The most evident and prominent utilization of medicinal service and IoT is remote well-being checking — which is now known as tally health. Along with the additions to these facts which minimize cost and dispose the requirements of certain appearance, it improves the patients’ natures in living through saving those burdens of movements. There could be a chance that a patient has restricted versatility or relies upon open transportation, something as straightforward as this could improve things significantly.

Guaranteeing This Availability and Accessibility of Critical Hardware In the given Figure 7 Remote health and monitoring in healthcare IoT has been defined. The present-day medical clinic requires cut edge programmed and equipped to work — some are evenly used for sparing or continuing human lives. “Like every single electronic device, this gear is inclined to various dangers — from power blackouts to framework disappointments — which could involve desperate.” Another IoT-driven arrangement through Philip, called e-Alert, intends to take care of this issue. Rather than trusting which device will come up short, Philips’ new framework adopts a proactive strategy for all intents and purposes observing restorative equipment and alarming medical clinic staff individuals if there’s an issue.

Figure 7. Remote health and monitoring in healthcare IoT (Mathur, 2022).

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Following Staff, Patients and Inventory Wellbeing “is the most extremist worry about any emergency clinics or medical offices or when nothing else it ought to be. It’s difficult to keep up this most extreme measure of security without this capacity to follow resources — staff individuals, patients and equipment — all through” this structure. It’s an assignment that is effectively accomplished in littler establishments, however, shouldn’t something be said about bigger offices whose component has various structures and grounds just as a great many patients and staff's individuals? Most are going for the IoT and ongoing area frameworks to encourage resource following. Not exclusively is it a cheap strategy for observing everyday exercises in an emergency clinic setting, yet it is subtle, successful and front line.

Improved Drug Management One of these most energizing achievements regarding human services and IoT comes in new types of doctor-prescribed prescriptions. It appears to be a work of sci-fi — yet pills containing minuscule sensors which are the size of a grain of rice could send a sign to an outside device — for the most part a fix worn on this body, to guarantee appropriate measurement and utilization. Such data could be precious with regards to guaranteeing patients to make sure to take their medicines and notwithstanding when endorsing future meds. Patients additionally approach this data, through a helpful cell phone application, to follow their presentation and improve their properties.

Tending to Chronic Disease In the given Figure 8, Data management through IoT in healthcare is explained. Repeating medical issues is never energizing, yet enormous leaps are being made in this treatment of such issues — and a lot of it is an immediate consequence of this IoT.

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Figure 8. Data management through IoT in healthcare (REALIGNS INC. 2021).

There isn’t one development or device which is helping treat unending sickness in this 21st century — it is this blend of wearable tech, cutting-edge examination and versatile network. Utilities like Fitbit utilize this IoT to screen individual well-being — such data could be imparted to a specialist to help comprehend repeating issues. An organization called Health Net Connect as of late settled a populace diabetic administration program to improve clinical treatment and lessen restorative expenses for patients — and they have just delivered some energizing outcomes.

Difficulties to Embrace IoT in Healthcare Security and Security of Patient Information Protection and security concerns are moderating this advancement for IoT to assume control over this division and demonstrate its potential. Human services are a very managed industry and require everything to be secure and safe since patient data needs to be ensured at any expense. Be as it can, security brakes still run wild in numerous pieces of this nation. It is obligatory to meet these consistent necessities under this Health Insurance Portability and Accountability Act (HIPAA). With regards to IoT, consistency is additionally required for application engineers, facilitating specialist organizations, distributed computing specialist organizations and fundamentally anybody, including subcontractors, associated with the social insurance space who can approach electronic patient well-being data (ePHI). In this way, no industry is more centered around virtualization security right now than medicinal services. In what manner could understanding information be verified?

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Curiously, a large number of these associations which endured breaks showed a disappointment in legitimate powers over physical devices. Monitoring physical devices is basic for picking up this certainty of patients in this utilization of IoT in social insurance. Suppliers, their partners, and merchant accomplices must guarantee conventions are set up and their representatives are prepared to tail them.

Lack of Consistency among Associated Cell Phones In the given Figure 9 Remote patient monitoring in healthcare is defined. The issue is that there are no basic models or correspondence conventions to encourage this way of collecting data from them. The multiplication of associated wellbeing and movement devices which many of us currently use makes it simple to perceive any reason why an absence of interoperability is a gigantic obstruction to advance. Wellbeing frameworks ought to keep up probably some basic standard for this sort of device kept in their office. It ought to be done to encourage the smoother transmission of information for faster bits of knowledge.

Figure 9. Remote patient monitoring in healthcare (Sahuja, 2014).

Vulnerable Information Transmissions Guaranteeing availability is a significant factor for IoT in human services. Information transmissions between devices, or between a device and this

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cloud must be expedient and persistent. Moreover, they ought to have the ability to have a lot of device associations in the meantime. Additionally, keeping up the quality and speed of transmissions is likewise a key factor for IoT working. To defeat these difficulties, this improvement of 5G innovation is now in progress. The other test is to make IoT sensors gather information regardless of whether there are issues with this system. Additionally, an IoT framework ought to have this option to tell you at whatever point a segment is detached, so these doctors know at each minute what is happening and if this framework is working appropriately.

Patient Status A non-specialized, critical factor is patient assent in embracing IoT. Patients are regularly confounded about this presentation of innovation in a part like medicinal services and might be reluctant to take to it. Doctors also can have their restraints about this equivalent. Thus, to conquer this test, patients should be made mindful of these potential advantages of IoT in medicinal services. In a world that is gradually, however relentlessly changing into a carefully determined society, these utilizations of IoT are monstrous.

Awareness About IoTs Understanding IoT from this shopper’s viewpoint isn’t a simple assignment. As these utilizations for these IoTs are extending and changing, there should be across-this-board mindfulness about them in this whole nation. Just through consistent push and advancement through human services experts, doctors, care groups and patients would this be able to happen.

Analysis Paralysis The given Figure 10 emphasizes Awareness of IoT. The flood of gigantic measures of information could prompt an investigation of the loss of motion. It implies that it tends to be awesome to go over each snippet of data displayed in this information. Extricating bits of knowledge from information for examination is the last phase of IoT usage, and it must be driven through intellectual innovations. Medical clinics and wellbeing frameworks must

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guarantee that the stage they settle on is equipped for trim as indicated through their prerequisites.

Figure 10. Awareness about IoTs (Chen, 2000).

Examples of IoT in Healthcare Treating This Development of Cancer June-July 2018, accepting medical advice for deadly development, information displayed in this ASCO annual meeting of an immoral clinical proposal of 358 cases. Each day use Bluetooth-strong weight scale and pulse sleeve.

Figure 11. Development of cancer through IoT (Sharma, 2020).

Shot Circle (Mechanized) Insulin Convection The given Figure 11 explains about Treating this development of cancer through IoT OpenAPS started in 2015 through Dana Lewis, and Scott Lybrand, who hacked DANA’s CGM and its insulin Stephen so that the

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convection of insulin be mechanized in structure. Using information fed from CGM, their own thing ends this circle and continually adjusts this measurement of this cephalon of insulin molecules. Computerizing this Insulin Convention provides various benefits which change the life of diabetes. Checking this level of a person’s blood glucose and consequently converting this insulin measurement into their structure, this APS keeps this blood sugar inside a safe range and prevents abusive highs and lows Although OpenAPS is not a “crate-out” system and hopes to be eager to build their structures from this people, it is pulling into a developing network of diabetics, which has its open source. Using their innovation hacks this insulin conversion. OpenApse site has announced that “By January 15, 2018, more and more (N = 1)* 1,078 + people are using other types of DIY closed circle used around this world. The OpenAPS group of people is not the main person to think this way. In 2013, Brian Mazzalish, a spouse and young child, both of whom have type 1 diabetes, made primary robotic and cloud-joined shut-circle fake fancy devices. In 2014, he found Smart Lock Labs which is currently known as Bigfoot biomedical to improve this mechanized insulin conventions framework, depending on his innovation, scale and market. The organization is now planning for a significant initial of its answer, these subtleties of which expects to declare between 2018 and mid-2019. Bigfoot today claims that this mechanized framework should be proposed financially in 2020, and FDA audit and endorsement will be pending (Mohd. M. N., 2020).

Associated In-Halers Same as diabetes, asthma is a condition or disease which will affect the life of many men in the whole world. Savvy innovation has started to give extended knowledge and authority about their side effects and remedies due to their associated inhaler.

Figure 12. Associated In-Halers through IoT (Chen, 2020).

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In the given Figure 12 the development of Associated In-Halers is showcased through IoT. The largest manufacturer of lover inhaler innovation is propeller health. In contrast to distributing all inhalers, propeller had made a machine which will combine to inhaler or Bluetooth Spiro-meter. It interrelates in this app and helps people with asthma and chronic obstructive pulmonary disease, which has emphysema and regular bronchitis. The drug tracks employs, and additionally gives the allergen gauge. The organization was established in 2010, and in 2014, with this intention of working with inhalers from important pharma organizations, FDA got two sensors: the discus inhaler of GlaxoSmithKline, and Respirate Inhaler from Boehringer Ingelheim. From which point onwards, Propeller has continued to work with various real manufacturers in-haler, and it is said that their sensor works with most inhalers and driving Bluetooth spirometer. Improvement in one of the gains of using an associated inhaler is followed - as it was, this drug was taken more and more frequently. Production of propeller sensor gives an account of an inhaler usage which could be provided to a patient’s primary care physician and could show whether they are using it regularly as it is supported. For patients, this inspires and, apart from this, demonstrates how their inhaler use is legally improving their condition.

The Vast Land scape of Healthcare Stakeholders and IoT Possibilités When we started posting every one of the executions, occasions, and use cases identified with the Internet of Things (IoT) in medical services, we immediately ran out of space. We needed to stop. Since medical care is particularly abrogating, and when you start to incorporate individual medical services, medication organizations, wellbeing inclusion, constant wellbeing data trade (RTHS), medical services building offices, automated frameworks, and biosensors just as remote observing and whatever else identified with medical services just as the distinctive wellbeing fortes and activities and even (therapies of) sicknesses, the rundown of Internet of Things applications in medical services develops dramatically. Neither the drug business nor the Internet of Things (and AI) in the treatment of sicknesses, nor the headways in particular fields like bionic appendages and other related fields, are the subject of this article in any capacity. The equivalent can be said for both the IoT use cases and the present reality utilizations of the Internet of Things in

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the medical care area, which are colossal in correlation. Regardless, as recently said, when we inspect applications and developments with respect to medical care suppliers and payers, certain utilization cases stand apart as especially essential.

Accélération in All IoT Use Case and Applications in Healthcare Ahead Two things are certain: 1) The most prevalent thing that Internet of Things uses a case in healthcare right now is (remote) health monitoring, at least in terms of IoT spending; and 2) The Internet of Things will soon be ubiquitous in healthcare and health-related practices and functions on a variety of levels in the coming years. We also observe that gadgets and Internet of Things applications that have historically been associated with consumers (e.g., personal health trackers) are increasingly being included in the interaction between consumers/patients and healthcare providers and payers (see Figure 1). Patient involvement and consumer awareness are key factors in this context, as are incentives and surcharges in the interaction with healthcare payers, among other things (compare with the use of telematics in insurance). Moreover, in a more Industrial Internet of Things environment, where healthcare providers such as hospitals use IoT in conjunction with applications and technology in robotics, artificial intelligence, and Big Data, there is significant development potential. The second emphasis area of IoT applications, which we stated in the beginning (monitoring, tracking, maintenance, and so on), is also expected to continue to develop; however, the rate of growth will vary depending on the hospital, nation, and other factors. The first level will include monitoring everything from hospital devices and clients to hospital investment avenues and beds, while the second and third phases will be more involved. Health information technology, particularly in the context of healthcare transformation, and the difficulties of informationdriven healthcare are discussed. When considering remote health monitoring – and the many other IoT applications in healthcare – it is important to consider the major problems that healthcare faces as well as the areas that have a clear benefit and/or purpose/possibility for innovation. When considering the Internet of Things in healthcare, it is important to consider how these advances will fit into the larger picture of the digital transformation of the different healthcare sectors. From an enabling standpoint, the IoT will be a

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cornerstone of the digital transformation of healthcare for at least the next decade.

The Internet of Things and Healthcare Information System With regards to well-being information, it is true that a lot of “information from clinical gadgets and observing frameworks at last winds up in Electronic Healthcare Records (EHR) Systems or in different utilizations that are connected to them and send the information to labs, specialists, attendants, and different gatherings included. “As increasingly more wellbeing-related information is assembled and made available continuously, it is turning out to be increasingly more connected with electronic wellbeing records (EHR). Continuous well-being frameworks (RTHS) will be a key application region for the Internet of Things in medical care as Big Data Analytics instruments and cycles are utilized to assess both static and dynamic information other than information examination as a component of the thorough medical care framework quality improvement,” as per Gartner. (Source: Mind Commerce, end of 2016) Electronic wellbeing record “(EHR) frameworks are a long way from pervasive, and most have not been planned with the Internet of Things, RFID, or ongoing information as a primary concern; they have been planned, if all works out in a good way, to make instruction quicker, more tolerant driven, all the more sensibly valued, and better from both a patient’s wellbeing and crafted by” medical care suppliers viewpoints, in light of to some degree static information. Although these goals are basic “in numerous IoT use cases in medical care, they are not generally refined. Besides, there are countless various techniques for the digitization of medical services information that an Internet of Things organization should think about these varieties in case it is” connected to a particular patient’s record. Just a piece of the well-being information gathered by connected gadgets makes it into the EHR/EMR environment. In light of the sort of information, the gadget, the extent of the framework, and its motivation, there are various “data frameworks and frameworks of knowledge. Accordingly, there is a pattern towards Real-Time Health Systems (RTHS), which goes past conventional EHRs to fuse mindfulness and constant information abilities from an Internet of Things and associated/wearable gadget perspective. This RTHS framework methodology consolidates EHR frameworks as a feature of a bigger setting and set of strategies that incorporates other RTHS frameworks. A new report distributed in the diary Mind Commerce found that “RTHS will be a significant region

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for IoT in medical care as Big Data Analytics instruments and methodology are utilized to break down both dynamic and static information for prescient examination as a component of general medical care frameworks improvement drives.”

Healthcare Data: Working with Propose and Security in Mind The data itself presents a second obstacle to overcome. “Healthcare information is very personal, and the selection of the necessary data must be done with the intended results in mind.” Patient outcomes and the organization of healthcare are improved when data is used to make better choices more quickly. This is especially true when it comes to the “capacity of physicians, specialists, nurses, and other healthcare professionals to make better decisions more quickly.” Furthermore, every IoT use case, project, or deployment must include security and privacy by design as an integral component of the overall architecture. The Internet of Things (IoT) and data analytics are being used to enhance and decrease mistakes and expenses. It is critical to ensure that it is not disclosed or utilized for inappropriate purposes. As previously stated in earlier articles, personal healthcare data must be handled differently from other types of data when it comes to security and compliance. Various laws in different parts of the world “drive the compliance agenda, but healthcare data security must go beyond compliance to be effective.” Meanwhile, healthcare organizations must pay greater attention to compliance, particularly in regions where tighter laws “are being implemented, such as the European Union’s General Data Protection Regulation (GDPR), where personal health data, as well as biological and other biomedical data, receive special attention and are regarded as extremely sensitive. It is obvious that any Internet of Things project involving personal health data must take these regulations, as well as the lawfulness, purpose, and dissemination requirements, to mention a few, into consideration before moving forward. When it comes to personal data, every Internet of Things project should be built with security and privacy in mind from the beginning. However, when you start comparing efforts and laws throughout the world, you will see that the stances taken on the protection and exploitation of health data are, to put it simply, quite different from one another (Arora, 2020).

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IoT and Its Background for COVID-19 Pandemic In layman’s terms, the Internet of Things (IoT) is an arrangement of associated gadgets and tasks that are completely consistent with all organization gadgets, including equipment, programming, web network, and some other advanced or PC implies that are expected to make them open by aiding information adjustment and assortment, in addition to other things. Proceeding with our conversation about the Internet of Things, we can say that it goes past an idea to incorporate the improvement of an overall engineering system that ultimately empowers the “mix and viable progression of information between the individual out of luck and specialist organizations. The powerlessness of prescriptions to be conveyed adequately to patients, which is the second most huge issue after the stress over immunization advancement (Farahnakian, Fahimeh, 2014).” is the wellspring of the vast majority of the troubles in the current common situation. With the Internet of Things thought, patients might be all the more effective came to, which ultimately supports the arrangement of more exhaustive clinical treatment to help them escape this sickness (He, Jianxing, 2019). The Internet of Things (IoT) is a state-of-the-art innovation that ensures that all influenced people because of this contamination are isolated. It is valuable to have a decent observing framework set up during isolation. All high-hazard patients can be found with no sweat on account of the web-based organization. “Circulatory strain, pulse, and glucose level are completely estimated utilizing this method (Mulajkar, Ashish,2019). The critical benefits of the Internet of Things for the COVID-19 Pandemic are displayed in Figure 13.”

Figure 13. Key merits of IoT for COVID-19 pandemic (Mohd. Javaid, 2021).

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The given Figure 13 showcases the Key merits of IoT for the COVID-19 pandemic. With the effective application of this technology, we may expect to see an increase in the efficiency of medical personnel while simultaneously seeing a decrease in their burden. Even in the event of a COVID-19 pandemic, the same principles may be used with fewer costs and errors. The Internet of Things (IoT) is a cutting-edge technology platform for combating the COVID19 pandemic and has the potential to meet major difficulties during a lockdown scenario. This technique is useful in capturing real-time data as well as other pertinent information about the afflicted person. The main procedures utilized by IoT for COVID-19 are shown in the picture below.”

Figure 14. Processes involved in IoT for COVID 19 (Keller, 2019).

The given Figure 14 emphasizes Processes involved in IoT for COVID19. The Internet of Things (IoT) is utilized in the first phase to collect health data from different places on the afflicted patient’s body and to handle all the data using a virtual management system. This technology aids in the control of data and the follow-up on the results of the report. As previously stated, the Internet of things idea makes use of a network of linked devices to facilitate the efficient flow and exchange of data. It also provides an opportunity for social workers, patients, citizens, and others to interact with service benefactors to address any issues and collaborate on them. As a result, by using the suggested Internet of Things strategy in the COVID-19 pandemic, the efficient tracking of patients, as well as the identification of questionable instances, can be ensured. The symptoms of the coronavirus are now wellrecognized to most people who have been exposed to it. The identification of the cluster may be greatly aided by forming a well-informed group of people who are all linked to one another via a network. It may also be possible to create a specific smartphone-based application to aid those in desperate need (John, 2019). The controller, i.e., doctors, physicians, caregivers, and others, must be kept up to speed on the correct “reporting of the symptoms and the

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recovery so that the impressive move may be opted out to optimize the total quarantine time. To combat and raise awareness among the public “about the COVID-19 pandemic in India, the Indian government has developed an Android smartphone application known as – ArogyaSetu, which is intended to create a link between the most characteristic of good healthcare service and the public in India. In a similar vein, the Chinese government has developed a smartphone application called Close Contact (English translation) for its” civilian population. Proximity to a corona-positive individual is indicated by this application, which notifies the app’s owner. Ensure that additional caution is used while going outdoors. The United States government will shortly create a similar kind of smartphone application for its citizens, which will be available at the end of April 2020. After China, Taiwan was the country that was most likely to have the highest number of COVID-19 cases. For the sake of the general public’s health, Taiwan swiftly militarized and established methods for identifying and suppressing any potential coronavirus cases, as well as for providing resources to those affected. For example, Taiwan supplied and incorporated its government health insurance dataset with its border agency, and it used a catalog to provoke the formation “of big data for data analysis. It also generated real-time warnings during an in-person clinical visit estimated travel antiquity and medical symptoms to aid in management development. Furthermore, they have taken use of the most recent technology, which includes scanning of QR codes, linked reporting of travel history,” and other features, to potentially identify those who have been infected (Tanvi, 2022).

Proposed Model Covid Med Map: A Health Monitoring System As the World is witnessing enormous amounts of challenges in this outgoing pandemic of the Corona Virus (2019) time has paused for everybody around Economies have collapsed Governments have failed miserably and the whole health system has panicked. Countless people have lost their loved ones due to this deadly disease in between all this there’s a big issue that needs to get enlightened and that is the mismanagement and the miscommunication between the patient and the family during the treatment of the Corona Virus. Once a patient is infected by the disease, he/she is immediately admitted to a hospital for treatment and due to the tough protocols issued by the Government the family members cannot visit their patient at the hospital premises during this process the family is unaware of the condition of the patient and the

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scenario during that periods gets tough to operate accordingly. So, to overcome this issue there is a certain method that will help the concerned users effectively during this tough time. The idea is to prepare health monitoring software based on The Internet of Things which will analyze the patient’s health in some specified concerned areas and will give certain data details which will be manually updated by the hospital IT Team. All this data will be received by the user with the help of a Mobile App which will be installed on their respective Mobile phones and is directly linked to the health band worn by the patient. This Software would be permitted by the Government and issued as compulsory for all the COVID Centers. The user will able to get the following information through the applications as: 1. 2. 3. 4. 5. 6. 7. 8. 9.

Temperature Oxygen level Blood sugar level Heart beat Pulse rate Blood pressure level X-rays, scans and the doctor’s prescription letter Display the Recovery Level of the patient. Saves Day to Day data on the patient’s health during his/her stay at the hospital.

There will be a chat box that directly connects the user to the IT Team which will timely give a brief on the condition of the patient as per the doctor. The calling facility will be available 24/7 and directly connects the call with the hospital staff on duty where the user can ask about the medicines that are injected into the patient and the amount of oxygen that has been given to him/her. The Patient’s data will be securely encrypted by high security. For a COVID Suffering Patient, the main health concerned areas are the oxygen level, temperature level of the body, heartbeat, pulse rate, and blood pressure level. So, there will be a health band that will be assigned to the patient’s hand, it consists of certain sensors which can track the following data: ● ● ●

Temperature Oxygen level, heartbeat Blood sugar level

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The sensors will have an inbuilt technology through which the wristband shown in Figure 15 can calculate all the terms, which need to be calculated such as Temperature, Oxygen level, Heartbeat.

Figure 15. Proposed IoT based patient care application.

The band will also be directly connected to the ECG Machine so that the user can also get access to the Pulse Rate of the patient. There are some manual tests like the Blood Sugar level which are directly done by the hospital staff and will also be updated by the IT Team of the medical Center in definite time intervals. The X-rays and the Scans a patient goes through during the period of his/her treatment will also be uploaded by the IT Team itself. The Doctor’s prescription which defines the medicines and injections that are given to the patient will also be uploaded on the patient’s profile. The proposed Healthcare Model builds an effective system in binding and controlling the technologies in a smart band and could save the lives of millions. This could be a new phase in the healthcare sector and so the beginning of better machine-human interaction. This Healthcare Model can be used as a self-learning-based decision support system that can initiate preventive measures when required in a medical emergency by the user. The focus of this invention is the availability of the technologies and the preciseness in their real-world applications. Our health statistics like heartbeats, blood pressure, and body temperature are continuously tracked by our lifesaving smart band. Any unusual variations lead to an emergency state where immediate help is provided to the user. The lifesaving IoT Healthcare Model will change the

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phase of the human race. The technologies which are present in today’s world can be used by every end user by using a single gadget. This enables everyone to keep track of their health without much dependency on multiple types of medical equipment (B. Sati, et al. 2022).

Issues and Future Scope of the Study When it comes to using the Internet of Things “in the current pandemic scenario COVID-19, the main point of concern is the security and privacy of the data that is received, which is unique and essential from the perspective of patient health. The second point to mention is the care that must be taken while integrating the data network among the many devices and protocols involved. The summary of problems and obstacles associated with adopting IoT for the COVID-19 pandemic” is shown in the figure below. Furthermore, future efforts should be centered on “data storage and management” techniques. In addition, the procedure of submitting “cost-effective adoption applications will be” taken into consideration in future research (S. Sahana, et al. 2022).

Figure 16. Various issues and future scope of the study (Jahmunah, Vicnesh, 2021).

Conclusion The given Figure 16 enlightens Various issues and the future scope of the study. The Internet of Medical Things (likewise called this things) is the use of the IoT for therapeutic and well-being-related purposes, information

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accumulation and examination for research, and observation. This ‘Savvy Healthcare,’ as it is likewise called, prompted the formation of a digitized medicinal services framework, associating accessible restorative assets and social insurance administrations. IoT devices could be utilized to empower remote well-being checking and crisis notice frameworks. It could likewise change itself to guarantee suitable weight and backing and is joined to the patient without the manual cooperation of nurses. IoT Healthcare Uses Diminishing Emergency Room Wait Times, Remote Health and Monitoring, guaranteeing the Availability and Accessibility of Critical Hardware, and Following Staff, and Patients and Inventory. Difficulties to embrace IOT in Healthcare: Security and security of patient information, lack of consistency among associated cell phones, vulnerable information transmissions. Improvement in one of these gains of using an associated inhaler is followed - as it was, this drug was taken more and more frequently. Production of a propeller sensor gives an account of inhaler usage which could be provided to a patient’s primary care physician and could show whether they are using it regularly as it is supported. For patients, this inspires and, apart from this, demonstrates how their inhaler use is legally improving their condition. Medical clinics and wellbeing frameworks must guarantee that the stage they settle on is equipped for trim as indicated through their prerequisites.

References Afor M. E. and Sahana, S. “The Internet Of Behaviour (IOB) and Its Significant Impact on Digital Marketing,” in 2022 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), 2022, pp. 7–12. Ahuja, Laxmi, Ajay Rana, and Siddharth Gupta. “Security and Privacy Model for Work from Home Paradigm.” In 2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), pp. 13511355. IEEE, 2020. Alcantara, Marlon F., Yu Cao, Benyuan Liu, Chang Liu, Ning Zhang, Pengfei Zhang, Terry Griffin, Walter H. Curioso, Cesar Ugarte-Gil, and Maria J. Brunette. “eRx–A technological advance to speed-up TB diagnostics.” Smart Health 16 (2020): 100117. Chen, Baozhan, Siyuan Qiao, Jie Zhao, Dongqing Liu, Xiaobing Shi, Minzhao Lyu, Haotian Chen, Huimin Lu, and Yunkai Zhai. “A security awareness and protection system for 5G smart healthcare based on zero-trust architecture.” IEEE Internet of Things Journal 8, no. 13 (2020): 10248-10263. Davison, Kathryn P., James W. Pennebaker, and Sally S. Dickerson. “Who talks? The social psychology of illness support groups.” American Psychologist 55, no. 2 (2000): 205.

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Farahnakian, Fahimeh, Adnan Ashraf, Tapio Pahikkala, Pasi Liljeberg, Juha Plosila, Ivan Porres, and Hannu Tenhunen. “Using ant colony system to consolidate VMs for green cloud computing.” IEEE Transactions on Services Computing 8, no. 2 (2014): 187198. Giallonardo, Vincenzo, Gaia Sampogna, Valeria Del Vecchio, Mario Luciano, Umberto Albert, Claudia Carmassi, Giuseppe Carrà et al. “The impact of quarantine and physical distancing following COVID-19 on mental health: study protocol of a multicentric Italian population trial.” Frontiers in psychiatry 11 (2020): 533. Grodi, Robin, Danda B. Rawat, and Fernando Rios-Gutierrez. “Smart parking: Parking occupancy monitoring and visualization system for smart cities.” In SoutheastCon 2016, pp. 1-5. IEEE, 2016. He, Jianxing, Sally L. Baxter, Jie Xu, Jiming Xu, Xingtao Zhou, and Kang Zhang. “The practical implementation of artificial intelligence technologies in medicine.” Nature medicine 25, no. 1 (2019): 30-36. https://dashtechinc.com/blog/internet-of-thin… | Internet technology, Iot, Blockchain technology. (n.d.). Pinterest. https://www. pinterest.com/pin/719027896743757470/. IoT and Security – Same as Always, Only More So. (2017, November 8). IT 29 Chronicles. https://itchronicles.com/iot/iot-security-always/). IoT based Wearables in Solving Healthcare Challenges. (2019, October 12). LIVE BLOG SPOT. https://liveblogspot.com/app-development/iot-based-wearables-devicessolving-healthcare-challenges/. IoT in Healthcare: 20 IoMT for better Smart Healthcare. (2019, December 20). FossGuru. https://www.fossguru.com/iot-in-healthcare-iomt-for-better-smart-healthcare/. Jahmunah, Vicnesh, Vidya K. Sudarshan, Shu Lih Oh, Raj Gururajan, Rashmi Gururajan, Xujuan Zhou, Xiaohui Tao, Faust, O., Ciaccio, E. J., Ng, K. H., & Acharya, U. R. “Future IoT tools for COVID‐19 contact tracing and prediction: a review of the state‐ of‐the‐science.” International journal of imaging systems and technology 31, no. 2 (2021): 455-471. Javaid, Mohd, and Ibrahim Haleem Khan. “Internet of Things (IoT) enabled healthcare helps to take the challenges of COVID-19 Pandemic.” Journal of Oral Biology and Craniofacial Research 11, no. 2 (2021): 209-214. Keller, T. (2019, January 28). “IoT data management: a guide on how to implement projects.” Bosch ConnectedWorld Blog. https://blog.bosch-si.com/bosch-iotsuite/iot-data-management-a-guide-on-how-to-implement-projects/. Lin, Yu-Hsiang, Zi-Tsan Chou, Chun-Wei Yu, and Rong-Hong Jan. “Optimal and maximized configurable power saving protocols for corona-based wireless sensor networks.” IEEE Transactions on Mobile computing 14, no. 12 (2015): 2544-2559. Mathur, S., and Arora, A. (2020). Internet of Things (IoT) and PKI-Based Security Architecture. In Industrial Internet of Things and Cyber-Physical Systems: Transforming the Conventional to Digital (pp. 25-46). IGI Global. Mathur, Sandeep, Aditya Shantanu, and Ajay Rana. “Estimating the imaging in medical science using image processing techniques.” In Journal of Physics: Conference Series, vol. 1714, no. 1, p. 012007. IOP Publishing, 2021. Mathur, Sandeep, and Ankita Arora. “IoT in 5th Generation Wireless Communication.” Cloud and IoT‐Based Vehicular Ad Hoc Networks (2021): 1-30.

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Mathur, Sandeep, and Krishnasheesh Datta. “Prediction of Covid-19 Cases in India Through Machine Learning Using Python.” In 2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), pp. 1-6. IEEE, 2021. Mathur, Sandeep, Tanvi Singla, Kunwar Bharat, and Ajay Rana. “AIIOT: Emerging IoT with AI Technologies.” In A Fusion of Artificial Intelligence and Internet of Things for Emerging Cyber Systems, pp. 269-291. Springer, Cham, 2022. Mathur, Sandeep. “Evolving IoT and Green IoT in Healthcare Perspective.” In Evolving Role of AI and IoMT in the Healthcare Market, pp. 175-197. Springer, Cham, 2021. Mathur, Sandeep. “Modeling the stubble burning generated airborne contamination with air pollution components through MATLAB.” Earth Science Informatics (2022): 1-10. Micklethwait, John, and Adrian Wooldridge. The Wake-Up Call: Why the pandemic has exposed the weakness of the West-and how to fix it. Hachette UK, 2020. Mohammed, M. N., S. F. Desyansah, S. Al-Zubaidi, and E. Yusuf. “An internet of thingsbased smart homes and healthcare monitoring and management system.” In Journal of Physics: Conference Series, vol. 1450, no. 1, p. 012079. IOP Publishing, 2020. Mulajkar, Ashish, Sanjeet K. Sinha, Vinod Bharat, Arundoy Lenka, and Govind Singh Patel. “The Role of IoT in Sustainable Healthcare.” In Machine Learning, Deep Learning, Big Data, and Internet of Things for Healthcare, pp. 125-135. Chapman and Hall/CRC, 2023.IoT in healthcare market to $136.8B by 2021. (n.d.). Today’s Medical Developments. https://www.todaysmedicaldevelopments.com/article/iot-internet-ofthings-healthcare-medical-111716/. Padikkapparambil, Jinesh, Cornelius Ncube, Krishna Kant Singh, and Akansha Singh. “Internet of Things technologies for elderly health-care applications.” In Emergence of pharmaceutical industry growth with industrial IoT approach, pp. 217-243. Academic Press, 2020. Parajuli, A. (2020, April 10). “How can IoT help within the COVID-19 crisis” The IOT Projects. https://theiotprojects.com/how-can-iot-help-within-the-covid-19-crisis/ Pendergrass, Sarah A., and Dana C. Crawford. “Using electronic health records to generate phenotypes for research.” Current protocols in human genetics 100, no. 1 (2019): e80. REALIGNS INC. (2021). Realignsinc.com. https://realignsinc.com/artificial-intelligenceiot-healthcare-investments/. Sati, B., S. Kumar, K. Rana, K. Saikia, S. Sahana, and S. Das, “An Intelligent Virtual System using Machine Learning,” in 2022 IEEE IAS Global Conference on Emerging Technologies (GlobConET), 2022, pp. 1123–1129. Sharma, Shally, and Sandeep Mathur. “Analyzing the patterns of Delhi’s air pollution.” In Advances in data sciences, security and applications, pp. 33-44. Springer, Singapore, 2020. Shuja, Junaid, Kashif Bilal, Sajjad A. Madani, Mazliza Othman, Rajiv Ranjan, Pavan Balaji, and Samee U. Khan. “Survey of techniques and architectures for designing energy-efficient data centers.” IEEE Systems Journal 10, no. 2 (2014): 507-519. Taiwo, O., and Ezugwu, A. E. (2020). Smart healthcare support for remote patient monitoring during COVID-19 quarantine. Informatics in medicine unlocked, 20, 100428.

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Chapter 6

A Healthcare Revolution in Cross Domain Applications Using Advanced Computational Techniques Ajay Sudhir Bale1,* N. Vinay2 Asma Zabi2 E. Eshwar2 and Suhaas Veera Raghavan Reddy3 1Department

of Electronics and Communication Engineering, New Horizon College of Engineering, Bengaluru, India 2Department of Electronics and Communication Engineering, CMR University, Bengaluru, India 3Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, Netherlands

Abstract This chapter gives an overview of the relationship of forensic science, COVID-19 and AI towards MEMS technology. Forensic science is one of the demanding technology due to increased crime rate. It is lacking behind due to mainly two reasons - complexity and low process of analytics with respect to time and accuracy. In this chapter, we have focused on overcoming these limitations using MEMS technology combined with forensic science. There are many types of corona tests by which an infected person can be detected, some of them include Genetic *

Corresponding Author’s Email: [email protected].

In: Intelligent Decision Support System for IoT Enabling Technologies Editors: Subrata Sahana, Anil Kumar Sagar, Sanjoy Das et al. ISBN: 979-8-89113-249-8 © 2024 Nova Science Publishers, Inc.

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Ajay Sudhir Bale, N. Vinay, Asma Zabi et al. tests, Antigenic tests and serological tests. FET -based Biosensors are used in detection of COVID 19. Many such tests are available for detection, but the RT-PCR method is highly accepted throughout the world. The chapter discusses about implementing RT-PCR test with MEMS technology to obtain greater results. When MEMS sensors are combined with Machine Learning (ML), they will solve a variety of realworld issues, including fitness tracking, obesity, human fall detection, robot status prediction, and rainfall prediction. In the categorization of robot status, the medium KNN approach demonstrated the greatest accuracy when compared to other algorithms, and the use of Long ShortTerm Memory (LSTM) exhibits excellent results to address the problem of accurate Rainfall prediction. We are optimistic that this research will lead the way for future biotechnology advancements.

Keywords: RT-PCR, Health Computational Techniques

Monitoring,

DNA,

SARS-COV-2,

Introduction Forensic science, also termed as criminalistics, on many situations deals with the crime investigation with the help of modern science and technology that includes biological, chemical, toxic, archaeological, geology, astronomy and many other domains. The application of criminology to an examination of crime is influenced by several stages of the decision-making process, from conduct an investigation at a site, gather and analyze evidence, and so forth (Bitzer et al., 2018). Multiple forces are exerting pressure on the criminology system’s investigation and prosecution phases. Due to procedural constraints, investigators must give quick outcomes, yet budget constraints limit the resources available to obtain these findings. While forensic science contributes significantly to judicial investigations, these limits have a substantial impact on the practice of forensic science, which frequently results in time-consuming phases and costly of analysis (Bitzer et al., 2019). Due to the advancement in crime the demand for efficient forensic science had become extremely important, with the other corresponding decision-making process (Bitzer et al., 2019). We have discussed about the various techniques of improving the forensic science using Micro-Electro-Mechanical Systems (MEMS) like micro fluids devices, fingerprint sensors, cantilever-based sensors. The need for sensing system with high intelligence is greatly aided by the use of a MEMS sensor and a machine learning approach (Zhu et al., 2019).

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This section exhibits various research involved in using MEMS sensors integrated with Machine Learning to Solve various problems in real life such as Fitness Tracking, Obesity, Human Fall detection, Robot status detection and prediction, and Rainfall prediction (Seo et al., 2014). This type of interactive system with future sensors gives consumers a more realistic experience and may be utilized in a variety of applications. MEMS based smart wearable devices help in monitoring the unexpected human falls, monitor our daily activities and calories burnt to overcome obesity (Shin et al., 2007). This approach of combining MEMS with Machine Learning helps us to predict the weather conditions and prepare for the calamities in advance. This approach also solves the problem of robot status prediction in real time. Corona Virus disease a newly emerging pandemic has shook the world to its core by effecting all human lives around the world, with the newly discovered mutations from this virus, there is a high chance that this pandemic could be the worst nightmare for the next two decades (Srivastava et al., 2021). While this pandemic is affecting people’s lives, it becomes very crucial for research in medical industries to find a cure, diagnosis and detection as early as possible, which not only can save lives but also can ensure people’s wellbeing for the next future generations. As a part of the research many diagnoses a detection test have been discovered throughout the world to ensure great sensitivity, reliability, stability, and accuracy is served. This chapter consists of various diagnosis and detection methods using Biosensors (Srivastava et al., 2021, Asif et al., 2020), MEMS (Khan et al., 2021), RT-PCR, Bio-MEMS and Bio-FEDs to detect and diagnose the Novel Corona Virus.

Forensic Science Criminalistics or forensic science is the peer reviewed study of the designation, collection, attribution, and evaluation of tangible evidence resulting from criminally or unlawful civil activity. Forensic science is breakdown in to following steps: examination, identification, collection, preservation, extraction, analysis and presentation that is represented and explained in figure. The introduction of MEMS technologies to the following steps will increase the accuracy and is depicted in Figure 1.

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Figure 1. Steps of forensic science.

Molecular Imprinting It is a technique that aims at the creation of cavities that are in template shape, with the help of polymer matric. Due to the formation of this cavities towards the targeted structure molecular imprinting will recognition capability in terms of both physical and chemical. Molecular imprint based synthetic polymers

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will be have the predefined selectiveness that leads to tailor-made polymeric material (Kempe, 2000, Cormack et al., 2000, Cormack and Mosbach, 1999). As molecular imprinting is a unique method of manufacturing molecular matrix with a selective recognition behaviour on molecules, it had its applications in various fields like Affinity separation, Antibody binding mimics, Enzyme mimics, Combinatorial library screening, Bio- mimetic sensor, enzyme behaviour study and so on. Molecular imprinting polymers are designed in different physical shapes and sizes that makes then capable as a smart material with multi- functional behaviour like stimuli-responsive, fluorescence labelling, magnetic etc. (Yılmaz et al., 2017). In the recent developments of molecular imprinting, it has increased its applications towards forensic behaviour. The forensic technology has been built mainly one two stages that are first to identity the evidence and then to find the organic of identified evidence using comparison technique. The molecular imprinting is capable of solving both stages of forensic, due to their recognition and stable feature (Uzun and Turner, 2016). The multi- functional behaviour of the Macular imprinting polymers described above makes them perfect technique in some of the forensic science applications (Yılmaz et al., 2017). At present the molecular implanting sensors serves a very critical role in the forensic for analyzing the targeted structures in analysis of crime related to criminology will have many cases that are related to the drug (benzodiazepines, marijuana, hashish, heroin, opium, nicotine etc.). Benzodiazepines can be detected using the hair sample with some limitations in its result. These limitations can be overcome by molecular imprinting technique due to its highly sensitive rate. Using blood and as the sample in molecularly imprinted solid-phase extraction systems, the greater results can be obtained (Anderson et al., 2008, Ariffin et al., 2007). The extracted plasma that is subjected to the molecular imprinting polymers will also showcase the greater results in benzodiazepines detection (Figueiredo et al., 2011). Opium and heroin can be detected computational techniques with the help of molecularly imprinted polymers (Piletska et al., 2005). Marijuana and hashish are detected with the help of urine and oral fluid samples subjected to watercompatible imprinted pills (Cela-Perez et al., 2016). The nicotine can be detected using urine and serum samples with the help of quartz crystal thickness-shear mode sensor coated with molecular imprint polymer coating (Tan et al., 2001). There are many other drugs that can be detected directly or indirectly using molecular imprinting method, like cyanide, nightshade, curare, caffeine, strychnine etc. (Jackson et al., 2010, Yılmaz et al., 2017).

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Explosion is one of the major areas that forensic study will he underdone. The most used explosives are trinitrotoluene, Nitrobenzene, dinitrotoluene etc. (Yılmaz et al., 2017). Such methods are also employed in the study of explosives in the forensic system. The core-shell imprinted particles are used for the detection of 2, 4, 6-trinitrotoluene (Gao et al., 2007), hollow polymer is used for trinitrotoluene detection (Guan et al., 2007). Gunshot detection is another challenging task in the forensic science, suspect is identifies based on the spills of gun powder or other related chemicals on their body or cloths. Gunpowder is a combination of inorganic and organic components. Molecular imprinting procedures are commonly utilized for sample preparation and detection in gunshot cases (Taudte et al., 2014). Some of the chemical components that are used in the gun power can also be directly identified by molecular imprint polymers (Pereira et al., 2014). The general molecular imprinting techniques are also used in fire accelerants detection for sample preparation and analysis. The fire causing residues like gasoline, alcohol and kerosene can be identified by sensors that are working on the bases if molecular imprinting (Alizadeh and Rezaloo, 2013). Molecular implanting has also solved some of the spectroscopy and optical problems that are faced by the forensic science like limitations of the Surface-enhanced Raman scattering (SERS) is overcome by combining (SERS) with molecular imprinting technique, which have shown the good results (Holthoff et al., 2011). Optical Surface Plasmon resonance sensors based on molecular imprinting are used to detect biological things like viruses, antibody, hormones etc. (Uzun et al., 2009). Golden nanoparticles that are molecular imprinting can be used for identification of vapor explosives, the produced forms by the combination of Golden nanoparticles and molecular imprinting had shown the greater sensitive rate with notable results (Riskin et al., 2011, Yılmaz et al., 2017).

Deoxyribonucleic Acid (DNA) Deoxyribonucleic acid commonly termed as DNA is a molecular composition that is present inside the cell that stores the genetic information that develops the function of organism. It is the main bridge for passing information for future generation. DNA is unique and it helps to differentiate the people in molecular level and also contains many information about the particular person. DNA can be biologically extracted and examined to identify the age, origin, death, and many more information about the specific person. Due to

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the uniqueness of the DNA it also majorly used in forensic science (ZbiećPiekarska et al., 2015). Human age is one of the most significant information to identify the unknown individual engaged in civil offence (Meissner and Ritz-Timme, 2010). In criminology the specific age of the victim is biologically identified to take in case in the narrow way when there are many suspects. This biological information is extracted from the DNA profile. It has been identified that human genome methylation changes with respect to age, so it is used to fine age in DNA level (Fraga and Esteller, 2007). The DNA extraction and information identification in forensic science mainly include sample collection and DNA extraction followed by methylation analysis by pyrosequencing and bisulfite conversion and then statistical analysis for identification (Zbieć-Piekarska et al., 2015). This information extraction also includes many molecular processes like accumulation of protein-based Daspartic acid (Dobberstein et al., 2008) and glycation end products (Sato et al., 2001), telomeres shortening (Tsuji et al., 2002), detection of mitochondrial DNA wit 4977 bp accumulation (Meissner et al., 1997) and reduce the sjTREC molecules (Zubakov et al., 2010). Micro electromechanical systems (MEMS) and nanoelectromechanical systems (NEMS) are proved to be highly sensitive towards the addition of mass (Pinto et al., 2020). The MEMS component that extremely used for mass sensing is cantilever, having anchored suspended beam at one end and other end is free. These cantilevers are used for detecting ppm level organic vapors (Then et al., 2006), virus particles (Gupta et al., 2004), 1, 1-difluoroethane (Li et al., 2007), DNA (Pinto et al., 2020) etc. The conventional method of small molecular detection was having a setup of large isolated equipment’s like gas chromatography-mass spectrometry, gas chromatography-mass spectrometry, this equipment’s were having huge space equipment’s and also maintenance. The miniature Lab-on-a-Chip (LoC) technology are replacing the traditional system, because to their high and accurate sensing capability. This LoC systems are highly suitable for point-of-care and point-of-use applications in biomedical industry (Culbertson et al., 2014). MEMS cantilever sensor works in two moods i.e., dynamic mood and stress mode. In stress mode, stress in created due to the molecular absorption in sensor at one of its surfaces, this stress leads to bending of sensor. The thiol-modified DNA probes are immobilized by a thin gold coating on top of the cantilever MEMS sensors, enabling it for stress-mode bio sensing of DNA Sequence (Pinto et al., 2020). There are many MEMS devices that are developed for biomedical applications, that includes devices like mixers, micro channels, cell sorters,

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valves etc. (Asif et al., 2020, Meldrum and Holl, 2002, Pal et al., 2004). The micro fluid divides are used for isolation of DNA and RNS from cells. DNS is extracted form white blood cells (WBC) using the micro fluid technology. As the starting step of this process DNS is first isolated from WBC with the help of high salt solution. Then DNA reversibly captured from lysed cells using binder. After binding the cleaning process will be undergone, during this process the DNA will be bounded to its surface that is later on subsequently eluted and pure DNA is obtained. A microfluidic chip made of silicon and glass can be used for the extraction of DNA, showcasing the good results (Chen et al., 2002, Goh et al., n.d.). This chip is made out of a built-in mixer, a filter having 3-m gap, two paraffin valves, and also a binder. The introduction of micro fluid devices in to forensic applications have changed the convent forensic science steps like tracing samples, sample workup, amplification reactions, detection, and secure storage as shown in Figure 2 (Bruijns et al., 2016).

Figure 2. Convenient and microfluidic device based forensic methodology (Bruijns et al., 2016). Reproduced from open access article.

Fingerprint Fingerprint is a mark or an impression of an individual person’s fingertip, its unique for every individual. Due to the unique nature of the fingerprint, it can be used as an identity pattern. Fingerprint serves an important vector in forensic biology, as it can be collected and preserved from different types of evidence (Ostojic and Wurmbach, 2017, Meuwly et al., 2017). Fingerprint identification includes following steps collection if the evidence from the crime scene, without any contamination. Extraction of fingerprint from the collected evidence. Storage and identification of fingerprint pattern with the help of many identification techniques. Convent method of identification of

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fingerprint was undertaken with the help of different chemical methods, which Is a difficult process. Due to the advancement of MEMS technology, different sensors are invented and manufactured for the fingerprint extraction and identification (Tang et al., 2018). There are different ways of extracting fingerprint that includes temperature, optical, acoustic, and capacitive and pressure (An et al., 2018). The optical fingerprint sensor uses a lens to acquire a fingerprint pattern and can penetrate thick glass. A thermal material is utilized in order to keep track of heat energy that transfers into the fingerprint. The sensor undergoes piezoelectric effect in this case. The technology of medical ultrasound is used in ultrasonic fingerprint sensors so that a visual representation of the fingerprint is created (Jiang et al., 2017). The Electrostatic Imaging based MEMS fingerprint sensor shows as the greater accuracy rate for fingerprint identification (Tang et al., 2018). The Monolithic based ultrasound fingerprint sensor developed on a single chip has shown the greater acoustic performance (Jiang, Lu, et al., 2017, Ganji and Nateri, 2012). Due to advanced and rapid action of MEMS (Sato et al., 2003) finger print sensor, they are used in many applications starting from smart phone to industrial applications. Similarly, MEMS based fingerprint sensors can be utilized in forensic science for extraction and identification of fingerprints.

MEMS and Machine Learning This section exhibits various research involved in using MEMS sensors integrated with Machine Learning (ML) to implement smart wearable health monitoring devices. There are many researches that claim the implementation of such MEMS sensors with ML to create a health monitoring system, most of these works use MEMS based accelerometers as a common device. Although they have accelerometers as a common device these devices as an individual server to solve different problems.

MEMS and ML as Smart Wearable Health Monitoring Devices There are many MEMS sensors with ML to create a Health monitoring system, most of these sensors use MEMS based accelerometers as a common device. Although they have accelerometers as a common device, these devices are individual servers to solve different problems. The work in (Fujita et al., 2008)

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demonstrates an approach for monitoring human activities and conditions using MEMS sensors. The proposed system contains MEMS based 3-axis accelerometer, temperature, and barometric pressure and relative humidity sensors. It also uses a PIC16LF877 microprocessor to store the measured data. The system uses peak hold circuit and sleep-mode of operation so that the system uses very low power and can monitor the activity for a long time (Holmes et al., 2002). We can determine the movement and direction of the human body by monitoring acceleration. Analog and digital signals are used to transport sensor output data to the microprocessor, it is then saved in flash memory or sent over the radio frequency module. The sensor system can be worn in a range of areas, including the waist, wrist, and ankle. The peak signal of the accelerometer is kept in the high hold circuit during sleep stage. Human activity conditions must be extracted from observed data in order to assess human energy consumption and analyses human life patterns. Based on the subject’s activity, the data is then divided into five categories: no activity, standing on a bus/train, sitting on a bus/train, bicycling, and walking. The evaluations were carried out at both long (15 seconds) and short (3 seconds) interval. The WEKA tool was trained using one day’s data and then used to approximate the classified activity for the other day’s test data. This human activity monitoring system could be very beneficial in preventive medical treatment, such as preventing metabolic syndromes from developing or measuring rehabilitation for postoperative monitoring. Obesity’s growth as a global epidemic makes it critical to keep track of one’s eating habits in the modern world. The research (Jain et al., 2017) covers the advancement of a portable ML-based system for detecting a human’s eating behaviour in real time. It utilizes a six-point calibrated wearable MEMS triaxial accelerometer (Liu et al., 2016). This accelerometer measures calories burnt for each step in the system and sends the data directly to the smartphone. The study (Tian and Chen, 2016) exhibits the novel method to collect data from both accelerometer and gyroscope using smart phones and use that data to recognize the daily human activity. Walking, running, moving upstairs, going downstairs, standing, sitting, cycling, and falling have been explored in the subject of machine learning. Ten volunteers (3 females and 7 males) were asked to use an Android smartphone to collect accelerometer and gyroscope data and this data was utilized in the model testing. Mean deviation of accelerometer and gyroscope signals, minimum and maximum value, RMS value of each axis, and signal magnitude are among the features retrieved. The frequency domain characteristics of maximum Frequency, power spectrum, median frequency, Cepstral coefficients, and Mel-Frequency were used to

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determine the correlation between the accelerometer and gyroscope axes. Support Vehicle Machine (SVM) was chosen to categorize seven different types of multi-label activity. Various tests were conducted to compare classification accuracy, with the SVM Method having the greatest accuracy of 96%. Falling among elderly people is one of the major causes for accident, disability and mortality (Noury et al., 2008, Sadigh et al., 2004). In controlled conditions, inertial sensor-based systems can detect falls. Using a wireless wearable accelerometer with a triaxial MEMS sensor, a study (Rescio et al., 2013) involves the advancement of extraction technique as well as the deployment of a ML approach for detecting people’s falls which is shown in Figure 3.

Figure 3. Accelerometer and its components (Rescio et al., 2013). Reproduced from open access article.

The backward fall for two situations were simulated. The first fall resulted in laying flat and the other resulted in recovery. Similarly, the forward fall for both the situation was simulated. The fall with sideways movement was also a part of the simulation. The gadget was held on the upper part of the torso using an elastic band in a distinct placement location for data collecting. To decrease noise caused by electrical components, the environment, and human tremor, 8 Hz cut-off FIR filter is used to filter out samples coming from the device. After the gadget

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was mounted, the calibration operation was completed by restoring the starting circumstances. The acceleration measurements recorded must be having very low value (~0) as the axes of acceleration tend to be orthogonal to earth gravity, showing that the hardware is installed properly. Both crucial and postfall phases of the feature extraction process are of importance. Because of the collision with the ground, the shock and a dynamic acceleration change is recorded. Because of the individual’s changing position in regard to the calibration phase, the static acceleration value represents a large movement. After the features have been retrieved, fall events are detected via a one-class support vector machine (OC-SVM). (Zhang et al., 2006, Cheng et al., 2011). To limit the identification of non-fall occurrences, basic voting-based filtering was added (Yu, 2008). If the SVM classifier’s alert level remains high for an extended length of time, it is identified as a fall event and hence the temporary anomalies were filtered. The dataset includes 200 daily events and 250 falls, with 50 activity of daily living (ADLs) and 40 falls, used for training and remaining 150 ADLs and 210 falls are used for testing. (Sangeetha and Kalpana, 2010) The polynomial and Gaussian Radial Basis Function (GRBF) kernels produce superior results. They have a better ability to notice a fall (more than 95% for sensitivity and specificity).

MEMS and ML in Real Time Robot status prediction MEMS sensors are used extensively in robotics and mechatronics because of their small size, low cost, and high sensitivity (Gigras and Gupta, 2012). The classification of robot state (calmness, longitudinal and latitude movement) using MEMS gyroscope data are demonstrated in the study (Nevlydov et al., 2018). The system comprises MPU-9265 devices, three-axis gyroscope, threeaxis accelerometer, and three-axis compass compressed in one chip, as well as a processor which has ability to process the algorithms which are based on motion fusion. Since the sensors records the raw signals which are noisy, they must be cleaned before further processing. Based on real data received by sensors, the robot motion activity identification system must determine the different types of longitudinal and lateral movements. The machine learning model was trained using three time-domain features (three gyroscope axes). The running window’s mean, standard deviation, minimum, and maximum signal were also utilized as features. For categorization, robot gyroscope signals are adequate. In identifying the robot state, weighted KNN and Bagged trees obtained the greatest accuracy of 88%. Another Research demonstrates

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the MEMS accelerometer‘s practical integration into a hexapod control system to address the challenge of categorizing its states in real time (Nevliudov et al., 2018). The hexapod has six limbs, each with two degrees of freedom, and is controlled by an Arduino board. In order to properly characterize the state in which the hexapod is positioned, seven distinct classes were chosen for identification: 1. 2. 3. 4. 5. 6. 7.

Rest state Forward motion Right turn Left turn Fall to the feet Fall with turn Fall with leaping legs

The Medium KNN technique of recognition during training provides 83.2% Accuracy in classifying the state of hexapod.

MEMS and ML in Rainfall Prediction Rainfall is one of the most severe natural events that may occur in a climatic system. It affects the ecosystems, and agriculture. Excessive rains aggravate landslides, floods, mudslides, and other natural disasters (Cramer et al., 2017). It is difficult to reduce the impact of such rainfall due to its unpredicted occurrence. Researchers have used MEMS sensors to solve this problem (Chao et al., 2018). The sensors for weather monitoring that use Microelectromechanical systems (MEMS) provide the best temporal and spatial resolution. This MEMS weather sensor is used in conjunction with an embedded method and a wireless communication interface to create an automated weather station. With a single server linked to six stations in different locations, a star topology sensor network was built. Seven sensors were installed in each station, the first was for predicting the wind speed, and the second was for sensing the temperature of the atmosphere. The other sensors were pressure, humidity, radiation and rainfall sensor. These stations use General Packet Radio Service (GPRS) to connect with the server and transmit weather data which are later stored in the server along with their corresponding information about the time and geographical location. Figure 4 shows the Star topology of a wireless sensor network (WSN).

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Two methods were tried to forecast rainfall with the use of LSTM (long short-term memory) (Hochreiter and Schmidhuber, 1997, Gers et al., 2000, Lipton et al., 2015, Tax et al., 2017, Han et al., 2018). Real-time prediction is one of them. Seasonal real-time prediction is the other. The findings of the experiment demonstrate that LSTM outperforms Auto Regressive–movingaverage model (ARMA), Radio frequency (RF), SVM, and Back-Propagation Neural Networks (BPNNs) in characterizing the seasonal rainfall process whereas in terms of real-time rainfall prediction, LSTM and ARMA perform similarly. The work concludes that LSTM is effective not only in predicting real-time rainfall but also in identifying seasonal rainfall trends.

Figure 4. Star topology of a WSN (Chao et al., 2018). Reproduced from open access article.

Machine Learning for Speaker Recognition in MEMS Microphone Voice recognition is considered to be one of the best interfaces for communication between the smart devices and human beings. The study (Han et al., 2018) illustrates how a flexible piezoelectric acoustic sensor (f-PAS)

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which has a capability of working with multiple resonant frequencies provide a unique platform for speaker detection, based on machine learning. The covariance matrix and mean vector are calculated using the Gaussian Mixture Model (GMM) technique. It utilizes weights sum of more than one probability density functions (PDF). The Expectation Maximization (EM) algorithm was used to train the parameters of the GMM algorithm, which is an iterative approach for determining the maximum posteriori or likelihood of parameters.

Corona Virus a Pandemic Corona Virus Disease 2019 (COVID 19) was declared as a Global pandemic on March 12, 2020. This disease was considered to be more dangerous due to its rapid spread from one human to another. Identification of the infected was a major challenge and many researchers have taken this challenge and many chapters were published which shows the detection of COVID 19. There are many tests by which an infected person can be classified from non-infected, some of them include Genetic tests, Antigenic tests, and serological tests. FET -based Biosensors are used in detection of COVID 19. Many such tests are available for detection, but the RT-PCR method is highly accepted throughout the world.

COVID-19 Tests COVID-19 can be detected through a range of tests such as Genetic test (Targets Viral Genome), Antigenic test (Targets Viral Proteins), and Serological tests (Targets Anti-Bodies against the virus) (Santiago, 2020, Lou et al., 2020) as shown in Figure 5. The detection methods also employ FET based biosensors to detect COVID-19 (Seo et al., 2020). Though the RT-PCR method is highly accepted throughout the world, other methods also serve their purpose very well. Corona Virus comprises of viral genes using which we can perform COVID-19 detection tests. They are Viral RNA, Proteins like Spike, Membrane, Envelope and Nucleocapsid (Liao et al., 2005, Poghossian et al., 2020).

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Figure 5. Types of tests for corona virus (Santiago, 2020). a) Tools used in the detection of COVID-19. b) Structure of SARS-COV-2. c) Time window for types of tests. Reproduced from open access article.

Genetic Antigenic and Serological Tests The Genetic test comprises of many steps which include sample collection, where nasopharyngeal or sputum samples are collected from a patient and then the sample is transported safely to a laboratory. It is then treated with a lysis buffer solution to inactivate the viral gene, The RNA is separated from the solution and purified using different available kits like fast-spin columns, paramagnetic beads or phenol-guanidine isothiocyanate (GITC)-based solutions (Santiago, 2020). One such kit is QIAamp Viral RNA Mini Kit developed by QIAGEN). RNA is converted to cDNA (Complementary DNA) as depicted in the Figure 6 by using Reverse Transcriptase enzyme. Amplification is performed with Polymerase Chain Reaction (PCR). Thermal cycling process takes about 2 hours of time. The DNA replication is achieved at room temperature using Isothermal amplification (Santiago, 2020). Due to this, the device complexity is reduced and the cost as well. RT- LAMP (Loop Mediated Isothermal Amplification) has been adopted by several groups for the detection of COVID-19. Optimizing the reaction conditions is one of the main challenges faced while adopting the RT-LAMP method (Santiago, 2020). The performance can be improved by integrating a detection method which can identify the amplified gene.

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Another method involves using highly specific Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) Associated Proteins (CAS) (Santiago, 2020). The nucleic acids in the sample are detected using this CRISPR gene editing tool. It is also used in the detection of COVID-19. The viral proteins presence is detected by Rapid antigen tests. The antibodies are targeted towards the SARS-COV-2 using serological tests. The time taken to acquire viral load for antigenic tests is about 5 days. With a stable serological test, the antibodies are developed in 7 days. The time taken to perform Molecular Diagnosis using RT-PCR is about 3 hours. The RNA preparation step is crucial as it can influence the accuracy of the diagnosis (Santiago, 2020).

Figure 6. Various Steps involved in the qRT-PCR test: a) Sample collection, b) Extraction of RNA c) Converted to cDNA. d) DNA polymerase resulting in increases fluorescence signal. e) The result of this test is positive or negative based on the level of fluorescence. Reproduced from open access article (Santiago, 2020).

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FET Based Biosensor FET based Biosensor is the best among the available diagnostic methods as it requires only small amounts of analytes for the instantaneous measurement in the detection of COVID-19 (Seo et al., 2020). A 2-Dimensional Graphene Sheet is adopted to develop a FET Bio sensing device which is used to detect SARS-COV-2 antigen protein with a limit of detection (LOD) of 1 fg/mL (Hochreiter and Schmidhuber, 1997). 1- Pyrenebutyric acid Nhydroxysuccinimide ester (PBASE) is used to block the SARS- COV-2 spike antibody from having a contact with the fabricated device. PBASE acts as a probe linker and is proven to be a best interface coupling agent. SARS-COV2 encodes different structural proteins, and this spike protein is found to be an ideal choice for diagnostics purposes as it has a major transmembrane protein, it exhibits amino acid sequence. The other reason for adapting this method is that the spike protein is highly immunogenic. The COVID-19 virus is most often detected using spike antibody receptor (Seo et al., 2020, Liao et al., 2005, Poghossian et al., 2020, Yang and Yan, 2020).

MEMS Based RT-PCR Chip The RT-PCR tests have been highly acknowledged throughout the world for its high specificity, sensitivity and reliability. A miniaturized portable MEMS based Reverse transcription - polymerase chain reaction (RT-PCR) system which has proven to detect RNA-based Viruses like EV-71 and Dengue-Virus type-2 follows two steps to amplify the RNA- based molecules (Liao et al., 2005). The systems consist of a microfluidic and micro temperature control module. These RT and PCR reagents are loaded into the reservoirs of microfluidic device. The functions of RT and PCR is twofold: ● ●

RT Reaction: cDNA is synthesized from the RNA molecules. PCR Reaction: These cDNA regions are amplified in order to detect the target virus.

This RT-PCR chip is helpful in automating the diagnosis and the detection process. This RT- PCR is battery driven and portable device which has been proven effective to detect two RNA-based Viruses (Liao et al., 2005) The thermal module is used to control thermal cycles appropriately.

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BIO FED’s Based Biosensor Due to the recent COVID-19 outbreak there is a desperate necessity of diagnostic tools which can use bio sensors in rapid detection of virus. These tools should be reliable at the same time cost effective (Christensen et al., 2007, Tahamtan and Ardebili, 2020). These devices should be capable of detecting the on-site clinical samples in a short duration of time (Poghossian et al., 2020, Poon et al., 2003, Leïchlé et al., 2020, Seo et al., 2020). Due to these capabilities Bio FED’s based sensors are the promising choice for early detection and diagnosis of RNA-based viruses. The Bio FED’s biosensors are robust in nature and promises to be compatible with latest fabrication technologies (Poghossian et al., 2020, Narita et al., 2021, Tymm et al., 2020, Lamprou, 2020, Seo et al., 2020). The approach in the detection of virus using such system can be categorized into 4 steps. They are as follows: 1. The virions which are intact virus particles are directly identified with the application of charge particle 2. The non-virions protein detection 3. RNA/DNA detection 4. The final step involves the detection of antibody which are produced by the immune system. The change in electrical properties of BioFEDs are considered in the detection mechanism. The first three above mentioned steps are used in diagnosing new cases. In order to determine whether an individual was previously infected with the virus, serological test which is an antibody detection technique (4th step) is used. A Silicon Nano Wire based FET Sensor (SiNW-FET) is a biosensor consisting of a silicon nano wire channel between the source and drain electrodes and is used in the detection of dangerous viruses. In this type, an application of gate voltage to the sensor results in functioning of receptors (single stranded DNA probes).

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Figure 7. FET sensor used in the detection of COVID-19.

The reverse transcription of virus RNA (extracted from patient) into cDNA is accomplished by RT-PCR. The microfluidic system discussed in (Poghossian et al., 2020) uses a PCR module for amplifying the nucleic acid targets. The SiNW module is also used in the detection of HINI and seasonal flu A. These are also derived from the sequence variations of viral RNA with the help of cDNA. The Figure 7 shows the operation of FET sensor in the detection of COVID-19 (Seo et al., 2020). It should be noted that the operation as shown in the Figure 7 is from (Seo et al., 2020).

Conclusion As the demand for the forensic science is growing, due to the increased crime rate, forensic science is leaving back due to its limitations mainly difficult level of analytics and low progress of analytics in complex situations, with respect to time and also accuracy. We have suggested a method of overcoming these limitations in forensic science with the help of MEMS technology. MIPs-based compounds are frequently used as pre-concentrators before being quantified using commercial methods. In biomedical industry, MEMS based LoC systems like micro fluids are ideal for point-of-care and point-of-use applications, especially for DNS and RNS extraction. There are many MEMS fingerprint sensors that are used in industrial sectors for various applications, same technology that are discussed in this study can be implemented for forensic science, which outcome the greater result. Researchers have developed devices which contain MEMS devices and use Machine Learning to predict certain patterns. The development of Smart wrist worn devices containing the MEMS device using Machine learning algorithms were used to monitor human behaviour in order to predict his/her

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Calories burnt, his/ her fall prediction, fitness tracking and much more. MEMS devices are now integrated with AI to predict the robot status (idle, moving up, moving down etc.) Weighted KNN and Bagged trees performed well in classifying the robot state. Researchers in China have used MEMS devices and ML for predicting weather conditions using LSTM (D. L. K. Reddy, et al. 2023). Corona Virus detection and diagnosis is the need of the hour. Various methods have been discussed like MEMS based RT-PCR system to automate the RT-PCR process using a MEMS based chip, also a FET based biosensor which detects specific viral gene related to SARS COV-2 by using 2D Graphene sheets using a specific antigen placed on the biosensor. Though various types can be used to detect COVID-19 such as CT scan, home based COVID-19 detection kits etc. Any Method that would detect the RNA based Viruses with high sensitivity, specificity, reliability, accuracy, low-cost production, minimal sample preparation steps might be the solution for detection and diagnosis for SARS-COV-2 another RNA viruses (I. Das, et al. 2019). We believe that this comprehensive study will benefit the researchers in developing more précised sensors in virus detection with high accuracy. As a future scope, we are keen in developing a study where in these concepts can be used in development of detection methods which are affordable and can be easily used at home for the detection of deadly viruses.

References Alizadeh, T. and Rezaloo, F. (2013), “A new chemiresistor sensor based on a blend of carbon nanotube, nano-sized molecularly imprinted polymer and poly methyl methacrylate for the selective and sensitive determination of ethanol vapor,” Sensors and Actuators. B, Chemical, Vol. 176, pp. 28–37. An, B. W., Heo, S., Ji, S., Bien, F. and Park, J. U. (2018), “Transparent and flexible fingerprint sensor array with multiplexed detection of tactile pressure and skin temperature,” Nature Communications, Vol. 9 No. 1, available at: https://doi.org/ 10.1038/s41467-018-04906-1. Anderson, R. A., Ariffin, M. M., Cormack, P. A. G. and Miller, E. I. (2008), “Comparison of molecularly imprinted solid-phase extraction (MISPE) with classical solid-phase extraction (SPE) for the detection of benzodiazepines in post-mortem hair samples,” Forensic Science International, Vol. 174 No. 1, pp. 40–46. Ariffin, M. M., Miller, E. I., Cormack, P. A. G. and Anderson, R. A. (2007), “Molecularly imprinted solid-phase extraction of diazepam and its metabolites from hair samples,” Analytical Chemistry, Vol. 79 No. 1, pp. 256–262.

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Chapter 7

Predictive Analytics and Deep Learning Models for the Prediction of the Length of Stay, Diabetes, Colorectal Cancer and Cardiovascular Diseases in Patients Arshpreet Kaur and Jagdeep Kaur* Computer Science and Engineering Department, Dr. B.R. Ambedkar National Institute of Technology, Jalandhar, Punjab, India

Abstract There is an abundance of information everywhere, especially in the healthcare sector, which needs to be objectively diagnosed. The healthcare sector has a gap where there are problems with e-records, data segmentation and early disease prediction. Numerous studies have been conducted in order to allocate the fewest medical resources, reduce patient expenditures, and optimize resources. This led a lot of researchers to use technological advancements like predictive analytics, data mining, and machine learning algorithms to gather the essential facts and create a set of conclusions. Predictive analytics, on the other hand, is a sort of technology that can help in predictive medical treatments in a variety of ways. Medical issues are receiving a lot of attention right now, but disregarding neglected communities and their unique circumstances could widen the digital gap. Deep Learning is still, in general, a complicated technique needing a high level of technical knowledge.

*

Corresponding Author’s E-mail: [email protected], [email protected].

In: Intelligent Decision Support System for IoT Enabling Technologies Editors: Subrata Sahana, Anil Kumar Sagar, Sanjoy Das et al. ISBN: 979-8-89113-249-8 © 2024 Nova Science Publishers, Inc.

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Arshpreet Kaur and Jagdeep Kaur Deep learning aids in the creation of effective research methodologies for dealing with issues obtained from the massive amounts of health-related data collected daily. A new strategy has been put out that combines machine learning and predictive analytics to forecast how long an elderly chronic patient will need to stay in the hospital and the prediction of diabetes, colorectal cancer, and cardiovascular diseases in patients. Several feature extraction sets were compared in order to determine the best among them. According to the experimental results, the extracted features can enable prediction models to better forecast the length of hospitalization of chronic patients as well as predict diabetes, colorectal cancer, and cardiovascular diseases in the patients. This chapter represents a comprehensive predictive system that combines network analytics and data mining, which not only accurately predicts patient fatality but also offers hospital administrators decision support and exemplifies the needed utility of system machine learning in the medical industry. The use of data mining tools in predictive analytics is crucial in the health sector because it empowers us to deal with diseases earlier than expected that can affect humans, young children, infants, and elderly people by anticipating a solution and assisting in decisionmaking.

Keywords: Comorbidity Network, Multimorbidity Network, Machine, Linear Support Vector, Patient Similarity Network

Abbreviations LOS: MN: RF: CCI: DCN: Dx2Vec: T2DM: LDA: ANNs: SVM: PSN: RMSE: ECI:

Length of stay; Multimorbidity network; Random Forest; Charlson comorbidity index; Disease cooccurrence network; Diagnoses to vector model; Type 2 diabetes mellitus; Linear discriminant analysis; Artificial neural networks; Support vector machine; Patient similarity network; Root mean square error; Elixhauser comorbidity index;

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Aortic stenosis; Coefficient of determination; Point of admission; Extreme gradient boosting; Patient similarity based on domain fusion; Relative risk; Electric health record; Unified message passaging model; Bayesian network; Mean absolute error; Chronic obstructive pulmonary disease; Eigenvector centrality; End stage kidney disease; Deep neural network; Hospital discharge records; Gradient boosting decision tree.

Introduction Prediction is more essential than explanation in the field of healthcare, where we find a huge set of data for management and therapy. In recent years, there have been several information systems that have been repeatedly built, highlighting the advantages of predictive analytics. Clinical data may come from several sources. To name a few, they are automated practitioner order inputs, medical records, and imaging equipment. These datasets are extraordinarily complex and dispersed in comparison to other industries, creating considerable problems for diagnosis, cure, and control. Improving them would be of immense worth.

Deep Learning and Predictive Health Care Analytics The data patterns generated using clinical as well as non-clinical data help to forecast future-related health consequences or events. By evaluating the patient’s past medical history and giving the patient the right therapy depending on actual symptoms and diagnostics, they have shown the potential to solve numerous challenges in discovering new drugs for patients. Study

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outcomes, including medical issues, re-admissions to the hospital, therapeutic responses, and mortality rates, are typically of great practical use in healthcare data analytics. Its significance is demonstrated by the recent trend of deep learning modalities in medical analysis of data. The application of predictive analytics has spread across a wide range of fields, including the market, manufacturing, education, and healthcare. In actuality, the healthcare industry sees predictive analytics as a chance to be able to anticipate the future and extract data that can be useful. The ability to identify hazards early, make better judgments, and save more lives can further change the healthcare industry into one that is not only predictive but also preventative. According to this study, it is essential to create a framework to direct the use of predictive analytics as well as a new comprehensive approach and strategy to improve outcomes and overcome obstacles like low revenue.

Healthcare Prediction Modelling Healthcare analytics research is increasingly integrating electronic health information gathered from multiple sources to create predictive models to identify patients’ ailments (EHRs). Predictive models can be used for several purposes, including feature generation selection, cross-validation of data, and data classification. To construct an efficient model, it is crucial to evaluate and enhance models created from several associates, mathematical outlines, and patient-related data. The goal of this research is to develop a predictive model and evaluate a platform for predictive modelling that may be applied to streamline and accelerate this process for health data (Knevel & Liao, 2022). Predictive analytical tools use data from hospital services, past medical histories, healthcare provider reports, and historical prescription lists to forecast patients’ long-term health. Additionally, as the amount of medical data increases quickly, computer prowess technical know-how may enable the medical business to transform in previously unimaginable ways. Running programs on input in the predictive model development produces predictions. Because the method is iterative, it educates the model that is most appropriate for achieving the objective or fulfilling the business needs. The following mathematical modelling stages are involved in the predictive model development:

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Data collection and cleaning- Data is gathered from all sources in order to extract the necessary information. Cleaning procedures are then performed to eliminate data redundancy so that predictions can be made with accuracy. Analysis of data- Whenever you begin developing your model, examine the data you have collected using a straightforward graphic. You must be capable of grasping the characteristics of the data and the relationships seen between variables. Users will not be capable of building a solid model if they cannot do so. You can obtain a solid notion of the solution to the issue you are trying to address based on the general tendency by making a simple map of your facts to explore it. Constructing a predictive model- Sometimes a certain algorithm or concept is well suited to the data. Sometimes the best course of action is not as obvious. Execute as many programs as possible while analyzing the data, then contrast the results. To evaluate the effectiveness of the classification algorithm against test suite, find test data and employ class labels. Integrate the model into your company’s operations- You must incorporate the model into the organization’s operations so that it may be included to enhance patient care for it to be useful for your healthcare facility.

The use of predictive analysis in healthcare helps to enhance patient safety and guarantee position in addition to many other things, it can anticipate quality control and improve medication, identify the riskiest patients who are in bad health and who would gain the most from assistance, develop conclusions from trends inpatient records in order to construct effective marketing campaigns, and a lot more.

Overview for Prediction in Length of Stay (LOS), Diabetes, Cardiovascular Diseases, and Colorectal Cancer ●

Length of stay (LOS)- Early prognostication of hospitalization among patients with long-term illnesses, particularly the elderly having multimorbidity, can assist hospital administrators in effectively allocating scarce resources, managing patient expenditure, enhancing

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all standards of healthcare solutions (Mekhaldi, Caulier, Chaabane, Chraibi, & Piechowiak, 2020) (Meadows, Gibbens, Gerrard, & Vuylsteke, 2018). Healthcare professionals’ strategizing, decisionmaking, and strategy-building would be aided by a precise estimate of LOS, re-admission, and death. It will inevitably result in better patient care, a reduction in readmissions, and a reduction in mortality following discharge. Diabetes- Diabetes is viewed as a chronic, non-transmittable disease because there is an imbalance between the levels amounts containing insulin, a pancreatic hormone required for the processing of glucose (M. A. R. Santos, 2017). There have been numerous sorts of diabetes, such as Type 1 Diabetes, in which the thyroid gland prevents secrete insulin, diabetes of type 2 which is frequently linked to obesity or overweight because the amount of insulin produced is insufficient compared to the blood glucose quantity, and the one which manifests in pregnancy is Gestational Diabetes (Contreras, Vehi, et al., 2018). The use of Machine Learning techniques under this field aids in the early detection of Diabetes based on patient clinical histories. Cardiovascular diseases- In many nations, cardiovascular illness remains the major cause of mortality. Cardiovascular illness is frequently identified by doctors according to the findings from previous clinical testing with their past knowledge dealing with patients who have comparable symptoms. Heart attack patients need an immediate diagnosis, early treatment, and continuing monitoring. The algorithm under discussion assesses the strength of the important characteristics that influence the prediction of heart disease. Through weighted associative rule mining, the study aims to forecast cardiovascular disease using the scores of relevant aspects.

Therefore, the key to reducing the number of cardiovascular diseases that cause death is the provision of effective treatments and, timely treatment. For patients with a high rate of heart disease, having access to these services is crucial (Cao, Chen, Yu, Li, & Chen, 2021).

Colorectal Cancer With approximately 1.8 million cases diagnosed in 2018 and indeed, colorectal cancer is the most frequent reason for death whenever the genders are united, it is the most prevalent cancer by occurrence (Sharma, 2020). The kind of procedure as well as the patient’s unique traits are two of the many

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variables that need to be considered while evaluating health outcomes following surgery. The patient demographics include BMI and performance status (ASA grade), echoing previous research that shows the effects of obesity on a variety of illnesses, particularly cancers (Candi et al., 2018). The prognosis and diagnosis of numerous health problems and diseases have recently shown tremendous potential using artificial intelligence (AI) and machine learning (ML) prediction models (Kourou, Exarchos, Exarchos, Karamouzis, & Fotiadis, 2015) (Pan et al., 2017). Without user intervention, machine learning seeks to identify patterns in data. One essential property that is modelled and learned from data using machine learning (ML) methods is the probabilistic dependency between a set of output and input variables. Numerous risk indicators can be incorporated into a forecasting model using ML, a data-driven approach (Passos, Mwangi, & Kapczinski, 2016). The model was built with age, blood cell count, and sexuality as input characteristics and has been demonstrated to be effective in diagnosing colorectal cancer (Hornbrook et al., 2017).

Literature Review Based on Predictive Analytics for Length of Stay (LOS), Diabetes, Cardiovascular Diseases, and Colorectal Cancer Due to machine learning’s exceptional nonlinear fit and great predictive capabilities, it has been routinely used to anticipate the LOS. Table 1 highlights relevant literature survey in predicting LOS. In order to estimate the length of stay (LOS) from insurance payout data, Xie et al. built a model called bagged regression trees. They discovered that medical information had a greater impact on LOS forecasts over demographic data. To predict the LOS among cardiac patients, Daghistani et al. used Bayesian Network (BN), Support Vector Machine (SVM), Artificial Neural Networks (ANNs) and Random Forest (RF). Considering the findings, RF seems to have the greatest performance and is easy to comprehend. The most significant than subsequent diagnostic stages are point of admission (PoA), as it could define a central feature for the services as well as asset management for family and patient counselling, only a handful of studies successfully estimated LOS (Alves et al., 2005). The minimal data that are available during such a preliminary phase of treatment makes it difficult to

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predict LOS during PoA. Typically, the PoA’s residential ward only has access to basic patient data, diagnostic information, and hospital features. Table 1. Literature review based on predictive analytics for Length of stay (LOS) Author Name (Xie et al., 2015)

Used Model

Type of Data

Bagged regression trees

Insurance claim data

(Daghistani et al., 2019)

Bayesian Network (BN), Random Forest (RF), Artificial Neural Networks (ANN), Support Vector Machine (SVM) Applied tree-based Models

Cardiac patients’ data

LOS

Extracted two network features from Disease Cooccurrence Network (DCN) Electric Health Record (EHR)

High- cost patients

The performance of the model could be greatly enhanced by feature sets.

Diabetic patient readmission

The results indicated that, compared to CCI, ECI features, accuracy prediction was increased from 4.65 to 5.75 percent using the network features.

(Srinivasan, Currim, & Ram, 2017)

(Sideris, Pourhomayoun, Kalan taria & Sarrafzadeh, 2016)

Made clusters of DCN to limit the dimensionality of data dimensionality

Type of Prediction LOS

Inference Medical metrics gives better results in comparison to demographic statistics in prediction Random Forest has best performance and good reasoning

Previous studies have sought to employ extracted features out from Elixhauser Co- morbidity Index (ECI) and Charlson Comorbidity Index (CCI) to determine LOS because of the importance of diagnostic information in predicting LOS at PoA (Tevis et al., 2015) (Henneman et al., 2013). In recent years, much emphasis has been paid to the Patient Similarity Network (PSN), in which the commonalities between paired patients are represented by the edges and the nodes represent patients, respectively. Network analysis is used

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to extract useful features from the PSN for a variety of health regression problems.

Predictive Modelling for Predicting Diabetes in Patients Related Work It has been a primary concern throughout our study as well as the creation of clever ideas to enhance its treatment because of the progression of diabetes in recent decades and the catastrophic consequences that occur from it. Table 2 highlights relevant literature survey in predicting Diabetes. Raj Sampath’s research examines the effectiveness of Naive Bayes (NB) and Support Vector Machines (SVM) methods for diagnosing sugar levels, while considering the patient ’s health condition, including blood glucose levels, blood pressure, or age. The findings revealed that the SVM predictive model outperforms the NB technique with an efficiency of 82% as opposed to 62.5%. However, Ghosh, Dutta, Paul concentrated on SVM, LR, and RF methods for identifying female diabetes. Table 2. Literature review based on predictive analytics for Diabetes Author Name (Raj, Sanjay, Kusuma, & Sampath, 2019)

Used Model

Type of Data

Support Vector Machines (SVM), Naive Bayes (NB)

Patient’s health condition, including blood glucose levels, blood pressure, or age

(Dutta, Paul, & Ghosh, 2018)

LR, SVM, and RF algorithms

Age, blood sugar levels, related to woman’s pregnancies

Type of Prediction Diabetes

Diabetes

Inference Findings revealed that the SVM predictive model outperforms the NB technique with an efficiency of 82% as opposed to 62.5% RF model had the highest accuracy (about 84%) and was the most useful to predict diabetes

Age, blood sugar levels, a woman’s previous pregnancies, insulin levels, and body mass index were among the data utilized in the problem modelling. As a result, they showed that the RF model had the highest accuracy (about 84%) and was the most useful to predict diabetes. A study that examined NB, DT, and SVM algorithms employing data linked to blood pressure, age, body temperature, and body mass index focused on pre-processing techniques such

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as Discretization and Principal Component Analysis. The outcomes showed the value of applying features extraction approaches prior to predictive models by raising the efficiency from 75.11% to 79.13% of the DT model and 75.88% to 79.41% for NB model, in contrast the accuracy deter from 76.36% to 75.59% for SVM model (Vijayan & Anjali, 2015).

Predictive Analytics for Cardiovascular Diseases Prediction Related Work Table 3. Literature review based on predictive analytics for cardiovascular diseases Author Name (Amin, Chiam, & Varathan, 2019)

Used Model ARM

Type of Data

(Khare & Gupta, 2016) (Chang et al., 2002) (Lochner & Cox, n.d.) (Xu, Zhang, & Yip, 2020)

ARM

Cardiac patients’ dataset UCI Sample

ARM

UCI Sample

ARM

UCI Sample

ARM

UCI Sample

Type of Prediction Cardiac accuracy rate Cardiac accuracy rate Cardiac accuracy rate Cardiac accuracy rate Cardiac accuracy rate

Inference Identified Characteristics that improve cardiac accuracy rate. Achieved an accuracy of 98.99% on the data set. Achieved an accuracy of 99% on the data set. Achieved an accuracy of 97.8% on the data set. Achieved an accuracy of 99.91% on the data set

Considering the high mortality rate, diagnostic and preventative interventions must be carried out successfully and effectively. Table 3 highlights relevant literature surveys in predicting cardiovascular diseases. To help with these problems, numerous data mining approaches have been employed. Most earlier studies focused on identifying characteristics that improve cardiac accuracy rate. An Associative Rule Mining (ARM) method can be used to determine the association between every feature that helps predict cardiovascular disease (Alsinglawi et al., 2020). The ARM method is well-liked in relational and transactional databases. Many big businesses have acquired an interest in understanding the patterns that might enable them to better their commercial decisions as a result of the secret information in massive datasets like business transactions (Agarwal and Mithal (Agarwal & Mittal, 2019)).

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For example, market basket analysis might reveal the products that clients purchase most frequently. This research examines the numerous goods that customers have in their shopping carts and finds the relationships between all of them. An excellent illustration might be that consumers who had been seeking to purchase milk were prone to also buy bread while at the store. This method is also frequently utilized in the healthcare sector, particularly in the privacy protection of health records (Domadiya & Rao, 2019), the prediction of protein complexes related to cancer (Dey & Mukhopadhyay, 2019), the prediction of sleep disturbance (Sim, Teh, & Ismail, 2017), and the prediction of co-diseases in hypothyroidism patients (Khan & Yadav, 2019). On the UCI sample, ARM has been applied by Khare and Gupta, Srinivas et al., Akbas et al., Lakshmi and Reddy and Shuriya and Rajendranb. Despite the excellent scores (99% by Sonet et al. (Sonet, Rahman, Mazumder, Reza, & Rahman, 2017) and 100% by Thanigaivel and Kumar (Thanigaivel & Kumar, 2016)) achieved from these datasets, the studies’ ability to be reproduced is limited because the datasets are not publicly available. On the other hand, Akbas et al. used the UCI sample to achieve a confidence score of 97.8%. The confidence score, however, indicated that there was no danger of heart disease in the population. A variation on ARM called Weighted Associative Rule Mining (WARM) uses weights to categorise the significance of the characteristics that are mined. Consider T to be a training sample, which consists of T = r1, r2, r3, . . . ri and has a collection of weights assigned to each pair of an attribute and its value. A set of parameters is present in each ith record ri and weights are associated with each attribute of the record’s ri tuple. Every record in a weighted framework is composed of the triples ai, vi, and wi, in which characteristic ai has a weight value wi and a value vi, and where 0¡wj ¡=1. WARM is frequently used to study hypothetical supermarket trolley scenarios and forecast buyer behavior. The topic of giving weight prior to and following ARM was researched by Chengis et al. (Cengiz, Birant, & Birant, 2019). Additionally, WARM was utilized to forecast disease complications using both clinical and genetic data (Lakshmi and Vadivu (Lakshmi & Vadivu, 2019)). Additionally, breast cancer is predicted using this method (Alwidian, Hammo, & Obeid, 2018). This method was recently employed in a study by Park and Lim (Park & Lim, 2021) to lower pre-alarming system design failures in the construction sector.

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Predictive Modeling to Predict Colorectal Cancer Related Work Table 4. Literature review based on predictive analytics for colorectal cancer Author Name (Ahmed, 2005)

(Kather et al., 2019)

Used Model

Type of Data

Three-layer feedforward neural network

Colon infected individuals

Deep convolution networks

100,000 histologic pictures of the patients

(Iizuka et al., Convolution and 2020) neural networks

colorectal cancer histology images

(Daniel & Thangavel, 2016)

30 patients with stomach ulcers and 49 patients with stomach cancer

Backpropagation neural network

(Zuo, Dai, Multivariate and &Ren, 2019) Univariate Cox regression analysis

CRC patients

Type of Prediction Survival of colon cancer patients

Inference

Prediction of the chance of survival of colon cancer patients was accurate rate of survival The gathered tissue of colorectal slides are divided up cancer into smaller pictures, which are then scored for deep stroma. Detect stomach Useful features were and small retrieved and the intestinal convolution-model and epithelial rnn-based are trained cancer on the extracted features. breathomics The backpropagation stomach cancer method is used to process the photos that have been obtained, classifying the health information of the patient by estimating levels of acetone, 2propanol, ethyl acetate, carbon isulfide and other abdomen substances. Determining An mRNA spectrum the risk of signature has been colon cancer developed to forecast survival rates.

In this, a few experts’ perspectives are examined to learn more about how colorectal cancer is detected. Table 4 highlights relevant literature survey in predicting colorectal cancer. Using an artificial neural network, Ahmed et al. prediction of the chance of survival of colon cancer patients was accurate. The analysis of the life expectancy employs a three-layer feed forward neural network. An artificial network which can be a non - linear regression tool, is initially used to gather and process data on colon infected individuals. Deep

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convolution networks were used by Kather et al. to evaluate the rate of survival of colorectal cancer. 100,000 histologic pictures of the patients’ tissue slides are gathered during this process. The gathered tissue slides are divided up into smaller pictures, which are then scored for deep stroma. Convolution and neural networks that recur were used by Iizuka et al. to develop an efficient method for detecting stomach and small intestinal epithelial cancer. Under this study, colorectal cancer histology images were gathered from patients during this procedure, and useful features were retrieved. The convolution model and RNN based are trained on the extracted features. Backpropagation neural network based automated software system for predicting breath omics stomach cancer was created by Daniel et al. Information on 30 patients with stomach ulcers and 49 patients with stomach cancer is gathered during the preliminary screening phase of this approach. The backpropagation method is used to process the photos that have been obtained, classifying the health information of patient by estimating the levels of ethyl acetate, acetone,2-propanol, carbon disulfide and other abdomen substances. Patients are divided into normal case, suspicious case, and positive case groups based on the criteria. The artificially intelligent method known as a neural network was crucial in the foreshadowing of colorectal cancer cases. This study uses an efficient ResNet deep neural network algorithm (Sekaran, Chandana, Krishna, & Kadry, 2020) to classify colorectal cancer by considering user opinions. Additionally, the developed system fails to extract detailed information from the colorectal picture since it uses a convolution model for feature extraction based on conventional methods. Therefore, the network extracts the low- and high-level properties that primarily aid in accurately identifying the colorectal pictures. The multivariate and univariate Cox regression analysis were recommended by Zuo et al. for determining the risk of colon cancer. Using multivariate and univariate Cox modelling analyses, an mRNA spectrum sig- nature has been developed to forecast survival rates in CRC patients. In their discussion of the future clinical investigation, (Ooft et al., 2019) show the viability of creating and evaluating patient-derived tumor organoids (PDOs) to gauge chemotherapy sensitivity. Without misclassifying patients who might have benefited from medication, the patient derived tumor tissue and cell test predicted the responsiveness of the biopsy samples in over 80 percent of total of patients sustained on irinotecan-based therapy.

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Predictive Methods for Length of Stay (LOS), Diabetes, Cardiovascular Diseases, and Colorectal Cancer Modelling and Evaluation Length of Stay (LOS) Prediction in Patients Modelling The performances of 5 machine learning algorithms were compared which utilized 5 distinct feature sets in order to assess the proposed methodology. The following five input feature combinations were available: Baseline, Baseline + PSN, Baseline + MN, and Baseline + History + MN + PSN. The four models for machine learning were DNN, RF (Liaw, Wiener, et al., 2002) XGBoost (Chen & Guestrin, 2016), LinearSVM (Smola & Scholkopf, 2004), GBDT (Friedman, 2001). We employ five machine learning methods to forecast the LOS, in order to assess the framework’s precision and effectiveness using our extensive dataset. The tree-based ensemble models XGBoost, RF, and GBDT exhibit excellent nonlinear fitting capability, resilience, and interpretability. Since radial function kernels are ineffective and unsuited for use with millions of samples, researchers do not examine them for SVM. As a result, we chose to make the kernel function of the SVM a linear one. The datasets were normalized as per standard procedure before being used to train the Linear SVM model. DNN (Daghistani et al., 2019) is yet another well-liked algorithm in the LOS region. The model’s ability to generalize is significantly influenced by the system architecture. ReLU served as the input layer, and mean square error served as gradient descent. In order to train the model, Adam optimizer with lr = 0.0005 and weight decay of 0.00001 was used. To prevent model overfitting, batch normalization and dropout were employed. The grid search algorithm decided the batch size to be 4096 and the epochs to be 200. Evaluation In order to evaluate the generalization abilities of the five models, the data was divided randomly into a training dataset (80%) and a test dataset (20%). Since there were more than a million samples in the training set, 20% of them were randomly divided to produce a validation set. This validation set was used to evaluate performance of the model as the grid search strategy’s parameters were being adjusted. All tests were conducted using Python 3.7.3 on a Linux

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server that had 48 Intel Xeon E5-2678 CPUs. The toolboxes utilized for model training and data pre-processing were sklearn 0.23.0 and Pandas 0.24.2, respectively. The DNN model was trained using Torch 0.3.0. The following figure (Figure 1) shows the working of the predictive model.

Figure 1. Predictive model flowchart (Hu, Qiu, Wang, & Shen, 2022).

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Modelling and Evaluation in Diabetes Prediction Modelling According to those found in prior studies, many classification methods were used, including Gradient Boosting Machines LR (GBM), SVM, RF, NN, NB and DT. Hyperparameter Tuning, Recursive Feature Elimination (RFE), and Cross Validation (CV) are implemented as part of the development of this model in order to improve results (HT). The first method involves choosing a portion of a dataset’s most pertinent features, and its use was intended to determine the significance of variables. The effectiveness of the strategy depends on the outcomes of the last forecasts. The reason CV is so well-liked as a resampling technique is that it measures the ability to make judgments on data left during the training process and evaluating its efficiency (Misra & Yadav, 2020). HT with grid search chooses a collection of suitable values for a training algorithm (Cortez & Cortez, 2014). A matrix of hyper - parameter variables is chosen, and grid search compares each one to the evaluation metrics (Probst, Boulesteix, & Bischl, 2019). Class balancing is a typical approach to classification issues, however in this instance, it was not necessary. To account for all possible outcomes, the scenarios corresponding to statistics including patient description, tests, and medical drugs (S1), Statistics containing patient chart and tests (S2), Statistics including patient’s medical record and pharmaceutical drugs (S3), and Statistics including diagnosis and treatment and allergy medicines were chosen (S4). 28 designs (four Instances, 7 Approaches) were induced to provide the desired outcomes. Evaluation The produced models are examined to see if they satisfy the defined organizational goals in order to enable a thorough examination of this research, including a review of the overall process as well as choice of next steps. To determine which algorithm produces the best results, metrics are employed. Below-mentioned values were outlined for the indicators considering the project’s starting context, information gained from related work, and data accessible for this study: SE (Sensitivity), PR (Precision), AC (Accuracy), and AUC (Area Under the ROC Curve): -75% for AC and AUC -70% for SE and PR.

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Figure 2. Proposed Methodology (Wang, 2022).

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Modelling and Evaluation for Prediction of Cardiovascular Diseases Modelling Apriori Algorithm The Apriori algorithm is an established methodology in WARM. Apriori was first proposed by Agrawal and Srikant (Sonka, Hlavac, & Boyle, 1993). The starting dataset for the algorithm only contains transactions that satisfy a userspecified threshold for common sets of items. The Apriori technique determines whether an object list X of size k is frequent when and only when each subdivision of X, of size k-1, is also determined to be frequent. This influences the search space in a major way, which expedites the discovery of new rules. Apriori constructs a rule of the form: s = > (f - s) if and only if the level of confidence of the rule is greater than the consumer’s threshold. Weighted Confidence To show how often the rule appears to be correct phenomenon of confidence interval is used. By dividing the weighted support of (X U Y) by the weighted support of the objective, Y, one can calculate the weighted confidence of a rule X Y. For example, the confidence for the rules “heart disease” and sex = “male” CA = “3” is 0.2/0.2 = 1.0. It means that a patient= “male” and has 3 CA (main arteries colored via fluoroscopy) will almost certainly get heart disease. Evaluation This stage of weighted associative rule mining produces rules using the Apriori algorithm. For the following, two sets of guidelines and confidence intervals were produced: (i) All 13 features are included here. (ii) A few key characteristics (8 features). Figure 2 shows proposed methodology for the predictive analysis.

Modelling and Evaluation for Colorectal Cancer Prediction Modelling Directly applied to the remaining deep learning network are a total of 100,000 224 224 tissue-related MRI pictures of histological colorectal cancer. The

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deep learning technique applies the kernel value to the pixel intensity. In the MR picture, the intensity value is neither constant nor identical; instead, it varies depending on the person. The colorectal image must be pre-processed using the data mining technique in order to identify colorectal cancer. The identical set of input photos are used in the normalization procedure while the image noise is eliminated. Additionally, doing so makes it much easier to process the picture weight and bias value steadily. In order to do this, the colorectal pictures are adjusted (Agrawal, Srikant, et al., 1994) based on pixel intensity. In order to do this, the min-max normalization procedure is used, where yi is the value of the normalized picture, the image’s maximum intensity value is given by max(x), and the minimum intensity value is given by min(x). The image is normalized during this step before being analyzed by the ResNet deep learning system.

Evaluation

Figure 3. Resnet Block Structure (Yazdani, Varathan, Chiam, Malik, & Wan Ahmad, 2021).

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BASED on 100,000 histological colon image data, the efficiency of the proposed IAECP system is assessed. The data collection consists of several photos that have been separated into groups for testing, training, and validation. In total, 8,820 of the various photos are used for testing. An approach called fivefold cross validation is employed in this procedure to evaluate the system’s effectiveness. In this instance, colon cancer histology images are collected and divided among k equal samples, one of which is used for training, validation, and the remaining portions for evaluation. The test photos are utilized as a search query in comparison to the training photos in order to forecast colorectal cancer images. The MATLAB R2017b program was used to create the IAECP colorectal cancer prediction images that are being discussed. Figure 3 shows Resnet Block Structure given below.

Results and Discussion Results Matrix for Predicting Length of Stay (LOS) in patients After LDA reduction, the decreased EVC characteristics were achieved. The best high energy was then chosen for each model by parameter tuning utilizing grid search. Figure (Figure 4) below compares the predictive capabilities of XGBoost, LinearSVM, GBDT, DNN, and RF on various itemsets. The R2 of our suggested method (Baseline + PSN + MN + History) was 0.285 on LinearSVM, 0.374 on GBDT, 0.330 on DNN and 0.347 on RF0, indicating that XGBoost beats its other versions. The R2 value of XGBoost seemed to be 0.250(Baseline), 0.304 (Baseline + MN), 0.316(Baseline + PSN) and 0.316(Baseline + History) for the other feature subsets, indicating that employing chronological features, MN features itself enhance the performance of the model in comparison to Baseline in an effective manner. In addition, when compared to the effectiveness of all other feature subsets, the experimental findings for Baseline + History + MN + PSN demonstrate the outstanding outcome with all models in all assessment criteria. Significantly, the R2 for XGBoost in MN + Baseline + PSN + History was enhanced to 18.75% in comparison to the R2 for XGBoost in Baseline + History, demonstrating that the performance of the model is significantly improved by the addition of network features.

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Results Matrix for Diabetes Prediction The algorithm’s best outcome and the corresponding scenario are displayed for the subject under investigation for diabetes diagnosis. The assessment values are displayed for every model. For example, AC measures how close a value is to a given value, while AUC measures how near a value is to all potential thresholds. You can assess the validity of the obtained results using the SE and PR measurements. The True Positive Rate is determined by SE, and PR is indeed the proportion of True Positives to All Positives (M. F. Santos & Azevedo, 2005).

Results Matrix for Prediction of Cardiovascular Diseases According to the regulations with the highest probability ratings. The number of factors available for producing this guideline is 3, and the utmost level of confidence for estimating the probability of developing heart disease is 96%. The rule specifies that such patients have an extremely strong possibility (confidence level = 96%) of possessing the heart disease risk if somehow the amount of Chest Pain (CP) is painless, its gradient is smooth, as well as the level of Thallium (Thal) remains bidirectional. Table 5. Choosing major characteristics from the highest performing results Feature\Occurrence Restecg Sex Oldpeak Trestbps Fbs Age Thal CA Exang CP Slope Chol Thalach

Best Accuracy 2 2 4 0 2 2 4 5 2 5 18 10 12

Precision 7 5 6 6 7 5 6 1 4 4 4 8 19

Best F-Score 7 4 4 4 7 4 4 2 2 4 5 12 14

Total 16 11 14 10 16 11 14 8 8 13 27 30 45

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Every one of the top twenty guidelines is identified by its rule number and the qualities it employs. Merely 6 of the top twenty rules forecast cardiovascular disease, while the remaining are healthy rules that indicate no cardiovascular disease. CP is the most crucial predictor of heart problems. All six of the created rules that identify cardiovascular disease have this trait. Among the six factors for heart disease prediction Oldpeak and Thal are present in four of them. Table 5 helps choose major characteristics from the highest performing results.

Results Matrix for Colorectal Cancer Prediction The effectiveness of the ResNet DLS using SACC method is assessed here. The 5-fold cross-validation procedure makes use of the system’s superiority, as was previously mentioned. Various block levels are deployed to inspect the system during training. Gradually, as a result of our trainable parameters, the system’s effectiveness increased. Additionally, the method of image segmentation as well as feature extraction makes use of a deep learning system. These methods effectively extract both high-level and detailed information out from colorectal images. The effectiveness of different cancer kinds in retrieving colorectal tumors was shown in Table 6. The IAECP system successfully obtains the various colon types of cancer. The fivefold cross-validation component efficiently derives five distinct types of colorectal cancer characteristics. Table 6. Effectiveness of the five-fold validation method for various cancer types Cancer type Adenocarcinoma

Gastrointestinal carcinoid tumor

Primary colorectal lymphoma

Gastrointestinal stromal tumor

Parameter Precision Recall F1-Score Precision Recall F1-Score Precision Recall F1-Score Precision Recall F1-Score

Set 1 98.79 99.49 99.1 99.96 99.89 99.1 99.79 99.49 99.1 98.79 98.49 99.1

Set 2 99.43 99.48 99.15 99.93 99.48 99.75 99.93 99.48 99.15 99.43 99.64 99.15

Set 3 98.67 98.47 99.3 98.67 98.47 99.3 98.66 98.47 99.3 98.67 99.47 99.3

Set 4 98.54 99.96 99.43 99.54 99.96 99.43 98.44 99.96 99.43 98.54 99.96 99.43

Set 5 99.1 98.13 98.78 98.1 98.13 98.98 99.85 98.13 98.78 99.1 98.13 98.78

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Additionally, the test picture features are used to assess the system’s perfection, and the results are shown in Table 6 below. According to following Table, they presented a deep learning methodology called ResNet which can identify the highest accuracy for colon cancer of 99.85%. Additionally, across all five sets of data, the IAECP algorithm accurately predicts cancer. Because of a successful feature training procedure and minimal variance between the request and test picture features, maximum recognition accuracy was achieved. In contrast to this correlation procedure, the system’s performance is measured against that of the current classifier. The ensuing Table 6 shows the results that were attained. It has demonstrated that the newly created IAECP system can compare the query image to the training image with 99.46 percent accuracy, select features from the database that are pertinent to the test image with 99.28 percent accuracy, and select features that are particular to the search query with 99.65 percent accuracy. The analysis of the data simply demonstrated that the SACC matching algorithm and ResNet with DLS system provides higher precision, in contrast to many research authors’ methods.

Future Work The research that was modelled demonstrates the potential to forecast a condition such as diabetes combining ML approaches with medical data, outlining the overall process of achieving prediction performance. The outcome can be divided into 4 approaches that rely on the metrics applied. The application of these strategies and the outcomes gained surpassed expectations considering the available data. Moreover, it is obvious how crucial information about a diabetic patient’s profile and medication is to the process of making a prediction. In respect of upcoming work, we have established restrictions of strategies to be applied to those that had the best outcomes. More data must be collected in order to increase the predictive power of the current algorithms. Future studies might focus on estimating heart disease risk levels because doing so will make it easier for individuals and medical professionals to assess the severity of their condition. The method employed in this chapter to measure weight could be further investigated for usage with different datasets to accommodate alternative weighted projections. The most widely utilized machine learning approaches for prediction of cardiovascular disease research are the only ones used in the attribute selection stage of this study. Future

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studies ought to investigate additional machine learning approaches for choosing the important traits (A. Anand, et al. 2021). Algorithms using the neural network could assist the clinics for screening lessen time locating cancer areas using the given approach to ordinary specimens with great accuracy. This innovative method might be very beneficial in the detection of colorectal cancer. Finally, the system’s effectiveness is assessed through MATLAB software, which shows that it predicts colorectal cancer with an accuracy rate of 99.85%. Future scope desires an enhanced algorithm procedure will be used to improve system’s efficiency (S. Sahana, et al. 2016).

Conclusion Length of Stay (LOS) This study offered a new method for predicting hospitalization LOS in older chronically ill patients by combining network science and machine learning. It demonstrated how to develop MN with thousands and even hundreds of thousands of large datasets using a storage and very efficient development process. The MN was used to extract the EVC characteristics, and the LDA was used to lessen the amount of EVC characteristics, that can hasten learning efficiency and improve performance of the model. To build the PSN, NMSLIB was used in order to utilize the neighbor’s data of patient. The investigation’s findings demonstrated that network features can greatly enhance performance of the model for a variety of models. XGBoost’s R2 on Baseline + MN + PSN+ History increased by 18.7% in comparison to its R2 on Baseline + History. In conclusion, our suggested method can forecast LOS earlier for senior patients, thereby providing hospital administrators with useful decision making. Resampling strategies that increase the amount of tailed LOS data may result in further advancements for our suggested methods (Naemi, Schmidt, Mansourvar, Ebrahimi, & Wiil, 2021). The inpatient LOS for older chronically ill patients at the time of PoA can be predicted with a specific level of precision due to the robustness of our suggested strategy. Future research will examine the suggested paths that will further increase accuracy.

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Diabetes Machine learning algorithms are used to glean data-driven insights. This can help in many situations when making forecasts as well as decisions. Machine learning algorithms are typically divided into supervised, semi-supervised, unsupervised, transfer learning and reinforcement learning. In order to accomplish collaborative filtering, grouping, and classification on a dataset, several strategies can be used. The accuracy of each method is determined by the results of a few parameters, such as error, score and accuracy. A diabetic person is said to have a critical condition in which their blood sugar levels are persistently high (either because their body produces insufficient amounts of insulin or because their cells do not respond to it). People’s health can suffer from major complications due to diabetes. The ability to forecast diabetes can be useful in making strategic health-prevention decisions. We provide a background analysis on tools of Big Data, machine learning techniques, and the comparative analysis in this chapter.

Cardiovascular Diseases The prediction of heart disease with the highest level of confidence was made by the WARM application to a small number of relevant characteristics, coming in at 98% as opposed to the 96% obtained from all features. WARM accurately predicts any chance for developing heart disorder. Among the topmost twenty rules extracted, exactly 6 are reliant across all the features. In contrast, 11 of the top 20 created rules were based on the chosen 8 traits. This study compared the rules produced in our study to those created by earlier studies utilizing the UCI dataset. Usage of WARM yielded the greatest confidence score for heart disease prediction using a small number of relevant characteristics, coming in at 98% as opposed to the 96% obtained from all features. WARM accurately predicts the chance of developing heart disease. Out of these just 6 rules lay their basis on all features among top 20 conditions produced. In contrast, 11 of the top 20 created rules were based on the chosen 8 traits. This study compared the rules produced in our study to those created by earlier studies utilizing the UCI dataset. This study helped to achieve the highest probability value when employing significant elements in WARM to predict heart disease. It has been demonstrated that giving weight values that are suitable enhances prediction performance. For the purpose of predicting heart disease, a group of important

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parameters with varying weights were used to reflect the severity of every feature. Towards the finest of our knowledge, our study is the first to implement WARM using its important aspects. Using the UCI dataset, this study has indeed helped to compile a list of the best rules for predicting heart disease. The benchmarking of the healthy norms and the unhealthy rules with the greatest confidence levels was done for the first in this study.

Colorectal Cancer IAECP was developed for the colorectal cancer screening system in accordance with the explanation above. The method of feature retrieval and feature training using ResNet deep learning determines how effective the system is. The attribute extraction is executed flawlessly in accordance with the outstanding training process. The relevance in between enquiry parameters is assessed using both values of the pixel intensity for the training as well as trial images. The analysis of the image boundaries, lines, and edges by several neural network layers is a crucial second aspect. The colorectal canceraffected area can be located by successfully extracting this area. Various deep learning attributes are automatically evaluated from the resultant region. Multiple convolutional layers, ReLu units, max-pooling layers, and batch normalization are used to train these features. These are the primary justifications for enhancing the IAECP’s approach for recognizing colorectal cancer. A fivefold cross-validation technique was used to evaluate the system, and the discussion guarantees 99.80% recognition accuracy. 100,000 pictures of histological colon tissue taken from several patients are used in this study. Colorectal cancer is evaluated using pertinent MRI images. To make computation simpler, the input images are levelled by determining the lowest and highest value. Each pixel of the MR picture is examined in order to pinpoint the area that is damaged. The convolution procedure is used on the image in numerous layers to retrieve its several attributes. The retrieved features are trained by residual deep learning networks. The difference in the expected and original values is calculated by the residual function throughout the feature training phase. While equating the training and lookup images, this computation reduces the variance.

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References Anand, A., S. P. Mishra, and S. Sahana, “Assistive Devices and IoT in Healthcare Functions,” in Deep Learning and IoT in Healthcare Systems, Apple Academic Press, 2021, pp. 103–130. Agarwal, R., & Mittal, M. (2019). Inventory classification using multi-level association rule mining. International Journal of Decision Support System Technology (IJDSST), 11(2), 1–12. Agrawal, Rakesh and Srikant, Ramakrishnan. (1994). Fast algorithms for mining association rules. In Proc. 20th int. conf. very large databases, vldb (Vol. 1215, pp. 487–499). Ahmed, F. E. (2005). Artificial neural networks for diagnosis and survival prediction in colon cancer. Molecular cancer, 4(1), 1–12. Alsinglawi, B., Alnajjar, F., Mubin, O., Novoa, M., Alorjani, M., Karajeh, O., & Darwish, Alves, A., Panis, Y., Mathieu, P., Mantion, G., Kwiatkowski, F., Slim, K. (2005). Postoperative mortality and morbidity in french patients undergoing colorectal surgery: results of a prospective multicenter study. Archives of surgery, 140(3), 278– 283. Alwidian, J., Hammo, B. H., & Obeid, N. (2018). Wcba: Weighted classification based on association rules algorithm for breast cancer disease. Applied Soft Computing, 62, 536–549. Amin, M. S., Chiam, Y. K., & Varathan, K. D. (2019). Identification of significant features and data mining techniques in predicting heart disease. Telematics and Informatics, 36, 82–93. Candi, E., Tesauro, M., Cardillo, C., Lena, A. M., Schinzari, F., Rodia, G. (2018). Metabolic profiling of visceral adipose tissue from obese subjects with or without metabolic syndrome. Biochemical Journal, 475(5), 1019–1035. Cao, W., Chen, H.-D., Yu, Y.-W., Li, N., & Chen, W.-Q. (2021). Changing profiles of cancer burden worldwide and in china: a secondary analysis of the global cancer statistics 2020. Chinese Medical Journal, 134(07), 783–791. Cengiz, A. B., Birant, K. U., & Birant, D. (2019). Analysis of pre-weighted and postweighted association rule mining. In 2019 innovations in intelligent systems and applications conference (asyu) (pp. 1–5). Chang, K.-C., Tseng, M.-C., Weng, H.-H., Lin, Y.-H., Liou, C.-W., & Tan, T.-Y. (2002). Contreras, Ivan and Vehi, Josep. (2018). Artificial intelligence for diabetes management and decision support: literature review. Journal of medical Internet research, 20(5), e10775. Cortez, P., & Cortez. (2014). Modern optimization with r. Springer. Daghistani, Tahani A., Elshawi, Radwa, Sakr, Sherif, Ahmed, Amjad M., Al-Thwayee, Abdullah, Al-Mallah, Mouaz H. (2019). Predictors of in-hospital length of stay among cardiac patients: a machine learning approach. International journal of cardiology, 288, 140–147. Daniel, D. A. P., & Thangavel, K. (2016). Breathomics for gastric cancer classification using back-propagation neural network. Journal of medical signals and sensors, 6(3), 172.

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Dey, L., & Mukhopadhyay, A. (2019). Biclustering-based association rule mining approach for predicting cancer-associated protein interactions. IET Systems Biology, 13(5), 234– 242. Domadiya, N., & Rao, U. P. (2019). Privacy preserving distributed association rule mining approach on vertically partitioned healthcare data. Procedia computer science, 148, 303–312. Dutta, D., Paul, D., & Ghosh, P. (2018). Analysing feature importances for diabetes prediction using machine learning. In 2018 IEEE 9th annual information technology, electronics and mobile communication conference (IEMCON) (pp. 924–928). Friedman, J. H. (2001). Greedy function approximation: a gradient boosting machine. Annals of statistics, 1189–1232. Henneman, D., Van Leersum, N., Ten Berge, M., Snijders, H., Fiocco, M., Wiggers, T.,Wouters, M. (2013). Failure-to-rescue after colorectal cancer surgery and the association with three structural hospital factors. Annals of surgical oncology, 20(11), 3370–3376. Hornbrook, M. C., Goshen, R., Choman, E., O’Keeffe-Rosetti, M., Kinar, Y., Liles, E. G., & Rust, K. C. (2017). Early colorectal cancer detected by machine learning model using gender, age, and complete blood count data. Digestive diseases and sciences, 62(10), 2719–2727. Hu, Z., Qiu, H., Wang, L., & Shen, M. (2022). Network analytics and machine learning for predicting length of stay in elderly patients with chronic diseases at point of admission. BMC Medical Informatics and Decision Making, 22(1), 1–15. Iizuka, O., Kanavati, F., Kato, K., Rambeau, M., Arihiro, K., & Tsuneki, M. (2020). Deep learning models for histopathological classification of gastric and colonic epithelial tumours. Scientific reports, 10(1), 1–11. Kather, J. N., Krisam, J., Charoentong, P., Luedde, T., Herpel, E., Weis. (2019). Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study. PLoS medicine, 16(1), e1002730. Khan, S. A., & Yadav, S. K. (2019). Class-based associative classification using super subsets to predict the by-diseases in thyroid disorders. In International conference on advances in computational intelligence and informatics (pp. 301–308). Khare, S., & Gupta, D. (2016). Association rule analysis in cardiovascular disease. In 2016 second international conference on cognitive computing and information processing (ccip) (pp. 1–6). Knevel, R., & Liao, K. P. (2022). From real-world electronic health record data to realworld results using artificial intelligence. Annals of the Rheumatic Diseases. Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V., & Fotiadis, D. I. (2015). Machine learning applications in cancer prognosis and prediction. Computational and structural biotechnology journal, 13, 8–17. Lakshmi, K., & Vadivu, G. (2019). A novel approach for disease comorbidity prediction using weighted association rule mining. Journal of Ambient Intelligence and Humanized Computing, 1–8. Liaw, A., Wiener, M., (2002). Classification and regression by randomforest. R news, 2(3), 18–22.

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Lochner, K., & Cox, C. (n.d.). Prevalence of multiple chronic conditions among medicare beneficiaries. United States. Meadows, K., Gibbens, R., Gerrard, C., & Vuylsteke, A. (2018). Prediction of patient length of stay on the intensive care unit following cardiac surgery: A logistic regression analysis based on the cardiac operative mortality risk calculator, euroscore. Journal of cardiothoracic and vascular anesthesia, 32(6), 2676–2682. Mekhaldi, R. N., Caulier, P., Chaabane, S., Chraibi, A., & Piechowiak, S. (2020). Using machine learning models to predict the length of stay in a hospital setting. In World conference on information systems and technologies (pp. 202–211). Misra, P., & Yadav, A. S. (2020). Improving the classification accuracy using recursive feature elimination with cross-validation. Int. J. Emerg. Technol, 11(3), 659–665. Naemi, A., Schmidt, T., Mansourvar, M., Ebrahimi, A., & Wiil, U. K. (2021). Quantifying the impact of addressing data challenges in prediction of length of stay. BMC Medical Informatics and Decision Making, 21(1), 1–13. Alsinglawi, B., Alnajjar, F., Mubin, O., Novoa, M., Alorjani, M., Karajeh, O., (2020). Predicting length of stay for cardiovascular hospitalizations in the in- tensive care unit: Machine learning approach. In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 5442– 5445). Ooft, S. N., Weeber, F., Dijkstra, K. K., McLean, C. M., Kaing, S., van Werkhoven, E.,others (2019). Patient-derived organoids can predict response to chemotherapy in metastatic colorectal cancer patients. Science translational medicine, 11(513), eaay2574. Pan, L., Liu, G., Lin, F., Zhong, S., Xia, H., Sun, X., & Liang, H. (2017). Machine learning applications for prediction of relapse in childhood acute lymphoblastic leukemia. Scientific reports, 7(1), 1–9. Park, H.-Y., & Lim, D.-J. (2021). A design failure pre-alarming system using score-and vote-based associative classification. Expert Systems with Applications, 164, 113950. Passos, I. C., Mwangi, B., & Kapczinski, F. (2016). Big data analytics and machine learning: 2015 and beyond. The Lancet Psychiatry, 3(1), 13–15. Prediction of length of stay of first-ever ischemic stroke. Stroke, 33(11), 2670–2674. Chen, T., & Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785–794). Probst, P., Boulesteix, A.-L., & Bischl, B. (2019). Tunability: Importance of hyperparameters of machine learning algorithms. The Journal of Machine Learning Research, 20(1), 1934–1965. Raj, R. S., Sanjay, D., Kusuma, M., & Sampath, S. (2019). Comparison of support vector machine and naive bayes classifiers for predicting diabetes. In 2019 1st international conference on advanced technologies in intelligent control, environment, computing & communication engineering (icatiece) (pp. 41–45). Sahana, S., K. Singh, S. Das, and R. Kumar, “Energy efficient shortest path routing protocol in underwater sensor networks,” in Computing, Communication and Automation (ICCCA), 2016 International Conference on, 2016, pp. 546–550.

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Santos, M. A. R. (2017). Prevaleˆncia e caracter´ısticas sociodemogra´ficas dos praticantes de corrida em portugal (Unpublished doctoral dissertation). Universidade de Lisboa (Portugal). Santos, M. F., & Azevedo, C. S. (2005). Prembulo [a]” data mining: descoberta de conhecimento em bases de dados”[Preamble [a]” data mining: discovery of knowledge in databases]. FCA-Editora de informtica, Lda. Sekaran, K., Chandana, P., Krishna, N. M., & Kadry, S. (2020). Deep learning convolutional neural network (cnn) with gaussian mixture model for predicting pancreatic cancer. Sharma, R. (2020). Descriptive epidemiology of incidence and mortality of primary liver cancer in 185 countries: evidence from globocan 2018. Japanese Journal of Clinical Oncology, 50(12), 1370–1379. Sideris, C., Pourhomayoun, M., Kalantarian, H., & Sarrafzadeh, M. (2016). A flexible datadriven comorbidity feature extraction framework. Computers in Biology and Medicine, 73, 165–172. Sim, D. Y. Y., Teh, C. S., & Ismail, A. I. (2017). Improved boosting algorithms by prepruning and associative rule mining on decision trees for predicting obstructive sleep apnea. Advanced Science Letters, 23(11), 11593–11598. Smola, A. J., & Scholkopf, B. (2004). A tutorial on support vector regression. Statistics and computing, 14(3), 199–222. Sonet, K. M. H., Rahman, M. M., Mazumder, P., Reza, A., & Rahman, R. M. (2017). Analyzing patterns of numerously occurring heart diseases using association rule mining. In 2017 twelfth international conference on digital information management (icdim) (pp. 38–45). Sonka, M., Hlavac, V., & Boyle, R. (1993). Image pre-processing. In Image processing, analysis and machine vision (pp. 56–111). Springer. Srinivasan, K., Currim, F., & Ram, S. (2017). Predicting high-cost patients at point of admission using network science. IEEE Journal of Biomedical and Health Informatics, 22(6), 1970–1977. Tevis, S. E., Carchman, E. H., Foley, E. F., Harms, B. A., Heise, C. P., & Kennedy, G. D. (2015). Postoperative ileus—more than just prolonged length of stay? Journal of Gastrointestinal Surgery, 19(9), 1684–1690. Thanigaivel, R., & Kumar, K. R. (2016). Boosted apriori: an effective data mining association rules for heart disease prediction system. Middle-East Journal of Scientific Research, 24(1), 192–200. Vijayan, V. V., & Anjali, C. (2015). Decision support systems for predicting diabetes mellitus—a review. In 2015 global conference on communication technologies (GCCT) (pp. 98–103). Wang, L. (2022). Predicting colorectal cancer using residual deep learning with nursing care. Contrast Media & Molecular Imaging, 2022. Xie, Y., Schreier, G., Chang, D. C., Neubauer, S., Liu, Y., Redmond, S. J., & Lovell, N. H. (2015). Predicting days in hospital using health insurance claims. IEEE journal of biomedical and health informatics, 19(4), 1224–1233.

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Chapter 8

Wireless Sensor Network Based Crop-Growth Monitoring Using Derived-Parameters in an Intelligent Greenhouse Suman Lata* and H. K. Verma Department of Electrical Electronics and Communication Engineering, Sharda School of Engineering and Technology, Sharda University, Greater Noida, India

Abstract Important factors responsible for appropriate plant growth are temperature of atmosphere and soil, amount of moisture in atmosphere and soil, and luminosity, etc. So, these factors are to be maintained within appropriate limits inside a greenhouse. For the same, continuous monitoring of such parameters is required. However, due to the interactive nature of the parameters, it is better to monitor derived parameters such as temperature humidity index (THI), soil respiration (SR) and vapour pressure deficit (VPD). The monitoring system should be designed in such a manner that it displays and stores the actual measurement readings. In addition, it should be able to display the trends in these measurements. In case the monitored parameters are going outside prefixed thresholds, appropriate alert signals should be generated by the monitoring system. This chapter aims at development of mathematical models and algorithms based on derived parameters, namely, VPD, THI and SR, for monitoring of the greenhouse environment. VPD is derived from the measured values of air humidity and temperature. For VPD modelling, *

Corresponding Author’s Email: [email protected].

In: Intelligent Decision Support System for IoT Enabling Technologies Editors: Subrata Sahana, Anil Kumar Sagar, Sanjoy Das et al. ISBN: 979-8-89113-249-8 © 2024 Nova Science Publishers, Inc.

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Suman Lata and H. K. Verma the Prenger model (Prenger, 2000b) and the standardized reference evapotranspiration equation are found in the literature. In this chapter the latter model has been considered. THI calculation for the atmosphere of the greenhouse, keeping in view its interactive nature is done using temperature and humidity. In the literature, for THI modelling regression models like Rothfusz model, as well as formula-based models like Kibler, 1964; NOAA, 1976; Scheon, 2005 are available. However, in the present work Scheon model has been considered for greenhouse monitoring. To derive soil respiration, measured values of the soil moisture as well as soil temperature have been considered. For mathematical modelling of soil respiration, Q10 model also known as Arrhenius model, Lloyd and Taylor model and Gaussian model have been reported in the literature. Reichstein model for SR modelling has been considered in this chapter.

Keywords: Greenhouse, Soil Respiration, Temperature Humidity Index, Vapor Pressure Deficit

Introduction For the well-being of humans, a decent food supply in terms of nutrition and calories is considered as one of the primary requirements (Stephen et. al., 2015). The world is expected to accommodate 9.5 billion populations by 2050. Due to urbanization, available land suitable for crop production is decreasing day by day. The current rate of increase in the crop is not going to yield a satisfactory increase in the demand for food, keeping in view the abovementioned factors. If present rates of increase in crop production per hectare are sustained in the future, by 2050, supply will surely fall significantly below the desired demand (Stephen et al., 2015). In contemplation, the required rate of increase in the crop in terms of quality as well as quantity with less cultivable land, an intelligent greenhouse which can create controlled environment for agriculture is an alternative solution (Loden et al., 2009). A greenhouse can be considered as a farm structure, specially designed, to create more appreciative environmental conditions for improved crop production as well as protection. In terms of size, such structures range from small sheds to industrial-sized buildings. A variety of covering materials for the roof as well as walls, like glass or plastic, are used so that sunlight is absorbed inside the structure. Commercial glass greenhouses with technological support offer controlled production facilities for vegetables,

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flowers or even medicinal herbs and plants. The greenhouse is fitted with equipment for heating, cooling, lighting, shading, etc. In an intelligent greenhouse, these equipments are automatically controlled by a computerbased digital controller to maximize the potential growth of the crops grown inside it. The general purpose of greenhouses is to create a self-restrained environment for crops, which allows them to grow under optimized conditions. It will result in increased crop production by maintaining a conducive environment for plant growth as well as protecting the crops from extreme weather conditions like heat waves or cold snaps, tropical cyclones, storms, and droughts, providing protection to crops against pathogens and making off-season production of crops possible. Major factors considered for testing the feasibility of greenhouses include geographical location, variety of crops being grown, and cost. The growth of the plants depends upon many atmospheric parameters, like temperature, vapor pressure deficit, duration and luminosity of light, humidity and CO2 level (Ellis et al., 1980). Various parameters in the soil or root zone, like moisture, temperature and level of macronutrients, also affect the growth of plants significantly.

Intelligent Greenhouse (IGH) In conventional or traditional greenhouses, there is no means for sensing and controlling the factors affecting the growth of the plants. However, in an Intelligent greenhouse (IGH), sensors are used for sensing various greenhouse parameters, and actuators respond to the control functions. A greenhouse can be considered as a complex multivariable system with inputs which are interactive in nature. As more than one physical parameter is affecting the environment of the greenhouse thus it is considered as multivariable. The interactive nature of greenhouses is because physical parameters are interdependent. In intelligent greenhouses precise regulation of the environmental conditions results in improved crop production as well as quality. This automation is a way forward for precision agriculture which will help farmers to grow high quality crops. A logical diagram of an intelligent greenhouse is given in Figure 1.

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Figure 1. Logic diagram of IGH.

WSN Based Monitoring in Intelligent Green house In order to create controlled environmental conditions in a traditional greenhouse, monitoring of the parameters affecting the growth of plants is the first step towards making it an intelligent greenhouse (IGH). In the beginning, for the development of IGH only one sensor was placed inside the greenhouse to measure the desired variable. But the single sensor approach is not capable of providing the real status of the micro-climatic conditions prevailing in the greenhouse. Single sensor-based monitoring was refined, and the sensors and data loggers were distributed throughout the greenhouse for obtaining an accurate profile of the variables. Such a type of monitoring system is known as distributed monitoring. Cabling for connecting the individual sensors is time consuming as well as costly. Also, altering the number of sensors becomes cumbersome in this monitoring technique. Because of the recent advancements in wireless sensor network (WSN) technologies the major concerns in cabling as well as addition and deletion of sensors have been overcome. Thus, single point sensing is being replaced by multipoint sensing in majority of applications, namely, environment monitoring (Loden et al., 2009; Mehdipour, 2013; Saeed et al., 2015), home and building automation (Elango et al., 2011; Kelly et al., 2013; Zhen-ya, 2014) health care services (Zigbee Alliance, 2009; Rajba et al., 2013; Suriyakrishnaan et al., 2016), habitat monitoring (Mainwaring et al., 2002; Naumowicz et al., 20; 10), defence & security ( He et al., 2006; Dudek et al., 2009; Prabhu et al., 2016 ), forest fire detection (Son et al., 2006; Yanjun Li, 2006; Garcia et al., 2006), health monitoring of structures (Zhao et al., 2007) and Monitoring of Greenhouses (Barker, 1990; Holder and Cockshull, 1990; Zolnier et al., 2000; Burrell et al., 2004; Ahonen et al., 2008; Park and Park 2011; Pahuja et al., 2012; Nasre et al., 2014; Mekki, 2015; Chung et al., 2016; Bouge, 2017; Ferentinos et al., 2018).

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A wireless sensor network (WSN) consists of sensor nodes deployed within the application area. The sensor nodes are communicating with each other wirelessly using a radio frequency (RF) link. It is an embedded and programmable network of nodes that has the capability of sensing, data acquisition, data processing, data storage and data networking. A typical schematic of wireless sensor network has been given in Figure 2.

Figure 2. Schematic representation of wireless sensor network.

Figure 3. Hardware and software components of a typical WS node.

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Greenhouse monitoring using WSN offers a number of advantages. Few of them are like, speedy distribution of sensors, easy shifting of wireless sensor nodes, flexible addition as well deletion of nodes as and when required and low maintenance and installation cost. Wireless sensor (WS) node is a fundamental component of unit a WSN. Figure 3 shows a typical architecture of wireless sensor node consisting of embedded hardware and software components. The hardware comprises the following modules: (i) Sensor unit (ii) Data acquisition (DAQ) unit (iii) Data processing unit (DPU) (iv) Communication unit (v) Power supply The software module consists of an operating system, sensor and actuator driver and data networking stack (DNS). The hardware units and software modules have been elaborated in the next two sections.

Hardware Unit Sensors Sensors are needed to sense the physical parameters. They form the front end of the node. The output of sensors may or may not be electrical in nature. Generally, smart sensors are preferred over traditional sensors as they have inbuilt signal conditioning modules, which are characterized by high reliability, compact size, and low power consumption. Conventional sensors additionally require a data acquisition unit.

Data Acquisition Unit The signal conditioning unit consists of a signal conditioning module and analog to digital converter (ADC). The output of conventional sensors, which is an analog electrical signal, needs to be conditioned so as to make it compatible with a data processing unit. Same is done with the help of a signal

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conditioning module. Various signal conditioning techniques may include amplification, linearization, filtration, etc. A multi- channel ADC is required for converting output of a signal conditioning module which is an analog signal into equivalent digital signals. In case a smart sensor having a digital signal is selected, there will be no need for DPU.

Data Processing Unit Data processing unit is a combination of data processor and memory. In a WS node, processing of data and control of the operation of the various components (data acquisition unit, memory and communication unit) is taken care of by data processing unit (DPU). This unit gathers the data from the network of sensor nodes and processes the same. DPU decides when and where the data is to be transmitted once it collaborates the data from all wireless sensor nodes. It also executes the number of programs like digital signal processing communication protocols to application programs. A microprocessor, FPGA i.e., field programmable gate array and digital signal processor can be a possible choice for a digital processing unit. An applicationspecific integrated circuit (ASIC) can also be used as a processing device. Cost, performance and flexibility are the major factors considered for choosing the processing device. For the storage of in between results RAM or flash memory of small size is used. For the storage of program code, either EEPROM or flash memory is used.

Communication Unit (RF Transceiver) The communication unit consists of two components, namely, a transceiver and an antenna. The trans-receiver is radio frequency (RF) based. The Communication unit is primarily used for transferring the data from one node to other nodes and the base station or a gateway node. This unit supports the conversion of DPU, digital data to radio waves and vice versa. Generally, an ISM radio frequency-based transceiver, operating in half-duplex mode, is used. Main characteristics of such types of trans receivers include low cost, low power and short range. While designing a trans-receiver the issues related to the physical layer and network layer are to be considered in addition to medium access control.

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Power Supply Unit Power supply is the crucial component that decides the lifetime of the WS node. Generally, sensor nodes are equipped with a battery followed by a DCto-DC converter for regulating the voltage delivered to all other units of the node. The battery used should have ample capacity and be lightweight. In addition, it should have an adequate energy density, recharging capability, and low self-discharge rate. In some cases, photovoltaic solar cells are being used along with rechargeable batteries. A WS node may also have some optional elements, such as actuators to control some physical variables or a micro-vision system for specialized applications. The over-all hardware design is motivated by advancements in enabling technologies, such as microelectronics, VLSI and micro-fabrication techniques with the objectives of miniaturizing the hardware, improving the energy efficiency and reducing the cost in an effective manner.

Software Modules In order to drive the hardware of the node, the requirement of software becomes inevitable. Overall functionality of the node is defined by the software. Main features of data acquisition and signal processing need to be defined by the software modules. Also, in-network computation, data compression, error control, encryption, communication, routing, forwarding and connectivity management are managed by software. The software has several modules, and their complexity can vary widely. The important software modules are described below:

Operating System Framework for developing any application primarily requires an operating system. It also reduces the burden of machine level functionality of microprocessor from the developer. The operating system should be small in itself and support fast implementation. Because of the resource constraints in nodes the operating system should ensure minimum application code size.

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Sensor/Actuator Drivers Drivers for sensors/actuators are the software modules which support various functions of sensors which are on board and actuators as well as are used for configuring and setting them. It separates the application software from the machine level functionality of these peripherals.

Data Networking Stack Data networking software module is required for implementation of data communication on the network. Out of the 7 layers of OSI model only 5 layers are included in the data networking stack of the WS node. These layers are application, transport, network, data and physical layers. Typical characteristics of WS network make them different from traditional TCP/IP-based wide area networks such as the internet. The WS network protocol supports multi-hop mesh routing, low data rates and shortrange transmission of real time, low volume, sensor-based data as against TCP/IP network that uses single-hop communication, high data rate and longdistance transmission of high-volume data.

Factors Affecting Plant Growth in Greenhouse For the proper growth of plants various atmospheric as well as soil parameters are responsible. These factors are to be maintained at optimized values as required by the plants in the greenhouse. Following are the main factors which need to be controlled to make a greenhouse an intelligent greenhouse and increase disease-free growth of crops.

Micro-Climatic Parameters A brief discussion regarding the micro-climatic parameters which impact the growth of plants is given in the following section.

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Temperature of the Atmosphere Inside Greenhouse Temperature is a prominent factor for the desired growth of plants. Each plant requires a different range of temperature for healthy growth. Temperature more or less than the required temperature, hampers the growth of the plants. The required temperature should be maintained throughout the crop cultivation. The impact of temperature on growth of plants is because it affects photosynthesis, transpiration and respiration. Activity of enzymes and rate of chemical reactions increases with an upward trend in temperature. The optimal atmospheric temperature controls the photosynthetic activity level. Every species of plant has inherent optimal values of air temperature for appropriate growth.

Humidity Inside the Green House The relative humidity is one factor that impacts the vapor-transpiration and stomata opening of the plants. Controlling humidity helps to seize the metastasize of diseases in plants. Relative humidity in the range of 50% to 70% ensures appropriate growth of plants. Temperature and humidity are interdependent in a greenhouse environment. The moisture holding capacity of warm air is more than the cold air. Therefore, reduced relative humidity is an indicator of increased air temperature.

Photosynthetic Active Radiation The greenhouse provides protection to the plants from severe weather. However, if the period of daylight is restrained due to the greenhouse structure, photosynthetic activity will be hampered, and plants will not grow. Controlling lighting hours will allow the farmer to extend the growing season. 10-12 hours of solar radiation are required normally for optimized growth of plants. However, when plants are producing fruits or flowers, there is an increase in the duration of lighting, which is approximately 16 hours. Solar radiation acts as the energy source for photosynthesis. Photosynthesis is a chemical reaction taking place in plants in the presence of solar energy. It converts carbon dioxide into organic compounds. A small portion of global radiation, namely, photosynthetic active radiation (PAR) is responsible for photosynthesis (Edmond et al. 1978). Out of the visible region of the

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electromagnetic spectrum, 400 nm to 500 nm and 600 nm to 700 nm wavelengths are the most effective regions, capable of supporting photosynthesis (Manaker, 1981). A deficient light intensity decreases chlorophyll content, resulting in reduced rates of light absorption and photosynthesis (Edmond et al., 1978). This in turn, will result in reduced plant growth and yield of crops. The impact of excessive light intensity is rapid transpiration and water loss, which in turn leads to leaf scorch and reduces crop yield. On the other hand, if the leaf temperature is high, it will inactivate the enzyme system which inhibits the rate of photosynthesis. Thus, maintaining appropriate duration as well as range of light intensity for improved quality and quantity of crop has to be ensured.

Carbon Dioxide Concentration During the photosynthesis process, the plant converts CO2, water, glucose, and oxygen in the presence of light. In the summer season, greenhouses may acquire the CO2 they need from the atmosphere itself when ventilation and roof windows are open. In winters growers can use burners to increase the CO2 level if required.

Soil Parameters Soil is crucial for plant life. It not only provides mechanical support to plants but also supplies nutrients and water to them. It acts as a major source of heat storage. The temperature of soil acts as a stimulant for many biological processes taking place in plants. For optimized plant growth, soil moisture, aeration and nutrient levels are to be maintained in appropriate limits. In the following section three soil parameters, soil moisture, soil temperature and macronutrient level, which are related to plant growth have been discussed.

Soil Moisture Soil moisture plays a key role in optimization of plant growth and thus maintaining it within correct limits is quite important. Due to conduction, heat

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is directly transferred to the soil structure. With low soil temperature water uptake decreases which results in reduction of soil moisture.

Soil Temperature Soil temperature regulates not only biological but also physio-chemical processes taking place in soil. It governs the gas exchange taking place between atmosphere and soil and indirectly, the growth of the plants. Thus, soil temperature impacts plant growth by altering uptake of water (Tonelli et al.) as well as nutrient uptake (Weigh et al.). With low soil temperature water uptake decreases which in turn decreases photosynthesis activity level. Increase in soil temperature stimulates metabolic activities and the availability of nutrients for plant growth.

Micro-Nutrient Level in Soil Prominent micronutrients available in the soil that impact the growth of plants are NPK i.e., nitrogen, potassium, and phosphorus. The excessive use of these macronutrients will result in contamination of the surface of earth and ground water. Thus, the application of the fertilizers should be optimized.

Derived Micro-Climatic Parameters Instead of controlling the above parameters individually, making a greenhouse an intelligent greenhouse following derived parameters may be a better option.

Vapor Pressure Deficit (VPD) Relative humidity of air is the ratio of actual vapor pressure (Vpa) to saturated vapor pressure (Vpsat). As Vpsat is dependent on-air temperature, indirectly, relative humidity is dependent on temperature. The interactive effect of Vpa, Vpsa and air temperature can be quantized using a single parameter, viz, vapor pressure deficit (VPD) which can be calculated by considering the difference

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between the actual amount of moisture in air and the amount it can hold at that temperature. 𝑉𝑃𝐷 = 𝑉𝑝𝑠𝑎𝑡 − 𝑉𝑝𝑎

(1)

where Vpsat = saturated vapour pressure and Vpais actual vapour pressure But the relative humidity RH is given by their ratio i.e., 𝑅𝐻 = 𝑉𝑝𝑎 / 𝑉𝑝𝑠𝑎𝑡 Therefore, 𝑉𝑝𝑎 = (𝑅𝐻 𝑥 𝑉𝑝𝑠𝑎𝑡)

(2)

Thus equation 1 can be modified as 𝑉𝑃𝐷 = 𝑉𝑝𝑠𝑎𝑡 − (𝑉𝑝𝑠𝑎𝑡 𝑥 𝑅𝐻)

(3)

As relative humidity of air is very important for the evapo-transpiration and stomata opening of the plants, monitoring of the VPD can be one of the parameters with the objective of maintaining a balance between the relative humidity and the temperature requirement of a crop. VPD includes the impact of temperature on the water holding capacity of the air, hence it can be selected as a better measure than relative humidity alone. At various temperatures, the levels of humidity required to maintain the comfort zone for plants, desired VPD limits will be different for different crops (Prenger, 2000) The High value of VPD is indicative of the high capacity of air to hold water and thus relative humidity is low. In this situation the transpiration process in plants will be induced, and loss of water vapor through the stomata of plants into the greenhouse atmosphere will take place. This situation mostly arises on dry and warm days, and the rate of transpiration in plants increases very drastically. It results in stress, dehydration, and the dropping of flowers in plants. Low VPD (humid and cool) means air cannot accept moisture, and the relative humidity is high, so the air cannot accept the moisture transpired from the leaves. This will be detrimental to plant growth. Too low VPD with a slight increase in temperature will promote dew, and hence, condensation on the leaves of the plants will take place. In such conditions, the possibility of spread of disease through pathogens will increase. The spread of diseases is

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most detrimental below 0.20 kPa. Thus, for the prevention of the spread of diseases, the VPD should be maintained above 0.20 kPa (Prenger, 2000b).

Temperature Humidity Index (THI) The moisture holding capacity of the air is related to the temperature. Warm air has the ability to hold more moisture than cool air. As there is a decrease in temperature, relative humidity will lead to low transpiration and spread of diseases in the case of plants. Again, if temperature falls below the dew point, condensation occurs in plant leaves. Thus, in addition to monitoring temperature and humidity separately, an index like temperature-humidity index (THI) should also be monitored. THI is a combined measure of atmospheric temperature and relative humidity and thus is a useful tool to assess the impact of heat stress. Like humans, plants also have a range of THI relating to their comfort zone. Mathematically 𝑇𝐻𝐼 = 𝑓 (𝑇, 𝑅𝐻)

(4)

Soil Respiration (SR) Soil respiration (SR) is a process by which carbon dioxide fixed by land plants is returned to the atmosphere (Schinner et al. 1996). It is because of the respiration of the microorganisms, plant roots, and fauna in the soil. It is measured in µmol CO2 m-2 s-1 i.e., the number of molecules of CO2 per meter square per second X106. It is also called below-ground respiration. It gives the rate of CO2 efflux from soil to atmosphere. Soil respiration rate is primarily dependent on the temperature of the soil and the moisture of the soil. Soil respiration follows temperature exponentially, like plant respiration. For every 10 °C rise in the temperature of the soil, microbial respiration approximately doubles. But this rise is up to a maximum of 35 to 40°C (Rochette, 2005). Beyond this temperature, soil moisture is reduced, resulting in a detrimental effect on plant growth. The soil respiration declines with respect to the dryness of the soil. As the soil moisture increases, soil respiration also increases up to a level. A further increase in the moisture level will cause declines in the soil respiration as the pores of the soil get filled and thus passage of oxygen to

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plant roots gets blocked. Soils which have good aeration and highwater absorption capacity have good soil respiration rate. Combining the above facts there is a desirable range of SR, which in turn depends on both soil temperature and soil moisture. Therefore, to ensure precision agriculture (best plant growth with minimum irrigation) it is proposed to monitor SR rather than soil temperature and soil moisture individually (F. Chauhan, et al. 2022).

Mathematical Models for Derived Parameters Vapor Pressure Deficit (VPD) The difference between the actual vapor pressure (ed) and saturated vapor pressure (es) is defined as vapor pressure deficit. It is correlated with transpiration and, thus, can regulate the quality of crops. With an increased air temperature, the saturation water vapor pressure increases exponentially. Estimation of plant evapotranspiration (ET), which gives a measure of water loss to the atmosphere, also depends on VPD. So, VPD can be considered as an indicator to determine how precise a closed-field environment is to saturation. VPD can more accurately reflect how the plant will be feeling as it takes into account the measurements of temperature as well as relative humidity (Zolner et al., 2000). In addition, VPD can assess condensation potential of a greenhouse crop and predict when it is likely to happen (A. K. Ray. Et al. 2022). From the literature various models have been reported for the calculation of the VPD. One such model was reported by (Prenger, 2000b) given in equation 5 can be used to compute VPD in kPa for a given T (°C) and RH (%). 𝑉𝑃𝐷 = 𝑒 (6.41+0.0727∗𝑇−3 10−4∗𝑇2 +1.18 10−6∗𝑇3 −3.86 10−9∗𝑇4 ) ∗(1−𝑅𝐻/100) (5) Another VPD based model for a greenhouse has been developed by calculating saturated vapor pressure (Vpsat) in kPa at air temperature T in °C, using ASCE, standardized reference evapotranspiration given by the following equation 6. 𝑉𝑝𝑠𝑎𝑡 = 0.6108 ∗ [𝑒 (17.27𝑋{𝑇/(𝑇+237.3)} ]

(6)

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Temperature Humidity Index Models Various mathematical models available in literature for calculation of the THI have been summarized below.

Regression Models Regression models which are empirically developed are:

Rothfusz Model This model is an eight-term empirical model for THI calculation (Rothfusz, 1990). The Rothfusz mathematical model is represented by equation (7) where T is the ambient dry bulb temperature measured in ˚F and R is the relative humidity in integer percentage. THI=-42.379+(2.04901523)T+(10.14333127)R –(0.22475541)TR – (6.83783*10-3)T2-(5.481717*10-2)R2 + 1.22874*10-3T2R +8.5282*104TR2-1.99*10-6T2R2 (7)

National Weather Service (NWS) Model This is a multiple-regression model with 16 terms and can be mathematically represented by equation (8). It calculates THI on the basis of dry bulb temperature and humidity values. THI=16.923+(1.852*10-1)T+5.37941R-(1.00254*10-1)T+9.41695*103T2+7.28898*10-3R2+3.45372*10-4T2R-8.14971*10-4TR2+(1.02102*105)T2R2-(3.8646*10-5)T3+(2.9158*10-5)R3+1.42721*106T3R+1.97483*10-7TR3-2.18429*10-8T3R2-(4.81975*10-11)T3R3 (8) Where temperature (T) is in degrees Fahrenheit and relative humidity (RH) is in per-cent.

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Formula Based Models Kibler Model For this model, THI is represented in terms of monthly average ambient temperature in °C, and monthly average relative humidity (fraction of the unit). The mathematical model for the same is given by equation (9). THI = 1.8Ta + 32 - (1 - RH) x (Ta - 14.3)

(9)

NOAA (1979) Model This model is given by the national ocean and atmospheric administration method calculates discomfort Index based on temperature and humidity calculations (NOAA, 1976). The mathematical equation is as given in equation 10. 𝑇𝐻𝐼 = 1.8𝑇 + 32 − (0.55 − 0.55𝑅𝐻){(1.8 ∗ 𝑇𝑎 + 32 − 58}

(10)

where T is average ambient monthly temperature in °C and RH is average monthly relative humidity.

Carl Scheon Model Equation 11 represents the empirical model which was given by Carl Scheon (Scheon, 2005) which is used for calculation of THI. 𝑇𝐻𝐼 = 𝑇 − 1.0799 ∗ 𝑒 0.037 55𝑇 ∗ [1 − 𝑒 0.0801(𝐷 − 14)]] (11) where T is the measured temperature in °C, and D is the dew point in °C. The Dew point is given as 𝐷 = (𝑔𝑥𝛼) / (ℎ − 𝛼)

and 𝛼 = ℎ ∗ 𝑇/(𝑔 + 𝑇) + 𝑙𝑛(𝑅𝐻 )

(12)

where h = 17.27, and g = 237.3 and RH is the measured relative humidity expressed as a decimal fraction. A Fahrenheit version of the Carl Scheon model is given by equation 11 given below. 𝑇𝐻𝐼 = 𝑇 − 0.9971 𝑒 0.020 86𝑇 [1 − 𝑒 0.0445 (𝐷 − 57.2)] where THI, T, and D are all in degrees Fahrenheit.

(13)

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SR Models In literature various mathematical and regression models which have been used to model soil respiration have been reported. Exponential Q10 model has been reported by many authors (Meyera et al., 2018; Davidson et al., 2005; Reichstein et al., 2002) which is also known as Arrhenius model as was given by Arrhenius in 1889 for relating soil respiration (SR) with soil temperature only. The mathematical model presented by the authors is give as: SR = R0 * e(β0*Tsoil), Q10 = e10*β0, fitted parameters are represented by R0 and β0 and Q10 represent sensitivity parameters of respiration. The limitation of the model is that it relates SR only with soil temperature. For the estimation of Q10 and SR use of Mid-infrared spectroscopy was used by Meyera et al. 2018. Another model was given by Lloyd and Taylor (1994) which was derived from the Arrhenius model. Above models were used by many authors, Reichstein et al. (2002), Meyera et al. (2018) Lellei-Kova´cs et al. (2016), etc. Another model known as Gaussian model for SR was given SR = e (a + bT + CT2), where T is soil temperature in Kelvin and the model constants are represented by a, b and c. Another method which has been derived from Davidson et al. (2006) model has been presented by Zanchi et al. (2009). Response of soil respiration versus temperature gets modified under different conditions of soil moisture has been reported by Wang et al. (2014) Exponential, Lloyd and Taylor and Gaussian models which are empirical SR models reported in literature were compared by authors. However, for the investigation of the performance of SR models soil moisture and temperature both have been considered. For the annual scale of time the annual effect of soil moisture on SR, was weak. Also, the exponential method was giving good prediction results of SR in the narrow range of temperature only. The mathematical model for the calculation of SR should have two independent variables, namely, soil temperature and soil moisture. Reichstein et al. (2003) proposed an SR model which relate both these independent variables with the SR. The dependent variable is given by equation (14), where soil water content at field capacity (RSWC) adds soil moisture variable in terms of an explicit water dependence to it. 𝑆𝑅 = 𝑅𝑟𝑒𝑓 𝑒 𝐸0 (𝑅𝑆𝑊𝐶) ∗ {(

1 𝑇𝑟𝑒𝑓 −𝑇0

)−(

1

𝑇𝑠𝑜𝑖𝑙 −𝑇0

)} ∗

𝑅𝑆𝑊𝐶 𝑅𝑆𝑊𝐶−𝑅𝑆𝑊𝐶 1 2

(14)

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The unit for soil respiration (SR) is µmol CO2 m-2 s-1 Rref is SR at reference temperature (25°C) E0 is activation energy in K-1 and T0 = lower critical temperature limit for soil respiration. As per the original model of Lloyd and Taylor (1994) the value of T0 is 46°C. RSWC = unit-less quantity and defined as the ratio of actual soil water content to the soil water content at field capacity i.e., 𝑅𝑆𝑊𝐶 = 𝑆𝑊𝐶/𝑆𝑊𝐶1/2

(15)

Where SWC is soil water content and SWC1/2 is soil water content for half maximal respiration. RSWC1/2 again is a dimensionless quantity is the relative soil water content where half-maximal respiration at a given temperature occurs and 𝐸0 (𝑅𝑆𝑊𝐶) = 𝑎 ∗ 𝑅𝐸𝑊 + 𝑏 𝑅𝐸𝑊 ∗ 𝑅𝑆𝑊𝐶

(16)

where Relative Extractable Water, which is the ratio of the amount of water that is extractable by the plant roots to the total available water in soil, a and b are the constants. REW, a and b are to be determined empirically for a given type of soil and plants.

Monitoring Algorithmes For developing the software modules for monitoring of greenhouse based on derived parameters, both upper and lower limits of all the parameters for selected crop are defined. For the development of the algorithms for practical greenhouses which are of large dimensions the greenhouse has been divided into multiple zones of varying dimensions. Again, practically there is a possibility that in each zone, a different variety of crops is being grown. Several sensors may be required in a particular zone for accurate monitoring as single-sensor monitoring is not going to provide the actual status of parameter of interest. There is a possibility that an unequal number of sensors are placed in different zones. The layout of the considered practical greenhouse has been given in Figure 4 below.

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Individual Parameters Monitoring Let us assume TL and TU levels are the lower and upper limits of temperature requirements of a particular crop (Suman Lata & H. K. Verma 2021). Let us assume that for a selected crop HL and HH indicate the lower and the upper levels of humidity, respectively.

Figure 4. Layout of the considered greenhouse.

For the measurement of individual parameters, the sampling time considered here is 5 minutes. That means the reading from the desired sensor is collected after every 5 minutes. Moving average with a widow width of 30 minutes is considered i.e., average of the latest six readings is calculated. For zone average, mean of the moving average values for all the sensors located within that zone will be considered. Let us consider that the number of zones in a greenhouse are ‘z’, the number of sensors in each zone are s, and the number of sensors in a zone are ‘n’. Again, it is assumed that j represents jth instant, Tzs MA (j) is the temperature measured at jth instant of time by a sensor in a zone. Then the mean average temperature measured at jth instant of time by a sensor ‘s’ in zone ‘z’ considering a moving window of L samples, denoted by TzsMA (j), can be given by generalized equation number 17 Tzs MA (j) =

1 L

∑L−1 k=0 Tzs MA (j − k)

(17)

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If the window width is 30 minutes and interval of measurement is of 5 minutes, then L = 30min/6min = 5. Thus, equation 17 will be reduced to equation 18. Tzs MA (j) = 1/6 ∑5k=0 Tzs MA (j − k)

(18)

To calculate the average temperature Tzavg for zone ‘z’, the average Tzs MA (j) of all the sensors in that zone is required. Mathematically, it can be evaluated using equation 19 given below. 1

TZ avg = ∑ns=1 Tzs MA (𝑗) n

(19)

For calculating all other parameters individually in a particular zone of a greenhouse, the same steps are to be followed. Let us assume H zs MA (j) represents the moving average humidity measured by a humidity sensor in a zone and Hz avg represents the average humidity measured by all the sensors in that zone. Equations (17) and (18) will change to equations (19) and (20) respectively. Hzs MA (j) = 1/L ∑L−1 k=0 Hzs MA (j − k)

(20)

Hzs MA (j) = 1/6 ∑5k=0 Hzs MA (j − k)

(21)

Also, equation 19 will change to equation 22. 1

HZ avg = ∑ns=1 Hzs MA (j) n

(22)

VPD Monitoring Algorithm To develop the VPD monitoring module, the lower and upper levels of the VPD should be predefined for a crop. Let us assume the lower and upper VPD levels as VPDL and VPDU, respectively. A VPD-based model for the greenhouse has been developed by calculating Vpsat using ASCE given by equation 6. So, T has been replaced by Tzavg of a zone and RH by Hzavg of a zone as given by equations 19 and 22 respectively. Thus equation 6 will change to equation 23.

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𝑉𝑝𝑠𝑎𝑡𝑧𝑎𝑣𝑔 = 0.6108 ∗ [𝑒

𝑇𝑧𝑎𝑣𝑔 } 𝑇𝑧𝑎𝑣𝑔 +237.3

(17.27∗{

]

(23)

where 𝑉𝑝𝑠𝑎𝑡𝑧𝑎𝑣𝑔 is the average saturated vapor pressure of a zone. Again equation 2 will get modified to equation 24 given below, where Vpazavg is average actual vapor pressure of a zone. 𝑉𝑝𝑠𝑎𝑡𝑧𝑎𝑣𝑔 = ( 𝐻𝑧𝑎𝑣𝑔 *𝑉𝑝𝑠𝑎𝑡𝑧𝑎𝑣𝑔 )

(24)

Thus, the average value of VPD of a zone (VPDzavg), will be given by equation 25. 𝑉𝑃𝐷𝑧𝑎𝑣𝑔 = [𝑉𝑝𝑠𝑎𝑡𝑧𝑎𝑣𝑔 - (𝑉𝑝𝑠𝑎𝑡𝑧𝑎𝑣𝑔 ∗ 𝐻𝑧𝑎𝑣𝑔 )

(25)

Stepwise algorithm for VPD monitoring is given below: Step 1: Calculate the moving average temperature and moving average relative humidity for a zone using equations 19 and 22, respectively. Step 2: Calculate the average actual vapor pressure of a zone (Vpsatzavg) using equation 23, followed by the calculation of Vpazavg given by equation 24. Step 3: Finally, VPDzavg is calculated using equation 25. The process is repeated for all the zones. If 𝑉𝑃𝐷𝑧𝑎𝑣𝑔 < VPDL, ventilation will be required, and if 𝑉𝑃𝐷𝑧𝑎𝑣𝑔 > 𝑉𝑃𝐷𝐿 the humidification process may be initiated. 𝑉𝑃𝐷𝑧𝑎𝑣𝑔 values are to be monitored continuously. Systematic flow chart for VPD monitoring is presented in Figure 5

THI Monitoring Module Scheon Model for THI is selected for greenhouse monitoring with following modifications: (i) The temperature here is 𝑇𝑍 𝑎𝑣𝑔 , i.e., is the average moving average temperature as given by equation 19. (ii) RH is here 𝐻𝑍 𝑎𝑣𝑔 , i.e., is the average moving average humidity of a zone as given by equation 22.

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Accordingly, equations 1 and 11 will change to equations 25 and 26 given below: 𝑇𝐻𝐼𝑧𝑎𝑣𝑔 (𝑔𝑟𝑒𝑒𝑛ℎ𝑜𝑢𝑠𝑒) = 𝑇𝑧𝑎𝑣𝑔 − 1.0799𝑒 0.03755𝑇𝑧𝑎𝑣𝑔 ∗ [1 − 𝑒 0.0801(𝐷−14) ]

(25)

Figure 5. Flow chart of VPD monitoring in a single zone.

𝐷=

𝑔∗∝ ℎ−∝

∝= 17.27 ∗

𝑇𝑧𝑎𝑣𝑔 237.3+𝑇𝑧𝑎𝑣𝑔

+ 𝐼𝑛(𝐻𝑧𝑎𝑣𝑔 )

(26)

SR Monitoring Module As discussed in section 2 that there will be lower and higher levels of the soil respiration SR for monitoring. Let it be SRL and SRU respectively. The

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running average values of soil temperature STzsMA(j) and soil moisture SMzsMA(j) are first obtained from the readings taken after every 5 min in the same as was done for air temperature and relative humidity. Thus STzavg, the average soil temperature of a zone will be given by equation 27. 1

STZ avg = ∑ns=1 STzs MA (j) n

(27)

Also, the SMzavg i.e., the average soil moisture of a zone will be given by equation 1

SMZ avg = ∑ns=1 SMzs MA (j) n

(28)

These values are used for calculating SR from equations 13, 14 and 15. It may be noted that T is equal to 𝑆𝑇𝑧𝑎𝑣𝑔 + 273 in K and SWC is equal to 𝑆𝑀𝑧𝑎𝑣𝑔 infractions.

Conclusion In this chapter three monitoring modules have been discussed for the monitoring of greenhouse, namely, THI monitoring, VPD monitoring and SR monitoring. Monitoring of individual parameters is essential so as to maintain these within the desired limits which are desired by individual crops, even though they may not ensure the optimized growth of plants. For the optimization of plant growth, the other two parameters, namely, THI and VPD, derived from temperature and humidity values may be more important. In addition, SR needs to be monitored for soil conditions so that it can be maintained within desired limits for a given crop. The ranges of the three derived parameters, namely, THI, VPD and SR, need to be determined as per the principles of precision agriculture, which is a separate domain.

References Ahonen, T., Virrankoski, R., and Elmusrati, M. (2008). Greenhouse monitoring with wireless sensor network. In IEEE/ASME: Proceedings of International Conference on Mechatronic and Embedded Systems and Applications, 2008, 12-15 October 2008, Beijing, China. pp. 403-408.

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Holder, R., and Cockshull, K. E. (1990). Effects of humidity alone, on the growth and yield of glasshouse tomatoes. Journal of Horticultural Science., 65(1) 31 -39. https://doi.org/10.1080/00221589.1990.11516025 .22. Kelly, S. D. T., Suryadevara, N. K., and Mukhopadhyay, S. C. (2013). Towards the Implementation of IoT for Environmental Condition Monitoring in Homes. IEEE Sensors Journal., 13(10) 3846-3853. doi: 10.1109/JSEN.2013.2263379. Kibler, H. H. (1964). Thermal effects of various temperature- humidity combinations on Holstein cattle as measured by eight physiological responses. Missouri Agricultural Experiment, Exp. Stn Res. Bull., 862, Mt Vernon. Environmental physiology and Shelter Engineering, LXVII. Lloyd, J., and Taylor, J. A. (1994). On the temperature dependence of soil respiration Functional Ecology., 8, 315-323. Loden, P., Han, Q., Porta, L., Illangasekare, T., and Jayasumana, A. P. (2009). “A wireless sensor system for validation of real-time automatic calibration of groundwater transport models”. Journal of Systems and Software, 82(11), 1859-1868. Mainwaring, A., Culler, A., Polastre, J., Szewczyk, R., and Anderson, J. (2002). Wireless sensor networks for habitat monitoring, In Proc. 1st ACM International Workshop on Wireless Sensor Networks and Applications, 2002, September 28, Atlanta, Georgia, USA. pp. 88-97. doi: 10.1145/570738.570751. Manaker, G. H. (1981). Interior Plantscapes: Installation, Maintenance, and Management. Englewood Cliffs, NJ: Prentice-Hall, Inc. 283 p. Measurements of soil respiration and simple models dependent on moisture and temperature for an Amazonian southwest tropical forest. Biogeosciences Discuss., 6, 6147–6177. Mehdipour, F., Nunna, K. C., and Murakami, K. J. (2013). “A Smart Cyber-Physical Systems-Based Solution for Pest Control,” 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing, Beijing, China, pp. 1248-1253. Mekki, O. Abdallah, Amin, M. B. M., Eltayeb, M., Abdalfatah, T., and Babiker, A. (2015). Greenhouse monitoring and control system based on wireless Sensor Network. In IEEE: Proceedings of International Conference on Computing, Control, Networking, Electronics and Embedded Systems Engineering (ICCNEEE), 7-9 Sept. 2015 Khartoum, Sudan. pp. 384-387. doi: 10.1109/ ICCNEEE.2015.7381396. Meyera, N., Meyerb, H., Welpa, G., and Amelung, W. (2018). Soil respiration and its temperature sensitivity (Q10): Rapid acquisition using mid-infrared spectroscopy. Geoderm. 323, 31-40. Nasre, A., Barai, R., and Walde, P. (2014). Design of greenhouse control system based on wireless sensor networks using MATLAB. Discovery., 19 (57), 56-58. National Oceanic and Atmospheric Administration (1976). Livestock hot weather stress. Kansas City, M. O Open Man Lett C- 31-76. Naumowicz, T., Freeman, R., Dean, B., Calsyn, M., Liers, A., Braendle, A., Guilford, T., and Jochen Schiller (2010). Wireless Sensor Network for habitat monitoring on Skomer Island. In IEEE: Proceedings of Local Computer Network Conference 2010, 10-14 Oct. 2010 Denver, CO, USA. pp. 882-889. doi: 10.1109/LCN.2010.5735827.

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Pahuja, R., Verma, H. K., and MoinUddin. (2012). Design and implementation of fuzzy temperature control system for WSN applications. International Journal of Computer Science and Network Security, 12(11), 1099-115. http://paper.ijcsns.org/ 07_book/201102/20110201.pdf. Park, D. H., and Park, J. W. (2011). Wireless sensor network–based greenhouse environment monitoring and automatic control for dew condensation prevention. Sensors., 11(4), 3640-3651. Prabhu, S. R. B., Pradeep, M., and Gajendran, E. (2017). Military applications of wireless sensor network system. A Multidisciplinary Journal of Scientific Research & Education., 2(12), 164-168. Prenger, J. J., and Ling, P. P. (2000b). Greenhouse condensation control, Fact Sheets (series) AEX-804. Ohio state university extension, Columbus, OH, 2000b. Prenger, J. J., and Peter, P. L. (2000a). Greenhouse Condensation Control Understanding and Using Vapor Pressure Deficit (VPD). Fact Extension Sheet: AEX-801 Food, Agriculture and Biological Engineering, 1680 Madison Ave. Wooster, OH44691. Rajba, S., Raif, P., Rajba., T., and Mahmud, M. (2013). Wireless sensor networks in application to patient’s health monitoring. In IEEE: Proceedings of Symposium on Computational Intelligence in Healthcare and e-health (CICARE), 2013, 16-19 April 2013, Singapore, Singapore. 94-98. Ray, A. K., Sahana, S., Das, S., and Das, I. (2022). “Cursor Motion Control Using Eye Tracking and Computer Vision,” in Advanced Communication and Intelligent Systems: First International Conference, ICACIS 2022, Virtual Event, October 2021, Revised Selected Papers, 2023, pp. 706–714. Reichstein, M., Rey, A., Freibauer, A., Tenhunen, J., Valentini, R., Banza, J., Casals, P., Cheng, Y., Grünzweig, J. M., and Irvine, J. (2003). Modeling temporal and large-scale spatial variability of soil respiration from soil water availability, temperature and vegetation productivity indices. Global Biogeochem Cycles., 17(4), 1104-119. Rochette, Philippe and Hutchinson, Gordon L. (2005). Measurement of Soil Respiration in situ: Chamber Techniques. Publications from USDA-ARS/UNL Faculty. 1379. Rothfusz, L. P. (1990). The heat index equation. Technical attachment, Scientific Services Division NWS Southern Region Headquarters, Fort Worth, TX, SR 90-23. Saeed, A., Nadeem, A., and Basit, A. (2015). Pest detection and control techniques using wireless sensor network: a review. Journal of Entomology and Zoology Studies. 3, 9299. Schoen C. (2005). A new empirical model of the temperature–humidity index. Journal of Applie Meteorology, 44 (9), 1413-1420. Son B., Her Y., and Kim J. (2006). A Design and Implementation of Forest Fire Surveillance System based on Wireless Sensor Network for South Korea. International Journal of Computer Science and Network Security, 6 (9B) 124-130. Stephen P. Long, Amy Marshall-Colon, and Xin-Guang Zhu, (2015). “Meeting the Global Food Demand of the Future by Engineering Crop Photosynthesis and Yield Potential” Cell, 161, March 26, ª2015 Elsevier Inc. Suman Lata, and Verma, H. K. (2021). Individual parameter based software monitoring modules for environment. Greenhouse. Congress on scientific research and recent trends, 25-38.

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Chapter 9

Machine Learning-Based Google App Store Cataloging Using a Naive Bayes Algorithm Jyothi Chinna Babu1,* Nuka Mallikharjuna Rao2 Potala Venkata Subbaiah3 Khalaf Osamah Ibrahim4 and Ghaida Muttashar Abdulsahib5 1Department

of Electronics and Communication Engineering, Annamacharya Institute of Technology and Sciences, Rajampet, Andhra Pradesh, India 2Department of Computer Science and Applications, Annamacharya Institute of Technology and Sciences, Rajampet, Andhra Pradesh, India 3Department of Marketing, GITAM School of Business at “Visakhapatnam Campus,” GITAM (Deemed to be University), India 4Al-Nahrain University, Al-Nahrain Nanorenewable Energy Research Center, Baghdad, Iraq 5Department of Computer Engineering, University of Technology, Baghdad, Iraq

Abstract As a part of application development, the Clients and App designers are creating a critical impact on market. The engineers want to accurately forestall submissions in the marketplace, accurate guessing results are important in showing client ratings that contribute to application success. In retrieving information, there is a lack of information. Currently, the information mine is become popular and precise to predicting the use of information mines. Through the use of evaluation instances, people *

Corresponding Author’s Email: [email protected].

In: Intelligent Decision Support System for IoT Enabling Technologies Editors: Subrata Sahana, Anil Kumar Sagar, Sanjoy Das et al. ISBN: 979-8-89113-249-8 © 2024 Nova Science Publishers, Inc.

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J. Chinna Babu, N. Mallikharjuna Rao, P. Venkata Subbaiah et al. would be able to forecast the future, make wise decisions, and demonstrate the data that has been deleted in a better manner. In light of their class, applications name, pricing, surveys, and form, the suggested work’s findings can predict the kind of apps that are the most frequently used. The Programmes offered here include humorous, educational, beautiful, commercial, gaming, sure, gadgets, and security, among other things. We can extract a plethora of data from this that is not mined to uncover secret information for accurate determination. Techniques for “communication excavation” are helpful for reviewing data from diverse metrics and identifying relationships. Because of this, we may use the Minimum Distance Algorithms and Support Vector Machine computation to describe and forecast the structure of both the Android Market applications. The outcome is higher than expected unless there is a defective reason, which has an accurate result of 75.8465%, and is best viewed and used an unusual forestry picture with an average accuracy of 91.4465%.

Keywords: Naive’s Bayes Classifier, Regression, Aggregation, Estimation

Introduction Artificial intelligence approaches dealing with a variety of difficulties. This study presents machine learning models and frameworks in depth in this study. Artificial Intelligence has a wide range of submissions and perspectives, in addition tremendous possibility for growth. In future, machine learning is probably to put up perfect hypotheses to show precise information. For the time being, unsupervised learning capabilities would improve as there is a huge amount of data generated at global level. It is also expected that neural system topologies would become increasingly unpredictable in order for them to be able to all of the more semantically significant items are separated by highlights. Furthermore, as support improves deep learning will become more robust, and studies would be able to use these areas of interest to complete more assignments.

Prediction and Analysis It is observed that mobile phones are playing an exponentially noteworthy role in public survival in today’s world. The rise of the mobile application industry has been shown to have a substantial influence on advanced innovation.

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However, as the flexible application market continues to grow, so does the number of portable application designers, resulting in increased revenue for the entire world portable application sector. There is huge rivalry between industries across the globe and it is crucial to move on in correct track by all the designers. The designers are to sustain their income in the market, it might be essential to continue their job in the market. One of the most popular app stores in the market is Google Play Store. There are many downloads from the Apple App Store, and it makes money. In this study scraped data from the Apple Play store and focused to investigate Portable (Mobile apps) which is became necessary part of human’s life, which leads the rapid development of modern cell. Regardless, it is difficult for us to keep up with the news and grasp because new applications are published every day, everything about them is important. It is accounted for that in September 2011; Android market has a substantial share of a million applications. The Google Play App Store now has 0.675 million Android applications available. Such a large number of applications are, by all accounts, a fantastic opportunity for customers to purchase from a wide range of options. In the study, I believe that online applications surveys have created a significant impact rather than paid applications. It is very difficult to sort out all the reviews and ratings in order to make decisions by potential clients / users. Furthermore, engineers who is developing applications have difficulty figuring out how to improve application performance based on generalized only on the basis of evaluations, and benefit from recognizing and comprehending a vast number of written remarks.

Problem Statement Mobile App stores are expanding because usage of smart phones is growing exponentially. Today, Apple’s App Store and Google Play stores are meeting the requirements of users across the globe. This study examined the Google Play store features, accessibility and forecasting the success of the App. Nevertheless, the question arises as to whether Google Play store is really essential in the market. Yes, almost 6,140 mobile applications are released by Google Play store every day according to statistical analysis presented in 38 Ahmed. F. Hussein (2022) As per Abdulsattar Hamad (2022). 1 million apps are outperformed by the end of July 2013. Approximately, 2.6 million Apps in the Google Play store are anticipated by the end of March 2018. It is tough to call the developers and Apps industries to come up with a

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unique concept-based Apps because, there were many numbers of application are releasing in the Play store every day. In view of this, all of your efforts will be for naught if the app fails to perform successfully on the Android market. The cellular sector is expanding quickly, which raises the level of competition. Nevertheless, competitive pressure also increases the likelihood of disaster. Since a lot of time, energy, and energy are spent in the procedure, the programmers must conduct proper research because the firm could manage to for application to collapse. According to predictions made based on the play company’s past performance and profit generation for the years 2016 and 2017. The store’s income increased by 34.2% while the proportion of apps available increased by 16.7%. The software must make money when it is released to compensate the development team, assist the business to turn a profit, or add investment to its funds. The developer, on the other hand, does not receive all the revenue generated by an app; instead, he receives a portion of it. Developer gets 70% of the full cost when an application is released on the Play Store, with the Play Store keeping the remaining 30%. There’s no minimal limit on how much money a software may make; any revenue exceeding $1 is transferred to the customer’s profile. The financial split in the Android Market is 70:30 49.M. Thus, developing an app is not a simple task to handle given the abundance of programmers that regularly produce apps. However, achieving that degree of accomplishment is what helps an application stand out from the competition. In all areas, one among software developers have fewer than 10,000 installations, and only 15% have broken the 1,000k installation threshold, in accordance with the State of Developers of Mobile Apps survey Hassan Jalel Hassan (2022) The likelihood that an app will succeed on the Android market in the future decreases as the quantity of downloads decreases. We try to address this important topic in our research. We looked at applications that, despite possessing amazing ideas, performed miserably on the app store Bharani (2022).

Review of Literature There have been numerous applications of machine learning in the industry; Amazon store, IBM e-commerce and others have employed machine learning in product classification as well as product recommendation. Advert placement and ad content design have been improved greatly by Google with machine learning. Machine learning has also been used extensively in image

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processing by Google for their image search. Although there are several machine learning algorithms available for various tasks, classification problems have be-come most predominant in this space. As described by Gill, H. S. (2022) classification is an example of supervised learning, where a training set of observations correctly identified are fed into the machine learning algorithm to train the system. The process allows the machine learning algorithm to identify correctly There have been numerous applications of machine learning in the industry; Amazon store, IBM ecommerce and others have employed machine learning in product classification as well as product recommendation. Advert placement and ad content design have been improved greatly by Google with machine learning. Machine learning has also been used extensively in image processing by Google for their image search. Although there are several machine learning algorithms available for various tasks, classification problems have be-come most predominant in this space. As described by Gill, H. S. (2022) classification is an example of supervised learning, where a training set of observations correctly identified are fed into the machine learning algorithm to train the system. The process allows the machine learning algorithm to identify correctly There have been numerous applications of machine learning in the industry; Amazon store, IBM e-commerce and others have employed machine learning in product classification as well as product recommendation. Advert placement and ad content design have been improved greatly by Google with machine learning. Machine learning has also been used extensively in image processing by Google for their image search. Although there are several machine learning algorithms available for various tasks, classification problems have be- come most predominant in this space. As described by Gill, H. S. (2022) classification is an example of supervised learning, where a training set of observations correctly identified are fed into the machine learning algorithm to train the system. The process allows the machine learning algorithm to identify correctly. There have been numerous applications of machine learning in the industry; Amazon store, IBM e-commerce and others have employed machine learning in product classification as well as product recommendation. Advert placement and ad content design have been improved greatly by Google with machine learning. Machine learning has also been used extensively in image processing by Google for their image search. Although there are several machine learning algorithms available for various tasks, classification problems have be-come most predominant in this space. As described by Gill, H. S. (2022) classification is an example of supervised learning, where a

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training set of observations correctly identified are fed into the machine learning algorithm to train the system. The process allows the machine learning algorithm to identify correctly Machine learning has been used in a variety of industries, including Amazon’s shop, IBM’ se-commerce, amongst other things. Machine learning has been used in product classification and suggestion. With machine intelligence, Google has substantially improved ad placement and ad content design. Google’s image search also makes heavy use of machine learning in picture processing. Despite the fact that there are a variety of machine learning methods for diverse applications, classification problems have become the most common in this area. Classification is an example of supervised learning, as described by O. I. D. Banumathy (2023) in which a training set of correctly classified observations is supplied into the train the system a machine learning algorithm is used. The training sets help to gain knowledge and such approach permits the machine learning algorithms to correctly recognize fresh data always. The unsupervised learning algorithm known as clustering involves classifying data into groups for measuring data similarity. In study reviewed many studies have been conducted to increase classification in a variety of domains; example in D. Banumathy (2022) reported structural MRI data, distinguished individuals with schizophrenia and bipolar disease and health patients by using machine learning algorithms. Support Vector Machine (SCV) leaning approach is created models using grey matter density pictures and similar study reported in SVM based product classification. In D. Maheswari (2014) consumers are focusing their searches for a certain item in the data set by adding some value to the SVM learning approach. In study Edeh MO (2022), identified the most detected feature to determine the polarity distribution namely, being optimistic, undesirable and impersonal in a shapeless review. And also, there is limited research on the proposed approach for product classification; the proposed approach has proven that it is quite effective in a diversity of the classification’s problems. The amount of personal and public information saved online has grown over time. This section includes linguistic information gleaned from bloggers, newsgroups, review sites, and other channels of social media. It is possible to automatically convert this unstructured input into structured data that reflects popular sentiment by using writeup prediction models. In order to determine how customers feel about specific apps, products, services, and brands, this relational database may then be employed. As a result, they can offer vital information for enhancing products and services. This style of sentiment analysis was used in the studies that followed.

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Using the Naive Bayes (NB) classifier, Kumari and other researchers (Arthur Chol 2016) categorized views as optimistic, unfavorable, or impartial. In O. I. D. Banumathy (2023), the review content is not influenced by rating. The users would try to write a favorable review, but at the end they would be given a lower rating only because of words they used. The study D. Maheswari (2014) proposed an approach for extracting polarity from the complete evaluation of the product by using classifier Naïve Bayes, which might be shown as optimistic, unfavorable and impartial. In another study Edeh MO (2022) machine learning approach outperform through traditional based classification approach, which falls short when it comes to sentiment analysis. To discover and organize text-based sentiments have been investigated by using information extraction approach. In some other studies Hanyang, H. (2019), propose a model for annotating a text low level demonstration of sentiments. A “scenario template” is used for summarizing the arguments stated in a text and that is based on personal judgment. These strategies are excellent for activities that require questions from a variety of sources. In study Hassan Jalel Hassan (2022) to extract the semantic orientations of text, it is recommended a spin model for statistical analysis. In the proposed spin model, estimated probability is computed using mean field approximation approach and these semantic orientations are provided rating and it shall say acceptable or not acceptable. This approach generates extremely accurate semantic orientations with a small number of seed words of English lexicon. In study Hemavathi, S. R. (2022), proposed a sentiment analysis algorithm which is used to summarize and ensembles of comments and reviews. The Gaussian distributions approach is used to get over the problems raised through sentiment analysis. This is a paradigmatic preparation idea that has never been utilized before. A machine learning strategy was put out in research Irfan Ali Kandhro (2022) to forecast a Google Play store rating using a database that included the app classification, the quantity and size of downloads, the kind of downloads, and the running android of the program. The techniques k-means aggregation, k-nearest neighbors, support vector machines decision trees, liner regression, logistic regression, NB classifications, and synthetic neural networks were employed to accomplish the required outcomes.

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Classification Issues Solvés Using Naive Bayes Algorithm A well-known statistically teaching method that has demonstrated success in a variety of situations utilizing irrelevant details is the Naive Bayes predictive model this paradigm is demonstrated by the word embedding known as the “bag - of - words,” where the wording is ignored. Faheem Ahmad Reegu (2022) made one of the first implementations of this paradigm to Information Extraction. Information extraction, which involves extracting specific information from text, has also played a significant role in making enormous datasets easier to comprehend for consumers. The authors demonstrate that a Naive Bayes Knowledge Extraction teaching method may perform better with a well-designed smoothing method. Gill (2022) compared Naïve Bayes classifier against alternative classification systems on a medical dataset. Their results demonstrate that Naive Bayes beat other algorithms in detecting medical information, and because of its ease of use and computational effectiveness, it may be used in medical data mining. The integration of Naive Bayes and other classification methods can eventually increase performance, despite the fact that Naive Bayes’ independence assumptions have been questioned. To enhance the Naive Bayes algorithm’s performance in the following applications, several enhancements have been suggested. The modified Naive Bayes technique developed by (Gnanavel, et al. 2022, H. S. Le 2015, H. Wang 2010, Hanyang 2019, Hassan Jalel Hassan 2022) for bettering the classification of Nepali texts serves as evidence of this. As Nepali text is utilized in this research, a language other than English, the lexicon domain pooling idea is applied to improve the performance of the classifiers. These texts include linguistic elements including part-of-speech tags, stop - word, and stemmers. The suggested method is so flexible that it aids the user in using languages other than English, Chinese, and Japanese. In another study (Hemavathi2022, Irfan Ali Kandhro 2022, J. Chinna Babu 2022, J. J. 2022) proposed naïve Bayes probabilistic model-multinomial event for text classification. The proposed multinomial nave Bayes approach exertion by pushing down greater counts if word frequency, if errors occur on same word in a text because this approach does not count the repetitions. In studies (K. A. Ogudo 2023, K. Dave 2003, Kavitha C 2022, Osamah Ibrahim Khalaf 2022, Khaparde 2022, L. Ting 2011) Spam identification is one of most popular applications of Nave Bayes approach. It proposed a nave Bayes spam detection process based on the generated decision trees, consequently the classifier error weight was also considered. The results show

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that the implementation is effective, whereas the incremental decision tree induction approach is not very effective. In studies Maalej 2013, Mohan, et al. 2022, N. Penchalaiah 2022), is propped to achieve ranking by using Naïve Bayes algorithm. In this study we used weighted naïve Bayes algorithm for ranking with each characteristic of weight of the content. The proposed weighted naïve Bayes algorithm outperforms as like as the ordinary naïve Bayes algorithm.

Algorithms Random Forest A tree-based technique, namely random forest involves creating massive trees (i.e., decision trees) and then merged their results to improve the generalization capabilities of a model. The Process of mixing the generated trees is known as a together approach. To create and build a dominant learner, a collective approach is unknown to the mixture of weak beginner. A random forest approach is a predicted the group of trees signed classifiers ℎ(𝑥, ∅𝑘 ), ∅𝑘 = 1. . 𝑛 , where 𝑘 is an autonomous identical dispersed random vector. In (P. M. Granitto 2006, R. Agrawal 2022, Radhakrishnan, et al. 2022, Rajendran 2021, Ramasamy 2022) these random vectors are dividing as trees by definite input. The proposed random forest algorithm is generating random finite set of trees discretely. The basic steps of random forest algorithm are as follows: Step 1: To create ‘N’ number of records at Random from the finite data set. Step 2: To Create a decision Tree using the ‘N’ number of records. Step 3: To repeat step 1 and 2 till end operation Step 4: To list out the decision tree generated through step 1 and step 2. Then, an original greatest is assumed to grouping that takes the most votes Figure 2.1 depicts various trees labeling the class in various ways. To develop a better model, ensemble takes mode (high level of possible) output shaped on different trees. To set it alternative method, random forest generates numerous decision trees and combines to get more accurate and consistent prediction.

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Support Vector Machines An SVM classifier is overseen Machine Learning organization technique produces an ideal hyper-plane for categorizing fresh samples based on considered drill data (Sharma, et al. 2022, Singh 2016, Subramani, et al. 2022, T. Fushiki 2011, Tanvi Puri 2022, V. Narayanan 2013, H. Wang 2010). SVM’s remained first presented in 1960s then developed in the 1990s. Though, due to their capacity to produce remarkable outcomes, they are only now becoming quite popular. SVM in a nutshell: Elementary Machine Learning seeks to create a border that splits the data in such a way that the misclassification mistake is diminished in case of linearly distinguishable facts in 2D, as illustrated in Figure 1. From Figure 1, you will notice all are multiple boundaries that separate the data points correctly. The data is properly classified by the two dashed lines and one solid line.

Figure 1. Multiple boundaries that separate correct data points.

SVM varies as of preceding organization approaches by selecting a verdict border that improves the coldness among the nearest data opinions for all classes. SVM not alone finds the result border, nonetheless, similarly classifies finest result border. One of most ideal decision boundaries will be the one with a large limit among all the adjacent points of classes. Supporting vectors are points very close to the verdict boundary that increase the distance between the verdict edge and points as shown in Figure 2. The supreme border classifier, or supreme border hyper-plane, is verdict edge for Support Vector Machines.

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Figure 2. Points very close to verdict boundary.

Figures 1 and 2 show the humble SVM technique is used to discover result limitations for linearly distinguishable data. A straight line, on the other hand, cannot be employed as the choice limit in the case of nonlinear data. The simple SVM algorithm cannot be applied with non-linearly separable data. Instead, Kernel SVM, a modified form of SVM is employed. Fundamentally, the Kernel SVM converts nonlinearly distinguishable data in inferior extents to linearly distinguishable data is advanced extents, so data points from distinct modules are assigned to various scopes.

Naive Bayes Algorithm The two names Naive and Bayes form the algorithm of Naive Bayes, which can be said as follows: 𝐴

𝑃 (𝐵) =

𝐵 𝐴

𝑃( )𝑃(𝐴) 𝑃(𝐵)

(1)

Naïve: It is so named as it is believed the existence of one element is not related to the other. When the results (1) color, shape and taste of fruit is used, the red, round, and sugary fruit is considered an apple. It results, each feature aids to identify the apple short of trusting on the other. O Bayes: It’s named Bayes since it was built on the Bayes’ Theorem premise.

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BAYES’ THEOREM: Bayes’ theorem, often recognized as Bayes’ Rule or Bayes’ law, is exact formulation used for calculating Probability of a theory created on earlier knowledge Y. Liu (2022). It is Conditional Probability that regulates this. O Bayes’ Theorem’s formula follows as: Here, P (A|B) = Possibility of upcoming: On the experimental event B, the possibility of suggestion A. P (B|A) = Possibility of Prospect: Assumed that the possibility of a premise is correct, the likelihood of indication Y. Liu (2022).

Random Forest Algorithm Random Forest is a well-known Machine Learning algorithm that uses the overseen knowledge way, which is depicted in Figure 3. In Machine Learning, this is used for together organization then regression matters. It’s based on collective knowledge; this technique of mixing numerous classifiers resolves a complex problematic then raises the replica’s presentation. “Random Forest is a classifier which comprises a no. of verdict trees on numerous subsections of a particular dataset then selects the cruel towards improve the projected efficiency of that dataset,” rendering the name. Instead, dependent on solitary verdict tree, the Random Forest assembles and estimates from each tree then expects the concluding output built on majority votes of calculations Hassan Jalel Hassan (2022). The larger number of trees in forest, the extra precise then the problem of over-fitting is evaded. The Random Forest method is depicted in the picture below: Step1: Go to the search bar and search for Anaconda Prompt Software. Step2: Command prompt will be opened with some location on the PC. Step3: Type “cd Play store” and click on Enter. Step4: Type “ipython notebook Final_code_px.ipynb” and again click on Enter. Step5: A web page will be opened in chrome tab with a title called Jupiter Notebook; it redirects the user to source code page. Step6: Jupiter Notebook will be opened as shown below. The program cells produces output if and only if we perform line-to-line execution of the

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code. So, to do this we should click on the “Run” button upto end line of the code.

Figure 3. Block schematic of Random Forest algorithm.

System Implementation System development was done for conducting operational and technological studies.

Input and Output Designs Logical Layout A systems rational strategy is an intellectual concept of the scheme’s information tides, inputs, and outputs. It is regularly proficient in modeling; it includes making an excessively intellectual perfection of the real scheme. Object Association Drawings, or ER Diagrams, remain a type of rational strategy (An Ali, etc. al. 2021). Design of Physical Architecture This carnal strategy is worried by the scheme’s actual input and output procedures. It defined how information is arrived in an organization, and the issue is validated / authorized, handled and shown as output. The architectural design phase determines the system’s requirements.

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● ● ● ● ●

Requirements for input Requirements for output Requirements for storage Requirements of processing Scheme management in addition backups /retrieval

To place it differently, the visual appearance of structure proposal is divided into 3 subtasks: 1. Design of the Operator Boundary 2. Visualization of Data 3. Proposal of the Process User Interface is engaged through the operator’s input data in a scheme then the information remains offered by organization. The system information is expressed then stored within the organization called fact proposal. To finish, System Plan is a process through the facts is vetted, secured, and/or altered by way of travels into, throughout, then obtainable to the organization. Certification defines 3 subtasks is developed then complete availability is following phase by the completion of systems strategy segment (K. Negi, et al. 2022). The physical layout doesn’t raise the carnal appearance of statistics scheme in this sense. To draw analogy, the physical design of a personal computer includes input through the controls, dispensation by the CPU, then outputs through a screen, printer, and other devices. This would not affect the physical layout of the hardware, which in the case of a PC would include a screen, motherboard, CPU, hard disks, cinematic/visuals postcards, modems, USB slots, and so on. It entails the creation of a detailed operator then it produces catalogue erection computer, as well as a switch workstation. For the proposed system, the hardware individual requirement is created.

Representation of Input and Output Input Design The input device obliges link among both user than the structure. It is proficient via examining the computer to deliver the information after printed

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or written file or having person’s crucial information system right. Limiting number of inputs required, limiting faults, minimizing suspensions, eliminating superfluous stages, and then making procedure mode stream in completely penalty area of input proposal. The input proposes security then suitability though upholding secrecy. These influences stay occupied in explanation through input proposal. ● ● ● ●

To learn what data should be provided essential input data? To know how data should be systematized? To know whether upkeep to assist working people in providing input? To know the behaviors to create input authentication, together with what to do if any errors occur.

Purposes The process of translating a user-oriented description of an input into a computer-based system is known as input design. A well-designed layout is acute for avoiding data admittance errors and leading management in the exact way for gathering precise information from the designed system. It is essential to design user friendly data admittance panels which can manage and avoid massive data and to make data admittance easier and error free all the time. This error-free and easy panel setup helps users to execute all the required data operations and it allows users to outlook and validate the submitted records. As a result, the main objective of input design is fulfilled and helps to push the data whenever required with easy-tofollow input layout.

Output Layout Design The objective of quality output layout design that satisfies the user’s needs, desires and visualize the specific data clearly and accurately. Obviously, user satisfaction is the ultimate aim of the developers. The interface between the user and system is enhanced thorough efficient and intelligent output layout design. a) To make system output should be a systematic, well-thought-out approach.

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b) To create the proper output layout design, assure that each output is attracted by the users. c) To make output layout simple and precise, that fulfill the standards when the users are interacting with the system. d) To design how data should be delivering e) To create a report in specific user required format for outcome visibility

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Output Screens Graphs In this study present various graphs were displayed based on the features and how the different algorithms generate outputs for the features that were considered. These are some of the features that considered for classification, they are: ● ● ● ●

Random Forest Algorithm (RFA) Decision Tree Algorithm (DTA) Support Vector Machines (SVM) Naive’s Bayes Algorithm (NBA)

In Figure 4, Figure 5 and Figure 6 is shown accuracy comparison of various algorithms with respect to review, price and apps respectively. Figure 4 states on the “reviews” function, this bar chart depicts the anticipated ultimate results given by all of the above-mentioned algorithms. The X-axis depicts various algorithms, while the Y-axis depicts the correctness of the output based on reviews. RFA = 0.45, DTA = 0.42, SVM = 0.37, NBA = 0.68 are the output outcomes for different algorithms. The Naive’s Bayes Algorithm estimation is the finest of all three algorithms. Because the number of positive evaluations for any classification should be as high as feasible, the findings will be more accurate. The accompanying bar Figure 5 depicts the projected end results generated by all of the algorithms listed above using the “price” attribute. The X-axis represents different algorithms; Where Y-axis represents the accuracy of the output based on pricing. RFA= 0.1, DTA= 0.25, SVM= 0.3, NBA= 0.05 are the output outcomes for the various algorithms. The Naive’s Bayes Algorithm prediction is the best of all three algorithms. Because the price for any categorization should be as low as possible while the results should be more precise. Depending on the “Rating score” function, Figure 6 graph demonstrates the predicted exact outcomes given by all of the above-mentioned algorithms. On the X-axis are different algorithms, and on the Y-axis is the predicted rating-score. The ratio of current performance ratings from the customer reviews to the estimated score value is used to generate the rating score.

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Figure 4. Accuracy comparison of various algorithms based on reviews.

Figure 5. Accuracy comparison of various algorithms based on price.

RFA = 0.21, DTA = 0.215, SVM = 0.216, NBA = 0.787 are the output outcomes for different algorithms. The Naive’s Bayes Algorithm estimation is the finest of all three algorithms. Since any classification requires a ratingscore value, it should be as high as possible in order for the findings to be as precise as possible.

Figure 6. Comparison of Various algorithms based on rating of apps.

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Figure 7. Comparison of various algorithms based on size of the apps.

The predicted final results provided by all of the above-mentioned algorithms based on the “size” attribute are shown in this bar chart. On the Xaxis, different algorithms are represented, while on the Y-axis, size is indicated in percentages. RFA = 43.93, DTA = 34.58, SVM = 44.06, NBA = 45.22 are the expected output outcomes for various algorithms shown in Figure 7. The predicted results of Naive’s Bayes Algorithm are the best of all three algorithms. Because the size of the predicted apps in every classification should be as small as possible, people will be more interested in utilizing the predicted apps. The expected final results given by all of the above-mentioned algorithms based on “accuracy” are shown in this bar chart. The X-axis represents several algorithms, while the Y-axis represents output accuracy. RFA = 0.77, DTA = 0.78, SVM = 0.79, and NBA = 0.86 are the expected output outcomes for different algorithms. The Naive’s Bayes Algorithm prediction accuracy is considerably superior than earlier algorithms. Because accuracy is more vital in any categorization result. The estimated final results given by all of the above-mentioned algorithms based on the “ratings” feature are shown in Figure 8. On the X-axis, several algorithms are displayed, and on the Y-axis, the number of apps predicted with ratings less than or more than 4 is represented. The number of predicted apps with ratings less than 4 is shown in blue, while the number of expected apps with ratings greater than 4 is shown in orange. RFA = 804:2940, DTA = 804:2940, SVM = 804:2940, and NBA = 1193:4423 are the expected output results for various algorithms shown in Figure 9. Although the first three algorithms produce similar outcomes, the Naive’s Bayes Algorithm predicts considerably better than the

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others. Because the number of predicted app ratings in any categorization should be as large as possible, people will be more interested in utilizing the higher rated apps.

Figure 8. Comparison of various algorithms based on Accuracy.

The predicted final results generated by all of the above-mentioned algorithms based on “speed of categorization” are shown Figure 10. The Xaxis displays various algorithms, while the Y-axis displays categorization speed in seconds. RFA= 0.55, DTA= 0.14, SVM= 0.09, and NBA= 0.03 are the expected output outcomes for distinct algorithms. The Naive’s Bayes Algorithm prediction is considerably superior than earlier algorithms. Because classification speed is very crucial in any categorization.

Figure 9. Comparison of various algorithms based on ratings of apps.

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Figure10. Results based on Speed of classification of the Apps.

The predicted final outcomes provided by all of the above-mentioned algorithms based on the “F1-score” are shown in this bar chart. On the X-axis, different algorithms are shown in Figure 11, while the f1-score is represented on the Y-axis. By taking the harmonic mean of a classifier’s precision and recall, the F1score integrates both into a single statistic. RFA = 3744, DTA = 3744, SVM = 3744, and NBA = 5616 are the expected output outcomes for different algorithms. Although the first three algorithms produce similar outcomes, the Naive’s Bayes Algorithm predicts considerably better than the others. Since the f1-score is more essential than the overall accuracy for any classification, it provides balanced precision and recall.

Figure 11. Comparison of various algorithms based F1 score of apps.

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The classification results based on rating, reviews, size, installs, type, price, content rating, genres, and last updated are shown in the table above. The study looked at the art and design area of apps and categorized more than ten apps based on their features, so that users may pick the best app for their needs. Similarly, based on the given parameters, classification of different kinds of apps will be performed.

Predictions

Output of Predictions The forecasted final results of the Naive’s Bayes classifier are shown in the table above, and the overall accuracy of the Naive’s Bayes classifier is 0.86. For apps with a rating of more than 4, the precision and recall values of predicted output are 0.89 and 0.32, respectively, demonstrating how well this classifier performs in classifications. Almost 5616 apps were categorized using NB classifier whereas the remaining classifiers categorized only 3744 apps. When different predictions are taken into account, the Naive’s Bayes classifier offers the most accurate results to both users and developers.

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Conclusion This data set includes a considerable amount of data that could be used in a number of ways. Furthermore, future developers and the Google Play Store crew could use Naive Bayes model generated with this set of data to analyze the Android Market and decide what application categories really should be made to make the Android Market appealing in the long term. This could be used to increase the worth of a business or the Play Store as a whole. It really is not only the issue we resolved. Upon the large dataset, we explored a variety of classification methods and determined that Naive Bayes is perfectly suited for our problem definition. Designers also learned that compared with other methods function in certain situations. Researchers discovered that now the Decision tree makes the model implementation easy to understand and explain while also saving processing power. The future work of study is focused on regression an approach which comprises the number of responses and installation of Apps. This approach is recognizing the various groups and statistics of maximum number of installed Apps and determining the similarity between the App size and versions.

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Osamah Ibrahim Khalaf. 2022. “Impact of Activation, Optimization, and Regularization Methods on the Facial Expression Model using CNN.” Computational Intelligence and Neuroscience 9. Kavitha C., Mani V., Srividhya S. R., Khalaf O. I. and Tavera Romero C. A. 2022. “EarlyStage Alzheimer’s Disease Prediction Using Machine Learning Models.” Front. Public Health 25-30. Khalaf, Osamah Ibrahim, Carlos Andrés Tavera Romero, Shahzad Hassan, Muhammad Taimoor Iqbal. 2022. “Mitigating Hotspot Issues in Heterogeneous Wireless Sensor Networks.” Journal of Sensors 1-14. Khaparde, A. R., Alassery, F., Kumar, A., Alotaibi, Y., Khalaf, O. I. 2022. “Differential Evolution Algorithm with Hierarchical Fair Competition Model.” Intelligent Automation & Soft Computing 1045–1062. Lawrence, K., S., and D. M. Pennock. 2003. “Mining the peanut gallery: Opinion extraction and semantic classification of product reviews.” World Wide Web 59-528. Le, H. S., T. V. Le, and T. V. Pham. 2015. “Aspect analysis for opinion mining of Vietnamese text, in Proc.” Int. Conf. Adv. Comput. Application 118-123. Liu, X., J., Liu, X., Osamah Ibrahim Khalaf, Jing Ji and Quan Ouyang2022. “Ship feature recognition methods for deep learning in complex marine environments.” Complex Intell. Syst 10-16. Liu. Y. 2022. “Interaction-Enhanced and Time-Aware Graph Convolutional Network for Successive Point-of-Interest Recommendation in Travelling Enterprises.” IEEE Transactions on Industrial Informatics 10. Maalej, D. Pagano and W. 2013. “User feedback in the app store: an empirical study.” IEEE 125-134. Maheswari, D., R. Nithya and. 2014. “Sentiment Analysis on Unstructured Review.” 2014 International Conference on Intelligent Computing Applications, Coimbatore, India: IEEE, 367-371. Meenakshi, R., R. Ponnusamy, S. Alghamdi, O. Ibrahim Khalaf and Y. Alotaibi. 2022. “Development of mobile app to support the mobility of visually impaired people.” Computers, Materials & Continua 3473-395. Mohan, P., N. Subramani, Y. Alotaibi, S. Alghamdi, O. I. Khalaf, and S Ulaganathan. 2022. “Improved Metaheuristics-Based Clustering with Multihop Routing Protocol for Underwater Wireless Sensor Networks.” Sensors 16-20. Narayanan, V., I. Arora, and A. Bhatia. 2013. “Fast and Accurate Sentiment Classification Using an Enhanced Naive Bayes Model in Intelligent Data Engineering and Automated Learning – IDEAL 2013.” SpringerBerlinHeidelberg 194-201. Negi, K., G. P. Kumar, G. Raj, S. Sahana, and V. Jain, “Degree of Accuracy in Credit Card Fraud Detection Using Local Outlier Factor and Isolation Forest Algorithm,” in 2022 12th International Conference on Cloud Computing, Data Science \& Engineering (Confluence), 2022, pp. 240–245. Ogudo, K. A., R. Surendran and O. I. Khalaf. 2023. “Optimal artificial intelligence based automated skin lesion detection and classification model.” Computer Systems Science and Engineering 693-707. Penchalaiah, N., Abeer S. Al-Humaimeedy, Mashael Maashi, J. Chinna Babu, Osamah Ibrahim Khalaf and Theyazn H. H. Aldhyani 2022. “Clustered single-board devices

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with docker container big stream processing architecture.” Computers, Materials & Continua 5349-5365. Radhakrishnan, K., D. Ramakrishnan, O. I. Khalaf, M. Uddin, C.-L. Chen, and C.-M Wu. 2022. “A Novel Deep Learning-Based Cooperative Communication Channel Model for Wireless Underground Sensor Networks.” Sensors 4475. Rajendran, Surendran, Osamah Ibrahim Khalaf, Youseef Alotaibi and Saleh Alghamdi 2021. “MapReduce-based big data classification model using feature subset selection and hyperparameter tuned deep belief network.” Scientifc Reports 138-145. Ramasamy, Viswanathan, Youseef Alotaibi, Osamah Ibrahim Khalaf, Pijush Samui and Jagan Jayabalan. 2022. “Prediction of groundwater table for Chennai Region using soft computing techniques.” Arab J Geosci 15827. Rawat, S. S., S. Alghamdi, G. Kumar, Y. Alotaibi, O. I. Khalaf, and L. P. Verma. 2022. “Infrared Small Target Detection Based on Partial Sum Minimization and Total Variation.” Mathematics 108-112. Reegu, Faheem Ahmad, Hafiza Abas, Abdoh Jabbari, Rudzidatul Akmam, Mueen Uddin, Chih-Ming Wu, Chin-Ling Chen, Osamah Ibrahim Khalaf. 2022. “Interoperability Requirements for Blockchain-Enabled Electronic Health Records in Healthcare: A systematic review and open research challenges Systematic Review and” Security and Communication Networks 11. Sharma, B., A. Hashmi, C. Gupta, O. I. Khalaf, G. M. Abdulsahib, and M. M Itani. 2022. “ Hybrid Sparrow Clustered (HSC) Algorithm for Top-N Recommendation System.” Symmetry 793-798. Srividhya, S. R., C. Kavitha, Wen-Cheng Lai, Vinodhini Mani, Osamah Ibrahim Khalaf. 2022. “ A Machine Learning Algorithm to Automate Vehicle Classification and License Plate Detection.” Wireless Communications and Mobile Computing 12. Subramani, N., P. Mohan, Y. Alotaibi, S. Alghamdi, and O. I. Khalaf. 2022. “ An Efficient Metaheuristic-Based Clustering with Routing Protocol for Underwater Wireless Sensor Networks.” Sensors 415-422. Tanvi Puri, Mukesh Soni, Gaurav Dhiman, Osamah Ibrahim Khalaf, Malik alazzam, Ihtiram Raza Khan,. 2022. ““Detection of Emotion of Speech for RAVDESS Audio Using Hybrid Convolution Neural Network.” Journal of Healthcare Engineering. Ting, L., W. H. Ip, and A. H. C. Tsang. 2011. “Is Naïve bayes a good classifier for document classification, vol. 5, no.3, pp. 37–46,2011.” International Journal of Software Engineering and its Applications 37-46. Wang, H., L. Yue, and C. Zhai,. 2010. “Latent aspect rating analysis on review text data: a rating regression approach.” Knowledge Discovery Data Mining 783-792.

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Chapter 10

Barriers to Digital Transformation: A Case Study of the Insurance Industry Vijay Anant Athavale1,* and Himanshu Jain2 1Walchand 2Panipat

Institute of Technology, Solapur, Maharashtra, India Institute of Engineering & Technology, Panipat, Haryana, India

Abstract Our way of life has changed as a result of information technology's introduction into our homes and workplaces. In order to develop new smart services, businesses are increasingly turning to technologies like the Internet of Things (IoT) and Artificial Intelligence (AI). Across various industries, digital transformation is currently taking place. A digital transformation requires a significant shift in the organization and its business model with the purpose of adding value. Because digital transformation is so extensive, there are strong incentives to fully analyze any existing barriers before change work can start. Because the digital transformation is so large, there are strong incentives to analyze any existing barriers before change work can start. Numerous studies have been done to investigate the barriers to digital progress in, say, the production industry. However, very few studies have looked at the challenges in the service industry. The objective of this study is to investigate the barriers that can create problems to an insurance company from going digital. Five semi-structured interviews with respondents from two insurance companies were conducted as part of a case study to address this research issue. The results show that there are numerous barriers; the majority of them are related to organizational, cultural, technical, and environmental ones. *

Corresponding Author’s E-mail: [email protected].

In: Intelligent Decision Support System for IoT Enabling Technologies Editors: Subrata Sahana, Anil Kumar Sagar, Sanjoy Das et al. ISBN: 979-8-89113-249-8 © 2024 Nova Science Publishers, Inc.

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Keywords: Digital Transformation, Fintech, Insurance Sector, IoT, AI

Introduction The advent of information technology (IT) in the home and work has changed our way of life. Today there are smart solutions for everything from heating the car to answering emails wherever we are. Today, we are completely dependent on these new technologies to make simple everyday chores and professional tasks. There is no indication that this development will slow down in the near future. According to Moore's law, computer power doubles every 18 months (Brynjolfsson & McAfee, 2015). As the development of technology takes place exponentially, we can rather expect that the development will go even faster, as (Brynjolfsson and McAfee, 2015) described this, we are now moving toward the other side of the chessboard and only the imagination will set the limits for tomorrow's technology. For many companies today, change is the only thing that is constant, which also entails new and changed working methods. Although it is common for companies to talk about developing their business with digital technology, there is still uncertainty about what digitization really is. At a basic level, digitization aims to make analog things digital, by encoding analog data into zeros and ones, we can store, process and even share material that was previously limited by its physical form. Digitization seldom creates new value in connection with transformation, something that digitalization does. Digitalization is changing everything from how we humans communicate to how we live, as well as how we work today (Van Dijk J, 2020). In recent years, digital transformation has become an increasingly important challenge for companies in many different industries. The rapid technological development that is taking place places new demands on companies at the same time as new opportunities are created. The concept of digital transformation relates to more comprehensive changes that permeate large parts of an organization. Thus, a digital transformation can involve several different digitization projects at the same time as it fundamentally changes a company's strategic business offering. (Athavale et al., 2021). The rapid development that is taking place in society is also expected to affect workplaces and the role of people in work. Processes previously performed by humans can be replaced by autonomous systems. A new report released by McKinsey & Company indicates that by 2030, as many as 800 million workers worldwide could be replaced at work by robots. This

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development will in turn contribute to humans being allowed to focus on more social or empathetic tasks while robots handle the monotonous (Folster et al., 2014; Floridi L. 2013; Balasubramanian et al., 2018). One industry that will be affected by the technical development to a great extent is the insurance industry. Traditionally, this industry has been completely dependent on human competence to set premiums and make risk assessments based on the customer's needs. Today, more and more of these processes can be automated and performed by smart systems using artificial intelligence (AI) that can make a calculation based on a large number of variables to determine how the premium should be set (Arora et al., 2020; Zimmer et al. 2021). New business opportunities have already been created because of new technologies such as the Internet of Things (IoT), which allows customers to connect, among other things, vehicles or household appliances to the internet. This creates new opportunities for players in the insurance industry to start working with, for example, User-Based Insurance (UBI), which allows customers to pay motor insurance based on their actual driving behavior. Smart home assistants will also be able to help protect customers' homes through autonomous systems that can detect everything from water damage to fire at an early stage (Sun Q. & Medaglia R, 2019). Policybazaar (31) and BankBazaar (11) are two fully digitalized players that are taking over ever larger parts of the insurance market. These players offer insurance with the help of smart technology solutions. Policybazaar and BankBazaar are examples of how companies in the insurance industry may work in the future to remain competitive. Insurance companies that do not keep up with technological development risk losing their market share to competitors who develop their services with smart systems. Many established insurance companies have a long history and tradition of working with and close to the customer. Although much research has dealt with what the future in the insurance industry may look like, little has been written about how traditional insurance companies can transform their operations digitally and which barriers can complicate the use of smart technology (Balasubramanian et al., 2018). The purpose of this study is to contribute knowledge about existing barriers in the service sector that may limit the possibility of development in a society characterized by rapid technological development. Against this background, the following question has been posed: ●

What barriers can complicate a digital transformation of an insurance company?

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To answer the question and fulfill the purpose of the study, we conducted a qualitative case study at an insurance company, where we interviewed people in leading roles who have the opportunity to influence the development of work within the organization. To analyze empirical data, (Vogelsang et al., 2019) framework for barriers is applied. This study also contributes knowledge about whether the barrier framework intended for the manufacturing industry applies to the service sector.

Digital Transformation Today you can see that the phenomenon of digital transformation includes several different development opportunities within an organization, for example, it can be about specific programs that are developed, processes, or the organization's business model. Researchers have explained the phenomenon in different ways. (Vial, 2021) defines digital transformation as follows: “a process that aims to improve an entity by triggering significant changes to its properties through combinations of information, computing, communication, and connectivity technologies”. According to this definition, digital transformation is a conscious attempt to create change on a device using a combination of information, computerization, communication and connected technology. (Berghaus and Back, 2016) in turn describe the phenomenon: “Digital transformation encompasses both process digitization with a focus on efficiency and digital innovation with a focus on enhancing existing physical products with digital capabilities”. They believe that it is about focusing on already physically existing products with the help of digital capacity. Digital transformation enables companies to integrate and engage with customers in a new way: “Digital transformation encompasses the digitization of sales and communication channels, which provide novel ways to interact and engage with customers, and the digitization of a firm's offerings (products and services), which replace or augment physical offerings. Digital transformation also describes the triggering of tactical or strategic business moves by datadriven insights and the launch of digital business models that allow new ways to capture value.” Digital transformation is seen here as primarily driven by digital product, service and business model innovation, which involves more direct and qualitative changes to what an organization does and how it does it”. Digital transformation as a phenomenon is driven by product, service and business

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model innovations, which include direct and qualitative changes in what a company does and also how they do it. All four definitions deal with some type of change in a company using digital means. (Berghaus and Back, 2016 and Vial, 2021) describe digital transformation as a less extensive change work where the entire business is not affected. (Vial, 2021) argues that digital transformation does not take place through increased digitization, but rather that strategy is the strongest driving force. According to (Berman, 2012), companies should focus on two main activities to succeed in a digital transformation of the business, namely to transform the value proposition to customers and to increase customer interaction and customer collaborations with the help of digital technology. For companies, digital transformation is often about creating value through the development or development of new products or services. In part, this development is further driven by society's rapid digital development, as ever-higher demands are placed on companies to develop in order to cope with the competition. At the same time, increased investments in transforming the business are not always the right way to go. Companies often talk about how great the opportunities are linked to digital transformation, but in reality, there is no guarantee that the company will profit from the investments in digital transformation (Andriole, 2017). If the business works as intended, attempts at digital transformation can instead damage the business's business model or simply be rejected as an expensive and fruitless project. Digital transformation is a challenge for companies, says (Soleymanian et al. 2019). By starting to work with new, smart technology, a company may need to review and adapt its existing business model. But it is also important to consider other important factors such as corporate culture, as a non-permissive culture of change can hamper development.

Internet of Things In recent years, there has been an explosive increase in products that can be connected to the Internet and transmit data between consumer electronics, applications and even manufacturing companies. Heating systems can today be connected to a mobile application that makes it possible to keep track of the heat in the summer cottage at a distance. IoT can help producer companies to see the diagnostics of their products out at customers in order to, for example, be able to schedule service on the product (Athavale et al., 2020).

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A contributing factor to this technology making great progress is the possibilities for the wireless connections that exist today (Athavale et al. 2022). In recent years, technical equipment such as computers and sensors have also become smaller and more energy-efficient (Saarikko et al. 2017). This development is usually popularly explained by Moore's law, which means that computer power doubles every 18 months (Brynjolfsson & McAfee, 2015). Wireless networks make it easier for the infrastructure in the home, in the past you were dependent on a fixed connection with cable, something that limited opportunities for digital development. IoT has made it possible for all people to connect their consumer electronics and even vehicles to the grid, which creates new business opportunities for companies. (Saarikko et al., 2017) believe that new types of core competencies will be needed to enable this development within companies and not least in the insurance industry. They mention three important competencies for organizations that want to work with IoT. An important competence is to be able to develop desirable products that are equipped with sensors that can convert real values into data. The next competence that they mention is the maintenance of the link between the product and the product developer, for example, John Deere's agricultural tractors send data regularly back to the factory to contribute to an increased understanding of how their products are used to the development department (Marr, 2016). The third competence that (Saarikko et al., 2017) consider it important is to process and analyze data in order to further develop the product, in order to create a service for a product and thus create added value.

Artificial Intelligence The modern history of artificial intelligence can be traced to when Isaac Asimov in 1942 published the short story run around which came to inspire many researchers in robotics and computer science, including the researcher, Marvin Minsky. Minsky later co-coined the term artificial intelligence with colleague John McCarthy (Shakti et al., 2021). Early AI was successful because it worked from a top-down structure with if-then propositions. In this way, the program always has an action plan based on the input received. The best-known example of this is IBM's Deep Blue chess computer, which worked from such a scheme combined with statistics to win over Gary Kasparov, then the reigning world champion in chess. Despite all this success

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for machines, none of these systems possesses real AI. The system depends on input in a specific form. AI is a system that has the ability to interpret and learn from external data and use that knowledge to meet challenges and achieve goals in a flexible way. Today, many individuals have access to different variants of AI in the home in the form of smart home assistants such as Google Home, Siri and Bixby on our smart mobile phones. Different types of analytical AI are also used in organizations, for example, to detect fraud in the financial world. (Davenport T., & Kalakota R., 2019) believe that AI will play a major role in how companies choose to integrate with external stakeholders such as customers. The big question is how it will affect these companies and what decisions will be made by AI and people. A common misconception is that AI always aims at a physical, selfdriving and social robot that will replace human work in a human way. In reality, there are several different types of AI and smart systems, most of which are completely desktop and computer-based. Social robots in the form of chat robots are already available today on some companies' websites, other companies use AI algorithms to calculate large amounts of variables and develop the systems using previous cases. So far, there are not too many cases of physical robots among us (Duffy, 2003; Dignum V. 2019; Mittelstadt B, 2019; W. H. O. Report, 2021).

Activities in Change - The Insurance Industry In this section of previous research, we will focus on how the insurance industry has changed and will continue to change with emerging digital technologies that enable new ways of working. The basic idea of insurance is that a policyholder pays a premium based on a calculated risk to guard against a possible accident. Once an accident occurs, the policyholder first pays a deductible and then receives a payment from the insurance company. Common types of insurance are property insurance which covers tangible property and personal insurance which covers cases of illness or life insurance. Traditionally, the risk has been calculated by a so-called underwriter who compiles data from a variety of parameters to determine how great the risk is for a certain outcome and to be able to determine the premium. As the insurance industry is digitized to a greater extent, tasks change, for example, claims adjusters previously worked analogously with the help of binders. When a customer reported damage, the claims adjuster had to search

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for information in a physical archive system in order to be able to compensate the customer. Today, claims settlers work with the help of computerized systems that facilitate the handling of customer matters and the information is gathered in one place. Despite new opportunities due to smart technologies such as Big Data and IoT, (Marr, 2016) believes that this industry has less developed services than the other finance-related sectors. In the US, the insurance industry is characterized by low customer satisfaction, something that technology can help change. As more players enter the market with new digital services, parts of the customer base are also taken over from traditional insurance companies (Stoeckli, 2018). Smart services like Policybazaar and Bankbazaar allow users to take out insurance smoothly on the internet where the premium is calculated by an autonomous algorithm. At the same time, there is today an increase in producer responsibility in society, primarily linked to newly produced homes and vehicles that also take up shares of the customer base in the insurance industry. In a rapidly changing world, we can then ask ourselves the question: Will there be room for classic insurance services in the future? Among other industries that have also experienced major changes as a result of technological development, we find the taxi industry with the advent of Ola or Uber. Ola or Uber fundamentally changed the taxi industry by allowing drivers to take rides ordered via their App, a concept so successful that Ola makes 7,50,000 rides every day. With the help of cheaper and more efficient sensors, we can today collect large amounts of data on how a driver behaves in traffic: It can be about various parameters such as average speed, fast braking and speed increases or even speed in curves that can be compiled to create a profile of how risk-averse a driver is. The modern insurance company can use this data as a basis for deciding what premium should be put on motor insurance. This phenomenon is called Usage Based Insurance and is already on the market. This is a difference compared to the traditional insurance model where the driver profile is created based on, among other things, age, gender, driving history and choice of vehicle. In a traditional insurance model, there is also no possibility for the policyholder to influence his premium himself. By offering motor insurance based on UBI, the policyholder's premium can be based on the actual driving behavior and the individual is given the opportunity to influence pricing by adapting their own driving (Soleymanian et al., 2019). Another future scenario is that the insurance industry will move more from a reactive role that acts after an injury has occurred to a proactive and preventive role, for example, a simple sensor can detect the moisture content

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in a basement and warn of potential moisture damage before it happens. Sensors are used today to a certain extent both in vehicles and in housing, which generates valuable data for insurance companies that want to work proactively (Ng & Wakenshaw, 2017). This data can also be used directly in real-time, for example, today's modern vehicles can detect distances to vehicles in front and take measures to maintain a safe distance (Balasubramanian et al., 2018). Smart systems based on algorithms or AI will create opportunities for insurance companies to automate certain processes, such as the risk assessment that underwriters do today. Smart systems will be able to collect and analyze data by reading and understanding various risk variables. These algorithmic systems are extremely efficient and can compile a decision in an instant (Diakopoulos, 2016). (Balasubramanian et al., 2018) believe that the role of the underwriter will change dramatically in the future. The need for the role will be substantially reduced and those who remain will depend on technology to an increasing degree. This, in turn, gives rise to new services that challenge the basic idea of the insurance companies' business concept, that everyone should be protected by comprehensive insurance. For example, most people today have home insurance, vehicle insurance and health insurance. New services such as micro-insurance include insurance that is only intended to cover a limited period and only protects against certain types of accidents. Micro-insurance has mainly occurred in developing countries but has recently found its place in the western world as well. Automated underwriter processes allow policyholders to micro-insure their borrowed car, or Airbnb apartment for themselves, for example, during a two-day festival visit. As different types of sharing economies become increasingly popular, the need for this type of insurance services may also increase. At the same time, new technologies such as IoT create the opportunity for insurance companies to work more proactively than they have done before. Sensors in the home can be connected to the internet to detect moisture damage or electrical faults before they cause further damage.

Framework – Barriers In this study, we will use a framework to be able to identify the barriers that an insurance company faces in order to be able to transform its operations. During the work, we have found research that describes what the insurance

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industry may look like in the future, but we do not find any studies that explain how it got there. (Kurti and Haftor, 2015) have created a barrier model based on different articles that all deal with different challenges. These challenges focus on obstacles and enabling factors linked to business development and business model change. A similar approach would have been feasible to use as a framework in our study as well, however, we believe that other variants are better suited for the purpose of the study. Another alternative that we investigated was to search for relevant literature that sheds light on individual barriers and that these are compiled in a self-constructed model that is designed according to the course of the study, depending on the data obtained. Although this approach appealed to us, we believe that there is strength in using a framework that has already been tested in some contexts. (Vogelsang et al., 2019) have studied the manufacturing industry and designed a barrier framework based on their own study. This framework contains five different categories, all of which are relevant to investigate for our study. As many companies in society struggle to drive their digital development forward, an understanding of the barriers the company has becomes important in order to be able to transform the company in a successful way.

Competence Barriers This category aims to shed light on the barriers that can be found linked to different types of competence requirements. It can be about employees' individual competence and organizational competence. Lack of relevant training for staff can be an obstacle as relevant knowledge may be lacking in order to successfully implement the desired digital transformation (Vogelsang et al., 2019). This step is dependent on several different competencies at different levels within the company, and certain innovative knowledge is required to capture ideas that in the long run can lead to a digital transformation. Then skills will be needed on a management plan to structure the investment and ensure that relevant resources are added to the project. Finally, skills will be needed to program and design the new system that will be added to the organization. Many of these skills can be obtained from various educations, but another important factor is the experience of the employees, both individually and collectively. Today, there are different requirements for workplaces than before and that tasks and communication have been moved to digital platforms. This creates an organizational need for a new pedagogical digital competence. By looking at the trend presented

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above during previous research with further technical advances, this type of competency will only become increasingly important (Balasubramanian et al., 2018). Thus, the supply of skills with the right workforce will be an important barrier to overcome for digital transformation. (Vogelsang et al. 2019) point out that the opportunity for personal development and the chance to make a career is a driving factor in employees choosing to stay within the organization. We believe that this is important to consider as a lack of skills supply can create barriers, if, companies have to regularly train newly recruited staff due to competent staff leaving for other companies. In addition to this, a digital transformation will create new professional categories which in turn have different competence requirements, thus the demand for new skills will increase at the same rate as the development takes place. Table 1 also includes Human Resources (HR) as a key concept and a factor for the supply of skills. Table 1. Framework for barriers (Vogelsang et al, 2019) Barriers Competence Technical Individual Organizational and Cultural Environment

Key concept Education, experience, workforce, careers, job growth and HR Mutual dependencies, security, compatibility, security and interfaces Acceptance, adaptability, transparency, fear and unemployment Organization, strategy, resistance to change, inertia, culture, mistakes, risk and investment Regulations and external societal factors

Technical Barriers Barriers can also arise linked to the existing technology. Within companies, there are usually several different existing systems that are used for different purposes in daily operations. In the insurance industry, there are various systems for dealing with various claims such as traffic, home or personal insurance. In addition to this, there may be customer and personnel management systems, but also a website and mobile application. Together, these parts form a network of systems that work together to enable the business to deliver value. These systems must often be able to communicate with each other and the same applies when implementing new systems. Compatibility risks creating a barrier unless the new system is designed to work with existing systems, which is not always easy. Older systems designed before the great

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digitalization wave may lack the common standard required for devices to be able to interact (Vogelsang et al., 2019). Other important requirements that are placed on the system and that can constitute barriers are the requirements which is provided at security, both at the organizational level and at the personal level. Insurance companies manage large amounts of information about private individuals and their property. Therefore, it is important that customers' security can be guaranteed, not least from intrusion on the internet. The extensive changes that a digital transformation entails require a holistic perspective over all parts of the business; therefore, it becomes important to carefully examine all subsystems that make up a company's structure. Smart technologies such as AI and IoT can easily be perceived as a major threshold to start working with, despite the overwhelming challenge, many believe that this is where development is headed (Balasubramanian et al., 2018; Soleymanian et al., 2019). Technical barriers and competence barriers are partly interdependent, as competence is important for developing new technology. Therefore, under this category, we will examine what can prevent companies from succeeding in digital transformation and allow competence requirements to aim at examining whether lack of competence creates barriers related to the development of the new technology. Within the insurance company, systems are often used by different categories of work, such as underwriters, claims adjusters and managers. These people have different requirements for the system and therefore the system needs to be adapted to meet everyone's needs. Websites will also need to be adapted so that it will be user-friendly for both customers and staff. If the customer does not understand how the website works, it can, for example, contribute to complex tasks not taking place automatically but being imposed on employees in the form of support calls.

Individual Barriers Change can also be opposed on an individual level, various factors such as the employees' attitude to different technologies or general acceptance of change can affect the outcome of a digital transformation. There is no guarantee that a system will be successful just because it is an improvement over the system that was replaced, ultimately the users working with the system will determine the degree of usability. As each individual has a unique relationship and expectation of the system, it will also lead to difficulties in implementing systems used by several individuals and sometimes across several companies.

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This is also affected by the individual's acceptance of change and/or new systems, but also the individual's adaptability. In addition to the willingness to change, individual technical competence is also required to carry out tasks on new, sometimes more advanced platforms. With digitalization, certain processes change from analog to digital, and as development progresses, it may also mean that other skills will be required to perform new and old tasks. This may constitute a barrier as employees may oppose a change if they consider that their own competence is insufficient and thus cannot cope with the transition to the new way of working (Carleton N. 2016). In addition, a feeling of lack of competence can create other barriers to further technical development. Fear among employees that smart systems and automated processes will replace their professional roles may create resistance to a digital transformation (Vogelsang et al., 2019). This trend of earlier human tasks being automated is also clearly visible in society. According to McKinsey & Company’s report by 2030, as many as 800 million workers worldwide could be replaced at work by robots, even if new jobs will arise with the development of society (Balasubramanian, 2018). An important factor in counteracting that company's own employees oppose new technology is that the management is careful with transparency, by being clear about how technology should help the company and what role the worker should have within the company even after parts of the business have been automated. Another barrier related to transparency is the one that exists toward customers. As more and more processes are automated and performed by systems rather than people, customers also risk losing human contact with the company. Processes that customers previously carried out with employees can now be carried out completely without human interaction by a self-learning algorithm. This creates a completely new need for transparency within the company towards the customer, questions regarding which variables are valued by the system and how the customer's data is processed become important for creating trust (Kumar et al., 2021). This type of barrier can appear after the transformation of the company has been completed and the new product is to be launched to the customer. Threads, TAM and dark side are concepts that we have excluded from the framework, as they do not contribute to the study's purpose of examining barriers in the insurance industry.

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Organizational and Cultural Barriers Barriers can also arise based on the organization's structure and culture. This category can theoretically include most of the barriers that can arise in digital transformation or change work. One of the biggest factors that determine a company's ability to develop is the company's strategy. A company's digital strategy is its action plan for how the company should develop and how technology should support the company in its daily operations, especially how IT can be used to capture and create value (Bharadwaj et al., 2013). A solid strategy can help the company to reach new levels while a poorly formulated and unrealistic strategy can hold the company back and thus form a barrier. According to (Bharadwaj et al., 2013), strategy is the major driving factor for development rather than technology. Furthermore, the company's structure can be a contributing factor that can create or counter barriers. Smaller companies can often let information flow freely between different parts of the company, so new ideas about technology and how it can be used can be shared and tested effectively. In larger companies, there is often a hierarchical structure both within the local office but also between the office and any parent/subsidiary. This type of structure can make it difficult for ideas to spread from the lower hierarchical levels in the company and form other barriers if different companies within the group have different needs than their sibling companies (Selander & Jarvenpaa, 2016). The company's culture also plays a role in the type of barriers that may arise, especially the prevailing innovation culture. A permissive culture may be more open to testing new technologies or systems, even if it is a risk-taking one. If a company avoids starting projects for fear of failure, it also means that they risk missing out on successful projects (Karimi &Walter 2015). The culture that characterizes the staff can also affect the opportunity for development. If the business today works desirably, it can be difficult to get the whole company involved in a change process. There is often an attitude among employees that the way of working that exists today works, after which no change is necessary (Vogelsang et al., 2019). This type of resistance to change can create barriers to further development.

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Environmental Barriers Environmental barriers can include which rules the industry must comply with and societal factors that affect it. (Vogelsang et al., 2019) mention that organizations should agree on what standards must be in place to be able to exchange information. It can mean how the staff communicates with each other but also how to communicate with customers in an equal way. There may be regulations that the staff must adapt to, rules that exist within the company on how a task is to be solved and laws such asPersonal Data Protection Bill, 2018. This is a law that came into force in 2019 and was enacted to, among other things, set stricter requirements for the handling of personal data and to protect individuals on the internet. The law applies to all industries and organizations that in some way save and handle sensitive information about their customers or employees. It, therefore, places higher demands on the company's processes and routines for the handling to be carried out in a safe manner. External requirements for processes and routines for companies can contribute to changes in the workplace. This may, for example, mean that the company must review its work roles and supplement them with competent personnel who are knowledgeable in handling data. If it is an organization that has not worked in such a way before, it can help that the work can go slower than you are used to. As society is constantly changing, it is required that the insurance industry is constantly flexible and tries to adapt its operations to society's infrastructure. Many individuals today have a smart home, which creates new opportunities for how home insurance can be designed, which places demands on the insurance provider to satisfy customers' wishes. Another environmental factor that can hinder the opportunities for this development is the degree of technical development on the part of customers. If customers lack modern homes and vehicles, it in itself creates a barrier for insurance companies to work with UBI and preventive IoT solutions. External societal factors are important to consider in order to remain competitive as a company and organization. One of the most famous examples of our time of a company that does not have time for the technological development of society is the one about Kodak with the introduction of the digital camera. At the same time as competitors are developing the next generation of digital cameras, other market shares were taken up by the growing smart mobile phone market, which eliminated the need for Kodak's product (Lucas & Goh, 2009).

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Method As the study is intended to investigate which barriers may complicate a digital transformation of an insurance company, it is appropriate to carry out a case study with semi-structured interviews. The advantage of semi-structured interviews for empirical collection is that it allows us to locate and examine the perceptions that our respondents have at their disposal (De Winter, J.C.F., Dodou, D, 2017). Furthermore, we believe that semi-structured interviews are the obvious choice for us because they allow us to adapt the basic questions from interview to interview, as we form a deeper understanding and the organization as a whole. By using a proven method, we give the research study credibility and a structured approach.

Case Study - Policybazaar and Bankbazaar The insurance industry is facing a change as new players have entered the market with new smart technology such as AI and IoT. These players are taking an increasing share of the customer base and it is uncertain how it will affect existing insurance companies in the future. Based on the purpose of the study, we contacted a nationwide insurance-providing company to be able to answer our question. The insurance company sells, among other things, non-life, personal, home, animal and traffic insurance. They have been on the market for many years and have great expertise in the field. Although they are established in the market, they lack as smart solutions as companies such as Policybazzar and Bankbazaar. The insurance company will therefore be interesting for us to study to find out what barriers exist so that they can take the next step in their digital transformation. The insurance company today offers the opportunity to take out some insurance on the website, but the majority of customers choose to call in to take out their insurance. The company has offices throughout India and has a common organizational structure, including a subsidiary that is responsible for technical development. The semi-structured interviews were conducted at one of the organization's offices in order to understand the respondents' shared experiences of digital transformation.

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Data Collection Five individuals were selected with different roles and competencies to contribute relevant information for the study. All respondents come from the same company where they hold a leading role within the company; we contacted these people via email to book an appointment for an interview. (Bryman, 2011) likens this to a goal-oriented selection. (Bryman, 2011) believes that telephonic interviews can be advantageous in comparison with direct interviews, as they can take place more efficiently without physical transport distance and booking of premises. So, all the interviews were planned to be conducted telephonically. The respondents are given the opportunity to be in a safe environment and to conduct the interview when it suits them best. Telephonic interviews can also facilitate conversations about sensitive topics, during a physical interview the informant risks feeling monitored. He also mentions that it can be a disadvantage to use a telephonic interview because, among other things, you miss the informant's body language when answering questions. As we intend to investigate which barriers can complicate a digital transformation of an insurance company, we have chosen to use a qualitative approach with semi-structured interviews. (Bryman, 2011) writes that interviewing is the most used method in qualitative research and that the flexibility of this method makes it so attractive. This method is used to create an understanding of the interviewees' own perceptions and views. To a large extent, it is possible for researchers to adapt their guide to the course of the conversation. Follow-up questions can be asked on answers that the respondents mention and that may be of interest to the study. In a qualitative interview, it is also possible to interview the same informant several times if the researchers feel that they want to supplement their study (Uwe Flick (Ed) 2013). We designed an interview guide based on the barrier framework to be able to identify different types of barriers within the insurance company. The structure of the interview guide consists of three main themes with accompanying follow-up questions. The first theme was about the informant and its role, tasks and experience. This is so that we as writers can get a picture of the respondents and that it is an easy way to start an interview. Theme two focused on the workplace, structure, culture and how they work within the company. Theme three focused on strategy, development at the company and how future IT solutions such as IoT and AI can be used in the industry. The interview guide ended with an open-ended question to the informant if there

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was anything he wished to add. This is to create a basic structure to relate to and at the same time leave open for follow-up questions. Prior to the interview, the respondents were not allowed to read the questions in the interview guide but were only informed about the purpose of the study. This was to avoid the respondents discussing the answers with each other before the interview. All five telephonic interviews were conducted individually. Four out of five interviews were recorded using a recording application on the telephonic and computer, to ensure that no data was lost due to technical problems. By recording the material, the authors were allowed to focus on the dynamics of the conversation. For example, how the informant expresses himself, and what tone of voice and pause pattern is used (Bryman, 2011). Because one informant wanted the conversation not to be recorded, notes were taken instead as substitutes. During this interview, one author took on the role of interview leader and the other author focused on taking notes. He writes that the recording should not be turned off until the interview is rounded off and completed. Table 2. Respondents from the Insurance Company Respondents Respondent_1 Respondent_2 Respondent_3 Respondent_4 Respondent_5

Time at the company More than 1 year More than 10 years More than 15 years More than 15 years More than 20 years

Work role

Call duration (min)

Manager Manager Leading position Manager Manager

47:28 36:25 34:36 30:07 39:02

This is because the informant can be more relaxed, open up and provide interesting information, which can be valuable for the study. Both authors participated in all interviews because it gave us the opportunity to capture more insights during the course of the interview and ask more inquisitive follow-up questions. All interviews lasted between 30–50 minutes (see Table 2). The table shows the respondents who have been interviewed, with approximate time at the company, title and how long the telephonic interview lasted. All the respondents are deidentified.

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Research Ethics Before the interviews took place, all respondents were informed that the study is based on the four requirements of the research ethics principles, the information requirement, the confidentiality requirement and the utilization requirement. This information was shared with each of the respondents via email explaining the purpose of the study and their role in the study. Participation in the study is voluntary and can cancel your participation at any time. If the respondents choose to terminate their participation in the study, all material will be removed and will not be reported in the results. Respondents will also not be asked against their will to continue the study under any circumstances. The material will be transcribed and faked and in this way, the respondents' anonymity can be maintained in the result. No information will be used for purposes other than the study in question.

Data Analysis After the interviews were completed, the recorded material was transcribed and then printed in physical form. Transcribing an oral interview involves a transformation, that is, changing one form to another. The fact that spoken and written language have different rhetorical forms is often forgotten when a research interview is transcribed. (Kvale and Brinkmann, 2009) mention that an interview transcript can be perceived as poor when it is taken out of context. The printouts create credibility and that can strengthen further analysis. The advantages of transcription are that it gives researchers the opportunity to go back and see what the respondents said. It also creates the opportunity to reproduce quotes in text form, which prevents researchers from letting their own values or preconceived notions color the result (Bryman, 2011). The data material was printed in paper form so that we could read, take notes and color code together. Based on the barrier framework, each category was given a color. The coding began with both authors going through an interview, sentence by sentence, as an initial test attempt (see Table 1). This is to create a consensus on how the framework should be applied to the material. After the initial test attempt, we continued to go through the complete material together in an equivalent way. The cases where we reasoned in different ways arose in fruitful discussions that resulted in a fair assessment of the real estate. According to (Kvale and Brinkmann, 2009), categorization and coding are the most common form of data analysis.

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During the data analysis, expressions that we listed as key concepts were identified (see Table 1). In order for an occurrence to be classified as legitimate, it was required that both authors agreed, if divided opinions arose, this was resolved through discussion. The coding was a process that took time, but it also contributed to the authors getting to know the material and it then simplified further analysis. The coding also facilitated the handling and created clarity of the data material. Based on the data material, a total of 160 text segments were coded, which were divided into five different categories based on the (Vogelsang et al., 2019) barrier framework (see Table 3).

Method Discussion (Bryman, 2011) mentions that conducting a telephonic interview can be a disadvantage because, among other things, you miss the informant's body language when answering questions. The unstructured nature of the qualitative method combined with the lack of the ability to read body language can further affect our subjectivity later in the analysis, where human behaviors such as irony can be misunderstood for opinions in a transcribed text. However, in our case, we feel that telephonic interviews have worked well for our study. The respondents we have been in contact with have been very cooperative and wanted to support the study to the best of their ability. An advantage of telephonic interviews is that they are an enormously smooth process. The respondents can be contacted via their mobile phones completely regardless of where they are, which has been crucial for conducting the study. What has worked less well is that we only have the respondents' statements and what we have been able to read on the company's websites; to relate to when we have to create our understanding of the insurance company. We have also experienced that it has been easier for the respondents to interrupt the interview as it takes place over the telephone than if the interview took place face to face (Bryman, 2011). During the majority of all interviews, there were situations when the informant asked us to clarify one or more questions, which may have meant that we influenced the respondents in their answers (Kvale & Brinkmann, 2009). Therefore, it became even more important for us to be critical of the answers given by the respondents to the questions that needed to be specified or given examples. As always with qualitative research where the interview has been chosen as the method, the result should not be taken as either definitive truth or untruth.

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Criticism has also been raised against interviews as a qualitative study as it can be difficult to know when the study is saturated with information (Bryman, 2011). We experience that our study achieved saturation precisely from respondents in leading roles as the results began to be repeated to a high degree already early in the study. As the study progressed, we felt that it would also have been of interest to interview people who do not hold a leading role within the company. This is because it could have given us the perspective from a different angle. In a way, respondents from leading roles contributed to us gaining a high degree of competence, but with such a homogeneous group, we lose perspective from other parts of the company that could also generate useful data. As always, the qualitative method is difficult to measure because it is based on individuals' experiences. Therefore, this study will not be reproducible even if it were to be performed on the same study object again. A new study on the same object but with respondents active in other parts of the company could generate completely different results. Five respondents participated in the study, which we believe was appropriate for the scope and purpose of the study.

Results and Analysis In the following sections, the results and analysis of the study will be presented. Using the (Vogelsang et al., 2019) barrier framework, a number of key concepts have been coded and categorized (see Table 3). Below, quotes will be presented linked to each individual barrier, to shape the type of text segment that results in an occurrence and to identify the types of barriers that can complicate a digital transformation. Results and analysis will later form a basis for discussion.

Competence Barriers This category includes key terms that have the second least occurrence in the material. The indications for barriers we have noticed are mainly linked to project realization, which in turn can be linked to education and experience.

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Table 3. Framework for barriers and number of occurrences (Vogelsang et al., 2019) Barriers

Key concept

Competence

Education, experience, workforce, careers, job growth and HR Mutual dependencies, security, compatibility, security and interfaces Acceptance, adaptability, transparency, fear and unemployment Organization, strategy, resistance to change, inertia, culture, mistakes, risk and investments Regulations and external societal factors

Technical Individual Organizationa l and cultural Environment

Quantity occurrence s 27 28 24 44 37

The insurance industry is currently facing major changes. Many companies find new solutions to streamline complex insurance cases, which creates competition that has not existed before. The respondents mentioned that new players are breaking into the market niche towards smaller market segments where smart automated solutions such as Policybazaar take an increasing share of the customer base. Policybazaar is an algorithm-driven insurance provider that automatically bases insurance premiums on input from policyholders. It is a new way of creating value for the policyholder. The customer goes to the website and enters the required details so that Policybazaar can assess the customer’s profile. It is a flexible solution for the customer and it goes very quickly. These new players in the marketplace demand the traditional insurance companies’ change, which also creates a need for new types of skills to be able to meet customers' wishes. After the interviews were completed, the recorded material was transcribed and then printed in physical form. Transcribing an oral interview involves a transformation, that is, changing one. “We see what Policybazaar is doing at present, even though we have the possibilities, we do not have as smart solutions [...]. If you go in and examine each part, we see that all parts exist, but we just cannot get it together to a simple and smart solution “ - Respondent_3.

Even though opportunities exist within the organization, it seems that the competence to be able to develop an equivalent solution does not exist. It can

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be difficult to find solutions that can fit into the traditional approach, which can also be a barrier to further change. At present, the respondents point out that there need to be individuals who possess the knowledge to drive this type of change with smart solutions. Man has long been the most important resource in the insurance industry. More and more people are now changing from people performing the work with a system as support to the system performing the tasks autonomously while people perform the more social tasks. Respondents find it easy to see the negative aspects of digital transformation with AI solutions. The idea that AI should take over human contact feels difficult and that is such a large part of the work today. Respondent_1 put it this way: “Even though there are great AI robots that look you in the eye too, [...] when the serious accidents happen and you really need understanding, someone who understands and shows empathy. I think AI is quite far from doing that…” Respondent_1. That an AI robot should take over human contact within a traditional insurance company feels very far away today. The fact that the respondents choose to elevate human contact as a barrier to further development with the help of AI indicates that the relevant underlying knowledge about technologies is deficient. This may be due to the fact that the employees are not organizationally provided with relevant training on smart technology in the insurance company and what opportunities this may entail. Lack of competence can have consequences in the form of other barriers such as acceptance and resistance to change. The focus should therefore be on automating simpler routine matters to allow staff to focus on human contact where needed.

Technical Barriers Under the category of technology, we find the key concepts of mutual dependence, security, compatibility, security and interface. The most common instances in this category were interfaces and compatibility. The instances encoded in the key concept of interface referred to the cases where the respondents pointed out that customers did not understand how cases could be resolved on the company's digital platforms. As many services are automated and today performed by the customer, we believe that an interface that is not intuitive enough can form a barrier for work shifts towards a more automated process for customers to have the full opportunity to carry out routine matters digitally without the need for support from employees.

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The insurance company places high demands on the technology being designed with usability in focus. “Today, more and more of our motor insurance policies are taken out directly online and it has not been possible for many years to do so. For that reason, it is not the case that fewer customers call in, on the contrary, more people call in today. [...] My feeling is that customers want to talk about insurance, you want to know what the insurance covers and what to fill in. I think you want the human contact for them.” - Respondent_2

Data Analysis The quote above describes how an increasingly automated process has rather led to customers calling more often than they did before. This is a clear indicator that the design of the interface is deficient as relevant information is lacking for the customer to feel informed enough to take out insurance entirely on their own. However, we cannot rule out that customers also value human contact with the company highly, but from this point, we assume that the opportunity to carry out routine matters independently should exist as an alternative. Another common barrier was identified linked to compatibility. Remarkable for this category is that several of the respondents refer to the same system when they highlight problems with compatibility. “We sit and regulate damage in an old system fora long time. We have a long journey to catch up with and this is usually the case in this industry and in such large organizations. We have a baggage and a history, so it's not just about changing systems. [...] It is not possible to connect anything, it is very cumbersome. Of course, it can affect our ability to develop. Then you feel that you have to make some movements first before you can look at the next one.” - Respondent_4

Lack of compatibility can create barriers, as new systems may need to communicate with existing systems to extract, store or manage data. When implementing new systems, it may therefore be necessary to review or even replace other existing systems, which in turn makes the change even more extensive. This existence of systems that are not compatible with other systems today constitutes a clear barrier to transforming the business.

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Individual Barriers Within individual barriers, we saw the least number of occurrences within the framework's categories. This may be because we have only interviewed leading people within the company and that these people have a great deal of knowledge individually, but it may also be because the respondents choose not to express their individual barriers. It could also be the case that we authors did not ask the right follow-up questions to capture relevant experiences. The key concepts we could read from the material were acceptance, adaptability, transparency and fear. The respondents did not experience any obstacles to their work role changing if they started working with algorithmic solutions. Today, they work more with a leading role towards employees and customers and if you started working with AI solutions, it would contribute to them instead of working more with coaching customers and employees. Changing work roles mean that new demands will be placed on employees to be prone to change. Respondent_4 expresses that it will be a surprise what it will look like in the future. “I cannot say that this or that profession will be replaced, I must admit that it may come as a surprise to us.” - Respondent_4.

A surprise can either be positive in the sense that it leads to the better or it will be that the company is negatively affected and customer contact is damaged. It is difficult to say whether digital transformation would lead to one or the other, but rather it is about how the staff behaves and accepts the change that takes place with digital transformation. The quote indicates that the respondents are not characterized by fear of change, but it is a matter for the organization. Micro-insurance is a relatively new concept that occurs, partly abroad but also in India. During the interview, there was shared knowledge about this phenomenon. - Respondent_2 points out that this may be due to the development taking place via other associate companies, i.e., that the responsibility for new technology does not lie at the individual level, but rather that the competence of the associate company is relied on. “No, not to my knowledge, in such cases, there should be a strategist at our associate company who is responsible for, but not what I know locally.” Respondent_2

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This in turn can constitute a barrier within the organization where a lack of transparency between associate companies and local companies prevents the spread of ideas from taking place. According to Joy's law, the smartest individuals work elsewhere and therefore they would be in the interest of the development responsible associate company to openly discuss possible development opportunities with the local companies. If leading people are negative about new technology, it can hamper development within the organization and innovations risk being lost. A more open innovation process with a high degree of transparency increases the opportunity for all parts of the organization to participate in development work and each individual becomes a potential source for the next innovation.

Organizational and Cultural Barriers Organizational and cultural barriers are the most common of all the barriers we identified in the study. The vast majority of all occurrences here can be linked to the organization's structure but also its strategy for technological development. The organization's structure with a large number of local companies with an associate company that is responsible for development issues can be seen as both the organization's greatest strength and weakness. Strength because it allows for the capture of innovations from all different local companies, weakness because innovations risk being lost if a local company is not supported by the other local companies. This creates a barrier for the individual local companies to transform their operations digitally on their own initiative, as all companies must relate to the associate company. “There are great opportunities to work with new technologies, then we have a company form that is, unfortunately, a bit complicated [...] our special ownership makes it difficult to reach out with new technologies.” Respondent_3

In addition to the complicated ownership relationships, there is also a risk that another barrier will arise as a result of the prevailing structure. The respondents stated that they did not know of a clear strategy for how the development work should proceed within the company. There are prompts that describe that the staff should capture what the customers' wishes are and then pass this on to the development company.

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As the responsibility for development work is shifted to an associate company, it can also contribute to the formation of a culture in local companies that are reluctant to take development initiatives. The strategic process today is a very slow process. Some respondents mentioned that once a proposal for change has been sent, it must be approved and taken further in a long process before the idea is realized. Below, Respondent_1 describes that you need to address this and that there may be disadvantages to the structure. “There is a very high awareness that this is something we need to act on, but we are a bit laid back in that we trust that this is handled by the associate company.” - Respondent_1

Although there is an understanding that the insurance industry is developing at a faster pace than ever before, this structure creates a situation where the responsibility lies outside the local company. This can create a barrier for potential innovations to be realized when employees choose not to express their ideas in the belief that the associate company will solve it. According to the respondents, ideas about potential development can come from both employees and customers and therefore it could be to the organization's advantage that part of the development work is structured to take place at the local level. The structure that prevails today creates a development system that is characterized by inertia. The road from idea to the finished system is both long and complicated, which is characteristic of large organizations where thorough controls determine which investments are reasonable to implement. Necessary pain in the innovation work constitutes another barrier to overcome in order to implement a digital transformation.

Environmental Barriers This category was one of the most common of the five. Regulations and external societal factors were the most frequent. All the respondents mentioned that they must always abide by rules. It can be an internal set of rules, such as how tasks are to be handled, and how to communicate with each other and with the customer, but also rules such as the Personal Data Protection Bill, 2019 were mentioned. Since the company is governed by a set of rules on many levels, the respondents could experience that the work could go slowly and that they had to control more today than

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they had done before. Respondent_1 tells of an example when a set of rules put a stop to development. “Unfortunately, the legislation on Personal Data Protection Bill, 2019 came at the same time as we sent an application to the Data Inspectorate for UBI solutions, which resulted in us not being granted a permit.” Respondent_1

The company had the motive to develop its premium setting on motor insurance, which would open up a new way of working and counteract injuries. As a result of the new law coming, this was slowed down and as far as we know, no new application has been submitted. This may be because you first had to familiarize yourself with the new regulations to understand how to work with them in order for customers to feel safe. It also contributed to the fact that you may have had to train your staff on how to use this and therefore you may not have had time to focus on your development of motor insurance. The insurance industry must constantly see what is happening in society. It can be new technology that customers use, it can be how vehicles are manufactured and smart households. These external societal factors contribute to the insurance industry having to actively work to find comprehensive solutions that suit the customer. Respondent_5 says that the new generation can make completely different demands on the industry in the future. “The market will place new demands on services to become more accessible. The new generation is more internet friendly and wants to solve things on their own digitally. “- Respondent_5

The new generation's way of life can therefore affect what the insurance industry will look like in the future. The respondents mention that there is a need to find solutions that can facilitate complex insurance matters and that it should be easy for the customer to report without them feeling monitored.

Discussion In this chapter, we will present and discuss the barriers we have observed in this study. We will also go deeper into how these barriers can prevent a digital transformation. Furthermore, we will evaluate the barrier framework and provide proposals for further research.

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Digital Transformation of the Insurance Industry During the course of the study, we have seen several indications that the insurance industry will change largely in the coming years (Balasubramanian et al., 2018; Soleymanian et al., 2019). New players enter the market with new services at the same time as existing players transform their operations and test new technologies. IoT gives insurance companies the ability to protect customers from accidents with proactive measures, something that fundamentally changes the way we look at insurance. There is no doubt that technology will play a major role in the insurance industry in the future. The big question we have encountered during the study is how human contact with the customer will be affected by a way of working characterized by a higher degree of automation. We do not believe that AI robots will take over the human role in the insurance company, but will rather focus on calculating premium settings and assessing risk. AI already has a given role in the production industry but has so far not made such an impact in the service sector. According to (Athavale et al., 2021), AI will have a major role in the insurance industry in the near future. Smart systems will both perform risk assessments, and premium settings and calculate claims settlement more efficiently than humans do today. This will allow humans to focus on the human to a greater degree than they do today. Automated systems will create opportunities to further develop existing services and thus create added value (Saarikko et al., 2017).

Identified Barriers in the Insurance Company In this chapter, we will discuss what barriers can complicate a digital transformation of an insurance company. Barriers to development work occur in all industries and in a variety of forms. The fact that barriers exist does not mean that change is impracticable, rather the presence of barriers will affect and sometimes hinder potential development. Within the insurance company, a great responsibility is placed on the individuals within the company, as the responsibility for new technology is placed on so-called ambassadors. The individual is then responsible for how the new technology is used and for the training of his colleagues. This requires that there is individual competence and that there is time outside the individual's regular work tasks. The competence barriers which we have identified based on the framework are mainly linked to training and

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experience. We feel that the company has short comings in training programs when it comes to AI´s areas of application. The respondents then expressed concern about whether AI could handle human contact, which indicates that there is uncertainty about what AI is expected to achieve in the insurance industry. Traditional insurance companies have experience with the organization's classic approach and previous services. Therefore, it becomes difficult to create new services that are different from the organization's family services they offer today. Therefore, we could also argue that there is a need for new skills that can develop new services without limiting traditional approaches (V. Maheshwari, et al. 2022). During technical barriers, we have identified the interface as a clear barrier for the customer to resolve matters independently on the internet. The insurance company today offers the opportunity to take out insurance matters on the website, but they have experienced that customers choose to call in instead. This may be due to the website interface being experienced as difficult and the information being deficient. Instead of carrying out the matter yourself as a customer, it is considered easier to call and ask for help. The usefulness of implementing a digital solution if the customer still chooses to call in can be questioned, as this creates more jobs for both the customer and for employees at the insurance company. However, we do not know whether the lack of an interface is due to the competence of the interface designer, the organization's extensive regulations or whether the organizational structure is what hinders this development. Although we cannot define where the problem with the interface is based, we can see that it currently constitutes a barrier to customers' ability to take out insurance independently. As we have previously pointed out, there are elements in the insurance company that is characterized by compatibility problems. Today, there are requirements that systems must be able to communicate with each other, something that also applies during the implementation of new systems. As the insurance company moves towards more advanced solutions and increased use of digital services, these outdated systems will need to be replaced or adapted. As a result of the industry's development towards connected units and vehicles, the insurance company's system will need to be able to communicate with other systems in a way that it cannot do today, which constitutes a major barrier to a digital transformation (I. Aggarwal, et al. 2022). Current individual barriers to acceptance, adaptability and transparency have been most prominent. In order for an insurance company to be able to carry out a digital transformation, it is required that the staff feels involved and accepts the change. In a change effort, the company may have to

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implement new systems, which may contribute to a change in the hierarchy within the company. Employees' ability to adapt becomes important in order to cope with the new tasks and employees who were knowledgeable in the old system risk not possessing the same status in the new way of working. During the study, we have also identified several cases that indicate a fear that technical development will result in the loss of work tasks. This fear of being replaced by new smart technology such as AI can further contribute to creating resistance to change on an organizational level. Does the company have one instead culture that talks about the possibilities with these aids and how it can facilitate the handling of insurance matters, the staff would instead feel safe and more open to this type of development. The strategy was the most common key concept linked to the category of organizational and cultural barriers. The respondents stated several times that they considered that there was no clear strategy at the company about how the development work would take place. According to (Bharadwaj et al., 2013) strategy is the most important factor for the success of the digital transformation. One organization that has a common strategy and a clear goal is easier to transform the business digitally because all employees understand where the development is going, something that can also increase the chance of capturing ideas from all parts of the business. About the company strategy does not include the entire business, there is a risk of forming barriers as the employees may feel that they have no impact on how the development takes place. Employees may also experience that there is certain inertia within the business that contributes to increased passivity. The insurance company's organizational structure with associate companies was classified as its greatest strength and weakness. While the current structure can create barriers for local offices that want to try new technology, the structure can create opportunities to take part in skills from different parts of the business, while local companies can act as pilots for new services. The organization's rigid structure means that changes are both difficult to implement and take a long time. The high degree of control minimizes the risk of the company’s continuing with unprofitable investments and at the same time contributes to the insurance company developing incrementally rather than radically. The fact that changes do not take place can often be because investing in new systems is costly, both in time and in the economic sense. In addition to this, there is a risk of changing something that has worked for many years. The employees feel secure in the systems and

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know how they work, while new systems can contribute to a certain amount of uncertainty. Although we observed several environmental barriers in the form of regulations and external societal factors, we do not believe that these are the barriers that a company should give the highest priority to counteracting before a digital transformation. These external factors will always affect whether a transformation succeeds or not, but as these factors are beyond the power of the company, they will never be able to circumvent or ignore them completely. This category is something companies need to relate to and adapt their business to. Many of the barriers that we have identified during the course of the study occur in more than one category, for example, the individual's acceptance of new technology can contribute to resistance to change at the organizational level. Although a barrier can be designed in different categories, a holistic perspective will be required in the work to counteract the prevailing barriers. Then this study is based on two insurance companies, it is unlikely that the results are generalizable to the entire insurance industry. However, this study can be a first step in understanding how the insurance industry views digital transformation and the barriers that exist in operations for further development with smart technology.

Barrier Framework for the Service Sector During the course of the study, we have chosen to work with Vogelsangs et al. (2019) barrier framework that is designed based on the production industry. One contribution of this study is to investigate whether this model is also applicable to the service sector. We believe that the barrier framework has had a good spread through the five categories and has worked well in an industry that we had limited prior knowledge of, which has contributed to a greater understanding of which barriers may occur. However, parts of the framework have proved completely irrelevant to our study, which may be due to the fact that we have only interviewed respondents who have been highly placed within the insurance company and that we have only focused on two insurance companies. If the study had included more offices in India, perhaps other parts of the framework would have been more influential. When (Vogelsang et al., 2019) used a number of key concepts, we chose to do the same, because we wanted to see if the barriers that occur in the production industry are the same as those that occur in the

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insurance industry. To develop the barrier framework, we present the following customized barrier framework (see Table 4). Table 4. Proposals for barrier frameworks for the service sector Barriers Competence Technical Individual Organizationa l Cultural Environment

Key concept Education, experience, workforce, careers, job growth, HR and individual competence Mutual dependencies, security, compatibility, security and interfaces Acceptance, adaptability, transparency, fear and unemployment Organization, strategy, inertia, mistakes, risk and investments Management culture, workplace culture, resistance to change and norms Regulations and external societal factors

Under the category of competence barriers, it has been difficult to identify barriers from only an organizational perspective. We interpret that this category only focuses on whether the insurance company possesses all the skills needed to conduct the line business. Knowledge should look the same throughout the business and everyone should have access to the same educational material. We see that within the insurance company, a great responsibility is placed on the individuals' competence and that these have an important role in the organization's development. We would like to add individual competence as a keyword in this category to be able to further specify barriers in future studies. As ambassadors play an important role in the implementation of new technology, their competence becomes important to consider. Organizational and cultural barriers is the category that has had the most occurrences during the study and we feel that it is a category that could be divided into two to be able to distinguish the barriers better. Our proposal is therefore to divide these and distribute the key concepts where we think they fit. Organizational barriers would focus on organization, strategy, inertia, mistakes, risk and investment. In this way, we can let the key concepts that occur within the organization refer to the prevailing structure of the company, such as the current way of working, routines and how to look at investments. In order to gain a better understanding of the barriers that are linked to the organization. Cultural barriers use key concepts like management culture, workplace culture, resistance to change and norms. By freeing culture into its own

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category, we can create a better understanding of the company's prevailing culture. Culture can look different within the company and therefore we chose to add key concepts such as management culture and work culture. It may be, for example, that the management has a permissive culture of change but that the staff is reluctant to change. This can contribute to a barrier in a changing work and therefore it will be interesting to examine both. Norms is also a keyword that we have lacked in the framework because it provides perspective on who may come up with proposals for change and implement change. If the study had been carried out at several offices within the organization, it would also be of interest to investigate how norms regarding business development differ between different offices. This is a relevant barrier to study as it can inhibit potential development.

Conclusion During the course of the study, we have seen how new technology can create new solutions and services with the help of IoT and AI. These services have the opportunity to change the way we look at insurance. The ability to work proactively rather than reactively may mean a paradigm shift for the insurance industry. Companies such as Policybazaar (35) and Bank Bazaar (10) as entered the industry with technical development with the help of smart systems and algorithms, these services may require that traditional insurance companies should review the development of their own business. The purpose of this study was to create an understanding of the barriers that an insurance company may face in digital transformation and we have concluded that organizational and cultural-, technical and environmental barriers are the most common. Furthermore, we have evaluated (Vogelsang et al., 2019) barrier framework and we believe that it can be used to our advantage to examine barriers in the service sector in general and the insurance industry specifically. We have also presented improvement proposals that can contribute to a deeper understanding of the barriers identified by the framework. As the scope of the study only extends to one insurance company, we cannot ensure that the result is generalizable to the remaining parts of the insurance industry, therefore more studies should be carried out in the area. As we only have respondents with a leading role, it would be interesting to see how employees view digital transformation and what barriers they experience.

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7d8bdd420a/Integration-of-blockchain-and-IoT-for-data-storage-andmanagement.pdf https://www.researchgate.net/profile/Vijay-Athavale/publication/354313006_Security_ Issues_in_Cloud_Computing_A_holistic_view/links/6130a94f2b40ec7d8bdcf450/Se curity-Issues-in-Cloud-Computing-A-holistic-view.pdf https://www.sciencedirect.com/science/article/abs/pii/S0740624X17304781 https://www.sciencedirect.com/science/article/abs/pii/S0921889002003743 https://www.sciencedirect.com/science/article/abs/pii/S0963868709000043 https://www.sciencedirect.com/science/article/abs/pii/S0963868717302196 https://www.sciencedirect.com/science/article/pii/S000768131730068X https://www.sciencedirect.com/science/article/pii/S0887618516300469 https://www.tandfonline.com/doi/full/10.1080/07421222.2015.1029380 https://www.taylorfrancis.com/chapters/edit/10.1201/9781003282914-5/problemsimplementation-blockchain-technology-decentralized-iot-authentication-vijay-anantathavale-ankit-bansal https://www.the-digital-insurer.com/wp-content/uploads/securepdfs/2021/08/1844McKinsey-insurance-2030-the-impact-of-ai-on-the-future-of-insurance-f.pdf https://www.who.int/publications/i/item/9789240029200 Karimi, Jahangir, and Zhiping Walter. “The role of dynamic capabilities in responding to digital disruption: A factor-based study of the newspaper industry.” Journal of Management Information Systems 32, no. 1 (2015): 39-81. https://doi.org/10.1080/ 07421222.2015.1029380 Kumar, Subodh, Vijay Anant Athavale, and Divye Kartikey. “Security Issues in Cloud Computing: A holistic view.” International Journal of Internet of Things and Web Services 6 (2021): 18-29. Kurti, Erdelina, and Darek Haftor. “Barriers and enablers of digital business model transformation.” In The European Conference on Information Systems Management, p. 262. Academic Conferences International Limited, 2015. Kvale, Steinar, and Svend Brinkmann. Interviews: Learning the craft of qualitative research interviewing. Sage, 2009. Lucas Jr, H. C., & Goh, J. M. (2009). Disruptive technology: How Kodak missed the digital photography revolution. The Journal of Strategic Information Systems, 18(1), 46-55. Marr, Bernard. Big data in practice: how 45 successful companies used big data analytics to deliver extraordinary results. John Wiley & Sons, 2016. https://doi.org/10. 1016/j.jsis.2009.01.002 Mittelstadt, Brent. “Principles alone cannot guarantee ethical AI.” Nature machine intelligence 1, no. 11 (2019): 501-507. https://doi.org/10.1038/s42256-019-0114-4 Saarikko, Ted, Ulrika H. Westergren, and Tomas Blomquist. “The Internet of Things: Are you ready for what’s coming?.” Business Horizons 60, no. 5 (2017): 667-676. https://doi.org/10.1016/j.bushor.2017.05.010 Selander, Lisen, and Sirkka L. Jarvenpaa. “Digital action repertoires and transforming a social movement organization.” MIS quarterly 40, no. 2 (2016): 331-352. Soleymanian, Miremad, Charles B. Weinberg, and Ting Zhu. “Sensor data and behavioral tracking: Does usage-based auto insurance benefit drivers?.” Marketing Science 38, no. 1 (2019): 21-43. https://doi.org/10.1287/mksc.2018.1126

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Stoeckli, Emanuel, Christian Dremel, and Falk Uebernickel. “Exploring characteristics and transformational capabilities of InsurTech innovations to understand insurance value creation in a digital world.” Electronic markets 28, no. 3 (2018): 287-305. Sun, Tara Qian, and Rony Medaglia. “Mapping the challenges of Artificial Intelligence in the public sector: Evidence from public healthcare.” Government Information Quarterly 36, no. 2 (2019): 368-383. https://doi.org/10.1016/j.giq.2018.09.008 Maheshwari V., S. Sahana, S. Das, I. Das, and A. Ghosh, “Factors Influencing Security Issues in Cloud Computing,” in Advanced Communication and Intelligent Systems: First International Conference, ICACIS 2022, Virtual Event, October 20-21, 2022, Revised Selected Papers, 2023, pp. 348–358. Van Dijk, Jan. The digital divide. John Wiley & Sons, 2020. Vial, Gregory. “Understanding digital transformation: A review and a research agenda.” Managing Digital Transformation (2021): 13-66. https://doi.org/10.1016/j.jsis. 2019.01.003 Vogelsang, Kristin, Kirsten Liere-Netheler, Sven Packmohr, and Uwe Hoppe. “Barriers to digital transformation in manufacturing: development of a research agenda.” (2019). Zimmer, Michael, Zeno Franco, Praveen Madiraju, C. Echeveste, K. Heindel, and J. Ogle. “Public Opinion Research on Artificial Intelligence in Public Health Responses: Results of Focus Groups with Four Communities.” AAAS. Washington, DC: AAAS Center for Public Engagement with Science and Technology: 2021-09

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Chapter 11

An IoT-Based Intelligent Healthcare System for Diabetes Prediction Navneet Verma* and Sumit Kumar Rana† Department of Computer Science and Engineering, Panipat Institute of Engineering and Technology, Samalkha, Panipat, Haryana, India

Abstract Diabetes is a global epidemic and one of the main causes of health crises. With the recent advancement in the Healthcare industry, Internet of Things (IoT) can be used to collect real-time patient data and for Diabetes forecasting Machine Learning (ML) techniques can make this possible. To collect real-time patient data, we have suggested a model in this chapter that makes use of the Adaptive Data Rate (ADR) algorithm for the Long Range (LoRa) IoT protocol under Low Power Wide Area Networks (LPWAN) technique which makes this model intelligent. Additionally, machine learning prediction uses classification techniques to identify different diabetes severity levels in data that has been gathered using the LoRa protocol. The Contiki Cooja simulator is used to assess the LoRa protocol’s performance. The proposed system utilizes the Python programming language to predict diabetes severity levels by employing a selection of machine learning classifiers, namely Gradient Boosting (GB), Decision Tree (DT), Random Forest (RF), k-nearest neighbors (k-nn), Support Vector Machine (SVM), Gaussian Naive Bayes (GNB), and Logistic Regression (LR).

* †

Corresponding Author’s Email: [email protected]. Corresponding Author’s Email: [email protected].

In: Intelligent Decision Support System for IoT Enabling Technologies Editors: Subrata Sahana, Anil Kumar Sagar, Sanjoy Das et al. ISBN: 979-8-89113-249-8 © 2024 Nova Science Publishers, Inc.

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Keywords: Internet of Things, Machine Learning, Lora, Contiki Cooja, Diabetes

Introduction An intelligent infrastructure called “smart healthcare” leverages cloud and fog computing (Majumder et al., 2019) as well as an IoT system to facilitate information transport. IoT in healthcare can be applied in various ways, including diagnosing and treating the elderly, monitoring and managing chronic conditions, preventing disorders, monitoring hazards, providing effective assistance, enabling smart hospitals, reducing wait times for critical situations, and assisting in drug research (Sinha et al., 2017). The engineering community and device manufacturing industry consider IoT as one of the most exciting research fields. Currently, we can connect two devices to interact with each other, aided by human intervention and the established internet. However, the true potential of IoT lies in connecting multiple “Things” without human assistance, forming a vast network of interconnected computational intelligence. Nevertheless, IoT protocols only facilitate the transfer of well-organized and valuable data. Due to the lack of standardized architecture, it becomes challenging to employ communication methods suitable for different applications. IoT serves as a significant source of big data, which, without proper analytics, remains untapped. Big data, machine learning, deep learning, and artificial intelligence analytics can enhance various IoT applications and offer significant value (Sinha et al., 2017). The emergence of new IoT technologies like Sigfox, NB-IoT, and LoRa (Long Range) under “LPWAN (low power wide area network)” enables effective long-distance transmission (Muhammad et al., 2020). The LoRa-enabled network is governed by LPWAN protocols, wherein LoRa-enabled devices are deployed across the designated area. Figure 1 illustrates how IoT equipment and technologies function in the health management sector. Diabetes, a metabolic condition, affects millions of individuals every year (Verma et al., 2022b). It is associated with a higher risk of critical organ failure and a lower quality of life. Regular monitoring and early identification are crucial for effective diabetes management (Zou et al., 2018). In healthcare research, diabetes is a significant focus, generating a vast amount of data due to its socioeconomic implications. Therefore, the diagnosis and prognosis of diabetes disorders are among the most well-known applications of this technique. Machine learning techniques can be valuable in extracting insights

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from large diabetes-related datasets. With the help of IoT-based sensors, we can gather information and remotely monitor patients. In this study, we exclusively utilized supervised machine learning algorithms to forecast diabetes, specifically decision tree (DT), gradient boosting (GB), support vector machine (SVM), K-nearest neighbor (KNN), Gaussian Naive Bayes (NB), logistic regression, and random forest, on the PIMA dataset (Saminathan et al., 2018). These algorithms were assessed based on their overall effectiveness and precision in predicting whether a patient will develop diabetes or not.

Figure 1. IoT in HealthCare.

IoT Architecture Figure 2 depicts the four levels that make up the IoT structural design (Dejana Ugrenovic, 2015). The following are the duties of the various tiers in an IoT structural design: I.

Perception layer: The device layer, which we may also refer to as the Perception Layer, is analogous to a physical layer. All sensors on this layer, including RFID, infrared sensors, and barcode scanners,

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II.

III.

IV.

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assemble data from the sensor node and send it to the network layer for more processing. Network layer: It is also known by the transmission layer as well. This layer operates securely by forwarding the information to the processing unit which is collected from the perception layer. For secure data communication, the transmission medium can be cable or wireless, and the network layer makes use of 3G, wi-fi, Bluetooth, and Zigbee technologies. As a result, the middleware layer receives the data. Middleware layer: Only when two IoT devices offer the same services can they be connected. It is up to the middleware layer to link it to the database while considering service management. The same middleware layer also does calculations and decision-making. Application layer: With the global management service, this layer is created. Depending on the device, there may be applications for smart agriculture, smart health, smart homes, intelligent transportation, and smart cities, among others.

Figure 2. IoT structural design.

Objective The term “Diabetes Mellitus (DM)” refers to a series of metabolic illnesses brought on by improper insulin release from our bodies. This condition is especially frequent among elderly people. When symptoms of DM appear, it

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can be very challenging for people who live in remote areas to find a doctor to consult. However, this model will be able to assess the severity of their condition without the need for a doctor’s help, and they can be guided remotely toward treatment by their family and nursing staff. The model will assist healthcare professionals in detecting and predicting DM in people who are suspected of having Diabetic Mellitus.

Organization of Chapter In this Chapter, a brief study of Diabetes Mellitus, IoT architecture, literature review, common issues with existing Healthcare systems and suggested design with system performance have been presented. The remaining chapters are structured as follows: section 1, defines Diabetes Mellitus and how IoT and ML are useful in healthcare systems followed by IoT architecture and objective. Section 2, the literature review describes the IoT Protocols and Machine Learning in Healthcare. Common issues with the existing system are discussed in section 3 followed by the suggested design and mechanism in section 4, performance analysis and results in section 5, and the conclusion in section 6 respectively.

Literature Review IoT Protocols in Healthcare LoRa/NB-IoT/Sigfox: Low power wide area networks (LPWAN) features including a range of radio communication, price, and standby time are advantages of the Sigfox, LoRa, and NB-IoT protocols (Hiraguri et al., 2015). While NB-IoT and LoRaWan both employ LTE encryption and AES 128b, respectively, Sigfox has neither authentication nor encryption. In a cuttingedge architecture for smart healthcare (Swaroop et al., 2019), communication is made possible through a LoRa link to fog computing. The monitoring of patients in faraway locations has been reported to be relayed up to several kilometers away; this approach does not place an undue strain on the cloud and is also a great example of power effectiveness. The goal of this study paper (Cho et al., 2016) is to assess the ability of LoRa technology to scale and offer a channel admittance method resolution to decrease collisions. The LPWAN

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category’s Sigfox/LoRa/NB-IoT protocols are without a doubt effective. This paper has concentrated on metrics like the count of packet crashes and network throughput to know the working of the LoRa transmission protocol. Different types of LoRa nodes are explained by this article that may join to the gateway simultaneously and yet follow the protocol’s restrictions. Numerous factors, including the data transmission speed, spreading factor, and duty cycle, have been researched to decrease the incidence of collisions and increase the effectiveness of the transmission channel. The author also offers several recommendations for reducing the number of collisions, including the use of ADR algorithm (Adaptive Data Rate|The Things Network, n.d.) that automatically changes the data speed when a collision is exposed. However, this strategy would increase the LoRa node’s energy consumption. Another option would be to stop using the ACK method, which would result in fewer collisions. In this study, a few further solutions were proposed, including limiting the maximum number of packet retransmissions if a packet is incorrectly received and one-way communication between a LoRa node and a gateway module. Continuous glucose monitoring (CGM) sensors that support Lora can use LoRa gateways to deliver data to the database. Limitations: Since there hasn’t been much NB-IoT marketable consumption in the healthcare industry, its lifespan and routine are uncertain. Internet access in remote areas is a problem that is anticipated to be resolved by 5G communication. The common flaw in the publications mentioned above is that they are just focusing on the communication aspect; they do not have a system in place to identify the sickness at an early stage. Bluetooth Low Energy (BLE): Due to its single-hop transmission capability, BLE is a power-saving and low-latency protocol. For the gateway node, the author of this study (Lavric, 2019), evaluated several microcontrollers before settling on the MCU node situated on its specs. Data gathered against the patient’s body via Bluetooth Low Energy was saved on an android app (BLE). The same software sent the gathered data to the cloud for later use, and BLE further offers a Cyclic Redundancy Check (CRC) for data encryption. The author of this study suggested a low-cost, dependable method. In a different experiment (Khanam et al., 2021), the author used the RPi3 as a gateway node to provide patient data to the mobile app using BLE, Wi-Fi, and GSM in case one method’s data is lost, the data against the remaining process may still be received. The idea has also been floated that web application software may serve as the user terminal for cloud databases. In this study (Pradhan et al., 2020), we found that the BLE protocol performs superior when the latency and energy characteristics are considered.

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Limitations: The aforementioned study is deficient in early sickness prediction methods, and all patient-sensed data is gathered entirely on the cloud exclusive of any data scrutiny, which might amplify the cloud’s massive data issue in coming years. BLE also backs single-hop transmission, although this type of transmission is less trustworthy than end-to-end transmission since it confides in the operation of intervening nodes. ZigBee: On the Internet of Things, the master-slave architecture’s contribution (Verma et al., 2021) is crucial. ZigBee, a superior protocol for short-range transmission, is used by the slave node to transmit various data impressions. The LoRaWAN protocol is then used with the sub-master node to transmit the information, it got against the slave node to the master node, which is frequently located considerably further away owing to cellular network restrictions. This document (Kharel et al., 2019) describes the correctness of the ZigBee protocol during communication, including precise position identification by the ZigBee terminal when it goes across the range of a ZigBee router. Approximately 99% of matches occurred when patientsensed data and manual reading were matched. The researcher in this work has presented an inexpensive IoT-based biomedical kit based on another communication protocol that is utilized (Verma et al., 2022a). Additionally, it may be applied to several people at once. The Raspberry Pi 3 serves as the system’s fundamental computing platform, and for data sensing and transmission, it is linked with a wireless transceiver and a microcontroller. In addition, the author claims that nRF is superior to ZigBee and BLE concerning cost, power utilization, backup power mode, and data transfer rate. Limitations: The speed of data by ZigBee is 250 kbit/s, and as can be seen, both ZigBee and nRF are intended for short-range communication. Bluetooth (IEEE 802.15.1): This research (Vineetha et al., 2020) demonstrates how the Internet of Things (IoT) significantly reduces heart attack-related mortality. Here, the two processes of the entire patient monitoring system—data collection and data transfer—are separated. Before being transmitted onto four dissimilar modules, the patient’s “Electrocardiogram (ECG), pulse, heart rate, blood fat, blood pressure, location of the patient, and glucose level in blood” are all examined during the data-collection phase. These modules include transmission on patient demand, synchronous continuous transmission for all information, continuous transmission in designated timeslots, and event-triggered transmission. Finally, a prototype of the implementation was created, in which the monitoring job was carried out using a Java application on an Android

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smartphone using sensed information that was supplied over Bluetooth and information that was stored server-side for doctor monitoring. Limitations: Additionally, Bluetooth is inflexible for long-distance transmission (Hiraguri et al., 2015). While BLE and Bluetooth both operate on a similar frequency and have a similar distance coverage area, Bluetooth (IEEE 802.15.1) has a transmission rate of up to 0.99- 2.99 Mbit/s compared to BLE’s 1 Mbit/s. MQTT: Throughput and end-to-end latency characteristics were used in this study (Mekki et al., 2019) to compare HTTP to the Message Queue Telemetry Transport (MQTT). MQTT protocol performs better than HTTP, according to OMNET++ network simulation, which was utilized to develop the simulation environment. Again, MQTT has been employed for health monitoring in this related article (Santamaria et al., 2018). The MQTT protocol is used by a wearable device to first obtain the data which is associated with health from the sensors, personalize it, and then transfer it to the cloud. The continuous input from the sensor is also accepted and filtered by a fuzzy classifier(Catarinucci et al., 2015) so that only a predetermined amount of data is sent to the cloud. Limitations: MQTT needs to utilize more power, and one more problem is that it employs text-to-topic names, which escalates the size of its messages. CoAP: Limited nodes inside constrained networks, which have little bandwidth and low availability, employ the CoAP, a web transfer protocol. It is similar to the HTTP protocol. In this study, the author (Singla et al., 2019) provides a hybrid system example. Due to the use of both the “6LoWPan and CoAP” techniques in the construction of this hybrid system, its efficiency has been significantly boosted. Similar to this (Sethi et al., 2017), the author implemented CoAP as an application layer protocol using a 6LoWPAN network and Contiki OS. In this case, the output is really good, and it was determined that Constrained Application Protocol is a trustworthy and efficient application technique. Limitations: CoAP has weak compatibility with other devices and is not a suitable congestion control solution.

Machine Learning in Healthcare In this study, the PIMA Indian dataset was used in conjunction with the SVM model, which underwent feature scaling, decision-making, enhancement, and assertion (Khanam et al., 2021). The performance indicators of this prototype

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have been graded as follows: precision 83.20 percent, recall 86.90 percent, and specificity 79 percent. A tenfold stratified cross-validation process was also employed. Although smartwatches and smartphones can be used by patients to transmit data, no specific backend processing procedure or protocol has been addressed in this work. By employing this proposed strategy, healthcare professionals may be able to make early decisions based on the threat predicted by the computer. According to the prediction made in this research (Saeedi et al., 2019), there will be 642 million diabetic patients worldwide by the year 2040. For this study, data from Luzhou city in China was collected, which included physical samples from approximately 70,000 individuals. These samples encompassed both healthy individuals and diabetic patients. Machine learning techniques such as neural networks, random forest, and decision trees with five-fold cross-validation were utilized to predict diabetes, while PCA and mRMR were applied to reduce dimensionality. The findings demonstrate that the random forest algorithm provided the highest accuracy forecast, at 0.8085. Additionally, this work emphasizes the importance of the classifier approach in identifying appropriate characteristics. Everywhere throughout the globe, especially in affluent countries, diabetes mellitus (DM) is a fatal illness. It has been a growing concern in underdeveloped countries like Nigeria for quite some time now. In this paper, supervised machine learning techniques including gradient boosting, Naive Bayes, support vector machines, logistic regression, k-nearest neighbor, and support vector machines were employed to create a model using the diagnostic diabetes dataset obtained from a local hospital in Kano (Hasan et al., 2018). Based on the receiver operating characteristic curve, the random forest and gradient boosting predictive learning-based models demonstrated the highest accuracy, with RFPL-based model accuracy reaching 86.28% and 88.76%, respectively. The developed model will aid doctors and other healthcare professionals in identifying and predicting type 2 diabetes. Table 1 (Abrardo et al., 2019), (Verma et al., 2022b) also provides a comparison between short-range and long-range communication techniques.

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0.90-1.25Mbps “AES-128b encryption, two keys used for identification and identity protection, secure pairing before key exchange”

245-255kbps “3GPP security including both user and device identity, entity authentication, confidentiality and data integrity” “LTE guard band”

0.20-5.55kpbs “Precise key distribution for data encryption, recognized only by the node and base station by using an individual key” 868MHz (Eu) 915MHz (US) 433MHz (AS)

“A private key signature, Encryption and scrambling Technique”

868MHz (Eu) 915MHz (US)

Endorsement and security

Operational Bandwidth

2.4GHz

150m

95-101bps

15km

7.2km

Good

BLE

9.5km

Good

NB-IoT

Average

LoRawan

Poor

Suitability for healthcare Exposure area Broadcast speed

SigFox

Table 1. Io Techniques for transmission

2.4GHz

“Network key shared throughout the network, optional link key to protecting communications at the application layer, and AES128b”

245-252kbps

30m

Average

ZigBee

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Common Issues with Existing System i)

ii)

iii)

iv)

v)

vi)

Integration: In essence, IoT deals with a wide range of networks, and the absence of a standardized design can sometimes make it challenging to select the appropriate communication protocol. When connecting multiple devices to the same application, it is not uncommon for some devices to disconnect and throw exceptions while others succeed. Data Accuracy &excess: ending data to a server or cloud without first evaluating its quality highlights the ineffective utilization of the Internet of Things, turning a small dataset into a large one unnecessarily. Correct Feature Selection: Many studies utilize the PIMA Indian Diabetes dataset, which includes information on age, pregnancies, blood pressure, skin thickness, insulin, glucose, and skin thickness. Data security &privacy: Data integrity, availability, and confidentiality are commonly used terms to describe data security. In other words, they encompass all the procedures and methods in place to ensure that data is not accessed or utilized by unauthorized parties or individuals. Cost: As we add more and more devices to the network, the cost of the infrastructure increases. Therefore, these devices need to either support multiple applications or be cost-effective. Incompleteness: Merely monitoring patients or uploading sensed data to the cloud without analyzing the dataset or determining the risk factors of the ailment is an insufficient approach.

Suggested Design and Mechanism Data sensing via IoT-enabled sensors, followed by the transmission of the sensed data, data analysis, and forecasting, are all aspects covered in this field. The proposed design is depicted in Figure 3, where the gateway node receives data from the patient’s body through LoRa (ADR)-equipped sensors and stores it in a database server. After data preparation, the normalization and machine learning classification procedures, which will be further explained, are performed. A sample of the PIMA diabetes dataset is also presented in Table 2 below.

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Figure 3. LoRa-based Diabetes Prediction Model.

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Pregnancies 2 3 7 2 3

Count

0 1 2 3 4

146 88 180 90 138

Glucose 75 76 70 70 65

Blood Pressure 33 30 30 25 37

Skin Thickness 0 0 0 96 178

Insulin 33.5 26.5 23.2 28.3 43.3

BMI

Table 2. PIMA Diabetes Dataset Sample Diabetes Pedigree Function 0.626 0.350 0.671 0.166 2.287 52 34 34 22 34

Age

0 1 0 1 0

Outcome

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Methodology Process flow of the complete method: a) Implant LoRa-capable sensors on the patient. b) Transmit the detected data to gateway nodes based on the ADR algorithm. c) The gateway provides the database system with the collected data. d) Preprocess the data by removing zero or non-existent values of specific characteristics. e) Generate a correlation and heat-map to determine the relative importance of these features. f) Split the entire dataset into training and testing datasets using a ratio of 0.75:0.25. g) Select a machine learning approach comparable to logistic regression, ensemble-random forest, k-nearest neighbor, decision tree, and support vector machine algorithms. h) Build a classification model using the training dataset and the chosen machine learning technique. i) Evaluate the trained model using the test dataset and the same machine learning strategy. j) Conduct an experimental comparison of the performance of each classifier and analyze the results. k) Determine the best-performing algorithm based on various analytical measures.

Performance Analysis and Results The following are the specifications of LoRa (ADR) Protocol: 1. The ADR frequency range, which can support thousands of end devices, is from 800MHz to 2100MHz. 2. It is specifically designed for scenarios with stable radio channel conditions. 3. The majority of ADR protocols currently in use are intended for homogeneous end devices. 4. End device data rate adaptation has not been defined yet.

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Figure 4. Packets received by 20 nodes in 30mins.

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As we utilize the LoRa (ADR) IoT protocol for patient monitoring and data collection, the performance is evaluated using the Contiki Cooja simulator, as depicted in Figure 4. To improve the diagnosis of the severity level of the disorder, we employ the PIMA Indian diabetes dataset. The standard parameters for LoRa (ADR) are presented in Table 3 below. Table 3. LoRa (ADR) Parameters Parameters Transmission Range (instant switching) Frequency Band Duty Cycle

Values 125kHz, 250kHz, 500kHz 24GHz-54GHz 1%

Results of Machine Learning Classifier Only properly framed data can be used with machine learning algorithms. Machine learning provides effective approaches for extracting knowledge from diagnostic medical datasets. In the case of diabetes prediction, we can utilize various classification and ensemble algorithms with the diabetes dataset. The primary objective of employing machine learning techniques is to understand the functionality of these classification methods, evaluate their accuracy, and identify the key factors essential for diagnosing diabetes. Machine learning problems can be categorized into three classes: supervised, unsupervised, and reinforcement learning. In this study, we utilize supervised learning, which involves training the model with a labeled dataset and making precise predictions. The cross-validation procedure (Kakria et al., 2015) continuously adjusts the weights of the model until it fits perfectly when provided with input data. The performance of each classification algorithm in this study was evaluated using a few standard parameters (Khan et al., 2016). The random state was set to 0, and the number of neighbors was set to 5 in the k-neighbors technique. To assess the effectiveness of each classification algorithm, specific statistical measures such as recall, accuracy, precision, and F1-score were taken into consideration, as shown in Table 3. These classifiers calculate the true positive (TP), true negative (TN), false positive (FP), and false negative (FN) values. When the patient has diabetes and the prediction findings are also positive, it is referred to as a true positive (TP). If both the patient’s and the forecasting findings are true negatives (TN), the patient does

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not have diabetes. False positive (FP) refers to a situation where the patient is free of diabetes, yet the forecasting findings are positive. False negatives (FN) occur when a patient has diabetes, but the prediction findings are negative (B. Sati, et al., 2022).

Figure 5. Features distribution of PIMA dataset.

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As shown in the preceding step-by-step execution, the data will be preprocessed before implementing the classification method. Therefore, we have displayed the feature distribution of the PIMA dataset in Figure 5. ML classifiers are implemented using the Python language. Table 4 below presents the complete classifier results, including the confusion matrix (M. E. Afor, et al., 2022). Table 4. Classifiers Results

i) Random Forest 0 1 Accuracy Macro avg Weighted avg Confusion Matrix ii) Gradient Boosting 0 1 Accuracy Macro avg Weighted avg Confusion Matrix

Precision

Recall

F1-Score

Support

0.78 0.75

0.91 0.52

0.77 0.77

0.72 0.78 [109 11] [30 33]

0.84 0.62 0.78 0.73 0.76

120 063 183 183 183

0.78 0.64

0.83 0.56

0.71 0.73

0.69 0.74 [100 20] [28 35]

0.81 0.59 0.74 0.70 0.73

120 063 183 183 183

0.82 0.59 0.75 0.70 0.74

120 063 183 183 183

0.81 0.64 0.75 0.72

120 063 183 183

iii) Logistic Regression 0 0.78 1 0.67 Accuracy Macro avg 0.72 Weighted avg 0.74 Confusion Matrix iv) Decision Tree 0 1 Accuracy Macro avg

0.87 0.52 0.70 0.75 [104 16] [30 33]

0.81 0.63

0.80 0.65

0.72

0.73

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Weighted avg Confusion Matrix v) k-nn 0 1 Accuracy Macro avg Weighted avg Confusion Matrix vi) GaussianNB 0 1 Accuracy Macro avg Weighted avg Confusion Matrix vii) SVM 0 1 Accuracy Macro avg Weighted avg Confusion Matrix

Precision 0.75

Recall 0.75 [96 24] [22 41]

F1-Score 0.75

Support 183

0.75 0.58

0.82 0.49

0.67 0.70

0.65 0.70 [98 22] [32 31]

0.78 0.53 0.70 0.66 0.70

120 063 183 183 183

0.81 0.70

0.86 0.62

0.75 0.77

0.74 0.78 [103 17] [24 39]

0.83 0.66 0.78 0.74 0.77

120 063 183 183 183

0.77 0.73

0.91 0.48

0.75 0.76

0.69 0.76 [109 11] [33 30]

0.83 0.58 0.76 0.70 0.74

120 063 183 183 183

293

Conclusion This chapter has covered a healthcare model that analyzes the health status of a patient and assesses the severity of their illness. In the previous investigation, monitoring and diagnosis were conducted separately. In the current scenario, the LoRa protocol based on the ADR algorithm from the IoT network is being utilized to measure performance on the Contiki Cooja simulator. The severity of the condition is then evaluated using a machine learning classifier on the patient’s dataset, even in the absence of a doctor.

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Figure 6(a). PIMA Classifier.

Figure 6(b). PIMA Accuracy after k-fold validation.

The GNB process exhibits the highest accuracy, as depicted in Figure 6(a), while the RF method, when utilizing k-fold validation, demonstrates the highest accuracy, as shown in Figure 6(b). This approach has been specifically developed for diabetic patients and aids in identifying the severity level of the

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disorder at an early stage. We can also store the information on the cloud and subsequently preprocess it through machine learning, allowing medical specialists to obtain accurate data for future research on this ailment. Additionally, the LoRa network can be utilized to physically implement the patient monitoring approach.

References Abrardo, A., & Pozzebon, A. (2019). A multi-hop lora linear sensor network for the monitoring of underground environments: The case of the medieval aqueducts in Siena, Italy. Sensors (Switzerland), 19(2). doi: 10.3390/s19020402. Adaptive Data Rate|The Things Network. (n.d.). Retrieved from https://www. thethingsnetwork.org/docs/lorawan/adaptive-data-rate/. Afor M. E. and Sahana S., “The Internet Of Behaviour (IOB) and Its Significant Impact on Digital Marketing,” in 2022 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), 2022, pp. 7–12. Catarinucci, L., De Donno, D., Mainetti, L., Palano, L., Patrono, L., Stefanizzi, M. L., & Tarricone, L. (2015). An IoT-Aware Architecture for Smart Healthcare Systems. IEEE Internet of Things Journal, 2(6), 515–526. doi: 10.1109/JIOT.2015.2417684. Cho, K., Park, G., Cho, W., Seo, J., & Han, K. (2016). Performance analysis of device discovery of Bluetooth Low Energy (BLE) networks. Computer Communications, 81, 72–85. doi: 10.1016/j.comcom.2015.10.008. Dejana Ugrenovic, G. G. (2015). CoAP protocol for web-based monitoring in IoT healthcare application. 23rd Telecommunications Forum TELFOR 2015, Novemner 2, 79–82. Hasan, H. M., & Jawad, S. A. (2018). IoT Protocols for Health Care Systems: A Comparative Study. International Journal of Computer Science and Mobile Computing, 7(11), 38–45. Retrieved from www.ijcsmc.com. Hiraguri, T., Aoyagi, M., Morino, Y., Akimoto, T., Nishimori, K., & Hiraguri, T. (2015). Proposal of ZigBee Systems for the Provision of Location Information and Transmission of Sensor Data in Medical Welfare. E-Health Telecommunication Systems and Networks, 04(03), 45–55. doi: 10.4236/etsn.2015.43005. Kakria, P., Tripathi, N. K., & Kitipawang, P. (2015). A real-time health monitoring system for remote cardiac patients using smartphone and wearable sensors. International Journal of Telemedicine and Applications, 2015. doi: 10.1155/2015/373474. Khan, M., & Kabir, M. (2016). Comparison Among Short Range Wireless Networks: Bluetooth, Zigbee, & Wi-Fi. January. Khanam, J. J., & Foo, S. Y. (2021). A comparison of machine learning algorithms for diabetes prediction. ICT Express, 7(4), 432–439. doi: 10.1016/j.icte.2021.02.004. Kharel, J., Reda, H. T., & Shin, S. Y. (2019). Fog Computing-Based Smart Health Monitoring System Deploying LoRa Wireless Communication. IETE Technical

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Review (Institution of Electronics and Telecommunication Engineers, India), 36(1), 69–82. doi: 10.1080/02564602.2017.1406828. Lavric, A. (2019). LoRa (long-range) high-density sensors for internet of things. Journal of Sensors, 2019. doi: 10.1155/2019/3502987. Majumder, A. J. A., Elsaadany, Y. A., Young, R., & Ucci, D. R. (2019). An Energy Efficient Wearable Smart IoT System to Predict Cardiac Arrest. Advances in HumanComputer Interaction, 2019. doi: 10.1155/2019/1507465. Mekki, K., Bajic, E., Chaxel, F., & Meyer, F. (2019). A comparative study of LPWAN technologies for large-scale IoT deployment. ICT Express, 5(1), 1–7. doi: 10.1016/ j.icte.2017.12.005. Muhammad, L. J., Algehyne, E. A., & Usman, S. S. (2020). Predictive Supervised Machine Learning Models for Diabetes Mellitus. SN Computer Science, 1(5), 1–10. doi: 10.1007/s42979-020-00250-8. Pradhan, N., Rani, G., Dhaka, V. S., & Poonia, R. C. (2020). Diabetes prediction using artificial neural network. Deep Learning Techniques for Biomedical and Health Informatics, 121, 327–339. doi: 10.1016/B978-0-12-819061-6.00014-8. Saeedi, P., Petersohn, I., Salpea, P., Malanda, B., Karuranga, S., Unwin, N., Colagiuri, S., Guariguata, L., Motala, A. A., Ogurtsova, K., Shaw, J. E., Bright, D., & Williams, R. (2019). Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9th edition. Diabetes Research and Clinical Practice, 157, 107843. doi: 10.1016/j. diabres.2019.107843. Saminathan, S., & Geetha, K. (2018). Real-time health care monitoring system using IoT. International Journal of Engineering and Technology(UAE), 7(2), 484–488. doi: 10.14419/ijet.v7i2.24.12141. Santamaria, A. F., De Rango, F., Serianni, A., & Raimondo, P. (2018). A real IoT device deployment for e-Health applications under lightweight communication protocols, activity classifier and edge data filtering. Computer Communications, 128, 60–73. doi: 10.1016/j.comcom.2018.06.010. Sati B., Kumar S., Rana K., Saikia K., Sahana S., and Das S., “An Intelligent Virtual System using Machine Learning,” in 2022 IEEE IAS Global Conference on Emerging Technologies (GlobConET), 2022, pp. 1123–1129. Sethi, P., & Sarangi, S. R. (2017). Internet of Things: Architectures, Protocols, and Applications. Journal of Electrical and Computer Engineering, 2017. doi: 10.1155/2017/9324035. Singla, R., Singla, A., Gupta, Y., & Kalra, S. (2019). Artificial intelligence/machine learning in diabetes care. Indian Journal of Endocrinology and Metabolism, 23(4), 495–497. doi: 10.4103/ijem.IJEM_228_19. Sinha, R. S., Wei, Y., & Hwang, S. H. (2017). A survey on LPWA technology: LoRa and NB-IoT. ICT Express, 3(1), 14–21. doi: 10.1016/j.icte.2017.03.004. Swaroop, K. N., Chandu, K., Gorrepotu, R., & Deb, S. (2019). A health monitoring system for vital signs using IoT. Internet of Things, 5, 116–129. doi: 10.1016/j.iot.2019. 01.004.

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Verma, N., Singh, S., & Prasad, D. (2021). A Review on existing IoT Architecture and Communication Protocols used in Healthcare Monitoring System. Journal of The Institution of Engineers (India): Series B. doi: 10.1007/s40031-021-00632-3. Verma, N., Singh, S., & Prasad, D. (2022a). Analysis for Early Prediction of Diabetes in Healthcare Using Classification Techniques. In Data Science for Effective Healthcare Systems (pp. 149–159). Chapman and Hall/CRC. doi: 10.1201/9781003215981-13. Verma, N., Singh, S., & Prasad, D. (2022b). Machine learning and IoT‐based model for patient monitoring and early prediction of diabetes. Concurrency and Computation: Practice and Experience. doi: 10.1002/cpe.7219. Vineetha, Y., Misra, Y., & Krishna Kishore, K. (2020). A real time IoT based patient health monitoring system using machine learning algorithms. European Journal of Molecular and Clinical Medicine, 7(4), 2912–2925. Zou, Q., Qu, K., Luo, Y., Yin, D., Ju, Y., & Tang, H. (2018). Predicting Diabetes Mellitus With Machine Learning Techniques. Frontiers in Genetics, 9(November), 1–10. doi: 10.3389/fgene.2018.00515.

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Chapter 12

Technological Scrutiny on Energy-Harvested Wireless Sensors for IoMT Healthcare Systems Bhanu Priyanka Valluri* and Nitin Sharma Department of Electronics and Communication Engineering, Chandigarh University, Gharuan, Punjab, India

Abstract Emerging technology provides a solution to transforming our lives in a way no one could think of before. Without hesitation, whether in security, commercial centres, the banking industry, or any industry, technology is proving an innovative and feasible solution for every question. One such domain is medical or health care services; technology is helping to simplify our lives by reducing or limiting the need for clinic/hospital visits. We are utilizing the genuine force of the Internet of Things (IoT) to eliminate the burden on medical / healthcare services outlined by permitting patients to associate with their medical advisors by moving the clinical information in a protected climate. The Internet of Medical Things (IoMT) is an assortment of various clinical appliances as per the applications that can associate with all the data networks of health services systems through different approaches. Using this technology, medical equipment will gather, examine and send data across the web. IoMT is making its place in the world at a fast pace, with 65% of global healthcare organizations already making use of it, and it is likely to increase by 27% by the mid of 2023. There is a high demand for energy harvesting schemes to power up different applications of IoT systems and make emerging technology sustainable. In this paper, we will discuss the *

Corresponding Author’s E-mail: [email protected]

In: Intelligent Decision Support System for IoT Enabling Technologies Editors: Subrata Sahana, Anil Kumar Sagar, Sanjoy Das et al. ISBN: 979-8-89113-249-8 © 2024 Nova Science Publishers, Inc.

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Bhanu Priyanka Valluri and Nitin Sharma introduction, application areas, types of IoMT devices, advantages, Challenges, and energy-efficient methods.

Keywords: Internet of Medical Things, Energy Efficient, Wireless Sensor Network, Healthcare

Introduction The Internet of Medical Things (IoMT) can revolutionize healthcare provision by improving its development, affordability, and dependability. By leveraging machine-to-machine interfaces, IoMT can enable the collection and transmission of real-time data about patients’ health status, which can be used to provide more personalized and effective healthcare services (Vishnu et al., 2020). Moreover, IoMT can increase patient engagement in decision-making by enabling them to monitor their health status and communicate with their healthcare providers more easily. This can improve the quality of care and make healthcare services more accessible and affordable. The increasing demand for IoMT technology is driving its market growth rate. The technology is expected to evolve rapidly in the coming years, leading to the development of new and innovative solutions to address the evolving needs of patients and healthcare providers. IoMT’s innovative approach to healthcare provision has the potential to transform the industry and improve patient outcomes. Providing individualized treatment based on real-time data and augmented equipment and devices can meet the growing demand for more effective and efficient healthcare services (Xiang et al., 2021). This technology is on the path to encouraging personalized care and providing a high living standard. Besides, recent research in developing fields such as significant data realms, cloud services, accessible sensor networks, security, artificial intelligence, machine learning, argument reality, etc. (Al-Turjman et al.,2020) is helping to lead affordable medical devices and allied health networks. This report showcases thorough, detailed analysis and methods and deliberates the future sides of IoMT technology and applications. Many queries were raised regarding understanding the concept of IoT in the medical technology field (MedTech) (Sathyapriya S et al., 2021). This report also attempts to answer a few of them. The healthcare sphere often involves interactions between patients, equipment, and devices, from bandages to surgical instruments, diabetic

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testing kits to monitoring devices, pacemakers to sophisticated scanners, etc. The MedTech industry is leading towards the designing and manufacturing a series of equipment to analyze, diagnose, observe, monitor, etc., to achieve reliable patient outcomes, minimize healthcare expenditures, improve efficiency and innovate ways of engaging and empowering patients. The flexibility in this field is possible due to the advancement in collaborative technologies such as wireless communication, computing techniques, etc. (Magsi H, et al., 2018) It is leading to the improvement of an increasing number of connected medical devices that can create, gather, evaluate and transmit data. In combination with all the stages of data and devices, they helped evolve the IoMT. The Internet of Things (IoT) is a reliable system that enables real-time data acquisition, device connectivity, data transmissions, and analytics to control end-user applications. The IoT ecosystem has a complex architecture with interconnected components to enable different end-user solutions. It provides a dependable environment that relies on physical systems and can adapt to social interference with computerized systems to accommodate datadriven decision-making processes. IoT has expanded to include technologies such as smart cities, intelligent towns, smart homes, and intelligent logistics, augmented through various devices such as sensors, actuators, tags, and more, along with communication network protocols. This expansion has allowed IoT to propose different concurrent solutions by integrating data analysis and sensors embedded in machines (Al-Turjman et al., 2020). Overall, integrating IoT with data analysis and sensors leads to new and innovative solutions in various fields, including healthcare, transportation, and logistics. The ability of IoT to collect and analyze real-time data from various sources can lead to more efficient and effective decision-making processes, improved productivity, and better outcomes. Many attempts are in MedTech to meet the healthcare sector’s requirements. This approach led to designing new regulations using the possibilities such as digitization, data methodologies, and collaborative technologies to develop a value-based healthcare system. This also showcased the change from product-based models to value-based systems driven by software-based services and solutions to produce a new value-based paradigm. IoMT can be encapsulated as a networked infrastructure of healthcare services, software applications, and medical equipment. MedTech’s role and interactions within health services are being rapidly enhanced by IoMT (Irfan M. et al., 2018). Integrating devices currently aims to enhance healthcare outcomes, reduce costs, and enhance efficiency. Therefore, the healthcare

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system was trained for a transformation through IoT into IoMT and its implementations by connecting devices that digitalized and transmitted sensor’s vital data in real-time following the other industry segments, such as energy and power distribution, construction, management, and manufacturing. IoMT tools transform healthcare delivery by leveraging their capabilities to collect, aggregate, analyze, interpret, and transmit data. These tools facilitate the tracking and prevention of chronic illnesses and hold immense potential to revolutionize the future of patient care and clinical practice. However, the underlying mechanisms of this connected ecosystem are complex and multifaceted. IoMT systems employ advanced sensor technology, data processing algorithms, and network protocols to enable real-time data acquisition, device connectivity, and transmission. The resulting data can be harnessed to generate insights and support informed decision-making by healthcare providers.

Figure 1. Components of IoMT.

The expansion of the IoMT ecosystem is driving the development of new technologies that will allow clinicians to monitor and treat patients remotely, addressing the increasing need for medical care, particularly in rural communities with limited access to medical professionals. This evolution will provide significant benefits, regardless of a patient’s environment, by improving access to care through connected medical devices (Rubí, J.N.S et al., 2020). In the MedTech industry, over half a million medical equipment and devices are manufactured, including wearable and implanted devices and

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stationary medical devices. Since most patient interactions with healthcare systems involve medical equipment and devices, IoMT has become known as healthcare IoT due to its sensor-based tools, including standalone devices and wearables, enabling remote patient monitoring. As a result, the ecosystem streamlines clinical workflow and improves patient care. Figure 1 explains the essential components that are involved in it.

Methodology The healthcare industry was delighted with the growth of IoT as it has given impeccable results. With the same inspiration, the study for this paper has started with the influence of IoT in the medical industry (Abdullah et al., 2016). During the search process, IoMT was introduced, and hence the study began by reporting observations of the online surveys conducted by various healthcare organizations, research experts, etc. Many companies have also discussed healthcare IoT’s benefits, trends, challenges, applications, and implementations on multiple platforms. This chapter mainly showcases the overview of IoMT along with its applications.

IoMT Ecosystem The proliferation of connected medical devices that can generate, collect, evaluate, and transmit health data or images and interface with healthcare provider networks to transmit such data to a cloud repository or internal servers drives the demand for IoMT. These devices operate through a complex system of sensors, data analysis algorithms, and network protocols to enable real-time data acquisition, device connectivity, and data transmission (Kotronis et al., 2019). The resulting data can then be processed and analyzed to generate actionable insights for healthcare providers, leading to better decision-making processes and improved patient outcomes. The technical intricacies of IoMT underline its potential to revolutionize healthcare delivery through the integration of advanced technology and data analytics. Figure 2 showcases the main stakeholders in this ecosystem. The healthcare IoT links bo th both physical and digital realms and can observe patient performance in real-time to manage different medical conditions.

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Figure 2. IoMT Designed Ecosystem.

Benefits and Limitations of IoMT The healthcare IoT refers to using internet-enabled medical devices and sensors, including implanted devices, wearables, and smart stethoscopes, to gather and transmit patient data for remote monitoring and healthcare management. These devices can generate vast amounts of data and transmit it over a network for real-time analysis and decision-making. Integrating these devices with advanced analytical tools holds immense potential for enhancing patient care and developing more effective treatment strategies. This paper explores the technical aspects of healthcare IoT and its implications for the future of healthcare delivery. While the healthcare IoT has the potential to revolutionize healthcare delivery and improve patient outcomes, it also presents some challenges and limitations. Some of the potential benefits of healthcare IoT include ●

Remote Patient Monitoring: IoMT enables remote patient monitoring, which allows healthcare providers to monitor patients’ health conditions in real time. This helps in early detection of diseases and improves patient outcomes.

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Personalized Medicine: IoMT facilitates the collection of large amounts of data from various sources, including wearable devices, electronic health records (EHRs), and patient-generated data. This data can be analyzed using machine learning and artificial intelligence algorithms to develop personalized patient treatment plans. Cost-Effective: IoMT reduces the cost of healthcare by reducing the need for hospital visits and readmissions and improving the efficiency of care delivery. Better Disease Management: IoMT can monitor patient’s vital signs, medication adherence, and other health metrics, which can help manage chronic diseases such as diabetes, hypertension, and heart disease.

To transform IoT into IoMT three factors are responsible for that. ● ● ●

The need for high-speed technology such as 5G (wireless technologies). The need for high-end data compression techniques using advanced computing power. The need to minimize complex and huge electronic medical devices.

Along with these factors, many others are also responsible for a smooth approach, such as infrastructure, portability of devices, equipment cost, maintenance, etc. The benefits can be categorized into patients, equipment manufacturers, and healthcare providers.

Patient Advantages ● ● ● ●

Cost reduction Real-time intervention Reducing financial burden Reduces human interaction in surgery

Equipment Manufacturers ● ● ● ●

Uniformity of data availability Standardization of data analysis Communicating information to remote location The capability of using high-end technologies

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Healthcare Providers ● ●

Optimization of resources Minimizing the response time in an emergency

The possibility of getting more excellent benefits by connecting devices or things in every industry is advantageous. The same has been confirmed with IoMT (Singh, R.P., et al., 2020). Not only the benefits mentioned above but also other advantages related to the system’s performance, such as accessibility, improvement in efficiency, faster implementation, low cost, etc.

Figure 3. Impact of IoMT on healthcare.

The IoMT significantly impacts healthcare by providing real-time data, remote monitoring, and personalized treatment plans. This technology enables clinicians to monitor and treat patients remotely, regardless of location, which is especially beneficial in rural areas where access to medical professionals can be limited. The IoMT also gives patients greater control over their health by enabling them to track and monitor their health data using wearable devices and mobile applications. Overall, the IoMT is improving patient outcomes, increasing efficiency, and reducing costs in the healthcare industry, as shown in Figure 3.

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The limitations can be categorized into two factors: technical and market challenges. The healthcare IoT also presents some challenges and limitations, including. ●

●

●

●

●

●

Security and Privacy Concerns: IoMT involves collecting and transmitting sensitive patient data, which can be vulnerable to security breaches and cyber-attacks. Therefore, it is important to implement strong security measures to protect patient data. Data Accuracy: The accuracy and reliability of data collected through IoT devices can be a challenge, especially regarding data collected from consumer-grade devices. This can affect the quality of the research outcomes. Standardization: Some devices need more standardization in IoMT devices, making it difficult to compare data from different devices and platforms. This can lead to challenges in data interpretation and analysis. Infrastructure and Technical Challenges: The deployment and maintenance of IoMT devices require a robust technical infrastructure and skilled personnel, which can be a challenge for healthcare providers, especially in resource-limited settings. Interoperability issues: There currently needs to be universal standards for healthcare IoT devices, making it difficult for different devices to communicate and share data. Regulatory challenges: The healthcare IoT is subject to various regulations and standards, including HIPAA, which can be challenging for healthcare providers to navigate.

Technical Challenges ● ● ● ● ● ● ● ●

Data security threats Lack of communication protocol and standards Errors in data handling Integration of data Lack of medical expertise Diversity Interoperability Performance

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Market Challenges ● ● ● ● ●

Compliance with security policy Mobile hesitation Healthcare faculty overloading data Storing patient information in devices Physical compliance

Although certain limitations have been identified, other challenges must be addressed to further advance research and development in the field of IoMT. These challenges include design issues, privacy and security concerns, and analyzing big data generated by IoMT devices.

Technologies Enduing IoMT About the micro-level architecture of IoT, the IoMT system integrates these three layers to provide a comprehensive solution for remote patient monitoring and healthcare management. The local system and control layer collect and process data, the device and data connectivity layer transmits data to cloudbased platforms, and the application and analytic solution layer provides insights and recommendations based on the data collected. As shown in Figure 4, The architecture of IoMT is a complex system that involves several layers of hardware, software, and communication protocols. At the bottom layer, the system consists of sensors and devices that collect and transmit data. The next layer comprises gateways that receive and preprocess the data before forwarding it to the cloud or on-premise servers. The cloud layer provides data storage, processing, and analysis capabilities. The application layer enables users to interact with the system and provides decision-making capabilities based on the analyzed data. The security layer ensures the confidentiality, integrity, and availability of the data and the system. This architecture facilitates the seamless integration of medical devices, data, and applications to enable real-time monitoring and management of patients.

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Figure 4. The Architecture of IoMT.

Local System and Control Layer Decentralized intelligence is a critical component of the Internet of Things (IoT) that plays a significant role in developing medical devices with intelligent control capabilities and interoperability techniques. This element of IoT aims to create a distributed network of intelligent devices that can operate autonomously and interact with other devices in the network to facilitate seamless data exchange and decision-making. By leveraging advanced algorithms and machine learning techniques, decentralized intelligence enables medical devices to analyze data in real-time and respond to changing conditions, improving patient outcomes and better healthcare devices (Limaye et al.; T., 2017). Generally, smart devices are equipped with things like sensors and actuators to measure parameters, digitize the inputs, process and analyze data to make real-time decisions reliable and also share the information with

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other devices and servers with efficient network interfaces such as diagnostic devices, wearable monitors, encrypting devices, etc. Along with the decentralized intelligence, compatibility and integration of advanced devices are driving factors to get effective solutions for real-time problems. An impactful ecosystem needs to be designed based on IoT architecture to transmit data and track performance. Protocols such as NFC, Bluetooth low (BLE), Zigbee, etc., for communication, wireless cellular, satellite technologies, and on-air programming for analysis are implemented for better performance.

Device Connectivity and Data Layer This layer focuses on data aggregation and implementation. The device collects data from the network devices and stores it in predefined data stores in the form of a database for further analysis. There are no specific technologies for this layer, though patient monitoring data is unique for a particular disease (Abdullah et al., 2016). The most crucial aspect that needs to be concentrated on is security. To process and analyze secure medical data transfer, technologies have also been improved to manage large volumes of data and ensure quality during processing at both the user and system end based on the requirement.

Application and Analytic Solution Layer In the felid of healthcare, many techniques are involved in being efficient results. The data is collets either a central server or remote server from multiple devices as per requirement over a network by considering their key components. The server is designed with in-built algorithms to analyze realtime data for conclusions. The methods will vary with changes in device utility. That is, the method of intelligent device analyses differs from techniques used in remote monitoring and chronic disease management.

Connected Medical Devices The intricate architecture of the IoT ecosystem involves multiple components interacting with each other to provide various solutions for the end-user,

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leading to an integrated and reliable environment. The adoption of IoT technology in healthcare, through IoMT, is paving the way for other innovative approaches, such as individual data-driven treatment and augmented equipment, to meet the increasing demand for physiological requirements. The IoMT is providing solutions by utilizing AI and ML techniques, thereby contributing to the evolution of healthcare services (Joyia, G.J. et al., 2017) shown Figure 5. The number of connected devices is increasing daily, so to make it more friendly even in the medical industry, the adoption of smartphones is also considered. Some of the key technologies that are essential to the IoMT include: ●

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Wireless communication: Wireless communication technologies like Bluetooth, Wi-Fi, and cellular networks connect medical devices to the internet and transmit data to healthcare professionals. Sensors: Sensors gather data from medical devices, including data on a patient’s vital signs, activity levels, and other health metrics. Cloud computing: Cloud computing systems store and process enormous amounts of data from medical devices, allowing healthcare providers to study and monitor patient health status in real time. Artificial intelligence (AI): AI algorithms analyze large amounts of data from medical devices, identify patterns and anomalies, and provide healthcare professionals with actionable insights.

Figure 5. Descriptive technologies involved in the improvement of IoMT.

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Blockchain: Blockchain technology can be used to secure and protect sensitive patient data, ensuring it remains private. Edge computing: Edge computing technology can process data from medical devices in real time, reducing latency and improving responsiveness. Augmented reality (AR) and virtual reality (VR): AR and VR technologies can provide healthcare professionals with real-time visualizations of patient data, allowing for more effective diagnosis and treatment. 5G: 5G cellular networks offer high-speed connectivity and low latency, making them ideal for real-time data transmission from medical devices.

These technologies continually evolve and improve, enabling the IoMT to become more efficient, effective, and accessible. As the IoMT continues to grow, new technologies will likely emerge, further improving the capabilities of medical devices and the quality of patient care. This growth includes the development of organizations, clinicians, physicians, and patients ready to accept IoMT solutions. Researchers have identified three categories of connected medical devices in the IoMT ecosystem: standalone devices, wearables, and implanted devices, all of which facilitate remote monitoring and healthcare management. These devices can collect and transmit patient data, albeit the selection of the specific type of device depends on various factors, including the patient’s medical condition, healthcare providers’ requirements, and available technology.

Stationary Medical Devices To measure the physiological parameters, devices such as mammography devices, CT and MRI scanners, Ultrasound machines, X-Ray, imaging devices, etc., are used physically; this process involves time consumption, physicians’ presence, cost, etc. However, instead of using manual input, highend devices that can transmit images remotely to physicians at any location improve the need for such devices (Joyia, G.J. et al., 2017). Hospitals, clinics, and diagnostics centres can be incorporated with sophisticated equipment to eventually observe and give real-time results. Stationary medical devices are critical to diagnosis and include high-end applications to overlap patient data handling for precise decision-making. Stationary medical devices refer to

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medical devices that are fixed in one location and connected to the internet or a network. These devices can collect, store, and transmit data about a patient’s health, vital signs, and medical conditions, among other things. Some examples of stationary medical devices in IoMT include: ●

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Patient monitors: These tools are used to measure the vital signs of a patient, including blood pressure, heart rate, and oxygen saturation. They can be connected to a hospital’s network or the internet, allowing healthcare professionals to monitor a patient’s health remotely. Imaging equipment: This includes X-ray machines, CT scanners, and MRI machines, which are used to diagnose and treat medical conditions. These devices can be connected to a network to store and transmit images to healthcare professionals. Infusion pumps: These devices are used to deliver medications or fluids to patients in a controlled manner. They can be programmed to deliver specific doses of medication at specific times and monitored remotely through a network. Electrocardiogram (ECG) machines: These machines are used to record the heart’s electrical activity. They can be connected to a network to transmit data to healthcare professionals for analysis. Telemedicine carts: These mobile carts are equipped with cameras, microphones, and monitors and provide remote consultations between healthcare professionals and patients.

Overall, stationary medical devices in IoMT play an important role in improving patient care and outcomes by enabling healthcare professionals to monitor and treat patients more effectively and efficiently.

Benefits of Stationary Medical Devices in IoMT ●

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Improved patient outcomes: Stationary medical devices in IoMT can help healthcare professionals monitor and treat patients more effectively, leading to better health outcomes. Remote monitoring: Stationary medical devices can be integrated with a network for remote patient monitoring of vital signs and health status, which is particularly beneficial for patients residing in distant or underserved locations.

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Increased efficiency: IoMT devices can automate routine tasks and eliminate manual processes, improving the efficiency of healthcare delivery. Data analysis: Stationary medical devices can collect and store large amounts of data, which can be analyzed to identify trends and patterns, helping healthcare professionals make better-informed decisions. Cost-effective: IoMT devices may lead to a decrease in face-to-face medical appointments and hospitalizations, thereby lowering healthcare expenses for patients and providers.

Limitations of Stationary Medical Devices in IoMT ● ●

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Security concerns: IoMT devices can be vulnerable to cybersecurity attacks, potentially compromising patient data and privacy. Reliability: Stationary medical devices must be highly reliable and accurate, as any malfunction or error could seriously affect patient health. Interoperability: Stationary medical devices must be able to communicate and share data with other devices and systems, which can be a challenge due to differences in data formats and protocols. Cost: Stationary medical devices can be expensive to purchase and maintain, limiting their availability in certain healthcare settings. Patient acceptance: Some patients may be reluctant to use IoMT devices due to concerns about privacy or a lack of familiarity with the technology.

Implanted Medical Devices These devices include biosensors that process signals, such as a pacemaker, hip replacement, cardiac conditions, diaphragm, nerve stimulators, etc. That relates to the patients how to require constant monitoring. In other words, as per the name, the device is implanted into the body by surgical or medical intervention. Implanted medical devices are IoMT devices implanted into a patient’s body to monitor or treat medical conditions. These devices can range from simple pacemakers that regulate a patient’s heart rate to complex neurostimulators that manage chronic pain or movement disorders.

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Some Examples of Implanted Medical Devices in IoMT Some examples of implanted medical devices in IoMT include: ● ●

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Cardiac pacemakers: These devices are implanted into patients’ chests to regulate their heart rate and prevent arrhythmias. Implantable cardioverter-defibrillators (ICDs): These devices are similar to pacemakers but can shock the heart to restore normal rhythm in a life-threatening arrhythmia. Neurostimulators: These devices can be implanted to manage chronic pain or movement disorders like Parkinson’s. Implantable glucose monitors: These devices can be implanted to continuously monitor a patient’s glucose levels, useful for managing conditions like diabetes.

Benefits of Implanted Medical Devices in IoMT ●

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Improved patient outcomes: Implanted medical devices can monitor and treat medical conditions more effectively than traditional treatments, leading to better patient health outcomes. Continuous monitoring: Implanted medical devices can monitor a patient’s health status, allowing for early detection of potential problems. Improved quality of life: Implanted medical devices can give patients greater freedom and independence, enabling them to manage their medical conditions with less disruption to their daily lives. Cost-effective: While the initial cost of implanted medical devices can be high, they can be cost-effective in the long run by reducing the need for hospitalizations and other medical interventions. Personalized treatment: Implanted medical devices can be tailored to each patient’s specific needs, providing personalized treatment that is more effective than a one-size-fits-all approach.

Limitations of Implanted Medical Devices in IoMT ● ●

Infection risk: Implantable medical devices carry a risk of infection, which can be serious and potentially life-threatening. Surgery risks: Implanting medical devices requires bleeding, infection, and anaesthesia complications.

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Battery life: Some implanted medical devices require battery replacement, which requires another surgical procedure. Device failure: Like any medical device, implanted medical devices can malfunction or fail, potentially leading to serious health consequences for patients. Limited compatibility: Not all patients are suitable candidates for implanted medical devices, and some may not be compatible with certain medical conditions or treatments.

Wearable External Medical Devices These devices include trackers that produce data monitored by medical experts or regular people. As the name suggests, they are in contact with the body externally, such as skin patches, smart watches, insulin pumps for diabetes monitoring, oxy meters to check the amount of oxygen present and BP ratings, etc. These devices are used to monitor patients, even with chronic conditions. Wearable external medical devices are IoMT devices that patients can wear to monitor their health and medical conditions. These devices can collect data on a patient’s vital signs, activity levels, and other health metrics and transmit that data to healthcare professionals for analysis and monitoring.

Some Examples of Wearable External Medical Devices in IoMT Some examples of wearable external medical devices in IoMT include: ●

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Smartwatches and fitness trackers: These devices can track a patient’s activity levels, heart rate, and other health metrics and can be used to monitor chronic conditions like diabetes and hypertension. Continuous glucose monitors: These devices can continuously measure a patient’s blood glucose levels and provide alerts when glucose levels are too high or too low. ECG monitors: Wearable ECG monitors can record the electrical activity of a patient’s heart, which can be useful for detecting arrhythmias and other heart conditions. Respiratory monitors: These devices can track a patient’s breathing patterns and can be used to monitor conditions like sleep apnea and chronic obstructive pulmonary disease (COPD).

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Smart contact lenses: These devices can monitor a patient’s intraocular pressure, which is useful for managing conditions like glaucoma.

Benefits of Wearable External Medical Devices in IoMT ●

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Improved patient outcomes: Wearable external medical devices can provide healthcare professionals with continuous real-time patient health data, thus enabling them to make informed clinical decisions and provide optimal care. Remote monitoring: Wearable external medical devices facilitate remote data transmission to healthcare professionals, enabling continuous monitoring of a patient’s health status. Patient engagement: Wearable external medical devices can encourage patients to manage their health actively, leading to improved outcomes and better adherence to treatment plans. Cost-effective: Incorporating wearable external medical devices into healthcare can reduce healthcare costs and in-person visits, as healthcare professionals can remotely monitor patients’ health data and intervene as necessary. Improved quality of life: Wearable external medical devices can provide patients with greater flexibility and independence, enabling them to manage their health conditions more effectively and with less disruption to their daily lives.

Limitations of Wearable External Medical Devices in IoMT ●

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Accuracy: Wearable external medical devices must be highly accurate and reliable, as errors or inaccuracies could seriously affect patient health. Data privacy and security: Wearable external medical devices can collect and transmit sensitive patient data, which must be protected from unauthorized access or disclosure. Technical limitations: Some wearable external medical devices may have limited battery life or connectivity issues, limiting their effectiveness for continuous monitoring. Patient acceptance: Some patients may be reluctant to use wearable external medical devices due to concerns about privacy or a lack of familiarity with the technology.

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Limited clinical evidence: While wearable external medical devices show promise for improving patient outcomes, there is a need for more clinical evidence to demonstrate their effectiveness and safety.

IoMT-Based Energy Efficiency System The exponential growth of IoT helps the technology achieve the concept of any time and place by connecting to anything. So this motive leads to the development of healthcare IoT which is nothing to it. This involves various devices, methods designed with different sensors, data analysis tools, display devices, and many more, which help build a user-friendly environment (Kotronis, C. et al., 2019). With the demand for increasingly intelligent devices and applications, increased power consumption is possible. There would also be consent on considering different technologies, along with the benefits. Among the various licenses, energy efficiency and power management are one. IoMT-based energy efficiency systems are important in healthcare applications to reduce energy consumption and increase the operational lifetime of medical devices. These systems utilize various techniques and algorithms to optimize the energy usage of connected medical devices while maintaining their performance and reliability. Implementing intelligent power management techniques is one approach to achieving energy efficiency in IoMT. These techniques can be used to control the power consumption of medical devices by dynamically adjusting their operating modes based on the current usage and performance requirements. For example, a medical device can be put into a low-power sleep mode when it is not actively being used, or its processing power can be scaled up or down depending on the current workload. Another approach is to use energy-harvesting technologies to power medical devices. Energy harvesting technologies can convert ambient energy from light, heat, or motion into electrical energy to power medical devices. This eliminates the need for batteries or external power sources, making the devices more portable and convenient. Machine learning and data analytics can also be used to optimize the energy usage of medical devices. Machine learning algorithms can analyze the usage patterns of the devices and make predictions about their energy consumption, allowing for proactive energy management. Data analytics can also be used to identify energy inefficiencies and opportunities for optimization.

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During the process of connecting things, there should be conservation between one machine and another, i.e., this type of conversation is called machine-to-machine communication (M2M). Figure 6 illustrates the M2M communication using IoT without human intervention. With the help of the internet or radio signals via a network, the application devices are connected M2M gateway using RFID and WSN technologies for further communication. The link can be a wired medium or wireless medium based on availability and device configuration. When talking about M2M communication in health care, the chip can be placed either inside the body or a wearable circuit externally. The IoT and WSN have created a new platform called wireless Body area network (WBAN), which offers mobility and flexibility in MedTech (Irfan M et al., 2018). We already understand that using IoMT as its implantations in chronic diseases, remote health care monitoring, physiological signal monitoring, fitness, etc., Almost all the WBANs are designed with small and lightweight batteries, which leads to the energy-driven system. Thus, energy-efficient IoMT healthcare systems are critically needed to maintain the network longer.

Figure 6. IoMT-based M2M communication Model.

CobeBlue, MASN, and other organizations aim to develop low-power sensor systems for monitoring significant body signals as per requirement. The transmission and reception of data in IoT involve communication among sensor nodes, gateways, and databases, wherein energy consumption is primarily observed during data sensing, processing, and communication

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(Dawood Butt et al., 2022). The multitasking process of data communication consists of various stages, increasing the likelihood of energy wastage. Energy consumption is influenced by packet collision, overheating, idle listening, and emitting. Typically, data transmission occurs in two phases. ● ●

Phase 1: Communicating between the sensor node and to system central node Phase 2: Communicating the gathered data to a remote medical station database.

Energy Conservation A continuous power source is required to operate any smart IoT system efficiently. During data communication, different technologies are involved in getting efficient results. With the help of wireless sensor systems, they are trying to provide secondary storage devices battery. Battery requires regular charging and has a restricted lifespan which may cause interrupts in network operations. So, to overcome this problem, a self-sustainable energy solution is needed (Abdullah et al., 2016; Amira et al., 2019). Different types of energy sources are present in the surroundings, such as solar energy, thermal energy, electrostatic energy, vibrational energy, acoustic energy, wind energy, tidal energy, RF energy, and many more. The energy sources depend on various factors such as climatic conditions, environment, difficulty in harvesting, storage, infrastructure, etc. These sources have their merits and demerits (D. L. Kavya Reddy, et al. 2022).

Factors for Energy Wastage and Communication As discussed earlier, In the entire process, energy wastage mostly happens to sensor nodes and during data communication processing. Optimizing data transmission is imperative for precise sensed data and an efficient healthcare monitoring system while transmitting and receiving data between sensors and devices. Detectors are battery-operated; if the sensor device is placed inside the human body, replacing or changing the battery would be tough without a medical procedure (Ghoumid K. et al., 2021). To ensure the reliability, costeffectiveness, flexibility, and practicality of long-term health monitoring systems that can operate for more than fifteen years without interruption, novel

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methods, and techniques are required. Such developments are made possible through emerging technologies such as WBAN and IoMT. The merits and demerits of various energy-harvested systems are discussed in Table 1. Advanced energy optimization methods that combine wireless devices’ physical and data link layer functionalities have been proposed to extend battery life. The choice of communication protocol, such as MAC, IEEE 802.15.4, and IEEE 802.15.1, is critical for ensuring data security and analysis while maintaining battery life. Table 1. Merits and demerits of energy harvesting systems S.No Energy Type 1 Solar Energy

2

3

4

5

6

7

Source --

Merits Demerits ● Most commonly used as it ● The source of energy is is easily accessible limited (daytime only). ● Nondepletable resources ● High installation and operational cost, low output efficiency Thermal Heat ● The energy can be directly ● It needs a more extensive Energy converted into electrical area but produces low energy based on a different power principle. ● Low output voltage ● Available all time. ● Very low conservation ● Scalable and durable efficiency ● Less power density Vibrational Vibration ● Systems are light in weight ● It needs a more prominent Energy ● High voltage output area; the power out is not ● The exploitation of waste fixed mechanical energy ● No appropriate for largescale harvesting Electrostatic Potential ● High output voltage ● Effects with environmental Energy energy ● Simple integration conditions ● Gathers low frequency ● ( insulators) ● Need of polarization source ● Complex power circuit mechanism ● Devices have a lifespan Wind Energy Wind ● Efficient use of landscape ● Intermittent ● Low operating cost ● Noise pollution ● Clean source of energy ● Depends on climatic ● High power received conditions Tidal Energy Waves ● Easy to operate and ● Depends on waves maintain ● It can only be used in the ● Short start-up time area where sustainable waves are produced. RF Energy EM ● Long-distance energy ● Ultra-low output power Waves transfer ● Low efficiency ● Freely available ● Distance is limited throughout, with no limit

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Applications of IoMT The IoMT devices offer a potential replacement for existing healthcare devices to monitor patients’ real-time data using advanced equipment such as sensors, signal converters, and communication devices. These devices have undergone various transformations based on their usage and requirements, including intelligent wearables, domestic and professional medical devices and kits, and remote or mobile applications that can be accessed by medical experts from any location (Singh, R.P. et al., 2020; Manogaran et al., 2018). These devices enable performance analysis and have potential applications for disease prevention, fitness, and remote intervention in emergencies. There are many applications of IoMT, but some of those applications areas are discussed as follows:

Tele Heath The implementation of healthcare automation requires the gathering and processing of data that is specific to the requirements of the healthcare industry. This enables the automation system to analyze the data against previous records to make informed decisions for future courses of action (Pratap Singh et al., 2021). The data collected from network devices is typically stored at a central location, often at the physician’s office. This technology-enabled intelligence facilitates the transfer of services for monitoring and administration to IoMT machines, leading to reduced costs and more efficient utilization of infrastructure resources. Additionally, the implementation of remote monitoring systems has reduced the rate of healthcare productivity, thereby reducing the need for personal assistance and guardianship, especially for continuous observation systems like cardiac monitoring. Remote mentoring systems have emerged as reliable solution by gathering patient information and observation data with security to provide accurate and dependable solutions (Sabban, A, 2019).

Improved Drug Management IoMT devices include various components, such as tags, crucial in managing drug availability problems. In particular, RFID tags are commonly utilized for medication packaging and supply chain management to ensure quality and compliance with FDA guidelines (Shepherd A. et al., 2020). Moreover, some organizations are developing smart pills using IoT technology, which is ingestible and can monitor medication doses administered to patients. This development represents a promising approach for enhancing patient drug monitoring and adherence (T. Kossi, et al. 2022).

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Chronic Disease Management To manage chronic disease conditions such as diabetes, cardiac failure, BP, etc., IoMT-embedded devices offer promising alternatives. These devices monitor various parameters inside the body based on the requirement and treatment method. To predict the disease, they help in processing high-level analysis of gathered data by real-time sources to provide future alternative treatments and dose changes eventually (Qureshi et al., 2018). This data can be collected and stored for further research and can help study the epidemiological trend of a particular disease. There are many other application domains (as shown in Figure 7 with the connected medical device collaboration model), such as: ● ● ●

Providing training paramedic staff Assisting in rehabilitation centres Providing access to health information electron records etc.

Figure 7. Connected medical device collaboration model.

Prospects of IoMT The Internet of Medical Things (IoMT) is a revolutionary new technology that has the potential to transform healthcare. By connecting medical devices, sensors, and data analytics through the internet, IoMT can enable continuous

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and remote patient monitoring, personalized medicine, and healthcare cost optimization. One of the most promising applications of IoMT is in continuous and remote patient monitoring. This technology can be used to track patients’ vital signs, such as heart rate, blood pressure, and blood sugar levels, in real time. This data can then be used to identify potential problems early on and intervene before they become serious. For example, IoMT can be used to track patients with chronic conditions, such as diabetes or heart disease, and send alerts to their healthcare providers if their vital signs start to deviate from their normal range. This can help to prevent unnecessary hospitalizations and improve patient outcomes. Another example, IoMT can be used to track patients’ responses to different medications and adjust their treatment plans accordingly. This can help to ensure that patients are receiving the most effective treatment for their individual needs. IoMT also has the potential to significantly impact healthcare cost optimization. By preventing avoidable hospitalizations, streamlining resource utilization, and automating administrative processes, IoMT can lead to more efficient operations and substantial cost savings. For example, IoMT can be used to track patients’ movements in hospitals and identify areas where resources are being underutilized. This information can then be used to optimize patient flow and reduce costs. While the prospects for IoMT are exciting, there are also challenges that must be addressed to ensure its successful implementation. One of the biggest challenges is security and privacy. IoMT devices collect and transmit a lot of sensitive patient data, and it is important to ensure that this data is protected from unauthorized access. Another challenge is interoperability. IoMT devices from different manufacturers often use different communication protocols, which makes it difficult to exchange data between them. Interoperability standards are needed to facilitate seamless data exchange and collaboration among various healthcare providers and systems. The regulatory landscape for IoMT is still evolving, and it is important for policymakers to be aware of the latest developments in order to make informed decisions. Embracing IoMT with these considerations in mind can lead to a transformative era in healthcare, optimizing patient outcomes and enhancing the overall healthcare landscape.

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Conclusion In the healthcare industry, IoT-based Medtech applications are still in the early stages of development, but the application of connected devices can significantly improve healthcare. Data flow will be transparent in all execution levels, from the device to cloud storage. Interoperability and remote monitoring techniques will reduce the burden of cost and compliance while also leveraging intelligent devices to provide instantaneous health status. While automation in healthcare will increase demand for operational efficiency, it may also increase the risk factor during implementation, including security. However, considering the benefits and challenges of IoMT, it is a promising approach to improving the healthcare sector. This technology represents a new era in personalized healthcare for better living standards worldwide by providing unique data-driven treatment methods. Additionally, recent research and development in various sectors, such as cloud storage and security, big data analytics, artificial intelligence, augmented reality networks, computing, and wireless communication, are also trying to provide their impact in this field to create an affordable smart medical system and connected healthcare ecosystem.

References Abdullah, Wan Aida Nadia Wan, Naimah Yaakob, Mohamed Elshaikh Elobaid, Mohd Nazri Mohd Warip, and Siti Asilah Yah. “Energy-Efficient Remote Healthcare Monitoring Using IoT.” Proceedings of the International Conference on Internet of Things and Cloud Computing, March 22, 2016. https://doi.org/10.1145/2896387. 2896414. Al-Turjman, Fadi, Muhammad Hassan Nawaz, and Umit Deniz Ulusar. “Intelligence in the Internet of Medical Things Era: A Systematic Review of Current and Future Trends.” Computer Communications 150 (January 15, 2020): 644–60. https:// doi.org/10.1016/j.comcom.2019.12.030. Amira, Abbes, Nazim Agoulmine, Faycal Bensaali, Amine Bermak, and George Dimitrakopoulos. “Special Issue: Empowering EHealth with Smart Internet of Things (IoT) Medical Devices.” Journal of Sensor and Actuator Networks 8, no. 2 (June 4, 2019): 33. https://doi.org/10.3390/jsan8020033. Ar-Reyouchi, El Miloud, Kamal Ghoumid, Doha Ar-Reyouchi, Salma Rattal, Reda Yahiaoui, and Omar Elmazria. “An Accelerated End-To-End Probing Protocol for Narrowband IoT Medical Devices.” IEEE Access 9 (2021): 34131–41. https:// doi.org/10.1109/access.2021.3061257.

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Dawood Butt, Arslan, Faisal Abrar, Muhammad Awais Qasim, Sarosh Ahmad, Muhammad Sajawal, and Muhammad Anas Attiq. “Design of an Efficient Rectenna for RF Energy Harvesting for IoT Medical Implants.” AI and IoT for Sustainable Development in Emerging Countries, 2022, 487–503. https://doi.org/10.1007/978-3-030-90618-4_24. Irfan, Mohammed, and Naim Ahmad. “Internet of Medical Things: Architectural Model, Motivational Factors and Impediments.” 2018 15th Learning and Technology Conference (L&T), February 2018. https://doi.org/10.1109/lt.2018.8368495. Joyia, Gulraiz J, Rao M Liaqat, Aftab Farooq, and Saad Rehman. “Internet of Medical Things (IOMT): Applications, Benefits and Future Challenges in Healthcare Domain.” Journal of Communications 12, no. 4 (2017). https://doi.org/10.12720/ jcm.12.4.240-247. Kavya Reddy, DL, K. Negi, D. R. Soumya, G. P. Kumar, S. Sahana, and A. K. Sagar, “Real-Time Face Mask Detection Using CNN in Covid-19 Aspect,” in Innovations in Electrical and Electronic Engineering: Proceedings of ICEEE 2022, Volume 2, Springer, 2022, pp. 327–344. Kossi, T and S Sahana. “Prediction of the evolution of corona-virus using Machine Learning Technique,” Glob. J. Nov. Res. Appl. Sci. (NRAS)[ISSN 2583-4487], vol. 1, no. 2, 2022. Kotronis, Christos, Ioannis Routis, Elena Politi, Mara Nikolaidou, George Dimitrakopoulos, Dimosthenis Anagnostopoulos, Abbes Amira, Faycal Bensaali, and Hamza Djelouat. “Evaluating Internet of Medical Things (IoMT)-Based Systems from a Human-Centric Perspective.” Internet of Things 8 (December 2019): 100125. https://doi.org/10.1016/j.iot.2019.100125. Limaye, Ankur, and Tosiron Adegbija. “A Workload Characterization for the Internet of Medical Things (IoMT).” IEEE Xplore, July 1, 2017, 302–7. https://doi.org/10. 1109/ISVLSI.2017.60. Magsi, Hina, Ali Hassan Sodhro, Faheem Akhtar Chachar, Saeed A. Khan Abro, Gul Hassan Sodhro, and Sandeep Pirbhulal. “Evolution of 5G in Internet of Medical Things.” 2018 International Conference on Computing, Mathematics and Engineering Technologies (ICoMET), March 2018. https://doi.org/10.1109/ icomet.2018.8346428. Manogaran, Gunasekaran, Naveen Chilamkurti, and Ching-Hsien Hsu. “Emerging Trends, Issues, and Challenges in Internet of Medical Things and Wireless Networks.” Personal and Ubiquitous Computing 22, no. 5-6 (September 27, 2018): 879–82. https://doi.org/10.1007/s00779-018-1178-6. Mavrogiorgou, Argyro, Athanasios Kiourtis, Konstantinos Perakis, Stamatios Pitsios, and Dimosthenis Kyriazis. “IoT in Healthcare: Achieving Interoperability of High-Quality Data Acquired by IoT Medical Devices.” Sensors 19, no. 9 (April 27, 2019): 1978. https://doi.org/10.3390/s19091978. Mohammed, Chnar Mustaf, and Shavan Askar. “Machine Learning for IoT HealthCare Applications: A Review.” International Journal of Science and Business 5, no. 3 (2021): 42–51. https://ideas.repec.org/a/aif/journl/v5y2021i3p42-51.html. Pratap Singh, Ravi, Mohd Javaid, Abid Haleem, Raju Vaishya, and Shokat Ali. “Internet of Medical Things (IoMT) for Orthopaedic in COVID-19 Pandemic: Roles,

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About the Editors

Dr. Subrata Sahana is working as an Associate Professor in the Department of CSE at Sharda University, Greater Noida, U.P. He received PhD in Computer Science from the JNU, New Delhi (Central University), India (NIRF Ranking-2020: 02), and MTech in Computer Science and Engineering from BIT, Mesra in 2010 (NIRF Ranking-2020 under 100) and BTech in Computer Science & Engineering from CEMK, Kolaghat affiliated from West Bengal University of Technology in 2007. He joined Sharda University as an Assistant Professor in August 2019 and was promoted as Associate Professor in January 2021. Before this, He was associated with Galgotias University as an Assistant Professor from 2012-2019, V.I.T. University, Vellore as an Assistant Professor from 2010-2012 and RVSCET, Jamshedpur as a lecturer from 2007-2008. He has rich experience in publication in many international journals and conferences with high repute. He is also serving many reputed journals as an editorial board member, and reviewer board member. Moreover, Dr. Sahana has also delivered expert talks, and guest lectures at international conference and served as the reviewer for journals of IEEE, Springer, IGI Global etc. His research area is Underwater Wireless Sensor Networks, Pattern Matching, Bio-informatics, IoT, Security, and Cryptography, etc. Dr. Anil Kumar Sagar is currently working as a Professor in the Department of Computer Science and Engineering, Sharda University Greater Noida, India. He did his BE and MTech, PhD in Computer Science. Before joining Sharda University, he has worked as a professor, at the School of Computing Science and Engineering, Galgotias University, India. His current research interest includes Mobile Ad hoc Networks and Vehicular Ad hoc Networks, IoT, Artificial Intelligence. He has published numerous papers in international journals and conferences including IEEE and Springer. He has received a Young Scientist Award for the year 2018–2019 from the Computer Society of India, the Best Faculty Award for the years 2006 and 2007 from SGI, Agra.

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330

About the Editors

Dr. Sanjoy Das is working as a Professor and Head, Department of Computer Science, Indira Gandhi National Tribal University (A Central Government University), Amarkantak, M.P. (Regional Campus Manipur)- India. He did his BE and MTech, PhD in Computer Science. He did his PhD from Jawaharlal Nehru University, New Delhi. Before joining IGNTU he has worked as Associate Professor, School of Computing Science and Engineering, Galgotias University, India. He also worked as Assistant Professor G. B. Pant Engineering College (A Govt. Institute), Uttarakhand, and Assam University (Central University), Silchar. He has 16+ Year experience in Teaching and Research. He has organized many international conferences as well as attended as Session Chair. He has published 70+ research papers in SCI, Scopus indexed, referred international journals and conferences, including publishers IEEE, Inderscience, Elsevier and Springer. He has been the editor of five books on Cloud Computing, Vehicular Ad hoc Networks,etc. His current research interest includes Mobile Ad hoc Networks and Vehicular Ad hoc Networks, Distributed Systems, and Data Mining. Dr. Vishal Jain is presently working as an Associate Professor at the Department of Computer Science and Engineering, Sharda School of Engineering and Technology, Sharda University, Greater Noida, U. P. India. Before that, he has worked for several years as an Associate Professor at Bharati Vidyapeeth’s Institute of Computer Applications and Management (BVICAM), New Delhi. He has more than 16 years of experience in the academics. He obtained PhD (CSE), M.Tech (CSE), MBA (HR), MCA, MCP and CCNA. He has more than 900 research citation indices with Google Scholar (h-index score 14 and i-10 index 25). He has authored more than 95 research papers in reputed conferences and journals, including Web of Science and Scopus. He has authored and edited more than 40 books with various reputed publishers, including Elsevier, Springer, DeGruyter, Apple Academic Press, CRC, Taylor and Francis Group, Scrivener, Wiley, Emerald and IGIGlobal. His research areas include information retrieval, semantic web, ontology engineering, data mining, ad hoc networks, and sensor networks. He received a Young Active Member Award for the year 2012–2013 from the Computer Society of India, Best Faculty Award for the year 2017 and Best Researcher Award for the year 2019 from BVICAM, New Delhi.

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Index

A

C

aggregation, 212, 217, 310 agricultural, vii, 1, 2, 3, 4, 5, 9, 10, 11, 14, 15, 16, 19, 20, 21, 22, 23, 24, 25, 27, 28, 29, 30, 31, 33, 39, 41, 46, 48, 59, 60, 61, 64, 70, 71, 208, 242 agricultural practices, 1, 2, 9, 23, 25, 70 anthropocene, 43 antigenic, 124, 137, 138, 139 architecture, 30, 40, 92, 94, 112, 119, 120, 164, 188, 223, 233, 236, 276, 277, 279, 281, 295, 297, 301, 308, 309, 310 artificial intelligence (AI), vii, 28, 41, 71, 73, 75, 81, 84, 85, 86, 87, 88, 91, 92, 93, 94, 95, 96, 109, 110, 120, 121, 123, 143, 145, 157, 177, 178, 212, 233, 234, 235, 237, 238, 239, 242, 243, 245, 248, 252, 253, 259, 261, 265, 266, 267, 270, 271, 272, 273, 274, 276, 296, 300, 305, 311, 325, 326, 329 automated irrigation system, 60

cardiovascular, 151, 152, 155, 156, 157, 160, 164, 168, 171, 172, 173, 175, 178, 179 cellular, 11, 12, 24, 25, 60, 149, 214, 281, 310, 311, 312 circular economy, 35, 36, 38, 55, 56, 57 cloud computing, 1, 5, 9, 10, 25, 30, 42, 57, 71, 84, 94, 96, 99, 120, 233, 235, 273, 274, 311, 325, 330 colorectal cancer, 151, 152, 155, 156, 157, 162, 163, 164, 168, 170, 172, 174, 176, 178, 179, 180, 181 communication, 4, 5, 9, 11, 12, 13, 24, 25, 31, 33, 35, 37, 39, 55, 56, 60, 62, 71, 92, 93, 94, 100, 119, 120, 123, 135, 136, 144, 146, 148, 178, 179, 180, 183, 188, 189, 190, 191, 209, 211, 212, 233, 236, 240, 246, 271, 274, 276, 278, 279, 280, 281, 283, 285, 295, 296, 297, 299, 301, 307, 308, 310, 311, 319, 320, 322, 324, 325 comorbidity network, 152, 181 computational techniques, 123, 124, 127 consumer, 38, 56, 86, 87, 110, 168, 241, 242, 307 Contiki Cooja, 275, 276, 290, 293

B bankbazaar, 244, 252, 272 barriers, 23, 30, 44, 237, 239, 240, 245, 246, 247, 248, 249, 250, 251, 252, 253, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 273, 274 biologic system, 95, 96 biosensor, 140, 141, 143, 147, 148 breach, 81

D deep learning, 26, 73, 74, 86, 89, 121, 151, 152, 153, 154, 168, 169, 172, 173, 176, 177, 178, 180, 212, 234, 235, 236, 276, 296

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332 detection, 11, 16, 17, 19, 26, 28, 29, 33, 44, 47, 57, 71, 81, 83, 92, 97, 124, 125, 127, 128, 129, 130, 137, 138, 139, 140, 141, 142, 143, 144, 145, 147, 148, 149, 156, 174, 186, 209, 218, 233, 234, 235, 236, 304, 315, 326 diabetes, 108, 151, 152, 155, 156, 157, 159, 164, 166, 171, 173, 175, 177, 178, 179, 180, 234, 275, 276, 278, 279, 283, 285, 286, 287, 290, 295, 296, 297, 305, 315, 316, 323, 324 digital humidity, 60, 62, 63, 64, 65 digital transformation, 110, 237, 238, 239, 240, 241, 246, 248, 249, 250, 252, 253, 257, 259, 261, 263, 264, 265, 266, 267, 268, 270, 271, 274 DNA, 124, 128, 129, 130, 138, 139, 141, 144, 145, 147, 149, 150

E efficiency, 6, 22, 23, 39, 41, 42, 43, 44, 66, 114, 159, 160, 166, 170, 174, 190, 222, 240, 282, 301, 305, 306, 314, 318, 321, 325 energy, 3, 5, 13, 19, 23, 25, 27, 31, 32, 37, 39, 42, 43, 45, 46, 49, 55, 60, 67, 68, 69, 100, 121, 131, 132, 145, 170, 179, 190, 192, 201, 207, 211, 214, 233, 242, 280, 295, 296, 299, 300, 302, 318, 319, 320, 321, 325, 326 energy conservation, 320 energy efficient, 31, 179, 207, 296, 300 environmental sustainability, 35, 36, 39, 41, 43, 44, 46, 48 estimation, 27, 30, 99, 145, 147, 148, 197, 200, 212, 227, 228

F fertilizer, 5, 7, 29, 30 fingerprint, 124, 130, 131, 142, 143, 145, 146, 147, 148, 149 fintech, 238

Index forensic science, 123, 124, 125, 126, 127, 128, 129, 130, 131, 142, 143, 144, 145, 147, 149

G greenhouse, 12, 13, 21, 29, 32, 35, 36, 42, 60, 61, 62, 63, 70, 183, 184, 185, 186, 188, 191, 192, 194, 195, 197, 201, 202, 203, 204, 206, 207, 208, 209 greenhouse system, 60, 61, 62, 63, 70

H hardware, 14, 37, 38, 43, 51, 53, 59, 61, 64, 66, 69, 70, 76, 88, 102, 119, 134, 187, 188, 190, 210, 224, 308, 327 health monitoring, 110, 115, 116, 124, 131, 186, 209, 210, 282, 295, 296, 297, 320 healthcare, vii, 5, 26, 52, 76, 84, 85, 95, 96, 97, 98, 99, 101, 102, 103, 104, 105, 107, 109, 110, 111, 112, 115, 117, 119, 120, 121, 123, 151, 153, 154, 155, 156, 161, 177, 178, 209, 236, 271, 274, 275, 276, 279, 280, 282, 283, 284, 293, 295, 297, 299, 300,301, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 316, 317, 318, 319, 320, 322, 323, 324, 325, 326, 327 humidity, 16, 39, 41, 45, 49, 61, 62, 63, 64, 65, 66, 69, 87, 132, 135, 183, 185, 192, 194, 195, 196, 197, 198, 199, 202, 203, 204, 206, 207, 208, 209

I implanted, 302, 304, 312, 314, 315, 316 industrial, vii, 3, 28, 31, 32, 38, 39, 44, 45, 47, 56, 72, 73, 74, 78, 84, 88, 92, 93, 94, 110, 120, 121, 131, 142, 184, 235 industrial applications, 73, 74, 78, 131 Industry 4.0, 56, 59, 60, 73, 74, 76, 88, 89, 90, 91, 93, 271 insulin, 107, 108, 156, 159, 175, 278, 285, 287, 316

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Index insurance sector, 238 intelligent greenhouse, 183, 184, 185, 186, 191, 194 intelligent manufacturing, 56, 74 internet of medical things, 118, 299, 300, 323, 325, 326, 327 irrigation, 4, 5, 6, 12, 13, 16, 47, 48, 59, 60, 61, 62, 63, 64, 67, 70, 72, 197

333 molecular, 126, 127, 128, 129, 139, 144, 146, 147, 148, 149, 177, 180, 234, 297 monitoring module, 203, 204, 205, 206, 209 monitoring system, 39, 47, 55, 131, 145, 183, 186, 207, 281, 296, 297, 320, 322 multimorbidity network, 152

O L linear support vector, 152 Lora, 276, 280, 295

M machine learning, vii, 22, 73, 74, 75, 76, 77, 78, 82, 83, 84, 85, 86, 87, 88, 90, 91, 92, 93, 94, 121, 124, 131, 132, 134, 136, 137, 142, 144, 145, 146, 147, 151, 152, 156, 157, 164, 173, 174, 175, 177, 178, 179, 181, 211, 212, 214, 215, 216, 217, 220, 222,233, 235, 236, 275, 276, 277, 279, 282, 283, 285, 288, 290, 293, 295, 296, 297, 300, 305, 309, 318, 326 maintenance, 11, 22, 42, 45, 50, 57, 63, 67, 74, 87, 88, 91, 92, 93, 110, 129, 188, 208, 242, 305, 307 medical devices, 146, 300, 301, 302, 303, 304, 305, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 322, 323, 325, 326 medicinal, 95, 96, 97, 98, 102, 104, 106, 119, 185 micro-climatic, 186, 191, 194 microphone, 11, 136 mobile, 5, 12, 17, 20, 27, 30, 32, 43, 51, 55, 65, 66, 67, 70, 86, 116, 120, 178, 207, 212, 213, 214, 235, 236, 241, 243, 247, 251, 256, 271, 280, 295, 306, 308, 313, 322, 329, 330 moisture, 6, 8, 12, 13, 17, 19, 29, 44, 61, 62, 63, 64, 65, 66, 67, 68, 69, 183, 184, 185, 192, 193, 195, 196, 197, 200, 206, 208, 210, 244, 245

operation management, 73, 74

P pandemic, 36, 113, 114, 115, 118, 120, 121, 125, 137, 326 paradigms, 77 parameters, 12, 17, 60, 61, 63, 64, 69, 70, 77, 137, 161, 164, 172, 175, 176, 183, 185, 186, 188, 191, 193, 194, 195, 197, 200, 201, 202, 203, 206, 232, 243, 244, 290, 309, 312, 323 patient similarity network, 152, 158 pest control, 4, 8, 208 policybazaar, 239, 244, 252, 258, 270, 272 prediction, 8, 29, 74, 88, 93, 94, 120, 121, 124, 125, 134, 135, 136, 143, 144, 145, 148, 149, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 164, 166, 168, 170, 171, 172, 173, 175, 177, 178, 179, 180, 181, 200, 212, 216, 219, 227, 229, 230, 234, 235, 236, 275, 281, 283, 286, 290, 295, 296, 297, 326

R rainfall, 19, 31, 124, 125, 135, 136, 144, 148 regression, 89, 157, 158, 159, 162, 163, 178, 179, 180, 184, 198, 200, 212, 217, 222, 233, 236, 275, 277, 283, 288, 292 reverse transcription - polymerase chain reaction (RT-PCR), 140 revolution, 5, 44, 59, 84, 95, 123, 273

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334 robot, 15, 26, 29, 31, 33, 124, 125, 134, 143, 145, 147, 243, 259, 272 robotics, 1, 2, 13, 14, 26, 33, 92, 110, 134, 242, 272

S SARS-COV-2, 124, 138, 139, 140, 143 SDG, 36 serological, 124, 137, 138, 139, 141 smart farming system, 60 software, 37, 38, 42, 43, 51, 62, 74, 81, 82, 83, 89, 91, 92, 93, 94, 116, 163, 174, 187, 188, 190, 191, 201, 208, 209, 214, 222, 234, 236, 280, 301, 308 soil moisture monitoring, 29, 60 soil respiration, 183, 184, 196, 200, 201, 205, 208, 209, 210

T temperature, 3, 13, 39, 41, 44, 45, 49, 52, 59, 60, 61, 62, 63, 64, 65, 66, 67, 69, 70, 87, 100, 116, 117, 131, 132, 135, 138, 140, 143, 159, 183, 184, 185, 192, 193,

Index 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 206, 208, 209, 210 temperature humidity index, 183, 184, 196, 198 temperature sensing, 60 therapeutic, 96, 99, 118, 154

U unmanned aerial vehicles, 2, 13

V vapor pressure deficit, 184, 185, 194, 197, 209, 210

W waste management, 48, 57 wearable, 99, 104, 111, 125, 131, 132, 133, 148, 149, 282, 295, 296, 302, 305, 306, 310, 316, 317, 318, 319, 327 wireless sensor network, 21, 27, 30, 32, 56, 72, 120, 135, 183, 186, 187, 206, 207, 208, 209, 210, 234, 235, 236, 300, 329 wireless technologies, 1, 2, 305

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