Preface
Page: i-ii (2)
Author: Parul Dubey, Mangala Madankar, Pushkar Dubey and Kailash Kumar Sahu
DOI: 10.2174/9789815256864124010001
The State of Artificial Intelligence Research and Development in the Present-Day Scenario
Page: 1-31 (31)
Author: Krupali Dhawale*, Shraddha Jha, Mishri Gube and Khwaish Asati
DOI: 10.2174/9789815256864124010003
PDF Price: $15
Abstract
Artificial intelligence is a field of computer science that focuses on humanlike intelligence in machines. Artificial intelligence is advancing in many areas to
increase the efficiency, accuracy, and speed of the decision-making process. The
chapters of this book provide a detailed overview of the AI journey and provide readers
with insights to improve their knowledge of AI. The chapters also cover the evolution
of artificial intelligence and the techniques used to create it. As artificial intelligence
continues to evolve and integrate into our daily lives, the chapters of this book discuss
the ethical and social implications of AI and the unpredictable growth and impact of
artificial intelligence in society. This chapter also contains thoughts on the future of
artificial intelligence, which has the potential to transform business, drive innovation,
solve complex problems, and provide justice to social and governance issues in a
better-explained way. Overall, this book chapter shapes one’s mind with the entire
concept of artificial intelligence.
Social Welfare and Artificial Intelligence's Role: A Comprehensive Summary of the Study
Page: 32-66 (35)
Author: Manjushree Nayak* and Jagannath Tiyadi
DOI: 10.2174/9789815256864124010004
PDF Price: $15
Abstract
AI is a strong instrument with the potential to transform social welfare
systems and improve individuals' and communities' overall well-being. This research
study examines the uses, advantages, ethical issues, and possible obstacles of artificial
intelligence in social welfare in depth. The concept of social welfare and its
fundamental objectives of promoting social justice, equality, and overall quality of life
are explained in detail at the beginning of the paper. It then digs into the different ways
in which artificial intelligence is being utilized to resolve complex social issues and
improve social government performance. Eliminating poverty is one crucial area where
AI has significant potential. By utilizing artificial intelligence innovations, for example,
information examination, prescient display, and choice of emotionally supportive
networks, state-run administrations, and associations can more readily grasp destitution
elements, distinguish weak populations, and configure designated mediations to ease
neediness and upgrade social versatility. Man-made intelligence likewise assumes a
vital role in further developing medical care access and conveyance. AI-powered
systems have the potential to revolutionize healthcare by enhancing diagnostic
accuracy, lowering costs, and facilitating personalized and preventive care, all the way
through to optimizing treatment plans and drug discovery. Training is another space
where computer-based intelligence can have a groundbreaking effect. AI-based
assessment tools, adaptive learning platforms, and intelligent tutoring systems can meet
individual learning needs, make it easier to get a personalized education, and close
educational gaps, making education more accessible and fairer to everyone. In addition,
AI technologies aid in crime prediction and prevention, which contributes to public
safety. AI algorithms can identify high-risk areas, detect suspicious activities, and
enable proactive law enforcement strategies by analyzing vast amounts of data, such as
crime patterns, social media feeds, and surveillance footage. This enables improved
outcomes for public safety. While the advantages of man-made intelligence in friendly
government assistance are critical, moral contemplations cannot be neglected. The
transparency and accountability of AI systems, algorithmic bias, and privacy concerns
are all examined in depth in this paper. It stresses the significance of developing and implementing AI in a manner that upholds social values, fairness, and individual rights.
In addition, the research paper discusses potential obstacles to applying AI to social
welfare, such as the requirement of trustworthy data, digital infrastructure, and
workforce upskilling.
Application of Artificial Intelligence Techniques in Healthcare
Page: 67-101 (35)
Author: Bhavana Singh*, Amrish Chandra, Deepika Joshi, Nidhi Semwal, Gauree Kukreti and Urvashi Saxena
DOI: 10.2174/9789815256864124010005
PDF Price: $15
Abstract
The integration of Artificial Intelligence (AI) into healthcare promises
improved medical evaluation, provided therapeutic solutions, and enhanced patient
care. Various AI branches, including machine learning, deep learning, and computer
vision, adeptly handle vast healthcare datasets ranging from electronic health records to
wearable data. These techniques extract vital patterns, forecast early-stage diseases, and
personalize patient treatments. In diagnostics, AI tools excel at identifying diseases and
predicting patient outcomes by automating image readings and pinpointing health risks.
AI also optimizes healthcare logistics, resource allocation, and overall patient care,
reducing clerical tasks and promoting data-driven decisions. Yet, there are challenges.
Concerns about data privacy, legislative compliance, ethics, and the need for
transparent AI results are paramount. Addressing these is crucial for successful AI
integration in healthcare. In essence, AI's integration into healthcare promises
revolutionary diagnostic and therapeutic advances. Navigating the challenges requires
collaboration between medical experts, AI specialists, lawmakers, and ethicists to fully
realize AI's transformative potential.
Advancements in Remote Heart Monitoring: Wearable Technology and AI-based Approaches for Cardiovascular Disease Detection
Page: 102-117 (16)
Author: Immaculate Joy S.* and Kanagamalliga S.
DOI: 10.2174/9789815256864124010006
PDF Price: $15
Abstract
In the era of precision medicine and individualized approaches, remote
monitoring and control of heart function have emerged as critical components of
patient evaluation and management. The integration of consumer-grade software and
hardware devices for health monitoring has gained popularity as technological
advancements become increasingly integrated into daily life. The cardiology
community must adapt to the demands of distant and decentralized care, as highlighted
during the COVID-19 pandemic. Wearable technology, such as vital sign monitors,
holds significant potential for monitoring heart disease and associated risk factors. This
book chapter explores the expanding applications of wearable technology in
cardiology, focusing on examples such as Holter-event recording and
electrocardiogram (ECG) patches. Textile-based sensors and wristbands are
implemented across various patient groups, emphasizing real-time deployment and the
evolving role of wearables in arrhythmia, cardiovascular disorders, and associated risk
factors. The importance of conducting clinical trials and using proper terminology for
clinical validation is also highlighted. To enhance the accuracy and efficiency of ECG
signal analysis, this chapter proposes a novel approach that combines AI-based
unsupervised Long Short-Term Memory (LSTM) with a recursive-based Ensemble
Neural Network (ENN). The LSTM component effectively denoises raw ECG signals
and enables faster convergence. The ENN, with its built-in deep layers, provides an
improved classification of cardiovascular diseases (CVD) present in the input ECG
data. The recursive approach employed by the ENN efficiently utilizes the available
parameters, even in the presence of noisy labels. The proposed method demonstrates
enhanced prediction and classification of CVD, with high precision, recall, and F1
score. The objective is to derive a checkpoint between clinical and research potentials,
identify gaps, and address potential risks associated with CVD detection using ECG
measurements. By leveraging wearable technology and advanced AI techniques,
clinicians and researchers can benefit from improved diagnostic accuracy, remote patient monitoring, and personalized care. The insights gained from this chapter will
contribute to the ongoing advancements in remote heart monitoring and facilitate the
adoption of innovative approaches in cardiovascular disease management.
Signs Unveiled: The Power and Promise of AI-Based Sign Recognition Systems
Page: 118-138 (21)
Author: Parul Dubey*, Priti Nilesh Bhagat, Gousia Hazra Anjum and Parvin Akhter
DOI: 10.2174/9789815256864124010007
PDF Price: $15
Abstract
Artificial intelligence (AI) has revolutionized the field of sign recognition,
enabling machines to accurately interpret and respond to signs, symbols, and gestures.
This chapter explores the dynamic landscape of AI-based sign recognition systems,
unveiling their components, functionalities, applications, and advantages over
traditional methods. By leveraging machine learning and computer vision, these
systems offer remarkable accuracy, adaptability, and real-time responsiveness. Their
impact extends across social and business domains, enhancing inclusivity for the
hearing impaired, enabling cross-cultural communication, and transforming industries
such as transportation and healthcare. Challenges and ethical considerations are also
addressed. The future trajectory of AI-based sign recognition promises a world where
machines seamlessly bridge the gap between human communication and technological
innovation.
Traffic Sign Detection and Recognition Using Convolutional Neural Networks
Page: 139-151 (13)
Author: R. Sreemathy*, Mousami Turuk, Harsh Patil, Saurabh Shastri, Ninad Lele and Soumya Khurana
DOI: 10.2174/9789815256864124010008
PDF Price: $15
Abstract
The advent of technology has brought about seismic shifts in our lives. It is
hard to imagine the world without the very technology that was considered
groundbreaking a while back, such as smartphones, the internet, etc. One such field that
has seen a tremendous rise in recent years is the field of artificial intelligence, along
with computer vision. Object detection and recognition is, and will continue to be, one
of the most important fields of research in the coming years due to our ever-increasing
demand for various technologies to substitute for the human eye. Traffic sign detection
and recognition is an important subset of this, having far-reaching real-world benefits.
Various methods and algorithms have been proposed to achieve this in the past few
years, with more novel technologies improving upon the previous works. The
emergence of advanced driving assistance systems (ADAS), used for driving
assistance, has led to many companies testing various systems on their novel car
models for better accuracy and reliability. There is still some way to go until object
recognition algorithms are deployed on these ADAS systems worldwide. One such
important part of this system is traffic sign detection and recognition. This work
proposes one such traffic sign recognition method. The proposed system is
implemented in two processes, namely detection and recognition. The former is
implemented using the You Only Look Once (YOLO) detection algorithm, which
performs grid classification on the image to predict the bounded boxes. This is
followed by finding the probability of a particular object’s presence in a particular grid.
For the latter process, a 4-layer CNN model is deployed to classify the object into 43
separate classes. The model is trained using the German traffic sign benchmark dataset.
Upon testing with other standard models such as VGGNet and ResNet-50, the proposed
model was found to be more accurate. Real-time implementation of the proposed
model gives a training accuracy of 99.51%, while the testing accuracy is found to be
97.13%.
Unlocking Business Insights: Leveraging the Synergy of Business Intelligence and Artifical Intelligence for Effective Data Analytics
Page: 152-168 (17)
Author: Manjushree Nayak* and Ashutosh Pattnaik
DOI: 10.2174/9789815256864124010009
PDF Price: $15
Abstract
Nowadays, the market is full of a variety of products for a single item, which
makes it very difficult for customers to choose which product is best for them
according to their price range. At the same time, it also creates a huge problem for
manufacturers and business people to sell their products in this market, with a variety
of customers who have different needs and expectations from the products they
purchase. One of the most important factors in selling a product is its price and
standard, which determine its sales and demand in the market. So, it is most important
to sell the product as per the customer's needs and also at a price so that they can afford
it. As there is a rapid evolution of technology, it is being used by other sectors to make
a profit and also to understand market needs and their performance in the market. So, it
is also very necessary to implement the technology in this sector, as it deals with a huge
number of customers and is very important for the businessperson to make a huge
amount of profit. A business intelligence tool is used, and its purpose is to analyze the
market. It collects complete information about a product in a huge quantity, which
helps it predict future results more accurately. By applying this tool in business, it
predicts the future sale of the product in the market, and if they decide to come up with
a new product in the market, what should be the selling price they need to fix for its
sale, and how can they increase the demand for that product in the market? One of the
biggest advantages of using this tool is that it also predicts the performance of the
manufacturer in the market and the different areas they should focus on to improve
their productivity. This concludes that using business intelligence in business solves
many problems for the manufacturer, starting from analyzing the market to
understanding the needs and expectations of the customers. Not only does it help
businesses, but it also helps customers so that they can purchase the product that meets
their expectations.
Multi-Agent Trading System Using Artificial Intelligence
Page: 169-194 (26)
Author: Vaishali Ingle*
DOI: 10.2174/9789815256864124010010
PDF Price: $15
Abstract
Multi-agent systems are concerned with decision-making tasks where
multiple agents act in a shared environment. Agents can observe their environment
(partially or fully), act to impact the environment, and might have different or aligned
goals. Multi-Agent Systems Artificial Intelligence (MAAI) is used for simulating enduser requirements. The models designed are examples of the use of AI in the business
world.
The concept of reinforcement learning can be applied to stock price prediction for a
specific stock, working in an agent-based system to predict higher returns based on the
current environment. The agent's reward will be either profit or loss. A multi-agent
system will use three types of agents: agent 1 (forecasting agent using a basic machine
learning algorithm), agent 2 (judgmental agent; the background algorithms to work on
it are reinforcement learning or fuzzy neural networks), and agent 3 (based on simple
trading rules or neural networks). Alert Agent (AA) guarantees proficient conveying
inside the schema. Signals are one of the alerts. The alert agent sends the foundation
agents (Agent 1, Agent 2, and Agent 3) signals (verdict) delivered by the superior
agent. Depending on these verdicts, the superior policies are understood to be presented
to the users (traders). Depending on the verdict by Superior, investment risk can be
minimized. The multi-agent framework verdict is combined with sentiment collected
from finance news for a particular company. The cognizant behavior of agents in the
stock market is also considered future research work for this framework. AI-based
stock trading systems must be strengthened in the future with the use of various
security measures.
Neural Network Models for Feature Extraction and Empirical Thresholding
Page: 195-221 (27)
Author: Krupali Dhawale*
DOI: 10.2174/9789815256864124010011
PDF Price: $15
Abstract
Neural Network Models for Feature Extraction and Empirical Thresholding
study the combination of neural network models and empirical thresholding methods to
improve the procedure for extracting features. For researchers and practitioners
working in the fields of feature extraction and machine learning, it illustrates the
advantages, approaches, and difficulties connected with this integration and offers
helpful insights. The basic concepts of feature extraction are covered in this book
chapter, along with an overview of the several neural network models that can be used
to accomplish this task, such as auto-encoders, convolutional neural networks (CNNs),
and recurrent neural networks (RNNs). This book chapter emphasizes the benefits,
methodologies, and challenges associated with this integration, providing valuable
insights for researchers and practitioners in the fields of feature extraction and machine
learning. This book chapter is useful for statistical analysis, domain expertise-driven
threshold selection, and validation metrics-based threshold choice as efficient
techniques for enhancing feature quality and lowering noise.
Comparing Different Machine Learning Techniques for Detecting Phishing Websites
Page: 222-234 (13)
Author: Astha Pathak*, Prashant Pandey and Vaishali Raheja
DOI: 10.2174/9789815256864124010012
PDF Price: $15
Abstract
Phishing site URLs are designed to gather confidential data such as user
identities, passwords, and transactions involving online money. Phishing strategies
have begun to advance quickly as technology advances; this could be avoided by using
anti-phishing tools to identify phishing. Employing machine learning techniques to
identify fraudulent websites was previously suggested and put into practice. This
project's primary goal is to develop the system in a way that is highly efficient,
accurate, and economical. Delivered to the system, the dataset of genuine and phishing
URLs is pre-processed to put the data in a format that can be used for analysis. Each
category has unique, defined phishing features against a dataset of real and fake URLs.
We evaluated the classifier's performance using a different test set after training it and
its values. A classifier has been created for phishing websites and tested for
effectiveness with a set of labeled phishing and legal URLs. When compared to seven
different classifiers of machine learning, the proposed model scored the greatest test
accuracy of up to 97.5% with the Gradient Boosting Classifier.
Cloud Integration in Artificial Intelligence (AI)
Page: 235-264 (30)
Author: Pranali Bhope*, Krupali Dhawale, Sejal Kumbhare and Kunika Dhapodkar
DOI: 10.2174/9789815256864124010013
PDF Price: $15
Abstract
We have entered the new era of the cloud age. Cloud integration has been
adopted by companies and is still growing in a rapid way. On the other hand, advances
in artificial intelligence have opened up new possibilities that can be used to improve
existing cloud technologies. This book chapter explores the integration of cloud
computing and artificial intelligence and focuses on the advantages and challenges
associated with this integration. It explores the economic benefits of cloud-based AI
systems, the cost-effectiveness of cloud integration in AI, potential security and privacy
issues associated with storing and processing sensitive AI data in the cloud, and how
the use of cloud-based AI platforms helps in resource optimization, knowledge sharing
among AI learners and integration of cloud services and AI development projects. The
study concludes with a comprehensive assessment of the current state of cloud
integration in AI and recommendations for organizations looking to adopt cloud-based
AI solutions.
Various Applications of Internet of Things-Based Artificial Intelligence in the Agriculture Sector
Page: 265-276 (12)
Author: Ruhi Uzma Sheikh, Madhuri Gupta, Devanand Bhonsle*, Veenita Swarnakar, Sapna Singh Kshatri and Shailendra Verma
DOI: 10.2174/9789815256864124010014
PDF Price: $15
Abstract
India is the most populous country in the world hence the challenge of
feeding the growing population is a matter of concern. Indian economy is dependent
upon agriculture, which is affected by various factors viz. climate change, drought,
heavy rain, insect attacks, etc. A few decades ago, agricultural practices were
performed by conventional techniques only but in the present era, Artificial Intelligent
(AI) has been introduced in the agriculture sector which brought a tremendous
revolution in this field. According to the World Economic Forum (WEF), over 7,000
farmers in India use AI-based technologies in the agriculture field to improve the
productivity of crops, crop yield prediction, soil testing, crop monitoring, disease
diagnosis, spraying, prediction of weather, crop infestation alert, precision forming, etc.
There are more than 1000 agriculture-based start-ups in India that provide solutions in
different stages of farming. It may also be used to maintain records of crops, digital
marketing, market prediction, digital finance, micro insurance, etc. However, it takes a
very long time to achieve the goal because of a lack of access to data, a high cost of
operations, and a lack of technical experts. In the long run, AI-powered machines and
other techniques may provide benefits to the farmers in such a way that they can make
better decisions and benefit. In this chapter, various applications have been discussed in
the field of AI-based agriculture. Various challenges have also been discussed so that
the obstacles can be reduced to achieve our goal in the AI-based agriculture sector.
The Role of Artificial Intelligence in Social Welfare: Harnessing AI For Positive Societal Impact
Page: 277-293 (17)
Author: Manas Rathore*
DOI: 10.2174/9789815256864124010015
PDF Price: $15
Abstract
This chapter explores the transformative role of Artificial Intelligence (AI)
in promoting social welfare and advancing the well-being of individuals and
communities. AI, with its remarkable capabilities in data analysis, pattern recognition,
and decision-making, has the potential to revolutionize various aspects of social
welfare, ranging from healthcare and education to poverty alleviation and disaster
response. The chapter begins by providing an overview of the concept of social welfare
and its significance in fostering inclusive and equitable societies. It highlights the
challenges faced by traditional approaches in addressing complex social issues and how
AI can act as a catalyst for change. The chapter explores how AI can enhance access to
quality healthcare, especially in underserved regions, and contribute to improved health
outcomes for vulnerable populations. Education is another domain greatly influenced
by AI. The chapter examines the potential of AI in personalized learning, adaptive
tutoring, and intelligent assessment systems. It explores how AI technologies can
bridge educational gaps, promote lifelong learning, and support inclusive education for
students with diverse needs.
Furthermore, the chapter discusses the role of AI in poverty alleviation and social
safety net programs. AI-driven analytics and predictive modeling can help identify
vulnerable populations, optimize resource allocation, and design targeted interventions
to reduce poverty and inequality. It emphasizes the importance of ethical
considerations and safeguards to ensure that AI solutions do not perpetuate biases or
deepen existing social divides. In addition, the chapter highlights the significance of AI
in disaster response and humanitarian aid. AI-powered algorithms can analyze vast
amounts of data from various sources, such as social media and satellite imagery, to
facilitate timely and effective disaster management. It explores how AI can enable
rapid needs assessment, aid distribution, and post-disaster recovery efforts, ultimately
saving lives and minimizing human suffering. Finally, the chapter addresses the
challenges and ethical implications associated with the use of AI in social welfare.
Subject Index
Page: 294-299 (6)
Author: Parul Dubey, Mangala Madankar, Pushkar Dubey and Kailash Kumar Sahu
DOI: 10.2174/9789815256864124010016
Introduction
AI in the Social and Business World: A Comprehensive Approach offers an in-depth exploration of the transformative impact of Artificial Intelligence (AI) across a wide range of sectors. This edited collection features 13 chapters, each penned by field experts, providing a comprehensive understanding of AI's theoretical foundations, practical applications, and societal implications. Each chapter offers strategic insights, case studies, and discussions on ethical considerations and future trends. Beginning with an overview of AI's historical evolution, the book navigates through its diverse applications in healthcare, social welfare, business intelligence, and more. Chapters systematically explore AI's role in enhancing healthcare delivery, optimizing business operations, and fostering social inclusion through innovative technologies like AI-based sign recognition and IoT in agriculture. With strategic insights, case studies, and discussions on ethical considerations and future trends, this book is a valuable resource for researchers, practitioners, and anyone interested in understanding AI's multifaceted influence. It is designed to foster informed discussions and strategic decisions in navigating the evolving landscape of AI in today's dynamic world. This book is an essential resource for researchers, practitioners, and anyone interested in understanding AI’s multifaceted influence across the social and business landscapes.