Preface
Page: ii-ii (1)
Author: Muhammad Ehsan Rana and Manoj Jayabalan
DOI: 10.2174/9789815080957123010002
PDF Price: $15
Determinants of Impulse Purchase Behaviours on e-Commerce Websites
Page: 1-10 (10)
Author: Mohammed Adnan Islam and Rajasvaran Logeswaran*
DOI: 10.2174/9789815080957123010004
PDF Price: $15
Abstract
This work investigates the various types and aspects of the determinants that
cause impulse purchase behaviour within the context of e-commerce websites. It delves
into finding the factors that trigger impulse purchase behaviour for consumers of both
male and female gender within the age brackets of earning potential. The findings of
this review highlight the factors that need to be in place before a purchase behaviour
from a consumer can be observed. These determinants above of impulse purchase
behaviour can generally be categorised into internal and external components, which
are analysed in this work.
Issuer Credit Rating Performance Report Using Sentiment Analysis
Page: 11-23 (13)
Author: Prabu Setyaji and Raja Rajeswari Ponnusamy*
DOI: 10.2174/9789815080957123010005
PDF Price: $15
Abstract
Indonesian Credit Rating Agency (CRA) is currently on its way to becoming
the early mover of digital transformation. CRA controls macroeconomics and has a
significant impact on many industries across the world. However, there are always
those that can exploit it through asymmetric information and human interaction. A
solution to reduce human interaction and enhancement is to build Natural Language
Processing (NLP) sentiment analysis models and then display the results using an
interactive dashboard story. Objectives are created for the aim of the project to be able
to conduct a feasibility study, develop a model based on a press release dataset,
conduct model evaluation, and display the results on an interactive dashboard. The
research aims to utilise press release documents with NLP sentiment analysis to
produce prescriptive analysis with interactive visualisation as the final output. Press
release files are processed by using several Machine Learning (ML) algorithms such as
Support Vector Machine (SVM), Multinomial Naive Bayes (MultinomialNB), Logistic
Regression (LR), and Multi-Layer Perceptron Artificial Neural Network (MLP-Ann).
This research will be carried out under Dynamic Systems Development (DSDM) and
Knowledge Discovery Database (KDD). This will allow the researchers to achieve all
objectives, permit models to perform very well, and let the output get displayed on a
dashboard as a storyboard.
Recommendations for Implementing an IoT-Based Inventory Tracking and Monitoring System
Page: 24-35 (12)
Author: Muhammad Ehsan Rana*, Kamalanathan Shanmugam and Chan Yu Hang
DOI: 10.2174/9789815080957123010006
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Abstract
Inventory management is vital in the industry because it allows
manufacturers and wholesalers to efficiently store and manage information in the
warehouse. Warehouse management provides the facility to control functions such as
storing newly shipped products in available locations and tracking inventory
information and product distribution for shipping. These complex processes in the
warehouse management system require manufacturers and wholesalers to ensure that
the warehouse operation is running smoothly and efficiently. The use of the Internet of
Things (IoT) for automating industrial and household processes has been increasing
rapidly in the last few years. This research focuses on IoT for tracking and monitoring
inventory, providing an automated and efficient system for manufacturers and
wholesalers. A comprehensive discussion has been conducted on IoT application layers
and recommended sensors for inventory tracking and monitoring systems. This
research also emphasizes applying IoT in inventory, logistics, and warehousing
operations. Researchers have proposed an IoT-based system specifically designed to
cater to the shortcomings of the existing inventory management systems.
Incorporate Artificial Intelligence into the Fitness Field to Curb Diabetes in Malaysia: Current and Future
Page: 36-47 (12)
Author: Wong Xin Yi, Mien May Chong* and Sivaguru A/L Subarmaniyan
DOI: 10.2174/9789815080957123010007
PDF Price: $15
Abstract
With the rapid technological change, most people are living an unhealthy
lifestyle and consuming processed food. Additionally, most people spend time on their
mobile phones instead of working on other activities such as exercise. Beginners
should have at least 2 to 3 days of working out per week, and the intermediate should
have 3 to 4 days of strength training. A set of stretching exercises is required after each
workout. Approximately 3.9 million people aged 18 and above are diagnosed with
diabetes in Malaysia. This means that 1 in 5 adults will be diagnosed with diabetes. The
prevalence rate has increased from 13.4% in 2015 to 18.3% in 2019. Some of the main
factors that can cause a person to acquire diabetes are obesity and consuming excessive
amounts of food with high sugar levels. The two types of diabetes are type 1 diabetes
and type 2 diabetes. Type 1 diabetes results in the body not producing insulin, whereas
type 2 diabetes causes the body to not respond to insulin even though it produces
insulin.
An RSA-based Secure E-hailing Application
Page: 48-61 (14)
Author: Loo Jun Hao*, Nik Sakinah Nik Ab Aziz and Nik Nurul Ain Nik Suki
DOI: 10.2174/9789815080957123010008
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Abstract
E-hailing and taxi services play a vital role in the transportation industry. It
inadvertently also provides opportunities for malicious hackers to hack into e-hailing
applications to gain private user information. With the increase in data breaches, there
is a need for an encryption technique to increase the security level and assure the safety
of users’ data. This study proposed SafeCar, a secure e-hailing application using the
RSA algorithm. SafeCar is developed using ASP.NET language. Testing has been done
to evaluate the security, input validation, and user satisfaction. The testing results
showed that 50% had a very good security level, another 50% with a good security
level, 100% user satisfaction, and 67% had very good input validation, with another
33% resulting in good input validation.
Digital Divide in Primary Schools
Page: 62-78 (17)
Author: Veerakumar Soundrapandian*
DOI: 10.2174/9789815080957123010009
PDF Price: $15
Abstract
The gap in the use of the Internet and computers in society is classified as
the level-two digital divide. Research papers from 61 popular journals published
between 2005 to 2018 were examined to carry out a Systematic Literature Review
(SLR). In addition, renowned organisations’ research publications from 2010 to 2018
were used. The adoption of technology, the type of use, the frequency of use, and the
effectiveness of use were the main areas of level-two Digital Divide research. This use
is further affected by the accessibility determinants of Affordability, Gender, Age,
Education Level, Race, and Digital Skills. None of the research used the accessibility
determinants as a moderator between availability and use.
Intrusion Detection System for the Internet of Medical Things (IoMT)
Page: 79-97 (19)
Author: Ameer A. N. Alasaad, Nor Azlina Abd Rahman* and Yusnita Yusof
DOI: 10.2174/9789815080957123010010
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Abstract
In this paper, the authors proposed the design of an Intrusion Detection
System (IDS) that can be used in the healthcare sector to increase cybersecurity. This
sector is facing high cyber threats. Similar IDS systems will be reviewed in the
following pages, followed by the justifications of why the authors decided to design the
IDS to be Signature-based. The experimental results showed that the developed IDS
could successfully capture the network traffic, record the logs and show an informative
alarm screen with a few other options within the dashboard to assist the user in
handling the situation and assure the hospital network security.
Cyber Security State of Industrial Internet of Things (IIoT)
Page: 98-116 (19)
Author: Ali Ahmed Mohammed Ali Alwashali, Nor Azlina Abdul Rahman and Mohammad Haziq Roszlan
DOI: 10.2174/9789815080957123010011
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Abstract
Cybersecurity is a critical component of technology and must be considered
during the early stages of the development of any system. Cyber security issues and
challenges faced by IIoT are discussed in this paper. The first section of this paper
focuses on Industrial Control System (ICS) environments where IIoT are deployed to
understand the nature of business and technology companies with IIoT networks,
followed by a comparison to understand the difference between Operational
Technology (OT) and Information Technology (IT) networks and how both can be
used to serve the need of business requirements. This paper evaluates the state of cyber
security in industrial networks and IIoT and the safety and privacy concerns found in
the literature. Solutions and improvement techniques introduced to cyber security
functions mainly focus on prevention, detection, and response. Moreover, IoT
organisational and operational security and cyber threat intelligence are also discussed.
Finally, an approach is presented on how to conduct a security assessment on IIoT
environments safely.
Machine Learning for Browser Privacy
Page: 117-126 (10)
Author: Kelvin Tan and Rajasvaran Logeswaran*
DOI: 10.2174/9789815080957123010012
PDF Price: $15
Abstract
Online privacy is an Internet user’s control of how much personal
information is shared with a third party. Unfortunately, some third parties, such as data
brokers, collect user data without permission to resell the data to other parties. Browser
tracking allows each Internet user to be uniquely identified, and in-depth user profiles
are built. Browser fingerprinting is one of the most effective methods of browser
tracking. It uniquely identifies each user through their devices’ configuration, even for
users using the same device models. Using Virtual Private Networks, the Tor browser
and specific browser extensions as a countermeasure against browser fingerprinting are
not widespread, so it often results in a compromised user experience. Researchers have
proposed various classification machine learning approaches to improve browser
privacy; some focus on recognising and blocking advertisements and website scripts
that track users. In contrast, others identify potential vulnerabilities in browser security
configurations. There is a need for more research in machine learning, especially
natural language processing, to enhance browser privacy.
ARP Spoofing in Launching Man-in-the-Middle Attack
Page: 127-140 (14)
Author: Soon Qi Huan and Vinesha Selvarajah*
DOI: 10.2174/9789815080957123010013
PDF Price: $15
Abstract
Man-in-the-Middle (MITM) attack is a typical eavesdropping cyberattack.
The attacker can launch a MITM attack whenever the attacker and victims are on the
same network. Here is a scenario: A MITM attacker connects to Subway’s Wi-Fi and
waits for the victim to connect to the Subway Wi-Fi. Eventually, Victim A walks in
and connects to Subway’s Wi-Fi. Once Victim A gets connected and is on the same
network as the attacker, the attacker can launch an attack to intercept the network
traffic of Victim A. Therefore, everyone on the same network connection with the
attacker can be the target of a MITM attack. In this paper, the MITM attack will be
introduced. The attacker can spy on the victim, steal sensitive credentials, disrupt
communications, or even corrupt the data through the said attack. To discover how the
MITM attack works, this paper explains it based on the ARP Spoofing attack, which
exploits the ARP protocol to send out forged ARP responses. ARP Spoofing attack is
one of the MITM attacks. This paper emphasises the MITM attack phases, different
types of MITM attacks, ARP Spoofing attacks, and how ARP works. The
demonstration of steps for launching an ARP Spoofing attack and the tools involved,
like Nmap, Arpspoof, and Wireshark, are also included.
Elderly Monitoring Using the Internet of Things (IoT)
Page: 141-149 (9)
Author: Matthew Tan Xian Long* and Intan Farahana Binti Kamsin
DOI: 10.2174/9789815080957123010014
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Abstract
As people get older and their bodies weaken, they become more prone to
illnesses and injuries. This paper reviews several research papers that discuss
implementing a monitoring system that monitors the elderly. This research aims to
improve the healthcare and safety of elderly people alone at home by implementing a
monitoring system based on IoT technology in Malaysia. This paper also discusses how
stratified sampling methods and surveys can help identify elderly peoples’ preferences
and improve the monitoring system. The system proposed in this research uses real-time pulse and temperature monitoring, real-time fall detection monitoring, a cloud
database, and a mobile application that could help reduce the effort and worry of taking
care of the elderly. The findings of this research will help to improve the lives of
elderly people that prefer staying in their own homes and will hopefully reduce the
effort needed to take care of them. Future system improvements will include blood
pressure and respiration rate monitoring, allowing more accurate monitoring of their
health.
IoT-Based Medical Ecosystem
Page: 150-162 (13)
Author: Wong Wan Jing, Nor Azlina Abdul Rahman and Daniel Mago Vistro*
DOI: 10.2174/9789815080957123010015
PDF Price: $15
Abstract
The Internet of Things (IoT) is an evolving technology in the emerging
digital transformation domain. The healthcare system is also growing, using IoT to
improve human life and save more lives. With the assistance of IoT technology,
physicians can easily monitor patients’ health conditions in real-time. A cardiac
pacemaker is a medical device connected to the IoT environment to improve the
efficiency of healthcare. However, low-quality IoT design will bring disadvantages,
such as cyber-attacks. Every process of building IoT medical devices should evaluate
the product before launching it to the market. Manufacturers or hospitals should
organise their critical infrastructure orderly to protect confidential data. The data
should achieve the confidentiality, integrity, and availability of the CIA triad, which is
the foundation of information security. This paper aims to study the vulnerabilities of
IoT medical devices, the methods of possible attacks from hackers, and organisational
and operational security to address cyber security in the healthcare industry. Moreover,
it proposes a framework for the IoT medical ecosystem between the patient and the
hospital to improve the existing IoT medical ecosystem.
Active Learning-based Mobile Learning System for Students of Asia Pacific University
Page: 163-174 (12)
Author: Hen Kian Jun* and Siti Azreena Binti Mubin
DOI: 10.2174/9789815080957123010016
PDF Price: $15
Abstract
In recent years, mobile technology has become increasingly more available
and advanced, especially in education. Mobile learning technology allows individuals
to have online distance learning in COVID-19 by transforming traditional Learning
from online Learning to mobile Learning. The implementation of mobile Learning in
higher education is essential because it allows students and tutors to stay connected and
allows students to access online materials for active Learning at any time. Therefore,
this research proposes a mobile learning system integrated with active learning
practices for Asia Pacific University students in the learning process. This will give
students more positive outcomes such as better academic performances and
achievements, increased motivation and attention in studies, increased learning
satisfaction in students, and training them to be active learners. This research is
conducted using the Quantitative method to the selected participations, and the
outcome of this research could contribute to the entire education field in promoting
active learning practices to improve academic performance and also provide other
researchers with an insight into exploring the mobile learning system into higher
education.
Analytics on Airline Customer Satisfaction Factors
Page: 175-184 (10)
Author: Pit Khien Leong and Rajasvaran Logeswaran*
DOI: 10.2174/9789815080957123010017
PDF Price: $15
Abstract
Dissatisfaction with the services provided causes customer loss and
customer churn in airline companies. Analytics conducted in assessing customer
satisfaction in airline companies and their analytical methods are reviewed to identify
the analysis's strengths, weaknesses, and gaps. Data analytics on assessing customer
satisfaction have been conducted on facilities and services provided, price, service
quality, reviews of customers, and flight catering. However, this research indicates that
only a few in-depth studies consider flight delays as a critical factor influencing
customer satisfaction. A flight is considered delayed if it departs or arrives 15 minutes
later than the scheduled time. Therefore, in this research, further analytics can be done
on the amount of time-of-flight delay in assessing customer satisfaction.
A Personalized Recommendation System for Academic Events
Page: 185-196 (12)
Author: Henry Khoo Shien Chen and Shubashini Rathina Velu*
DOI: 10.2174/9789815080957123010018
PDF Price: $15
Abstract
Academic events are growing in numbers worldwide annually for
researchers to discuss their work. The research on recommendation systems in
academic domains has high significance for researchers. The classical approach to the
recommender system uses content-based and collaborative filtering that tends to
produce poor results. The focus of the study is to determine the factors involving the
selection of academic events and create a user-based personalised recommender system
for academic events. A survey will be conducted to identify the factors affecting the
choice of events. The system will filter the results of the events using a matching
matrix by conducting a factor analysis and receiving input to find the most relevant
academic events from the database. The study's approach evaluates the result based on
the pre-processed data and the similarity measures between a similar user (Top-n) and
an active user for events with a higher probability of participation. The weighted
average of the neighbour’s ratings will be generated for the predictions of the events.
The study’s outcome will prove that the personalised recommendation system is better
than the classical approach in finding the most relevant events. The recommendation
system can be optimised in domains.
e-Health Web Application with Electronic Medical Records (EMR) and Virtual Appointments
Page: 197-209 (13)
Author: Faridzuan Bin Barakath Rahman, Tanveer Khaleel Shaikh* and Nurul Husna Binti Mohd Saad
DOI: 10.2174/9789815080957123010019
PDF Price: $15
Abstract
Relying on papers to document medical records hinders the United Nations’
goal of creating a more sustainable world for future generations. With the excess usage
of paper, the world is contributing towards global warming. Medical staff tends to take
a long time to pull out a patient’s medical record when it can be done in a matter of
seconds. Being paperless does not immediately mean it is effective or transparent, as
medical providers don’t usually share the content of a patient’s medical record with the
patient itself, let alone a different medical provider. Another issue is the lack of
transparency within the healthcare sector. This research focuses on the need for
Malaysia to have an e-Health system, preparation and participation towards the e-Health system, and the security of migrating towards the e-Health system.
Subject Index
Page: 210-214 (5)
Author: Muhammad Ehsan Rana and Manoj Jayabalan
DOI: 10.2174/9789815080957123010020
PDF Price: $15
Introduction
Emerging Technologies for Digital Infrastructure Development is a comprehensive and insightful book that reviews the transformative impact of cutting-edge technologies on the digital landscape. It presents 16 topics, from e-commerce consumer behavior to AI applications in healthcare and cybersecurity, this book offers a detailed overview of the role of technology in shaping the modern world. With a focus on bridging the digital divide in education, the book presents innovative solutions to contemporary challenges. The editors also emphasize the importance of privacy and security in an interconnected world by discussing cybersecurity measures and threat detection strategies. The book serves as a valuable resource for technology professionals, researchers, and academics, offering a deep dive into the latest trends and applications in digital infrastructure. It also caters to business leaders, policy makers, and students seeking to understand the transformative potential of emerging technologies. Key technologies highlighted in the book include machine learning and AI models, IoT, data analytics, recommendation systems and e-learning systems. Applications of these technologies are demonstrated for healthcare, e-commerce, cybersecurity, aviation and education sectors. Emerging Technologies for Digital Infrastructure Development offers insights and solutions that pave the way for a secure, efficient, and inclusive digital future.