bluuebunny/crossref_metadata_2025 huggingface dataset:
Unleashing the E-Wallet Enchantment
Authors: Hashim Ahmad Azwan bin Meor , Ab Aziz Muhammad Ridhwan , Abu Seman Junaidah
Published: November 2024

Due to the increasing use of e-wallets, it is crucial to evaluate their compliance with Shariah to meet the needs of Muslim customers in line with Islamic principles. This chapter seeks to offer understanding of maqasid al-Shariah in the context of e-wallet usage and financial well-being. In connection with this, the development of financial technology will expand the capabilities and services of e-wallets beyond being just a basic payment tool. This expansion includes multiple funding sources, such as credit cards, buy now, pay later (BNPL), and short-term loans. These multi-sourcing fundings may trigger expenditure, aid, or impede financial well-being. Therefore, a user's spending behaviour can affect the e-wallet usage and their financial well-being. Understanding the results of this research would partly contribute to the general knowledge of Islamic finance and fintech, with the intention of developing Shariah-compliant digital financial solutions suited to the needs of Muslims all over the world.
The Geographic Review of Short-Term Rentals in Minsk, Belarus
Authors: Bezruchonak Andrei
Published: November 2024

Short-term rentals in Minsk, the largest city in Belarus, have become an essential aspect of the urban socio-economic landscape, affecting the accommodations for international and domestic tourists. Due to booking flexibility and the diversity of locations, STRs are often considered a cost-effective alternative to traditional forms of collective accommodation. This study aims to present a brief historical, socio-economic, regulatory, and geographic overview of the current state of STRs in Minsk. The analysis of the spatial distribution of 1677 STRs in Minsk, collected from one of the oldest and largest booking platforms, revealed the areas with the highest concentrations of listings due to proximity to the CBD, historic centre, transport infrastructure, or new affordable housing. The chapter also addresses how external factors (such as COVID-19 in 2020-2021, sanctions, etc.) affected the STR market in the capital city of Belarus. This material should be considered a contribution to the chapter on various case studies.
Integrating AI Into One's Teaching Philosophies and Dispositions in Higher Education
Authors: Wang Viktor , Ellington Linda
Published: November 2024

AI's role in higher education is multifaceted, influencing how teachers perceive and implement their teaching philosophies. Teachers who embrace AI tools may develop dispositions that value data-driven decision-making, continuous assessment, and the customization of instruction to meet diverse student needs. Furthermore, AI can reinforce a philosophy of lifelong learning among educators as they continually adapt to new technologies and methodologies. This ongoing professional development shapes teaching dispositions open to innovation, reflective practice, and a collaborative learning community. The authors assert that as AI becomes more embedded in educational practices, it transforms teaching philosophies and reshapes educators' fundamental dispositions, aligning them more closely with modern, technology-enhanced teaching philosophies.
What Would Confucius Do?
Authors: Schenck Andrew
Published: November 2024

Although AI will undoubtedly have a prominent role in the cultivation of language learning, its application in modern-day English as a Foreign Language (EFL) classrooms remains limited. The failure to adapt theory to practice may be the result of assumptions about technology, which posit that all learners will benefit equally from unilateral adoption of new techniques. In reality, the benefits of specific AI technologies may differ significantly based on characteristics of an educational environment. In Confucian countries, for example, traditional cultural values for group action may adversely impact autonomous learning styles that value LLM “tutors” like ChatGPT. It is essential that cultural and contextual factors of an educational environment be considered before AI technologies are utilized. Therefore, the present book chapter examines educational variables associated with Confucian contexts, yielding insights for the improvement of EFL instruction.
Redefining Learning in the Digital Age
Authors: Baek Eun-ok , Wilson Romina Villaflor , Liu Chang
Published: November 2024

This chapter explores the potential alignment and challenges between generative AI tools and constructivist principles in the context of education. Despite the significant advancements in generative AI technology, there is still a need for further investigation and research when integrating AI into education. The integration of generative AI technologies into the educational setting holds immense potential for enhancing constructivist learning approaches. By leveraging the natural language processing capabilities of AI models, interactive, learner-centered learning environments can be created, promoting active knowledge construction, meaning-making, and authentic learning experiences. However, the effective integration of generative AI in education requires a deep understanding of its affordances, potential challenges, and ethical considerations. This chapter delves into the fundamental principles of constructivism and generative AI, examining their interplay within the educational sphere. It includes case studies and addresses ethical considerations.
AI in Education
Authors: Kaiser Greg , Kaiser Sue
Published: November 2024

This chapter serves as a culmination of our exploration into the integration of artificial intelligence (AI) in both pedagogical and andragogical education. It synthesizes the comprehensive analysis presented throughout the book on how AI is reshaping teaching and learning for children and adults. The chapter underscores the transformative potential of AI in creating personalized, engaging, and effective educational environments while addressing the critical challenges of data privacy, algorithmic bias, and the digital divide. Through a review of key findings, ethical considerations, and practical implications, the chapter provides a forward-looking perspective on the future of AI in education. It emphasizes the need for a collaborative effort among educators, technologists, and policymakers to harness AI's potential effectively, supported by case studies that illustrate successful implementations. The insights offered aim to inspire and guide educators, researchers, and decision-makers as they navigate the evolving landscape of AI in education.
Integrating AI in Formative Assessment Under UDL Principles in Diverse Learning Environments
Authors: Teles Rui Oliva , Moreira Filipe T.
Published: November 2024

The integration of artificial intelligence (AI) in formative assessments within the universal design for learning (UDL) framework presents promising opportunities to enhance educational inclusivity and effectiveness. UDL emphasizes flexible teaching strategies to accommodate the diverse needs of learners. AI also offers the potential to personalize learning by providing real-time adaptive feedback, aligning with UDL's core principles of multiple means of engagement, representation, and expression. However, challenges remain, such as addressing ethical concerns, preventing algorithmic bias, and ensuring the equitable use of student data. The integration of AI-driven formative assessments in UDL-based learning environments requires thoughtful consideration of its implications to foster inclusive and equitable education for all students. This chapter takes a reflective look at this issue and presents alternative perspectives.
A Comprehensive Study of Advancements in Intelligent Tutoring Systems Through Artificial Intelligent Education Platforms
Authors: Gomes Dipta
Published: November 2024

Intelligent tutoring systems offer personalized, adaptive learning experiences through digital platforms, contrasting with traditional methods. The integration of AI enhances ITS capabilities, providing dynamic adaptation, real-time feedback, and optimized outcomes. Ethical considerations, including privacy and bias mitigation, are paramount. Comparative analysis reveals AI's potential to reshape education, with future directions including sophisticated algorithms, diverse educational settings, and innovative pedagogical approaches. Practical implementation and evaluation will drive advancements, empowering students and educators in personalized learning journeys. The integration of AI in ITS represents a significant advancement, enabling systems to analyze vast amounts of data and tailor educational experiences to meet the unique needs and preferences of each learner. Through a comparative analysis, this chapter highlights the distinctive features and benefits of ITS compared to traditional learning methods.
Revolutionizing Student Evaluation
Authors: Giri Akshyata
Published: November 2024

This chapter explores the transformative potential of AI-powered tools in student evaluation, focusing on their ability to enhance educational assessment practices. It delves into the integration of artificial intelligence in plagiarism detection and automated grading systems, highlighting the benefits of accuracy, efficiency, and fairness these technologies bring to the educational landscape. By examining various AI-driven assessment models, the chapter demonstrates how these innovations can provide detailed, individualized feedback, promote academic integrity, and streamline the evaluation process for educators. Additionally, the chapter addresses potential challenges and ethical considerations associated with AI in education, advocating for a balanced approach to harnessing its full potential while ensuring transparency and equity. Ultimately, this chapter underscores the significance of AI-powered assessment as a catalyst for revolutionizing student evaluation and fostering a more effective and inclusive educational environment.
Introduction to Neuromorphic Computing
Authors: Londhe Supriya Amol , Shivthare Sunayana Kundan , Sharma YogeshKumar
Published: November 2024

Neuromorphic computing is an innovative and fast-evolving field poised to significantly impact human life in the coming decades. By emulating the brain's neural architecture using modern electronic systems, it aims to enhance computer architectures and revolutionize computing and machine learning tasks. Neuromorphic systems transcend traditional computing paradigms by integrating memory and computation in a massively parallel fashion, unlocking unprecedented computational efficiency and performance. This approach promises breakthroughs in pattern recognition, natural language processing, and autonomous decision-making. Additionally, neuromorphic computing could advance electronics and biology integration, leading to innovations like brain-computer interfaces and neuroprosthetic devices that revolutionize healthcare and human augmentation.
A Review of GAN-Synthesized Brain MR Image Applications
Authors: Tiwari Ankita , Tavse Sampada , Bachute Mrinal , Bhola Abhishek
Published: November 2024

Recent advancements in brain imaging technology have led to a rise in the use of magnetic resonance imaging (MRI) for clinical diagnosis. Deep learning (DL) techniques have emerged as a valuable tool for automatically detecting abnormalities in brain images without manual intervention. Meanwhile, generative adversarial networks (GANs) have shown promise in generating synthetic brain images for a variety of applications, such as image translation, registration, super-resolution, denoising, motion correction, segmentation, reconstruction, and contrast enhancement. This chapter conducts a comprehensive review of the literature on the use of GAN-synthesized images for diagnosing brain diseases, drawing on data from studies in the Web of Science and Scopus databases from the past decade. The review examines the various loss functions and software tools used in processing brain MRI images, as well as provides a comparative analysis of evaluation metrics for GAN-synthesized images to assist researchers in selecting the most appropriate metric for their specific needs.
Classification of Moderate and Advanced Dementia Patients Using Gradient Boosting Machine Technique
Authors: Gowroju Swathi , Choudhary Shilpa , Jain Arpit , Srilakshmi R.
Published: November 2024

In the twenty-first century, caring for persons with Dementia's has become extremely difficult due to the prevalence of dementia cases. Using data from the OASIS (Open Access Series of Imaging Studies) program provided by the University of Washington Dementia's Disease Research Center, the study presents a new predictive model for Dementia's. Dementia, a chronic condition and it's become a serious health concern in adults. Various methods of data imputation, preprocessing, and transformation were used to prepare the data for model training. Machine learning algorithms, including AdaBoost (AB), Decision Tree (DT), Exclusion Tree (ET), Gradient Boost (GB), K-Nearest Neighbor (KNN), Logistic Regression (LR), Naive Bayes (NB;, Random Forest (RF), and Support Vector Machine (SVM), were used in this field. These algorithms were evaluated on both the complete feature set and a subset of features selected via the Least Absolute Shrinkage and Selection Operator (LASSO) method. Comparative analysis based on accuracy, precision, and other metrics showed that the proposed method achieved the highest accuracy of 96.77% using Support Vector Machine (SVM) with all feature sets, further refined and applied, has great potential for the diagnosis of early Dementia's disease (AD) disease.
Analysis and Data Exploration of Dynamic Formula 1 Using the Ergast API Machine Learning
Authors: Vishwakarma Harshlata , Gupta Sumit , Baronia Arpita , Kumar Satrughan , Naik Granth , Kumar Munish
Published: November 2024

F1 is the top engineering, strategy, and driving class. Early on, F1 prioritises beauty and data science. Teams examine massive car construction, racing strategy, and performance data after each race to dominate. The study emphasises F1 business intelligence. Historical and real-time data help teams improve vehicle performance, make strategic choices, and anticipate race results. Additional sports technology and racing consequences are considered. It considers F1 economically significant because to its worldwide appeal, high pricing, meaningful contributions, and vehicle upgrades. Race analysis follows pre-race checks and performance data processing. Demands real-time processing and analysis to maximise data use. F1's competitive data visualisation and analysis tools are contrasted. Decision-making, data analysis Supervised, unsupervised, reinforcement, and deep learning tests. Predictive maintenance, performance modelling, and failure detection benefit from supervised learning, including regression and classification. Race clustering and outlier identification are unsupervised.
Neuromorphic Software Tools and Development Environments
Authors: Sailaja D. , Sharma Yogesh Kumar , Nune V. L. Manaswini
Published: November 2024

We are seeing a technological transformation now that was unthinkable ten years ago. Although introducing artificial intelligence (AI) in contemporary business theoretically permits unrestricted expansion, the dreaded power-wall issue in the parallel computing paradigm prevents us from fully using AI's potential. Because they are expected to operate at extremely low power, modern Neuromorphic accelerators provide a profitable substitute for conventional artificial neural network (ANN) accelerators for deep learning (DL). Neuromorphic accelerators are centred on Spiking Neural Networks (SNN), which seek to mimic the extremely energy-efficient mechanism operating in our brains. This chapter covers the general overview of Neuromorphic software tools and development environments, including platforms, frameworks, and best practices.
Neuromorphic Computing
Authors: Pandey Devendra G. , Sharma Yogesh Kumar , Kumar Nimish
Published: November 2024

The exponential growth of data and information has stimulated technological progress in computing systems that utilize them to effectively discover patterns and produce important insights. Neural network algorithms have been applied to conventional silicon transistor-based hardware to do highly parallel computations, drawing inspiration from the structure and functions of biological synapses and neurons in the brain. Nevertheless, synapses composed of many transistors are limited to storing binary data, and the utilization of intricate silicon neuron circuits to handle these digital states poses challenges in achieving low-power and low-latency computing. This study examines the significance of developing memories and switches for synaptic and neural components in building Neuromorphic systems that can efficiently conduct cognitive tasks and recognition. This chapter closely examines and rates the latest progress in Neuromorphic computing, focusing on how these changes impact edge and Internet of Things technologies. It is also being thought about how to use tiny switches and short-term memory to copy the action of neurons. Once this is done, more Studies in many areas should be able to focus on the design, circuitry, and devices of Neuromorphic systems.
Social Media Influencers Shaping Ecotourism Destination Branding
Authors: Rehman Saqib , Arshad Nabeela , Ullah Aman , Noor Nadia
Published: November 2024

Influencers can use large online followings to create awareness about the destination, answer the tourists' queries, and structure the destination image, environment, and sustainable accommodations while staying in a particular landscape. Moreover, influencers may promote the ecotourists' destination, grab the trust of their followers, and be a carrier of promoting eco-friendly activities at destinations. The theory of planned behaviour sets the direction for this study. It offers a thorough framework for clarifying the factors that may affect or limit persons' intentions to perform certain behaviours. TPB can assist tourists in understanding the motivations behind their choice of destination by analyzing the elements influencing ecotourism. Influencers who engage with ecotourists can shape their perceptions of a region by emphasizing its unique attributes, like captivating natural scenery and sustainable tourism practices.
Building Brand Authenticity and Strategies for Genuine Destination Experiences
Authors: Jamil Khalid , Anwar Aliya , Baig Sajjad Ahmad
Published: November 2024

This chapter is devoted to understanding the concept of constructing the destination brand authenticity, focusing on the delicate interplay between tourism and culture. This chapter discusses over-tourism, cultural commodification, environmental degradation, and economic dependency issues based on Bali, Venice, and Kyoto case studies. Every case presented in the paper describes specific actions taken to ensure that genuine values are preserved, including the involvement of the local population, the promotion of ecotourism, the use of the legal framework, cultural conservation, and the diversification of the economy. The measures taken by Bali regarding sustainable tourism, measures taken by Venice in terms of regulatory and artistic support, and measures taken by Kyoto in terms of cultural preservation and implementation of sustainable practices help preserve the true identity of places popular among tourists. The chapter ends by stressing that the approaches discussed should be integrated and dynamic, including community involvement and sustainability.
Destination Branding and Luxury Experiences in the Digital Age
Authors: Martin Mario Sierra , de Brito Correia Marisol , deMatos Nelson da Silva , Caquero Álvaro Díaz , Urbistondo Pilar Alarcón
Published: November 2024

In an era where the digital image of a destination can be as influential as actual experiences, this chapter will evaluate how Spain and Portugal use digital platforms to cultivate and promote their identities as luxury destinations. Through a methodology that combines quantitative and qualitative analysis, the effectiveness of the digital marketing techniques employed will be examined, including content management on social media, user interaction, and the adaptation of marketing strategies to changes in consumer preferences and behaviours. Additionally, our research leverages user-generated content, specifically comments extracted from TripAdvisor, to gauge perceptions of luxury hotels in both countries. By analyzing these comments, we gain insights into customer satisfaction, expectations, and the overall reputation of the hotels. This comprehensive approach allows us to assess the strategies deployed by these destinations and their real-world impact on the luxury tourism market.
Fostering Ecotourism Brands Through Local Engagement in Peru and the Maldives
Authors: Hashmi Gulen , Roberts Julie
Published: November 2024

As a subset of sustainable tourism, ecotourism has increased in protected tourism sites and biodiversity hotspots as increasingly more tourists look for meaningful natural experiences. This necessitates understanding how to leverage tourist-local community engagement in ecotourism branding. The chapter focuses on two case studies from two contexts, which harness tourist-local community engagement in their ecotourism branding. While the first case study relates to the conservation of nature and cultural heritage within an Amazonian rainforest in Peru, the latter refers to preserving the ecosystems of the ocean in the Maldives. Although both cases leverage tourist-local community engagement to a certain extent, they heavily focus on uplifting local communities, guest education and scientific research in their ecotourism branding. The study lacks empirical evidence, yet it has managerial and policy implications. It advises managers and policymakers to foster a sustainable ecotourism brand with the tourist-local community interaction in mind.
Microfinance Governance
Authors: Hussain Mohammad Delwar
Published: November 2024

The only financial sector where stakeholders may gain from both poverty alleviation and sustainability is microfinance. Microfinance practitioners argue that the absence of governance standards in microfinance institutions (MFIs) is the reason for this dual role. The focus will be on understanding how effective governance can enhance the performance and sustainability of MFIs, ensure accountability, and protect the interests of stakeholders, including borrowers, investors, and the community. The focus of this research is on data from a frontier market economy, and the research framework takes into account multiple theories, including three management-based theories (resource dependence, stakeholder, and stewardship) and the economic-based agency theory. The chapter will provide a blend of theoretical insights, practical examples, and case studies to offer a comprehensive understanding of the critical role governance plays in the microfinance sector. It was discovered that the profitability of MFI has a non-linear impact on its services for the impoverished, taking into account both “social-welfare and commercial logic.” MFIs' outreach typically declines when they aim for large profits. Consequently, the study's conclusions recommend creating a governance handbook that prioritizes social welfare reasoning while increasing MFI services for clients who are less fortunate. It is also necessary to generate institutional structures and separate governance guideline, based on the social welfare logic. As part of their responsibility to ensure that the MFIs adhere to the social mission, the microfinance board of directors may designate a social director. These directors have been acting on behalf of the MFIs' clients.
Governance and Performance of Local Authorities in Morocco
Authors: El Moujahid Lamia , Taoussi Ghita , Tahri Wadi , Cherkaoui Mariem , Safiry Mounia
Published: November 2024

This study explores the relationships between governance and the performance of local governments in Morocco. The adopted methodology facilitates a detailed analysis of the causal links between various dimensions of governance—such as transparency, participation, convergence, and partnership—and the performance of local governments, evaluated in terms of relevance, effectiveness, efficiency, and impact. The findings reveal that specific governance dimensions significantly influence local government performance in Morocco. For instance, transparency and citizen participation emerge as critical factors positively affecting the quality of local public services. By employing structural equation modeling, these relationships are quantitatively identified, offering valuable insights for decision-makers and practitioners seeking to enhance local governance in Morocco.
Sol-Gel Synthesis of a New Composition of Bioactive Glass
Authors: El Bouami Halima , Mabrouk Assia , Mercier Cyrille , Follet Claudine , Bachar Ahmed
Published: November 2024

Bioactive glasses hold immense promise for tissue engineering and bone regeneration due to their ability to bond with living tissues and stimulate bone growth. This chapter explores the sol-gel method, a versatile technique offering advantages like lower processing temperatures and superior compositional control, for synthesizing a new bioactive glass composition. We discuss the fundamental principles of sol-gel chemistry and precursor selection for achieving the desired elements in the final glass. The chapter details the design rationale behind the new compositions, targeting the effect of therapeutic ions promotion specific antibacterial and angiogenesis properties and enhancement in bioactivity and osteoblast production. Additionally, the chapter offers a brief overview of in vitro bioactivity assessment methods for evaluating the glass's interaction with physiological fluids. Finally, we discuss the potential applications of the newly developed bioactive glass and propose future research directions for further optimization and exploration of its functionalities.
Electrochemical Performance of Biomass-Derived AC and CNTs-Based Supercapacitors
Authors: Atamanov Meiram , Atamanova Tolganay , Taurbekov Azamat , Mansurov Zulkhair
Published: November 2024

This study investigates the electrochemical performance of activated carbon (AC) derived from two biomass sources, rice husk (RH) and walnut shell (WS), utilizing two activation methods: chemical activation with potassium hydroxide (KOH) and physical activation with carbon dioxide (CO2). The aim is to evaluate the potential of these bio-derived carbon materials in energy storage applications, particularly for supercapacitors. The electrochemical behavior of the activated carbon electrodes was assessed using cyclic voltammetry (CV) and galvanostatic charge-discharge (GCD) techniques, along with electrochemical impedance spectroscopy (EIS), to evaluate the specific capacitance, energy density, charge transfer resistance, and ion diffusion properties.
Engineering the Invisible
Authors: Kumar Krishnappa Madhu , Kumar Manjappa Kiran , Kumar Nagaraj Sasi , Kumar Lakshman N. Sampath
Published: November 2024

Nanoporous materials are a major research focus due to their unique structures and diverse applications in catalysis, drug delivery, and environmental remediation. This chapter reviews the synthesis, characterization, and practical applications of nanoporous materials. It examines various synthesis methods, detailing their principles, advantages, and limitations. Key characterization techniques such as scanning electron microscopy (SEM), X-ray diffraction (XRD), transmission electron microscopy (TEM), and atomic force microscopy (AFM) are discussed for assessing important properties like surface area and pore size. The chapter also highlights the performance of nanoporous materials in enhancing reaction rates in catalysis, improving drug delivery systems, and aiding in pollutant degradation. Ideally, this chapter contributes to providing researchers and practitioners with the knowledge necessary to advance the further development and application of nanoporous materials in various technological and industrial areas.
Advancements and Challenges in the Internet of Drones Security Issues
Authors: Abu-Zaid Amani , Aljaidi Mohammad , Al-Na'amneh Qais , Samara Ghassan , Alsarhan Ayoub , Qadoumi Braa
Published: November 2024

This academic review paper offers an in-depth evaluation of the Internet of Drones (IoD), specializing in advancements, demanding situations, and future research avenues inside this emerging area. By inspecting key regions which include ethics, sustainability, energy fashions, and security in IoD networks, the paper offers a complete evaluation of the complexities and considerations concerned with drone technology integration. It severely evaluates unresolved issues in UAV-primarily based delivery systems. Through the identification of study gaps, and proposed answers, . This scholarly evaluation paper notably explores the dynamic panorama of the Internet of Drones (IoD), delving into cutting-edge advancements, chronic demanding situations, and promising study trajectories within this evolving area. By scrutinizing ethical implications, sustainability concerns, power modeling intricacies, safety paradigms, and protection imperatives in IoD ecosystems, the research gives a nuanced perspective on the intricate interplay of things shaping the future of drone generation integration.
DIS Flooding Attack Impact in RPL-Based 6LowPAN Network
Authors: Al-Na'amneh Qais , Dhifallah Walid , Hazaymih Rahaf , Almaiah Mohammed Amin , Alsheyab Asalla , Alshinwan Mohammad , Qadoumi Braa
Published: November 2024

Networks that use RPL are vulnerable to security concerns called RPL flood attacks. The IPv6 Routing Protocol for Low-Power and Lossy Networks mitigation technique Secure-RPL is proposed in this study to effectively identify and prevent DIS flooding attacks with minimal overhead. Malicious nodes overload available resources, obstruct communication, and impede data transfer, which may result in performance issues and service outages. It is essential to use countermeasures like message filtering and intrusion detection systems. This chapter shows that the DIS flooding assault increases the network's control packet overhead, greatly degrading network performance. This raises the network nodes' power consumption. To solve this issue, a mitigation strategy called Secure-RPL is proposed. The suggested approach considerably reduces the impact of a DIS flooding attack on the network performance. The effectiveness of the suggested Secure-RPL system is compared to the normal RPL protocol.
Revisiting the Paradigm of Abortion-Related Reproductive Health Laws in the European Union
Authors: Sodha Ravisha , Singh Hemendra
Published: November 2024

This chapter provides a detailed examination of abortion-related reproductive health laws within the European Union, focusing on their historical development, current legal frameworks, and the socio-economic factors that influence access to abortion services. It explores the diverse range of abortion laws across EU member states, highlighting the contrast between liberal approaches in countries such as Sweden and France and restrictive policies in nations like Poland and Hungary. The chapter also considers the impact of judicial interventions on shaping these laws and the broader implications for reproductive rights. Through this comprehensive analysis, the chapter aims to shed light on the complexities of abortion law within the EU and its relevance to women's reproductive health.
Men's Role in Reproductive Health and Rights
Authors: Rituraj Ankita , Jha Ayush
Published: November 2024

Reproductive health and rights encompass a broad range of issues, including family planning, maternal health, sexual health education, and access to safe and legal abortion. While these areas are often viewed through a lens that primarily considers women's roles and responsibilities, the involvement of men is equally crucial. Lack of male engagement in reproductive health perpetuates gender inequality. By reinforcing the notion that reproductive health is solely a woman's domain, societal norms continue to marginalize women and restrict their autonomy. This not only affects women's health and well-being but also impedes progress towards achieving gender equality and empowering women in India.
Complexities of Assisted Reproductive Technology
Authors: Khattri Upasana , Shandilya Rajat , Saxena Sanyukta
Published: November 2024

Assistive Reproductive Technologies (ART) has become topic of discussion in the recent times. The introduction of ART in the field of medical science has been subject to controversies. Various laws and guidelines to address the issues related to ART but due to its rapid advancement ART requires re-evaluation. The paper will analyse the existing legal framework, religious beliefs and the ethical issues surrounding the topic of ART. The progressive increment in the use of these techniques ranging from but not limited to vitro fertilisation, genetic editing and surrogacy has brought remarkable change in the field of human reproduction. As it is a common saying the every coin has two sides, same is the case here as on a positive note, this particular advancement by the way of ART has given hope to many individual and couples who are aspiring to be parents but are unfortunately suffering from infertility and genetic disorders and on the contrary, this specific advancement can be held to be the reason behind the rise of multiple ethical, legal, and religious concerns and challenges.
A Comprehensive Review of Ontologies in Cybersecurity
Authors: Shah Vatsal Hasmukhbhai , Maniar Rahil
Published: November 2024

With the evolution of Semantic Web architecture and advancements in Generative AI, the demand for a universally accepted ontology in cybersecurity has significantly increased. This paper reviews five notable ontological frameworks: STIX, STUCCO, UCO, CTIM, and WAVED. It evaluates their strengths and limitations, providing a comparative analysis. The study offers recommendations for developing an optimal ontology, aimed at enhancing interoperability, security, and automation in cybersecurity systems, thereby addressing current gaps and promoting a more cohesive cybersecurity landscape.
Cyber Security Techniques for 5G Networks
Authors: Das Himadri Sekhar , Samanta Sandipan , Metia Rajrupa , Samanta Debasish , Bag Banibrata
Published: November 2024

The rapid deployment of 5G networks introduces new cybersecurity challenges due to their enhanced speed, low latency, and massive connectivity. To safeguard these networks, advanced techniques are essential, including network slicing for isolating threats, artificial intelligence and machine learning for real-time threat detection, and blockchain for securing transactions. Additionally, Zero Trust architectures ensure that every access request is authenticated, regardless of origin. These strategies, combined with robust encryption and anomaly detection mechanisms, are critical in addressing the diverse and evolving threats in the 5G ecosystem, ensuring secure and reliable communication for users and industries alike.
Neptune Security
Authors: Shah Vruddhi K , Doshi Jignesh
Published: November 2024

With the inclusion of various data such as structure, semi-structure and unstructured data, protecting one's online privacy is crucial in the fast-paced technological age we live in, and Neptune Security stands out as a major player in this space. The analysis emphasizes the crucial part that Neptune Security plays in managing rising privacy issues, especially in India's changing digital landscape. The impact of big data on privacy is an essential aspect of this study. Big data has completely transformed how data is analysed and decisions are made. It can be recognized by its immense scale and complexity. However, privacy is naturally challenged by the amount of data. In this scenario, Neptune Security plays a crucial role by using cutting-edge encryption techniques and adaptive access controls to quickly neutralize such threats. This study explores the complex relationship between privacy and big data to offer an in-depth analysis of the changing risks associated with the digital era.
Security Challenges in Internet of Things
Authors: Joshi Purva , Joshi Seema
Published: November 2024

Industrial and domestic appliances are IoT-connected. Sensors, phones, and security are examples. The benefits of device communication are various. Monitor IoT security risks. Simple methods and technology may secure devices and detect attackers. Security challenges in IoT include authentication and authorization, encryption, firmware and software vulnerabilities, insecure connections, and device patching and updates. Threats from IoT vulnerabilities Worse passwords allow IoT device theft. Used IoT devices can join larger networks. Low-encryption IoT devices may leak and misuse consumer data. IoT user mistrust, identity theft, and financial fraud may arise. Weak IoT devices can be hijacked by malicious networks to launch massive DDoS attacks on services and infrastructure. Damage, disruption, and death can result from industrial and critical infrastructure IoT devices. Hackers could break into IoT networks and access more systems and data. The chapter explains IoT security risks. Examine case studies last. Technology and industry cannot apply all attack avoidance measures.
An Implementation of Decentralized Secured Tweeting Platform
Authors: Pawar Sumathi , Suma K. , P. Ankitha , B. S. Vandana , Samaga RajaLakshmi
Published: November 2024

A decentralized secured tweeting platform utilizes blockchain technology to distribute control and data across a network of participants, challenging the centralized model. It provides enhanced privacy, reduced content manipulation, and resistance to centralized control of user content. This system represents a decentralized tweeting social media platform with the objective of furnishing users with a communication environment that's both secure and characterized by transparency. Through the integration of smart contracts, users can transact directly and securely without the need for intermediaries. ReactJS empowers a responsive and user-friendly interface, fostering real-time interaction among users to create a decentralized social media platform, enhancing security and transparency to unprecedented levels. Blockchain technology confirms that data is securely stored, immutable and transparent. The proposed system enables users to post tweets, follow other users, like and comment on posts and engage in real-time messaging. This article also presents the implementation of the system.
Intrusion Detection in Networks Using Adversarial Networks and Weighted Encoder Components
Authors: Chaubey Nirbhay Kumar , Dahiya Ruby , Venkateswaran R. , R. V. S. Praveen , Hemavathi U. , Subramaniam Sangeetha
Published: November 2024

This study introduces an innovative approach to network intrusion detection by harnessing the capabilities of adversarial networks and underscoring the importance of encoder components. Conventional intrusion detection systems frequently grapple with the real-time identification of intricate and evolving threats, rendering them susceptible to both false positives and negatives. Our proposed method aims to confront these inherent challenges. The proposed strategy, referred to as ID-ANWE, entails the integration of adversarial networks into the system. This augmentation enhances the system's ability to differentiate between typical network behavior and irregular, potentially malicious activities. Through a training process, these adversarial networks become proficient in distinguishing between legitimate network traffic and potential intrusion attempts, contributing to the establishment of a dynamic and adaptable security framework.
Exploring Public Perspectives on Climate Change With AI Analysis
Authors: Ghosh Debjyoti , Kujur Abhay Gyan P. , Salim Abu
Published: November 2024

In the digital age, climate change has become a critical global challenge, driving widespread discussions across social media platforms. Social media, especially platforms like Twitter, plays a key role in shaping public perception of climate change, making it essential to understand how people discuss and feel about this issue online. This study focuses on analyzing Twitter conversations to identify the main topics and sentiments related to climate change. By using Latent Dirichlet Allocation (LDA), the study uncovers key themes in climate change discussions, such as global warming, renewable energy, and government policies. These insights are crucial for tailoring communication strategies to address public concerns effectively. Sentiment analysis, conducted using the BERT (Bidirectional Encoder Representations from Transformers) model, allows for a nuanced understanding of the emotions expressed in these discussions. BERT's ability to grasp context ensures accurate interpretation of sentiments, revealing both the concerns and hopes people have regarding climate change.
Eco-Intelligence
Authors: Rahman Md. Adnan , Emon Md Mehedi Hasan , Khan Tahsina , Hamid Abu Bakar Abdul , Yaakub Noor Inayah
Published: November 2024

This chapter, titled “Eco-Intelligence: AI's Contribution to a Sustainable World,” explores the significant influence of artificial intelligence (AI) on environmental sustainability. The text examines the impact of AI in several areas such as climate change mitigation, biodiversity protection, smart agriculture, urban sustainability, and circular economy projects. The chapter focuses on the use of AI in predictive modeling for climate scenarios, the optimization of renewable energy systems, and the real-time monitoring of emissions. These applications together contribute to the reduction of greenhouse gas emissions. The emphasis on biodiversity conservation is on AI's skills in species identification, habitat mapping, and anti-poaching initiatives. The chapter also explores the use of AI in augmenting precision farming, boosting crop monitoring, and managing sustainable fisheries. The integration of AI technology revolutionizes urban planning and waste management by enhancing the optimization of resource use and energy efficiency.
Bridging the Gap
Authors: Khan Tahsina , Emon Md Mehedi Hasan , Rahman Md. Adnan , Hamid Abu Bakar Abdul , Yaakub Noor Inayah
Published: November 2024

This chapter, titled “Bridging the Gap: Realizing GreenTech Potential,” explores the critical barriers hindering the widespread adoption and implementation of Green Technology (GreenTech) and proposes strategic frameworks to overcome them. The discussion covers key challenges, including technological innovation gaps, financial constraints, regulatory hurdles, and societal resistance, which collectively impede GreenTech's progress. The chapter emphasizes the importance of collaborative innovation, financial incentives, supportive regulatory frameworks, and public engagement in accelerating GreenTech deployment. Through case studies of successful renewable energy projects, sustainable agriculture, and green building initiatives, the chapter provides practical insights and lessons learned. By addressing these barriers and leveraging strategic approaches, the chapter outlines a pathway for realizing the full potential of GreenTech, contributing to a more sustainable and environmentally responsible future.
Deep Learning and Blockchain Application in Smart Agriculture and Farming
Authors: Kumari Namrata , Praveen Nushrat
Published: November 2024

Agriculture is undergoing a transformation through the integration of deep learning and blockchain technology, addressing productivity, sustainability, and transparency challenges. This revolutionary approach in smart agriculture leverages Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to analyze agricultural data accurately and promptly. This analysis aids in yield prediction, insect identification, and crop health monitoring, enhancing decision-making and resource allocation. Consequently, crop yields are increased, and resource waste is minimized. The combination of deep learning and blockchain technology in smart agriculture creates a powerful synergy, enhancing productivity, profitability, and sustainability. Deep learning provides accurate data analysis, while blockchain ensures the transparency and security of this data.
Deep Learning Framework for Early Detection of Oral Cancer
Authors: Kumar Jeevan , Pandey Vijay , Tiwari Rajesh Kumar
Published: November 2024

One of the most hazardous diseases is cancer, poses a significant threat to human health due to its ability to manifest in various parts of the body as abnormal, proliferative cells. In the case of oral cancer, diverse behavioural patterns may be observed, making early detection and accurate prognosis crucial for effective and timely treatment. Deep learning (DL), a branch of artificial intelligence (AI), has emerged as an attractive way to tackle this problem and revolutionise cancer management by improving diagnostic accuracy and prognostication. In this research, we investigate the possibility for identifying and forecasting early-stage detection of oral cancer by using deep learning models. We apply visual geometry group16 (VGG16) model of deep learning for analysis of oral cancer. Furthermore, we highlight unresolved research issues in the field of cancer disease, providing valuable insights for future research endeavours aimed at advancing cancer diagnosis and treatment.
Effect of Age, Breed, Sex, Reproductive Status, Body Size, and Five-Point Body Condition Score on Body Fat Percentage of Dogs Calculated Using Morphometrics
Authors: Gamachchige D. , Rajapaksha R. A. D. E. I. , Ariyarathna H. M. H. S.
Published: November 2024

There are many canine body fat percentage assessment methods available including dual-energy X-ray absorptiometry (DEXA), deuterium oxide method (D2O), computed tomography and quantitative magnetic resonance imaging. These methods are technically sophisticated and therefore have limited practical utilities. The calculation of body fat percentage of dogs using morphometrics is a simple and inexpensive method. However, the interference of signalment of dogs in assessing body fat percentage using morphometrics remains unknown. This study was carried out to determine whether the body fat percentage of dogs calculated using morphometrics is affected by age, breed, sex, reproductive status, body size, and the five-point body condition score (BCS). The body fat percentage determined by morphometrics in 129 small, mediums and large size dogs was correlated with multiple signalment criteria. Body fat percentage poorly correlated with age, sex, reproductive status, body size, and breed. However, there was a significant correlation between body fat percentage and BCS (rho=0.81, p=<0.001). In addition, the variability of body fat percentage between dogs was best explained by the variation of BCS (R2 =0.747, F=316.7, p=<0.001). The results suggest that body fat percentage calculated using morphometrics can be reliably used in medium to large size dogs above or at the age of 6 months upto14 years irrespective of sex or reproductive status. Further, the present findings suggest that body fat percentage calculated using morphometrics which significantly correlates with the five-point BCS is an easy and cost-effective tool to identify dogs deviating from ideal body condition.