bluuebunny/crossref_metadata_2025 huggingface dataset:
Revolutionizing Responsible Consumption and Production of Food In Hospitality Industry
Authors: Singh Bhupinder , Kaunert Christian
Published: November 2024

The hospitality industry is pivotal in addressing responsible consumption and food production challenges. The hotel sector is leading the way in tackling important issues related to responsible food production and consumption to lead revolutionary solutions using deep learning and machine learning technology. Integrating modern technical solutions becomes crucial in the current setting, where global poverty, malnutrition, and the complex dynamics of food adulteration considerably affect the industry's demand-supply mechanism. Keeping a close eye on the larger global scene, this study promotes practical actions beyond simple technology implementations. In the face of global issues such as hunger, malnutrition and food adulteration, the assimilation of advanced technological strategies becomes imperative. This chapter explores the role of ML and DL in achieving zero waste within the hospitality sector, addressing challenges and presenting innovative strategies for adequate food loss-waste management.
Personalized Language Learning With AI for Pedagogical and Andragogical Approaches
Authors: Yingsoon Goh Ying , Rahman Nurul Ajleaa Abdul
Published: November 2024

Language learning has traditionally relied on standardized pedagogical approaches for children and andragogical methods for adults. However, the emergence of Artificial Intelligence (AI) offers exciting possibilities for personalizing language education to cater to individual needs and learning styles across all ages. This chapter explores how AI can be leveraged to create effective personalized learning experiences within both pedagogical and andragogical frameworks. Furthermore, the integration of AI in language instruction has the potential to democratize access to quality education, ensuring that learners of diverse backgrounds can benefit from tailored learning paths that enhance retention and proficiency.
Leveraging AI in Educational Assessment
Authors: V. Mohana Sundari , Vaishnavi V. , Kumar G.
Published: November 2024

This study examines the effectiveness of AI-enabled assessment methods in providing personalized feedback and enhancing student engagement. Dependent variables include the effectiveness of these methods, student engagement, and academic performance, while independent variables encompass AI use in grading, oral presentations, peer reviews, gamification, and creative project assessments. Employing a quantitative analysis, the study surveys 247 academicians in India to explore emerging trends in AI-enabled educational assessment. Purposive sampling ensures representation across disciplines and institutions. Findings reveal positive perceptions of AI-enabled methods, noting benefits like personalized feedback and increased engagement, but also highlight concerns about algorithm reliability and bias. Recommendations include training faculty to use AI tools effectively and conducting further research on algorithmic fairness.
Curipod's Artificial Intelligence Assistant in the Proofreading Process
Authors: Moura Adelina , Tchiang Van Man Isabel
Published: November 2024

The use of artificial intelligence (AI) tools in writing and proofreading is beginning to develop. Studies show that AI tools can positively influence students' writing and proofreading skills. This study presents the perceptions of vocational education students regarding the assessments and suggestions for improvement provided by the AI assistant Curipod and followed by students in the proofreading phase. It centres on a case study, with data collected using a survey with open and closed questions, participant observation, and an interview. The students positively perceived the feedback they received from the AI assistant on their initial text and consider that it helped them to revise and improve the final versions of the texts written on paper and digitally. The students are interested in using tools like these in writing revision activities, as they see the potential they have for the classroom and autonomous learning.
AI Grading Systems in Education
Authors: Ragolane Mahlatse , Patel Shahiem
Published: November 2024

This chapter will review the use and implementation of automated grading systems (i.e., AI grading) in South African higher education focusing on the exploration of whether it serves as a solution or a challenge. This chapter will explore this enquiry through the lens of socio-technical theory to understand the application of AI grading systems in higher education and how they serve the social and technical dimensions of the sector. This chapter calls for higher education institutions, educators, government, and other stakeholders to take part in the implementation of AI-based systems as they hold the potential to revolutionise how traditional methods were done before and now in the 21st century. This chapter argues that the role of technology in South African education is clear and that it will drive progress and prosperity, but it will be up to leaders to guide this transformation as much of the future potential for the education system is already achievable with the technology available today.
Evaluating the Impact of AI Tools on Teaching Effectiveness and Student Outcomes
Authors: Joel Robinson , Lakshmi N. , Shanthakumar P. , T. D. Jeba Freeda , Siva A. , R. Thalapathi Rajasekaran
Published: November 2024

The study examines AI technologies' impact on education, focusing on teaching strategies and student outcomes. AI-driven tools like intelligent tutoring systems and automated grading enhance personalized learning, boost engagement, and reduce administrative tasks for teachers. The research includes qualitative and quantitative data, such as student performance and teacher feedback. While AI greatly benefits tailored learning, challenges like high costs, ongoing maintenance, and the risk of widening educational disparities are highlighted. The study underscores the need for teacher training and equitable access to AI, offering insights for effective AI integration in education.
Managing Ethical Challenges
Authors: Dar Amir Ahmad , Yadav Shipra Shivkumar , Tripathi Rajendra Kumar , Albalawi Olayan , Jain Akshat , Gautam Prem Lata
Published: November 2024

In an educational environment where technology is advancing at an unstoppable pace, the incorporation of artificial intelligence (AI) into assessment techniques marks a turning point in the development of teaching and learning. AI-driven assessment tools have the potential to transform educational practices worldwide by improving objectivity, streamlining processes, and personalizing learning experiences. The study provides a basic investigation that explains the theories underlying artificial intelligence in educational assessment. It clarifies the revolutionary potential of AI-driven evaluation instruments in transforming conventional approaches. This chapter highlights the need to maintain integrity and equity in using AI technologies, based on theoretical frameworks and empirical research findings.
Foundations of AI and Machine Learning in Real Estate Valuation
Authors: Yalgashev Olimjon , Natarajan Arul Kumar , Galety Mohammad Gouse
Published: November 2024

This chapter delves into the application of Artificial Intelligence (AI) and Machine Learning (ML) within the field of real estate valuation, utilizing the California Housing Prices dataset to demonstrate practical implementations. By employing and contrasting various regression models, including linear Regression, decision trees, and ensemble methods like Random Forest and Gradient Boosting, this study highlights the capabilities and limitations of these approaches. The research meticulously evaluates each model's performance, offering a comprehensive analysis that underscores the significant potential of AI and ML to enhance predictive accuracy and efficiency in real estate markets. Through detailed data preprocessing, model application, and performance evaluation, the chapter provides valuable insights into the integration of sophisticated AI methodologies in the valuation process, making it accessible and actionable for both practitioners and researchers.
Predicting Exchange Rates Volatility Using Hybrid ARIMA-GARCH Model
Authors: Dinga Bruno , Claver Jimbo Henri , Kum Cletus Kwa , Che Shu Felix
Published: November 2024

For the last few decades, the Autoregressive Integrated Moving Average (ARIMA) model has been a popular linear model in predicting time series. Recent research has shown that the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model can be a promising alternative to the traditional linear models. A time series can be considered to comprise a linear and non-linear part. This chapter aims to highlight in a relevant way the need to properly define the dynamics of the linear and non-linear parts of the time series as far as the time series prediction is concerned. Using the GARCH model and the combined ARIMA-GARCH model in the analysis and prediction of exchange rates, this study shows that the combined ARIMA-GARCH-T model, which defines the dynamics of the linear and non-linear parts of the exchange rate time series, has a higher predictive power when compared to the GARCH-T model.
A Network-Based Approach to the Implications of the COVID-19 Pandemic on the Latin American Minimum Variance Portfolio
Authors: de Souza Lucas Pagel , Katenka Natallia , Ribberink Natalia
Published: November 2024

Since the COVID-19 pandemic outbreak a growing body of literature has observed uncertainty among the main factors driving investor behavior, with these becoming more likely to seek risk minimization strategies, such as the minimum variance approach based on Modern Portfolio Theory. In this chapter a network-based minimum variance approach to portfolio diversification is carried out with assets from Latin America's five largest stock exchanges between 2018 and 2021. Global Minimum Variance Portfolios are obtained with the use of structural information contained in partial-correlation networks and a community detection process. The main objective is the investigation of the implications of the pandemic on minimum variance diversification in the region through an analysis of portfolio asset selection and allocation results, including risk and return performance, benchmarked against naïve investment portfolios.
Statistical Optimization of Option Pricing Factors
Authors: Ahmad dar Amir , Sharma Garima , Siddiqui Shavej Ali , Albalawi Olayan , Chat Bilal Ahmad , Yadav Princy
Published: November 2024

Options are a highly versatile trading tool renowned for mitigating downside risk while offering unlimited upside potential. The effectiveness and profitability of trading options hinge on selecting the right option at the appropriate price. Traditionally, the Binomial option pricing model (BOPM) is employed to estimate the fair value of options. This study delves into the impact of factors such as underlying asset price, strike price, volatility, and period (with a constant interest rate) on put option values. Leveraging Taguchi's design of experiment methodology, the research aims to optimize these factors for option valuation, marking the first instance of such optimization using Taguchi's method. Through a design of experiment (DOE), analysis of variance (ANOVA), regression analysis, and analysis of mean (ANOM), the study examines the effects of input factors, employing MINITAB 18 software for analysis.
Neurogenesis of Intelligence Principles of Brain-Inspired Computing
Authors: Sharma Yogesh Kumar , Padmanaban Harish , Kumar Nimish
Published: November 2024

In machine learning, artificial neural networks (ANNs) are becoming indispensable tools, showing impressive results in various applications such as robotics, game development, picture and speech synthesis, and more. Nevertheless, there are inherent disparities between the operational mechanisms of artificial neural networks and the real brain, specifically concerning learning procedures. This chapter covers the overview of Brain-Inspired Computing, its key principles, importance, applications, and future directions. This work also thoroughly examines the learning patterns inspired by the brain in neural network models. We explore the incorporation of biologically realistic processes, including plasticity in synapses, to enhance the potential of these networks. Furthermore, we thoroughly examine this method's possible benefits and difficulties. This review identifies potential areas of investigation for further studies in this fast-progressing discipline, which may lead us to a deeper comprehension of the fundamental nature of intelligence.
Leveraging Social Media for Destination Branding
Authors: Murtza Muhammad Hamid , Rasheed Muhammad Imran
Published: November 2024

This chapter examines and understands social media's significance in destination branding. Social media has become instrumental in shaping perceptions, effectively interacting with and forming destination brands. This section will highlight the critical role of social media in inventing and promoting destination brands, provide a complete analysis of its upshots, and describe theoretical application and support from the literature. With the advent of social media, the methods for advertising destinations have significantly affected customer perceptions. Social media outlets have wide-ranging outreach with interactional features, portraying destinations in previously impossible ways. These outlets can easily express distinctive value propositions to travellers, establishing influential, enduring brands. This chapter will also cover the fundamental concepts and theories related to social media, such as social identity theory and the theory of planned behaviour and its uses and gratifications.
Balancing Bias and Sustainability in Heritage Tourism
Authors: Nag Aditi , Rathore Anurag Singh
Published: November 2024

This paper examines the complex dynamics of biases in ecotourism and destination branding within the context of heritage site management. It explores how these biases influence perceptions, visitor experiences, and sustainable practices at iconic destinations such as the Taj Mahal, Machu Picchu and Serengeti National Park. The study identifies critical challenges and opportunities in promoting inclusive, authentic, and sustainable tourism by integrating insights from predictive analytics, cultural sensitivity, and ethical considerations. Drawing on case studies and theoretical frameworks, the paper offers practical implications for mitigating biases, enhancing visitor engagement, and safeguarding cultural and natural heritage. It concludes by advocating for integrated strategies that balance effective branding with responsible stewardship to ensure the long-term preservation and equitable enjoyment of heritage sites worldwide.
Village Tourism Balances Brand Identity and Sustainability in Montegridolfo
Authors: Presutti Manuela , Barbini Francesco Maria , lo Presti Valeria
Published: November 2024

This paper explores the role of village tourism in promoting sustainable practices and enhancing destination branding, using the case study of Montegridolfo and Palazzo Viviani, a luxury hotel in Italy's Riviera Romagnola. Through ethnographic research, we investigate the motivations behind establishing a high-end hotel in a small,rich village far from mainstream tourist routes. The study examines how luxury tourism can foster sustainable development, support unique destination branding, and balance tourism growth with the preservation of cultural heritage and local identity. The case of Palazzo Viviani demonstrates the integration of eco-sustainable strategies within the luxury sector, highlighting the interplay between environmental responsibility, economic growth, and cultural revitalization. Our findings suggest that luxury tourism when properly managed, can provide ethical and economically sound development opportunities for rural destinations. The paper concludes by offering strategic insights for developing sustainable luxury tourism models in similar small village settings
Integrating Green Finance and Sustainability-Oriented Governance
Authors: Ed-Dafali Slimane , Kobiyh Miloudi , Mohiuddin Muhammad , Abidar Brahim
Published: November 2024

Recent investment practices have introduced new financing methods and organizational models aimed at addressing climate challenges. Green finance has become a pivotal tool for reducing emissions and fostering sustainability by incorporating environmental considerations into financial decision-making. This chapter provides a comprehensive overview of integrating non-financial elements into financial decision-making, advocating for a model that combines green finance with environmental governance. It analyzes the role of financial development and the effectiveness of environmental governance in guiding financing and investment operations toward improved environmental performance. The authors conclude that this approach is crucial for fostering a sustainable economy, with benefits extending beyond investors to society as a whole. Based on this work, they suggest that investments aimed at generating wealth, supporting economic growth, and combating climate damage must be accompanied by effective environmental governance to align economic efficiency with environmental sustainability.
How and When Does Female Board Representation Influence Corporate Social Responsibility Performance?
Authors: Pang Zhuoyue
Published: November 2024

Many previous studies found an inconsistent relationship between female board representation and corporate social responsibility performance. However, the underlying processes that produce the mixed relationship are still unclear. Building on stakeholder theories and social exchange theories, the author proposes that the moderating influence from the institutional environment can explain this mixed relationship. Specifically, the author suggests that organizations in countries with high democratic voice and accountability (i.e., more diverse regulatory regimes in such countries, which include more open and transparent voices from citizens) will strengthen the positive effect of female board representation on corporate social responsibility performance toward stakeholders who have less institutional power. The main contribution is to explicate the moderating effect of democratic voice and accountability that potentially produces the mixed relationship between female board representation and corporate social responsibility performance and reconcile inconclusive findings in the literature.
Corporate Governance and Managerial Overconfidence
Authors: Cherkaoui Adil , Oudrhough Youness
Published: November 2024

This chapter draws attention to the fact that corporate governance can help in moderating overconfidence, particularly in corporate strategic decisions. CEOs with overconfidence are more likely to engage in risky investment. Agency theory does endorse performance-related pay and independent directors. The Sarbanes-Oxley Act and gender diversity are cited as useful for maintaining an equitable decision-making system. However, a great void persists within the landscape of bibliometric analyses in this area. With the R studio program and the PRISMA procedure, this study makes a critical literature review on overconfidence and corporate governance. Through an exhaustive review of 283 papers on the Web of Science (WOS) and Scopus, this study summarizes major themes, principal authors, and key gaps in the field of overconfidence and strategic decision-making. This is the reason why this chapter aims at assessing the nature of the relationship between overconfidence and corporate innovation, R&D, financial and investment, acquisition decisions, and CSR, and calling for improved governance solutions, multidisciplinary research, and stakeholders' involvement.
The Role of Digital Platforms in Transforming Corporate Governance
Authors: Salamzadeh Aidin , Hadizadeh Morteza , Feyzabadi Javad Ghaffari , Yasin Naveed , Paramba Jumana Nalakam
Published: November 2024

Contemporary corporate governance models traditionally focused on maximizing shareholder value and encounter significant challenges in encountering the complexities of sustainability. The digital age necessitates the development of innovative frameworks that integrate environmental, social, and governance (ESG) factors into core decision-making processes within corporations. This chapter investigates the transformative potential of digital platforms in promoting sustainable governance practices. Digital platforms possess a unique suite of features that can revolutionize traditional governance structures. These features include enhanced data transparency and accessibility, fostering rapid social responsiveness, optimizing supply chains through blockchain technologies, and leveraging data analytics to predict customer behavior patterns. By promoting transparency, accountability, and stakeholder engagement, digital platforms can contribute to a reduction in resource waste and an improvement in environmental efficiency.
Family Business Governance
Authors: Nidaazzi Hamza , Zaher Jihane
Published: November 2024

This chapter examines family business governance as a model for sustainability, drawing on agency theory, stewardship theory, stakeholder theory, and socioemotional wealth theory. The aim is to understand how governance mechanisms in family firms contribute to long-term success and how these can be applied to other corporate forms. The study focuses on three key areas: implementing long-term incentive structures, cultivating a culture of stewardship and legacy, and balancing financial and non-financial goals in corporate governance. The analysis suggests that these elements can significantly enhance organizational sustainability when adapted to non-family contexts. This chapter contributes to the understanding of sustainable governance practices and offers practical implications for businesses seeking to enhance their long-term viability and societal impact.
Examining Board Characteristics as Moderators in the Relationship Between CSR Practices and Financial Performance
Authors: Dawar Gaurav , Bhatia Shivangi
Published: November 2024

This study aims to examine the potential effect that ESG Scores have on the financial performance of any organisation using the moderating role of independent directors and women directors. To find out the moderating effect between ESG and the firm's performance with the moderating effect of board characteristics, the authors have applied regression analysis where they have selected Top 100 Companies Listed in NIFTY 100. Out of these 100 companies, they have chosen 93 companies who have also been ranked as per the ESG parameters. The results of the research suggest that there is no relationship between a company's financial performance and its commitment to corporate social responsibility (identified from the ESG scores). So according to the test results, they have seen that even if a firm spends more on CSR operations there may not be a proportionate increase in the firm's financial performance. In Addition, this study contributes to the promotion of the relationship between sustainable practises and independent directors. The findings of this study have practical implications and extra incentives for practitioners, especially CEOs and other high-ranking corporate governance authorities. It has significance for international regulators and decision-makers further on providing data to the reader a better understanding of how to evaluate the company's financial performance and potential for future growth in a setting where corporate governance and social responsibility play a major role in business valuation.
Internal Governance Mechanisms, Board Composition, and Social Performance
Authors: Mahdi El Koudsi El , Simmou Samira , Simmou Walid , Bengrich Mustapha , Alourhzal Hajar
Published: November 2024

Corporate governance plays a pivotal role in shaping organizational behavior and outcomes. This study empirically investigates the influence of internal governance mechanisms, particularly board composition, on social performance within Moroccan industrial firms. Drawing on data from 86 listed firms, the authors employ partial least squares structural equation modeling (PLS-SEM) to analyze the relationships between board size, CEO duality, the presence of a corporate social responsibility (CSR) committee, and social performance metrics. The findings reveal that larger boards and the establishment of CSR committees significantly enhance social performance, while CEO duality exerts a negative impact. These results highlight the importance of effective board governance and dedicated CSR structures in driving socially responsible business practices. This study contributes to the growing body of literature on corporate governance and social performance, offering valuable insights for policymakers, practitioners, and researchers seeking to improve corporate sustainability.
Governance in Morocco's Oil Sector
Authors: Touhami Amina , Barakat Ahmed
Published: November 2024

Since the Moroccan Refinery (SAMIR) closure, the current model of governance and strategic approaches to the oil sector in Morocco has become a concern to public authorities. This model is the subject of many studies and discussions because this sector is of particular importance due to the industry's strategic and sensitive nature. The objective in this chapter is to analyze how Moroccan oil energy companies will be able to respond effectively to the challenges imposed by the closure of local refining capacities. How can they ensure more or less stable and sustainable governance in energy supply at the national level? To what extent does the governance of Moroccan oil companies adopt multifaceted strategies to ensure the sustainability of operations and the stability of the Moroccan oil market? In addition, engagement with regulatory authorities and stakeholders also allows for guaranteeing quality requirements, fiscal conditions, and favorable adjustments. In addition, expanding of distribution and sales networks in detail is an essential element that maintains market access and consumer reception.
Economic Analysis of the Impact of Digital Transformation and Governance on Tax Revenues in Morocco
Authors: Hajjaj Mohammed Amine , Mkaddem Zakariae Bel
Published: November 2024

The development of digital technologies is radically changing society, stimulating economic and social development while remodelling the world of work and the management of organisations. Public governance plays a central role in this context by facilitating technological integration and maximising the benefits of digital technologies and foreign investment. This study analyses the short- and long-term effects of information and communication technology (ICT) imports, gross domestic product (GDP) growth, unemployment, and productivity on government tax revenues in Morocco from 2000 to 2022 using the autoregressive distributed lag (ARDL) model. The results show that ICT imports have a significant positive impact on government revenues in the long term, most likely due to the role of digital technologies in improving tax collection and public sector efficiency. These results highlight the importance of promoting technological integration and effective governance to manage the short-term economic disruptions resulting from these investments.
Financial Inclusion Initiatives by Cooperative Societies in Selected Regions of North India
Authors: Gupta Vipul , Sharma Parveen Kumar
Published: November 2024

The farmers of North Indian regions are small and marginal and finance to these farmers were arranged through cooperative societies. The present study aims to analyze the financial inclusion initiatives of cooperative societies in the selected areas of North India, especially Jammu and Kashmir (UT), to improve their corporate governance. The current research considered both the primary and secondary data. The primary data was collected from field surveys using purposive sampling for the month of November 2023. The secondary data was collected from the statistical surveys of 2016 and 2018 published by National Cooperative Union of India. The research used descriptive statistics and principal component analysis to analyze the collected data. The study found that farmers had bank accounts in the nearby areas but were more dependent on cooperative societies for credit rather than banks. Additionally, cooperatives helped farmers to face uncertainty in agricultural marketing. The study concluded that cooperatives played a leading role in the financial inclusion initiatives of rural India and thus improved their corporate governance.
Tourist Satisfaction and Sustainable Tourism Governance in Mediterranean Island Destinations
Authors: Kontogeorgis Georgios , Livas Christos , Koutoulas Dimitris , Pappa Evangelia , Garefalakis Alexandros
Published: November 2024

The study examines international tourists' satisfaction with Corfu and the factors affecting it, with the purpose of informing sustainable tourism governance and development. Primary data were collected from a sample of 237 international tourists. The findings were evaluated by a panel of key stakeholders of the Corfiot tourist industry. Overall, results suggest that excluding price level, participants demonstrated satisfaction with most aspects of Corfu's tourism product. However, they identified problems with the road network, cleanliness, and environmental protection. Bivariate and multivariate statistical analyses indicate that satisfaction is influenced by demographics, activities at the destination and perceived problems. The research demonstrates that tourists' satisfaction can be determined both prior and during their stay on the small island. Destination marketing organizations are encouraged to focus on targeting receptive tourists and introduce targeted actions to improve tourists' experience at the destination, while ensuring sustainable governance practices.
Khadi, Arts, and Handicrafts as India's Symbol of Freedom and Fashion in Today's Internet and Circular Economy
Authors: Gopalan Balaji
Published: November 2024

This chapter focuses on a relevant topic, an element of India's freedom struggle called Khadi or the homespun cloth made of cotton on spinning wheels as an example of product stewardship in terms of ethics and environmental impact. Product stewardship is identified as businesses with commitments to a healthy, safe, and environmentally friendly society. This closely aligns with the philosophy of a circular economy that adheres to lean production principles of manufacturing and business processes that are eco-friendly and sustainable. Textile industries around the world are integrating manufacturing towards a circular economy and green practices due to an increasing need to reduce the environmental impact. Social entrepreneurship as an alternative has been in focus as a sustainable business model and the popularity of Khadi, arts, crafts, and cottage industries in India is growing in today's internet economy. This study has explored how social entrepreneurship is providing numerous opportunities for growth and sustainability and a solution to social problems.
Organizing in Democratic Organizational Forms and Constitution of Collective Intelligence
Authors: Camacho Magali Pérez
Published: November 2024

This chapter explores how democratic organizational forms enhance collective intelligence, which is a group's ability to collaborate, reason, and solve problems. It highlights how such organizations promote participation, decentralization, and diversity, creating environments where information flows freely and diverse perspectives are valued. Mechanisms like shared sensemaking and self-organization support this. Theories from Karl Weick and Barbara Czarniawska are used to explain these processes, with case studies like the Political Task Committees in Gentofte and “Sager der Samler” in Aarhus illustrating the effectiveness of decentralized approaches. The chapter concludes that democratic organizations are well-suited for developing collective intelligence and calls for more research on optimizing organizational structures.
Security Challenges and Solutions in IoT
Authors: El Hajla Salah , Ennaji El Mahfoud , Maleh Yassine , Mounir Soufyane
Published: November 2024

The Internet of Things (IoT) connects devices that collect, communicate, and act on data, but also introduces significant security challenges. This chapter explores the complex landscape of IoT security, focusing on issues like device heterogeneity, resource constraints, and evolving threats. It examines IoT security architecture and communication protocols, highlighting the need for scalable solutions across diverse environments. Major IoT attacks such as DDoS, MitM, and data breaches are classified and analyzed for their impact on IoT ecosystems. The chapter reviews IoT-specific Intrusion Detection Systems (IDS), with an emphasis on machine learning-based real-time anomaly detection, addressing challenges like high false-positive rates, scalability, and resource limitations. Emerging trends, including blockchain-based solutions, Federated Learning, and AI-driven defenses, are also discussed. This overview synthesizes current research to offer insights for enhancing security frameworks across IoT domains such as smart homes, healthcare, industry, and urban infrastructure.
Artificial Intelligence, Terrorism, and Cyber Security
Authors: Chitadze Nika
Published: November 2024

Artificial intelligence is not just another technology. It is the most important global event of the modern era, completely changing the modern social, economic, and political reality. On the one hand, it has enormous potential to significantly improve society's life and humanity to take development to a new height, however, on the other hand, it is one of the most dangerous and Unpredictable technologies, which, as the UN Agency for Disarmament Affairs declared in an open letter to the committee, it may be unimaginable to put humanity in front of large-scale conflicts and catastrophes. The paper analyzed the events based on real facts about Artificial Intelligence`s Terrorism and Counter-Terrorism potential.
Phishing URL Detection Using BiLSTM With Attention Mechanism
Authors: K. S. Jishnu , Arthi B.
Published: November 2024

Abstract. Phishing attacks have emerged as a formidable threat to cyber security, continuously evolving to exploit vulnerabilities in users' online behavior. These attacks often employ deceptive URLs and malicious websites to deceive individuals into divulging sensitive information, making effective detection crucial for safeguarding digital environments. To address this pressing need this approach leverages a deep learning model based on Bidirectional Long Short-Term Memory networks with attention mechanism. A dynamic learning rate scheduling technique is also incorporated to optimize model convergence. Empirical evaluations on a dataset comprising 11,430 URLs demonstrate that this methodology significantly improves Accuracy and robustness in identifying phishing URLs, achieving an impressive accuracy of 96.63%. This substantial enhancement contributes to bolstering cyber security measures. This research equips cyber security practitioners and researchers with practical tools to combat evolving phishing threats in today's digital landscape.
Cyber Risk Assessment Using Machine Learning Algorithms
Authors: Andrae Silvio
Published: November 2024

A dedicated cyber risk assessment framework is essential for proactive risk mitigation. The chapter presents a framework that integrates machine learning techniques. It is a data-driven approach to quantitatively assessing cyber risks. In addition, various machine learning models—especially supervised machine learning—are presented. Overall, the chapter makes an innovative contribution to using machine learning to establish a robust and standard cyber risk management system that can be valuable for practitioners in different industries. Given cyber risk's complex, dynamic nature, it is necessary to go beyond typical risk management and technical approaches and incorporate various methods such as threat intelligence, vulnerability management, and incident response. Cyber resilience aims to consistently deliver the intended business outcome even in the face of unfavourable cyber incidents.
Machine Learning Techniques for Network Security
Authors: Qamar Roheen , Zardari Baqar Ali
Published: November 2024

Machine learning can be used to identify security risks in networks by continuously observing network behavior and looking for irregularities. Massive volumes of data are processed in almost real time by machine learning engines, which then identify crucial incidents. These methods enable the identification of unknown malware, insider threats, and policy infractions. By identifying “bad neighborhoods” online, machine learning can also assist users in avoiding connecting to dangerous websites. Every Internet user needs online security. Ensuring online security is critical for all internet users, as most cyber attackers are opportunistic, targeting widespread weaknesses rather than specific websites or organizations. Machine learning (ML) may educate robots to spot patterns and detect harmful or anomalous activity more efficiently than people or traditional software.
Ethical and Legal Implications of AI in Cybersecurity
Authors: Vashishth Tarun Kumar , Sharma Vikas , Samania Barkha , Sharma Rajeev , Singh Savita , Jajoria Pooja
Published: November 2024

The integration of artificial intelligence (AI) in cybersecurity presents significant advancements in threat detection, response, and overall system protection. However, this rapid development brings forth critical ethical and legal challenges that need to be addressed to ensure responsible and lawful AI deployment. This research explores the ethical implications of AI in cybersecurity, focusing on privacy and data protection, bias and fairness, accountability and responsibility, and transparency and explainability. It also examines the legal landscape, reviewing existing regulations, emerging standards, intellectual property rights, and liability issues. Through comprehensive analysis and case studies, the research identifies several key findings: AI-driven cybersecurity systems can enhance precision in threat detection but often lack transparency, potentially leading to bias and accountability issues. The study also highlights a gap between current legal frameworks and the pace of AI development, necessitating adaptive regulations.
Deep Learning and Blockchain Application in Smart Agriculture and Farming
Authors: Ritu  , Bhuyan Ajatray Swagat , Beg Mirza Samiulla , Farooq Sheikh Afaan , Haq Sharaz UI
Published: November 2024

Artificial Intelligence (AI) enables farmers to increase production, limit input consumption, and improve resource allocation through the integration of drones, sensor networks, and satellite imaging, thereby promoting environmental stewardship. Predictive analytics powered by AI is also essential for reducing the hazards to agriculture brought on by extreme weather and climate change. In order to anticipate pest infestations, disease outbreaks, and agricultural yields, machine learning algorithms evaluate historical data. This allows for proactive measures to be taken to protect the resilience and health of crops. AI also transforms supply chain management by improving efficiency, traceability, and transparency across the whole value chain of agriculture. When combined with AI algorithms, blockchain technology allows for the unchangeable recording of transactions, guaranteeing the authenticity and traceability of food.
Unlocking the Potential of Robotic Process Automation for Digital Transformation in Logistics and Supply Chain Management
Authors: Tsang Y. P. , Tang Valerie , Wu C. H. , Li Fuqing
Published: November 2024

Digital transformation (DT) and automation technologies have significantly impacted logistics and supply chain management (LSCM), enhancing business performance. Efforts to automate processes alongside machine and robotic automation aim to improve reliability, consistency, efficiency, and cost-effectiveness. While existing research has explored specific aspects of DT and robotic process automation (RPA), the deployment and implementation of RPA as a service (RPAaaS) in LSCM are under-explored. This study focuses on RPA in LSCM and aims to improve organisational processes associated with DT. A systematic review identifies five knowledge clusters regarding RPA deployment for DT in LSCM. Real-life RPA implementation cases in a third-party logistics company highlight challenges and opportunities related to RPA deployment. Insights on the timing and choice of the RPAaaS platform to facilitate DT in enterprises are discussed. This paper provides extensive theoretical insights into RPA for DT in LSCM and offers practical guidance for streamlined implementation.
AVALIAÇÃO DA ATIVIDADE MODULATÓRIA DE TIMOL NA AÇÃO DE ANTIMICROBIANOS EM ESPÉCIES DO COMPLEXO Mycobacterium avium
Authors: Alexandre de Oliveira Renata , Karla Pereira da Silva Giulienne , Trevisolli Palomo Carolina , Sayuri Murase Letícia , Yukari Okochi Camile , Julia Faria Leticia , Hiroyuki Ito Juliano , Fressatti Cardoso Rosilene
Published: November 2024

As micobactérias não tuberculosas (MNTs), são patógenos oportunistas com uma crescente relevância clínica, especialmente em pacientes imunocomprometidos. Atualmente, o tratamento das infecções causadas por complexo Mycobacterium avium (MAC) envolve uma terapia combinada de múltiplos fármacos, como etambutol (EMB) e linezolida (LNZ), por exemplo. Porém a eficácia dessa terapia é limitada devido a facilidade com que espécies de MAC desenvolvem resistência e pelos efeitos colaterais dos medicamentos. Para contornar esse problema, a busca por novos fármacos e estratégias terapêuticas é uma necessidade. Portanto, o objetivo deste trabalho foi avaliar a atividade modulatória de timol (TIM) sobre a ação de antimicrobianos utilizados no tratamento de infecções por MAC. A concentração inibitória mínima (CIM) foi determinada em três linhagens de MAC pelo método Resazurin Microtiter Plate Assay. Os antimicrobianos EMB e LNZ foram utilizados como controle. TIM apresentou baixa atividade contra MAC com CIM 128 µg/mL, EMB 16 µg/mL e LNZ variou de 16 a 32 µg/mL. A atividade combinatória de TIM com EMB e LNZ foi avaliada com o ensaio "resazurin drug combination microtiter assay”. TIM reduziu a CIM de EMB de 8 vezes e para LNZ de 8 a 32 vezes. Estudos com um número maior de linhagens de MAC são necessários para um melhor entendimento da interação entre TIM e EMB e LNZ de tratamento nesse complexo bacteriano.
Study on the Usage of Ceramic Tile Waste as a Partial Replacement for Sand in Block Manufacturing
Authors: Jayasinghe D. G. M. P. , Dassanayake A. H. M. D. R.
Published: November 2024

Due to heavy usage, natural materials have become scarce. As a solution, several studies have been conducted to investigate alternative materials including industrial and construction waste. For example, research studies have been conducted to use fly ash along with cement in concrete. Furthermore, research studies are being done to investigate alternative materials to replace river sand in concrete and masonry. In this context, ceramic tile waste (CTW) has emerged as a viable alternative for sand. The objective of the current study was to utilize CTW as a replacement for sand in the manufacturing of masonry blocks, with an investigation into the optimal utilization of sand properties in CTW-based blocks. The properties of CTW and the mechanical properties of masonry blocks manufactured with CTW were investigated. Sieve analysis was carried out for sand and CTW to investigate the particle size distribution of both materials. Solid masonry blocks having the size 215 mm x100 mm x 65 mm were cast with a mix proportion of 1:6 Cement: Sand. CTW as a replacement for fine aggregates was used in varying percentages: 0%, 10%, 20% 30%, and 100%. The compressive strength of the masonry blocks was determined by crushing them at the age of 7 and 28 days. Additionally, the water absorption properties and material cost of the blocks were investigated. The results of the sieve analysis test indicated a similar distribution of particle sizes of sand and CTW, suggesting that sand could be effectively replaced by using CTW in the manufacturing of masonry blocks. Moreover, compressive strength and water absorption test results showed that sand can be replaced up to 20% by CTW in masonry blocks.
Dermatitis artefacta: a challenging diagnosis
Authors: Ciccarese Giulia , Salvia Giorgia , Fidanzi Cristian , Mastrolonardo Mario , Drago Francesco
Published: November 2024

Dear Editor,
Dermatitis factitia (DF), also known as dermatitis artefacta, is a rare condition characterized by self-inflicted skin lesions. These lesions can be a result of compulsive habits driven by emotional distress, serving as a means of emotional release during episodes of anxiety or distress. Alternatively, they may be deliberately induced by the patient with the intention of gaining medical attention. DF is essentially the physical manifestation of underlying psychiatric disorders, making it a particularly challenging condition within the realm of medical practice. Being a diagnosis primarily based on the exclusion of an underlying disorder often requires multiple investigations that can impose a significant economic burden. [...]
‘Justice be with you’: Transcending the peace talk rhetoric in the Zion Christian Church
Authors: Kgatle Mookgo S.
Published: November 2024

The Zion Christian Church is one of the largest, most powerful and influential African Independent Churches (AICs). Its membership of about 6 million makes up about 50% of the total membership of AICs. The church has been preaching peace from its inception which became one of the cores of its theology and practice. Thus, the church is known for its peace talk rhetoric expressed in Northern Sotho, ‘Kgotso ebe le lena’ which means ‘Peace be with you’ or even ‘kgotso ebe le batho’ which means ‘peace be with the people’. This peace talk rhetoric is usually used by the church’s leaders and the church’s ordinary members. Through this rhetoric, the church played a meaningful role in moving South Africa towards peace and reconciliation in 1994. This article argues that the theology of peace and reconciliation should be constructed by transcending the peace talk rhetoric to meaningful works of justice. The wrongs of the past should be corrected rather than just a mere talk about peace. The theology of peace and reconciliation should not only be a ‘peace be with you’ rhetoric but also a ‘justice be with you’. The reviewed literature in this article focussed on peace and reconciliation in the Zion Christian Church, peace talks and peace theology. A case study method was used for the selection of the Zion Christian Church.Contribution: The theology of peace and reconciliation is discussed by incorporating justice in addition to the peace talk rhetoric in the Zion Christian Church.