In Wireless Sensor Networks (WSNs), an efficient clustering technique is critical in optimizing the energy level of networked sensors and prolonging the network lifetime. While the traditional bee colony optimization technique has been widely used as a clustering technique in WSN, it mostly suffers from energy efficiency and network performance. This study proposes a Bee Colony Optimization that synergistically combines K-mean algorithms (referred to as K-BCO) for efficient clustering in heterogeneous sensor networks. This is to develop a robust and efficient clustering algorithm that addresses the challenges of energy consumption and network performance in WSNs. The K-BCO algorithm outperformed comparative clustering algorithms such as H-LEACH, DBCP, and ABC-ACO in average error rate (AER), average data delivery rate (ADDR), and average energy consumption (AEC) for transmitting data packets from sensors to cluster heads. The K-BCO outperformed other algorithms in terms of ADDR at 95.00% against H-LEACH (75.86%), DBCP (72.07%) and ABC-ACO (90.08%). The findings indicate that the K-BCO not only optimizes energy consumption but also guarantees more stable and robust solutions, thereby extending the network lifetime of WSNs. Thus, K-BCO is recommended to practitioners in wireless sensor networks as it paves the way for more efficient and sustainable wireless communication.

Insider threats remain a persistent challenge in cybersecurity, as malicious or negligent insiders exploit legitimate access to compromise systems and data. This study presents a bibliometric review of 325 peer-reviewed publications from 2015 to 2025 to examine how machine learning (ML) and deep learning (DL) techniques for insider threat detection have evolved. The analysis investigates temporal publication trends, influential authors, international collaboration networks, thematic shifts, and algorithmic preferences. Results show a steady rise in research output and a transition from traditional ML models, such as decision trees and random forests, toward advanced DL methods, including long short-term memory (LSTM) networks, autoencoders, and hybrid ML–DL frameworks. Co-authorship mapping highlights China, India, and the United States as leading contributors, while keyword analysis underscores the increasing focus on behavior-based and eXplainable AI models. Symmetry emerges as a central theme, reflected in balancing detection accuracy with computational efficiency, and minimizing false positives while avoiding false negatives. The study recommends adaptive hybrid architectures, particularly Bidirectional LSTM–Variational Auto-Encoder (BiLSTM-VAE) models with eXplainable AI, as promising solutions that restore symmetry between detection accuracy and transparency, strengthening both technical performance and organizational trust.

The use of biometric identification technology has become increasingly prevalent in modern society, with potential benefits for various populations. However, despite the widespread adoption of biometric identification, there is still a significant gap in research on the benefits that older adults may derive from this technology and how it may improve their quality of life. This study draws on a mixed methodology and the flow theory to understand how biometric identification improves the quality of life of older adults. The findings show that older adults obtain socio-economic impacts, social inclusion, improved access to healthcare and access to information that serves their health, psychological, emotional and mental needs through the use of biometric identification. Drawing on artificial neural network, we ranked the derived benefits and used partial least square-structural equation modelling (PLS-SEM) to investigate how these benefits translate to the quality of life of older adults. The results showed that the most significant biometric factor that promotes the quality of life of older adults is improved healthcare access, followed by information access and socio-economic development. The PLS-SEM results show that social inclusion is essential but does not improve the quality of life of older adults. The findings of this study offer valuable information for policy-makers, technology developers and practitioners working to improve the lives of older adults.

Financial and insider fraud increasingly intersect with broader cybercrime ecosystems, creating attack vectors that undermine national cyber resilience and the integrity of digital financial infrastructures. As organizations turn to machine learning (ML) and deep learning (DL) models for automated fraud and insider-threat detection, the opacity of these systems presents strategic risks for cyber defense: unexplainable alerts weaken analyst trust, complicate incident response, and challenge regulatory and forensic accountability. This study presents a systematic review of 107 empirically validated works (2015–2025) examining how Explainable Artificial Intelligence (XAI) techniques enhance transparency, trustworthiness, and operational readiness in AI-driven fraud detection systems. Using a mixed bibliometric–thematic methodology, the review maps the evolution of ML/DL architectures, XAI adoption patterns, evaluation practices, and dataset limitations within security-critical environments. The findings highlight a sector-wide dependence on post-hoc feature attribution and reveal emerging shifts toward intrinsic interpretability through attention mechanisms and hybrid temporal models. Despite progress, gaps persist: limited use of sequential behavioral models, narrow evaluation metrics, and overreliance on structured datasets weaken real-world resilience against adaptive adversaries. To address these challenges, the paper proposes a Three-Pillar Framework: Algorithmic Transparency, Evaluation Accountability, and Data Traceability that positions explainability as a foundational architectural property for cyber defense systems. By aligning model interpretability with security operations, regulatory requirements, and analyst cognition, the framework strengthens organizational readiness against insider threats, financial fraud, and AI-targeted adversarial manipulation, key considerations in modern cyber warfare and security operations.

In a world increasingly driven by data, most developed economies are leveraging big data to achieve greater feats in various sectors of their economies. From advertisement, commerce, healthcare, and energy to defense, big data has given new insights into the huge volume of data accumulated over the past few decades that is helping reshape our knowledge and understanding of these sectors. Unfortunately, the same cannot be said about the state of big data in the developing world, where investments in IT infrastructure are dangerously low, keeping huge proportions of the population offline. This chapter discussed the challenges that exist in developing countries, which affect the smooth take-off of big data and data science as well as recommendations as to how countries and companies in the developing world can overcome these challenges to harness the benefits and opportunities presented by this technology.