Recent advancements in Explainable Artificial Intelligence (XAI) aim to bridge the gap between complex artificial intelligence (AI) models and human understanding, fostering trust and usability in AI systems. However, challenges persist in comprehensively interpreting these models, hindering their widespread adoption. This study addresses these challenges by exploring recently emerging techniques in XAI. The primary problem addressed is the lack of transparency and interpretability in AI models to humanity for institution-wide use, which undermines user trust and inhibits their integration into critical decision-making processes. Through an in-depth review, this study identifies the objectives of enhancing the interpretability of AI models and improving human understanding of their decision-making processes. Various methodological approaches, including post-hoc explanations, model transparency methods, and interactive visualization techniques, are investigated to elucidate AI model behaviours. We further present techniques and methods to make AI models more interpretable and understandable to humans including their strengths and weaknesses to demonstrate promising advancements in model interpretability, facilitating better comprehension of complex AI systems by humans. In addition, we provide the application of XAI in local use cases. Challenges, solutions, and open research directions were highlighted to clarify these compelling XAI utilization challenges. The implications of this research are profound, as enhanced interpretability fosters trust in AI systems across diverse applications, from healthcare to finance. By empowering users to understand and scrutinize AI decisions, these techniques pave the way for more responsible and accountable AI deployment.

Big climate change data have become a pressing issue that organizations face with methods to analyze data generated from various data types. Moreover, storage, processing, and analysis of data generated from climate change activities are becoming very massive, and are challenging for the current algorithms to handle. Therefore, big data analytics methods are designed for significantly large amounts of data required to enhance seasonal change monitoring and understand and ascertain the health risks of climate change. In addition, analysis of climate change data would improve the allocation, and utilisation of natural resources. This paper provides an extensive discussion of big data analytic methods for climate data analysis and investigates how climate change and sustainability issues can be analyzed through these approaches. We further present the big data analytic methods, strengths, and weaknesses, and the essence of analyzing big climate change using these methods. The common datasets, implementation frameworks for climate change modeling, and future research directions were also presented to enhance the clarity of these compelling climate change analysis challenges. This big data analytics method is well-timed to solve the inherent issues of data analysis and easy realization of sustainable development goals.

Integrating artificial intelligence (AI) and machine learning (ML) into smart home systems has significantly advanced and improved residential energy efficiency, addressing growing concerns around energy conservation and sustainability. Choosing appropriate AI/ML techniques to optimize energy consumption in the dynamic and contemporary smart home environment remains a complex challenge. This study investigates a range of AI/ML algorithms such as regression models, deep learning, clustering, and decision trees to enhance energy management in smart homes. The study highlights the core processes of smart home energy optimization, including data acquisition, feature extraction, and model evaluation, as well as the specific roles of each AI/ML technique in optimizing energy usage. The study also discusses the strengths and weaknesses of the AI/ML techniques used for smart homes. It further explores the application areas and emerging challenges such as data security risks, high implementation costs, and gaps in existing technology that impact the scalability of AI/ML solutions in smart home contexts. The findings reveal that AI/ML techniques can effectively transform energy management in smart homes, enabling real-time optimization and adaptive decision-making to minimize energy consumption and reduce costs. Additionally, the study highlights future research directions.

Mobile healthcare (mHealth) delivery has revolutionized the healthcare industry 5.0. With the move toward mHealth, access to healthcare services has gradually increased, allowing individualized treatment routines, and real-time health monitoring. However, cybersecurity threats to mHealth systems also increase as their use increases. The continuous rise of cyber threats has recently affected the healthcare delivery sector. These have caused much financial damage and fears in the past few years. This paper aims to systematically assess and categorize the cyber threats in mHealth applications to guide the industry-wide adoption and prospects. A systematic literature review was carried out between 2018 and 2025 including 40 primary study papers were synthesized out of 24,854 search articles. The study discussed mHealth, trends, features, and main technologies to explore the advancement of mHealth. It presents cyber threats in mHealth applications where the common cyber threats were identified and security challenges such as data compromise, malicious attacks, insecure systems, and user vulnerabilities. We provided mitigation strategies to address the inherent challenges. It further highlights traditional and contemporary and centralized and decentralized detection approaches to cyber threats for effective implementation of patient health data. The findings showed that much work is left undone which we offered in open research prospects. The outlined open research prospects will improve the security and privacy of health-sensitive data against cyber threats. The practical implication of this study is that it will guide all health stakeholders, such as patients, health professionals, agencies, government and policymakers, and researchers, in improving the security and privacy of patients’ data in digital cyberspace.