In past few decades, solar energy plays a vital role in energy production among the different renewable energy resources. In shaded/unshaded photovoltaic (PV) systems, tracking of maximum power under different environmental conditions is provided by maximum power point tracking (MPPT). In recent years many works available on different types of MPPT techniques to track maximum power from PV systems with own pros and cons. This article comprehensively reviews the different traditional methods like perturb and observation (P&O), open circuit voltage (OCV), short circuit current (SCC), hill climbing (HC), incremental conductance (IC). Also recall the advanced MPPT techniques like particle swarm optimization (PSO), grey wolf optimization (GWO), cuckoo search (CS), artificial neural networks (ANN), fuzzy logic controller (FLC) available in literature. This review is conducted based on implementation, accuracy, tracking speed, cost, merits, and demerits of each technique. Traditional MPPT methods can’t able to track global maximum power point under partial shaded conditions and exhibits less efficiency when compared with advanced soft computing methods. Hybrid methods provide good efficiency and performance than traditional and advanced methods. Authors powerfully confirm that this article offers convenient information’s to enthusiastic engineers and new researchers those who are all working in solar PV systems.

This article focuses on the early prediction of cardiovascular disease (CVD) through the application of machine learning to health records. This study systematically reviews existing literature and employs advanced machine learning algorithms to discern predictive factors within electronic health data. Key findings highlight the significance of genetic predispositions, lifestyle choices, and clinical markers as influential contributors to CVD development. The integration of these factors into machine learning models demonstrates notable accuracy in preemptive risk assessment. The implications of this research are profound, offering potential advancements in preventive healthcare strategies, personalized interventions, and resource allocation for populations at heightened cardiovascular risk.

The realm of healthcare is undergoing a revolutionary transformation through the integration of artificial intelligence (AI) for early cancer detection, ushering in a new era of enhanced well-being. This review paper delves into the paradigm shift brought about by AI, highlighting its potential to identify diseases in their nascent stages, thereby revolutionizing preventive healthcare. Harnessing the computational prowess of AI, this paradigm empowers us to analyze intricate medical data, including internal imagery and health records, facilitating the identification of subtle disease markers imperceptible to the human eye. The key focus lies not only in early detection but also in ensuring diagnostic accuracy. A comprehensive analysis of various studies underscores AI’s superiority in comparison to traditional methods, manifesting in quicker and more precise identification of anomalies. This transformation translates to expedited medical interventions and improved patient outcomes. The crux of this evolution lies in AI’s capacity to redefine healthcare, transforming it into a proactive endeavor that identifies and addresses health concerns while they remain manageable.

This Review explores the transformative impact of artificial intelligence (AI) on the early detection of skin cancer, with a specific focus on melanoma, a potentially lethal form of the disease. Beginning with an overview of traditional diagnostic methods and their limitations, this paper delves into the evolution of AI within dermatology, emphasizing its application in image analysis and pattern recognition. A comprehensive examination of AI algorithms for melanoma detection, including machine learning and deep learning models, is provided. This Review critically assesses the performance metrics, training datasets, and comparative analyses with traditional methods. Addressing challenges such as data quality, interpretability, and ethical considerations, this paper outlines future directions, emphasizing ongoing research, algorithm improvements, and integration with clinical practices. Case studies and success stories highlight the real-world impact of AI in dermatology. This Review concludes by summarizing key findings and underlining the pivotal role of AI in revolutionizing early melanoma detection, with implications for personalized medicine and enhanced patient outcomes.

This paper explores the synergy between early diabetes detection and artificial intelligence (AI)-based diabetic retinopathy screening. It emphasizes the crucial role of integration for enhancing patient care. Diabetes and its complications, such as diabetic retinopathy, can be better managed with early intervention. AI techniques are revolutionizing medical diagnostics, offering potential for accurate prediction of diabetes risk and automated analysis of retinal images for retinopathy detection. By combining these aspects, we uncover a comprehensive strategy that not only identifies diabetes in its early stages but also ensures timely management of its ocular complications. This integrated approach shows promise for personalized care, improved outcomes, and efficient resource utilization. This paper underscores the need for collaboration between medical and AI experts to maximize the potential of this dual-focused approach for transforming diabetes management.