The paper covers topics ranging from hyperspectral imaging applications to innovative algorithms that have enhanced the analysis of hyperspectral data. We delve into the practical applications, including Agriculture, Food Quality and Safety, Earth Sciences, Exploration and Monitoring, Healthcare, Pharmaceuticals, Medical Imaging, Industrial Manufacturing, Management, Conservation, Safety and Security. Our goal is to provide a holistic understanding of how hyperspectral imaging is transforming these fields and driving new possibilities. We also discussed detailed algorithmic development in Hyperspectral Imaging including Supervised and Unsupervised algorithms. We reviewed algorithmic development from the early 1980â\euro™s to recent developments including Artificial Intelligence involving algorithms from Machine Learning to Deep Learning. We believe our paper will be of particular interest to your readers for those who wish to study algorithm development from simple image processing procedures to AI based approaches and how these algorithms are used in diverse applications to make intelligent decisions for increasing productivity and efficiency in Agriculture, Exploration and Management and identification of diseases and natural resources. Our paper is well organized into various sections as per algorithms and applications. We reviewed more than 250 papers with proper citations and authors’ research work.

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Scene boundary detection is a crucial task in video analysis, facilitating tasks such as video summarization, indexing, and content-based retrieval. In this paper, we present a comprehensive review of various approaches and methodologies employed for scene boundary detection. We analyze the strengths and limitations of different techniques and highlight emerging trends and challenges in the field.

This paper introduces an EEG signal classification approach, leveraging machine learning algorithms. The methodology involves the extraction of features from EEG signal datasets through discrete wavelet transform (DWT). Optimal feature selection is then accomplished using evolutionary algorithms, specifically Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). To identify the most effective classification method, various machine learning algorithms, including Support Vector Machines (SVM), Naive Bayes, Decision Trees, and Random Forest, are systematically compared. This comprehensive evaluation aims to enhance the accuracy and efficiency of EEG signal classification for improved diagnosis and understanding of neurological conditions.