Hyperspectral Imaging (HSI) provides detailed spectral information for each pixel in an image, which involves acquiring images at numerous narrow and contiguous wavelength bands. Comprehensive spatial and spectral information deposits in hyperspectral images acquired by sensors, cameras, and various data acquisition sources lead to a wide range of applications across multiple fields from agriculture, and environment to biology. Various Image Processing and Artificial Intelligence algorithms have been developed periodically to analyze the data acquired through HSI. This review paper presents a comprehensive analysis of HSI focusing on its various aspects and potential implications. We explore detailed applications and key algorithms of HSI and discuss the associated advancements and challenges. Through an extensive literature review, we identify the state of research and methodologies related to HSI. Our study covers a wide range of HSI applications such as Earth Sciences, Exploration, Monitoring, Agriculture, Security, Conservation, Security, Healthcare, and Medical Imaging, and how Hyperspectral Imaging algorithms benefit these applications. Additionally, we discuss emerging trends and future directions in HSI providing insights into the promising avenues for further research.

Analyzing videos presents a unique challenge due to their rich content compared to images. Furthermore, processing lengthy videos efficiently necessitates segmenting them into scenes. Focusing on individual scene analysis offers an efficient alternative to analyzing entire videos. To facilitate this approach, images are extracted from these scenes, and each image is subjected to processing and analysis. In this study, we introduce a novel framework for video analysis employing state-of-the-art open-source models, including Contrastive Language–Image Pre-training (CLIP) for image classification, Bidirectional and Auto-Regressive Transformer (BART) language model for text classification, WHat-If Scenario Planning for Event Recognition (WHISPER) for Speech Recognition, and Yet Another Multiscale Convolutional Neural Network (YAMNET) audio model for audio classification. The proposed methodology starts with a video, and then, audio features are harnessed to extract scenes. A multimodal approach is applied to these scenes, capturing audio features, and text features through Automatic Speech Recognition (ASR), and image classification using CLIP. This fusion of multimodal features enhances the video intelligence process, making it a highly effective approach. The application of this approach extends to a variety of Video Intelligence tasks, from surveillance applications to comprehensive video analytics. By capitalizing on opensource foundation models and leveraging audio and text features, our framework offers a versatile solution to the intricate task of video analysis, catering to a multitude of real-world applications. We published the code at https URL [https://github.com/akewarmayur/VideoIntelligence] for future consideration and study.

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.