Rehabilitation is undergoing a revolution with machine learning (ML) enabling personalized solutions based on patient-specific data. Unlike current rehabilitation practices that lack adaptability, ML optimizes physical, cognitive, and psychological aspects through real-time monitoring, modeling, and intervention. Computer vision enhances movement analysis, NLP supports psychological assessments, and predictive models optimize treatment plans. Applications such as wearable technology, telerehabilitation, and ML-driven robotics improve patient interaction and supervision, offering flexible and remote care. Integrating ML with technologies like VR and IoT creates patient-centric, accessible solutions with superior prognostic value. These advancements usher in a new era of algorithms designed for better patient care, safety, and personalized therapeutic processes.

Introduction: Facial micro expressions have a significant role in communicating hidden emotions, offering profound implications in multiple areas such as psychology, cybersecurity, and the study of human-computer interaction. However, recognizing micro-expressions is tough because of their transient nature. Furthermore,Micro expressions are significantly shaped by the visual characteristics of the face and the interactions among its various sub-regions. Recent advances in computer vision have improved self-supervised learning. Traditional CNNs for vision disorders learn only from whole photos or videos and cannot consistently distinguish facial micro expressions. Some existing CNN models might lack advanced attention mechanisms to effectively prioritize subtle micro-expression features within the complex facial data. They may leading to potential misinterpretation of expressions. Objectives: This research offers the Attention Convolutional Gated Recurrent Neural Network with RMSProp (ACGRNN- RMSProp) classifier aims to achieve high accuracy and robustness in micro facial expression recognition. The attention mechanism sharpens the model's emphasis on important facial regions, while convolutional layers gather spatial features, and GRUs effectively capture temporal dynamics. The RMSProp optimizer ensures efficient and stable training, making this approach well-suited for the complex task of recognizing subtle and fleeting micro expressions.. This suggested approach is applied to the FER2013 dataset and compared to conventional micro expression recognition baseline methods. Methods: The FER2013 dataset is initially loaded and processed. Facial landmarks in this dataset are detected using a Multi-Task Cascaded Convolutional Neural Network, after which features are extracted from the processed data using EfficientNetB3 and InceptionResNetV2. The features obtained from extraction are then fused and subsequently classified using an Attention Convolutional Gated Recurrent Neural Network with RMSProp optimization. Results:This hybrid model demonstrates exceptional classification performance, achieving precision, recall, and F1-score of 94.5% each, along with an accuracy of 96.43%. Furthermore, it includes additional metrics such as the Matthews Correlation Coefficient (MCC), Receiver Operating Characteristic(ROC) Curve in this field. Conclusions:This study showcases enhanced recognition accuracy when compared to a range of leading models in the field,eliminated the requirement for system preprocessing procedures such as contrast enhancement, gradient operators, or dimensionality reduction for accurate results.Leverage the inherent relationships between face identification and other face analysis tasks to enhance future performance.

The present computer era is based in large part on research, processing and safe storage in distributed computer networks of confidential data. In view of the development of various cryptographical algorithms, several research studies have addressed this issue. In addition to the stressful cryptographic studies, it is noted that the protection of data on the server side is vulnerable to handle, and there is need to build and effectively provable security framework that will boost the efficiency of Cloud Server data processing. This paper proposes a new protection scheme which authenticates evidence that sensitive data are stored safely when a cloud-central cloud storage architecture is deployed. This article introduces an innovative computer security mechanism that solves the issue of internal nodes by reducing the overhead computer with powerful security algorithms. The experimental study reveals that the proposed mechanism exceeds most current defense mechanisms.

The adoption of green computing is crucial due to cost reduction and environmental responsibility. However, challenges hinder progress. This research explores solutions for balancing industrial growth with sustainability and addresses green computing obstacles. Recognizing industry's importance while mitigating its environmental impact is vital. Strategies to reduce waste and energy use, like cloud computing and virtualization, can help. Implementing a circular economy approach makes products regenerative and less wasteful. This research provides real-world case studies and insights, aiding businesses in adopting greener, more sustainable computing practices for a cost-effective, environmentally responsible tech landscape.

The explosion in the amount of textual data in the last several years has created an abundant source of resources for data extraction and analysis. It is necessary to summarize this data in order to access relevant information in a fair amount of time. Here we take a look at some of the more current methods for summarizing abstractive texts with deep learning models. A hotspot for research in the field of Natural Language Processing (NLP) is summarization, the process of condensing a document without losing any of its significance. You can classify summarization methods as either “extractive” or “abstractive” depending on whether they use natural language processing to construct new phrases or keep the exact ones from the source text. Extensive research has been conducted on extractive summarization, and the issue has now attained maturity. At this point, the focus of the study is on providing an abstract summary. Abstractive Text Summarization (ATS) is a tough and complicated operation because of the inherent complexity of the natural language content. Researchers can get a thorough overview of Deep Learning based A TS in this work. This paper provides a comprehensive survey of deep learning models employed for abstractive summarization, delving into their architectures and methodologies. Furthermore, it scrutinizes prevalent datasets utilized for training and evaluation purposes, offering insights into their characteristics and suitability for benchmarking. The study evaluates the performance of various abstractive summarization methods on these datasets, shedding light on their effectiveness and limitations. Lastly, emerging themes in the field are discussed, along with unresolved challenges, aiming to foster deeper understanding and advancement in DL-based abstractive text summarization among researchers.

Automatic emotion identification has emerged as a prominent area of research during the past decade, with applications in healthcare, human-computer interaction, and behavioral analysis. Although facial expressions and verbal communication have been thoroughly examined, the identification of emotions via body gestures is still inadequately investigated. Body gestures, an essential aspect of "body language" offer significant contextual indicators shaped by gender and culture variations. Recent breakthroughs in deep learning have facilitated the development of robust models capable of accurately capturing complex human movements, hence enhancing emotion recognition precision and adaptability. This study presents a thorough framework for the automatic recognition of emotional body gestures, encompassing essential elements such as individual detection, position estimation, and representation learning. High computational costs and the need for advanced algorithms to fuse multimodal data add to these hurdles. Recent advancements in deep learning, have shown great potential to overcome these issues and improve accuracy. This work highlights the applications, challenges, and future directions in emotion recognition from body gestures, emphasizing the need for scalable, robust, and real-world-ready systems that can enable emotionally intelligent technologies.