https://www.matjournals.net/engineering/index.php/RRMLCC <p><strong>RRMLCC</strong> is a peer reviewed journal in the discipline of Computer Science published by the MAT Journals Pvt. Ltd. It is a print and e-journal focused towards the rapid publication of fundamental research papers on all areas of Artificial Intelligence. The Journal aims to promote high quality empirical Research, Review articles, case studies and short communications mainly focused on Machine Learning, Cloud Computing, Bayesian Learning, Supervised Semi-Supervised and Unsupervised Learning, Decision Support Systems, Human-Computer Interaction and Systems, Problem Solving and Planning, Clustering, Classification, Neural Information Processing, Heterogeneous and Streaming Data, Probabilistic Models and Methods, Data Mining, Knowledge Discovery, Web Mining, Robotics and Control, Bioinformatics will be taken for consideration additionally.</p> en-US Research & Review: Machine Learning and Cloud Computing (e-ISSN: 2583-4835) https://www.matjournals.net/engineering/index.php/RRMLCC/article/view/3644 <p><em>Feedback from mobile app reviews helps uncover how applications perform, feel, and function in real-world usage. However, interpreting large volumes of unstructured textual data remains a significant challenge. A hybrid approach combining traditional Natural Language Processing techniques with Large Language Models forms the foundation of this methodology. The process begins with automated data collection followed by text preprocessing to improve data quality. Aspect extraction is primarily performed using LLM-based semantic understanding, with fallback statistical methods ensuring robustness and completeness. Sentiment classification relies on lexicon-based approaches to determine emotional polarity efficiently. Summarization is achieved using transformer-based models capable of generating concise and meaningful representations of user feedback. Visualization techniques, such as word clouds and sentiment distribution charts, highlight frequently discussed topics and overall sentiment trends. By integrating statistical and semantic techniques, the system improves resilience and contextual accuracy compared to standalone approaches. Experimental results demonstrate effective identification of key aspects such as performance issues and functional defects, along with their associated sentiment polarity. These insights support developers in aligning application improvements with actual user feedback.</em></p> Ramtilak Nadar Maya Nair Copyright (c) 2026 Research & Review: Machine Learning and Cloud Computing (e-ISSN: 2583-4835) 2026-05-30 2026-05-30 5 2 12 20 https://www.matjournals.net/engineering/index.php/RRMLCC/article/view/3643 <p><em>The Internet of Things (IoT) has developed significantly in recent years. Cloud computing plays an important role in data storage and processing. During COVID-19 many people lost their lives due to a lack of monitoring of vitals. Keeping track of that many vitals was the challenge. This Smart Health monitoring system (SHMS) effectively monitors patients’ health status and saves lives on time. SHMS helps in monitoring many vital signs like body Temperature (BT), heart Rate (HR), toileting habits, blood pressure, as well as sleep patterns.</em><em> Think of this system as a smart health companion that quietly looks after the user. It uses small sensors, a data collection unit, and a microcontroller, like Arduino, to keep track of the health in real time. All this information is instantly sent to a secure cloud platform, where intelligent tools continuously check for anything unusual and quickly alert a doctor if something seems off.</em></p> <p><em>It doesn’t just stop there; the system can also connect with a decision support system (DSS) to create clear and helpful medical reports, making it easier to understand health over time. With an accuracy of 91.68%, it offers a dependable and efficient way to stay on top of well-being, especially when regular hospital visits aren’t possible. What makes it even more valuable is its ability to adapt during health emergencies. It can monitor additional vital signs like breathing rate and oxygen levels, which are especially important during illnesses such as COVID-19 or the flu. Overall, this system makes healthcare more accessible, helps detect problems early, supports timely treatment, and reduces the burden on hospitals—acting like a constant, caring presence for health.</em></p> Dipali Tupe Anshurani Singh Nehal Sinha Tanishka Laute Zeel Panchal Copyright (c) 2026 Research & Review: Machine Learning and Cloud Computing (e-ISSN: 2583-4835) 2026-05-30 2026-05-30 5 2 1 11