https://matjournals.net/engineering/index.php/RRMLCC/issue/feed 2026-09-14T06:48:45+00:00 Open Journal Systems <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> https://matjournals.net/engineering/index.php/RRMLCC/article/view/4063 2026-09-02T09:07:34+00:00 Raman Shukla ramanshukla63077@gmail.com Akarshi Tiwari ramanshukla63077@gmail.com

<p><em>Social media fake news attacks free speech and makes people question the media. This heavily affects elections and public health. Human fact-checkers cannot keep up with the amount of content posted on the web. Because of this, there is great interest in the use of Machine Learning (ML) for news verification. This paper surveys five ML techniques used for fake news verification: Naïve Bayes, Logistic Regression, Support Vector Machine (SVM), Random Forest, and Long Short-Term Memory (LSTM). The effectiveness of the techniques is compared using accuracy, precision, recall, and F1-score based on previous studies performed on three commonly used benchmark datasets. The survey shows that with the right number of resources, LSTM can provide the best accuracy, while Naïve Bayes and Support Vector Machine (SVM) are the best and most resource-efficient algorithms. The paper lists the limitations of existing systems such as the lack of real-time verification, the difficulty of detection in a different context (domain), and the sparseness of labelled training data.</em></p>

2026-09-02T00:00:00+00:00 Copyright (c) 2026 Research & Review: Machine Learning and Cloud Computing (e-ISSN: 2583-4835) https://matjournals.net/engineering/index.php/RRMLCC/article/view/4112 2026-09-14T06:48:45+00:00 Suraj R. Nalawade asimsayyed28122004@gmail.com Tapase H. O. asimsayyed28122004@gmail.com Asim Sayyed asimsayyed28122004@gmail.com

<p><em>Serverless computing has moved from an experimental deployment style to a mainstream option for building cloud-native applications, shifting responsibility for provisioning, scaling, and fault tolerance from the developer to the platform. This paper synthesizes findings from two large-scale systematic reviews of the serverless computing literature, one spanning 164 papers and another spanning 275 papers published between 2016 and 2020, together with supporting studies on architecture, platform performance, and application domains, to build a consolidated picture of the field. The synthesis indicates that research attention has concentrated heavily on performance engineering, programming frameworks, and resource management, while comparatively less work addresses benchmarking standardization, resource-aware pricing, and privacy protection for Internet-of-Things deployments. Commercial platforms such as AWS Lambda, Microsoft Azure Functions, and Google Cloud Functions dominate industry adoption, while open-source, container-based alternatives offer greater portability at the cost of additional operational overhead. The review concludes that closing these specific gaps rather than further validating already well-studied performance claims represents the most productive direction for near-term serverless computing research.</em></p>

2026-09-14T00:00:00+00:00 Copyright (c) 2026 Research & Review: Machine Learning and Cloud Computing (e-ISSN: 2583-4835)