An Optimized Intelligent Open-Source MLaaS Framework for User-Friendly Clustering and Anomaly Detection

An Optimized Intelligent Open-Source MLaaS Framework for User-Friendly Clustering and Anomaly Detection | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article An Optimized Intelligent Open-Source MLaaS Framework for User-Friendly Clustering and Anomaly Detection Kamal A. ElDahshan, Gaber E. Abutaleb, Berihan R. Elemary, Ebeid A. Ebeid, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3958757/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 24 You are reading this latest preprint version Abstract In the big data era, businesses are in dire need of intelligent solutions to rapidly extract valuable insights. However, not all companies possess the specialized expertise required to operate machine learning algorithms. To bridge this gap, this paper introduces a cost-effective, user-friendly, dependable, adaptable, and scalable solution for visualizing, analyzing, processing, and extracting valuable insights from data. The proposed solution is an optimized open-source unsupervised machine learning as a service (MLaaS) framework that caters to both experts and non-experts in machine learning. The framework aims to assist companies and organizations in solving problems related to clustering and anomaly detection, even without prior experience or internal infrastructure. With a focus on several clustering and anomaly detection techniques, the proposed framework automates data processing while allowing user intervention. Furthermore, the proposed solution is expandable; it may include additional algorithms. It is versatile and capable of handling diverse datasets by generating separate rapid artificial intelligence (AI) models for each dataset and facilitating their comparison rapidly. The proposed framework provides a solution through a Representational State Transfer (RESTful) Application Programming Interface (API), enabling seamless integration with various systems. Real-world testing of the proposed framework on customer segmentation and fraud detection data demonstrates that it is reliable, efficient, cost-effective, and time-saving. With this innovative MLaaS framework, companies may harness the full potential of business analysis. Machine Learning as a Service Unsupervised Machine Learning Business Analysis Clustering Anomaly Detection Fraud Detection Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 04 Jul, 2024 Reviews received at journal 03 Jul, 2024 Reviewers agreed at journal 03 Jul, 2024 Reviews received at journal 02 Jul, 2024 Reviewers agreed at journal 24 Jun, 2024 Reviewers agreed at journal 24 Jun, 2024 Reviews received at journal 23 Jun, 2024 Reviewers agreed at journal 21 Jun, 2024 Reviews received at journal 21 Jun, 2024 Reviewers agreed at journal 21 Jun, 2024 Reviewers agreed at journal 20 Jun, 2024 Reviewers agreed at journal 20 Jun, 2024 Reviewers agreed at journal 20 Jun, 2024 Reviewers agreed at journal 19 Jun, 2024 Reviewers agreed at journal 19 Jun, 2024 Reviewers agreed at journal 19 Jun, 2024 Reviewers agreed at journal 19 Jun, 2024 Reviewers agreed at journal 19 Jun, 2024 Reviewers agreed at journal 18 Jun, 2024 Reviewers agreed at journal 18 Jun, 2024 Reviewers invited by journal 18 Jun, 2024 Editor assigned by journal 19 Feb, 2024 Submission checks completed at journal 19 Feb, 2024 First submitted to journal 15 Feb, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3958757","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":273681133,"identity":"9b2cee7e-8d79-4b44-bce6-ce5de7b773a7","order_by":0,"name":"Kamal A. 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