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@article{167305,
        author = {Ms.Sushmita Sheeba Dsa},
        title = {Java Libraries for Machine Learning: An In-depth Analysis of Weka, Deeplearning4j, and MOA},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {3},
        pages = {1027-1033},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=167305},
        abstract = {This paper provides an in-depth analysis of three prominent Java libraries for machine learning: Weka, Deeplearning4j (DL4J), and MOA. These libraries are examined in terms of their architecture, algorithm support, scalability, performance, ease of use, and application suitability. Weka, known for its extensive range of algorithms and user-friendly interface, is evaluated for its effectiveness in educational settings and small to medium-scale projects. Deeplearning4j, a robust deep learning library, is assessed for its capabilities in handling complex neural networks and large-scale data through distributed computing. MOA, specializing in data stream mining, is analyzed for its ability to perform real-time analytics on continuously flowing data. By comparing these libraries across various dimensions, this study aims to guide practitioners and researchers in selecting the most appropriate tool for their specific machine learning needs. The findings highlight the unique strengths and limitations of each library, offering insights into their optimal use cases and potential integration into Java-based machine learning applications.},
        keywords = {Machine Learning, Java Libraries, Weka, Deeplearning4j, MOA, Data Mining},
        month = {September},
        }