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    <title><![CDATA[A Machine Learning-Based Sentiment Analysis of Article 370 Tweets to Support Government Policy Decisions]]></title>
    <link>https://thescipub/abstract/jcssp.2026.1968.1990</link>
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        <![CDATA[<p>Journal of Computer Science, Published online: 2 July 2026; <a href="https://thescipub.com/abstract/jcssp.2026.1968.1990">doi:10.3844/jcssp.2026.1968.1990</a></p>This study proposes a robust sentiment analysis framework to evaluate public opinion on the abrogation of Article 370 using Twitter data. The methodology begins with tweet collection through the Twitt...]]></content:encoded>
    <dc:title><![CDATA[A Machine Learning-Based Sentiment Analysis of Article 370 Tweets to Support Government Policy Decisions]]></dc:title><dc:creator>Subhasis   Mohapatra</dc:creator><dc:creator>Sudhir Kumar Mohapatra</dc:creator><dc:creator>Sweta   Samantaray</dc:creator><dc:creator>Aliazar Deneke Deferisha</dc:creator><dc:creator>Prasanta Kumar Bal</dc:creator><dc:identifier>doi:10.3844/jcssp.2026.1968.1990</dc:identifier>
    <dc:source>Journal of Computer Science, Published online: 2026-07-02; | doi:10.3844/jcssp.2026.1968.1990</dc:source>
    <dc:date>2026-07-02</dc:date>
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    <title><![CDATA[Intrusion-Resistant Multi-Hop Communication Protocol for Secure Wireless Sensor Networks]]></title>
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        <![CDATA[<p>Journal of Computer Science, Published online: 1 July 2026; <a href="https://thescipub.com/abstract/jcssp.2026.1959.1967">doi:10.3844/jcssp.2026.1959.1967</a></p>Modern Internet of Things (IoT) systems are based on Wireless Sensor Networks (WSNs), which can be distributed to various environments to perform a range of sensing and data aggregation. Their intrins...]]></content:encoded>
    <dc:title><![CDATA[Intrusion-Resistant Multi-Hop Communication Protocol for Secure Wireless Sensor Networks]]></dc:title><dc:creator>Rakesh   Ranjan</dc:creator><dc:creator>Vaishali   Singh</dc:creator><dc:creator>Hitendra   Singh</dc:creator><dc:identifier>doi:10.3844/jcssp.2026.1959.1967</dc:identifier>
    <dc:source>Journal of Computer Science, Published online: 2026-07-01; | doi:10.3844/jcssp.2026.1959.1967</dc:source>
    <dc:date>2026-07-01</dc:date>
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    <title><![CDATA[MCWDRL: Multi-Cloud Workflow Scheduling Using Deep Reinforcement Learning and Improved Workflow Segmentation for Multi-Cloud Environments]]></title>
    <link>https://thescipub/abstract/jcssp.2026.1949.1958</link>
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        <![CDATA[<p>Journal of Computer Science, Published online: 1 July 2026; <a href="https://thescipub.com/abstract/jcssp.2026.1949.1958">doi:10.3844/jcssp.2026.1949.1958</a></p>The rapid growth of cloud computing has led to complex workflow scheduling challenges in multi-cloud environments, where efficient resource utilization, minimized makespan, and reduced costs are param...]]></content:encoded>
    <dc:title><![CDATA[MCWDRL: Multi-Cloud Workflow Scheduling Using Deep Reinforcement Learning and Improved Workflow Segmentation for Multi-Cloud Environments]]></dc:title><dc:creator>S.   Gowri</dc:creator><dc:creator>A.   Sumathi</dc:creator><dc:identifier>doi:10.3844/jcssp.2026.1949.1958</dc:identifier>
    <dc:source>Journal of Computer Science, Published online: 2026-07-01; | doi:10.3844/jcssp.2026.1949.1958</dc:source>
    <dc:date>2026-07-01</dc:date>
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    <prism:doi>10.3844/jcssp.2026.1949.1958</prism:doi>
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    <title><![CDATA[A Novel Crow Search Optimization Based Feature Selection With Optimal DNN for Big Data Classification]]></title>
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        <![CDATA[<p>Journal of Computer Science, Published online: 30 June 2026; <a href="https://thescipub.com/abstract/jcssp.2026.1933.1948">doi:10.3844/jcssp.2026.1933.1948</a></p>Big data analytics has become popular due to its applicability in various real-time applications. To attain better performance, big data can be analyzed using the machine learning and deep learning mo...]]></content:encoded>
    <dc:title><![CDATA[A Novel Crow Search Optimization Based Feature Selection With Optimal DNN for Big Data Classification]]></dc:title><dc:creator>C.   Mahesh</dc:creator><dc:creator>J. Ruby Elizabeth</dc:creator><dc:creator>S. Gnana Selvan</dc:creator><dc:creator>S.   Jagadeesh</dc:creator><dc:creator>R.   Umanesan</dc:creator><dc:creator>S. Samsudeen Shaffi</dc:creator><dc:identifier>doi:10.3844/jcssp.2026.1933.1948</dc:identifier>
    <dc:source>Journal of Computer Science, Published online: 2026-06-30; | doi:10.3844/jcssp.2026.1933.1948</dc:source>
    <dc:date>2026-06-30</dc:date>
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    <title><![CDATA[Volatility-Aware Hybrid Memory Architecture for Real-Time and Persistent Big Data Systems]]></title>
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        <![CDATA[<p>Journal of Computer Science, Published online: 27 June 2026; <a href="https://thescipub.com/abstract/jcssp.2026.1923.1932">doi:10.3844/jcssp.2026.1923.1932</a></p>As data volumes and real-time analytics demands grow, DRAM only memory systems struggle to scale cost-efficiently and sustainably. We present VA-HMA, a volatility-aware hybrid memory architecture that...]]></content:encoded>
    <dc:title><![CDATA[Volatility-Aware Hybrid Memory Architecture for Real-Time and Persistent Big Data Systems]]></dc:title><dc:creator>Mohammed Elhabib Maicha</dc:creator><dc:creator>Mohammed Redha Bouzidi</dc:creator><dc:identifier>doi:10.3844/jcssp.2026.1923.1932</dc:identifier>
    <dc:source>Journal of Computer Science, Published online: 2026-06-27; | doi:10.3844/jcssp.2026.1923.1932</dc:source>
    <dc:date>2026-06-27</dc:date>
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