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    <title><![CDATA[Bridging Traditional Craftsmanship and Digital Interaction through User-Centered Design: Designing Intuitive Interfaces]]></title>
    <link>https://thescipub/abstract/jcssp.2026.1767.1784</link>
    <content:encoded>
        <![CDATA[<p>Journal of Computer Science, Published online: 5 June 2026; <a href="https://thescipub.com/abstract/jcssp.2026.1767.1784">doi:10.3844/jcssp.2026.1767.1784</a></p>Traditional craftsmanship served as both a manifestation of cultural heritage and a vital pillar of the global creative economy. However, its digital integration was frequently hindered by low technol...]]></content:encoded>
    <dc:title><![CDATA[Bridging Traditional Craftsmanship and Digital Interaction through User-Centered Design: Designing Intuitive Interfaces]]></dc:title><dc:creator>Christopher Kevin Gunawan</dc:creator><dc:creator>Ravi Ardiza Putra</dc:creator><dc:creator>Steven   Albert</dc:creator><dc:creator>Sugiarto   Hartono</dc:creator><dc:creator>Fathy   Radhia</dc:creator><dc:identifier>doi:10.3844/jcssp.2026.1767.1784</dc:identifier>
    <dc:source>Journal of Computer Science, Published online: 2026-06-05; | doi:10.3844/jcssp.2026.1767.1784</dc:source>
    <dc:date>2026-06-05</dc:date>
    <prism:publicationName>Science Publications</prism:publicationName>
    <prism:doi>10.3844/jcssp.2026.1767.1784</prism:doi>
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    </item><item rdf:about="https://thescipub/abstract/jcssp.2026.1785.1796">
    <title><![CDATA[Hybrid CNN-Based Transformer Pipeline With Radiomic Fusion for Multi-Class Lung Cancer Detection]]></title>
    <link>https://thescipub/abstract/jcssp.2026.1785.1796</link>
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        <![CDATA[<p>Journal of Computer Science, Published online: 3 June 2026; <a href="https://thescipub.com/abstract/jcssp.2026.1785.1796">doi:10.3844/jcssp.2026.1785.1796</a></p>Early detection of lung cancer remains challenging due to high intra-class variation and inter-class similarity in Computed Tomography (CT) images. In this paper, we propose a hybrid deep learning mod...]]></content:encoded>
    <dc:title><![CDATA[Hybrid CNN-Based Transformer Pipeline With Radiomic Fusion for Multi-Class Lung Cancer Detection]]></dc:title><dc:creator>Aanchal   Vij</dc:creator><dc:creator>Kuldeep Singh Kaswan</dc:creator><dc:creator>Anand   Nayyar</dc:creator><dc:identifier>doi:10.3844/jcssp.2026.1785.1796</dc:identifier>
    <dc:source>Journal of Computer Science, Published online: 2026-06-03; | doi:10.3844/jcssp.2026.1785.1796</dc:source>
    <dc:date>2026-06-03</dc:date>
    <prism:publicationName>Science Publications</prism:publicationName>
    <prism:doi>10.3844/jcssp.2026.1785.1796</prism:doi>
    <prism:url>https://thescipub/abstract/jcssp.2026.1785.1796</prism:url>
    </item><item rdf:about="https://thescipub/abstract/jcssp.2026.1797.1810">
    <title><![CDATA[Optimized Cloud Load Balancing Using Deep Q-Learning With Inverted S-Shaped Hierarchical Reverse Superb Fairy-Wren Optimization]]></title>
    <link>https://thescipub/abstract/jcssp.2026.1797.1810</link>
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        <![CDATA[<p>Journal of Computer Science, Published online: 4 June 2026; <a href="https://thescipub.com/abstract/jcssp.2026.1797.1810">doi:10.3844/jcssp.2026.1797.1810</a></p>Cloud computing offers a scalable and cost-effective platform by providing on-demand access to shared computational resources. However, effective load balancing is essential to maintain optimal perfor...]]></content:encoded>
    <dc:title><![CDATA[Optimized Cloud Load Balancing Using Deep Q-Learning With Inverted S-Shaped Hierarchical Reverse Superb Fairy-Wren Optimization]]></dc:title><dc:creator>B. R. Yogeetha</dc:creator><dc:creator>S. P. Anandaraj</dc:creator><dc:identifier>doi:10.3844/jcssp.2026.1797.1810</dc:identifier>
    <dc:source>Journal of Computer Science, Published online: 2026-06-04; | doi:10.3844/jcssp.2026.1797.1810</dc:source>
    <dc:date>2026-06-04</dc:date>
    <prism:publicationName>Science Publications</prism:publicationName>
    <prism:doi>10.3844/jcssp.2026.1797.1810</prism:doi>
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    </item><item rdf:about="https://thescipub/abstract/jcssp.2026.1811.1822">
    <title><![CDATA[Toward Intelligent Evaluation of Digital Public Services: Evidence from Indonesia’s SIM Online Platform]]></title>
    <link>https://thescipub/abstract/jcssp.2026.1811.1822</link>
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        <![CDATA[<p>Journal of Computer Science, Published online: 15 June 2026; <a href="https://thescipub.com/abstract/jcssp.2026.1811.1822">doi:10.3844/jcssp.2026.1811.1822</a></p>This investigation examines more than 65,000 user evaluations from the Google Play Store concerning sentiment toward the Digital Korlantas POLRI application. Reviews were systematically processed and ...]]></content:encoded>
    <dc:title><![CDATA[Toward Intelligent Evaluation of Digital Public Services: Evidence from Indonesia’s SIM Online Platform]]></dc:title><dc:creator>Evaristus Didik Madyatmadja</dc:creator><dc:creator>Ricky   Kosasih</dc:creator><dc:creator>Najla Aurelia Evanthe</dc:creator><dc:creator>    Rudy</dc:creator><dc:creator>Betley Heru Susanto</dc:creator><dc:identifier>doi:10.3844/jcssp.2026.1811.1822</dc:identifier>
    <dc:source>Journal of Computer Science, Published online: 2026-06-15; | doi:10.3844/jcssp.2026.1811.1822</dc:source>
    <dc:date>2026-06-15</dc:date>
    <prism:publicationName>Science Publications</prism:publicationName>
    <prism:doi>10.3844/jcssp.2026.1811.1822</prism:doi>
    <prism:url>https://thescipub/abstract/jcssp.2026.1811.1822</prism:url>
    </item><item rdf:about="https://thescipub/abstract/jcssp.2026.1823.1834">
    <title><![CDATA[Application of Machine Learning for the Detection of Depression and Mindfulness as a Mitigation Method]]></title>
    <link>https://thescipub/abstract/jcssp.2026.1823.1834</link>
    <content:encoded>
        <![CDATA[<p>Journal of Computer Science, Published online: 16 June 2026; <a href="https://thescipub.com/abstract/jcssp.2026.1823.1834">doi:10.3844/jcssp.2026.1823.1834</a></p>Depression is a global mental health problem with various causes. Nowadays, with Artificial Intelligence, it can be predicted in order to take preventive measures, whether at a psychotherapeutic or ph...]]></content:encoded>
    <dc:title><![CDATA[Application of Machine Learning for the Detection of Depression and Mindfulness as a Mitigation Method]]></dc:title><dc:creator>Santiago Domingo Moquillaza Henriquez</dc:creator><dc:creator>Santiago Domingo Moquillaza Henriquez</dc:creator><dc:creator>Liliana Ruth Huam&aacute;n Rond&oacute;n</dc:creator><dc:creator>Nestor Marcial Alvarado Bravo</dc:creator><dc:creator>Roberto Jos&eacute; Antonio Carbonel Pezo</dc:creator><dc:creator>Juan Faustino Infantes Loo</dc:creator><dc:creator>Juan Faustino Infantes Loo</dc:creator><dc:identifier>doi:10.3844/jcssp.2026.1823.1834</dc:identifier>
    <dc:source>Journal of Computer Science, Published online: 2026-06-16; | doi:10.3844/jcssp.2026.1823.1834</dc:source>
    <dc:date>2026-06-16</dc:date>
    <prism:publicationName>Science Publications</prism:publicationName>
    <prism:doi>10.3844/jcssp.2026.1823.1834</prism:doi>
    <prism:url>https://thescipub/abstract/jcssp.2026.1823.1834</prism:url>
    </item><item rdf:about="https://thescipub/abstract/jcssp.2026.1835.1842">
    <title><![CDATA[Less Biased Approach for Sandstone Pore Segmentation]]></title>
    <link>https://thescipub/abstract/jcssp.2026.1835.1842</link>
    <content:encoded>
        <![CDATA[<p>Journal of Computer Science, Published online: 15 June 2026; <a href="https://thescipub.com/abstract/jcssp.2026.1835.1842">doi:10.3844/jcssp.2026.1835.1842</a></p>Reservoir characterization is a fundamental process in estimating reserves within petroleum and hydrogeological systems, where the precise determination of pore space dictates the validity of fluid fl...]]></content:encoded>
    <dc:title><![CDATA[Less Biased Approach for Sandstone Pore Segmentation]]></dc:title><dc:creator>Daffa Abiyyu Murtadha Kurnia</dc:creator><dc:creator>Iman Herwidiana Kartowisastro</dc:creator><dc:creator>Iman Herwidiana Kartowisastro</dc:creator><dc:identifier>doi:10.3844/jcssp.2026.1835.1842</dc:identifier>
    <dc:source>Journal of Computer Science, Published online: 2026-06-15; | doi:10.3844/jcssp.2026.1835.1842</dc:source>
    <dc:date>2026-06-15</dc:date>
    <prism:publicationName>Science Publications</prism:publicationName>
    <prism:doi>10.3844/jcssp.2026.1835.1842</prism:doi>
    <prism:url>https://thescipub/abstract/jcssp.2026.1835.1842</prism:url>
    </item><item rdf:about="https://thescipub/abstract/jcssp.2026.1841.1865">
    <title><![CDATA[Designing the Future: A Blockchain-Based Framework for Transparent and Secure Elections]]></title>
    <link>https://thescipub/abstract/jcssp.2026.1841.1865</link>
    <content:encoded>
        <![CDATA[<p>Journal of Computer Science, Published online: 16 June 2026; <a href="https://thescipub.com/abstract/jcssp.2026.1841.1865">doi:10.3844/jcssp.2026.1841.1865</a></p>Blockchain-based electronic voting systems have been identified as a solution to enhance the transparency, security, and efficiency of modern electoral processes. However, the existing system has thre...]]></content:encoded>
    <dc:title><![CDATA[Designing the Future: A Blockchain-Based Framework for Transparent and Secure Elections]]></dc:title><dc:creator>Jayesh   Solanki</dc:creator><dc:creator>Divyakant   Meva</dc:creator><dc:identifier>doi:10.3844/jcssp.2026.1841.1865</dc:identifier>
    <dc:source>Journal of Computer Science, Published online: 2026-06-16; | doi:10.3844/jcssp.2026.1841.1865</dc:source>
    <dc:date>2026-06-16</dc:date>
    <prism:publicationName>Science Publications</prism:publicationName>
    <prism:doi>10.3844/jcssp.2026.1841.1865</prism:doi>
    <prism:url>https://thescipub/abstract/jcssp.2026.1841.1865</prism:url>
    </item><item rdf:about="https://thescipub/abstract/jcssp.2026.1866.1880">
    <title><![CDATA[Polar Disentangled Non-Local Convolutional Neural Network for Skin Cancer Classification Using Dermoscopy Images]]></title>
    <link>https://thescipub/abstract/jcssp.2026.1866.1880</link>
    <content:encoded>
        <![CDATA[<p>Journal of Computer Science, Published online: 16 June 2026; <a href="https://thescipub.com/abstract/jcssp.2026.1866.1880">doi:10.3844/jcssp.2026.1866.1880</a></p>Skin cancer classification is the process of identifying and categorizing various types of skin lesions into cancerous and non-cancerous. This process is performed to achieve accurate detection, which...]]></content:encoded>
    <dc:title><![CDATA[Polar Disentangled Non-Local Convolutional Neural Network for Skin Cancer Classification Using Dermoscopy Images]]></dc:title><dc:creator>Usha   Thirugnanam</dc:creator><dc:creator>Nalini   Joseph</dc:creator><dc:identifier>doi:10.3844/jcssp.2026.1866.1880</dc:identifier>
    <dc:source>Journal of Computer Science, Published online: 2026-06-16; | doi:10.3844/jcssp.2026.1866.1880</dc:source>
    <dc:date>2026-06-16</dc:date>
    <prism:publicationName>Science Publications</prism:publicationName>
    <prism:doi>10.3844/jcssp.2026.1866.1880</prism:doi>
    <prism:url>https://thescipub/abstract/jcssp.2026.1866.1880</prism:url>
    </item><item rdf:about="https://thescipub/abstract/jcssp.2026.1881.1893">
    <title><![CDATA[Blockchain-Enabled Privacy-Preserving Access Control for Electronic Health Records Using Smart Contracts]]></title>
    <link>https://thescipub/abstract/jcssp.2026.1881.1893</link>
    <content:encoded>
        <![CDATA[<p>Journal of Computer Science, Published online: 22 June 2026; <a href="https://thescipub.com/abstract/jcssp.2026.1881.1893">doi:10.3844/jcssp.2026.1881.1893</a></p>Electronic medical records are crucial for providing high-quality healthcare, but they also raise serious privacy and security issues. Traditional centralised storage systems are vulnerable to manipul...]]></content:encoded>
    <dc:title><![CDATA[Blockchain-Enabled Privacy-Preserving Access Control for Electronic Health Records Using Smart Contracts]]></dc:title><dc:creator>G. Bhanu Prasad</dc:creator><dc:creator>M. Venkateswara Rao</dc:creator><dc:identifier>doi:10.3844/jcssp.2026.1881.1893</dc:identifier>
    <dc:source>Journal of Computer Science, Published online: 2026-06-22; | doi:10.3844/jcssp.2026.1881.1893</dc:source>
    <dc:date>2026-06-22</dc:date>
    <prism:publicationName>Science Publications</prism:publicationName>
    <prism:doi>10.3844/jcssp.2026.1881.1893</prism:doi>
    <prism:url>https://thescipub/abstract/jcssp.2026.1881.1893</prism:url>
    </item><item rdf:about="https://thescipub/abstract/jcssp.2026.1894.1911">
    <title><![CDATA[UzNER: A Human-Reviewed Benchmark for Uzbek Named Entity Recognition With Gazetteer-Augmented Transformer Models]]></title>
    <link>https://thescipub/abstract/jcssp.2026.1894.1911</link>
    <content:encoded>
        <![CDATA[<p>Journal of Computer Science, Published online: 22 June 2026; <a href="https://thescipub.com/abstract/jcssp.2026.1894.1911">doi:10.3844/jcssp.2026.1894.1911</a></p>UzNER-100K is a large-scale human-reviewed benchmark for Uzbek named entity recognition with 100,000 training sentences, 18 fine-grained entity types and 200,083 entity mentions across 114,269 sentenc...]]></content:encoded>
    <dc:title><![CDATA[UzNER: A Human-Reviewed Benchmark for Uzbek Named Entity Recognition With Gazetteer-Augmented Transformer Models]]></dc:title><dc:creator>Bobur   Saidov</dc:creator><dc:creator>Bobur   Saidov</dc:creator><dc:creator>Vladimir   Barakhnin</dc:creator><dc:creator>Vladimir   Barakhnin</dc:creator><dc:creator>Zarnigor   Fayzullaeva</dc:creator><dc:creator>Umid   Ibragimov</dc:creator><dc:creator>Ulugbek   Tursunov</dc:creator><dc:identifier>doi:10.3844/jcssp.2026.1894.1911</dc:identifier>
    <dc:source>Journal of Computer Science, Published online: 2026-06-22; | doi:10.3844/jcssp.2026.1894.1911</dc:source>
    <dc:date>2026-06-22</dc:date>
    <prism:publicationName>Science Publications</prism:publicationName>
    <prism:doi>10.3844/jcssp.2026.1894.1911</prism:doi>
    <prism:url>https://thescipub/abstract/jcssp.2026.1894.1911</prism:url>
    </item><item rdf:about="https://thescipub/abstract/jcssp.2026.1912.1922">
    <title><![CDATA[Comparative Analysis of Fraud Detection Methods in Banking Using Machine Learning Techniques]]></title>
    <link>https://thescipub/abstract/jcssp.2026.1912.1922</link>
    <content:encoded>
        <![CDATA[<p>Journal of Computer Science, Published online: 22 June 2026; <a href="https://thescipub.com/abstract/jcssp.2026.1912.1922">doi:10.3844/jcssp.2026.1912.1922</a></p>Fraud detection in banking requires algorithms that balance classification performance, computational efficiency, and regulatory interpretability, criteria that are rarely benchmarked together. We pre...]]></content:encoded>
    <dc:title><![CDATA[Comparative Analysis of Fraud Detection Methods in Banking Using Machine Learning Techniques]]></dc:title><dc:creator>Youssef   Tounsi</dc:creator><dc:creator>Ennouri   Tazi</dc:creator><dc:identifier>doi:10.3844/jcssp.2026.1912.1922</dc:identifier>
    <dc:source>Journal of Computer Science, Published online: 2026-06-22; | doi:10.3844/jcssp.2026.1912.1922</dc:source>
    <dc:date>2026-06-22</dc:date>
    <prism:publicationName>Science Publications</prism:publicationName>
    <prism:doi>10.3844/jcssp.2026.1912.1922</prism:doi>
    <prism:url>https://thescipub/abstract/jcssp.2026.1912.1922</prism:url>
    </item><item rdf:about="https://thescipub/abstract/jcssp.2026.1923.1932">
    <title><![CDATA[Volatility-Aware Hybrid Memory Architecture for Real-Time and Persistent Big Data Systems]]></title>
    <link>https://thescipub/abstract/jcssp.2026.1923.1932</link>
    <content:encoded>
        <![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>
    <prism:publicationName>Science Publications</prism:publicationName>
    <prism:doi>10.3844/jcssp.2026.1923.1932</prism:doi>
    <prism:url>https://thescipub/abstract/jcssp.2026.1923.1932</prism:url>
    </item><item rdf:about="https://thescipub/abstract/jcssp.2026.1933.1948">
    <title><![CDATA[A Novel Crow Search Optimization Based Feature Selection With Optimal DNN for Big Data Classification]]></title>
    <link>https://thescipub/abstract/jcssp.2026.1933.1948</link>
    <content:encoded>
        <![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>
    <prism:publicationName>Science Publications</prism:publicationName>
    <prism:doi>10.3844/jcssp.2026.1933.1948</prism:doi>
    <prism:url>https://thescipub/abstract/jcssp.2026.1933.1948</prism:url>
    </item><item rdf:about="https://thescipub/abstract/jcssp.2026.1949.1958">
    <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>
    <content:encoded>
        <![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>
    <prism:publicationName>Science Publications</prism:publicationName>
    <prism:doi>10.3844/jcssp.2026.1949.1958</prism:doi>
    <prism:url>https://thescipub/abstract/jcssp.2026.1949.1958</prism:url>
    </item><item rdf:about="https://thescipub/abstract/jcssp.2026.1959.1967">
    <title><![CDATA[Intrusion-Resistant Multi-Hop Communication Protocol for Secure Wireless Sensor Networks]]></title>
    <link>https://thescipub/abstract/jcssp.2026.1959.1967</link>
    <content:encoded>
        <![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>
    <prism:publicationName>Science Publications</prism:publicationName>
    <prism:doi>10.3844/jcssp.2026.1959.1967</prism:doi>
    <prism:url>https://thescipub/abstract/jcssp.2026.1959.1967</prism:url>
    </item><item rdf:about="https://thescipub/abstract/jcssp.2026.1968.1990">
    <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>
    <content:encoded>
        <![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>
    <prism:publicationName>Science Publications</prism:publicationName>
    <prism:doi>10.3844/jcssp.2026.1968.1990</prism:doi>
    <prism:url>https://thescipub/abstract/jcssp.2026.1968.1990</prism:url>
    </item></rdf:RDF>