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    <title>Journal of Computer Sciences and Applications</title>
    <link>http://www.sciepub.com/journal/JCSA</link>
    <description>Journal of Computer Sciences and Applications is a peer-reviewed, open access journal that provides rapid publication of articles in all areas of computer sciences and applications. The goal of this journal is to provide a platform for scientists and academicians all over the world to promote, share, and discuss various new issues and developments in different areas of computer sciences and applications.</description>
    <dc:publisher>Science and Education Publishing</dc:publisher>
		<dc:language>en</dc:language>
		<dc:rights>2013 Science and Education Publishing Co. Ltd All rights reserved.</dc:rights>
		<prism:publicationName>Journal of Computer Sciences and Applications</prism:publicationName>
		14
		1
		January 2026
		<prism:copyright>2013 Science and Education Publishing Co. Ltd All rights reserved.</prism:copyright>
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<title>
Revolutionizing Multi-Campus Communication: A Next-Generation OSPF-Based Network Design for NVSU's Distributed Learning Environment
</title>
<link>http://pubs.sciepub.com/jcsa/14/1/1</link>
<description>
<![CDATA[<i>Revolutionizing Multi-Campus Communication: A Next-Generation OSPF-Based Network Design for NVSU's Distributed Learning Environment</i> addresses the urgent need for reliable network infrastructure in multi-campus universities: a need that has been intensified by the COVID-19 pandemic. Nueva Vizcaya State University (NVSU) requires a distributed learning environment that ensures educational continuity while complying with social distancing measures. Such an environment demands a robust and scalable network capable of supporting seamless communication and collaboration among students, faculty, and staff across geographically dispersed campuses. This study proposes a next-generation Open Shortest Path First (OSPF)–based network design to meet these increasing communication requirements. Previous research demonstrates that OSPF-based network architectures provide scalability, resiliency, and efficient routing, particularly well-suited for complex multi-campus networks. Furthermore, integrating emerging technologies such as Software-Defined Networking (SDN) and Network Function Virtualization (NFV) enhances network flexibility, performance, and manageability. The proposed research aims to design and implement an advanced OSPF-based network infrastructure that supports remote collaboration, ensures reliable access to online learning resources, and improves overall network efficiency. The significance of this study lies in its potential to revolutionize multi-campus communication at NVSU, enhance the quality of distributed learning, and serve as a reference model for other higher education institutions facing similar networking challenges.]]>
</description>
<dc:creator>
Fidel  G. Patricio Jr., Christopher  A. Gurat, Joan  Hazel V. Tiongson, Emmanuel  Danguilan, Armilyn  H. Wais, Keziah  Ann V. Rosini
</dc:creator>
<dc:date>2026-02-10</dc:date>
<dc:publisher>Science and Education Publishing</dc:publisher>
<prism:publicationDate>2026-02-10</prism:publicationDate>
<prism:number>1</prism:number>
<prism:volume>14</prism:volume>
<prism:startingPage>1</prism:startingPage>
<prism:endingPage>7</prism:endingPage>
<prism:doi>10.12691/jcsa-14-1-1</prism:doi>
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<item rdf:about="http://pubs.sciepub.com/jcsa/14/1/2">
<title>
An Interpretable, Imbalance-Aware Ensemble for Citrus Leaf Disease Classification using DenseNet169 and MobileNetV2
</title>
<link>http://pubs.sciepub.com/jcsa/14/1/2</link>
<description>
<![CDATA[Citrus leaf diseases pose a significant threat to agricultural productivity, particularly in regions dependent on smallholder farming. Early and accurate detection is essential, yet traditional diagnostic methods are labor-intensive and prone to subjectivity. This study proposes a logit-level weighted ensemble framework integrating DenseNet169 and MobileNetV2 for automated citrus disease classification under class imbalance conditions. A publicly available dataset of 594 images across four classes was utilized, employing a stratified 70/30 train-validation split and class-weighted cross-entropy loss to address imbalance. Results show that MobileNetV2 achieved the highest macro F1-score (0.9417), outperforming DenseNet169 (0.9327) and the proposed ensemble (0.9283). This indicates that a well-optimized single model can outperform ensemble methods in terms of peak accuracy, particularly when predictions are highly correlated. However, the ensemble demonstrated smoother convergence and more stable validation performance, emphasizing its strength in improving training stability and prediction consistency. Grad-CAM visualization confirmed that models focused on biologically relevant lesion regions. The framework was implemented using CPU-based computation, demonstrating feasibility in resource-constrained environments. Overall, ensemble learning enhances stability even when peak accuracy gains are limited.]]>
</description>
<dc:creator>
Carmelo  Alejo D. Bisquera, Romeo  P. Evangelista, Michael  John R. Robles, Von  P. Gabayan Jr.
</dc:creator>
<dc:date>2026-03-29</dc:date>
<dc:publisher>Science and Education Publishing</dc:publisher>
<prism:publicationDate>2026-03-29</prism:publicationDate>
<prism:number>1</prism:number>
<prism:volume>14</prism:volume>
<prism:startingPage>8</prism:startingPage>
<prism:endingPage>14</prism:endingPage>
<prism:doi>10.12691/jcsa-14-1-2</prism:doi>
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<item rdf:about="http://pubs.sciepub.com/jcsa/14/1/3">
<title>
Mapping Graduate Performance: The Role of Alumni Tracing Systems in Assessing Employability of the BSIT Graduates from the College of Information Technology Education
</title>
<link>http://pubs.sciepub.com/jcsa/14/1/3</link>
<description>
<![CDATA[This study systematically maps the professional trajectories of Bachelor of Science in Information Technology (BSIT) graduates from the College of Information Technology Education (CITE) at Nueva Vizcaya State University, Bayombong Campus, aiming to cover the period from 2021 to 2025. Employing a concurrent mixed-methods research design, the study triangulates quantitative performance metrics with qualitative thematic insights. Data were collected through Google Forms surveys and Messenger-based outreach, followed by rigorous manual cleaning and processing in a Google Colab environment using Pandas. Exploratory Data Analysis (EDA) and visualization through Matplotlib and Seaborn were utilized to identify longitudinal trends. Results indicate a resilient employability profile, with a peak employment rate of 77.61% in 2022 and a significant recovery in job relevance reaching 73.8% in 2024. The findings reveal a balanced absorption across both public and private sectors, with graduates successfully securing specialized roles in software engineering, technical support, and public sector IT management. The study concludes that the BSIT program maintains high market responsiveness and supports Sustainable Development Goals 4 (Quality Education) and 8 (Decent Work and Economic Growth). Recommendations focus on institutionalizing a permanent, data-driven "Alumni Engagement Ecosystem" and an Industry-Alumni Advisory Board to maintain a continuous feedback loop for curriculum enhancement and strategic resource allocation.]]>
</description>
<dc:creator>
Bartolome  Laarnie O., Bisquera  Carmelo Alejo D., Robles  Michael John R., Segundo  Carlita G., Tito  Paul Louie L., Wais  Armilyn H.
</dc:creator>
<dc:date>2026-04-13</dc:date>
<dc:publisher>Science and Education Publishing</dc:publisher>
<prism:publicationDate>2026-04-13</prism:publicationDate>
<prism:number>1</prism:number>
<prism:volume>14</prism:volume>
<prism:startingPage>15</prism:startingPage>
<prism:endingPage>20</prism:endingPage>
<prism:doi>10.12691/jcsa-14-1-3</prism:doi>
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