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All Journal International Journal of Smart Systems International Journal of Technology and Modeling
Delgado, Samantha Joyce
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics

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The Role of AI-Powered Analytics in Building a Human-Centered Smart Campus Delgado, Samantha Joyce; Panganiban, Nathaniel Joseph; Robles, Kimberly Anne
International Journal of Smart Systems Vol. 1 No. 3 (2023): August
Publisher : Etunas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijss.v1i3.81

Abstract

The rapid digital transformation of higher education has accelerated the adoption of smart campus technologies integrating artificial intelligence (AI), Internet of Things (IoT), and cloud computing. While existing initiatives often emphasize operational efficiency and infrastructure optimization, limited attention has been given to building human-centered smart campuses that prioritize student engagement, well-being, and academic success. This study investigates the role of AI-powered analytics in shaping adaptive, inclusive, and student-focused campus ecosystems, with an observational study conducted at De La Salle University (DLSU), Manila, Philippines. AI-driven analytics were deployed to process multi-source datasets, including IoT-enabled classroom sensors, learning management system (LMS) activity logs, and student survey feedback. The system generated predictive insights to identify at-risk learners, support personalized learning pathways, and recommend interventions for improved academic outcomes. Preliminary findings from the DLSU pilot revealed a 19% increase in course participation and a 12% reduction in dropout risk among vulnerable student groups. Additionally, real-time analytics enhanced campus services by optimizing space utilization, energy efficiency, and scheduling flexibility, indirectly improving student comfort and productivity. The results suggest that AI-powered analytics extend the smart campus paradigm beyond efficiency, enabling higher education institutions to foster human-centered learning environments that integrate inclusivity, well-being, and sustainability. By demonstrating how data-driven systems can support both academic and non-academic aspects of student life, this research positions AI as not only a technological enabler but also a catalyst for equitable and student-centered digital transformation in higher education.
Advancements in Deep Learning: A Comprehensive Survey on Architectures, Optimization Techniques, and Applications Delgado, Samantha Joyce; Panganiban, Nathaniel Joseph; Robles, Kimberly Anne; Buenaventura, Anthony Daniel; Vergara, Melissa Jane; Evangelista, Christian Noel
International Journal of Technology and Modeling Vol. 3 No. 2 (2024)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v3i2.141

Abstract

Deep learning has revolutionized the field of artificial intelligence by enabling significant advancements across various domains, including computer vision, natural language processing, and speech recognition. This survey provides a comprehensive overview of recent developments in deep learning, focusing on three core aspects: architectural innovations, optimization strategies, and real-world applications. We explore the evolution of neural network architectures, from classical feedforward networks to cutting-edge models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and graph neural networks (GNNs). In addition, we examine state-of-the-art optimization techniques, including adaptive learning rate methods, regularization strategies, and training heuristics that address challenges like vanishing gradients and overfitting. Finally, we present a broad spectrum of deep learning applications, highlighting breakthroughs in autonomous systems, healthcare, finance, and more. By synthesizing recent research trends and identifying emerging challenges, this survey aims to serve as a valuable resource for researchers and practitioners seeking to navigate the rapidly evolving landscape of deep learning.
Co-Authors Buenaventura, Anthony Daniel Evangelista, Christian Noel Panganiban, Nathaniel Joseph Robles, Kimberly Anne Vergara, Melissa Jane