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All Journal International Journal of Smart Systems International Journal of Technology and Modeling
Panganiban, Nathaniel Joseph
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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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Journal : International Journal of Technology and Modeling

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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.
Modelling Smart Cities: Integration of IoT, Big Data, and Analytics Anne Robles, Kimberly; Delgado, Samantha Joyce; Panganiban, Nathaniel Joseph
International Journal of Technology and Modeling Vol. 4 No. 3 (2025)
Publisher : Etunas Sukses Sistem

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63876/ijtm.v4i3.160

Abstract

The rapid urbanization and technological advancement have catalyzed the emergence of smart cities as a transformative paradigm for sustainable urban development. This paper presents a comprehensive framework for modeling smart cities through the systematic integration of Internet of Things (IoT), big data, and analytics technologies. We propose a multi-layered architectural model that addresses the technical, operational, and governance challenges inherent in smart city implementations. The research examines how IoT sensors and devices generate massive volumes of heterogeneous data, which are subsequently processed through big data platforms to extract actionable insights via advanced analytics techniques. Our framework encompasses data acquisition, storage, processing, and visualization layers, while incorporating machine learning algorithms and real-time analytics for intelligent decision-making. Through case studies of various smart city domains including transportation, energy management, public safety, and healthcare, we demonstrate the practical applicability of our integrated model. The paper also addresses critical challenges such as data privacy, security, interoperability, and scalability that must be overcome for successful smart city deployment. Our findings reveal that effective integration of these three technological pillars enables cities to optimize resource allocation, enhance service delivery, improve quality of life for citizens, and achieve sustainability goals. The proposed model provides urban planners, policymakers, and technology implementers with a structured approach to design and deploy smart city solutions that are both technologically robust and contextually relevant.
Co-Authors Anne Robles, Kimberly Buenaventura, Anthony Daniel Delgado, Samantha Joyce Evangelista, Christian Noel Robles, Kimberly Anne Vergara, Melissa Jane