Scopus ID: 105030174366
2024 IEEE 16th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2025), pp. 1-6
Conference Paper | Published: December 3, 2025
Abstract
The decision to select a college degree program plays a fundamental role in shaping students' academic and professional futures. While much research has examined the factors affecting students' choices prior to enrollment, fewer studies have explored their perceptions and expectations after they have entered their chosen programs. This study seeks to bridge this research gap by investigating the visions and aspirations of freshmen using natural language processing techniques. The textual analysis involves text tokenization, word frequency counts, visual representation of data through word clouds, and sentiment classification. The findings suggest the demand for tailored academic support and curriculum development that align with the specific aspirations of students in different disciplines. Overall, this study contributes to the growing literature on student decision-making and expectations of degree programs, while demonstrating the value of sentiment analysis as a tool for understanding students' academic trajectories.
Scopus ID: 105030166329
2024 IEEE 16th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2025), pp. 1-5
Conference Paper | Published: December 3, 2025
Abstract
This study utilized purposive random sampling during beta testing of the game “O Ektos,” a 3D-PC real-time strategy game aimed at raising awareness about clean water and sanitation. Over 100 respondents, including BSIT students specializing in game development, web management, and digital arts, as well as executives and staff from the MWF organization, participated in the evaluation. Respondents tested the gameplay, promotional website, and overall aesthetics, assessing aspects such as mechanics, graphics, user interface, sound design, and storyline. Results showed that the game was well-received across all categories, highlighting its effective design and alignment with the United Nations' Sustainable Development Goal No. 6. The game's combination of contemporary technology, engaging gameplay, and meaningful content positions it as a feasible tool for raising awareness about water pollution and sanitation issues.
Scopus ID: 105030165159
2024 IEEE 16th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2025), pp. 1-5
Conference Paper | Published: December 3, 2025
Abstract
“TRACK2TRACK: A Digital Communication Plan on Passenger Information Dissemination for LRTA” outlines an innovative initiative aimed at revolutionizing the way information is disseminated to passengers within the Light Rail Transit Authority (LRTA). The project, known as TRACK2TRACK, leverages digital communication strategies, including real-time updates, interactive maps, and mobile applications, to provide accurate and timely information to commuters. Complementing this approach are eight short-form videos, strategically designed to convey essential aspects of the digital communication plan concisely. This comprehensive strategy aims to enhance overall transit experiences, improve system efficiency, and potentially serve as a model for other transit authorities seeking effective and engaging passenger communication solutions.
Scopus ID: 105030165309
2024 IEEE 16th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2025), pp. 1-5
Conference Paper | Published: December 3, 2025
Abstract
The development of reliable wireless networks is crucial for advancing sustainability. Not only does it facilitate remote work and telecommunication which are critical remote services such as telemedicine and distance education, they are also essential in supporting sustainable practices like the application of IoT in monitoring environmental conditions and energy usage. To ensure that these networks work optimally, it is essential that the datasets used in their development are not only accurate but are also distinct. This study contributes to this end by analyzing the datasets generated by the COST 2100, a model that is used extensively in wireless communications. Using ANOVA, the researchers determined if the dataset are indeed distinct as signals bounce about multiple clustering which use Multiple Input, Multiple Output (MIMO) Technology similar to modern wireless systems like 5G. Results show that the different variables or dimensions are distinct from each other. Thus, the datasets generated by COST2100 are suitable to be utilized in further preprocessing methods of wireless multipath clustering, ultimately contributing to building a more sustainable wireless communication system.
Scopus ID: 105031877203
2025 International Conference on Mobile, Military, Maritime IT Convergence (ICMIC), (2025), pp. 206-209
Conference Paper | Published: November 28, 2025
Abstract
Blue carbon ecosystems (mangroves, saltmarshes, seagrasses) are globally significant carbon sinks, absorbing an estimated 50% of oceanic carbon despite covering only 2% of the ocean surface. However, these important habitats face rapid degradation, with 25% to 50% loss over the past 50–70 years, transforming them into carbon sources. This paper presents a climate-smart communication and sensing framework for real-time monitoring of these critical marine ecosystems. It integrates Artificial Intelligence (AI) with advanced sensing technologies, including satellite, Uncrewed Aerial Vehicles (UAVs), LiDAR, and in-situ sensors, for comprehensive data acquisition and analysis. The framework evaluates energy-efficient and secure underwater (acoustic, optical) and overwater (satellite, cellular, LoRaWAN) communication strategies to ensure continuous data flow. AI algorithms enhance data processing, pattern recognition, predictive modeling, and autonomous operations of platforms like Autonomous Underwater Vehicles (AUVs). This integrated approach not only supports accurate carbon accounting but also yields substantial co-benefits for climate change mitigation and adaptation, biodiversity conservation, and maritime security by deterring illegal activities and pollution. The framework provides a transformative pathway for sustainable blue economy and resilient coastal communities.
Scopus ID: 105031905094
2025 International Conference on Mobile, Military, Maritime IT Convergence (ICMIC), (2025), pp. 229-232
James Paul Tan, Margrette Yebes, ... Gabriel Avelino Sampedro
Conference Paper | Published: November 28, 2025
Abstract
In military and expeditionary (maritime) health care environments where isolation, safety, limited personnel, and resource constraints can threaten the delivery of frontline health care, access to timely and knowledgeable medical assistance can be extremely valuable. The purpose of this paper is to investigate the use of large language models (LLMs) in military and maritime health care environments by creating an AI-powered, Over-the-Counter (OTC) Medication Assistance Bot using the Mistral-7B model. The bot is intended to be deployed within tactical, or even shipboard systems, and it would empower autonomous, in-the-moment recommendations of medications for specific symptoms, while mitigating the risks associated with deploying personnel self-medicating through potential non-fundamental use errors. For this work, we employed Low-Rank Adaptation (LoRA) to fine-tune the system, and the bot was trained on a specific dataset derived from material on pharmacological sources, contextualized for medical practices in the Philippines. Based on evaluation the model achieved an average F1-score of 0.7296, which is above the 0.60-0.70 expected levels of performance for medical dialogue systems. The research shows promise for the model as it enhances combat and maritime healthcare readiness by providing consistent, low-bandwidth, and local medical assistance when connected medical supervision may not be immediately available.
Scopus ID: 105031905269
2025 International Conference on Mobile, Military, Maritime IT Convergence (ICMIC), (2025), pp. 170-173
Conference Paper | Published: November 28, 2025
Abstract
This study assesses the potential of advanced materials, specifically graphene, perovskites, and nanostructured ceramics to enhance the energy efficiency, durability, and environmental resilience of 5G and 6G communication systems deployed in harsh environments. A comparative evaluation was conducted based on electrical conductivity, thermal stability, mechanical strength, optical performance, and corrosion resistance, drawing on recent experimental data and life-cycle analyses. Graphene demonstrates electrical conductivity near 10^8 S/m and thermal conductivity up to 5000 W/m-K, enabling transistors with 200 times higher speeds and coatings reducing corrosion by over 90%. Perovskite-based devices achieve solar cell efficiencies up to 34% and optical modulators operating at 170 Gbps. Nanostructured ceramics offer low dielectric loss and stability above 1000°C, supporting high-frequency operation in challenging conditions. Integrating these materials is projected to extend device lifespans by up to 40% and reduce energy and cooling demands by 30%. These findings indicate that adopting advanced materials can significantly improve the performance and sustainability of next-generation communication infrastructure.
Scopus ID: 105031884370
2025 International Conference on Mobile, Military, Maritime IT Convergence (ICMIC), (2025), pp. 274-277
Conference Paper | Published: November 28, 2025
Abstract
Accurate resource yield prediction is critical for military logistics, planning, and operational readiness, yet remains challenging due to numerous influencing factors such as environmental conditions, resource quality, and logistical constraints. This study examines the effectiveness of quantile-based data binning on classical machine learning algorithms in predicting resource yields pertinent to military applications. Furthermore, the effectiveness of Backpropagation Artificial Neural Networks (BP-ANN) and Naive Bayes classifiers with regression models such as K-Nearest Neighbors (KNN), Linear Regression, and Multi-Layer Perceptron Regressors (MLPRegressor) are compared using a robust dataset representative of global resource metrics. The results indicate that binning continuous data into quartiles substantially enhances model accuracy, precision, recall, and computational efficiency. In particular, the binned data enables the BP-ANN to achieve an accuracy of approximately 90.4%, with regression models such as KNN and MLPRegressor outperforming this benchmark by attaining accuracies exceeding 93%. Additionally, binning drastically reduced hyperparameter tuning duration from around 149 minutes to less than 10 minutes, underscoring its computational efficiency advantage. Overall, this research demonstrates that quantile-based data binning is a valuable preprocessing technique that improves predictive accuracy, reduces computational cost, and enhances the reliability of classical machine learning models for military resource forecasting.
Scopus ID: 105031875924
2025 International Conference on Mobile, Military, Maritime IT Convergence (ICMIC), (2025), pp. 224-228
Conference Paper | Published: November 28, 2025
Abstract
Climate change is intensifying maritime risks, including sea level rise, stronger storms, and disrupted ocean currents—posing threats to coastal infrastructure, navigation, and security. Traditional monitoring systems lack the predictive capabilities and coverage to address these evolving challenges. This paper explores the integration of Artificial Intelligence (AI) and satellite-based Earth observation as a next-generation early warning system (EWS) for maritime security. We assess observed and projected hazard trends, propose an AI-enhanced system architecture, and evaluate readiness in climate-vulnerable geographies. The findings highlight actionable strategies to improve forecasting, risk detection, and climate resilience in coastal and maritime domains.
Scopus ID: 105031909872
2025 International Conference on Mobile, Military, Maritime IT Convergence (ICMIC), (2025), pp. 329-332
Conference Paper | Published: November 28, 2025
Abstract
This study proposes a Blockchain-Integrated Circular Economy Framework to improve lifecycle tracking and emissions reporting for maritime ICT and energy materials. The system combines Digital Product Passports, IoT telemetry, and smart contracts on a permissioned blockchain to record operational data and end-of-life events for batteries, photovoltaic modules, and navigation equipment. A parametric algorithm calculates emissions by combining production impacts, usage profiles, and recycling credits, while automated incentives promote material recovery. Synthetic data simulations illustrate the framework's ability to monitor degradation, quantify emissions, and enforce circularity incentives. Results indicate that production emissions dominate lifecycle impacts, highlighting the value of integrated tracking and verified recovery to support low-carbon maritime operations.