Romano Q. Neyra
14 Publications
Scopus ID: 105030132921
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
A sustainable electronics engineering program effectively prepares graduates to tackle the changing technological, environmental, and societal challenges. In this context, this study examines the relationship between Outcome-Based Education (OBE), quality assurance mechanisms, and student performance in the Electronics Engineering Licensure Examination with the goal of enhancing the development of the program and making it more sustainable. To do this, the paper analyzed a five-year dataset to examine key factors such as accreditation by the Philippine Technological Council (PTC), international rankings (QS and THE), and recognition as Centers of Excellence (COE) or Centers of Development (COD) by the Commission on Higher Education (CHED). Regression modeling of the data gathered revealed that the linear interaction model most effectively predicts student performance, with an R-squared value of 0.85, highlighting the emphasis on OBE and quality assurance to improve academic results. The study concluded that emphasizing interactions among program attributes can guide curriculum revisions to enhance student success and ultimately, its sustainability. It suggested that future studies integrate machine learning (ML) techniques to improve the predictive capabilities of model to enhance quality assurance measures. This may be done by utilizing ML methodologies from related fields such as human detection systems and ECG analysis and apply it to educational research. Such an implementation can enhance data-driven decision-making processes, thereby improving the quality of education and student performance in the Electronics Engineering Licensure Examination and ultimately, making the program more sustainable.
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: 105016784943
2025 IEEE Applied Sensing Conference (APSCON), (2025), pp. 367-370
Conference Paper | Published: April 9, 2025
Abstract
The integration of Internet of Things (IoT) technology in the agricultural sector using advanced sensor systems has garnered significant interest in the recent year, especially in promoting food security. This study shows an application of this to Spondias purpurea (Philippine Pias Prunes), an important fruit in the Philippine Archipelago. The developed system places significant importance on the connection of Internet of Things (IoT) devices and the Google Cloud Platform. This integration enables real-time monitoring, data storage, and analysis, therefore providing valuable insights into enhancing the drying process and mitigating spoilage by maintaining moisture levels within the recommended range of 12-14%. The technology provides farmers with the opportunity to extend the shelf life of the prunes, reduce food wastage and increased profitability. While the system focused on Spondias purpurea, the system is highly adaptable and scalable to other fruits and crops. The research employed a DHT11 sensor that is linked to a Raspberry Pi Microcontroller, together with a Google Cloud-Based Platform for the purpose of data storage and processing. Results of the experiments indicate that the temperature measurements remain consistent at varying conditions. Moreover, the humidity levels remain to be high while the prune’s moisture content continue to be steady. To enhance the system's functionality, future endeavours should focus on integrating the system with other agricultural processes. Additionally, it is recommended to broaden the scope of the cost-benefit analysis by considering aspects such as the initial investment, maintenance costs, energy consumption, and potential rewards in terms of product quality, loss reduction, and increased output.
Scopus ID: 105008218295
2025 IEEE Global Engineering Education Conference (EDUCON), (2025), pp. 1-6
Conference Paper | Published: January 1, 2025
Abstract
The fast progression of Artificial Intelligence (AI) technology has elicited substantial ethical issues, especially within engineering fields that directly impact society. This study seeks to establish a framework for integrating Ethical AI ideas into engineering curriculum, therefore preparing future engineers to address the moral, social, and legal ramifications of AI. The framework incorporates Ethical AI principles into current course formats, encompassing introductory, enabling, and demonstrative courses, with particular focus on subjects like Science, Technology, and Society, Professional Engineering Ethics, and thesis/capstone projects. The paper recommends a curriculum update that complies with industry norms and equips students to embrace responsible AI practices, based on a thorough analysis of pertinent Commission on Higher Education (CHED) Memorandum Orders (CMOs) and literature. The research also presents evaluation rubrics to gauge students' comprehension and implementation of Ethical AI concepts in their academic projects. The paper suggests that integrating Ethical AI into engineering education enables universities to cultivate engineers who possess both technical proficiency and a robust ethical framework about AI technology.
Scopus ID: 85212826598
DOI: 10.1063/5.0162584
AIP Conference Proceedings, (2023), Vol. 2868, pp. 020011
Conference Paper | Published: August 10, 2023
Abstract
Due to the hardships brought about by the pandemic, Filipino farmers resort to creative ways of drying fruits. Since there is limited movement during the pandemic brought about by restrictions, farmers have no choice but to employ fruit drying on the roof of their houses to preserve the food. Currently, this is done at the mercy of the elements. However, modern fruit-drying requires monitoring of temperature and humidity so that proper measures can be taken when it is about to rain. Otherwise, rain can cause food spoilage which would cause farmer’s wastage of already scarce resources. The crop chosen for this study was Spondias sp. However, a minor adjustment can also be applied to other fruits, vegetables, and even meat. Once the drying process is complete, the farmer will receive a notification so that the dried product may be processed for packing and selling. Results indicate that the prototype can meet the specifications and that farmers found it helpful. For future works, it is recommended that solar panels be used to utilize the power from the sun. It is also recommended that direct current be used in future project modifications to minimize errors during a power outage.
Scopus ID: 85199879034
2023 IEEE 15th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2023), pp. 1-5
Conference Paper | Published: January 1, 2023
Abstract
Diabetes mellitus is a disease incorporated with carbohydrate metabolism whereas the body becomes unable to generate or react with insulin which leads to abnormal levels of blood sugar (glucose). In a worldwide perspective, Diabetes mellitus is ranked as the 9th leading cause of death based on the records of the World Health Organization and according to the International Diabetes Federation, there are about 463 million diabetic people worldwide in 2019 which is projected to increase to 700 million diabetic people by year 2045. In a regional perspective, about 251 million (45%) diabetic people resides on the Western Pacific and Southeast Asian region, whereas about 140 million people are undiagnosed of the disease. In this study, a genetic algorithm-optimized neural network using MATLAB was developed based on the risk factors. The experimental results show that the best validation performance has a value of 0.014129 and with a regression model coefficient R2 value of 0.95864.
Scopus ID: 85199915106
2023 IEEE 15th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2023), pp. 1-6
Conference Paper | Published: January 1, 2023
Abstract
This study aims to build upon the authors’ previous work investigating the complex connection relating energy consumption, emission reduction, and economic development. Specifically, it focuses on the Association of Southeast Asian Nations (ASEAN) within the context of post-pandemic economic recovery. The methodology includes implementation of regression modelling using the MATLAB Regression Learner program that utilizes World Energy Council (WEC) Trilemma ratings as input predictors. A range of regression models are utilized and undergo thorough assessment using established metrics, such as Mean Absolute Error (MAE), R-squared Coefficient of Determination, Root Mean Squared Error (RMSE), and Mean Squared Error (MSE). Additionally, practical metrics such as prediction time and training time are considered. Through a comparative analysis of the results achieved by the 2023 model in relation to its predecessor, an evaluation is conducted to determine the suitability of previous model. This assessment leads to the identification of relevant policy implications that may contribute to sustainable energy trajectory of the region. This academic pursuit hopes to enhance the scientific dialogue by integrating empirical research results with policy imperatives, in order to promote the development of ecologically sustainable and economically resilient energy frameworks in the ASEAN region. Based on the results, the key discovery of this study pertains to the ever-changing nature of energy dynamics and the significance of flexible modelling in influencing regional energy strategies. Hence, it is essential for policymakers to remain agile to the ever-changing factors that impact environmental sustainability, energy security and energy equity.
Scopus ID: 85159458476
2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2022), pp. 1-6
Conference Paper | Published: January 1, 2022
Abstract
The liver is the human body's largest internal organ. Globally, liver disease is considered the cause of approximately 2 million yearly death – whereas the 11th and 16th worldwide leading causes of death are cirrhosis and liver cancer. In the Philippines, according to the Department of Health (DOH), liver cancer is ranked as the 3rd leading cause of death. In most cases, surgery may be considered a possible cure if detected at an early stage. However, there is no efficient early detection method for liver cancer. In this paper, multiple machine learning methodologies are modeled to provide diagnosis classification of liver disease based on the laboratory parameter readings. Based on the results for all models, the most accurate prediction is made by ANN at 89%, followed by SVM at 79.5%. The results establish that AI-based machine learning approaches may be utilized for assisting medical-related diagnosis.
Scopus ID: 85159403218
2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2022), pp. 1-6
Conference Paper | Published: January 1, 2022
Abstract
With the pandemic coming to an end, the world is expected to revert to most, if not all, of its economic activities prior to the pandemic to ensure economic recovery. However, this pursuit also impacts energy consumption, economic growth, and emission reduction. There have been several studies that have tackled this but only on a macro-scale and were focused on developed countries, so the context is different from Southeast Asia. Using regression modelling of World Energy Council (WEC) trilemma scores of the member countries of the Association of Southeast Asian Nations (ASEAN), its mathematical relationships with energy consumption, economic growth and emission reduction were determined, as well as its implications to sustainable energy policy in the ASEAN. The study found out that Gaussian Process Regression – Exponential GPR is the best fit model to use for this purpose. Considering the findings, policies to (1) encourage investments in clean energy, (2) push for clean energy transition, (3) reduce energy-related carbon dioxide emissions, (4) study the differences in energy use and efficiency across groups and (5) advocate energy savings were forwarded.
Scopus ID: 85127555504
2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2021)
Conference Paper | Published: January 1, 2021
Abstract
In a worldwide perspective of the most common cancer diseases, cervical cancer is ranked fourth most frequent whereas the worldwide mortality rate is at 54.56%. In the Philippines, the second leading site among women is cervical cancer next to breast cancer. Research shows that cervical cancer is one of the most treatable cancer forms if detected and managed early. Currently, the most reliable diagnosis and prevention method of cervical cancer is thru a regular testing via Pap Smear test and HPV vaccination being performed in hospitals worldwide. However, according to the Centers for Disease Control and Prevention in California, the cervical cancer screening rate of regular testing in hospitals went down significantly during the stay-at-home order by the government due to the COVID-19 pandemic. Also, there are limited research based on the behavior information in relation to cervical cancer risk prediction, but existing studies proves the possibility of the risk prediction based on behavior information. This paper presents an Artificial Neural Network-based model for early cervical cancer risk detection based on behavior information. The neural network was trained using scaled conjugate gradient back propagation. The system showed 98% overall correctness in early cervical cancer risk prediction.