Scopus ID: 85199900806
2023 IEEE 15th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2023), pp. 1-6
Danilyn Joy O. Aquino, Alvin Roland M. Alcedo, ... Kenneth Russell K. Torralba
Conference Paper | Published: January 1, 2023
Abstract
Modern advancements have the potential to help farmers maximize food production that will result to conservation of resources and profitability maximization. It is beneficial for farmers and to our local industry and economy because it approaches the issues regarding agricultural farming with the help of an up-and- coming field of studies. It would be a step towards food sustainability and conservation of resources and the environment. With this, the researchers came up with an idea of incorporating Artificial Intelligence (AI) through Deep-Learning Neural Network Technology to our food production. With the help of Raspberry Pi Microcomputers, we will develop an AI that will learn the parameters that are needed to control for optimal food production, and then implement the monitoring system through Swarm- based Drone Technology which will perform monitoring and crop maintenance autonomously. All operations shall be processed and deployed through Python Language.
Scopus ID: 85199862569
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
The transcription of music is essential since it preserves the originality of the music and the compositional technique used by the composers. In this manner, native music can be reproduced, and documents of certain tunes can be passed on to succeeding generations without hearing the original music. A very limited amount of research has been conducted on the application of automation in music transcription using deep learning, particularly in native music instruments. This research is an effort to preserve and conserve Filipino culture in the context of native music and musical instruments particularly the Diwdiw-as, a native Filipino bamboo flute. Using signal processing and deep neural networks, the proposed study aims to automate music transcription. The system is capable of classifying pitches based on the fundamental frequency and is also capable of classifying notes based on their duration. Diwdiw-as pitch can be transcribable to the music sheet using the following pitches: CS, DS, ES, FS, GS, AS, BS, C6, while the note values are whole, eighth, quarter, half, dotted eighth, dotted half, dotted quarter. A web-based application has been developed to assist in the automatic music transcription (AMT) of Diwdiw-as. A pdf file of the transcribed music sheet can then be downloaded. According to the confusion matrices, the system's accuracy is high in terms of the transcription of pitches and notes in the music sheet.
Scopus ID: 85199893175
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
Water is a resource that all living things require, especially humans. Water contamination is prevalent worldwide due to progress and rapid industrialization. This study aims to conceptualize a low-cost and straightforward to install solar photocatalytic reactor (PCR) prototype for the degradation of Methylene Blue (DMB), one of the contaminants released into water resources, with the aid of Biochar and Titanium Dioxide as nano catalysts and assess its efficiency. A 3mL sample was collected before the start of the experiment, another 3mL sample from an unilluminated setting, and a 3mL sample at the end of the 2-hour photocatalytic investigation. The samples were processed and observed from an external laboratory. Results showed a 90.53% adsorption efficiency rate, a 1.1% and 9.9% difference from another study that utilized the same contaminant and time duration, and a 0.0196/min degradation rate. Based on this result, it was assessed that the proposed photoreactor was solar adsorption efficient and had a photodegradation potential to reduce the Methylene Blue (MB) contaminant.
Scopus ID: 85199876371
2023 IEEE 15th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2023), pp. 1-6
Laurence Keith P. Alquiza, King Harold A. Recto, ... Jesús Villalobos
Conference Paper | Published: January 1, 2023
Abstract
The Republic of the Philippines is confronted with rebuilding its energy landscape, which now depends heavily on imported fossil fuels for a substantial supply. The Department of Energy (DOE) has established lofty objectives to enhance the nation's renewable energy (RE) capability; nevertheless, these objectives are still to be achieved. This research supports DOE's goals by studying other possible renewable energy sources. In particular, the primary aim of this research is to examine the viability of Offshore Wind Converters (OWCs) and Wave Energy Converters (WECs) as viable sustainable energy options for the Philippines. Ocean wave converters (OWCs) provide inherent benefits in terms of dependability and have widespread societal acceptance. Conversely, wave energy converters (WECs) harness the vast energy potential contained within ocean waves. A comparative evaluation was undertaken to analyze the differences between these two potential renewable energy sources. The assessment concludes that OWCs possess a minor advantage over WECs regarding their economic viability and higher societal acceptability. It recommended that the government adopts a diversified energy portfolio, which may include the incorporation of WECs to effectively navigate the changing dynamics of the energy sector, enhance sustainability, and ensure the long-term security of the nation's energy supply.
Scopus ID: 85199888295
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
The coffee rust disease threatens coffee production in the Philippines with widespread defoliation and reduced yield. Identifying rust infection and its severity is critical for implementing effective mitigation strategies. As an alternative to recent methods that rely on deep learning approaches, our vision-based approach utilizes Artificial Neural Networks, K-Nearest Neighbors, and Thresholding methods to identify rust infection on coffee leaves and estimate severity, providing a computationally lightweight alternative for agricultural disease management. Twenty-four (24) color and texture features of a collected dataset of coffee leaf images were extracted as inputs for an ANN classifier. The percentage of damage on coffee leaves was determined by comparing the damaged pixels to the total area of the leaf using KNN and thresholding segmentation techniques. Through the use of confusion matrix and RMSE, the decision support system has demonstrated promising results in identifying coffee leaf health and estimating severity of coffee rust infection.
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: 85199917118
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
In the context of higher education, ensuring timely and successful graduation for students is a pivotal objective, necessitating a comprehensive understanding of their academic performance and tailored interventions. Evaluating ongoing academic records is crucial for effective pedagogical interventions, but limited research on student performance in completing degrees has introduced challenges. To address these, academic institutions are adopting flexible curricula designs, prompting the need for diverse course offerings. Amidst this, two student categories emerge: regular and irregular, each presenting unique challenges. A Rule-Based Suggestion Platform for Academic Program Completion was conceptualized, designed, developed, and rigorously evaluated through the use of the Technology Acceptance Model (TAM). This innovative platform, which harnesses the power of rule-based decision-making, was created to address the intricate challenges surrounding students' timely and successful program completion within the academic landscape. The platform's underlying architecture and functionality were crafted to provide students with personalized and optimized recommendations, guiding them towards informed decisions in shaping their educational journey. The development process involved the integration of advanced rule-building mechanisms, enabling the system to analyze individual student profiles, academic progress, and program requirements. This data-driven approach empowers the platform to generate customized study plans that not only consider the students' academic ambitions but also adhere to predefined constraints and parameters. By evaluating the platform's performance through the Technology Acceptance Model, this study assesses the users' perception and acceptance of this novel tool, shedding light on its effectiveness and potential impact on enhancing the academic planning process.
Scopus ID: 85199929124
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
Providing support is one thing, generating an automatic ticket category based purely on the textual data provided is another. This study is working towards encouraging the educational landscape to start integrating AI in further enhancing the way students learn and the way teachers are giving their lessons. The focus of this study is to use the subset of AI that concentrates on making machines understand how humans talk which is known as NLP. By using several Python libraries, 3 text classification algorithms – namely SVM, Naïve Bayes, and logistic regression were used to train the previously collected dataset and choose the model that will be integrated to the web-based helpdesk system called HelpTech. With the help of the model, the system instantly categorizes the issue submitted by the end users resulting to an easier way to use the educational tools available which assist the stakeholders in developing their digital literacy.
Scopus ID: 85165994980
Journal of Loss and Trauma, (2023), Vol. 28, No. 8, pp. 784-786
Letter to the Editor | Published: January 1, 2023
Abstract
As the educational technology director of our institution, I often find myself at the forefront of discussions surrounding the integration of artificial intelligence (AI) and its impact on our lives. Recently, a former student approached me with a thought-provoking question: "In times of grief and loss, can ChatGPT offer the comfort and consolation we seek?" The weight of this inquiry bore down on me, for I realized that answering it was not a task I could take lightly. I hesitated, acutely aware that I was not a health professional equipped with the expertise to navigate the depths of grief and loss. Moreover, my role as an educational technology director means that I have had the opportunity to witness the transformative potential of AI, leading me to wonder whether I possess a natural inclination to embrace technology as a solution. Therefore, I felt compelled to engage health professionals, the true authorities on matters of emotional well-being and mental health, to join me in an open and honest exploration of this complex question.
Scopus ID: 85166667236
2023 IEEE World AI IoT Congress (AIIoT), (2023), pp. 0421-0426
Pitz Gerald G. Lagrazon, Jennifer Edytha E. Japor, ... Arnold B. Platon
Conference Paper | Published: January 1, 2023
Abstract
Rainfall is crucial for flood prevention and comprehending the correlation between rainfall and flooding. Cavite province in the Philippines is vulnerable to flooding caused by heavy rainfall and climate change impacts. Early detection of flooding through early warning systems can prevent excessive damage loss and potentially save lives. It can also provide major savings in terms of monetary benefit and increased interagency coordination for rapid decision-making. Machine learning is an important tool for predicting rainfall which can be used to predict rainfall in the province. The objective of this study is to conduct a comparative analysis of various models for predicting daily rainfall, using relevant atmospheric features such as maximum, minimum, and mean temperature, relative humidity, wind speed, wind direction, cloud cover, pressure, and evaporation. The study seeks to identify the most effective model for accurately predicting rainfall in the Cavite Province to benefit the local community. Among the five machine learning models evaluated, the Gaussian Process Regression model demonstrated the highest accuracy in predicting daily rainfall. The findings of this study can be leveraged to mitigate the damage caused by flooding in the Cavite Province and serve as a useful reference for similar studies in other regions prone to flooding.