Scopus ID: 85152198192
2022 2nd International Conference in Information and Computing Research (iCORE), (2022), pp. 20-24
Conference Paper | Published: January 1, 2022
Abstract
This project aims to develop an augmented reality mobile application about endangered marine species. The program has made use of augmented reality technology to scan target photos on the card and show three-dimensional reconstructions of six endangered marine species. When a target image is detected, the program will show the 3D model of the sea species to the user. The application was built using Vuforia and Unity, the 3D models were modeled with Substance Painter and ZBrush, and the card layout was designed with Adobe Illustrator. The created system is one of the country's first augmented reality (AR) apps regarding endangered marine species, with the purpose of spreading information and increasing awareness about the situation of endangered marine species via AR. To demonstrate that the program is productive and user-friendly, the researchers surveyed ten IT experts. The survey findings demonstrate that the approach is effective and useable for sharing information and increasing awareness about the state of endangered marine species. Future researchers may enhance the system by incorporating some of the features and refining the mobile application so that people can still use it without the visual book.
Scopus ID: 85152187938
2022 2nd International Conference in Information and Computing Research (iCORE), (2022), pp. 139-143
Jake Dave M. Esteban, Michaela D. Gipala, ... Renato R. Maaliw
Conference Paper | Published: January 1, 2022
Abstract
Oclean's goal is to raise awareness about the harmful effects of plastic waste in the ocean. The game was created for use on a mobile device. It's a 2D game with a single player endless mode. A scoring system and a timer were also included in the game. In addition, the game has a gallery where players could see what is happening in real life. This initiative believes that little acts can generate significant effects in the fight against plastic pollution in our oceans. In addition, interactive information about the issues of marine plastic trash was also provided. The game's set of objectives were completed, and an unending casual game about ocean cleaning and marine plastic garbage was created effectively.
Scopus ID: 85132208686
2022 10th International Conference on Information and Education Technology (ICIET), (2022), pp. 58-63
Conference Paper | Published: January 1, 2022
Abstract
The evaluation process to teachers is a method to ensure that instruction quality has been delivered to various stakeholders. Assessing the performance of faculty members is now an integral part of the educational system as it aims to assess faculty performance using sets of criteria and provide necessary academic intervention program anchored in the development of comprehensive faculty development program. With this, the researchers developed faculty evaluation system integrating decision support mechanism that can provide automatic report generation in terms of evaluation. In addition, a rule-based engine has been integrated as a mean to provide decision support mechanism in terms of automatic development plan. The evolutionary prototyping model was used in the development of the system. Based on expert evaluation result, using the ISO selected criteria, the system recorded a grand mean 4.31 which has an interpretation of “Very Satisfactory” This means that the system is ready for deployment.
Scopus ID: 85132198588
2022 10th International Conference on Information and Education Technology (ICIET), (2022), pp. 124-129
Conference Paper | Published: January 1, 2022
Abstract
The project endeavored to design and develop a Web-based Performance Evaluation System with integration of rule-based algorithm that will analyze the performance of each reviewee based on the scores from their examination. The algorithm automatically provides suggested reading materials based on the result of the assessment. With this, the system will help the reviewees assess their own performance. The system was developed using Agile Methodology Model of Software Development. The software produced was tested using Alpha and Beta software tastings. The FURPS model was used to assess the software quality as perceived by the selected group of respondents. The system was found to be functional, meeting the designed functional requirements specifications.
Scopus ID: 85132193982
2022 10th International Conference on Information and Education Technology (ICIET), (2022), pp. 390-397
Conference Paper | Published: January 1, 2022
Abstract
Forecasting the number of incidences of medical cases is important in planning institutional health program strategies to draft intervention and allocate resources. The utilization of advancements in computing and the use of massive health data create possibilities for the generation of tools in a recommender system. This study focused on medical cases forecasting using exponential smoothing model for the development of resource allocation recommender system. Different data pre-processing techniques were used such as imputation and data cleaning in the historical dataset. To determine which set of alpha values can be considered and be used in the development of online resource allocation recommender system for Mabalacat City Health Unit, the mean absolute percent error and mean absolute deviation were used. Exponential smoothing with an alpha value of 0.9 and 0.3 have high forecasted values than that of Exponential smoothing using 0.1, 0.5 and 0.7 respectively.
Scopus ID: 85132186753
2022 10th International Conference on Information and Education Technology (ICIET), (2022), pp. 93-98
Riegie D. Tan, Keno Piad, ... Joseph Espino
Conference Paper | Published: January 1, 2022
Abstract
The emergence of information technology used in all factors of our everyday lives has exponentially increased the amount of unstructured data. This huge quantity of records is a great source for finding and thus, may be used for extracting actionable information. In the academe, for instance, teachers and school administrators can adjust their approach to teaching/learning by getting feedback from students through a Learning Management System that can automatically analyze the semantic orientation of words and contextual polarity of these feedbacks - categorizing them into positive and negative. Identifying and classifying words expressed in the students' feedback about learning materials can provide structured information that can guide the teacher, impact its design and target the students' needs. This study implemented a lexicon-based strategy for automatic sentiment analysis using VADER as a model. Student feedbacks are extracted from an LMS developed to demonstrate the usability and effectiveness of the adopted approach; among other features of LMS that will help teachers improve its implementation. Results of the LMS sentiment analysis are compared to human-annotated sentiments to verify and validate the output, as well as, check its accuracy using Confusion Matrix. It aims to create a structured representation of student sentiments through LMS to help teachers improve the design of learning materials.
Scopus ID: 85138253909
2022 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), (2022), pp. 1-5
Dennis C. Malunao, Roger S. Tamargo, ... Roldan D. Jallorina
Conference Paper | Published: January 1, 2022
Abstract
Immediate identification of plant disease is one of the important solutions in Agricultural problems. In this study, the researchers develop an early detection system for tomato leaf diseases. It is important to create a system that will detect and classify a certain disease present in the leaf to prevent further loss. In order to do that, the researchers used an algorithm called YOLOv3 for training a model that accurately detects specific diseases for tomato leaves. The proposed model is able to classify the diseases and has a mean average precision(mAP) of 98.28 %. The result of the trained model varied with the accuracies ranging from 75% - 99%, for detecting the two common tomato leaf diseases such as, Early Blight and Septoria Leaf Spot.
Scopus ID: 85130206509
2022 International Conference on Decision Aid Sciences and Applications (DASA), (2022), pp. 1469-1473
Jhon Michael C. Manalo, Alvin Sarraga Alon, ... Ricky C. Sandil
Conference Paper | Published: January 1, 2022
Abstract
Pothole detection is critical in defining optimal road management solutions and maintenance. The researcher used deep learning and yolov3 to create a pothole detection system in this study. A deep learning algorithm called YOLOv3 is used to develop a model that can successfully identify potholes. The detection model had an average precision of 95.43%, and identified potholes had accuracies ranging from 33% to 69%, which is to be anticipated given the numerous various forms and sizes of potholes.
Scopus ID: 85159411512
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
Determination of relay pairs from breakpoints in a given network is essential for maintaining the current protection system. Pairs of relays as primary or backup maintain the operation of the protection scheme within its zone of protection in tandem. All calculations and assumptions that are made in protection systems are based on breakpoints. It is inadequately documented that machine learning can be used to determine breakpoint sets and relay pairs. This paper presents the implementation of supervised decision tree machine learning approach for determining directional overcurrent relay breakpoint set in 3-bus networks. Using the one-hot encoding method, 45 input features are extracted from a matrix derived from 3-bus, 5-line network data. Bayesian optimization is used to further optimize the hyperparameters of each model for each of the break point set outputs. Tree diagrams are also provided here to assist in the interpretation of the decision rule resulting from the regression analysis. Experiment tests indicated that the proposed method shows promising results in determining breakpoint set in terms of RMSE.
Scopus ID: 85134876150
2022 IEEE World AI IoT Congress (AIIoT), (2022), pp. 039-045
Renato R. Maaliw, Karen Anne C. Quing, ... Ranie B. Canlas
Conference Paper | Published: January 1, 2022
Abstract
Grit plays a crucial role in determining high individual success more than intellectual talent alone. However, there is no existing literature that ventured into the trait identification in an e-learning environment. This study presents a comprehensive computational-driven strategy for detecting a learner's grit using machine learning. Empirical results show that DBSCAN and Random Forest models produce average accurate prediction consistency of 92.67% against the questionnaire method. Knowledge interpretation using feature importance and association mining quantifies perseverance and sustained interest as the most pressing component of grit. Correlational analysis reveals that grit has a weak connection with course grades (short-term goal) but demonstrates a strong positive association with professional achievement (long-term goal) and maturation. Collectively, our findings substantiate that breakthrough accomplishment is contingent not solely on cognitive ability but on constant interests and resilience.