Scopus ID: 85159461464
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 daily maximum multi-annual series including the rainfall frequency analysis, are one of the inputs for the design process for stormwater management, that entails numerous procedures: (a) rainfall data gathering from Philippine Atmospheric Geophysical and Astronomical Services Administration (PAGASA), (b) information gathering, and (c) checking all received datasets for missing or different data. To address these setbacks, 6 rain gauge stations located in Region VIII, Visayas, Philippines were used to first determine whether or not the Gumbel Extreme Value (GEV) was the better suitable method to use in producing Rainfall Intensity Duration Frequency (RIDF) than Log-Pearson Type III (LP3) by performing Chi-square test; secondly, to select the better RIDF values; and lastly, the isohyetal maps should be developed for return periods of 2, 5, 10, 25, 50, and 100 years. GEV was a better fit for the x2 values (27.96, 54.59, 52.82, 87.96, 11.78, 7.66) obtained through chi-square test were close to or smaller than the critical value of 30.144. The RIDFs produced in GEV were used in plotting isohyetal maps. In all return periods, Borongan generated the highest rainfall intensity value.
Scopus ID: 85134882268
2022 IEEE World AI IoT Congress (AIIoT), (2022), pp. 489-493
Laily Mariz A. Bengua, Vanessa Jane D. De Guzman, ... Alvin S. Alon
Conference Paper | Published: January 1, 2022
Abstract
The electro-mechanical salted egg grading system was developed to support producers by streamlining the cleaning process, delivering a sorted outcome, saving time, decrease human resources needs, labor costs, and minimized egg breakage, consequently boosting production efficiency. OpenCV (Open Source Computer Vision Library) was employed as a development platform and the Raspberry Pi 3 Model B as a microcomputer due to its speedier and more powerful CPU, which is required to operate the system's components and process the acquired images for classification. In addition, a Raspberry Pi camera module V2 was employed to capture the images for scanning, LED bulb for candling, and an SG90 micro servo for sorting. Furthermore, we used B66 and B35 V-belts for the conveyor assembly. An induction motor of 0.125 horse power is used to rotate the conveyor assembly, a chain, and sprocket to reduce its speed. The researchers also used soft bristles brushes which are ideal for cleaning the eggshell. For cleansing, sprinklers were used along with the water PVC pipe that holds pressurized water of 30 psi. The camera's captured images are categorized as clean, dirty, well-pickled, and spoilt eggs. Empirical results exhibited that the detection accuracy achieved 96% and 93% for cleanliness and quality, respectively. It establishes the model and prototype's robustness in cleaning, sorting, and grading salted eggs.
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: 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.
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: 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: 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: 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: 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: 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.