Ace C. Lagman
95 Publications
Scopus ID: 85144089053
2022 IEEE 13th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), (2022), pp. 0119-0123
Bernard H. Ugalde, Renato R. Maaliw, ... Maurine C. Panergo
Conference Paper | Published: January 1, 2022
Abstract
It is always difficult to travel alone in a wheelchair without prior knowledge of the accessibility of the planned route. The majority of people prefer the shorter route. On the other hand, those with ambulatory limitations may prefer a longer route with proper ramps and drop curbs. This study aims to design obstacle management so that a registered user can report the accessibility of a ramp. The research includes an algorithm for generating barrier-free routes on the derived graph paths. When a wheelchair user encounters an obstacle while navigating the suggested route, the algorithm redirects them to their destination. A simulation test was performed, and the entire approach was evaluated using the survey method. The results showed that the proposed routing algorithm could find the shortest paths and reroute users to an unobstructed path. Respondents were highly pleased with the proposed navigation system’s performance and thought it was accessible, usable, and reliable. As a result, the study may provide a novel approach to designing a geographic information system for use in a wheelchair navigation system.
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: 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: 85143620903
2022 IEEE 13th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), (2022), pp. 0074-0081
Renato R. Maaliw, Alvin S. Alon, ... Roselyn A. Maaño
Conference Paper | Published: January 1, 2022
Abstract
Artificial intelligence-based medical image analysis promises an efficient and reliable diagnosis in today's healthcare. Traditional approaches for cataract screening by medical practitioners often results in subjectivity due to their varying levels of knowledge and expertise. Using transfer learning, ensembles of pre-trained convolutional neural networks, and stacked long short-term memory networks, we developed a non-invasive and streamlined pipeline for automatic cataract severity classification. Empirical results show that our proposed combined models of AlexNet, InceptionV3, Xception, and InceptionResNetV2 using a weighted average algorithm produces 99.20% (normal vs. cataract) and 97.76% (normal to severe) accuracies compared to standalone models. Furthermore, the ensemble model reduces classification error rates by an average of 2.17%. This study has the potential to help doctors to specify the magnitude of cataract stages with highly acceptable precision.
Scopus ID: 85137142818
2022 IEEE 13th Control and System Graduate Research Colloquium (ICSGRC), (2022), pp. 47-50
Conference Paper | Published: January 1, 2022
Abstract
Early disease identification and detection have been an interest of experts to enhance productivity and performance in agriculture. This study aims to use deep learning algorithms to classify sugarcane diseases using leaf images. Deep learning algorithms are implemented to create models that can classify sugarcane diseases using 16,800 images of training data, 4,800 images for validation tasks, and 2400 images for testing. Results show that the InceptionV4 algorithm outperforms other models in classifying sugarcane leaf diseases at 99.61 accuracy. Different models such as VGG16, ResnetV2-152, and AlexNet achieve high accuracies of 98.88%, 99.23%, and 99.24%, respectively. Hence, this study provides evidence that deep learning models can perform better in classification problems. This study suggests some improvements to further its contribution.
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: 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.