Ace C. Lagman
106 Publications
Scopus ID: 85149976940
Lecture Notes in Networks and Systems, (2023), pp. 19-27
Marr Darwin T. Antonio, John Matthew B. Clemente, ... Carl Ivan M. Yap
Book Chapter | Published: January 1, 2023
Abstract
This capstone project aims to create a visual book with an augmented reality feature and a CMS-based website. The application uses augmented reality technology to track target images on the book and display 3D models and animations of the 30 endangered bird species in the Philippines. To elaborate, when a target image is found, the application will display the 3D model of the bird and two different animations for the user to explore. The application was developed using Vuforia and Unity; the 3D models of the endangered bird species will be modeled and animated using Autodesk Maya, Substance Painter, and ZBrush, and the design and layout of the book were created using Adobe Photoshop and Adobe InDesign. The developed system is one of the first AR books about endangered bird species in the country, attempting to disseminate information and raise awareness about the status of endangered birds using augmented reality. To prove that the application is practical and usable, the researchers surveyed 70 respondents consisting of ten from the client’s organization, 40 from the general public, ten bird lovers, and ten I.T. professionals. Based on the survey results, the system proves to be practical and usable in disseminating information and raising awareness about the status of endangered birds. Future researchers can improve the system by adopting some features and enhancing the application so that users can still utilize the mobile application even without the visual book. The researchers also encourage future researchers to implement the application in other devices with different operating systems, such as iOS and Windows, to cater to a broader range of users.
Scopus ID: 85156264475
2023 IEEE 13th Annual Computing and Communication Workshop and Conference (CCWC), (2023), pp. 0168-0175
Renato R. Maaliw, Zoren P. Mabunga, ... Rhowel M. Dellosa
Conference Paper | Published: January 1, 2023
Abstract
Diabetic retinopathy is a serious complication needing prompt diagnosis and medication to avert vision loss. Lesions caused by the condition are difficult to track because they are hidden behind the eye's structure in small and subtle forms. To extract relevant features., we created a robust pipeline using multiple preprocessing techniques., image segmentation architecture (DR-UNet) with atrous spatial pyramid pooling., and an attention-aware deep learning convolutional network with different modules based on ResidualNet. Empirical results show that our framework has segmentation accuracies of 87.10% (intersection over union) and 84.50% (dice similarity coefficient). Moreover., classification performance of 99.20% provided better results than existing schemes., as reinforced by the smooth convergence of training/validation loss and accuracy. This study has the potential to supplement traditional diagnosis to identify better the ailment in its early and advanced stages.
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.
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: 85199916542
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
Ensuring students' timely and satisfactory graduation requires evaluating their future performance based on ongoing academic records and implementing pedagogical interventions. Within an educational context, students can be categorized as regular or irregular, each subject to distinct academic rules. Regular students follow a predetermined curriculum, enjoying a clear path to graduation and improved access to required courses, facilitating efficient progress toward degree completion. Conversely, irregular students face challenges such as disruptions and delays, necessitating additional time and support to meet degree requirements. Guiding both regular and irregular students and enhancing their study plans requires proper guidance and academic intervention. To bridge the existing research gap, this study introduces a Decision-based Recommendation System towards Program Completion Using Forward Chaining Algorithm. This system automatically generates a study plan by considering defined constraints and parameters, enabling students to assess the term and year of their degree program completion. Leveraging the forward chaining algorithm with fuzzy IF-THEN-ELSE rules, the system's predictive model captures intricate relationships and dependencies within the data, yielding valuable insights and predictions. This adaptive approach refines predictions with the availability of new data, enhancing accuracy and usefulness in guiding decision-making processes related to generating a study plan.
Scopus ID: 85174292970
2023 International Conference on Information Technology Research and Innovation (ICITRI), (2023), pp. 118-122
Conference Paper | Published: January 1, 2023
Abstract
Wind power is a challenge in power generation. The tortuous process stages in generating voltage become a significant problem to be solved properly. One indicator of the process is the determination of the right wind speed because it always changes at any time and under circumstances. For this reason, accurate predictions are needed so as to maintain the smooth integration of wind power into the overall system. Machine learning is used as a promising approach to dealing with wind intermittent power because wind speed prediction methods have been developed in recent years. This study explores climate patterns in the Philippines using data collected from PAGASA. The data is trained and tested with a machine learning model to predict wind speed. This research resulted in the Gaussian Process Regression (GPR) model outperforming other models and is very suitable for datasets in achieving accurate and reliable predictions.
Scopus ID: 85199923471
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
Sleep is critical, particularly for young adults aged 18 to 25. Young adults should get at least 7 to 8 hours of sleep per night. Most young adults struggle to get enough sleep due to a variety of factors such as anxiety, depression, and excessive use of technology. As a result of continuing the same sleepless night routine in their daily lives, young adults become sleep deprived. The most common side effects of sleep deprivation are health issues such as heart disease, obesity, kidney failure, high blood pressure, and many more. Since this issue is very timely and relevant nowadays, the researchers develop a major project, an 2D Animated Interactive Video, and a minor project, a Podcast that can help young adults to be relaxed in order to get a better sleep. The 2D animated interactive video will present two environments from which users can choose. It will be uploaded to a Wix site, and each option will be unlisted so that users can interact with the animated video. The podcast, on the other hand, will be storytelling that is related to the story of the major project. It will consist of four episodes excluding the pilot episode. This project will also be uploaded to YouTube for easy and free access.
Scopus ID: 85147989172
Lecture Notes in Networks and Systems, (2023), pp. 225-232
Book Chapter | Published: January 1, 2023
Abstract
FEU Tech’s College of Computer Studies hosts an annual event known as Technology Innovation on Capstone Project (TICaP), which exhibits the different capstone projects of each specialization. This event allows the students to view the different projects of the FEU Tech PBL students and recognizes the group with the best capstone project and other special awards. However, problems were encountered while managing the event resulting in disorganized planning and management. The proponents developed an event management system named TICaP HUB to solve the different problems experienced by the organizers in planning and managing the event. The team conducted a system evaluation and survey of the PBL and non-PBL students, including FEU Tech faculties. Using the Likert scale for the system evaluation, the Web application has an overall weighted mean of 4.72 and 4.71 for the mobile application. This outcome can be interpreted that the users strongly agreed with the FURPS evaluation of the system. With the results acquired from the respondents, the proponents conclude that using the TICaP HUB can significantly help the users efficiently manage the TICaP event and lessen the time consumed in planning and organizing for the said event.
Scopus ID: 85199903161
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
The Philippines has unique and rich culture; thus, it is also part of its identity. A preliminary test the researchers conducted revealed that the consumers do not consider street food as part of the culture. The Hybrid 2D Animation Documentary tackles the role of street food and street food vendors in the cultural aspect. The evaluation process used in the study is a comparative analysis of the set of pre-assessment and post-assessment evaluations from 30 respondents. The result showed that there had been an increase in value in recognition and acknowledgment of street food after consuming the material. The researchers recommended that street food vendors start acknowledging their cultural role, that multimedia art students create more content featuring the cultural role of street foods, that the teachers introduce the representation of street foods, and that future researchers explore the sanitation issues of street foods.
Scopus ID: 85199914523
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 assessment of students' academic performance based on ongoing records plays a vital role in enabling timely interventions for successful graduation. Variances in degree program completion times emphasize the need to tailor subjects and courses to individual student requirements. However, limited research exists on student degree completion, presenting challenges related to diverse student backgrounds, information gaps in courses, and accommodating evolving progress in predictions. Educational institutions are adapting flexible curricula, necessitating a diverse range of courses and informed decision-making for academic planning. Structured course sequences in universities ensure a gradual buildup of knowledge and skills, aiding students in tackling more complex subjects progressively. A decision-based recommendation system for predicting graduation time was developed and evaluated using the ISO/IEC 25010 Software Quality Model. This model included parameters like functionality, performance, compatibility, usability, reliability, security, maintainability, and portability. Each parameter was assessed to ensure the system's functionality, performance optimization, cross-platform compatibility, user-friendliness, reliability, security, ease of maintenance, and adaptability to different environments. Through this evaluation, the system's quality and efficacy were comprehensively validated, confirming its ability to achieve intended objectives and meet user requirements.