Scopus ID: 85148206655
International Journal of GEOMATE, (2023), Vol. 24, No. 101
Journal Article | Published: January 1, 2023
Abstract
Corrosion is the leading reason for reinforced concrete structures reduced service life. Structures such as ports and harbors and bridges and other offshore and near shore are prone to chloride-induced corrosion. This research evaluates the use of self-compacting concrete (SCC) as an alternative concrete for such structures. In theory, SCC reduced water content, and high cement and powder content will help protect the reinforcement from chloride intrusion because of its lower porosity and the alkalinity that the cement provided. Sixteen different mixtures of SCCs were mixed and tested for rheology, compressive strength, rapid chloride ion penetration test (RCPT), and impressed current (IC). Water content is the significant factor that affects both RCPT and IC. The segregation of SCC when too much water-cement ratio is combined with a high amount of superplasticizer resulted in a high level of corrosion in the reinforcement. The formation of cracks accelerates the corrosion due to the increased flow of current in the IC set-up. The impressed current technique is the suggested method for determining the corrosion resistance of concrete since it simulates the similar effect of corrosion to concrete which is cracking. It also stimulates the effect of rust on the flow of current. A rapid chloride penetration test is a good indicator of the durability of concrete but may be insignificant for predicting the corrosion level of reinforcement for SCC. Segregation negatively affects the total charge passed in the impressed current and the corrosion level of the rebar.
Scopus ID: 85171267066
International Journal of Intelligent Engineering and Systems, (2023), Vol. 16, No. 5, pp. 488-500
Journal Article | Published: January 1, 2023
Abstract
Serious game is one of the pedagogical media capable of transferring knowledge to its players. This game genre requires a support system that adaptively selects the appropriate scenario for players to increase their interest and comfort. Therefore, this study proposed an adaptive scenario selection (ASS) system using a finite state machine based on an artificial neural network (ANN). The game scenario is selected by ASS based on five player preferences, including work, hobbies/interests, origin, group members, and repetition. Furthermore, the multi-layer perceptron (MLP) architecture was used in the scenario selection process for the proposed ANN method. The experimental stage was carried out using the theme of travel in several tourism destinations in Batu City, East Java, Indonesia. The experimental results show that ASS succeeded in generating adaptive game scenario choices for players based on their preference data with an accuracy of 67.25%.
Scopus ID: 85199879034
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
Diabetes mellitus is a disease incorporated with carbohydrate metabolism whereas the body becomes unable to generate or react with insulin which leads to abnormal levels of blood sugar (glucose). In a worldwide perspective, Diabetes mellitus is ranked as the 9th leading cause of death based on the records of the World Health Organization and according to the International Diabetes Federation, there are about 463 million diabetic people worldwide in 2019 which is projected to increase to 700 million diabetic people by year 2045. In a regional perspective, about 251 million (45%) diabetic people resides on the Western Pacific and Southeast Asian region, whereas about 140 million people are undiagnosed of the disease. In this study, a genetic algorithm-optimized neural network using MATLAB was developed based on the risk factors. The experimental results show that the best validation performance has a value of 0.014129 and with a regression model coefficient R2 value of 0.95864.
Scopus ID: 85190895371
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 presents a profound exploration into the synthesis of cultural mythology and video gaming, epitomized by the creation of “Anito: Battle of the Gods.” With meticulous attention to detail, the game artfully merges immersive gameplay with the captivating narrative of the Philippines' rich cultural heritage. Grounded in the Scrum methodology, the project unfolded through iterative collaboration, ensuring both efficiency and alignment with player preferences. Extensive quantitative data was amassed through game evaluation sheets and purposive sampling, spotlighting the game's diverse aspects. Results unveiled resounding satisfaction among participants in categories spanning gameplay, aesthetics, user interface, player experience, and website usability. Theoretical implications accentuate the efficacy of cultural storytelling within games, while practical insights highlight the successful amalgamation of entertainment and education. Technically, the study underscores the potency of agile methodologies in crafting an engaging gaming experience. These findings hold far-reaching significance, demonstrating the power of culturally infused games to captivate and educate players. In this tapestry of research, the study signifies the convergence of creativity, culture, and technology, elevating gaming beyond leisure to an instrument of cultural exploration and enrichment.
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: 85199864750
2023 IEEE 15th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2023), pp. 1-6
Teodoro F. Revano, Jr.
& Ronaldo A. JuanatasConference Paper | Published: January 1, 2023
Abstract
The technology-enabled learning and teaching ecosystem (TELTE) enhances accessibility, personalization, engagement, collaboration, and continuous learning, while also providing valuable data for assessment and improvement. TELTE is essential in education as it empowers learners, expands access to knowledge, fosters engagement and collaboration, and prepares individuals for the demands of a digital world. However, most frameworks focused on the needs of learners, leaving other stakeholders, such as teachers, underrepresented. Therefore, the study focused on developing a technology management framework for TELTE (TMF-TELTE), addressing both students and teachers. The framework development was anchored to the challenges concerning resource allocation, delivery methods, support systems, management support, technology infrastructure, and policy development. A mixed-methods approach using an explanatory sequential design was adopted. The experts, students, and teachers from various HEIs who participated evaluated the framework in terms of usefulness and convenience. The results show that the students, teachers, and experts all agreed that the developed framework was acceptable. TMF-TELTE was therefore recommended to be included in either the revision of their curriculum and other academic materials or the development of new technology- based learning and teaching ecosystems.
Scopus ID: 85199881223
2023 IEEE 15th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2023), pp. 1-6
Reylwin N. Caña, Rozanna Dixie D. Berbano, ... Rex Paolo C. Gamara
Conference Paper | Published: January 1, 2023
Abstract
Despite awareness of food waste consequences on environmental, societal, and financial aspects, no behavioral change towards food waste reduction is seen in the majority of Filipino Households. This study focuses on the creation of a food waste monitoring system that provides accurate figures to incite constructive behavioral changes. Thru the utilization of Convolutional Neural Networks coupled with image processing techniques, the system identifies and classifies food waste items, measures weight, and collects data for the consumers' viewing in a manner that is both informative and user- friendly. The device developed by the proponents involves two Raspberry Pi 4 Boards and one Arduino Uno board which are the main components of the device that simultaneously communicates to process the given food waste. The other components such as two RPI Cameras that can record in HD, detect food waste, and send the following data to be processed. Then, the weight is taken using height input from the user and EfficientDet algorithm is used for detecting different classes of food waste. Using 2D images that were captured the RPI Cameras, the proponents gathered images to make data sets and to train the device in such a way that it can determine weight and food class. After these steps, the food waste can now be thrown away automatically via the moving platform. After conducting tests, we successfully created a device with an impressive detection accuracy rate of 94.0952%, accompanied by an error rate of only 5.9075% and a precision level of 96.42156%. When it comes to weight detection, our device achieves an overall mean absolute percentage error rate of 9.9898%. Additionally, the tilting platform demonstrates a remarkable relative accuracy of 98.8889% with a success rate of 100%.
Scopus ID: 85199910971
2023 IEEE 15th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2023), pp. 1-4
Conference Paper | Published: January 1, 2023
Abstract
This research study employed machine learning algorithm in This research study employed a machine learning algorithm in predicting student academic performance in the Data Structures and Algorithm (DSA) course which is based on student lifestyle to analyze the factors that affect the high or low performance result. A total number of 251 Bachelor of Science in Computer Science (BSCS) students participated in the study where 207 or 82% were male and 44 or 18% were female. A oneshot case study was conducted that led to data collection through the administration of an online survey on former enrollees of the said course. The dataset was extracted with 43 features and was analyzed using Python on Jupyter Notebook. Randomly selected 70% of these, 176 observations, are used to train the classifier models. The remaining 30%, 75 observations, were used as the test data. In order to classify academic performance students, eight machine learning algorithms were applied based on random forest (RF), decision tree (DT), support vector machines (SVM), K-nearest neighbors (KNN), logistic regression (LR), Gaussian Naive Bayes (GNB), stochastic gradient descent (SGD), and perceptron. Although SGD and Perceptron classifier models show comparably low classification performances, both random forest and decision tree classifiers provided the highest metric performance. The study indicated that the lifestyles of students contributed to whether the student performance became high or low in their grade performance.
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: 85199857565
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
Personal safety is paramount in today's society, where crimes like theft and assault are prevalent. However, many self-defense tools are inaccessible or impractical for everyone. This creates a demand for innovative, user-friendly tools that can provide reliable protection in emergency situations. The Flash'n Alert, a handheld warning device with an integrated alarm system designed to empower individuals to protect themselves effectively. The Flash 'n Alert is a handheld device designed and developed with an activation button as input to the microcontroller that will trigger the four emergency modules - LED for high-intensity flashing of light, speaker for loud alarm, GPS to determine the location of the incident and GSM to send SMS to emergency contacts. This device combines a high-intensity flashing light with a loud alarm, deterring potential attackers and alerting nearby people in emergencies. When activated, the device emits a blinding flash that disorients attackers and provides an opportunity for escape. The accompanying alarm system grabs attention and signals an emergency in progress. Notably, the Flash 'n Alert also notifies emergency contacts, making it invaluable when the user is unable to call for help. With a simple activation button, this device is effortless to use. And with its two rechargeable battery feature, the Flash'n Alert is cost-effective and eco-friendly, ensuring it remains ready for use with its long-lasting battery life.