Scopus ID: 85105008189
2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2020), pp. 1-5
Conference Paper | Published: December 3, 2020
Abstract
Diabetes is a disease which chronic in nature, which is caused by an elevated blood sugar (or blood glucose) level. The metabolic disease is linked to several potential serious organ complications including nerves, kidneys, eyes, blood vessels, and the heart. According to the International Diabetes Federation, in 2019, about 2 million deaths were recorded worldwide due to diabetes. Furthermore, according to Philippine Statistics Authority (PSA), Diabetes Mellitus is considered as the fifth main cause of in the Philippines in the past years and in a 2015 study, about 1.7 million Filipinos are still undiagnosed of diabetes. Therefore, several machine learning-based techniques were developed for diabetes risk prediction. However, these works have yet to utilize artificial neural networks using the symptom information of suspected diabetic patients. This research paper demonstrated an ANN-based diabetes risk classification based on the symptom information of patients. The scaled conjugate gradient backpropagation technique was utilized for neural network training process. The classification system showed 99.2% overall correctness in determining the likelihood of diabetes.
Scopus ID: 85105023525
2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2020), pp. 1-6
Conference Paper | Published: December 3, 2020
Abstract
Myocardial Infarction (MI) better known as heart attack, is considered as one of the most alarming diseases that used to harm a great percentage of the male populace. However, the number of female patients suffering from the condition that was formerly known as the “old-man disease” is gradually increasing at the present time. Taking that into consideration, the researchers gathered enough data to come up with a predictive model that could be utilized in identifying the risk indicators for the mortality of female patients suffering from MI. By using different tools in data mining that contribute to a lot of great data discoveries up to date, the researchers made use of logistic regression, random forest, and decision tree to evaluate which technique can generate a diagnostic model with a higher accuracy rate. The generated prognostic models were based on a total of 9 significant attributes that were used to determine the risk indicators for the mortality of female patients suffering from MI. Upon conclusion, it turns out that among the three data mining techniques used in this study, logistic regression has the highest accuracy rate of 79% while random forest and decision tree resulted in 77% and 73% respectively. Medical practitioners could also use this study in discovering the characteristics that made up the clusters or groups of Myocardial Infarction patients that survived and characteristics that made up the clusters or groups of Myocardial Infarction patients that didn't. Determining the risk indicators of a female patient surviving MI tailors a more personalized way of treating the disease.
Scopus ID: 85104966191
2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2020), pp. 1-4
Conference Paper | Published: December 3, 2020
Abstract
Application of corrosion inhibitor has been practiced to counter the development of corrosion in reinforced concrete. However, utilization of corrosion inhibitor may compromise the bond between concrete and steel bar. The study investigated the bond performance of corroded rebar in concrete with corrosion inhibitor using impressed current technique. The relationship of bond strength and corrosion accumulated by reinforcement, non-coated (NC) and coated (C) with corrosion inhibitor are the main variables of this study. Thirty samples were subjected to impressed current technique to accelerate corrosion. For every 7-day interval within a 28-day period, 6 samples (NC and C) were tested using single pull-out test to determine the bond strength. Upon using ANOVA, results demonstrated an increasing trend of corrosion level for samples exposed longer to impressed current, with a P-value of 5.11 e-07. On the other hand, a SLRA derived model for bond strength as a function of corrosion level showed that bond strength of reinforcement (C and NC) decreases by 0.5508 MPa, and 1.2078 MPa respectively. Both models are deemed to be significant having a P-value and Multiple R of 0.0371, 0.5415 and 0.0009, 0.7667, for C and NC respectively.
Scopus ID: 85104972599
2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2020), pp. 1-4
Conference Paper | Published: December 3, 2020
Abstract
The West Valley Fault Line stretches across Metro Manila and School buildings are essential facilities as it serves as an evacuation center for such events. Rapid Visual Survey is a method in screening structures for potential earthquake hazards. This method was employed to assess seismic hazards of the public schools in Metro Manila within 5km from the fault line. Data collection form from the FEMA P-154 which includes building identification information regarding the target schools was used. Age of the building, irregularities, and soil type were determined on site and during the planning stage. A corresponding score was derived and used as level indicator of the potential seismic hazard of the building. It was found out that 14 buildings out of 139 were potentially seismically hazardous and requires further seismic assessment by the LGUs. A hazard map created using ARCGIS showed effectively the distribution and potential grade of damage of every building in each city. The map can be used to raise social awareness and baseline information to promote safety of the communities in the study area.
Scopus ID: 85104960144
2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2020), pp. 1-6
Elijah Alixtair L. Estolas, Agatha Faith V. Malimban, ... Toru L. Takahashi
Conference Paper | Published: December 3, 2020
Abstract
The study is aimed to develop an Automatic Beatmap with Genre Detection, called “Efflorescence”, a mobile application which can generate a rhythm game for people who would like to improve their reflexive functions. This study also provides different music genres that will be detected during the generation process so that users are able to distinguish different types of music among the songs they have chosen and/or uploaded to play. The researchers also aim in determining known music genres and its alternatives, and to be able to generate non-fixed beat maps to give the users a little challenge than most rhythm games produced. For the researchers to create the application, the following algorithms were used: Music Information Retrieval, Onset Detection, Tempo Detection, and Machine Learning. To prove that the application is feasible, the researchers conducted a survey among 50 respondents, all composed of FEU Institute of Technology CS and IT. The respondents rated the application average of being able to produce the result they wanted towards the game. The system can be further improved by future researchers through updating the system by putting up more functions and data required for the genre detection. It is also recommended that future researchers would apply it on different other platforms that were not and to lessen the specifications of the hardware itself. Lastly, future researchers can add more interactive features to make the game more challenging yet fun at the same time.
Scopus ID: 85098263057
2020 IEEE 10th International Conference on System Engineering and Technology (ICSET), (2020), pp. 22-25
Aimee G. Acoba, Christopher Franco Cunanan, ... Michael Angelo D. Ligayo
Conference Paper | Published: November 9, 2020
Abstract
The area of information system (IS) has created a whole different environment for information and communication technology (ICT), and the Internet plays a vital role in this work. This paper introduces an IS web-based implementation structure for the judicial system in the Philippines' Regional Trial Court (RTC). The original structure centers on helping the Judicial Officers and the Lawyer workflow. In the other hand, the administrator (authorized person) views other files: status reports, list of cases and list of clients. The research methodology includes an extensive study of information systems in the judiciary system. Two important concepts, Case Information Management System (CIMS) and Electronic Court Record-Keeping (ECRK), form the basis for the initial design. Software and the system architectures are presented.
Scopus ID: 85098261698
2020 IEEE 10th International Conference on System Engineering and Technology (ICSET), (2020), pp. 17-21
Cherry D. Casuat, Julius C. Castro, ... Christina P. Atal
Conference Paper | Published: November 9, 2020
Abstract
Predicting students' employability prior to graduation can be a great tool for every HEI's career center to intervene timely and to take steps on how to improve the weaknesses of the students to become more employable. At present, there is no tool that can be used to determine undergraduate students who are at risk of unemployment or becoming disadvantaged because vulnerabilities are not detected early. In this study the principal component analysis (PCA) and logistic regression were used to determine the most predictive features in the students' employability prediction system (STEPS). The Dataset used consist of 1000 information of engineering students who took their on-the Job training from School year 2017-2018 to School year 2019. The features used were professionalism and branding, confidence, comprehension, communication skills, growth potential, student performance rating. Upon using PCA, the experiments resulted to communication skills growth potential and student performance rating obtained the most predictive attributes that affects the employability prediction.
Scopus ID: 85096897096
2020 11th IEEE Control and System Graduate Research Colloquium (ICSGRC), (2020), pp. 312-317
Conference Paper | Published: August 1, 2020
Abstract
The value of money can be quickly grasped by the fact that virtually all economic, social, and other tasks are carried out and done using money. With dramatic shifts in global growth and other total human needs, the value of money is rising day after day. Money is an important asset that will allow you to run your life. The exchange of goods for products is an older custom, and you cannot buy something you want without any money. It's straightforward to use money, you just have to glance at the currency instantly, pull the appropriate sum of cash out, and that's it. But that may become a challenging job for those who cannot see. Among the most significant issues facing visually impaired people is the identification of money, particularly for paper currency. In this study, it focuses on this problem and is based on a machine vision technique called object detection. The study used Raspberry Pi 4 as a microcontroller, Pi Camera as its capturing device, and a speaker for audio in announcing the detected bill. EyeBill-PH has been done with an overall testing accuracy of 86.3%.
Scopus ID: 85096936405
2020 11th IEEE Control and System Graduate Research Colloquium (ICSGRC), (2020), pp. 328-333
Conference Paper | Published: August 1, 2020
Abstract
One way to determine whether or not the student is conscientious in the classroom is by facial expressions. Facial expressions are facial changes in response to a person's internal mental states, thoughts, or social contact. The application of machine learning and computer vision methods have made very useful in area of automated assessment. In this paper, an experimental setup was installed for data collection. The researchers aim to present a new approach of predicting student behavior (attentive or not attentive) based from face recognition during class session. This demonstrate a real-time detection of student behavior. Using deep learning approach, the acquired data utilized the YOLO (you only look once) v3 algorithm in predicting student behavior inside the classroom. The evaluation was created right after the live feed review. Generated models were tested using mAP to decide which model is appropriate for object detection. The mAP (mean average accuracy) is a common measure used to determine the precision of the artifacts being observed. This measure was focused on the following class: high = Attentive and low = Not Attentive. The experimental testing shows that model accuracy is 88.606%. Tests indicate that this method offers reasonable pace of identification and positive outcomes for the measurement of student interest dependent on observable student actions in classroom instruction.
Scopus ID: 85096893634
2020 11th IEEE Control and System Graduate Research Colloquium (ICSGRC), (2020), pp. 323-327
Conference Paper | Published: August 1, 2020
Abstract
Liquefied Petroleum Gas (LPG) is readily available to consumers around the world because LPG is a clean, portable, and efficient source of energy. LPG is currently used by hundreds of millions of people in the world and there are more than 1000 applications. LPG cylinder may leak as a liquid or a gas. If the source of ignition and gas meets LPG can explode or burn. LPG can make the lives of the people in the house endangered and can cause cold burns to the skin. To ensure the safety and security of the consumers using LPG the study aims to develop a device that can detect gas, smoke, and flame from the LPG cylinder and can notify the owner via text messages applying a rule-based approach. The system can display also a warning message and can alarm the owner using a buzzer. The researchers conducted functionality testing with twenty (20) trials in checking the accuracy of the system were obtained a 100% accuracy. This proves that the system is reliable and efficient when using at home by the consumers.