Scopus ID: 85127580968
2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2021), pp. 1-5
Conference Paper | Published: January 1, 2021
Abstract
Vital sign monitoring is a core component of nursing care as information regarding ‘vital functions’ strengthen medical diagnoses. Consequently, various measurement devices have been proposed and utilized from wearable devices to custom engineered equipment. To contribute to the existing innovations of monitoring devices for vital signs, this study proposed a different variation of such a medical device by incorporating the principles of an expert system together with its own wearable vital sign analyzer. At this stage of the project, the device (subsequently referred to as iVital) was a proposal with a prototype as final the product. The primary goal of iVital is to provide both patients and healthcare experts to continuously monitor vital sign measurements remotely. Further, and most importantly, it integrated an expert system where other important data (e.g., patient history and medical records) detectearly signs of clinical deterioration. This work, albeit prefatory, is evidence of how expert systems can be used in healthcare.
Scopus ID: 85113487106
2020 IEEE 7th International Conference on Engineering Technologies and Applied Sciences (ICETAS), (2020), pp. 1-4
Conference Paper | Published: December 18, 2020
Abstract
The most common cause of injuries in the construction site was caused by falls, slips, and trips. As a response to the Occupational Safety and Health Administration (OSHA), this agency conducted training such as fall prevention. Despite these initiatives, there are still incidents and accidents that happened on the site. According to the study conducted by previous researchers, those fatalities can be reduced by wearing a hard hat. That is why OSHA requires all construction sites to strictly implemented the wearing of hard-hat within the vicinity of the construction site. This study developed a hard hat detection system to determine if the worker is wearing a hard-hat properly. Image processing was used in this study. The proponents used the public datasets with hard hat-wearing images to evaluate the performance by using the mean average precision (mAp) where the proponents obtained an average accuracy of 79.246. The proponents of the detection system of hardhats concluded that regardless of their size, color, types, and angles with an average Training and Validation accuracy of 97.29 and 92.55, average evaluation accuracy of 79.24% with the highest model accuracy of 86.89%, and testing accuracy of 86.67%. The system works properly.
Scopus ID: 85113543634
2020 IEEE 7th International Conference on Engineering Technologies and Applied Sciences (ICETAS), (2020)
Conference Paper | Published: December 18, 2020
Abstract
In the Philippines, at least 16 million Filipinos reported smoking cigarettes amid the campaign against tobacco products due to various concerns about their adverse health effects. Due to health, environmental, and safety concerns, the President of the Philippines issued Executive Order 26 s. 2017, imposing a nationwide ban on smoking (use of tobacco including e-cigarettes) in all public places in the Philippines. Despite the implementation of this order, many are still seen smoking in prohibited smoking areas. A smoke detector can be helpful in this situation. This study proposed a smoker detection system that uses a deep learning algorithm that can detect people that are smoking cigarettes. The study used the Pascal VOC format and LabelImg tool for annotating the datasets. Training, validation, and evaluation of the system is done by presenting images, videos, and live detection using the webcam of a camera. Overall, the system produced 90% testing accuracy.
Scopus ID: 85113507662
2020 IEEE 7th International Conference on Engineering Technologies and Applied Sciences (ICETAS), (2020)
Helcy D. Alon, Michael Angelo D. Ligayo, ... Marites V. Fontanilla
Conference Paper | Published: December 18, 2020
Abstract
Dementia is a common term for memory loss, speech, problem-solving, and other cognitive skills that are serious enough to interfere with everyday life, and Alzheimer's is the leading cause of dementia. Alzheimer's disease is presumed to develop 20 years or more before symptoms occur, with degenerative changes that are unapparent to the person affected. The deep learning approach for early detection and Alzheimer's disease classification has recently gained significant attention. This study proposed disease detection trained by utilizing the YOLO v3 algorithm that aims to detect Alzheimer's disease based solely on Magnetic Resonance Imaging (MRI). Pascal VOC format and LabelImg tool are used for annotating the datasets, categorizing the image as non-demented and mild-demented. Model 4 was used in the system having 98.617% training accuracy, 98.8207% validation accuracy, and mAP of 96.17%. To test the accuracy of the used model, images of MRI scans are presented and it recorded 80% testing accuracy.
Scopus ID: 85088750851
Journal of Cleaner Production, (2020), Vol. 276, pp. 1-10
Ivan Henderson V. Gue, Michael Angelo B. Promentilla, ... Aristotle T. Ubando
Journal Article | Published: December 10, 2020
Abstract
The shift to a circular economy requires careful planning, the first step of which is to understand the drivers of the transition. There have been few papers in the literature that have analyzed and mapped interrelationships of these transition drivers from the perspective of different sectors. This work presents a methodological framework for mapping causality networks for macro-level transition towards circular economy based on sector perceptions. Fuzzy DEMATEL is used to allow linguistic inputs to be quantified. This procedure allows drivers to be characterized as causes or effects based on their position in the causality network. A case study presents the Philippines as a representative developing country for circular economy transition. The inputs of seventeen respondents from retail and trade, manufacturing, construction, water services, food services, electricity services, academic services, and health services were elicited through a survey. These responses were then aggregated into the industry and service sectors. The drivers considered were government support, company culture, consumer demand, social recognition, economic attractiveness, and information to practitioners. Results show that economic attractiveness and consumer demand are unanimously seen as the causal drivers. All sectors identify company culture as an effect driver. The findings also indicate varying perceptions among sectors. Although these findings apply specifically to the Philippines, this methodology itself can be used for mapping driver interrelationships of other countries and regions.
Scopus ID: 85104936823
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
Design Thinking is commonly used by businesses as a mindset and approach for problem-solving, learning, and collaboration. Such methodology is a beneficial addition to the pedagogy selections used in the education landscape especially to fields that build products (e.g., computer systems) requiring significant considerations to its functional designs. In this study, the use of Design Thinking Curriculum was explored in Higher Education Institutions particularly on Information Technology and Computer Science programs to determine its impact to the skills and abilities of future computing professionals. To do this, a self-assessment scale that comprises of 31 measurement items divided into seven dimensions was given to computing students. Findings establish that computing students enrolled in a Design Thinking Curriculum have significantly improved in all scales compared to those who are not. Therefore, this study validates the application of Design Thinking Curriculum in education as an approach to encourage innovation in the computing field.
Scopus ID: 85104995169
2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2020)
Conference Paper | Published: December 3, 2020
Abstract
Digitalization of different industries led to new systems that provide accurate information that results in efficient and effective services. This information is vital for decision-making and on the larger scale, policymaking especially in the health sector. In the Philippines, some healthcare establishments have not adjusted to this digital change. This study aims to develop an enhanced model of healthcare management system that can perform digitization of data, predictive health analytics and sales trend analysis. The researchers identified these three features as the focus of the system because it improves data quality, accessibility, reliability, and autonomy. The system is based on prescriptive analytics - a type of analytics that uses machine learning to process historical and predictive data. The artificially intelligent management system caters to the needs of the healthcare sector in this digital age to improve its services to the people.
Scopus ID: 85104983562
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
Controlled environment agriculture are being developed with the purpose of increasing production yield in farms. For optimal yield, it is very important to have an understanding about the relationship of environmental factors such as radiation, temperature, nutrients, water, and in relation with the growth state of the crop. Growth monitoring of cherry tomato crops in traditional methods are extremely labor-intensive, destructive, and costly in terms of time and money. Thus, application of computer vision has become an area of interest in the study of monitoring tomatoes' growth. In this study, image processing techniques are employed to identify the growth stage of cherry tomato as fruiting, flowering, and leafing stage. Confusion matrix with True Positive rate and False negative rate, and ROC are used to evaluate the decision support system developed. Experimental results show a high performance in determining the growth stage of test cherry tomato images.
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.