Teodoro F. Revano, Jr.
21 Publications
Scopus ID: 85159218924
2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2022), pp. 1-5
Conference Paper | Published: January 1, 2022
Abstract
The Virtual Dietitian (VD) application is a nutrition knowledge-based system that generates personalized meal plans in accordance with the one-size-does-not-fit-all concept of precision nutrition. A subset of the population that was not involved in its four-part developmental study was gym and fitness enthusiasts despite them being important target users. As part of our quality improvement (QI) plan, we initiated a two-phase user testing to inform modifications to VD. We recruited a total of 30 users with prior experience in nutrition applications. In phase 1, they used the current version of VD for a week and answered a mixed-form questionnaire afterward. We used the same questionnaire from our previous study, which is composed of System Usability Scale (SUS) items and open-ended questions. After months of system modification, the same set of users evaluated again the new VD version after another week of use. A paired-sample t-test showed a statistically significant difference in SUS scores before (SUS = 79) and after (SUS = 82) modifying VD based on the suggestions of the participants (p = 0.005). Some new features include water tracker and reminder modules, Google Fit integration, and other nutrition support services (e.g., teleconsultation with registered dietitians). Although further refinements to VD are still needed, we were able to incorporate a QI initiative typically employed by healthcare organizations into software development for a better and improved personalized nutrition application.
Scopus ID: 85127559713
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
Coding is widely regarded as a fundamental skill of the 21st century. Yet, there is still a shortage of programmers worldwide which disproportionately affect the innovation goals of many sectors. In this study, we evaluated the installment of a Python programming gamified course in higher education, and measure its effect on students’ knowledge, attitude, self-efficacy, and skills performance. Two sections with 50 students each were randomly assigned to experimental or control groups. After one semester, the experimental group exhibited significantly higher scores in laboratory activities (skills performance) compared to the control group. Furthermore, they demonstrated a significant improvement with reference to attitude and self-efficacy before and after intervention. Therefore, we concluded that the use of a Python programming gamified course was an effective method for students to learn coding and programming concepts. The use and installation of a gamified course in learning other computer programming languages is highly recommended.
Scopus ID: 85127556927
2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2021), pp. 1-6
Conference Paper | Published: January 1, 2021
Abstract
It is clear that since the COVID-19 pandemic started in the Philippines, education is one of the most affected areas. After more than a year of struggling with different community lockdowns and the alarming consistency with the increasing number of confirmed cases each day, students and teachers are now left with the choice to voice out their frustrations, activism, opinions, and ideas regarding online classes through different social networking sites, most especially Twitter. With the influx of tweets available in the internet sphere, the authors of this study decided to conduct a sentiment analysis to categorize the overall opinions of Filipino citizens about the current state of education after more than a year of adapting with the distance learning practices that are now considered as the new normal. The authors utilized rtweet, a built-in package available in R programming to perform opinion mining on Twitter data collected through the package related to online class during pandemic. Through sentiment lexicons available in R such as bing and afinn, the results show that most of the tweets about online learning in the Philippines turned out to be neutral. The positive responses are 55.77% while 44.23% of the sentiments collected are negative. To evaluate the accuracy rates of results, the authors used three classification techniques namely Naïve Bayes, logistic regression, and random forest. Naïve bayes and logistic regression both show 69.23% accuracy rate and random forest calculated 71.15% accuracy in identifying whether the given tweet is a positive, negative, or neutral sentiment.
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: 85126632773
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
Higher education institutions (HEIs) are looking for new methods to assess and monitor student learning outcomes, as well as objectively determine the circumstances that contribute to their growth in different courses. Advances in new analytics tools that put visualizations and dashboards on top of live student data are making learning analytics more powerful than ever. This study utilized a participatory design (PD) technique to formulate an analytics dashboard intended for higher education. The rationale behind the study lies on the belief that an information system must be designed for users, rather than users having to accommodate a wide range of adjustments just to utilize such application. Students and teachers were recruited for their feedback and observations, respectively. After multiple PD sessions, four main crucial factors were derived: (1) who has access to data, (2) importance of time, (3) learning analytics should help students make the transition to university life, and (4) it should be discipline-specific. This study opens up a discussion on the importance of human-centered design through the use of PD and how learning analytics dashboard can be maximized to its potential when deployed in the academe.
Scopus ID: 85127569499
2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2021), pp. 1-6
Conference Paper | Published: January 1, 2021
Abstract
One of the most crucial parts of online learning is online testing. It is advantageous to users to save material resources while conducting an effective, quick, and secure inspection. The researchers created and built a web-based assessment system. This article discusses the system’s primary operations, objectives, and principles, as well as auto-generating test papers and questionnaires utilizing algorithmic analyses and presenting the system’s security.
Scopus ID: 85121327537
Proceedings of the 9th International Conference on Computer and Communications Management, (2021), pp. 46-51
Conference Paper | Published: January 1, 2021
Abstract
Assistive technologies gained traction in the medical field over the last few decades. Novel approaches have been developed in order to support people with disability to communicate effectively. However, little research has been conducted on the other side of the coin, that is, assistive technologies to help people who do not have a disability to understand and comprehend the language of disabled. This study describes the early development of a hand alphabet recognition that intends to accomplish a functioning dactylology conversion from sign language to English print in a live streaming video. Through a video analysis, each frame is processed using a segmentation technique to partition it into different segments (e.g., pixels of hand gesture). The dactylology conversion algorithm was implemented in a mobile application where users can watch video containing an on-screen sign language interpreter and understand fingerspelling used as a communication by hearing- and speech-impaired people. Through the sample dataset of 13 videos of American Sign Language manually collected (N=10) and recorded (N=3), the application was tested for its accuracy in detecting the alphabet in a video (94.16%), and the correctness of conversion of the detected alphabet into English print (89.65%). This study contributes to the list of existing novel approaches that aims to promote social positive effects as well as improve the quality of life for both disabled and all the people they socialize with.
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: 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: 85064127351
2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM), (2018), pp. 1-5
Conference Paper | Published: July 2, 2018
Abstract
The combination of multimedia technology and Internet provides an amiss channel for pornographic contents accessible by certain sensitive groups of people. Furthermore, the same channel provides the easiest medium to distribute illicit images and videos without an autonomous content supervision process. In this study, an application was developed grounded from a pixel-based approach and a skin tone detection filter to identify images and videos with a large skin color count and considered as pornographic in nature. With nudity detection algorithm as the foundation of the system, all multimedia files were preprocessed, segmented, and filtered to analyze skin-colored pixels by processing in YCbCr space and then classifying it as skin or non-skin pixels. Afterwards, the percentage of skin pixels relative to the size of the frames is calculated to be part of the mean baseline for nudity and non-nudity materials. Lastly, the application classifies the files as nude or not, and then filter it. The application was evaluated by supplying a dataset of 1,239 multimedia files (Images = 986; Videos = 253) collected from the Web. On the final testing set, the application obtained a precision of 90.33% and accuracy of 80.23% using the supplied dataset.