University of the Cordilleras

About: University of the Cordilleras is a education organization based out in Baguio City, Philippines. It is known for research contribution in the topics: Higher education & Turnaround time. The organization has 70 authors who have published 95 publications receiving 173 citations. The organization is also known as: UC.

Rice Pest and Disease Detection Using Convolutional Neural Network

Abstract: Detection of rice pest and diseases, and proper management and control of pest infested rice fields may result to a higher rice crop production. According to the International Rice Research Institute, farmers lose an average of 37% of their rice crops due to pest and diseases, yearly. Using modern technologies, like smart phones, farmers can be aided in detecting and identifying the type of pests and diseases found in their rice fields. This study proposed an application that will help farmers in detecting rice insect pests and diseases using Convolutional Neural Network(CNN) and image processing. It looked into the different pests that attack rice fields; information on how they can be controlled and managed was considered; farmers' knowledge in different rice pests and diseases, and how they control these pests was regarded in this study; the study also looked into the reporting mechanism of farmers to government agencies. Using CNN and image processing, the application that detects rice pests and diseases was developed. The searching and comparison of captured images to a stack of rice pest images was implemented using a model based on CNN. Collected images were pre-processed and were used in training the model. The model was able to achieve a final training accuracy of 90.9 percent. Cross-entropy was low, which implies that the trained model can perform prediction or can classify images with low percentage of error. Through the developed application, farmers were provided with information and procedures on how to control and manage rice pest infestation. Future researchers may look into multiple pest comparison to a stack of images for faster retrieval of information.

Students' perception on e-learning: A basis for the development of e-learning framework in higher education institutions

Abstract: As learnings styles evolve along with modernizing society, educational technology also expands. A current trend in education brought about by technological advances is the e-learning system where teachers and students can discuss lessons online and exchange learning resources. This study explored on the areas of e-learning and provided a review on current e-learning frameworks from different studies. A recent study of Debattista presented a comprehensive rubric for e-learning and it is adopted by this paper as basis for gathering student expectations, feedback, and problems encountered in e-learning. These rubrics were rated by students according to importance. Statistical findings show a significant difference between ratings of students from public and private institutions. Similarly, there is a significant difference between the ratings of male and female students. The difference might spring from the level of interest of students towards learning as factored in by type of institution and gender. Students' learning expectations in an e-learning environment were also gathered in this study as a basis for a proposed e-learning framework. All specific standards presented by Debattista were labelled very important by respondents and are therefore adopted into the proposed framework. Along with these rubrics are proposed additional standards that focus on the enrichment of student experience and enhancement of learning. It is still highly recommended that strict and proper implementation of such standards are supervised by concerned administrative departments.

NutriTrack: Android-based food recognition app for nutrition awareness

Abstract: One of the emergent concerns of human life is about health and wellness. Undeniably, health and nutrition are one of the valuable aspects of life. Thus, technological innovations to help enhance and even promote health awareness is essential. With the advent of mobile computing, it is much easier to be aware of health information because of its mobility and availability. Much mobile application is being developed to serve as a tool for health monitoring and nutritional guide. Mobile applications have the ability to support health needs like detecting heart rate, classifying food, and many more. Taking advantage of technology, utilization of it hereby addresses certain issue and problems of human life, especially in health. In this study, the researcher's attempts to design and develop an Android-based food recognition application that could be used as a health awareness tool for non-health conscious individual. The application lets the user take the photo of the food and show its nutritional contents. Implementing Mifflin-St Jeor method in determining daily calorie consumption, users shall be aware of their required calorie intake. Moreover, the researchers' have studied its effect on people's health awareness on food nutrition by the randomly selected respondents. Finally, this paper presents an analysis of the impact of the food recognition app to change people's concept of food nutrition.

Predicting Student's Board Examination Performance using Classification Algorithms

Abstract: Educational Data Mining can help stakeholders give appropriate decisions to improve educational experiences. New knowledge or models are realized when data mining techniques are applied on educational data. This study focuses on building classification model by utilizing data mining techniques for predicting the likelihood of a student to pass the Licensure Examination for Teachers (LET). Several well-known data mining algorithms such as Neural Network, Support Vector Machine, C4.5 Decision Tree, Naive Bayes, and Logistic Regression are used to build the models. The performance of these models in terms of accuracy to predict the student's performance in the Licensure Examination for Teachers, F1 Measure and Area under the Curve (AUC) value were compared to determine which among these classification algorithms performs best. Results show that C4.5 turns to be the most suitable algorithm for the model. It has an accuracy of 73.10%, F1 measure of 62.53% and Area under the Curve value of 0.730. The identified model could be able to identify students who will likely fail the Licensure Examination for Teachers. These students should be given higher priority during their mock board review and be able to pass the board examination. Aside from helping these students, it also helps the institution get higher percentage of passers in the Licensure Examination for Teachers and can be of help during accreditation.

IoT based model for monitoring and controlling water distribution

Abstract: These days, because of increment in relocation from a provincial territory to urban ranges, the population in urban areas is obviously expanding quickly together with the requirement for comfortable living. With increase in population, urban areas have expanded, water becomes one of the major problems in a city particularly water distribution, interfered with water supply, water protection, water utilization and furthermore the water quality. To overcome water supply related problems proper monitoring and controlling system must be implemented. The developed system consist of different IoT devices like water pressure sensor, ultrasonic sensor, solid state relay switch, motorized electric water valve, Raspberry PI, GSM module and Arduino UNO micro-controller. This paper focused on the monitoring and controlling of water distribution using IOT based model. It aims to design and develop a low cost reliable and efficient technique to improve water distribution in the community. A prototype was developed to simulate the operation of a water distribution. Also, a web application was created as a front-end system for monitoring the status of the different pumping stations as well as controlling. Also, fuzzy logic algorithm was integrated into the developed prototype system to be more scientific in making decision. As a result, the experiment was successful and passed all the conditions set for monitoring and controlling water distribution using IoT based model.