UV Express is one of the most used modes of public transportation in the Philippines. However, the inconvenience in commuting, such as long queues of passengers at UV Express terminals and extended waiting time for UV Express drivers and passengers, is still present. The Book-Express aims to help UV Express, which operates on a terminal-to-terminal basis, by providing a reservation and booking system through a website and mobile application. The system’s primary functionalities were validated through user acceptance testing using the metrics of functionality, usability, reliability, performance, and supportability. Consequently, the system garnered an overall score for the mobile application of 4.60, which is "Excellent", and an overall score for the website of 4.53, which is "Excellent," as well.

Agriculture has a big contribution in the life of most of the residents of Isabela having rice as the main crop. Having a large farm cannot be easily harvested manually so using a harvester machine helps the farmers get the rice and sell immediately without hustle. Through this, researchers aim to provide an easier and more comfortable way of purchasing harvester parts to the consumers without going on the physical store. This online ordering with recommender system displays the products and suggest relevant items to consumers. The integration of augmented reality in the physical brochure shows the 3D animations and descriptions of the 10 major parts of the machine which gives the consumers the knowledge about a certain part before it will be used in farm operations. In summary, the developed system is aimed at giving agricultural marketing information to all the farm owners in Isabela as well as to help increase the sales of the company.

According to National Center for Education Statistics, almost half of the first-time freshmen full time students who began seeking a bachelor’s degree do not graduate. The imbalance between the student enrolment and student graduation can be solved by early predicting and identifying students who are prone of not having graduation on time, so proper remediation and retention policies can be formulated and implemented by institutions. The study focused on the application of the ensemble models in predicting student graduation. Ensemble modeling is the process of running two or more related but different analytical models and then synthesizing the results into a single score or spread in order to improve the accuracy of predictive analytics and data mining applications. The study recorded an increase of classification accuracy in predicting student graduation using ensemble models and combining multiple algorithms.

The study aims to integrate neural network algorithm that predicts students' vulnerability of not having graduation on time to an adaptive learning management system. Neural network is one of the popular machine learning techniques because of its learning algorithm. The learning algorithm is focused on updating weights of the edges in order to produce minimal mean squared error between actual and predicted values. The integration of this platform could lead to much efficient learning management system as LMS is mainly driven to provide individualized and personalized learning tailored to specific requirements and learning preferences. The neural network algorithm is designed to classify students with learning difficulty so that administrators can formulate remediation and academic support policies.

Medicine intake, as prescribed by physicians and health care providers, is important not only for minimizing the risk of relapse but also to treating conditions and improving one’s overall well-being. However, adherence to a medication routine seems to be a problem for some people which is usually affected by a variety of factors such as hectic day-to-day activity schedules, poor prescription instruction, concurrent intake of multiple medications, and forgetfulness. Medication adherence has been then considered as one of the major medical problems globally. In such cases, a medical device that could alert and remind patients in taking their medicines on time will come in handy. Consequently, this study aimed to design and develop a pocket-sized electronic pillbox device using TFT LCD display, Arduino microcontroller, Piezo Buzzer (for sound notification), Eccentric Rotating Mass (for vibration notification), Lithium Ion battery as power source, and plastic organizer as the main body. The said pillbox device will act as a countermeasure for medication non-adherence particularly by patients under the case of polypharmacy. Thus, this study focused on the design and development of the prototype, hardware testing and system qualification only. Furthermore, this paper is part of a future study where the assessment and measure of device behavior and adherence will be conducted to compare whether the utilization of pillbox device has an impact to the people who are using it.