With the fast-paced growth of technology in our generation today, a lot of different applications has been developed in order to make people's lives more convenient and comfortable. However, due to the people's attachment to these technology, they have grown more and more sensitive with what they read from the internet (especially social media) — this in return takes toll on their mental health capabilities and provides problems for them and their families. This study aims to promote the presence of a mental health issue that has been frequently discussed by a lot of people through the years — this disorder is known to be depression. The researchers’ aim in this study is to develop a game that can aid people in understanding how depression affects the character through the said game's story — and help alleviate some possible factors that saddens or disrupts the player's mind from the gameplay and music. One of the main elements from this study is that interviews from psychologists, psychiatrists, and guidance counselors would be conducted and analyzed in order to know the possible problems that the target audience of the project may or may not face in their own lives — the researchers would then conduct a survey accompanied with the Patient Health Questionnaire (PHQ-8) in order to test the game's effectiveness and enjoyment. Based from the overall results of the study, the researchers were able to analyze the gathered PHQ-8 survey and has successfully rejected their null hypothesis — which means that the project has an effect on decreasing the severity of depression of the players. Overall, the researchers were able to satisfy their objectives which makes the project a step closer in developing an application that can be positively used for both entertainment and depression awareness in the future.

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.

Blood banking is the process of collecting, separating and warehousing blood. There are numerous file-based repositories of blood bank management that exist for storing data for blood bank ecosystem such as hospitals and centers. This functions for maintaining the information of donors, availability of blood, and transaction information. Currently, these systems are effort intensive, costly, and failed to achieve efficiency in terms of its filtering mechanism which makes repository penetrating faster and reliable. This paper introduces a new design for blood banking ecosystem with proper filtering solution using frame-based approach. The system has three major features: (1) blood camp setup module, (2) stocks management module which includes the blood donation and blood releasing, and (3) the filtering system module which shows the nearest blood camp with the available blood type based on the patients’ needs. Also, with the use of frame-based approach as filtering method, the system is more efficient and reliable compared to other blood banking repository systems. The system’s functionality was tested for its efficiency, usability, and reliability and the results are revealed in the survey. Conclusions and future work were also provided in this paper.

In the Philippines, according to Philippine Authority of Statistics, there is an imbalance between the student enrollment and student graduation. Almost half of the first-time freshmen full time students who began seeking a bachelor's degree do not graduate on time. The study aims to utilize how Naïve Bayes algorithm - a data classification algorithm that is based on probabilistic analysis - can be used in educational data mining specifically in student graduation. The study is focused on the application of the Naïve Bayes algorithm in predicting student graduation by generating a model that could early predict and identify students who are prone of not having graduation on time, so proper remediation and retention policies can be formulated and implemented by institutions.