Sentiment analysis has been extensively researched for the purpose of analyzing qualitative data using a computation technique. However, there are only a few research papers that focus on analyzing sentiments in terms of teachers’ evaluation. Analyzing comments on teacher evaluations can lead to understanding more of what faculty development programs can be provided to improve teachers’ academic performance. Thus, this study presents an opinion-mining system utilizing the Bayesian technique of the Naïve Bayes algorithm. The descriptive research method was used in this study, with a questionnaire serving as the instrument for testing the acceptability of the application. One hundred (100) evaluators were surveyed for the teachers’ evaluations. The application performance attributes are defined using the functionality, usability, reliability, performance and security (FURPS) model. The mean formula was used to analyze the data. The usability and security were evaluated as perfectly acceptable, with a weighted mean of 4.64, and 4.56, respectively. Furthermore, the functionality, reliability, and performance were evaluated at acceptable evaluation ratings with a weighted mean of 4.29, 4.02, and 4.11. The overall quality of the system was given an acceptable rating with a weighted mean of 4.32, indicating that the application provided and managed ratings and comments on individual teachers’ performance.

Supervision regarding the wellness and security of the students of NBCA International School is insufficient. As for short break times and only one concessionaire, negligence in the school’s canteen is prominent. To administer and enrich the wellbeing of each student, MoniPay intends to monitor the consumption and expenditures of food purchased using NFC or Near-Field Communication cards where parents are notified of the student’s procuring activities. Reports of student's daily and weekly expenditures are accessible in the parent application. This study emphasizes the need for the system to fulfill what is lacking in administering the wellness of the students in the school. This study is quantitative research wherein data is collected by way of surveys answered by respondents chosen through a purposive sampling technique to improve the system. The results and objectives have been met in line with the gathered information from research, interview, and surveys.

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.

The blockchain phenomena is no longer about Bitcoin or cryptocurrency, it is beyond a common protocol to make it nearly impossible to create fraudulent transaction. Blockchain based architecture overall performance is subjected to storage expenses with high computational cost. This paper designed a new consensus protocol for Blockchain using Content Addressable Pattern with the adaptation of modified SHA256 algorithm. Although government, business and other entities interest of adapting blockchain to their processes, the complexity issues and operational cost is still a challenge to date. With the newly design consensus protocol the process of validating the transaction that involves tedious mining or solving cryptographic puzzles has been eliminated and move towards using signature to authenticate the transaction. Concatenation of all these elements is a generated hash value using modified SHA256. Since the hash is secured, the transaction is secured. Thus, the implementation of off-chain chanel instead of global consensus addresses the complexity and high computational cost of blockchain technology.

Data analysis is an integral part of research. Most researchers examine their results by using graphs, tables, charts, and figures. These methods are effective, but knowledge transfer is limited because it only depends on what the authors or researchers have presented. The need to scrutinise further the given data is essential. One way of addressing this problem is to utilise a graphical user interface (GUI), wherein a user can manually choose some parameters of an extensive dataset to display and analyse. In this paper, the results of the four variants of clustering techniques, namely the Ant Colony Optimization (ACO), Gaussian Mixture Model (GMM), K-Power Means (KPM), and Kernel-Power Density-Based Estimation (KPD), in grouping the wireless multipath propagations, are evaluated through the use of a GUI. The accuracy performance of each clustering algorithm can be obtained by choosing in the GUI the corresponding channel scenario that the user would like to investigate. A deeper analysis of the clustering characteristics can also be done by selecting other parameters in the GUI. This selection gives a better understanding of the behaviour of each clustering technique and provides an effective way of presenting and analysing the different sets of data.

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.