Academic database is considered as the heart and soul of every higher education institutions. This database contains a vast amount of useful information that is useful for analysis. Algorithms for machine learning play a significant role in mining academic databases and have been proven to be effective when applied in the academic field. Prediction models are made using relevant classification algorithms for dropout analysis. The success of the prediction model depends on the performance of the feature selection algorithm used for dimensionality reduction. The study utilized the Modified Mutated Firefly Algorithm (MMFA) as a dimensionality reduction strategy to enhance the accuracy of the prediction model for dropout analysis. The results of the simulation revealed that the Decision Tree (DT) classifier outperformed the Naïve Bayesian using the three UCI datasets. After the test of benchmark datasets, a students' cumulative dataset was used to come up with a predictive model for dropout analysis of Davao del Norte State College, Davao del Norte, Philippines. The results of the experiment confirmed that the MMFA+DT obtained an accuracy rate of 95.82%, while MMFA+NB only has 92.85% using 10-fold cross-validation.

For effective optimization, metaheuristics should maintain the proper balance between exploration and exploitation. However, the standard firefly algorithm (FA) posted some limitations in its exploration process that can eventually lead to premature convergence, affecting its performance and adding uncertainty to the optimization results. To address these constraints, this study introduces an additional novel search mechanism for the standard FA inspired by the behavior of the scout bee in the artificial bee colony (ABC) algorithm, termed the "Scouting FA". Specifically, fireflies stuck in the local optima will take directed extra random walks to escape toward the region of the optimum solution, thus improving convergence accuracy. Empirical findings on the five standard benchmark functions have validated the effects of this modification and revealed that Scouting FA is superior to its original version.

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The use of Android devices nowadays is almost inevitable. Having been able to get a big slice of the mobile operating systems, Android has become a wide target for malware attacks. Malware detection analysis in this study is done to contribute to the many various ways in doing the malware analysis using classification algorithm using Random Forest and Naive Bayesian. This study used a static method of analyzing and detecting malware applications through the permission requests made by each Android application as analyzed by Virus Total website. This study utilized fifty actual Android samples downloaded from the Internet in which the samples were composed of twenty-five benign apps and twenty-five malware applications.