The graduates in every institution reflect the skills developed and competencies acquired by the tudents through the education offered by the institution that is suitable in the companies. Employability of graduates becomes one of the performance indicators for higher educational institutions (HEIs). Therefore, it is important to accentuate the employability of graduates. This is the reason why this research is being carried out. This study involved twenty-seven thousand (27,000) information consist of three thousand (3000) observations and twelve (12) features of student's mock job interview evaluation results (MJI), on-the-job training (OJT) student's performance rating and general point average (GPA) of students enrolled in the on-the-job training course of SY 2015 to SY 2018. To address the issue in imbalance datasets where the minority class, the researchers used synthetic minority over-sampling technique (SMOTE) were applied in this study to address the issue in imbalanced datasets Six learning algorithms with SMOTE were used such as Decision Trees (DT), Random Forest (RF), and Support vector machine (SVM), K-Nearest Neighbor (KNN), Logistic Regression (LR) to understand how students, get employed. The six algorithms were evaluated through the performance matrix as accuracy measures, precision and recall measures, f1-score, and support measures. During the experiments, Support Vector Machine (SVM) obtained 91.22% inaccuracy measures which were significantly better than all of the learning algorithms, DT 85%, RF 84%. The learning curve produced during the experiment displays the training error results which were above the one for validation error while the validation curve displays the testing output where gamma was best at 10 to 100 in gamma 5. This concludes that the model produced with SVM was not under fitted and over-fit. This study is very promising which leads the researchers to be motivated to enhance the process and to validate the produced predictive model for further study.

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The current system of checking and grading egg quality in the Philippines was done manually one by one using the traditional way where graders exert great effort that resulted in graders' visual stress. To address the problem identified the researchers proposed a scientific way of checking and grading the egg quality by using image processing based non-destructive and cost-effective technique to detect various cracks, dirt, and defect in eggs. Upon testing, the system obtained a total of 91.33% as high-quality eggs and the presence of either crack or dirt while 8.66% were inspected as low quality. For the internal part of each egg, the system achieved 100% detection of the yolk. The main results achieved have been quite promising; the researchers are encouraged to continue the labor of improving the generation of internal and external egg detection.

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Many kinds of research focused on the flood detection and monitoring, flood management, flood risk management and flood forecasting in urban areas, wherein a large number of populations lies chaos in mobility is high. Owing to natural disasters, flooding in these regions can lead to an increase in mortality rates. This project is primarily focused on the detection of a flood by installing a flood detector device with a camera beside the bridge column. The camera is facing the three lines with different colors. If one of the colors was tempered by the river water, the device will send an alarm to the community that the water level in the river is high. This aims to alert the community and the authorities to be aware and be ready for the approaching flood. Flood-Level Detection and Alert System proved 87.1%, 73.6%, and 95.69% testing accuracy of Green, Blue, and Red respectively. Overall, the accuracy of the whole system produced 85.46%.

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