Part of speech tagging (POS) is crucial in natural language processing, identifying the grammatical categories of words in sentences. This research highlights the lack of focus on POS tagging for Asian languages, particularly Cebuano. Inadequate research on Cebuano religious text has hindered linguistic documentation and understanding its grammar and vocabulary. This study introduces a Parts-of-Speech Tagging for Cebuano utilizing a Modified Viterbi Algorithm. The researchers also apply a method for handling unfamiliar words. Results indicate that the algorithm performs exceptionally well on a religious text corpus comprising 50,000 datasets, achieving an accuracy of93%,precision of90%, recall of 90. 52%, and an F1-score of92%. These results highlight the algorithm's effectiveness in tackling language challenges within specific genres. Furthermore, the research supports the Sustainable Development Goals (SDGs) by promoting linguistic diversity and advancing inclusive language technologies. The study also provides valuable insights into Cebuano's linguistic characteristics and grammatical structures, laying a solid foundation for future research in natural language processing.

This research study employed machine learning algorithm in This research study employed a machine learning algorithm in predicting student academic performance in the Data Structures and Algorithm (DSA) course which is based on student lifestyle to analyze the factors that affect the high or low performance result. A total number of 251 Bachelor of Science in Computer Science (BSCS) students participated in the study where 207 or 82% were male and 44 or 18% were female. A oneshot case study was conducted that led to data collection through the administration of an online survey on former enrollees of the said course. The dataset was extracted with 43 features and was analyzed using Python on Jupyter Notebook. Randomly selected 70% of these, 176 observations, are used to train the classifier models. The remaining 30%, 75 observations, were used as the test data. In order to classify academic performance students, eight machine learning algorithms were applied based on random forest (RF), decision tree (DT), support vector machines (SVM), K-nearest neighbors (KNN), logistic regression (LR), Gaussian Naive Bayes (GNB), stochastic gradient descent (SGD), and perceptron. Although SGD and Perceptron classifier models show comparably low classification performances, both random forest and decision tree classifiers provided the highest metric performance. The study indicated that the lifestyles of students contributed to whether the student performance became high or low in their grade performance.

Computer programming is now one of the most critical competencies taught in computer courses. [1]. Students require any assistance they can get when learning programming in order to acquire the necessary abilities to excel in the field of computing [2]. This paper aims to investigate the C compiler error logs of Computer Science freshmen students. A prototype was developed and pilot-tested to obtain C source code snippets focusing on assignment statements. The dataset consisting of 1013 logs were extracted from the initial prototype then followed the data science approach of [3] for pre-processing. A Person correlational analysis was conducted on eight features to investigate the relationship between all variables in the dataset. Results of the study show that there is a strong relationship between wrong expression and operator (0.806), wrong expression and numeric value (0.794), operator and numeric value (0.663). Implications of this study is also helpful to computing instructors to improvise the delivery of their teaching pedagogy.

Computer programming is one of the fundamental skills in the field of computing [1]. In a computing class, students are expected to learn the skills rather than remembering materials only. This study aims to develop a constraint-based student model (CBM) by analyzing the computing students' C compilation error logs. The proposed modified CBM will be used as input to develop a user behavior of an ongoing study for an intelligent tutoring system. The prototype was developed to obtain compilation error logs from the selected students, it contains five (5) C programming questions that focus on assignment statements. The prototype of the study was pilot tested on two (2) online programming classes with a total of thirty-one (31) freshman college students composed of nine (9) BSCS and twenty-two (22) BSIT participants with a mean age of 18.68, where nineteen (19) or 61.3% are males and twelve (12) or 38.7% are females. The study uses convenience sampling to determine the total number of student participants. The dataset was extracted from the prototype and feature identification was performed on one thousand thirteen (1013) C programming logs which resulted to obtain eight (8) error types. The paper of Khodeir, Wanas, & Elazhary (2018) [2] and Karaci (2018) [3] on constraint-based modeling was reviewed to develop a proposed constraint-based model in the context of C programming focusing on assignment statements. By mapping a student error on the suggested constraint relevance (Cr) and constraint satisfaction, the database for constraints was finished (Cs).

Complete Blood Count is one of the most commonly performed medical laboratory procedure today. It is required to detect various types of diseases. Presently, some small-scale clinics in the country still does the tedious, manual method of counting the blood cell. With Complete Blood Count Analysis System through Image Processing, automated CBC can be performed by mounting the smart phone camera on the viewer of the microscope. The input image will go through several image processing algorithms such as: Binary Thresholding, Clustering, and Hough Circle Technique. The result will be computed through the formulas used in the manual method of the CBC process. Experimental results show the developed system gains 94% of accuracy for counting the Hematocrit, Hemoglobin, Red Blood Cell, and White Blood Cell values.

Flood is imminent when heavy rain occurs, identifying the level of water in plain sight is difficult to achieve. There are currently available ways to detect flood water but usually are very expensive and needs a huge equipment with sensors. The research has proposed an alternative solution to expensive ways on detecting flood and water levels. The study created an application to detect body of water by using image processing technique called Region-based segmentation algorithm to detect water on the image and Canny Edge Detection with computation using Pixel Ratio on a selected water region to determine the height of the water or flood. A CCTV camera was used to capture the image and was fed on the application through the network infrastructure. Once captured, the image was processed to detect the body of water and measurement of its level. The testing of the application was done on a controlled environment and the application was able to detect the water body on the picture. It was able to detect the edge of the water based on a selected region where the water is found. The measurement of the actual height of the water, closely matches the height of stated in the application. Thus, the research has found a way to detect body of water and gauge its water level using image processing, in which, have found a way to detect and measure water affordably. This research can be a step, in future research like monitoring the streets’ flood level when heavy rains occurs. This is a much more safe and affordable way to monitoring the increase and decrease of flood.

In this paper, we analyze the effect of the representative spectral indices, normalized difference vegetation index (NDVI), normalized difference water index (NDWI) and normalized difference built-up index (NDBI) on classification accuracies of Landsat-8 OLI image. After creating these spectral index images, we propose five methods to select the spectral index images as classification features together with Landsat-8 OLI bands from 1 to 7. From the experiments we observed that when the spectral index image of NDVI or NDWI is used as one of the classification features together with the Landsat-8 OLI bands from 1 to 7, we can obtain higher overall accuracy and kappa coefficient than the method using only Landsat-8 OLI 7 bands. In contrast, the classification method, which selected only NDBI as classification feature together with Landsat-8 OLI 7 bands did not show the improvement in classification accuracies.