Artificial intelligence (AI) has transformed smartphone use and enhanced the entire mobile experience. AI integration in mobile phones has become a game changer for the future of technology. This paper evaluates the factors that influence AI smartphone continuance usage intention. The study is anchored to technology continuance theory (TCT) with additional factors. Data were obtained from 745 Gen Z from various cities in Metro Manila, Philippines, and examined using partial least squares structural equation modeling (PLS-SEM). The study found that utilitarian and hedonic value, confirmation, and perceived usefulness had a positive influence on satisfaction, which was the most significant predictor of AI smartphone continuance usage intention. Further, personal innovativeness and attitude of users had a significant influence on continuance usage intention. Surprisingly, perceived usefulness and AI smartphone continuance usage intention had an insignificant relationship. The study contributes as the first empirical investigation of the continuance usage intention of AI smartphones among Gen Z in a developing nation like the Philippines.

This paper seeks to predict a consumer's green purchase intention and classify them as green consumers or not through a set of cognitive and behavioral factors. Data were obtained from 526 Generation Z in the National Capital Region (NCR), Philippines, and evaluated through various machine learning techniques, namely, Decision Trees, Random Forests, Gradient Boosting, XGBoost, K-Nearest Neighbors, and Support Vector Machines. Various performance metrics were used to validate these models. The findings show that most of the models achieved above 80% classification performance. Further, the study revealed that perceived behavioral control, green perceived value, and social media impact were the most crucial factors of green purchase intention, followed by environmental consciousness, green perceived quality, and environmental knowledge. Interestingly, green self-identification achieved the lowest rank, which suggests that green purchase intention among Filipino Generation Z is driven more by practical and psychological factors than just environmental awareness and identity. Finally, theoretical and practical implications are offered at the end of the study.

This study addresses the challenge of identifying students at risk of academic underperformance in a BS Information Technology program. Using a predictive analytics framework aligned with the Philippine Computer Society’s Information and Computing Accreditation Board (PICAB) Criterion 3 on Student Outcomes, a decision tree model was developed in Python using Google Colab. The dataset included grades from key academic indicators such as OJT, Capstone, GPA, Programming, Math, Ethics, and Communication. The trained model achieved an accuracy of 83.33%, effectively distinguishing patterns of academic risk. Specifically, students with Capstone grades of 4.00 or higher, or multiple failing grades in core subjects, were frequently classified as "At-Risk." These findings provide actionable insights for academic intervention, curriculum refinement, and program enhancement. The research supports evidence-based decision-making and contributes to Sustainable Development Goal 4 which is Quality Education by promoting inclusive and data-driven approaches to student success.

Early disease identification and detection have been an interest of experts to enhance productivity and performance in agriculture. This study aims to use deep learning algorithms to classify sugarcane diseases using leaf images. Deep learning algorithms are implemented to create models that can classify sugarcane diseases using 16,800 images of training data, 4,800 images for validation tasks, and 2400 images for testing. Results show that the InceptionV4 algorithm outperforms other models in classifying sugarcane leaf diseases at 99.61 accuracy. Different models such as VGG16, ResnetV2-152, and AlexNet achieve high accuracies of 98.88%, 99.23%, and 99.24%, respectively. Hence, this study provides evidence that deep learning models can perform better in classification problems. This study suggests some improvements to further its contribution.

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.