Over the past decade, several studies have been conducted to discover a better-performing multipath clustering technique. Developing a multipath clustering technique with better accuracy performance is a big challenge considering the varying properties of the multipath propagations that change over time. In this study, several clustering techniques have been investigated and compared to the newly-developed technique for performance analysis. Using the Jaccard score as a metric for the accuracy of grouping correctly the wireless multipaths, the performance of the different clustering techniques has been determined and compared to the newly-developed technique. The proposed clustering algorithm shows improved performance in the indoor channel scenarios but needs further investigation in the semi-urban environment.

E-commerce shows steady growth in the marketplace since it makes the lives of people easier. Today’s generation is more inclined toward convenience in purchasing goods. The COVID-19 shut down many food establishments across the Philippines, resulting in an online bakeries boom. Pastries are one of the most sought goods online, especially with the pandemic surge where physical stores are seldomly open. E-commerce with a recommender system is the trend that helps customers choose products, which helps in decision-making on what to purchase. On the other hand, a mobile application counterpart could increase brand recognition and customer engagement as it is now the most effective, direct, and personalized way to deliver product information. In this study, the descriptive research method was used, with a questionnaire using the functionality, usability, reliability, performance and supportability (FURPS) serving as the instrument for testing the acceptability. The overall quality of the system was given an acceptable rating with a weighted mean of 4.07, indicating that the system’s functions were well integrated, that navigation was simple, performed consistently, and that the system was accessible regardless of device.

This study aims to build upon the authors’ previous work investigating the complex connection relating energy consumption, emission reduction, and economic development. Specifically, it focuses on the Association of Southeast Asian Nations (ASEAN) within the context of post-pandemic economic recovery. The methodology includes implementation of regression modelling using the MATLAB Regression Learner program that utilizes World Energy Council (WEC) Trilemma ratings as input predictors. A range of regression models are utilized and undergo thorough assessment using established metrics, such as Mean Absolute Error (MAE), R-squared Coefficient of Determination, Root Mean Squared Error (RMSE), and Mean Squared Error (MSE). Additionally, practical metrics such as prediction time and training time are considered. Through a comparative analysis of the results achieved by the 2023 model in relation to its predecessor, an evaluation is conducted to determine the suitability of previous model. This assessment leads to the identification of relevant policy implications that may contribute to sustainable energy trajectory of the region. This academic pursuit hopes to enhance the scientific dialogue by integrating empirical research results with policy imperatives, in order to promote the development of ecologically sustainable and economically resilient energy frameworks in the ASEAN region. Based on the results, the key discovery of this study pertains to the ever-changing nature of energy dynamics and the significance of flexible modelling in influencing regional energy strategies. Hence, it is essential for policymakers to remain agile to the ever-changing factors that impact environmental sustainability, energy security and energy equity.

The coffee rust disease threatens coffee production in the Philippines with widespread defoliation and reduced yield. Identifying rust infection and its severity is critical for implementing effective mitigation strategies. As an alternative to recent methods that rely on deep learning approaches, our vision-based approach utilizes Artificial Neural Networks, K-Nearest Neighbors, and Thresholding methods to identify rust infection on coffee leaves and estimate severity, providing a computationally lightweight alternative for agricultural disease management. Twenty-four (24) color and texture features of a collected dataset of coffee leaf images were extracted as inputs for an ANN classifier. The percentage of damage on coffee leaves was determined by comparing the damaged pixels to the total area of the leaf using KNN and thresholding segmentation techniques. Through the use of confusion matrix and RMSE, the decision support system has demonstrated promising results in identifying coffee leaf health and estimating severity of coffee rust infection.

As the educational technology director of our institution, I often find myself at the forefront of discussions surrounding the integration of artificial intelligence (AI) and its impact on our lives. Recently, a former student approached me with a thought-provoking question: "In times of grief and loss, can ChatGPT offer the comfort and consolation we seek?" The weight of this inquiry bore down on me, for I realized that answering it was not a task I could take lightly. I hesitated, acutely aware that I was not a health professional equipped with the expertise to navigate the depths of grief and loss. Moreover, my role as an educational technology director means that I have had the opportunity to witness the transformative potential of AI, leading me to wonder whether I possess a natural inclination to embrace technology as a solution. Therefore, I felt compelled to engage health professionals, the true authorities on matters of emotional well-being and mental health, to join me in an open and honest exploration of this complex question.

Online shopping has become one of the most prominent forms of retail for businesses. This is due to the advancement of web services, and mobile applications that have become accessible, and effective with the utilization of the Internet. Accordingly, this study aims to further scrutinize the discussed application of online shopping. Therefore, an online retail system with mobile application through Android was developed, deployed with the purpose of managing the products, and services that are offered by the company, with the standardization of data forecasting to make accurate prediction of future trends. To standardize and validate the attributes of the said system, a descriptive research method that used a survey instrument based on the Likert scale, and the functionality, usability, reliability, performance, and supportability (FURPS) model. The said survey instrument collected 200 responses with purposeful sampling treatment and converted into distinct inputs with the use of the weighted mean formula. The functionality, usability, and reliability were rated as acceptable, with weighted means of 4.5, 4.5, and 4.5, respectively. The performance and supportability were rated as perfectly acceptable, with weighted mean scores of 4.7 and 4.6, accordingly. The system’s overall attributes were rated perfectly acceptable, with a weighted mean of 4.6, suggesting that it managed and analyzed sales, services, and inventory data.

In the context of higher education, ensuring timely and successful graduation for students is a pivotal objective, necessitating a comprehensive understanding of their academic performance and tailored interventions. Evaluating ongoing academic records is crucial for effective pedagogical interventions, but limited research on student performance in completing degrees has introduced challenges. To address these, academic institutions are adopting flexible curricula designs, prompting the need for diverse course offerings. Amidst this, two student categories emerge: regular and irregular, each presenting unique challenges. A Rule-Based Suggestion Platform for Academic Program Completion was conceptualized, designed, developed, and rigorously evaluated through the use of the Technology Acceptance Model (TAM). This innovative platform, which harnesses the power of rule-based decision-making, was created to address the intricate challenges surrounding students' timely and successful program completion within the academic landscape. The platform's underlying architecture and functionality were crafted to provide students with personalized and optimized recommendations, guiding them towards informed decisions in shaping their educational journey. The development process involved the integration of advanced rule-building mechanisms, enabling the system to analyze individual student profiles, academic progress, and program requirements. This data-driven approach empowers the platform to generate customized study plans that not only consider the students' academic ambitions but also adhere to predefined constraints and parameters. By evaluating the platform's performance through the Technology Acceptance Model, this study assesses the users' perception and acceptance of this novel tool, shedding light on its effectiveness and potential impact on enhancing the academic planning process.

Providing support is one thing, generating an automatic ticket category based purely on the textual data provided is another. This study is working towards encouraging the educational landscape to start integrating AI in further enhancing the way students learn and the way teachers are giving their lessons. The focus of this study is to use the subset of AI that concentrates on making machines understand how humans talk which is known as NLP. By using several Python libraries, 3 text classification algorithms – namely SVM, Naïve Bayes, and logistic regression were used to train the previously collected dataset and choose the model that will be integrated to the web-based helpdesk system called HelpTech. With the help of the model, the system instantly categorizes the issue submitted by the end users resulting to an easier way to use the educational tools available which assist the stakeholders in developing their digital literacy.

Technological advancements and modernization of different industries and disciplines contributed to more consumption of oil and electricity which powers these industries. Aligned with the United Nations (UN) Sustainable Development Goals (SDG), the use of alternative and renewable energy (RE) sources is encouraged as it allows the utilization of clean energy resources and access of populations in developing countries to electricity and energy. Forecasting and maximizing the harvest for renewable energy requires an understanding of the mechanics behind the variables that impact solar photovoltaic production. 755 datasets were created from 150 days of recorded data and used in the model building and sensitivity analysis. The approach used in this study to identify the variable importance of each meteorological variable to the solar photovoltaic (PV) production was the Garson’s algorithm (GA). In this study, an artificial neural network (ANN)-based sensitivity analysis (SA) using Garson’s algorithm (GA) was implemented to identify the relative importance (RI) of the factors influencing the solar PV output including the solar irradiance (SI), rainfall, maximum temperature (MaT), minimum temperature (MiT), relative humidity (RH), and wind speed (WS). The model also considers the relative significance of these parameters to the solar PV output. Results indicate that, with a relative value of 29.48% and 5.01%, respectively, solar irradiance and wind speed are the most and least important factors.

Rainfall is crucial for flood prevention and comprehending the correlation between rainfall and flooding. Cavite province in the Philippines is vulnerable to flooding caused by heavy rainfall and climate change impacts. Early detection of flooding through early warning systems can prevent excessive damage loss and potentially save lives. It can also provide major savings in terms of monetary benefit and increased interagency coordination for rapid decision-making. Machine learning is an important tool for predicting rainfall which can be used to predict rainfall in the province. The objective of this study is to conduct a comparative analysis of various models for predicting daily rainfall, using relevant atmospheric features such as maximum, minimum, and mean temperature, relative humidity, wind speed, wind direction, cloud cover, pressure, and evaporation. The study seeks to identify the most effective model for accurately predicting rainfall in the Cavite Province to benefit the local community. Among the five machine learning models evaluated, the Gaussian Process Regression model demonstrated the highest accuracy in predicting daily rainfall. The findings of this study can be leveraged to mitigate the damage caused by flooding in the Cavite Province and serve as a useful reference for similar studies in other regions prone to flooding.