Abstract : Entrepreneurship is helping drive local economic growth in the Philippines, but many new business owners find it hard to meet regulatory requirements and pick the right business locations. These issues are especially common in the food service sector, where several government agencies enforce strict legal, zoning, and health rules. Although spatial analysis and the Gravity Model are often used in urban planning, public health, and market analysis, there is still a lack of decision-support systems that combine regulatory compliance and location recommendations for entrepreneurs at the local government level. This study developed a web-based recommendation system that reviews business regulations and finds the best business locations using the Gravity Model. The system gives entrepreneurs a list of regulatory requirements based on their chosen business type and suggests sites in Janiuay, Iloilo. System Development Life Cycle (SDLC) and gathered data from municipal agencies, population and spatial records, and business guidelines. The system was evaluated using ISO 25010 Software Quality Standards. Results show that the system works very well in terms of functionality, usability, security, reliability, and performance. This proves it can help entrepreneurs with legal compliance and choosing business locations. The system also adds value to both research and practice by combining spatial data analysis with regulatory automation. It offers a tool that supports evidence-based entrepreneurship, helps local governments make decisions, and improves business planning..
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Abstract : This study developed an intelligent decision-support tool, the Student Track Recommendation System Using Data-Driven Methods, to identify the best specialization track fit for incoming third-year Information and Communications Technology students based on their theoretical and practical examination results. Realizing that the choice of specialization is an essential academic decision that often receives limited guidance and thus relies heavily on subjective judgment, this system will analyze patterns of student performance using data-driven techniques, namely collaborative filtering and content-based filtering, to develop personalized, objective recommendations. To achieve this goal, it involves data gathering, preprocessing, algorithmic modeling, system design, and implementation to ensure the system supports structured, evidence-based analysis. This systematic assessment of students' competencies will help improve academic decision-making, support the faculty and the administration in determining readiness for specialization, and foster a more transparent, data-informed guidance process. The present study aims to contribute to the field of educational technology by demonstrating the value of implementing data-driven methodologies to facilitate improved student placement and further align academic pathways with demonstrated strengths. Other variables to be used in further development include soft skills, experiential learning, and labor market insights, to further increase the precision of recommendations and the system's adaptability..
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Abstract : The Philippines' agriculture, particularly crop cultivation, plays a crucial role in the economy. Still, many farmers face poverty, with a significant portion struggling due to a lack of education and resources. This leads to the rise of middlemen who buy crops from the farmers at low prices and sell them at a much higher price. Also, even though agricultural production has improved, farmers in the country still have trouble getting goods to consumers directly, limiting their income and access to fair prices. To solve these problems, the researcher developed a web-based farmer-to-consumer marketplace designed to facilitate direct transactions between farmers and consumers, limiting the involvement of middlemen. It applied an enhanced item-based k-nearest neighbor (k-NN) algorithm with weighted metrics (ratings, purchases, and searches) to generate personalized product recommendations for consumers to help them make better decisions in terms of product reducing decision fatigue and giving them direct access to fresh crops..
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Abstract : Soil used in earthwork is typically rated as “excellent-to-good” when it effectively supports building foundations, embankments, pavements, and other civil infrastructure. Expansive clays are problematic soils due to their tendency to swell and shrink under varying moisture “conditions”, and represent a significant challenge in geotechnical engineering, often resulting in considerable economic losses for construction projects. These soils exhibit high vulnerability to failure mechanisms, including excessive settlement and subgrade instability, which exacerbate their performance issues compared to other soil types. Given the scarcity of suitable construction sites, improving the engineering properties of these soils is imperative for their application in construction projects. Stabilization techniques play a crucial role in enhancing the characteristics of native or granular soils used in pavement layers. This study presents an experimental investigation into the combined effects of admixtures, specifically RoadCem and cement, on the geotechnical properties of expansive soils. The findings revealed that the incorporation of these stabilizers resulted in an increase in optimum moisture content (OMC), a decrease in maximum dry density (MDD), and significant improvements in unconfined compressive strength (UCS) and California Bearing Ratio (CBR)..
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Abstract : Across all industries, digital service platforms have a significant problem from customer attrition. In order to improve churn prediction accuracy and cross-domain generalization, this paper suggests a cross-domain deep learning architecture that makes use of transfer learning. Through knowledge transfer across several industries, including food delivery, streaming, and mobility services, the suggested method enhances predictive performance in areas with less labeled data, allowing for more resilient and flexible churn control solutions. The first dataset used to train a Fully Connected Neural Network (FCNN) had 388 samples and 55 characteristics related to food delivery. The trained model was then applied to two different domains to assess its generalization ability: Uber (50,000 samples, 14 features) and Netflix (1,000 samples, 26 features). Training was conducted using the Synthetic Minority Oversampling Technique (SMOTE) to rectify the imbalance in classes. Significant performance improvements across domains were shown by the transferred model. With a churn recall of 0.79 and an accuracy of 66.73% on the Uber dataset, it outperformed XGBoost and CatBoost by 16.2% and 41.1%, respectively. The recall for Netflix was 1.5% better than CatBoost and 23.2% better than XGBoost. With a churn recall of 0.90 in the source domain (food delivery), the model outperformed XGBoost and CatBoost by 15.4% and 5.9%, respectively. In target domains, the suggested FCNN with transfer learning improved churn recall by up to 10.1%, consistently outperforming both baseline and hybrid models. Because it allows for the early identification of at-risk clients utilizing information transferred from related domains, this method is especially advantageous for sectors with minimal labeled data. In society's expanding digital economy, the concept helps to improve service continuity and minimize corporate losses by increasing proactive client retention..
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