The traditional use of an electronic spreadsheet is poorly suited to operationalize and manage the visualization and analysis of datasets related to mud crab production in the Philippines because of the long process of constructing equations to produce the desired diagram. A mud crab-specific analytic platform is needed to accurately visualize and analyze the datasets that will eventually result in better and data-driven decision-making on the part of farmers and stakeholders. This paper introduces the design and development of a state-of-the-art Mud crab Analytics Platform (MAP) utilizing the K-means algorithm. A holistic system development approach was applied which consists of three phases namely; 1) capture, 2) consumption, and 3) integration and transformation. As a result, MAP was able to provide significant insights in terms of descriptive, predictive, and prescriptive analytics. This real-time and dynamic analytic platform may provide farmers and stakeholders a powerful means to conduct data analysis on a community level to obtain actionable insights.

This study provided the concept of applying information technology in the aquaculture sector that offers an appropriate growth management system for mud crabs (Scylla spp.) through the utilization of a patented device and software application known as Information and Communication Technology-Assisted Growth Management System (ICT-AGMAS). This utility model (UM) is intended for real-time monitoring of the morphometric development of mud crabs. The automated nature of this technology avoids the farmers from manually checking the crabs within the pond, thus saving time and labor costs. To test its performance, the system was deployed at one of the polyculture aquaculture farms at San Juan I, Ternate, Cavite for five months. A comparison of the actual number of sampled crabs per unit time between the conventional sampling method (manual) and ICT-AGMAS was made by applying deep learning. Evaluation results revealed that the system can offer a faster and easier way of sampling mud crab with a sampling time difference of 143.25 seconds compared to the conventional sampling protocol. In addition, statistics and analysis of the use of the system disclosed a positive user acceptance of applying the technology based on identified descriptive variables. Estimated financial parameters such as payback period, the return of investment (ROI), and net present value (NPV) were also found to be positive and viable.

Resource centers of gender and development (GAD) in local government use the traditional method of disseminating information about GAD awareness, such as distributing printed campaign materials and conducting gender sensitivity training (GST) on faculty and staff, students, and selected barangay communities in the Philippines. Some recipients of campaign materials are text-heavy and unappealing to read, which makes them less interested. However, faculty and students conducting research are not aware if their study is gender-responsive or if GAD is invisible. Hence, this study examines the user acceptance of the GAD app mobile application using the modified technology acceptance model (TAM) with a machine learning (ML) algorithm applied. The results of statistics and analyses from the intended users (N=100) were presented including data-driven modeling using a support vector machine (SVM) to show precise findings for the research on how this technology was used and accepted. The study’s findings show widespread acceptance among experts and users of the mobile application employing external factors like self-efficacy (SE) and specific anxiety (SA) and moderating variables such as age, sex, highest educational attainment (HEA), and knowledge in GAD implementation, which are crucial for predicting and understanding the consequences of the research made clear.

Many data accessed from different information systems are commonly not clear on how these interpret in a meaningful way to produce concrete information and how to embrace this innovation that could be used for a decision-making approach. Hence, this study involved predicting the occupation progress of a person using decision tree-based analysis. Using the Information System for Sex-disaggregated Data (ISSDD) platform, the datasets of residents' information were collected and cleaned for confidentiality. This involves the recording condition of inhabitants that could give an action by the government to improve the quality of life in their communities. Hence, the prediction made in this study analyzed the occupation progress of a resident based on their age, civil status, and highest educational attainment. The data mining approach was used to develop new knowledge for the in-depth interpretation of data such as identification of the source of information, collection of data points to be analyzed, extraction of relevant data content, classification of key values from the extracted data set, and evaluation and recording of results. The percent accuracy of the confusion matrix is 77.69% where 90.48% of single residents have a chance to be a success in their occupation over the 71.59% for the married.

This study investigates user acceptance of the IBON Monitoring system, a mobile app that uses image recognition to identify bird species. Using the Technology Acceptance Model (TAM), it surveyed 100 faculty and students at Romblon State University to assess factors like perceived usefulness, ease of use, computer literacy, and self-efficacy. Results showed that usefulness and ease of use significantly influence user attitudes and intentions. The findings suggest actionable recommendations for improving IBON system adoption, including training programs to enhance computer literacy and self-efficacy and strategies to demonstrate the system’s relevance to user needs. Future research should explore additional external factors, such as cultural influences and user experience design, and conduct longitudinal studies to assess sustained use and impact on biodiversity monitoring outcomes. This study underscores the importance of fostering user acceptance to maximize the potential of innovative technologies like IBON Monitoring in advancing biodiversity conservation efforts.