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.

Generative AI is transforming digital creation by providing artists with tools that enhance efficiency and unlock new creative possibilities. In multimedia arts, including the growing digital art scene in the Philippines, AI-powered platforms are being used to accelerate workflows, automate tasks, and introduce innovative artistic techniques. While these advancements enable Filipino digital creators to experiment and increase productivity, they also raise concerns about originality, authenticity, and the diminishing value of traditional artistic skills. This study examines student perspectives on AI's impact using aspect-based sentiment analysis. The findings reveal that while students appreciate AI's accessibility and time-saving benefits, many remain cautious about its long-term influence on artistic growth. The Diffusion of Innovations Theory was applied to analyze AI adoption, highlighting both its advantages and challenges. Ethical concerns, such as copyright issues, fair attribution, and the potential devaluation of human-created art, continue to spark debate. These results emphasize the importance of an educational approach that integrates AI as a supportive tool rather than a replacement for human creativity. For Filipino digital creators, especially those in animation, graphic design, and content creation, it is crucial to balance AI's advantages with a strong foundation in artistic expression. Future studies should explore how AI can be leveraged to empower artists while preserving the unique cultural and creative identity of Filipino multimedia arts.

The accelerating integration of artificial intelligence (AI) within the field of integrated marketing has prompted a reevaluation of the competencies required of future creative professionals. This study examines the preparedness of multimedia art students in the Philippines to engage with AIenhanced marketing environments. Anchored in the TaskTechnology Fit (TTF) Model, the research investigates students' familiarity with and attitudes toward key AI applications, including automated audience targeting, personalized content delivery, and real-time campaign optimization. The findings indicate a growing awareness of AI's relevance; however, this is accompanied by critical gaps in training access, institutional infrastructure, and ethical literacy-particularly in areas concerning data privacy and algorithmic fairness. While respondents exhibit cautious optimism regarding AI's capacity to augment creative and strategic functions, the study underscores the urgent need for curriculum development, crosssector collaboration, and ethical guidelines to support humanAI collaboration. These efforts are essential to cultivating a resilient, future-ready marketing workforce capable of navigating the evolving dynamics of AI-driven industries.

This study investigates the dating app experiences of queer Filipino women during the COVID-19 pandemic to address the lack of representation and understanding of their unique narratives. Using a qualitative case study approach grounded in intersectional feminist methods, the research involved in-depth interviews with seven self-identifying queer Filipino women. Four key themes emerged: exploration and personal growth, app functionality and user experience, challenges in virtual interaction and safety, and shifting social dynamics. These findings were transformed into a five-episode motion graphics series, integrating visuals, audio, and aesthetics rooted in the research to offer a holistic presentation of the topic. The study highlights how dating apps, while promoting inclusivity and freedom of gender identity expression, continue to be influenced by traditional patriarchal structures. It also emphasizes the potential of multimedia as an innovative learning framework to amplify marginalized voices, particularly through participatory production methods for authentic representation. Further exploration of digital dating across various socioeconomic backgrounds and the continued development of inclusive educational tools are recommended to deepen the understanding of queer Filipino women's experiences within the evolving digital landscape.

The construction sector is a substantial generator of waste and carbon dioxide emissions worldwide. The use of sustainable materials in construction could minimize its negative effects on the environment. This research is intended to offer a soft computing model for predicting the deformation and parallel bending strength (PBS) of Guadua angustifolia applying an artificial neural network (ANN)-particle swarm optimization (PSO) approach and employing the data obtained from the experimental tests performed in the study. The input parameters (IP) utilized in the modeling process include the outside diameter, wall thickness, minimum length, external taper, perpendicular distance of bow, ISO ovality, eccentricity, actual shear span, area, modulus of elasticity, density, and linear mass. The resulting models showed R values of 0.99076 and 0.99976 and MAPE of 0.936% and 0.345% for deformation and PBS, respectively. The findings of the sensitivity analysis (SA) also exhibited that ISO ovality and eccentricity were the most important parameters to the deformation and PBS models. Research outcomes demonstrated the effectiveness of the ANN-PSO approach for predicting the deformation and parallel bending strength characteristics of Guadua angustifolia. The modeling approach proposed in this study could be utilized for speeding up the material characterization phase of similar construction materials.

As part of economic progress, there has been a surge in construction projects in the past few years. It is known that construction has negative and detrimental effects on the environment. The use of sustainable practices needs to be integrated into the construction processes to minimize these negative impacts. This paper introduces a prioritization method for determining the most influential factor to the implementation of environmental smart guidelines for sustaining environmentally friendly programs using sensitivity index (SI) method. Several areas were considered in this study including environmental, ecological, social, and economic impacts. Using the backpropagation-neural network (BPNN) modeling, four models for environmental, ecological, social, and economic impact ratings were developed with 15-31-1, 4-9-1, 9-19-1, and 2-5-1 network topologies (input neuron-hidden neuron-output neuron) for environmental, ecological, social, and economic impact rating, respectively. The R values for the models were observed to range from 0.97652 to 0.99901. To determine the trend of the impact of the subsets of each areas, sensitivity index method was used and the findings revealed that the water pollution reduction in the project is the most influential subset to the environmental impact rating, presence of planting area in the project for the ecological impact rating, fair sharing of benefits of the project for the social impact rating, and self-liquidation capacity of the project for the economic impact rating. The results of the study could assist managers and planners in addressing key areas and concerns in the effective implementation of smart and sustainable practices in projects.

The world is rapidly changing and experiencing a rapid increase in population, especially in cities and urban areas. The growth in population in these urban areas results in a need for a more competitive and sustainable system. In the onset of the fourth industrial revolution, the trend in equipping these cities with advanced mechanisms in improving the quality of life and service in these cities is needed. In this study, a neural network - based approach for factor prioritization was implemented to determine the most influential factor in the smart city (SC) development implementation in the Philippines. Using the neural network internal characteristics including the Levenberg-Marquardt (LM) as the training algorithm (TA) and the hyperbolic tangent sigmoid (HTS) as the transfer function (TF). The study utilized the 18-37-1 network structure for the neural network model with an R value of 0.95003 and MSE of 0.032609. The connection weights (CW) from this network were utilized to calculate the relative importance (RI) of the factors affecting the smart city implementation through Garson's Algorithm (GA). The results of the study revealed that the most influential parameter (MIP) to the smart city implementation is the SCPD2 - analyzing solutions fit with strategic objectives. Moreover, the results and findings of the study could assist the city planners and SC strategy development authorities in the integration of different systems in the SC implementation.

The rise of digital platforms has led to a massive influx of textual data. While traditional textual analysis techniques have been effective, analyzing large datasets is becoming impractical due to the required time and resources. To demonstrate the usefulness of text mining as an alternative, this study analyzed data extracted from an emergency remote learning (ERL) environment. Free-form responses from a series of cross-sectional surveys (2020–2022) were analyzed using word frequency, collocation, concordance, topic modeling, and sentiment analyses. According to the findings, the most commonly occurring unigram and bigram in the text corpus were “hard” and “mental health,” respectively. Three primary themes based on lived experiences were identified, namely individual, academic, and technological challenges, and another three themes emerged from coping strategies, including entertainment, relationship, and health-related mechanisms. Negative sentiment toward the ERL setup was also evident in the text corpus. Overall, the combination of text mining techniques allowed for a comprehensive exploration of the linguistic features of the corpus and provided a multifaceted understanding of the selected phenomenon. Consequently, this study endorses text mining as a methodology for analyzing large volumes of textual data.

Loan management in transportation cooperatives involves the assessment, approval, disbursal, and monitoring of loans for various member needs, ensuring compliance with regulations, and providing support throughout the loan lifecycle. The traditional manual processes and paperwork used in loan management are prone to errors and data redundancy, are time-consuming and inefficient, lack transparency, and cause difficulty in data analysis and reporting, highlighting the need for a proficient Loan Management Website and Mobile Application. This study presents the development of a comprehensive system that efficiently handles data, integrates the credit scoring system, and leverages the FICO score for accurate loan amount recommendations, enhancing decision-making and enabling improved money monitoring, risk management, and record storage. The system's quality was evaluated using ISO 9126's software characteristics such as functionality, usability, reliability, and portability, yielding an overall mean score of 4.71 for the web application and 4.68 for the mobile application, indicating its seamless facilitation of loan transactions. The integration of additional technologies like Decision Support and SMS notifications enhances risk management and financial transparency, fostering increased capital generation for cooperatives, thereby providing opportunities for business growth and sustainability. The developed Loan Management System offers a streamlined approach compared to traditional methods, facilitating faster loan application, approval, and payment processes. Overall, this research contributes to the advancement of loan management practices in transportation cooperatives by leveraging technology, enhancing decision-making processes, and promoting financial efficiency and transparency.

The adoption of information technology in healthcare has resulted in novel solutions for improving patient care and operational efficiency. This study details the design and implementation of a web-based clinic management system augmented with sentiment analysis to evaluate and enhance patient satisfaction. The system utilizes natural language processing and machine learning to autonomously assess patient feedback, understanding feelings (negative, positive, and neutral) regarding critical aspects such as waiting times, doctor-patient interactions, care efficacy, and overall clinic experience. The system underwent alpha and beta testing, commencing with controlled trials and then involving real-world evaluations with clinic attendants, physicians, and patients. An evaluation conducted in a dermatology clinic revealed the system's effectiveness in detecting service deficiencies and informing enhancements. Thus, this study suggests that the integration of sentiment analysis in clinical management systems enhances data-driven decision-making, hence improving patient experiences and optimizing operations.