Authors - And Julie D. Rosal Abstract - This study checks on emerging research trends and conducts a bibliographic analysis on smart waste bins as foundational elements for cost-effective, automated waste management solutions. The escalating urbanization and environmental pressures necessitate advanced waste management technologies, where smart trash bins equipped with IoT (Internet of Things), AI (Artificial Intelligence), and robotics offer transformative solutions such as automated segregation, real-time monitoring, and optimized collection routes. Employing a systematic literature review facilitated by Scopus AI, the study identifies consistent, rising, and novel themes in smart bin research. IoT-enabled systems consistently emerged, underscoring their role in operational efficiency and sustainability. Rising trends prominently featured AI integration, robotic automation, and advanced waste segregation methods, indicating a clear shift towards intelligent automation. Security and privacy issues constitute novel themes, reflecting the growing importance of ensuring trust and reliability in technology adoption. The bibliometric analysis revealed significant global research contributions with notable peaks aligning with heightened global sustainability goals. Additionally, the study proposes a conceptual prototype integrating sensors, AI-driven categorization, and autonomous collection mechanisms, validated through simulation testing. Findings demonstrated improved operational efficiency, higher recycling accuracy, and substantial cost savings. Addressing integration challenges, infrastructure compatibility, and public acceptance, the study provides critical insights for urban planners, policymakers, and stake-holders. This work significantly contributes to the discourse on sustainability and smart city initiatives, highlighting technological innovation as pivotal in solving contemporary environmental management issues.
Authors - Akheel Mohammed, Sameera Khanam, Ayesha, Mohammed Shah Alam Rasool Khan Abstract - The proliferation of cloud computing presents a significant challenge for enterprises: entrusting mission-critical data to remote service providers. The widespread adoption of cloud services is fundamentally hampered by a lack of robust and reliable trust evaluation mechanisms. To address this critical obstacle, this paper introduces a novel reputation-based trust model designed to accurately assess the trustworthiness of Cloud Service Providers (CSPs). Our proposed model uniquely integrates three key metrics: direct customer feedback, historical server rejection rates, and real-time server workload. A specialized trust evaluation algorithm processes these inputs to generate a comprehensive reputation score. Experimental results validate the efficacy of our approach, demonstrating a more efficient and accurate evaluation of CSP trustworthiness compared to existing models.
Authors - Ashwini Matange, Jibi Abraham Abstract - Major component of research is searching for relevant publications in the domain of research from varied sources, exploring them, organizing them and finding interconnections between them. Different platforms ranging from Google Scholar, PubMed, IEEE Xplore, ScienceDirect, Springer are to be visited and explored to find research papers in the relevant field of the researchers. Research Rabbit is a free to use literature mapping tool that provides a one stop venue to locate papers of interest effortlessly with effective visualizations and smooth navigation.
Authors - Seema Shrawne, Aishwarya Jadhav, Saniya Gupte, Sayali Panch, Sakshi Thombre, Vaibhav Dhore, Vijay Sambhe Abstract - Accurately detecting and counting fruits is crucial for improving yield estimates in Precision Agriculture. However, challenges such as varying fruit types, overlapping objects, and cluttered backgrounds make this task difficult. In this study, we use RetinaNet, a deep learning model known for detecting dense objects, to address these issues. By replacing its ResNet50 backbone with SEResNet18, we improve both accuracy and speed, increasing mAP by 1.65%at IoU 0.5 and 1.79%at IoU 0.7. We also fine-tune the model using Differential Evolution to optimize anchors, further boosting mAP by 0.61% and 7.3% at IoU 0.5 and 0.7, respectively. Our enhanced model performs better on KFuji apples, mangoes, and holly fruits, while the default model slightly outperforms ours on strawberries and oranges at higher IoU thresholds.
Authors - Meeradevi, Maria Rufina P, Prathik B, C S Parthasarathy, Janya V, Neeha Ritvika Mandava Abstract - PathoVision is an AI-powered framework that enhances cancer diagnosis by integrating histopathology images with clinical text using multimodal learning. Unlike traditional systems focused solely on image analysis, it employs the PLIP model to generate combined visual-text embeddings for more accurate and contextual understanding. A custom decoder pro- cesses these embeddings to classify cancer subtypes, while Grad- CAM provides interpretable heatmaps, ensuring transparency in decision-making. Designed for use in clinical diagnostics, research, and education, PathoVision addresses the limitations of manual pathology—such as subjectivity and time constraints—by offering a reliable, interpretable, and accessible solution. This framework represents a significant advancement in computational pathology, blending modern AI with traditional diagnostics to support expert pathologists and improve patient outcomes.
Authors - And Julie D. Rosal, David Livingstone T. Ramos, Randy Joy M. Ventayen Abstract - This study explores the quality assurance (QA) practices of Pangasinan State University (PSU) from 2016 to 2025, emphasizing key strategies that enhanced its national and global reputation. Using a multi-method approach—including literature review, surveys, and interviews, examines stake-holder perceptions, institutional challenges, and achievements in quality assurance recognition, and improved international rankings. The findings highlight the importance of strong leadership, continuous improvement, faculty development, and stakeholder engagement in fostering a culture of quality. These elements have strengthened PSU’s quality management system and its responsive-ness to academic and institutional demands. As a forward strategy, the study presents a five-year roadmap for 2026–2030 focused on sustaining quality through digital transformation, global benchmarking, research-driven planning, and performance-based evaluation. This roadmap is intended to solidify PSU’s status as a globally competitive university and offers practical insights for other state universities aiming to institutionalize QA and improve their academic standing at both national and international levels.
Authors - David Livingstone T. Ramos Abstract - Quality assurance (QA) in higher education is a critical mechanism for promoting academic excellence, institutional ac-countability, and societal impact. State Universities and Colleges (SUCs) in the Philippines serve as gateways to education for under-served regions but face significant challenges in maintaining and im-proving quality due to the absence of standardized frameworks, reliance on manual processes, and limited technological integration. These issues hinder their efficiency, compliance with accreditation standards, and ability to respond to evolving educational demands. This study examines the role of a Quality Assurance Information System (QAIS) as a solution for addressing these challenges. Employing a literature review methodology, it evaluates current QA practices in SUCs and identifies gaps, such as fragmented processes, inconsistent standards, and insufficient data management capabilities. The research emphasizes the potential of QAIS to streamline processes, automate routine tasks, and provide real-time data for evidence-based decision-making. Key components of an effective QAIS include modular design for scalability, compliance with international standards, user-friendly interfaces, and robust analytics for monitoring and continuous improvement. By implementing QAIS, SUCs can align their practices with global benchmarks, enhance stakeholder engagement, and improve institutional performance. The study also highlights challenges in adopting QAIS, including resource limitations and resistance to change, and proposes strategies to mitigate these barriers. The findings offer actionable recommendations for SUCs to implement and scale QAIS effectively, aligning with national and international quality standards while addressing local needs. By leveraging QAIS, SUCs can enhance their competitive-ness, improve educational outcomes, and contribute to broader national development goals.
Authors - Pravitha N R, Sumedha N Varma Abstract - This study reflects the pivotal role of financial technology (Fin-tech) in reshaping the IT and Corporate sectors while playing a role in achieving sustainable development. Fintech has evolved into a significant role as a game changer, driving innovation in financial contribution through digital platforms, blockchain technology, artificial technology, and big data analytics. In the IT sector, Fintech facilitates secure, efficient transactions and enhances digital infrastructure, leading to improved service delivery and client engagement. Within the corporate domain, it streamlines operations, increases financial inclusion, and supports strategic decision-making. In addition, the integration of Fintech services with principles of sustainability is enhancing green finance and promoting efficient digital solutions. This paper studies the factors affecting the adoption of fin-tech services and how sustainable development goals are achieved by adopting fin-tech services. By using regression analysis, the study expresses that Digital Financial Literacy and Perceived Performance Expectancy have a positive impact on the Behavioural Intention in adopting fintech services. The analysis further states that the actual use of fin-tech services plays a vital role in promoting sustainable development.
Authors - Sasini Gandhari Dabare, Indra Mahakalanda, Prasanga Jayathunga, Nathali Fernando Abstract - Media Mix Modeling (mMM) is a data-driven analytical approach that quantifies the influence of multiple advertising channels on sales performance while adjusting for external factors such as seasonality and economic conditions. This study explores machine learning-based mMM to predict weekly sales performance in the Sri Lankan home appliances industry by combining ad spending variables with external factors such as exchange rates and holidays. The study applies time series decomposition, ad stock transformation, and data normalization to capture delayed and nonlinear media effects. TimeSeriesSplit cross-validation was used to test four predictive models: Linear Regression, XGBoost Regression, Support Vector Regression (SVR), and Bayesian Ridge Regression, along with key metrics for performance (MSE, RMSE, R² Score, MAPE). The tuned XGBoost model had the best accuracy (R² = 0.9145; MAPE = 2.87%), making it most useful for predicting. The findings demonstrate the importance of machine learning-enhanced MMM as an effective strategy for optimizing media spending and increasing advertising efficiency in competitive, budget-constrained markets.
Authors - Hera Khairunnisa, Nayla Nandhita Nuril Hadi, Surya Anugrah, Rochma Sudiati, Windy Permata Suyono Abstract - First objective of this study is evaluating MSMEs tax compliance with Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis. Second objective of this study is evaluating the role of Core Tax Administration System (CTAS) for MSME. The study uses a descriptive qualitative approach, with semi-structured interviews with three tax consultants with extensive experience in MSME taxation. This study also employs the literature review approach. From the interview, this study shows that MSMEs typically have straightforward cost structures given their simple business operations. Government support, such as the reduction of the final tax rate for MSMEs from 1% to 0.5%, is viewed as an opportunity in taxation. However, the study highlights ongoing challenges in MSME operators' tax competence, particularly their understanding of recent tax regulations and literacy in tax matters. This study also shows that the implementation of the Core Tax System serves as a strategic advancement in enhancing tax administration for MSMEs in Indonesia. By leveraging digital integration and automation, the system aims to promote greater efficiency, transparency, and ac-countability in tax processes.
