Authors - Maria Cecilia L. Pangan , Jolou Vincent M. Jala, Ralph Vendel E. Musni, Everly A. Nacalaban, Nenon Roy A. Sandinao, Randy Joy M. Ventayen Abstract - As digital revolution, globalization, and cross-border collaboration re-shape academic landscapes, internationalization of higher education has emerged as a strategic focus for institutions globally. With this, technology-driven innovations particularly learning management systems (LMS), digital platforms, virtual mobility tools and artificial intelligence (AI) have augmented international engagement beyond physical boundaries. Artificial intelligence has immense potential to be a universal technology that boosts product innovation and productivity across a range of industries. This study explores how technological innovations improve internationalization in higher education. Most specifically by how technological innovations improve internationalization in higher education through Technology-Enabled Teaching and Learning, Virtual Mobility and Global Collaboration, Research and Knowledge Exchange, Institutional Governance and Global Competitiveness. Notably, the rapid growth of digital and global academic engagement also causes meaningful implications for students’ and faculty members’ mental health and well-being. This is because when internationalization becomes progressively technology-mediated, matters such as academic pressure, digital fatigue, time-zone differences in global collaboration, and constant online connectivity may contribute to anxiety, stress, and burnout. To attain this goal, the proponents critically examined 156 papers in the body of literature that were indexed by Scopus to examine the advancement of Internationalization in Higher Education through Technology-Driven Innovations Using a systematic review of recent literature, this paper synthesizes global and international perspectives. The findings emphasize that Technology-driven revolutions have redefined the practices and scope of internationalization in higher education. Conversely, obstacles and challenges such as deficient infrastructure, Digital divides, and unfair access to technology deter inclusive involvement and participation
Authors - Gaurav Gupta, Kumar Shashvat, Gunjan Abstract - In India, dengue fever poses a significant threat to public health which continues to worsen. Forecasting methods are crucial to developing effective disease surveillance systems. This study provides an empirical comparison between classical time series forecasting methods, and various machine learning techniques, applied to dengue forecasting for the period of 2013 - 2019 in Chandigarh, India. Seven methods are explored - ARIMA, SARIMA, Exponential Smoothing (ETS), AutoReg, Linear Regression with lagged variables, Decision Tree Regression, and Random Forest Regression. The models are evaluated on multiple criteria which include Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Scaled Error (MASE), and for the statistical models, the Akaike Information Criterion and the Bayesian Information Criterion (AIC/BIC) are used. Random Forest Regression produced the lowest predicted error (MAE 26.95, MASE 0.19), while SARIMA, with seasonal modeling, demonstrated the best and most useful epidemiological interpretability (MAE 45.36, MASE 0.39) of the models. The outcome of the study shows the balance between predictive power of a public health forecasting model, and the interpretability of the model. In this case, SARIMA had the best balance of both and thus, is recommended as the best model for dengue surveillance systems.
Authors - Made Ratih Nurmalasari, Putu Diah Kumalasari, Mirah Candra Adi Saputri Abstract - Through an emphasis on the function of Audit Committee in trying to enhance the correlation of Green Banking Disclosure and also Sustainable Firm Value, this study tried to do investigating how banking firms in Indonesia might have benefits from this practice. As sustainability gets increasingly significant for businesses and also stakeholders alike, banks are under pressure to show transparent of the environmental initiatives. By applying data from Indonesian banks registered in the year of 2021, 2022, and also 2023, this study tended to examine whether banks that actively disclose their efforts of green banking are better allocated to help enhancing their value. The Committee is defined as a moderating variable, shown the significant role to help ensuring good governance and also the disclosures credibility. Data analysis was done by using SPSS with models of multiple regression, as like terms of interaction to help assessing the moderation influences. The outcomes stated that Sustainable Firm Value is greatly improved by Green Banking Disclosure. It is also getting amplified at the time an effective Audit Committee is in place, hoping that good governance is able to increase the influence and also value of sustainability. This research also emphasizes the merging necessity of transparent sustainability measures with strong frameworks of governance to help providing enduring value. The out-comes have actionable information for banking regulators, executives, and also legislators to help integrating sustainability with expansion of corporate.
Authors - Made Ermawan Yoga Antara Abstract - This study is to examine how sustainable MSMEs are impacted by digital innovation and entrepreneurial mindset, mediated by entrepreneurial resilience. This study was carried out in the province of Bali using a quantitative methodology, focusing on MSMEs in the creative economy craft sub-sector. The study sample consisted of 361 MSME owners and leaders selected using proportional random sampling from a total population of 3,745 business units. Data were collected using a Likert-scale questionnaire and analyzed using SEM-PLS with the assistance of SmartPLS software. The results showed that digital innovation and entrepreneurial mindset have a positive and significant effect on both entrepreneurial resilience and sustainability in MSMEs. Additionally, sustainable MSMEs benefit greatly from entrepreneurial resilience. The association between digital innovation and entrepreneurial mindset on sustainable MSMEs is partially mediated by entrepreneurial resilience, according to the mediation test results. Digital innovation has the largest influence on entrepreneurial resilience, while entrepreneurial mindset has the largest direct influence on sustainable MSMEs. These findings emphasize the importance of integrating digital technology adoption and internal entrepreneurial capabilities in driving business sustainability. This research supports dynamic capabilities theory, which emphasizes sensing, seizing, and transforming capabilities in enhancing the resilience and sustainability of MSMEs. Practically, MSMEs need to strengthen digital innovation, entrepreneurial mindsets, and business resilience to adapt to environmental dynamics. This research contributes to the development of sustainable entrepreneurship literature, particularly in the creative economy in developing countries.
Authors - Criscel Jay F. Nayve, Lord Francis B. Navarro,Karen Aparicio Doblas, Elvan Budiongan, Darrel A. Cardana, Max Angelo D. Perin Abstract - This study evaluates online tourist feedback on the Chocolate Hills in Carmen, Bohol, Philippines, using Natural Language Processing (NLP) techniques. Although the destination consistently receives high ratings, negative reviews contain critical insights that can guide tourism management. A total of 4,059 Google Maps reviews were collected, of which 2,011 contained textual content suitable for analysis. The dataset underwent preprocessing using Python and Orange Data Mining before applying sentiment analysis and Latent Dirichlet Allocation (LDA) topic modeling. Results show that, while overall sentiment toward the Chocolate Hills remains strongly positive, negative reviews highlight key concerns related to accessibility, and crowding. Topic modeling identified five dominant themes: scenic appreciation, environmental ambience, crowd density and photo-taking behavior, physical effort required for climbing viewpoints, and perceived cost–benefit value. Sentiment trends from 2020 to 2025 indicate stable positive perceptions despite pandemic-related fluctuations in review volume. Findings suggest that tourists’ satisfaction is primarily driven by the site’s natural beauty, but logistical challenges require targeted management interventions. The study contributes to localized tourism analytics in the Philippines and demonstrates the usefulness of NLP for extracting actionable insights from large volumes of user-generated content.
Authors - Sarvesha Nakharekar, Seedhi Kundap, Suman Madan Abstract - When cyberattacks become ever more extensive and complicated, the demand for intelligent systems capable of executing cyber threat intelligence, digital forensics, and risk management efficiently has increased. We have focused on the important point where digital forensics and cyber threat intelligence meet through this article. In order to build and evaluate the classification models, a publicly accessible intrusion detection dataset was used. The models are Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors, and Multilayer Perceptron .The models were evaluated from the perspective of their probable employment in cyber threat intelligence and forensics, based on their performance indicators such as accuracy, precision, recall, F1- score, and computing efficiency .Through a critical discussion, the article also contains a number of significant problems that have been touched upon: the explainability of the attacks, the existence of adversarial attacks, the data imbalance problem, and the limitations of real time processing. The investigation, however, brings up the possibility of using machine learning based on detection outcomes to improve cyber risk management by threat prioritization and thereby making informed decisions. The document is an essential resource for both researchers and field specialists interested in exploring the use of ML to significantly improve threat forecasting, speed incident handling, and strengthen risk management even in a more and more unfriendly domain of cyberattacks.
Authors - Md Tanzid, Md. Foridul Haque, Md. Ismail Hossain, Mohammad Golam Sarowar Abstract - The rice supply chain has been susceptible to quality deterioration, expiry, and a low level of transparency, which are risks to consumer health and food security. To overcome these challenges, the current research suggests a consortium-based blockchain and IoT-enabled smart contract framework to provide a holistic, traceable, and automated governance model. The framework facilitates a consortium of all key stakeholders in the rice supply chain from farmers to retailers as a blockchain network that is co-controlled and resistant to tampering. At key storage infrastructures, Internet of Things (IoT) sensors are deployed to provide the variable storage conditions (humidity and temperature) in real-time that are important in storing rice. The monitoring variables are sent to smart contracts that generate a two-tiered governance system. Upon data showing that a rice lot reached 90% of its shelf life, the intervening automated process will promulgate notifications. Upon expiration of the rice, monitoring will render a smart contract disables the ability to purchase or distribute in the supply chain. The automated process therefore notifies users of public health risks by preventing the introduction and sale of products deemed unsafe for consumption. The framework ensures the sustainable, validated, and tamper evident functionality for continuous monitoring and rule-based execution of perishable products on a public ledger to facilitate enhanced food governance, to lower food safety risks to consumer health, and to promote consumer trust in the rice supply chain.
Authors - Aksh Modi, Agrim Gairola, Suryansh Shah, Sahil Singh, Malvinder Singh Bali Abstract - The increasing degradation of global coral reef ecosystem heavily needs scalable, automated monitoring solution that are capable of operating in resource constrained underwater ecosystem. Though the ongoing State of the Art approaches, such as Vision Transformer and Efficient Net, achieve high classification accuracy, they heavily suffer from computational latency and power requirement that makes them unsuitable for Autonomous Underwater Vehicles (AUVs) or diver held devices. This paper presents a lightweight, real time detection model using the YOLOv8s-cls architecture, which is optimized for edge deployment. Our model achieves a Top 1 Accuracy of 89.84%, conquering the official NOAA Vision Transformer baseline (85.0%) and recent YOLOv8 benchmark at 88.0% accuracy when tested on NOAA-PIFSC-ESD dataset. Crucially, this performance is achieved with a fraction of the computational overhead, enabling high-frequency inference without reliance on cloud connectivity. These results demonstrate that lightweight Convolutional Neural Networks (CNNs) can outperform complex Transformerbased models in texture-centric underwater tasks, providing a viable pathway for immediate, in-situ bleaching assessment by low-power marine robotics.
