Authors - Megha Potdar, Andhe Dharani, Ch.Ram Mohan Reddy Abstract - Semiconductor fabrication processes suffer significant yield losses, often exceeding 20%, due to equipment anomalies in critical stages like plasma etching and lithography, where traditional Statistical Process Control fails to detect subtle, non-linear drifts in multivariate sensor data such as temperature, pressure, and gas flow. This paper proposes a novel hybrid AI framework combining Long Short-Term Memory Autoencoder for unsupervised reconstruction-based anomaly detection with Isolation Forest for robust outlier scoring and severity ranking, enabling real-time predictive maintenance and Remaining Useful Life estimation. The LSTM-AE compresses temporal sequences into a latent space and flags anomalies via elevated Mean Squared Error thresholds (>95th percentile), while Isolation Forest filters multivariate errors to minimize false positives. RUL prediction employs linear regression on error trends for proactive scheduling. Implemented in a Keras/TensorFlow MLOps pipeline with
Authors - Kamasani Vishnuvardhan Reddy, Anjan Babu G Abstract - Universities maintain extensive repositories of institutional knowledge, yet students struggle to extract accurate information from disparate sources such as PDF circulars, web portals, and notice boards. Rule-based chatbots handle only narrow query sets, while general-purpose large language models (LLMs) produce fluent but sometimes fabricated responses—a phenomenon termed hallucination. This paper presents the Intelligent Campus Assistant Chatbot for Sri Venkateswara University (SVU), employing a Retrieval-Augmented Generation (RAG) pipeline that grounds every response in verified institutional documents via dense semantic vector search and deterministic keyword retrieval fused through Reciprocal Rank Fusion (RRF). Evaluation on a 200-query benchmark yields 94.2% factual correctness, hallucination rates below 1%, mean latency of 0.8 s, and inter-rater agreement κ = 0.87 across English, Telugu, and Hindi.
Authors - Ronald S. Cordova, Rowena O. Sibayan, Hazel C. Tagalog Abstract - Digital marketing teams often struggle less with access to algorithms than with choosing the right one for a specific decision. This paper presents a comparative study on the selection of the three most suitable algorithms for two related digital marketing tasks: customer segmentation and promotion-response prediction. Based on the example of Oman's retail industry, a benchmark is established using first-party customer data, including recency, purchase frequency, monetary value, product-category behavior, campaign participation, website visits, and engagement ratio. For customer segmentation, the study focuses on Kmeans, DBSCAN, and Gaussian mixture model because they provide a practical balance of scalability, noise handling, and probabilistic customer-state representation. For promotion-response prediction, the selected models are logistic regression, random forest, and XGBoost because they offer a staged balance between transparency, nonlinear learning, and campaign-ranking performance. For benchmarking and explainability, the same preprocessing approach, leakage prevention, temporal splitting, tuning strategies, and metrics such as silhouette quality, stability, ROC-AUC, PR-AUC, Brier score, calibration, and top-decile lift are employed. Explainability is treated as a condition for adoption rather than an optional reporting activity.
Authors - Mike Philip T. Ramos, Andres R. Vicedo, Jocelyn O. Padallan, Jonardo R. Asor, Genemarck B. Catedrilla Abstract - This research aims to develop a model for plankton species classification by analyzing images utilizing a convolutional neural network or CNN to simplify the task of classifying plankton species. The use of CNN and other transfer learning models will be used to recognize different freshwater plankton species in order to identify the genus of plankton easily. There were several layers in the CNN architecture used in this study; (1) Layer 1 has convolutional data with 32 filters and 3x3 kernel with max pool of 2x2 kernel; (2) Layer 2 has convolutional data with 64 filters and 3x3 kernel with max pool of 2x2 kernel; and (3) Layer 3 has conventional data with 128 filters and 3x3 kernel with mas pool of 2x2 kernel. After the validation and training in terms of accuracy and loss for CNN and pre-trained models, it is observed that MobileNetv2 showed the highest positive scores with 0.99 in train accuracy, 0.93 in validation accuracy, 0.07 in train loss, and 0.12 in validation loss, which makes it more viable to be used in this study. CNN's capacity to extract characteristics from photos has shown to be highly effective at classifying images. Additionally, it has been determined that transfer learning strategies can aid CNN in enhancing its picture categorization capabilities. The use of pre-trained learning like MobileNetv2 with a small data set and image classification studies can be a greater help for identification than CNN, Convolutional Network, Rest- Net50 and EfficientNetB0
Authors - Wannakorn Phornprasert, Nisarat Onthong, Thapanapong Sararat, Wongpanya S. Nuankaew, Pratya Nuankaew Abstract - This study proposes an Educational Data Analytics approach to understanding students' digital behavior and academic achievement using Descriptive and Cognitive Analytics. Data were collected from 40 purposively selected students using questionnaires that covered general information, social media usage, sleep behavior, Kolb-based learning style, and GPA. Descriptive Analytics was applied to summarize frequencies, percentages, means, and key behavioral patterns, while Cognitive Analytics was used to interpret these patterns in relation to learning readiness, self-regulation, and academic achievement. The findings showed that students had an average GPA of 3.38, spent an average of 7.53 hours on social media per day, and most frequently used social media between 20:01 and 00:00. The most common bedtime was 01:00, and Concrete Experience was the dominant learning style. The results suggest that small-scale learner data can support understanding of digital behavior, sleep patterns, and academic achievement in Thai higher education.
Authors - Kuljira S. Nuankaew, Kaewpanya S. Nuankaew, Wongpanya S. Nuankaew, Keingkrai Buakeaw, Thapanapong Sararat, Pratya Nuankaew Abstract - This research presents the development of an Explainable Intelligent Document Recognition system and a decision support system for assessing tax deductions in Thailand. The system uses image processing and data extraction technologies to analyze photographic documents and PDF files. It incorporates image quality enhancement, text recognition, key information extraction, and tax condition assessment, along with a rationale to enhance transparency in decision-making. Experimental results demonstrate efficient and accurate data recognition and extraction, and the system can handle diverse document types. Furthermore, a web-based prototype evaluation by 30 users showed high satisfaction, particularly regarding understanding the results and explanations. However, the system exhibits limitations with low-quality and complex documents. This research highlights the potential for applying such technology to taxation and for future expansion to improve flexibility and efficiency.
Authors - Najera R. Umpar, Apolinar P. Datu, Minsoware S. Bacolod, Soraya R. Umpar, Darwin B. Reyes, Albert Lee A. Catibayan, Klifford L. Carlos, Francia F. Murao Abstract - The widespread use of artificial intelligence (AI) tools into the world of academia has brought about substantial changes to the way scholars write. This paper examines how faculty view AI tools for use in academic writing through their own experiences of using these tools. Also, it explored their capacity to produce research publications and the integrity of the research being produced. Using a qualitative research design, the study gathered data through semi-structured interviews with faculty selected purposively using AI enabled tools such as ChatGPT, Sci.ai and Grammarly) during their writing process to collect data. Thematic Analysis was utilized to identify common themes within faculty member's accounts of their experiences. The findings of the study indicate that faculty perceive AI tools as valuable to enhance the speed in which they complete writing tasks, and also to improve grammar usage while writing, and to assist in idea generation; however, there were concerns voiced about overusing AI tools, ethical concerns with using AI tools, and how AI tools affect a faculty member's ability to think critically and produce work that is original. Additionally, the digital literacy level of faculty members who participated in this study reflects their ability to be able to adopt and incorporate these technologies into their daily teaching and research activities; thus, varying levels of digital literacy influence how a faculty member adopts and incorporates these technologies into their academic productivity. The study underscores the need for clear institutional guidelines and capacity-building initiatives to ensure the responsible and effective use of AI in academic writing. By providing insights into faculty experiences, this research contributes to the growing discourse on AI integration in higher education and offers implications for policy development, pedagogical practices, and future research.
Authors - Pradeep Kumar, Balasubramanian, Dhivyalakshumi Abstract - This paper analyzes the role of archetypal storytelling as an ethical brand meaning construction strategic tool in cause-related advertising as applied longitudinally to the Jaago Re campaign created by Tata Tea. Jaago Re is a cause marketing effort spanning more than 10 years and dealing with civic engagement, gender equity, community health and climate accountability. Based on the theory of archetypal branding, the paper examines thirteen aired and online advertisements published between 2008 and 2023 to learn how archetypes are utilized and redefined based on the changing socio-cultural and ethical issues. Based on the principles of a qualitative content analysis, guided by the Archetypal Criticism framework and Cultural Branding theory, the research paper recognizes the primary and secondary archetypes and investigates their narrative and ideological roles. The results have shown that archetypes that include the Hero, Sage, Caregiver, Everyman, and Outlaw, together with the Magician are well-planned layers that deploy civic actions, promote ethical contemplation, and maintain symbolic continuity in the campaign stages. The work proves that Jaago Re goes beyond episodic cause promotion, including the responsibility and social awareness as a part of the cultural identity of the brand. This study can be of use in the literature on ethical branding and responsible advertising because it links the progression of archetypal arrangements through time, providing a conceptual framework to build a sustained social and cultural value in the marketing communications.
