Authors - Nway Nway Zaw Win, Aye Nyein Mon, Win Lelt Lelt Phyu Abstract - Generating natural language descriptions for visual content is a key task bridging Computer Vision and Natural Language Processing. Conventional CNN-based approaches often struggle to capture global contextual information, limiting semantic consistency. This paper presents a multimodal video captioning framework for Myanmar-script generation based on a Vision Transformer (ViT) encoder and a Gated Recurrent Unit (GRU) decoder. Global visual representations are derived from transformer-based self-attention, while a class-prefixing mechanism is introduced to improve semantic grounding in a low-resource language setting. Experimental results evaluated using BLEU, CHRF, and TER metrics demonstrate that the proposed ViT–GRU model outperforms CNN–RNN baselines. PCA and t-SNE visualizations further confirm the effectiveness of transformer-based visual representations.
Authors - Kaveti Nani Kartik, Tanuja Pattanshetti Abstract - The proliferation of Large Language Models (LLMs) has raised concerns about embedded social biases and violations of fairness. Previous work has explored bias detection in word embeddings, fairnessaware algorithmic interventions, and system-level auditing frameworks. However, these approaches are still scattered across datasets, evaluation strategies and implementation pipelines. In this paper, we present a comprehensive literature survey to summarize the previous work on bias detection and fairness auditing, and categorize the contributions based on multiple phases of the research. Moreover, coverage and consistency limitations on popular benchmark datasets are analyzed. To address these problems, we present a unified dataset integration pipeline and a modular bias auditing framework. Identified critical research gaps include lack of intersectional bias modeling, lack of standardized metrics, and limited scalability in real-time auditing systems.
Authors - Mohit Apte Abstract - We develop systematic pairs trading strategies exploiting price adjustment lags between commodity-exporting currencies and their underlying commodities using CME futures. Two signal generation methods are compared: a rolling Z-score with Optuna-optimized hysteresis, and walk-forward Ridge regression on fourteen engineered features. Backtests on nine currency-commodity pairs over ten years of hourly data (2016–2026) show the ungated fundamental signal achieves Sharpe 0.56 under realistic costs. Adding rolling cointegration gating improves Sharpe to 0.64 while halving maximum drawdown from 23% to 12%. The ML signal reaches Sharpe 0.92, with strongest results on INR-Gold, AUDCopper, and CAD-Copper pairs. PCA-denoised Equal Risk Contribution sizing pushes ML Sharpe above 1.0 at the cost of higher drawdowns. Results confirm a tradable but risk-sensitive commodity-currency relationship at intraday frequencies.
Authors - Sayra Islam Saki, Qaium Hossain, Nadia Jahan, Abir Sen Gupta, Md. Tafshir Jaman Takib, Rajia Sultana, S.M. Sayem Abstract - This study examines how AI Systems, Big Data Analytics, Internet of Things (IoT) and E-Procurement enhances Sustainable Supply Chain Performance (SCP), with a particular focus on Supply Chain Resilience (SCR) as mediator. Primary data were obtained from 307 respondents of manufacturing industries through structured questionnaire. Partial Least Squares Structural Equation Modelling (PLS-SEM) approach was utilized for data analysis. The findings indicate that AI Systems, Big Data Analytics, Internet of Things and Supply Chain Resilience positively influence Sustainable Supply Chain Performance. On the contrary, E-Procurement doesn’t portray any significant direct effect. In terms of indirect pathways, SCR has positive mediating relationships between AI Systems and SSCP, as well as between IoT and SSCP. The mediation effect of SCR in the links between Big Data Analytics and E-Procurement with SCP is however not significant. These results provide subtle guidance to the practitioners in the industrial contexts, highlighting the need to prioritize those technologies that will promote resilience, specifically to AI Systems and IoT and re-examine the strategic contribution of E-Procurement to Sustainable Supply Chain models.
Authors - Swe Swe Htun, Aye Nyein Mon Abstract - Text classification has become a crucial task in natural language processing, especially for low-resourced languages in which limited annotated data and linguistic resources remain main challenges. This work presents inductive graph-based approach, GraphSAGE (Graph Sample and Aggregate), for text classification applying different word embedding models for Myanmar News classification. Experiments are conducted on eight Myanmar News categories (Business, Crime, Culture&Tourism, Educa-tion&Technology, Entertainment, Health, Politics, and Sports). The experiments show the effectiveness of BiLSTM (Bidirectional Long Short-Term Memory) and GraphSAGE architectures integrated with traditional and contextual embedding meth-ods, including TF-IDF (Term Frequency–Inverse Document Frequency), MyanBERTa, and mBERT (Multilingual Bidirectional Encoder Representations from Transformers). In the proposed work, transformer-based embeddings from pre-trained language models are extracted and combined with graph neural networks to capture both semantic and structural relationships among documents. A similarity graph is built by utilizing cosine similarity and k-nearest neighbor methods, and GraphSAGE is used to aggregate neighborhood information for inductive learning. The performance of graph-based models is compared to sequential deep learning technique based on BiLSTM. Experi-mental results reveal that graph-based approaches achieve better performance than BiLSTM-based models in all embedding settings. Among the evaluated models, mBERT with GraphSAGE gets the highest classification accuracy of 63%, followed by MyanBERTa with GraphSAGE with 60%. In contrast, MyanBERTa with BiLSTM and mBERT with BiLSTM yield 47% and 52% accuracy, respectively, whereas TF-IDF with GraphSAGE obtains 57% accuracy. The findings show that combining the contex-tual transformer embeddings with graph neural networks substantially enhance text classification performance by efficiently modeling semantic and relational information.
Authors - Edgar G. Cue, Felix JR Q. Pocong, Darwin Catalan, Santa M. Faltado, Ethel Reyes-Chua, Randy Joy M. Ventayen Abstract - This qualitative study examined business leaders' use of artificial intelligence (AI) in the workplace and employees' adaptation to AI-related changes. To gain a more thorough understanding of how various leadership practices are used, the employee experience, and the organizational response to the integration of AI into their operations, the researcher employed a qualitative research design. The research included business leaders and employees from organizations that have previously implemented or are currently implementing AI technologies in their operations as the study's subject population. Participants were selected using purposive sampling based on their direct involvement or knowledge of AI integration efforts within their organizations. The researcher collected data through interviews and coded it using thematic analysis to identify recurring themes and patterns related to leadership strategy, employee adaptation, organizational challenges, and workplace transformational changes resulting from the integration of AI. Major findings of this study indicate that, in implementing AI, leaders pre-dominantly used phased, strategic methods while considering employee readiness, continuous training, ethical governance, and partnerships to successfully implement AI within their organizations. On the other hand, employees exhibited both optimism and anxiety about AI adoption, with particular concerns about job security, technological skills, and organizational support. The study also established that transformational leadership, participative decision-making, transparent communication, a supportive leadership culture, and continuous capacity development are the most effective practices for facilitating employee adaptation and successful AI integration. The study concludes that successful AI integration requires not only technology but also a high degree of human-centered leader-ship, ethical accountability, and an organizational commitment to continuous development and change management to facilitate sustainable transformational change in organizations.
Authors - Divy Awasthi, Rushil Jariwala, Pearl Patel, Dhiren Patel Abstract - Artificial intelligence is deployed at scale across high-stakes domains—healthcare, autonomous systems, finance, and critical infrastructure— yet the pace of capability development has outrun our ability to ensure these systems behave safely, transparently, and in accordance with human values. While individual aspects of AI safety have been studied in isolation, a unified treatment spanning technical vulnerabilities, ethical risks, security threats, and governance failures remains lacking. This paper addresses that gap with a structured survey of Safe AI organized around a four-layer taxonomy of challenges—data, model, system, and societal—and a corresponding set of mitigation strategies at each layer. We trace AI’s evolution across three generations of increasing capability and opacity, examine domain-specific safety risks in healthcare, autonomous vehicles, manufacturing, and large language models, analyze the alignment problem through robustness, interpretability, controllability, and ethical adherence, and consolidate ten cross-layer directives for safe deployment. We review the global regulatory landscape, including the EU AI Act, GDPR, and national AI safety initiatives across the US, UK, and India, and identify open challenges in scalable oversight, formal verification, and the governance of increasingly autonomous AI systems.
Authors - Rowena Ocier Sibayan, Hazel C. Tagalog, Salvacion M. Domingo Abstract - Artificial intelligence (AI)–based writing tools are increasingly integrated into higher education as part of institutional technological‑intelligence infrastructures, providing automated feedback that can improve students’ writing quality and efficiency. This study evaluates AI writing tools as intelligent decision‑support systems and examines their impact on academic performance, student learning behavior, and institutional decisions about AI integration in higher education. A convergent parallel mixed‑methods design was adopted, combining quantitative analysis of writing performance with qualitative insights into student experiences. Data were collected from 100 undergraduate students with prior exposure to AI writing tools; quantitative measures included pre‑ and post‑intervention writing scores, rubric‑based assessments, and usage frequency, while qualitative data were gathered through structured questionnaires and reflective responses. Findings reveal statistically significant, large improvements in writing confidence, perceived clarity, and assignment performance, with mean grades increasing from 68.5% to 73.2%. Students also reported greater perceived independence in writing, although qualitative data indicate variability in engagement, ranging from critical use of AI feedback to more passive reliance. Concerns about data privacy showed minimal change and remained an area of uncertainty, underscoring the importance of governance and risk management in institutional AI deployments. The study concludes that AI writing tools enhance measurable writing outcomes but do not automatically foster deeper cognitive development. Their effectiveness depends on how students interpret and engage with AI feedback, underscoring the need for pedagogically guided and ethically responsible integration of AI in higher education.
Authors - Sowmini Devi Veeramachaneni Abstract - This paper addresses the challenge of balancing economic performance and environmental sustainability in supply chain optimization. We propose a bi-level hybrid optimization framework that integrates Particle SwarmOptimization (PSO) with Linear Programming (LP) for carbonaware business decision making. At the upper level, PSO dynamically optimizes the carbon penalty parameter, while at the lower level, LP ensures optimal and feasible operational decisions under supply chain constraints. The proposed framework automatically learns the trade-off between profit and emissions, eliminating the need for manual parameter tuning. Experimental results on both synthetic and real-world datasets demonstrate that the method effectively identifies Pareto-optimal solutions, achieves stable convergence, and exhibits strong robustness compared to standalone optimization approaches.
Authors - Vinca Valenia, Chelsea Calissta Liman Lim, Ichwan Masnadi Abstract - The swift embrace of artificial intelligence (AI) in the hospitality field has deeply modified the way services are provided and how customers interact, especially in the context of robot waiter systems in restaurant settings. Previous research mainly focused on operational efficiency; however, little has been done to understand how such technologies affect customer experience and their subsequent behaviors. This paper first determines customers' perception factors of AI-based robot waiter systems and their emotional involvement and satisfaction as consequences of the service encounter. Based on the Technology Acceptance Model (TAM), this study examines perceived usefulness and perceived ease of use in their contribution to customer attitudes formation toward AI-enabled services. Furthermore, emotional involvement as the main affective reaction that alters the customer attitudes-satisfaction link has been included in this investigation. Participants were selected based on their familiarity or interest in AI-based service technologies, and the quantitative method was used for the model testing. These results may shed light on the ways in which customer experience and satisfaction can be improved through AI-driven service innovations that take into account the cognitive and emotional aspects of consumer behavior. This paper is a significant addition to the field.
Authors - Brandon Octavianus, Charles Jonathan, Julia Christina, Ichwan Masnadi Abstract - The introduction of AI-driven self-service in restaurants has been swift, fundamentally altering the nature of customer service interactions. Customers’ experiences dining at these AI-enabled restaurants have also revealed that intelligent systems need to be more human-centered. The intention of this research is to discover the influence of Technology Readiness to Attitudes Toward Using restaurant self-order technology device with Perceived Ease of Use, Perceived Usefulness, and Perceived Speed as the mediators. Through a quantitative analysis of 200 respondents located in the JABOTABEK region that have experience using restaurant self-ordering technology. The data was evaluated through PLS-SEM system. This research reveals a positive effect of Technology Readiness on each variable, but it does not have considerable direct impact on Attitude Toward Using. The analysis of mediations revealed that customer attitude was positively impacted by Perceived Ease of Use and Perceived Speed, whereas Perceived Usefulness displayed insignificant effect. Overall, Perceived Speed was revealed as the strongest predictor implying that customers prioritize fast and easy service over useful functionality when interacting with intelligent restaurant systems. This study builds upon existing knowledge with an additional layer of understanding about human-centric AI implementation. Intelligent service technologies are meant to benefit both humans and organizations, but restaurants should also focus on providing quick, seamless, and easy customer experience through this technology. Keywords:
Authors - Agnes Gracia Hosiana, Catherine Puspita Sari, Tiurida Lily Anita Abstract - The rapid expansion of quick-commerce mobile applications has re-shaped how consumers purchase everyday essentials through digital platforms. Unlike traditional e-commerce, quick-commerce operates in a time-sensitive and mobile-first environment, making interface usability and trust particularly important in shaping user adoption. In this research, Technology Acceptance Model (TAM) is extended by adding interface usability and trust into the model with the aim of understand the factors that affect the users' behavioral intention toward the usage of ASTRO mobile application. This research used quantitative methodology through surveys conducted among 258 active users of ASTRO. The pro-posed model in this research was evaluated utilizing Partial Least Square Structural Equation Modeling (PLS-SEM). The findings show that interface usability significantly influences perceived ease of use and perceived usefulness. Further-more, trust positively impacts both attitude toward use and behavioral intention to use. Both perceived usefulness and perceived ease of use also positively impact user attitude. These results confirm that TAM remains relevant in the quick-commerce context, while also demonstrating that interface usability and trust enhance its explanatory power in mobile retail environments. This research offers contributions to the technology adoption literature by providing a context-sensitive ex-tension of TAM for quick-commerce applications and delivers practical recommendations for platform developers to optimize user experience, strengthen trust, and encourage sustained adoption.
Authors - Tanvi Pawar, Sachin S. Pande, Emmanuel M Abstract - Sarcasm detection in social media text is a NLP challenge, as sarcastic statements inverse meaning of the statement as sarcastic statements hide the real meaning. This problem intensified on platforms like Reddit by informal phrasing, community-specific references, and implicit cultural knowledge. This paper introduces a RoBERTa-based classification framework which addresses three core issues: contextual impoverishment of isolated comments, unstable training caused by random initialization, and catastrophic forgetting during fine-tuning. These are handled via inline textual metadata fusion (encoding subreddit identity and upvote score into the input sequence), a structured multi-layer classification head, and a biphasic two-stage training method with differential learning rates. Trained on a balanced 500,000-sample subset of the SARC dataset, the model achieves 68.36% accuracy with stable, monotonic convergence across all training epochs. Near-symmetric false positive and false negative rates shows that the model does not favor a single class. Future directions include knowledge graph integration, model distillation, multi-class sarcasm taxonomy, and multilingual extension.
Authors - Augustus Abbey, Benjamin Ghansah, Stephen Opoku Oppong, Joseph Kwabena Essibu, Charles Buabeng-Andoh, Christopher Yarkwah, Mathias Abgeko Abstract - The adoption of Learning Management Systems (LMSs) in higher education has transformed teaching and learning by enhancing digital content delivery, assessment processes, and collaborative engagement. Despite their widespread use, variations in students’ learning experiences and academic outcomes suggest that the effectiveness of LMS platforms is influenced by both system features and learner characteristics. This study investigates the extent to which specific LMS functionalities contribute to students’ academic performance and examines how demographic and learner-related factors moderate LMS usage and learning outcomes. A cross-sectional survey design was employed, involving 381 students from the University of Education, Winneba. Data were collected using structured questionnaires and analyzed through descriptive statistics, correlation analysis, and multiple regression techniques. The findings reveal that key LMS dimensions, including content delivery mechanisms, communication and interaction tools, navigation usability, and system accessibility, significantly influence students’ academic performance and learning experiences. Further-more, demographic and learner-specific variables such as age, socioeconomic back-ground, language proficiency, and learning preferences were found to shape the effectiveness and utilization of LMS platforms. The study underscores the importance of inclusive and user-centered LMS design approaches that accommodate diverse learner needs and promote equitable access to digital learning environments. The findings con-tribute to the growing discourse on technology-enhanced learning by providing empirical insights for educational institutions, LMS developers, and policymakers seeking to optimize the accessibility, usability, and pedagogical effectiveness of LMS platforms in higher education.
Authors - Sowmini Devi Veeramachaneni Abstract - Modern supply chain systems must balance economic efficiency with environmental sustainability. Traditional optimization approaches, such as linear programming (LP), provide optimal solutions but often struggle with scalability in large-scale networks. This paper proposes a clustering-based framework to reduce the computational complexity of supply chain optimization while preserving solution quality. The method groups suppliers and demand points using feature-aware clustering based on cost and emission profiles, and solves a reduced transportation problem using LP. Experimental results on a real-world dataset demonstrate that the proposed approach achieves near-optimal performance, with less than 7% deviation in profit and less than 2% deviation in emissions, while reducing computation time by nearly an order of magnitude. An ablation study further highlights the trade-off between computational efficiency and solution fidelity controlled by the number of clusters. The proposed framework provides a practical and scalable solution for large-scale, sustainability-aware supply chain optimization.
Authors - SMT. DIVYASHREE D V, D RAMESH Abstract - In India, obesity has become a serious public health concern, especially in urban and semi-urban areas that are seeing fast changes in diet and lifestyle. Predictive modelling has advanced globally, but there are still very few techniques tailored to a given region that take into consideration Indi's distinct socioeconomic, environmental, and cultural context. The study is conducted from the local population in Tumkur city by creating an ANN model that predicts the obesity risk from diverse age groups. The model is built with the physiological, behavioural and environmental parameters that make deeper study to analyse the risk through multi-faceted dataset. A mobile application is developed to close the gap and monitor the obesity risk through recommendation given by interactive monitoring tool. This tool will provide the real time risk evaluations to the individuals by giving warnings and progress updates that supports health tracking for timely behavioural and physiological changes. The research mainly focusses on predicting the obesity risk, designing a mobile health monitoring tool and assessing the obesity risk by validating the hypothesis risk framework by one-way ANOVA statistical analysis on primary data on region specific.
Authors - Jann Alfred A. Quinto, Mark Teddy D. Quiban Abstract - The increasing integration of generative artificial intelligence (AI) in educational settings calls for stronger development of AI literacy, particularly among students at the start of their academic programs. This study explored the extent to which exposure to generative AI technologies can enhance the AI literacy of first-year Bachelor of Science in Information Technology (BSIT) students. Instructional design was informed by the Technological Pedagogical Content Knowledge (TPACK) and Substitution–Augmentation–Modification–Redefinition (SAMR) frameworks to support meaningful and pedagogically aligned use of technology. The intervention emphasized early conceptual grounding, critical engagement, and responsible interaction with AI systems. To examine its impact, a quasi-experimental one-group pretest–posttest design was employed with 45 participants. A validated AI literacy instrument was administered before and after the intervention. Learning activities incorporated guided interaction with generative AI technologies, structured tasks, and reflective exercises addressing both functional use and ethical considerations. Statistical analysis using a paired-sample t-test was conducted to evaluate changes in performance. Results indicated a statistically significant improvement in posttest scores (p < 0.05). These outcomes suggest that a structured and framework-guided approach to integrating generative AI can strengthen students’ conceptual understanding, applied capabilities, and awareness of ethical issues. Introducing AI literacy early in the BSIT curriculum may help prepare students for the demands of AI-influenced academic and professional environments.
Authors - Phillip Queroda Abstract - This study examined the implementation of inclusive education strategies within the Open and Distance e-Learning (ODeL) system of Pangasinan State University–Open University Systems (PSU-OUS). Utilizing a quantitative descriptive research design with stratified random sampling, data were collected via an online questionnaire from faculty and students. Findings revealed a high level of implementation across four domains: Universal Design for Learning (UDL)-based instructional design, collaborative learning, accommodations and modifications, and personalized learning. Instructional resources and activities consistently provided multiple means of representation, engagement, and expression, successfully fostering learner interaction and addressing diverse needs. However, implementation gaps were identified in the integration of assistive technologies and the development of systematic monitoring and evaluation mechanisms. The study concludes that while PSU-OUS demonstrates a strong institutional commitment to inclusive online education, enhancing technological integration and establishing data-driven monitoring systems are essential for long-term sustainability and effectiveness.
Authors - Zahrah Meidila Hafizhah, Jurry Hatammimi Abstract - The digital creative economy is increasingly driving live streaming to become one of the most promising business models, particularly within the gaming community. Here, creators are competing to produce the most engaging content possible in order to generate revenue from their social media channels. This study examines how entrepreneurial efforts and opportunity costs influence the monetisation performance of Valorant micro-streamers in Indonesia. A quantitative method was employed, utilising data from 100 respondents via an online questionnaire, which was subsequently analysed using SEM-PLS. The results support all three hypotheses: entrepreneurial effort has a positive effect on monetisation performance, whilst opportunity cost has a stronger positive effect. Together, these two variables account for 30.3 percent of the variation in monetisation. The significant difference in effect sizes suggests that monetisation outcomes are not primarily determined by the extent of effort expended, but rather by economic conditions that influence how deeply an individual can commit to streaming. These findings extend the study of digital entrepreneurship to the context of streaming outside western countries, which tends to be mobile-based, whilst also suggesting that platforms wishing to support micro-streamers need to consider not only content quality, but also the incentive systems that influence creators’ sustainability.
Authors - Ronnel A. dela Cruz Abstract - This study presents a machine learning–based predictive analytics framework[1][2][3] for forecasting faculty promotion outcomes in state universities using institutional performance data and OCR-based document processing[ 4][5]. Faculty demographic information, Individual Performance Commitment Review (IPCR) indicators, and digitized faculty documents were utilized to develop predictive classification models. A dataset consisting of 1,000 faculty records was preprocessed through data cleaning, normalization, feature engineering, and SMOTE balancing applied only to the training dataset. Ada- Boost, Gradient Boosting, and XGBoost classifiers were evaluated using Accuracy, Precision, Recall, F1-score, and ROC-AUC metrics. Among the evaluated models, AdaBoost achieved the strongest performance with 97.33% accuracy and 98.36% ROC-AUC. Feature importance analysis identified teaching effectiveness, curriculum development, and mentorship services as dominant predictors of promotion outcomes. The findings demonstrate the potential of integrating machine learning and OCR-driven document processing to support transparent, scalable, and evidence-based faculty promotion systems in higher education institutions.
Authors - Apolinar P. Datu, Jesielitlyn B. Gloria, Barnard J. Maraon, Jhoan P. Sarimos, Jenny B. Unico, Garry G. Garcia Abstract - Adobo, often regarded as the Philippines’ unofficial national dish, holds significance both as a culinary staple and as a symbol of cultural heritage. This study explores consumer satisfaction and preferences between traditional and modern adobo preparations. Specifically, it aims to: (1) identify sensory and cultural factors influencing consumer choices, (2) compare satisfaction levels between traditional and modern versions, and (3) examine how demographics such as age, lifestyle, and exposure to food trends shape these preferences. Using a quantitative survey design, data were collected through a structured questionnaire administered to a diverse group of respondents. Perceptions were measured across five dimensions—taste, aroma, presentation, health considerations, and cultural relevance—while descriptive statistics and comparative analyses were employed to assess variations in consumer satisfaction. The findings reveal that traditional adobo remains preferred for its authenticity, flavor consistency, and nostalgic value, reflecting its cultural importance. In contrast, modern adaptations—marked by fusion styles, innovative presentation, and health-conscious alternatives—resonate with younger and lifestyle-driven consumers. Satisfaction, therefore, extends beyond taste, encompassing identity, innovation, and cultural pride. This study highlights how culinary heritage evolves within modern gastronomy, offering insights for restaurateurs, culinary educators, and food entrepreneurs to balance tradition with innovation in sustaining adobo’s cultural significance.
Authors - Bryly Brord Mirah, Anderes Gui Abstract - The rapid integration of Artificial Intelligence (AI) in the financial sector has fundamentally transformed service delivery through the emergence of Digital Human Advisors. This research examines the factors influencing the intention to adopt these AI-driven services in Indonesia by synthesizing the Technology Acceptance Model (TAM) with Perceived Trust and Multidimensional Perceived Value, including functional, emotional, and conditional dimensions. Employing a quantitative methodology with purposive sampling, data were gathered from a predominantly Generation Z population. The analysis, conducted through Partial Least Squares Structural Equation Modeling (PLS-SEM), reveals that Perceived Ease of Use serves as the primary cornerstone in shaping Perceived Usefulness, indicating that the simplicity of the interface is a critical pre-requisite for users to recognize the technology's benefits. Furthermore, Intention to Use is significantly driven by Perceived Trust, Functional Value, and Perceived Usefulness. Conversely, the insignificance of emotional and conditional values suggests a highly pragmatic mindset among users in high-stakes financial environments. These findings imply that financial institutions should prioritize a "utility-first" strategy, focusing on systemic integrity and seamless navigation to foster long-term adoption.
Authors - Jann Alfred A. Quinto Abstract - Artificial Intelligence (AI) continues to modify education hence, need for AI Literate teachers becomes increasingly critical. There remains limited data on teachers’ AI competency in terms of knowledge, attitudes, ethical understanding, and use of technologies. This study sought to assess the level of AI literacy progression among Teachers using a UNESCO Competency Framework for Teachers (CFT), profile, relationships, differences in the competency levels. Findings showed that majority of teacher are novice (1-5 Years in service) in the teaching profession, and with no trainings attended related to AI, and occupying a position equivalent to Teacher 1 to 3. Teachers strongly agree that they “Acquired” basic AI knowledge, skills and ethics along Human-centered mindset, Ethics of AI, Foundations and applications, AI pedagogy, and AI for professional growth. In addition, there is no significant difference in AI literacy competency progression level across profile. This shows that teachers, with or without training and new in service “Acquired” the basic principles and applications of AI competencies through self-exploration. Literacy competency among the respondents is on the “acquired level”. Furthermore, there was a significant gap between Human-centered mindset and AI professional growth domain. This implies, awareness on AI importance, belief that AI is human led and appreciating AI capacities is high while exploration of AI tools to enhance professional development, utilization of AI tools confidently for sharing resources is low. This suggest that there should be training on the use of AI tools in teaching before useful programs become obsolete due to rapid change in technology.
Authors - Ovia Rizvi, Sadman Kabir, Md. Tafshir Jaman Takib, Abir Sen Gupta, Sayra Islam Saki, S.M. Sayem Abstract - The global shift toward cashless payment systems has transformed financial transactions, yet adoption in developing countries such as Bangladesh remains limited. This study investigates the determinants of cashless payment adoption in Bangladesh by examining user perceptions and behavioral drivers. Drawing on survey-based evidence from 369 respondents, the PLS-SEM analysis identifies facilitating conditions, perceived security, initial trust and intrinsic motivation as the most influential factors shaping adoption. In contrast, digital financial literacy, social influence and IT innovation acceptance were found to have little impact, suggesting that peer effects and novelty alone do not encourage sustained use. Moreover, initial trust and intrinsic motivation showed significant mediating impact between the drivers and the adoption of cashless payment systems. The findings highlight the importance of robust infrastructure, strong security protocols and user-centric design in promoting digital financial inclusion. Policy implications emphasize collaborative efforts by regulators and service providers to expand infrastructure, enforce cybersecurity standards and foster user trust. These measures are critical for accelerating Bangladesh’s transition toward a secure and inclusive cashless society.
Authors - Aditya Nova Putra, Tri Wiyana, Setiani Putri Hendratno, Nora Fitriawati, Ida Bagus Putu Aditya Abstract - The world of social media marketing is shifting from traditional con-tent delivery to personalized solutions, algorithmic recommendation systems, AI generated content and Automated Customer Service Chat. While these technologies can increase relevance and responsiveness, the consumer impact of AI-powered brand communications is conditional upon perceptions of brand messaging as being trustworthy and human-centered, socially meaningful. The study constructs a consumer-behavior framework of the impact of human-centric AI-driven social media intelligence on trust in AI-based brand content, e-WOM, pur-chase intention and perceived sustainability. Situated in the fields of digital marketing, social media intelligence and behavioral consumer analytics, this study aims to investigate a quantitative survey conducted among Indonesian social media users who have been exposed to AI-assisted or AI-generated brand communication. Data was analyzed with PLS-SEM with trust being treated as the inner psychological mechanism and e-WOM as the outer social amplification mechanism transferring AI-enabled marketing to purchase intention and perception of Sustainability. Moving beyond technological adoption, this research on AI marketing highlights customer intelligence, consumer trust construction, online recommendations and responsible digital value creation. In practice, the framework guides firms in designing AI-enabled social media strategies that are persuasive, credible, customer- and sustainability-oriented.
Authors - Chaithra G, Ambika P R, Manjunath R, Shivashankar, Niranjan R, Sowmya Naik P Abstract - Gross Domestic Product (GDP) forecasting using traditional Econometric models is indispensable for evidence-based decision-making. However, these models are often limited in their ability to handle linear relationships and adapt to high-dimensional data. This paper introduces EcoVision, an open-source web-based forecasting platform that incorporates AI and machine learning to accurately predict GDP and other associated socio-economic variables using the Gap minder dataset (175 countries, 1998-2018). Four machine learning models were used: Support Vector Machine Regression, Polynomial Regression, Decision Tree Regression, and Random Forest Regression. These were built using Python and the Flask/Scikit-learn stacks. Models were evaluated using Average Absolute Error, Squared Error, and R² values. Results show that the Decision Tree Regression model has a perfect fit (R² = 1.0, AAE = 0, SE = 0), making it the best model compared to the other models. The web interface is built using pure HTML5/CSS3/Chart.js. The integrated "Gemini API Module" enables the automatic generation of easily understandable policy summaries, thus allowing for faster extraction of insights. Results from testing the system on 3532 clean data records proved that the system is accurate in forecasting ≥ 85%, Artificial Intelligence summary relevance ≥ 80%, and export success 100%, making it a potential decision-support system for economists, researchers, and governments.
Authors - R Suganya, S Priya, Sheeja Pon Chakravarthy,Pragadheesh Thirumal M Abstract - This paper presents a real-time intelligent surveillance system designed to detect weapons and violent activities using deep learning techniques [2]. The system integrates the YOLOv7 object detection model [7] for weapon recognition and a CNN-based violence detection module for behavioral analysis. Real-time video streams from CCTV cameras are processed to identify potential threats, and alerts are transmitted via MQTT for immediate notification. Experimental evaluation demonstrates that the YOLOv7 model achieves a mean Average Precision ([email protected]) of 55.3% for weapon detection, while the CNN model [11] attains 96% accuracy in classifying violent actions. The system operates at an average speed of 25–30 frames per second with low latency, confirming its feasibility for live surveillance applications. The proposed architecture enhances public safety by providing automated, accurate, and real-time monitoring capabilities.
Authors - Melati Budi Srikandi, Jonathan Jacob Paul Latupeirissa, Yolanda Masnita, Rizki Dewantara, Ni Made Prasiwi Bestari, Ayu Made Bianca Juarez Abstract - The shift from human gatekeepers to AI-driven algorithmic curation has fundamentally changed the concept of "Agenda-Setting" theory in the digital age. This change is significant because AI now influences public issue salience; however, there is a notable gap in public awareness. This study examines AI's role in social media information curation and its effects on public discourse and agenda setting. To do this, the research employs a systematic literature review guided by PRISMA principles, analyzing data collected from the Scopus database to identify current research trends. Moving from “handheld” to “automatic” curation results in more personalized interfaces that foster “filter bubbles” and “echo chambers,” according to the analysis. It demonstrates that understandings of “algorithmic news bias” are more influenced by user partisanship and ideological cues than purely technical causes. In conclusion, this suggests that media theories need to be refined to include automated gatekeeping as a core component. Algorithmic literacy serves as a filter against distortions, aiming to reduce disinformation and digital conflict within society. To address the bottlenecks in communication processes, future research and policy should focus on improving algorithmic literacy, given its undeniable influence over human decision-making.
Authors - Helmy Wijaya, Vallencia Ricca Widjaja, Fernand Jetshen Clevanno, Ichwan Masnadi Abstract - With rapid digitalization happening in the hospitality industry today, hotels are now able to interact more digitally with their guests using innovative customer service solutions. Aspects of technology adoption can be studied from the perspective of customer intelligence to gain behavioral insights about customers, which in turn can help hoteliers improve their user experience with such technologies and create a higher rate of adoption amongst customers. This research explores what factors affect hotel customers' intention to adopt online check-in technology by implementing the Technology Acceptance Model (TAM) with an exploratory factor analysis aimed at customer behavioral insights. Using quantitative explanatory research methods, data from 150 respondents in Jakarta was gathered through online questionnaires. Structural equation modeling was analyzed through Partial Least Squares SEM (PLS-SEM). Empirical results showed that PU and PEOU affected users' attitude toward using online check-in technology. The users' disposition exerted a considerable influence on their aspiration to utilize the digital check-in technology. The effect of PU and PEOU on intention was fully mediated by attitude, implying how affective evaluation by customers has an impact on customers' behavioral intention.
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.
Authors - Valencia Vannessa Taslim, Melissa Anastasia, Shalva Andena Rizaldi, Tiurida Lily Anita Abstract - Online hospitality reviews provide valuable insights into guest experiences, service quality, and operational performance. However, the unstructured and noisy nature of review text makes large-scale analysis difficult, especially for Indonesian-language reviews that often contain informal expressions, abbreviations, spelling variations, and inconsistent sentence structures. Although sentiment analysis has been widely applied in hospitality research, studies focusing on Indonesian-language hospitality reviews remain limited, and few have presented a reproducible Natural Language Processing (NLP) workflow for multiclass sentiment classification. This study proposes a reproducible Indonesian NLP pipeline for classifying hospitality reviews into positive, neutral, and negative sentiment categories. The workflow integrates review collection, sentiment annotation, Indonesian text preprocessing, TF-IDF feature extraction, and super-vised classification using Naïve Bayes, Support Vector Machine (SVM), and Logistic Regression. The dataset consists of 450 Indonesian-language hotel reviews collected from Google Reviews across three hotel segments: budget, mid-scale, and upscale. The experimental results show that SVM achieved the best overall performance, with 91.78% accuracy, 91.35% precision, 91.78% recall, and 91.50% F1-score, outperforming Naïve Bayes and Logistic Regression under the same experimental setting. These findings indicate that classical machine learning, when supported by systematic preprocessing and consistent feature representation, remains highly effective for Indonesian hospitality review analytics. This study contributes a practical and reproducible baseline for Indonesian-language sentiment classification and provides a foundation for future intelligent review monitoring systems in the hospitality sector.
Authors - Jolou Vincent M. Jala, Everly A. Nacalaban, Nenon Roy A. Sandinao, Erlinda D. Rivera, Hilfiger L. Cubarrubia Abstract - This article explored the adoption of Artificial Intelligence in Financial Management Systems of Higher Education Institutions (HEIs) by utilizing a systematic review of related literature. The study focuses on reviewing pre-sent literature on Artificial Intelligence adoption in financial management systems, recognizing the benefits of AI integration, scrutinizing the challenges and barriers to implementation, and offer recommendations for effective and successful AI integration in HEIs. The findings disclosed that artificial intelligence has the capability to meaningfully enhance financial management systems in Higher Education Institutions through automated financial reporting systems, budgeting forecasting and predictive analytics, fraud detection and risk management, and expense tracking and optimization. Adoption of Artificial Intelligence improves efficiency, enhances accuracy, provides better decision-making and cost optimization. More-over, it enhances operational efficiency by systematizing monotonous financial tasks, enhances accuracy by plummeting human faults, supports better decision-making through actual financial data and predictive analytics, and helps to long-term cost optimization and financial sustainability. These improvements permit institutions to alter from manual and volatile financial management routines toward more data-driven, calculated and strategic, financial planning and re-source provision. Conversely, the study also found several challenges that deter AI adoption in Higher Education Institutions, specifically in developing countries such as the Philippines. These challenges include high initial investment and maintenance costs, limited technical skills among staff, data privacy and cybersecurity risks and resistance to organizational change. Numerous HEIs are still in the developing stage of digital transformation and depend chiefly on enterprise systems and basic accounting rather than advanced Artificial Intelligence technologies. The article concludes that successful AI in- corporation requires institutional readiness, strategic planning, capability building, infrastructure progress, and robust data governance policies to completely maximize the advantages of Artificial Intelligence in financial management systems.
Authors - Denver Novencido Abstract - An organization’s operational efficiency, productivity, and reliability can be adversely affected by using manual-based systems. Some of the issues associated with using a manual-based approach include inefficient processes, inconsistent documentation, difficulty in monitoring and validating records, and limited accessibility. The development of information systems provides a solution to address the limitations and challenges of a manual-based approach in organizations. This study presents the design and implementation of a cloud-based information system integrated with decision support capabilities to streamline organizational operations, enhance data storage and retrieval, and facilitate strategic planning. The system was created using the Agile Unified Process (AUP) software development methodology. Evaluation results indicate strong compliance with ISO software quality standards, making it a suitable tool for managing organizational operations.
Authors - Felix Kabwe, Jackson Phiri Abstract - This study explores how blockchain-based Identity and Access Management (IAM) systems can enhance the security and efficiency of Digital Financial Services (DFS). As DFS environments grow more complex and involve multiple stakeholders, traditional IAM systems face challenges such as centralization, limited interoperability, and scalability constraints. Blockchain offers a compelling alternative by enabling decentralized, transparent, and tamper-resistant identity management. The study compares three main IAM models: centralized systems supported by blockchain, federated identity management, and Self-Sovereign Identity (SSI). Using the Technology-Organization-Environment (TOE) framework alongside a semi-quantitative scoring approach, the research evaluates these models across key factors including security, privacy, usability, scalability, governance, cost, and regulatory alignment. The findings highlight clear trade-offs. Centralized systems excel in performance, cost efficiency, and regulatory compliance but are vulnerable to single points of failure. Federated models strike a balance by improving interoperability and user experience, though they introduce governance complexity. SSI provides strong privacy and user control but faces challenges in usability, scalability, and regulatory acceptance. Overall, no single model fully meets DFS needs. Federated systems are currently the most practical, while hybrid federated–SSI approaches offer the most flexible, scalable, and user-focused solution.
Authors - Md. Monowar Hossain, Fahima Hossain, Md. Shahidul Islam, Md. Tanvir Ahmed, Reduan Ahmed Abstract - This automated image captioning is on one hand a Computer Vision (CV) and Natural Language Processing (NLP) application, but on the other hand, conventional CNN-RNN models suffer from feature loss and long-range dependency. The proposed model in this study is a parameter balanced multi-modal model that consists of a dual-encoder network which combines Effi-cientNet-B4 for hierarchical features and MobileNetV2 for geometric efficiency, as well as a multi-head Transformer decoder. The model was evaluated on Flickr8k, and tested with a dynamic scalar weight mechanism and teacher-forced optimization, the BLEU-1 was 0.5774 and METEOR was 0.4129. Interestingly, the ablation results also showed that although the dual-encoder method is competitive, the pathway of the standalone MobileNetV2 is slightly better than the fused pathway in terms of BLEU-4 (0.2284 vs. 0.20). This indicates that the pathway may be redundant during the concatenation process. This study validates the possibility of using Transformer decoders instead of RNN bottle-necks and offers important considerations for the optimization of real-time feature fusion for vision tasks.
Authors - Aarthi R, Aniketha Prasad, Dhamini Manoj, Manasvi G, Meghaa Sunil Abstract - Early and accurate diagnosis of dermatological disorders remains one of the main issues in clinical dermatology, especially with regard to diseases with similar appearances. Despite the achievements of deep learning methodologies in the classification of cutaneous lesions with the help of images, structured clinical metadata is not used to the fullest, despite its significant diagnostic potential. In a practical clinical setting, dermatologists do not solely use visual evaluation of the case but also use patient-specific metadata, which includes age, lesion progression, pruritus, hemorrhage, anatomic location, prior biopsy, and family history. The current study presents a fully explainable, metadata based, multi-class classification of skin diseases, using the PAD-UFES-20 database, and concentrated on 6 distinct diagnostic categories. Although the dataset is dermoscopic, the predictive quality of formal metadata variables are mainly under consideration in the present work. The explainability analyses revealed that biopsy status, elevation, itch, region and age are attributes that have significant effects on the classification results. However, empirical evidence shows that the reduced model consisting of the premier five features lowers accuracy, which highlights the importance of a thorough combination of metadata features to determine skin disease rather than limited combination. Comparative studies indicate that the Multi-Layer Perceptron shows an improvement in a model performance with a corresponding increase of the number of selected features. The suggested framework thus highlights interpretability in line with predictive efficacy thus enhancing the importance of transparent artificial intelligence systems in medical decision-making.
Authors - Veeravalli Sri Satya, Anjan Babu G Abstract - Human-computer interaction with smart consumer electronics predominantly requires physical peripherals, which introduce limitations regarding hardware degradation, shared-surface hygiene, and usability in hands-free environments. Voice-activated systems provide an alternative but exhibit high latency and degraded performance under ambient noise. This paper presents a multi-layered touchless gesture control framework that translates human hand kinematics into direct system actuation. The architecture utilizes a standard web camera and the Google MediaPipe framework to extract 21 three-dimensional hand landmarks in real time. To bypass the computational bottlenecks of traditional Convolutional Neural Networks (CNNs), the system employs a custom heuristic algorithm to classify eight distinct static and dynamic gestures by analyzing the geometric relationships between finger joints. The framework processes these classifications locally and actuates Android-based Smart TVs over Wi-Fi utilizing Android Debug Bridge (ADB) protocols [11]. Evaluated in a controlled environment, the pipeline achieved an average processing time of 35 milliseconds per frame (approximately 30 frames per second) with a network transmission delay of 50 to 80 milliseconds. The results suggest that computationally lightweight computer vision models, when paired with structured state-machine logic, can effectively replace physical remote controls without requiring dedicated GPU hardware.
