Authors - Thapanapong Sararat, Ratanachote Thienmongkol, Ruethai Nimnoi, Wongpanya S. Nuankaew, Pratya Nuankaew Abstract - Ensuring equitable access to library information systems is crucial in the digital era, particularly for visually impaired users who rely on assistive technologies. WebOPACs are key gateways to resources, but many remain difficult to use despite referencing accessibility standards. This study proposes a Disability-Centered Framework to improve accessibility and Universal Design in Thailand’s WebOPACs. Developed through design-based research, it integrates international accessibility literature, Universal Design principles, WCAG 2.1, and evaluation insights. The framework emphasizes three components: disability-focused design principles, classification of visually impaired users and needs, and task-specific accessibility requirements across perception, navigation, interaction, and assistive-technology compatibility. It also incorporates Thai linguistic, cultural, and technological conditions to bridge global standards and local implementation. Findings indicate that meaningful accessibility requires iterative testing and ongoing refinement rather than a one-time compliance check. This framework guides libraries, developers, and policymakers in enhancing WebOPAC accessibility and supporting inclusive access for visually impaired users in Thailand.
Authors - Srishti Mathur, Hrishita Patra, Suhani Verma, Dhruva R Prasad, Shylaja S.S Abstract - The conventional way of preparing an advertisement is an elaborate process incorporating human subjectivity and human resources heavily dependent on creativity. Making advertisements by human effort can be regarded as an inefficient utilization of capital for small to medium-scale businesses due to increased cost of production. Even in current advancements in the development of generative techniques including LLM-based strategies for Advertisement Generation with Prompts, creating apt prompts for the depiction of products requires human expertise, making them less accessible. In order to overcome the challenges presented by the current models, we introduce a fast, affordable, and scalable platform for the automation of advertisement generation for products leveraging the capabilities of pre-trained diffusion models. The proposed system requires no training or fine-tuning since everything is performed at the inference level. The AI-aware system for designing assists in the identification of color schemes and attributes from the images of the products, whereas the descriptions and categories of the items help identify the theme and pattern recommendations for advertisements. These recommendations are channeled through a pre-trained Stable diffusion model guided by the LLaMA language model.
Authors - Sneha Visveswaran, Tanmay Praveen, Vidula Gurudutta, Yamini Sridhar, Chaithra T S5 Abstract - Arecanut crop management has traditionally depended on manual inspection for disease identification and harvest readiness assessment, a method that is both time-consuming and susceptible to human error. This study introduces an automated, image-based system designed to address two primary tasks: disease classification and ripeness assessment. The proposed pipeline initiates with data preparation, including resizing, normalization, and augmentation of arecanut images to enhance model robustness. A convolutional neural network architecture, incorporating additional feature extraction and optimization layers, is utilized to detect disease symptoms. A comparable deep-learning model is trained to classify ripeness stages based on visual characteristics. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics to ensure reliability. The system is implemented via a user-friendly web interface, which allows real-time image uploads and immediate predictions, thereby facilitating practical application for farmers and agricultural stakeholders. This integrated solution provides a scalable and cost-effective approach to improving crop monitoring and supporting data-driven decision-making in arecanut cultivation.
Authors - Kate Lorreine M. Colot, Anjeneth G. Molina, Freely M. Wasawas, Ferlyn P. Calanda, Shem L. Gonzales, Richard B. Colasito Abstract - Despite the availability of digital voting systems, prior studies continue to identify gaps such as weak or voter authentication, security vulnerabilities and insufficient fraud prevention mechanisms. This paper presents BotoSafe, a secure and user-centered electronic voting (e-voting) platform developed for student government elections within educational institutions. The system implements multifactor authentication (MFA) using one-time password (OTP) verification and facial recognition with an anti-spoofing mechanism. To ensure the confidentiality and integrity of the voting process we employ the Advanced Encryption Standard in Galois/Counter Mode (AES-GCM). A developmental research design with a quantitative approach was used for the system development and evaluation. A mock election involving 84 students from Western Mindanao State University–Pagadian Campus was conducted, followed by a post-assessment survey. Results from the System Usability Scale (SUS) yielded a score of 72.08, indicating acceptable usability. User responses further showed that the system is easy to use, safe, and trustworthy for student elections. These findings indicate that BotoSafe is a viable e-voting solution for student government elections and may be further enhanced in future studies.
Authors - Eliza Borkute, Michael Savariapitchai, Vijeyandra Shahu, Deepak Sharma Chetan Parlikar Abstract - The current study aims to examine the significance of trust, perceived security, and awareness as factors that influence the adoption rate of UPI among private sector employees within the region of Chandrapur. The structured ques tionnaire has been designed to measure the following: a) trust factor regarding data protection and the correctness of the operations; b) perceived security level of UPI; c) awareness and knowledge about UPI functions; d) demographic characteristics related to education level, annual earning capacity, and age; and e) actual level of UPI adoption involving the use rate, continuous use of UPI, recommendations, and its integration with financial activities. Nonparametric statistical methods were used, including Spearman's rank correlation by investi gating the relationships of trust, security perception, awareness, and adoption. Kruskal-Wallis tests were conducted for finding group differences between ed ucation level and usage frequency. The results have accounted for strong, posi tive, and statistically significant associations between consumer trust, perceived security, awareness, and UPI adoption indicators. Education level revealed a partial moderating effect. Educated respondents tend to show higher trust and usage frequency in selected trust dimensions. However, this is not the case in all the aspects of this dimension. Additionally, the frequent users of UPI exhibit greater trust compared to the occasional users.
Authors - Tanay Balakrishna, Vishal Kumar Rahul, Yugabharathi E, Samanvi P, Vinay Joshi Abstract - The rapid spread of online news has made it more difficult to distinguish factually based reporting from misleading content. Many factchecking systems fail to detect false articles that appear professional and realistic, which leads to widespread disinformation. Most models rely on surface characteristics and neglect semantic coherence and factual consistency. An Improved Hybrid Fact-Checking System that combines language understanding, adversarial training, rule-based plausibility checks, and claim level web verification. These components run together in an ensemble model using BERT, BiLSTM, and an XGBoost meta-classifier to merge multiple evidence sources. Experiments on benchmark and curated datasets show an accuracy of 96.84% and a recall of 98%, outperforming existing deep learning methods. The results show that blending linguistic analysis with external verification leads to a robust and interpretable approach for automated fact-checking
Authors - Shraddha Mankar, Tanishq Thuse, Prasanna Khebade, Ritik Kumar Singh, Shravani Shirpurkar Abstract - Coronal Mass Ejections (CMEs) occurring in halo configuration are considered one of the most serious threats coming from space weather that can cause disruptions to most of the Earth’s geomagnetic facilities. The present study is about a hybrid machine learning system that detects the halo CMEs and predicts their Earth impact in real-time using the particle data coming from the in-situ India’s Aditya-L1 mission placed at L1 Lagrange point. We apply physics-informed feature extraction from SWIS-ASPEX payload measurements, obtaining alpha-to-proton density ratios, bulk velocity gradients, thermal parameters, and velocity anisotropy indices as CME markings. A Long Short-Term Memory (LSTM) neural network tuned through the Spider Cuckoo Optimization Algorithm processes 24-hour sequential windows of these features to distinguish between CME and non-CME events. The system also includes the modeling of Parker spiral propagation for Earth arrival time estimation and it is made available through a React-based dashboard with explainable AI components. The performance of the system reveals that it achieves a 98% detection rate along with a mean absolute error of 0.001 in the prediction of the normalized impact index. A comparison with historic halo CME catalogs indicates that our method has reduced false alarms by 85% when compared with threshold-based techniques while keeping the recall rate at 90%. The operational version of the system grants a 45-60 minute notification for the arrival of the CME, thus enabling the sensitive infrastructure to take preventive measures.
Authors - Sabo Hermawan, Ryna Parlyna, Surya Anugrah, Inkreswari Retno Hardini, Bayu Suhendry, Ria Rahma Nida, Windy Permata Suyono, Nur Lisa Rahmaningtyas, Eka Septariana Puspa, Cornellius Seno Adriano, Alifah Nur Rahmawati Abstract - Smart parking systems have developed as a critical solution to urban challenges such as traffic congestion, disorganized space utilization, and delays in manual parking searches. This study presents a smart parking framework that employs a Raspberry Pi 4GB, a camera module, and a servo motor for automated parking management. The system integrates a Haar Cascade classifier and YOLOv11 for accurate vehicle detection, while utilizing IR and ultrasonic sensors for obstacle identification. Real-time slot availability is displayed through an LCD interface. To ensure uninterrupted functionality, the system is powered by a solar panel with a rechargeable battery, enabling autonomous operation during power outages. Experimental results validate the reliability of vehicle recognition under varying illumination conditions, efficient gate control, and improved accuracy compared to conventional sensor-based approaches. This design offers a scalable, cost-effective, and energy-sustainable framework for urban parking solutions. Future work includes integration with cloud-based IoT platforms for centralized monitoring, optimization of YOLOv11 through lightweight variants for edge deployment, and extension to multi-level parking facilities with dynamic slot availability updates.
Authors - Sabo Hermawan, Ryna Parlyna, Surya Anugrah, Inkreswari Retno Hardini, Bayu Suhendry, Ria Rahma Nida, Eka Dewi Utari, Nur Lisa Rahmaningtyas, Cornellius Seno Adriano, Alifah Nur Rahmawati Abstract - This research investigates the performance of transformer-based models, BERT, ALBERT, and RoBERTa, fine-tuned for sentiment classification on the Women’s Clothing E-Commerce Reviews dataset. The overall task was executed under both 3-class and 5-class sentiment classification schemes. Each model was trained under the same conditions and evaluated comprehensively. In the 3-class task, RoBERTa achieved an F1-score of 91.7% and an AUC of 0.967, surpassing previous best-reported results. BERT also showed competitive performance with an F1-score of 90.2% and an AUC of 0.951. These results establish the superior generalisation ability and discriminative power of transformer models, particularly RoBERTa, in classifying sentiment from unstructured review text. ALBERT, while computationally efficient, showed reduced accuracy and AUC, indicating that extensive parameter sharing can hinder fine-grained sentiment resolution. The models exhibit broadly consistent behaviour in the 5-class setting, with RoBERTa maintaining a lead. A modest decline in F1 and AUC is evident, reflecting the greater difficulty introduced by finer class granularity. This research validates transformer architectures in a commercial Natural Language Processing scenario, demonstrating the superiority of transformer-based models over traditional baselines in both accuracy and robustness.
Authors - Jitesh Kriplani, Michael Savariapitchai, Vijeyandra Shahu, Deepak Sharma, Chetan Parlikar Abstract - The present investigation discusses the influence of social media in fluencers on the choices made by consumers and their buying behavior, espe cially in connection with important personality traits of the influencer, such as emotional engagement, authenticity, and reliability. The scientists conducted a well-organized survey questionnaire that collected primary information from 360 respondents in the Wardha District. Using Spearman's rank correlations re sults indicated strong, positive and statistically significant relationships between influencer behaviors and consumer purchase behaviors indicating that influenc ers have a significant impact on consuming behaviors of consumers. The results of a one-way ANOVA found that perceptions of influencer credibility (includ ing honesty and sponsorship disclosure), as well as perceptions of emotional engagement and authenticity, were significantly different depending on the fre quency of social media use by the participant. The demographic analysis also examined differences in consumer reactions depending on age, gender, and in come, finding no significant difference across age groups, but significant differ ences related to income and gender. The study concludes that consumer en gagement increases with more frequent social media use and influencer effec tiveness is significantly related to the authenticity, transparency, and credibility of the communication. Findings highlighted the need for focused influencer marketing content based on demographics providing empirical evidence of in fluencer marketing on consumer behavior.
