Authors - Jason Elroy Martis, Ronith, Anvitha Rao, Vignesh Salian, Apoorva Shetty, Philomina Princiya Mascarenhas Abstract - The task of recovering high-level architectures from embedded software systems is error-prone and difficult, and state-of-the-art methods still rely on static analysis or heuristics and lack explainability. To address these challenges, an explainable and automated method for recovering high-level architectural diagrams directly from source code is suggested. Specifically, this method begins with the generation of function call graphs at the function level via static analysis and functions grouping into domain-agnostic component classes, generating a component graph. Components are then augmented with semantic attributes learned via CodeBERT embeddings, facilitating a light graph convolutional network (light GCN) model for learning-component interactions reflecting structure and semantics. Methods for explainability via gradients are incorporated for emphasizing prominent components and edges, helping in developer understanding, validation, and tuning of predicted architectures. The performance of this method on several embedded projects showed accuracy as high as 91.87%, precision of 96.48%, recall of 86.90%, and an F1-score of 91.44%. Use cases have shown successful extraction and interpretation of critical paths, bottlenecks, and unusual architectures and highlight explainable insights that enable efficient analysis and thus make it a highly significant progress in explainable AI for embedded software.
Authors - Nazia Sultana, Kumar P K Abstract - This research details the design and implementation of the AI-Driven Penalty Performance Analysis System, a desktop application aimed at bridging the technological divide in football analytics. The system focuses particularly on environmental and situational influences, such as crowd size, match context, and time of day, on penalty outcomes. The system employs a robust data pipeline and a comparative evaluation of multiple machine learning classifiers to predict the likelihood of penalty kick success. Using a dataset of professional penalties, we engineered novel features such as a ‘PressureIndex‘ to quantify situational fac tors. A suite of models, including Logistic Regression, K-Nearest Neighbours, Decision Tree, Random Forest, and Gradient Boosting, was trained and evalu ated. The optimal Gradient Boosting model achieved an accuracy of 79.1% and an AUC-ROC score of 0.87. A critical contribution is the integration of Explain able AI (XAI) using SHapley Additive exPlanations (SHAP), which transforms the system from a predictive ’black box’ into a transparent, diagnostic tool. This provides coaches and players with actionable, data-driven insights, validating the system’s potential to democratize advanced sports analytics.
Authors - Ankita Manohar Walawalkar, Chun-Wei Remen Lin, Suman Kumar, Ming-Yen Wang Abstract - The growing dependence on digital platforms for service discovery has revealed a substantial visibility gap for local businesses and independent service providers. Skilled professionals, in-cluding electricians, beauticians, bakers, tutors, mechanics, tailors, and photographers, frequently encounter challenges in reaching potential customers due to limited marketing expertise, financial barriers, and the lack of an integrated digital marketplace. This study introduces SkillBizz, a mo-bile platform intended to connect local service providers and businesses with nearby users through a community-driven, location-aware interface. The application features a scrollable home feed that prioritizes services and businesses based on geographical proximity, allowing users to refine their results using filters such as service category, budget range, distance, and popularity. Service providers can promote their offerings through multimedia posts that highlight services, offers, and announcements, while users engage through familiar social media features, including likes, comments, saves, and shares. By facilitating free and organic visibility without reliance on paid advertising, SkillBizz aims to support local entrepreneurship and foster trust-based service discovery. The proposed platform aims to create a digital marketplace that seeks to enhance com-munity engagement, improve service accessibility, and promote sustainable economic growth. In a short survey, students rated the app’s ease of navigation and overall usefulness highly, with an average satisfaction score of 4.5/5, indicating strong acceptance and positive user experience. Shop owners noted that the app provides an easy way to share product updates, promotions, and service news directly with local customers, with 80% expressing interest in continued usage due to time-saving benefits and improved customer reach.
Authors - Karuna A. Katakadhond, Manohar Madgi Abstract - Groundnut being a major oilseed crops, contributes to nearly 10% of the total value of produce from agricultural crops in India. Several researches indicate that disease infestations at different stages of crop growth can lead to 30-70% of yield reduction and significant economic losses. This challenge can be addressed by using Artificial Intelligence (AI) based smart monitoring and recommendation systems through early detection, identification, and prediction of crop diseases. The primary objective of the study is to develop an AI driven smart monitoring framework capable of detecting, identifying, and predicting biotic and abiotic factors responsible for major disease occurrences in groundnut plants. Additionally, the systems goal is to provide an effective and efficient recommendation system for sustainable agriculture from an integrated and practical perspective with its technical and economic performance to the farmers for managing the field level infestations. This includes prediction of diseases and timely recommendation of plant protection chemicals which may reduce the yield loss and enhance the productivity of the crop.
Authors - Usman Ali, Ghulam Mohayud Din, Sajid, Ayesha Ali, Munawar Hussain, Muhammad Mujeeb Akbar Abstract - The proliferation of misinformation on social media poses significant social, political and economic risks. This research proposes an AI-based fake news detection system that leverages deep learning (BERT and LSTM) and Explainable Artificial Intelligence (XAI) frameworks to classify online fake news as Fake or True. The proposed architecture processes textual data through Natural Language Processing (NLP) techniques for semantic and contextual analysis. To ensure Interpretability, SHAP and LIME is Integrated to visualize the rationale behind classification results. The system was trained using balanced datasets augmented through SMOTE, achieving over 95% accuracy. A web-based interface was developed to facilitate real-time text and URL verification, providing confidence scores and explanations. This approach minimizes human intervention, enhances transparency and explainable frameworks yields an accurate and trust-worthy tool for combating misinformation.
Authors - Suphawatchara Malanond, Pongsarun Boonyopakorn Abstract - In the food supply industry, differentiating between cultivated and weedy rice is crucial since the latter interferes with production and competes for essential resources. This research utilizes the YOLOv8 object detection model to automate the classification of rice grains to improve the separation process. The dataset was gathered during the harvesting phase and annotated utilizing a typical bounding-box methodology. Multiple configurations were evaluated with different model sizes (nano, small, medium) and training epochs. The optimal results attained a precision of 0.845, a recall of 0.779, and a mAP@50 of 0.822. These findings indicate that YOLOv8 enables near real-time identification at the grain level, diminishing dependence on manual verification. The study yielded a lightweight prototype developed to demonstrate and reflect the application of the trained model for rapid, image-based screening by non-technical users. The significance of the study lies in its support for more effective rice quality management and its contribution to strengthening food security and sustainable agriculture.
Authors - Wongpanya S. Nuankaew, Parichat Janjom, Khwanchiwa Khumdaeng, Rattiyaporn Laemchat, Thapanapong Sararat, Pratya Nuankaew Abstract - Communication has been a topic as ancient as man and at the same time so important that, over time, various forms have been cre- ated to facilitate it, among which stand out: mail, telephony, telegrams, and fax, to name a few. Nowadays many people use instant messaging applications to communicate with each other by feeling that their con- versations are protected. However, that feeling could not be further from reality and should not be taken lightly, since there are always groups focused on taking advantage of the vulnerability of this kind of applica- tions, resulting in users’ privacy being compromised. In this paper, we present the development of an instant messaging application that inte- grates a novel key establishment protocol based on a quantum-resistant algorithm. Our application employs cutting-edge lattice-based crypto- graphic techniques, ensuring robust security against quantum attacks while maintaining operational efficiency. Obtained results show the ap- plication’s viability by offering a practical solution to safeguard mobile communication in the impending quantum era.
Authors - Rashmi Shivanadhuni, Martha Sheshikala Abstract - The rapid expansion of QR-code payment systems has positioned QRIS as a key component of Indonesia’s national digital payment infrastructure. While prior studies have largely focused on initial adoption, limited empirical evidence explains the factors that sustain long-term usage of QR-code payments in mobile banking. This study investigates the determinants of sustained QRIS adoption by examining the roles of perceived usefulness, perceived ease of use, trust, and perceived security, with user satisfaction as a mediating variable. Using a quantitative approach, survey data were collected from QRIS users of mobile banking applications and analyzed using Structural Equation Modeling (SEM). The results indicate that perceived usefulness, trust, and perceived security significantly enhance user satisfaction, which in turn strongly predicts sustained adoption of QRIS in mobile banking. Perceived ease of use shows a weaker direct effect, suggesting that post-adoption behavior is driven more by value realization and trust than by usability alone. These findings contribute to ICT and fintech literature by highlighting user satisfaction as a critical post-adoption mechanism for sustaining engagement with national digital payment systems. Practically, the study offers insights for policymakers, banks, and system designers to strengthen the long-term viability of QR-based payment infrastructures through trust-building and value-enhancing strategies.
Authors - Suman Kumar, Yeneneh Tamirat Negash, Ankita Manohar Walawalkar, Ming-Yen Wang Abstract - The backbone of modern data infrastructure which demands strategies to ensure data availability and uptime is Cloud Storage. This paper provides a complete overview of redundancy models and storage techniques that are used to maintain data availability and uptime in cloud storage systems. It covers core redundancy methods like data replication, erasure coding, Raid and disk-level redundancy, multi-cloud redundancy and hybrid models. This paper also provides storage techniques that support data availability like distributed file systems and object storage platforms for scalability and flexible access. Additionally, the paper also presents a literature review of key research findings and compares models that demonstrates substantial improvements in reliability and storage efficiency. It also covers the challenges related to computational complexity and monitoring precision. By synthesizing theoretical and practical perspectives, this research guides the design of cloud storage solution which balance availability, cost and recovery objectives and also help stakeholders to meet stringent service level agreements in increasingly heterogeneous and large-scale cloud infrastructure.
Authors - Massoud Moslehpour, Suman Kumar, Hanif Rizaldy, Ankita Manohar Walawalkar, Thanaporn Phattanaviroj Abstract - Accurate identification of paddy crop growth stages plays a crucial role in effective agricultural planning, crop management, and yield estimation. Paddy cultivation is highly sensitive to environmental conditions, disease progression, and growth variability, making continuous and automated monitoring essential. This paper presents an AI-driven framework for automated paddy growth stage identification and yield readiness estimation using deep convolutional neural networks. The proposed system employs the EfficientNetV2-S architecture trained on heterogeneous paddy plant image datasets collected from multiple public sources. To address inconsistencies in labeling across datasets, a semantic stage-mapping mechanism is introduced to map dataset-specific visual classes into standardized paddy growth stages. Furthermore, a confidence-weighted yield readiness index is formulated to provide an interpretable estimate of crop maturity and harvest readiness based on predicted growth stages. The trained model is deployed using a Flask-based web application that supports real-time inference, result visualization, and storage of historical predictions. Experimental results demonstrate stable convergence, high classification accuracy, and reliable generalization across different growth stages. The proposed framework effectively bridges visual growth stage classification and yield estimation, offering a practical and scalable solution for precision agriculture and decision support systems.
