Authors - Konstantina Rigou, George Dimitrakopoulos Abstract - The rapid adoption of Artificial Intelligence (AI) in high-impact domains (healthcare, finance, justice) creates an urgent need for sys tems that are legally compliant, explainable, ethical and transparent. Decision Support Systems (DSS) aim to assist managerial and professional decision-making, yet few works translate legal and ethical principles into concrete technical design constraints for explainable AI (XAI). This paper proposes a Legal Explainability Framework (LEF) that maps legal obligations (General Data Protection Regulation, European Union Artificial Intelligence Act) and ethical principles to measurable XAI requirements and implementation steps, and demonstrates the approach with a prototype using an open legal dataset derived from judgments of the European Court of Human Rights (ECtHR). The results show that legally compliant XAI is not merely a normative aspiration, but a technically feasible and practically implementable design paradigm.
Authors - P.Pandiaraja, N.Shiva Kumar, B.Vishnu Vardhan, C.Sevarathi, Charles Prabu V, S.Jagan Abstract - Retrieval-Augmented Generation (RAG) chatbots represent a significant advancement in intelligent conversational systems, grounded in the prin-ciples of natural communication, accuracy, and reliability. Traditional chatbots are constrained by pre-trained knowledge or rule-based responses, limiting their effectiveness in dynamic and complex real-world scenarios. RAG-based systems integrate information retrieval mechanisms with sophisticated language generation models to identify relevant knowledge in real time and produce contextually appropriate responses. The proposed system employs sentence-transformers (all-MiniLM-L6-v2) for dense vector embeddings and FAISS as the vector data-base backend, enabling fast and semantically accurate document retrieval. Ex-perimen- tal results demonstrate a mean retrieval accuracy of 87.4%, an average response latency of 1.3 s, and a user satisfaction score of 4.2 out of 5, confirm-ing the system’s readiness for real-world deployment.
Authors - Manjula K, Vijayarekha K, Venkatraman B Abstract - The fabrication of components across various industries is accom plished through welding. Although welding has been practiced for more than a hundred years, defects may still occur during the welding process. Thus, indus trial standards require welded joints to be inspected and evaluated to ensure their quality and reliability. Conventional ultrasonic testing (UT) has long been widely used in industry for detecting and evaluating defects in weld specimens. Over the last few decades, advances in sensor technology and signal analysis techniques have significantly advanced ultrasonic testing methods. Advanced methods, such as Time Of Flight Diffraction (TOFD), are more likely to detect linear defects. However, one of the major challenges in applying TOFD to the inspection of austenitic stainless steel (ASS) weldments is noise in the signals. Various signal processing approaches have been developed to suppress such noise, each with its own advantages and limitations. In this work, the focus is placed on the applica tion of multi-level discrete wavelet transform (DWT) decompositions with ‘n’- order wavelet filters for de-noising ultrasonic TOFD A-scan signals. The results show that this approach achieves greater improvement in signal-to-noise ratio (SNR) while requiring less computational time.
Authors - Likhitha Ragha Ramya Nakka, Anuradha Andra, Appalaswami Ravada, Vinay Kumar Pamula Abstract - This study uses Roland Barthes' semiotic approach to analyze how meaning is represented in HMNS' Untitled Humans ad on Instagram Reels. Understanding how storytelling campaigns create and communicate meaning has become crucial for successful digital marketing as social media plays a big-ger role in brand communication strategies. This study examines a selection of Instagram Reels content from the official Instagram @hmns account using a qualitative-descriptive methodology, emphasizing how text, sound, and visual components interact to provide multiple interpretations. The study methodically sign how everyday occurrences, human relationships, and nature scenery are turned into symbolic representations of authenticity, freedom, and personal identity using Roland Barthes' three-level semiotic framework: denotation, connotation, and myth. Direct observation and content documentation of Reels recordings are used for data gathering, and triangulation is used for analysis to guarantee validity and thoroughness. Results show that by creating an existential story that prioritizes closeness, introspection, and human connection, the campaign goes beyond traditional product advertising. Authentic, unconstructed life imagery is presented at the denotative level, visual and musical elements evoke emotion and personal memory at the connotative level, and perfume, rather than being a commercial product, becomes a symbol of emotional intimacy and identity exploration at the mythic level.
Authors - Deepak Mane, Siddhi Dhamal, Shivam Devkar, Divit Maheshwari, Riddhi Kaulage, Diya Nair, Deepak R. More Abstract - The evaluation of handwritten answers sheet has so many challenges since from many years due to variability in handwriting, linguistic barrier and personal bias. This is very time-consuming method and inconsistent method which highlights the need for automated subjective answers evaluation. Here, proposed automated handwritten answers evaluation system uses TrOCR based handwritten answer detection, NLTK tokenization, WordNet lemmatization and semantic similarity check between teacher’s and student’s answer based on meaning. This advanced multi-model system overcomes traditional keyword matching technique and improves contextual accuracy. This system also overcomes traditional manual checking and results in fast evaluation. The system promotes the fairness, fast and accurate processing. Moreover, the suggested framework removes human fatigue, encourages fair grading, and offers a solution that can be used for large-scale academic tests. The results show that this automated method not only works like a human brain but also makes the evaluation process more fair and open.0
Authors - Deepak Mane, Deepak R. More, Arya Kale, Ravina Jagtap , Soumya Dubewar , Diya Nair Abstract - Timely detection of crop diseases is essential to ensuring high agricultural produc- tivity; thus, early and accurate detection has always been a priority for the farmers. So we pro- posed a deep learning based framework that classifies the condition of basil leaves in three cat- egories - wilting, infection by mildew and healthy - through an EfficientNet-B0 convolutional neural network fine-tuned using transfer learning. We leverage a curated dataset of 1,442 plant images available at the Roboflow platform, splitting the dataset into 70% training, 20% valida- tion and 10% testing. Transfer learning was used where we started EfficientNet-B0 with weights learned on large scale ImageNet pretraining. Training was done in two stages: first the whole model was trained with the backbone frozen and only the newly added classification head being trained, followed by unfreeze the last 100 layers and perform fine-tuning to the domain. Leaf orientation and illumination variability were treated by a group of data augmentation methods including random horizontal flipping, rotational transforms, zoom perturbations, and contrast adjustments. The proposed system achieved a remarkable result with high generalization of 96.6% training accuracy and 97.8% test accuracy. The detailed analysis of the confusion matrix and the ROC-AUC curves corroborate faithful multi-class discrimination. A Streamlit-based web interface was also developed to facilitate live inference, farmers and agronomists are now able to make immediate predictions of the disease with confidence estimates. The results showed that the well optimized EfficientNet-B0 model can be a feasible and scalable solution for automated monitoring of crop diseases in the context of smart agriculture.0
Authors - Vinodkumar Bhutnal, Prajwal Vijay Sonawane, Om Vinod Chaudhari, Avinash Golande, Mohit Ashok Tajane, Sujal Kishor Papdeja Abstract - There is no more pressing issue in modern cities, industries, and public venues than nighttime security, as the conventional approach of patrolling in-person only works well until fatigue and coverage become challenges, when humanity and human error become a finite issue that requires short delay interruptions. Urbanization, increased crime rates, and the inadequacy of current traditional patrolling to provide a sufficient security posture have led to the proposal of an Intelligent Night Patrolling System that uses edge-cloud frameworks, IoT-enabled CCTV camera technology, and artificial intelligence video analytics to significantly reduce the presence gap. This system will provide continuous, real-time proactive surveillance of locations and even be equipped with advanced deep learning models like Cummings Neural Networks (CNNs) and Long Short term Memory (LSTM) to detect suspicious activity, anomalies, intrusions, and violent types of activities. This research introduces the concept of Night Patrolling System designed to assist security personnel during night surveillance.The proposed system achieves an estimated accuraxy of over 90% with a reduced latency , demonstarting it’s effectiveness for a real time survillence applications.
Authors - Deepak T. Mane, Deepak R. More, Gopal D. Upadhye, Rucha C. Samant, Hemlata U. Karne, Suraksha Suryawanshi, Prem Borse Abstract - Efficient vehicle type classification is vital for intelligent transportation systems, traffic monitoring, and urban mobility planning. This paper presents a Real-time Multimodal Vehicle Type Classification System that leverages both visual and acoustic data to identify and categorize vehicles such as cars, buses, trucks, and motorcycles from live video streams. The proposed system integrates CNN-based and Transformer- based models for feature extraction across modalities, enhancing detection robustness under diverse lighting, weather, and traffic conditions. A lightweight preprocessing pipeline performs synchronized frame extraction, audio segmentation, and feature fusion while ensuring minimal latency in real-time environments. The proposed multimodal architecture combines late fusion of visual and audio features to enhance the reliability of classification when either modality is suffering from low visibility or occlusion. Experimental evaluations demonstrate that the proposed framework achieves a classification accuracy of 96.2% at 28 fps, outperforming unimodal baselines with real-time efficiency. This system is deployable for intelligent traffic surveillance, automated tolling, and urban safety analytics.
Authors - Shwetha Ramadas, Krutthika Hirebasur Krishnappa, Sudhir Trivedi Abstract - Methane (CH4) emission from rice paddies is a significant source of greenhouse gas emissions from agriculture. Currently, most models for methane prediction from rice paddies depend on collecting field data and sending it to a server. In this new paradigm, several privacy concerns arise, model scalability is restricted, and a large number of data points are exposed to the attacker. This paper addresses all privacy con cerns by providing an edge-based solution for modeling methane emis sions from rice paddies that leverages data from edge sensors at respec tive locations, while keeping individual sensor data private. The method employs different machine learning (ML) algorithms, including Linear Regression, Random Forest, XGBoost, and a Feedforward Neural Net work (FNN), implemented using TensorFlow Federated (TFF) in both centralized and federated learning (FL) frameworks. The FL-based FNN achieved an R2 score of 0.91, which was superior to both centralized classical and centralized FL models, especially for highly non-IID client side data distributions in sensor datasets. In summary, this paper extends the current literature on modeling methane emissions from rice paddies and provides a comprehensive evaluation of our proposed FL system ar chitecture, an in-depth discussion of the communication resources re quired for FL implementation, and an examination of the effects of abla tion studies on clients’ data heterogeneity. Therefore, the proposed FL approach is efficient and scalable, enabling safe, privacy-preserving modeling of methane emissions from rice paddies to effectively imple ment Climate Smart Agriculture (CSA) and mitigate global warming while supporting sustainable rice cultivation.
Authors - P. Pandiaraja, P.Krishna Kishore, E. Ganesh, C. Selvarathi, Charles Prabu V, S. Jagan Abstract - Large Language Models have facilitated the development of sophist i-cated smart platforms that are actively leveraged in the provision of financialservices to various classes of customers. This advancement has enabled peopleto obtain individual financial advice. This paper presents a framework for buil d-ing a financial chatbot that incorporates Retrieval Augmented Generation(RAG) technology and several SQL agents to improve reliability. The proposedapproach addresses five fundamental challenges in financial artificial inte ll igence: eradicating hallucinations, obtaining up to date information, utilising u s-er facts to tailor individual suggestions, safeguarding user privacy, and provi d-ing clear explanations. RAG is used to retrieve verified financial knowledge,while SQL agen ts query databases to produce accurate outputs. The solutionprovides advisory responses that are relevant to users and protect sensitive i n-formation through a zero trust security architecture. The system architecture i n-corporates multiple validation check points and is dynamically configured tomeet individual user requirements. Experimental results demonstrate a 96.2%accuracy rate in handling financial queries with a 3.8% error rate and a mean r e-sponse time of 1.5 seconds, outperforming comparable solutio ns. The proposedarchitecture establishes a reliable baseline for financial professionals seekingdependable advisory services.
