Authors - Sanket Shah, Jenice Bhavsar, Bhumi Shah, Jishan Shaikh, Khevana Raval, Ekta Vyas Abstract - Dyslexia is a neurodevelopmental condition that impairs reading fluency and phonological processing across languages. Early identification in school settings remains difficult because the Dyslexia Assessment for Languages of India (DALI) assessment tool requires expert administration which makes it difficult to implement in practice. The latest developments in artificial intelligence allow researchers to evaluate reading patterns through inexpensive devices which people commonly use. The research presents a system framework that uses multiple methods to combine webcam-based eye-tracking with voice analysis and machine learning methods for early dyslexia detection. The system examines tabular gaze and speech features through gradient-boosted models while using convolutional neural networks to encode spatial gaze patterns which include a meta-learning layer for multimodal fusion. The proposed framework enables practical implementation through its web-based interface which connects to secure backend services, thus providing schools with a privacy-protected and scalable method to conduct dyslexia assessments and provide personalized learning assistance in their resource-limited classrooms.
Authors - Geethashree A, Surabhi M R, Varshitha H N, Vipul S, Vivek M R Abstract - The RISC-V Vector Extension (RVV) enables scalable data-parallel processing through a flexible vector length architecture, offers a standardized and scalable approach to vector computing. Derived from an analysis of existing RVV architectures, this paper presents a focused architectural study and implementation of a basic RVV-based vector extension. Unlike complex, high-performance designs, the proposed architecture prioritizes simplicity and clarity, implementing only essential vector arithmetic and memory instructions. The vector extension is integrated with a single-cycle scalar RISC-V core, and instruction decoding is implemented and verified at RTL level. Functional simulation confirms correctness of RVV instruction decoding. This work bridges the gap between theoretical RVV studies and practical step-by-step hardware implementation.
Authors - Lalitha R, Husna Sarirah Husin, Suriana Ismail, Nikitha S, Kavya Darshini S, Pooja M Abstract - The data from Tamil Nadu government MSME programs is a treasure trove, but the information is fragmented and scattered in different kinds of documents. Consequently, it becomes a task for both the public and the analysts to process the data and get important insights. The paper introduces LKD-RAG, an explainable hybrid retrieval-augmented generation (RAG) system that relies on LLMs and KGs to make natural language queries possible on the data of these schemes collected from different sources. In the initial phase, the LLM started autonomously to discover entities, relations, and attributes, which eventually led to the creation of structured triples that signify factual statements (subject-predicate-object). The knowledge represented by these triples was loaded into Neo4j, thereby producing a MSME Scheme KG that is specific to the domain. Also, a document embedding layer was set up with SentenceTransformer ("all-MiniLM-L6-v2") that made it possible to do semantic retrieval of supporting textual evidence. When a query is made, Gemini decodes the person’s inquiry, finds relevant KG subgraphs and text embeddings, and constructs a response that is grounded on the evidence. The subgraph that corresponds to the answer is shown to the user, so the user can check what knowledge the model is relying on for its reasoning. Thus, the process facilitates transparency and the use of explainable AI (XAI) in policy analytics. The results of the experiments indicate that the hybrid RAG model not only has the ability to generate factually accurate responses but also to provide interpretation through different Tamil Nadu MSME programs.
Authors - Krashn Kumar Tripathi, Sachin B. Jadhav Abstract - In digital world, cyber-attacks are becoming more sophisticated and popular. The conventional intrusion detection models are not adequate in challenging threat escapes. Importantly, the major reason for increasing demand in the networks, unauthorized access is increasing their interests in these areas. Various network environments and organizations are tackling numerous of attacks on their network at frequent times. Traditionally, various manual methods are used for intrusion detection such as packet and flow analysis, traffic log reviewers and monitoring the security. Nevertheless, the manual techniques for such type of the detections takes too much time and also the result obtained is not up to the mark, so due to this it is difficult to predict all types of attacks and intrusions for network security. To overcome these issues, several conventional researches have concentrated on intrusion detection models to offer effective security to the networks. Conversely, it results with accuracy and speed lacks. For enhancing the intrusion detection, research make use of a Deep Learning (DL) Unravelled Spatial Features in Multilayer Perceptron with Gradient Jacobian Matrix. Gaussian Activation is used to enhance the Intrusion detection system for an effective classification. In the proposed research work we are using the RT-IoT dataset and the final efficiency has been analyzed by using various parameters like overall correctness, actually correct, correctly identified by the model,and the balance between the both values of recall and precision (Harmonic Mean). Furthermore, the current work and the proposed model is developed to contribute to avoid the different cyber threats by timely identifying such type of intrusion in the networks.
Authors - Maulana Amirul Adha, Maulana Paramaditya Ananta, Bayu Suhendry, Ria Rahma Nida, Eka Dewi Utari, Nur Athirah Sumardi Abstract - The challenge of generating accurate and contextually complete mod-els and prompts in Model-Driven Engineering (MDE) using Large Language Models (LLMs) is based on the current limitations in understanding the complex structured data. The significance of this issue lies at the heart of modern software development where MDE has taken the lead to advance development in the field moving towards with the aim of automating manual processes. To increase this automation, the application of LLMs holds the potential to reduce the manual effort and reduce human error involved in the process. To address this, we pro-pose a context-based prompt generation framework that integrates the techniques of Retrieval-Augmented Generation (RAG) with LLMs such as GPT-4 and CodeLlama to produce prompts that are contextually accurate and sound. Along with these LLMs, tools like FAISS, LangChain, and PlantUML are also em-ployed to produce detailed and structurally accurate UML models and prompt to enhance MDE understandability. In summary, the proposed framework aims to improve the accuracy and completeness of model generation by providing a con-textually correct prompt with a high level of accuracy and enhances the interpret-ability and ability of trust in AI-generated artifacts, creating the way for more efficient, automated, and user-friendly MDE processes.
Authors - Pierre Buys, Tevin Moodley Abstract - This paper presents a real-time chessboard state detection system that leverages computer vision and deep learning to automate a digital representation of a physical chess game. Traditional digitization systems either require manual input or specialized equipment. However, the proposed system addresses this problem by capturing a chess game in real time through the use of a smartphone camera. Detected piece positions are mapped to standard board coordinates and translated into Forsyth-Edwards Notation (FEN), enabling seamless integration with existing chess engines for analysis and move suggestions. The system works by firstly localizing the chessboard via Canny edge detection as well as a Hough transform. Thereafter, multi-class object detection is addressed by developing a two-stage R-CNN model alongside a single-stage YOLO model, allowing for a comparative evaluation of their respective methodologies and performance. The described system achieves a localization precision of 98.77% per board coordinate, whilst the two-stage R-CNN and single-stage YOLO models achieve a piece detection accuracy of 83.62% and 99.47%, respectively.
Authors - Hardik Modi, Mayur Makwana, Sagarkumar Patel, Dharmendra Chauhan, Siddhi Patel, Dhara Soni, Malvi Patel Abstract - Early and accurate detection of brain tumors is a critical requirement in modern clinical diagnostics, as it directly affects treatment planning, disease prognosis, and patient survival rates. The rapid increase in the availability and complexity of medical imaging data has intensified the need for reliable computer-aided diagnosis (CAD) systems to assist radiologists in consistent and precise tumor identification. Among various CAD techniques, medical image segmentation plays a pivotal role in differentiating abnormal tumor tissue from healthy brain structures in diagnostic images. This paper presents an automated brain tumor detection framework based on medical image analysis, implemented using a MATLAB-based graphical user interface. The proposed system processes Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scans through a structured processing pipeline that includes image acquisition, noise reduction, contrast enhancement, feature-based segmentation, and tumor region visualization. The segmentation methodology is designed to accurately localize tumor boundaries while minimizing false-negative detections, which is a crucial requirement for clinical decision-making. The developed interface enables interactive visualization of segmented regions, allowing efficient analysis without the need for extensive computational expertise. The proposed framework offers a user-friendly and computationally efficient platform that reduces reliance on manual interpretation and improves diagnostic repeatability across clinical environments. The novelty of this work lies in the seamless integration of automated tumor detection, structured segmentation techniques, and real-time visual interpretation within a unified MATLAB-based environment, providing a practical and accessible CAD solution without dependence on complex hardware or deep learning infrastructures. Experimental observations indicate that the system enhances analysis efficiency and supports medical professionals in making faster, more reliable, and time-effective diagnostic decisions.
Authors - Najera R. Umpar Abstract - Artificial Intelligence (AI), as a technology, has the potential to change the manner in which organizations are run in the world. However, small and medium-sized enterprises (SMEs) in the Philippines have unique limitations in the use of AI in running the business. The study aims to explore the perceptions of SME managers in the Philippines on the use of AI, with particular reference to the limitations and facilitators in the use of the technology in the business environment. In this study, the researcher interviewed five SME managers from different sectors, including retail, manufacturing, and service sectors. The researcher used thematic analysis to identify the commonalities in the decisions made by the SME managers on the use of AI in the business environment. The study revealed the perceptions of the SME managers on the use of AI in the business environment in the Philippines, with the limitations and facilitators in the use of the technology in the business environment. The study provides practical insights that can guide strategies aimed at strengthening AI readiness and responsible adoption among SMEs in the Philippines.
Authors - Nilay Shah, Darsh Pandya, Nisarg Patel, Rudra Shah, Umang Shah, Dhaval Patel, Priteshkumar Prajapati Abstract - Early and accurate detection of brain tumors is a critical requirement in modern clinical diagnostics, as it directly affects treatment planning, disease prognosis, and patient survival rates. The rapid increase in the availability and complexity of medical imaging data has intensified the need for reliable computer-aided diagnosis (CAD) systems to assist radiologists in consistent and precise tumor identification. Among various CAD techniques, medical image segmentation plays a pivotal role in differentiating abnormal tumor tissue from healthy brain structures in diagnostic images. This paper presents an automated brain tumor detection framework based on medical image analysis, implemented using a MATLAB-based graphical user interface. The proposed system processes Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scans through a structured processing pipeline that includes image acquisition, noise reduction, contrast enhancement, feature-based segmentation, and tumor region visualization. The segmentation methodology is designed to accurately localize tumor boundaries while minimizing false-negative detections, which is a crucial requirement for clinical decision-making. The developed interface enables interactive visualization of segmented regions, allowing efficient analysis without the need for extensive computational expertise. The proposed framework offers a user-friendly and computationally efficient platform that reduces reliance on manual interpretation and improves diagnostic repeatability across clinical environments. The novelty of this work lies in the seamless integration of automated tumor detection, structured segmentation techniques, and real-time visual interpretation within a unified MATLAB-based environment, providing a practical and accessible CAD solution without dependence on complex hardware or deep learning infrastructures. Experimental observations indicate that the system enhances analysis efficiency and supports medical professionals in making faster, more reliable, and time-effective diagnostic decisions.
Authors - D.K. Chaturvedi, Tipu Sultan Abstract - A real-time operating system (RTOS) should be able to recover from interruptions. Since RTOS systems are used in safety-critical environments, this function is essential for ensuring system availability and reliability. However, while many of the current anomaly detection techniques can detect faults, they do not provide any means for recovery. Therefore, in this paper, I propose a self-repairing RTOS framework that utilizes reinforcement learning (RL) to automatically select the best course of action to take when an anomalous event arises. I propose a Q-Learning agent that learns to recover from six types of common faults, including: sensor degradation, stuck sensor, priority inversion, memory leaks, sporadic overloads, and task starvation. The framework is built on FreeRTOS, and the agent utilizes an 8-dimensional state space and the six different types of recovery options available for each fault. The overall success rate of the system was 99.2 % after 5,000 training episodes, with average success rates of 98.0 % and 99.9 % when handling individual faults. The RL agent completely prevented system crashes and returned the system to normal operation within an average of 0.06 ms after an interruption occurred. The training results provide strong evidence that the model learned to operate effectively and consistently, with its success rate improving from 97.0 % during early training stages to 100 % after training was completed. Therefore, this study demonstrates a practical, production-ready method to implement autonomous fault recoveries in RTOSs in automotive applications. To our knowledge, this is the first successful implementation of RL for autonomous, self-repairing behaviors in this area.
