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.
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.
Authors - Chaitra Sai Chakravarthi Ganapaneni, Rishik Reddy Cheruku, Venkata Karthik Chamarthi, Venkata Sasidhar Kommu, Malathi P Abstract - Academic websites function as institutional interfaces connecting universi-ties with multiple stakeholder groups. Many institutions face challenges in developing web presences that address usability, accessibility, and stakeholder needs simultaneously. Existing frameworks address isolated dimensions without providing integrated guidance. This research proposes a conceptual design framework for academic websites that integrates Web Con-tent Accessibility Guidelines (WCAG) 2.1 Level AA standards with Nor-man's design principles. The framework consists of four core segments (In-terface Design, Content Accessibility, Technical Performance, User Experience) and four modular add-ons categories (Career and Job Opportunities, Student Projects Showcase, Alumni Community, Industry Collaboration). Framework validation employed dual evaluation methods to ensure both conceptual soundness and stakeholder relevance. Expert judgment assessment (n=5) achieved complete agreement on conceptual soundness. Quantitative user assessment (n=450) across six stakeholder groups showed that framework components achieved good performance levels (mean scores 3.58 to 3.70) and add-ons features received high priority classifications (mean scores 3.62 to 3.80). The framework contributes systematic integration of accessibility standards with design principles and provides guidance for institutions developing academic websites.
Authors - Amulya Saxena, Pratibha Joshi, Adwitiya Sinha Abstract - Global food security and hunger mitigation is one of the major challenges ahead of us. The global population specifically from underdeveloped countries are quite vulnerable to climate change and its impact in abnormal weather conditions and related bad crop leading to food shortages. In today’s globalised world, where a disruption in food supply chain has its own impact on potentially everyone in the planet is a mounting challenge to surpass. The advent of Artificial Intelligence, specifically Computer Vision techniques prove to be extremely helpful in identifying the data pattern of the images of the cultivated land, its anomalies and is insightful in giving the challenges of farming such as affect of bad weather, bad crop prediction, crop distribution etc. The availability of high-quality geospatial data from the satellites such as Sentinel 1/2, Landsat is extremely helpful for advanced ML techniques to provide timely predictions so that a corrective action can be taken in time. This study focuses on an AI-driven approach that predicts land where Rice will be produced vs. no crop land using satellite optical data and its variates, radar logs, weather data and location information.
Authors - Arin Bansal, Pranshu CBS Negi Abstract - The research provides a description of WaveTrust, which is a trust-conscious and energy-efficient routing protocol that is applied to Underwater Wireless Sensor Networks (UWSNs) based on reinforced Q-learning and trust assessment. Neutral trust and network deployment initiate the protocol. During the process of routing data in real time, monitoring of the behavior by the nodes is required with respect to four metrics namely Packet Forwarding Ratio, Energy Behavior Consistency, Latency Observance and Link Quality Indicator. The calculation of the trust is performed according to the direct and indirect observations and makes it possible to determine malicious nodes. Q-learning routing strategy The routing strategy uses weighted rewards according to energy, trust and latency in updating paths such that it favors nodes with high-trust and high-Q-value. The nodes dynamically revise the trust and Q-values about the received feedback during transmission of data. The sink node keeps on broadcasting the global updates of the updated trust thresholds and routing updates. The simulation outcomes have indicated that WaveTrust is better than T-AODV, FuzzyTrust on the basis of packet delivery ratio, detection accuracy, energy consumption, routing overhead and an apparent strength on the capability to work in dynamic and resource limited underwater setting. This creates the impression that WaveTrust is quite flexible protocol and has the capability of providing secure and energy efficient routing in UWSNs.
Authors - Harita Venkatesan Abstract - Fusion-based multimodal models typically assume full modality availability at inference, an assumption that often fails in real-world settings. When a modality is missing, common strategies such as zerovector masking or unimodal fallback can lead to unstable predictions. We propose CORE, an embedding-level framework that completes multimodal representations by integrating original and cross-modally reconstructed embeddings in a fusion-consistent manner prior to fusion. CORE employs lightweight bidirectional cross-modal imagination networks with a cycle-consistency constraint to preserve shared semantic structure across modalities. The model is trained with stochastic modality dropout, enabling unified inference under complete and incomplete modality configurations. Experiments on a multimodal MRI–text classification task for lumbar spine analysis demonstrate that CORE yields more stable predictions than zero-vector masking under severe modality absence, while maintaining comparable performance when all modalities are present.
Authors - Latha N. R., Pallavi G B, Shyamala G., Abubakar Mohammedshafee Matte, Aditya Dinesh Netrakar, Akshara Singa, Akshata Hosmani Abstract - Tourism has become a strategic pillar in China’s transition toward a service-oriented economy, the world cultural heritage sites play an important role in promoting cultural–tourism integration in both China and global. The Dazu Rock Carvings is located in Chongqing, well known by their unique synthesis of Buddhist, and Taoist ideas and their wonderful stone-carving artistry. Recently, the Dazu site received growing number in tourist arrivals and tourism-related revenue due to the regional rapid development as well as the strategic support; however, compared with other outstanding heritage destinations such as the Mogao Grottoes, the reception capacity, product diversity, brand influence, and market performance of Dazu still remain relatively weak. This study adopts a mixed qualitative–quantitative case study design. Data are collected from official tourism statistics and cultural heritage management reports published by national and local authorities in between 2018-2024. Descriptive analysis is used to explore the trends in tourist arrivals, tourism revenue, and related industrial effects. Based on the findings, the study identifies key dimensisons on sustainable development and proposes a marketing path centered on cultural IP empowerment, industrial ecosystem construction, and digital technology-driven innovation, offering practical guidance for similar heritage destinations.
Authors - Deepa V, Atul Anilkumar, Sheena Susan Andrews Abstract - Organizations are rapidly embedding artificial intelligence (AI), including generative AI, into core business functions, but making AI sustainable across environmental, social, and economic dimensions is still challenging, especially when data governance is weak. Public estimates suggest data centres consumed roughly 415 TWh of electricity in 2024 and may rise toward ~945 TWh by 2030 under a base-case trajectory, while reported AI-related incidents reached a new high in 2024. In parallel, industry signals point to fast enterprise adoption of GenAI and ongoing leakage of sensitive information through tools that are not properly governed. Taken together, these patterns increase sustainability risks that are often data-mediated in practiceshaped by data quality and representativeness, provenance and documentation, access control, privacy protections, and end-to-end lifecycle management. Although data governance is widely seen as “foundational” to responsible AI, the concrete mechanisms linking governance capabilities to sustainable AI outcomes, and the ways to measure them, remain dispersed across data management, AI governance, and sustainability research. This paper consolidates peer-reviewed research, public standards, and open industry evidence to position data governance as an operational, measurable capability for Sustainable AI, one that converts sustainability goals into decision rights, lifecycle controls, and auditable outcomes. It contributes: (i) a capability-based taxonomy of data governance tailored to AI lifecycles; (ii) six evidence-grounded impact pathways showing how governance mechanisms influence outcomes (quality and fairness; documentation and auditability; privacy and security; interoperability and reuse; lifecycle stewardship; and sustainability instrumentation); and (iii) the Sustainable AI Data Governance Impact Model (SAI-DGIM), accompanied by testable hypotheses (H1–H8) and a KPI-oriented measurement framework that can be validated using survey constructs, system telemetry, and governance artifacts. For practitioners, the model offers a practical roadmap to embed governance controls directly into AI delivery workflows and treat sustainability metrics as release criteria, not just retrospective reporting. For researchers, it provides aligned constructs, hypotheses, and measurement guidance to rigorously assess how organizational data governance shapes Sustainable AI outcomes at scale.
Authors - Nhat Ho Minh, Long Le Pham Tien, Kien Nguyen Trung, An Pham Nam, Trong Nhan Phan Abstract - The fast increase in the number of unstructured digital documents in academic, industrial, and personal fields has generated an urgent requirement to have intelligent systems to read, arrange and structure document automatically. Traditional document organization methods have traditionally been heavily based on either manual intervention or rule-based methods, neither scalable nor efficient nor error free. The current paper is a multimodal AI architecture to assist document under-understanding and structuring that uses large language models (LLMs) and vision language models to handle heterogeneous document types. The suggested framework does semantic metadata extraction, classification of documents as well as structural organization of textual and visual documents. It uses a modular three-layer design, including an AI processing layer, service oriented backend, and cross platform user interfaces. The system is also developed to support secure functioning in the offline mode, which guarantees the privacy of data and the low-latency processing. The effectiveness of the pro-posed frame-work has been proved through experimental assessment, as it will be seen that classifying documents and categorizing images are very precise. The findings show that multimodal AI is remarkably better in document understanding and automation than traditional systems.
Authors - S M Mazharul Hoque Chowdhury, Ruth West, Stephanie Ludi Abstract - The prediction of liver disease through clinical data analysis faces difficulties because current machine learning methods fail to handle class imbalance and produce incorrect probability assessments. The existing supervised and ensemble methods use fixed decision thresholds together with heuristic weighting methods which results in biased predictions that compromise their ability to achieve balanced performance. The research introduces CAL-WE++ which serves as a Calibration- Weighted Ensemble system that uses an MCC-Optimized Threshold to forecast liver disease. The system employs five-fold stratified cross-validation without data leakage to produce out-of-fold probability results. The model weights are determined by evaluating both the model's ability to distinguish between outcomes (measured through ROC-AUC) and its accuracy in predicting probabilities (assessed through Expected Calibration Error ECE). The Matthews Correlation Coefficient (MCC) serves as the optimization method to determine the final classification threshold which helps to solve class imbalance problems. The Indian Liver Patient Dataset (583 records; 416 diseased, 167 non-diseased) experiments show that CAL-WE++ achieves a mean cross-validation MCC of 0.3474 and a test MCC of 0.4487 which exceeds the performance of baseline classifiers. The model achieves a ROC-AUC score of 0.8140 and a PR-AUC score of 0.9272 while maintaining a low ECE value of 0.0774 which demonstrates strong ability to distinguish between different outcomes and accurate probability assessments. The CAL-WE++ framework offers medical professionals a decision-making system that maintains balance between multiple criteria while delivering dependable outcomes for medical datasets with unequal class distributions.
Authors - Nidhi Pruthi, Rajiv Singh, Swati Nigam Abstract - Automatic Speech Recognition (ASR) systems have achieved remarkable progress through deep learning and Transformer-based architectures, demonstrating near-human accuracy on clean audio. However, their performance degrades significantly under challenging conditions and specialized domains. This comprehensive study evaluates leading commercial ASR APIs—Google Cloud Speech-to-Text, Microsoft Azure Speech Service, AssemblyAI, Deepgram, OpenAI Whisper, Speechmatics, and others—across multiple dimensions: general speech recognition, low-quality forensic-like audio, domain-specific mathematical notation, and personalized speaker adaptation. Results demonstrate 100% accuracy on clean audio for leading systems (Deepgram, Speechmatics, Webkit SpeechRecognition), but dramatic performance degradation to 10− 81% word error rates on forensic-like audio. Analysis of domain-specific challenges reveals that none of the tested commercial ASR systems natively support direct transcription of mathematical symbols and Greek letters into structured symbolic output (e.g., LaTeX). The study identifies critical limitations in robustness, modularity, and domain adaptation, while highlighting promising customization mechanisms including custom vocabularies, language models, and post-processing integration. Performance improvements through speaker personalization ranged from 3% for natural voices to 10% for synthetic voices. Despite notable advances in end-to-end and Transformer-based approaches, ASR systems remain unsuitable for forensic applications and specialized domains without substantial customization and post-processing. Future research must address low-resource performance, linguistic diversity, robustness in extreme noise, and the integration of Large Language Models for semantic understanding. This paper synthesizes recent advances and critical gaps, providing a roadmap for advancing ASR technology in specialized and challenging acoustic environments.
Authors - G Naga sree suma, A. Kamala kumari Abstract - The existence of a growing social media has created complex cyber systems in which vast quantities of interactions constitute substantial issues regarding misinformation, privacy invasion, deception of identities, and destructive behavioural tendencies. The regularity of involvement in this type of big systems requires sophisticated systems that are able to judge the motive of the user, content validity and suspicious activities within real time. Overall interest will be to develop a universal trust calculation system that will be more secure and effective in ensuring privacy and increasing the accuracy of suspicious or malicious users in social sites. The proposed Multi-Layer Federated Trust Framework algorithm is a combination of peer-based user reputation scoring, feature-based content authenticity detection, federated trust indicators aggregation, and anomaly detection with the help of behavioural anomalies. These approaches cooperate with secure aggregation and decentralized learning in removing the uncoded information exposure and enable the computation of trust at scale. The proposed algorithm is experimentally confirmed, and the obtained results are 95.2, 94.1, 93.5, and 93.8, corresponding to a minimum latency of 65 ms and a privacy preservation score of 0.98. The general results indicate a viable and holistic response that adds to secure interactions, blocks malicious acts and encourages trust in the actual social media settings.
