Authors - Stuti Kumari, Kunal Dey Abstract - Teen suicide remains a significant public health concern in the Unit ed States, with substantial geographic variation across counties. Understanding how socio-environmental and healthcare access factors relate to suicide risk can help identify communities that may benefit from targeted interventions. This study aims to support this effort by analyzing county-level teen suicide patterns using K-means clustering, an unsupervised machine learning technique. A da taset of 248 U.S. counties with reported teen suicide data was constructed using five-year aggregated suicide crude rates (2019-2023) alongside multiple socio environmental and healthcare indicators, including hospitalization rates, mental health provider availability, primary care provider rates, social association rates, uninsured population percentages, poverty levels, food insecurity, and rural population share. K-means clustering was then applied to identify county-level risk profiles. The results reveal two distinct county groups: one characterized by lower suicide rates, greater healthcare provider availability, stronger social as sociations, and lower socioeconomic disadvantage; and another characterized by higher suicide rates, reduced healthcare access, higher poverty and food in security, and greater rural residency. These findings highlight meaningful coun ty-level disparities and demonstrate the utility of machine learning approaches to identify regional risk profiles associated with teen suicide. The results may help inform public health strategies and policy efforts aimed at prioritizing re sources and expanding mental health services in high-risk communities.
Authors - Sanjida Karim Peuly, Sharmin Alam Mou, Tamanna Hossain Badhon Abstract - Diabetes diagnosis at the early stages is an important factor in avoiding long-term complications. The existing body of literature tends to be based on small, saturated datasets that are not very interpretable and externalized. This pa-per will suggest a powerful machine learning model to predict diseases at the first stage of diabetes on the basis of a symptom-based dataset of One thousand five hundred and sixty cases. Six classifiers, including Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors, Naive Bayes, and XGBoost, were considered on the stratified cross-validation and independent test sets. Systematic hyperparameter optimization using GridSearchCV was used to prevent overfit-ting and improve the generalization. Additionally, a Stacking Ensemble model was provided; the Logistic Regression, Random Forest, and XGBoost were com-bined to obtain a high level of predictive stability. Experimental evidence has shown that ensemble-based methods are more effective than single classifiers, as XGBoost and Stacking Ensemble have the highest accuracy and ROC-AUC val-ues. The analysis of feature importance suggested polyuria and polydipsia as the most important clinical signs, which is consistent with medical knowledge. This study offers a practical and interpretable decision support model in screening early diabetes, which bridges the predictive performance and clinical utility gap.
Authors - Jose R. Rosas-Bustos, Mark Pecen, Jesse Van Griensven The, Roydon Andrew Fraser, Nadeem Said, Sebastian Ratto Valderrama, Andy Thanos Abstract - Post-quantum migration is increasingly constrained by time: deployed cryptographic mechanisms may need to be retired, hybridized, or re-keyed before effective security margins fall below asset-specific pol icy thresholds. This timing problem is complicated by uncertainty in clas sical hardware acceleration, algorithmic progress, implementation ero sion, and the arrival of cryptographically relevant quantum comput ers. This paper presents a compact probabilistic pipeline that translates evolving assumptions and evidence into decision-facing migration guid ance. The approach couples three layers: (i) a security-trajectory model that encodes expected margin erosion under scenario parameters, (ii) a latent-regime model that represents partially observed risk states and updates them as evidence changes, and (iii) an option-style timing layer that quantifies the diminishing value of delaying migration as thresholds approach. Outputs are conditional on stated assumptions and are in tended to be reported with sensitivity bands and lead-time constraints. In practice, the pipeline is intended to be re-run as assumptions and evidence evolve, preserving an auditable trail from scenario inputs to in termediate states and final decision artifacts. The primary deliverables are comparative rankings and conservative “start-by” windows under stated assumptions, rather than single predicted break dates.
Nadeem Said is a computer engineer with research and professional interests in artificial intelligence, machine learning, cryptography, and secure computational systems. Currently pursuing his Master’s, his academic work includes peer-reviewed contributions to quantum security... Read More →
Authors - Ronald S. Cordova, Rowena O. Sibayan, Hazel C. Tagalog, Rolou Lyn R. Maata Abstract - Awareness regarding consumer sentiments will benefit a business entity and/or a company in making their marketing strategies more effective and engaging in the current digital marketing context. In traditional marketing scenarios, since there is a lack of actual emotional aspect in expressing views in real-time contexts, it has always been challenging for a business to perform a significant adjustment in their marketing campaigns and achieve a greater success rate. The proposed idea focuses on AI and ML-based approaches for sentiment analysis in digital marketing. The framework is made up of seven core steps: data collection, preprocessing and data cleaning, sentiment analysis models, feature extraction and model training, sentiment classification and analysis, insights and decision-making, and application in digital marketing. From social media to e-commerce reviews to online discussions, consumer sentiment data comes from many digital sources. The text for analysis is standardized, and noise is cleaned in data preparation. Then, apart from other artificial intelligence-based sentiment classification models, sentiments are classified as positive, negative, or neutral using lexicon-based, machine learning, and deep learning approaches. The learned knowledge enables businesses to react dynamically to consumer sentiment, target advertisements, and adjust marketing strategies. Businesses will be able to conduct more profitable promotions, communicate with customers better, and monitor real-time sentiment through this AI-driven sentiment analysis platform. The paper emphasizes the benefit of incorporating artificial intelligence in decision-making within digital marketing, even in addressing issues like ambiguous sentiment expression management and multi-language data. This paper provides a strategic way towards maximum customer interaction and brand loyalty and also emphasizes the need for sentiment analysis that is sustained by available data in modern digital marketing.
Authors - Mandala Nagarjuna Naidu, Bandi Hemalatha, Kadavakallu Viswanath, Kotapati Venkata Pavan, Ms.Ragavarthini Abstract - Autonomous vehicles rely on powerful perception systems with real-time object detection and tracking capabilities. Our paper presents a unified deep learning framework based on YOLOv8n and ByteTrack for multi-class detection of vehicles, pedestrians, traffic signs and lights on roads. Our work maintains consistent tracking between frames without the limitations of previous works that rely on static images or single-object-type detection. The lightweight model, with only 3.2 million parameters in YOLOv8n, provides a good trade-off between accuracy and efficiency for embedded automotive hardware. Experiments conducted on the COCO validation dataset, achieving 52.11% mAP @ 0.5,with precision and recall values of 63.42% and 47.44% respectively.It runs real-time on traffic videos with an average frame rate of 62 FPS and a mean inference time of 10.10 ms.Results for tests on traffic videos show, on average 10.15 objects detected with 68.29% confidence.These findings make this approach apt for both autonomous navigation and intelligent traffic monitoring.
Authors - Mazdak Zamani, Mohammad Naderi Dehkordi, Riham Hilal, Azizah Abdul Manaf, Achyut Shankar, Touraj Khodadadi Abstract - Access to formal financial services remains limited in many develop ing regions, largely due to economic and infrastructural constraints. This study uses the ISO/IEC 25010 as the evaluation framework to present a software quality assessment of a lending automation system installed in a financial insti tution in Butuan City, Philippines. The evaluation focuses on five essential as pects of software quality: usability, reliability, functional suitability, perfor mance efficiency, and security. Usability surveys using SUS and UMUX-Lite, operational and performance testing, and an evaluation of security and data pri vacy compliance were used to gather empirical data. According to the results, the system achieved high performance with an average inference latency of 0.208 ms per record, uptime reliability of ≥99.5%, excellent usability with a mean SUS score of 82.5, and full compliance with data privacy regulations. Predictive analytics, specifically the Random Forest model with isotonic cali bration, further enhanced the automated loan assessment’s interpretability and reliability. The system proved that it is appropriate for real-world applications and can encourage financial inclusion in resource-constrained environments, as it exceeded the intended benchmarks for each quality model. To guarantee the long-term adoption of lending automation technologies, the study emphasizes the significance of thorough software quality evaluation in addition to predic tive accuracy.
Authors - Sai Sundarakrishna, Vedant Maheshwari Abstract - Recent literature has posed LLMs as nonlinear dynamical systems. LLM safety, in these modern LLMs is about the systematic and critical monitoring of logit based oscillations, hidden state rotations and entropy fluctuations. Many of these important factors are spectral proxies for the generation of imaginary eigenvalues. These imaginary eigenvalues are, in a way, determinants of the latent oscillation energy. Though the system in its original state space is inherently nonlinear, through the Koopman operator, we can linearize the evolution in the lifted space of observables. We design a spectral jailbreak detector that has a Sparsely regularized koopman autoencoder as its backbone. We obtain the koopman operator through this SR-KAE, and also obtain the imaginary component of the eigenvalues of that spectral operator, A new risk score metric is proposed that is used to classify prompts as either jailbreak or safe. This becomes a physics-style stability classifier on prompts. We present several test cases, while we discuss the strengths and limitations of this new system.
Authors - Mazdak Zamani, Mohammad Naderi Dehkordi, Riham Hilal, Azizah Abdul Manaf, Achyut Shankar, Touraj Khodadadi Abstract - The rushed development of edge computers, including Internet-of-things (IoT) nodes, wearable similes, and embedded cyber-physical systems has enhanced the necessity to deploy machine-learning (ML) models with a high diligence to function within harsh resource restraint conditions. Although traditional deep-learning models have high predictive accuracy, they usually require significant computational resources, memory and power which makes them infeasible in these settings. This paper provides a thorough proposal of accuracy-efficiency trade-off of lightweight ML models adapted to resource-constrained resource providers. We compare classical and modern lightweight methods of determining classification: linear frameworks, tree-based learners, shallow and compressed neural networks, on various performance metrics of accuracy, inference latency, memory base, and energy usage. Experimental outcomes based on commonly used benchmark datasets show that lightweight models can achieve competitive accuracy at significantly reduced overall computation overhead. The results also provide useful recommendations to select and design ML models in edge intelligence, real-time decision-making, and low-power AI models.
Authors - Sri Kavya Swarna, Varun Kumar Reddy Kola, DS Bhupal Naik, Dinesh Reddy Tiyyagura, Lakshmi Charitha Bandaru, Srinivasa Rao P. Abstract - This paper presents PricePulse, a web-based price comparison system that supports consumers with real-time multi-platform price analysis and AI-powered shopping insights. The system aggregates product data from Amazon, Flipkart, and Meesho via SerpAPI’s Google Shopping API and enriches results with recommendations generated by Google’s Gemini AI. Built on Next.js and Flask, PricePulse addresses gaps in the e-commerce ecosystem by eliminating manual price comparison across platforms. The system uses JWT-based authentication, maintains search history in SQLite, and provides an intuitive interface with React and Tailwind CSS. Evaluation shows average response times under 2 seconds and 95% accuracy in price extraction, demonstrating significant potential to help consumers make informed purchasing decisions and save on purchases.
Authors - Mazdak Zamani, Mohammad Naderi Dehkordi, Riham Hilal, Azizah Abdul Manaf, Achyut Shankar, Touraj Khodadadi Abstract - Nowadays, small networks are commonly used by people at home, in laboratories, or by small offices. These networks are not secured and an attacker can easily attempt to intrude these networks. To prevent this we need to continue to monitor the network and detect wrong activity early. Our simple system is called NetSentinels, and was developed in this project. It monitors the network traffic at all times and displays alerts message in case of a questionable event. We have used Snort which is free and open source tool. It assists in identifying attacks such as port scans, ICMP floods and multiple attempts of logging in. This system does not require the use of sophisticated devices thus can be installed in ordinary computers. NetSentinels can be applied in small networks to remain safe against attackers and enhance general security practices. In addition to real-time monitoring, the system also stores alert logs which can be used for later analysis and understanding attack patterns. The use of a virtual machine environment ensures safe deployment and easy portability across different systems. The system is designed to consume minimal CPU and memory, making it suitable for continuous operation without affecting system performance. Overall, NetSentinels provides a simple, low- cost and practical approach for improving network visibility and security awareness in small-scale environments.
Authors - Shabnam Praveen, Shubham Kumar, Tulika Roy, Sanskriti Sahu, Subhangi Raj, Ranjita Kumari Dash Abstract - The implementation and design of a covert communication channel that embeds hidden information within TCP/IP packet headers rather than within the actual payload of the packets is presented as a project. This is different than traditional embedding methods (steganography), which typically embed data into multime dia files, in that steganography in this case utilizes header fields that are not cur rently in use or can be modified so that TCP/IP packets can transmit hidden data. The fields that are used to transmit hidden data are the IP Identification Field, TCP Sequence Number, TCP Acknowledgment Number, and TCP Window Size. The sender module encodes and generates packets, and the receiver retrieves packets, extracts encoded bits, and reassembles data from the encoded bits found in the packets. The integrity of the data is verified using a checksum (SHA-256) and packet loss is reported. The lack of a payload will further enhance the stealth various data transmission methods may enjoy as it will circumvent conventional intrusion detection techniques (which primarily examine the payload data within packets). This project will demonstrate the ability to use this or similar covert communication channels to implement covert communication systems. In addi tion, covert communication channels can be used for different types of files and demonstrate the security and educational value of covert channel research in net work security.
Authors - Prajakta Shinkar, Madhuri Suryavanshi, Sakshi Satav, Mahima Thakre, Saisha Chaudhary Abstract - The contemporary academic and professional world requires smart systems of productivity that are not limited to the old task managers. The paper introduces an intelligent personal productivity assistant powered by AI that consists of generative AI, dynamical schedule, behavioral analytics, and gamification using a mobile-first structure. The system is based on a Flutter frontend and FastAPI backend and a hybrid AI architecture to create conversational tasks and understand their context. A burnout detection module is a behavioral module that analyzes workload trends, tasks owed and completion trends to give early risk alerts. A smart scheduling system aggressively plans on a daily basis with priority-based model, conflict resolution and Pomodoro-based segmentation. The proposed system combines conversational AI, predictive analytics, and motivational reinforcement to increase productivity and decrease cognitive load and help avoid burnout in managing tasks.
Authors - Ashvini Jadhav, Pankaj Chandre Abstract - The contemporary academic and professional world requires smart systems of productivity that are not limited to the old task managers. The paper introduces an intelligent personal productivity assistant powered by AI that consists of generative AI, dynamical schedule, behavioral analytics, and gamification using a mobile-first structure. The system is based on a Flutter frontend and FastAPI backend and a hybrid AI architecture to create conversational tasks and understand their context. A burnout detection module is a behavioral module that analyzes workload trends, tasks owed and completion trends to give early risk alerts. A smart scheduling system aggressively plans on a daily basis with priority-based model, conflict resolution and Pomodoro-based segmentation. The proposed system combines conversational AI, predictive analytics, and motivational reinforcement to increase productivity and decrease cognitive load and help avoid burnout in managing tasks.
Authors - NamUook, Kim, Gihwan Bong, Yoon Seok, Chang Abstract - The heterogeneity of data sources makes the design of traditional da ta ware-houses complex and time-consuming. Indeed, the data warehouse system must process structured, semi-structured, and unstructured sources. To over-come this challenge, we propose an interactive approach to data ware house design based on a federated ontology. The ontology serves as a unified conceptual layer that integrates heterogeneous data sources and facilitates the building of the data warehouse. Our approach allows decision-makers to in teractively select the subdomain of the federated ontology according to their needs and generate their data warehouse. The generation of the data ware-house in the constellation schema is automated using algorithms. It also ensures the maintenance of the data warehouse to take into account various changes in data and decision-makers' needs. The proposed methodology is summarized through architectures defined at each stage, each addressing a specific challenge. At the ontology construction level, it resolves issues related to data heterogeneity while enabling interoperability among multiple do-main ontologies. It also provides a complete scenario for the decision-maker to assist in the full construction of a data warehouse from an ontology. Finally, it facilitates querying the constructed data warehouse using requests ex-pressed by the decision-maker in natural language.
Authors - C Nitheeshwaran, M Saravanan, S Mukesh, K S Anuvarshini Abstract - The present study explores the online privacy concerns of young Indian consumers. Using the segmentation approach popularized by Dr Alan Wes-tin in the U.S., this study identifies the segments within Indian youth. This study is based on a survey conducted on a sample of Indian university students. Hierarchical and non-hierarchical cluster analysis techniques were applied to identify segments within young Indian consumers based on their privacy concerns. The study identified three consumer segments: highly concerned, moderately concerned, and less concerned based on online privacy concerns. The findings also reveal important differences among the three segments in terms of out-come variables such as perceived effectiveness of legal/regulatory policy, fabricating personal information, and software usage for protection. The results indicate an overall increased level of concern for online privacy among young Indian consumers. The results suggest similarities and dissimilarities with Westin’s approach. While previous research on online privacy has been chiefly based on the Western context, this study offers a window to look at the Eastern context by examining the privacy concerns of young Indian consumers, who have not been studied, and hence provides an important contribution to the existing literature.
Authors - Quang-Thinh Bui, Lan T.T. Tran Abstract - The digital transformation of the construction industry has intensified the demand for standardized methods of information exchange. Building Infor mation Modeling (BIM) has become a cornerstone of this transformation, ena bling interdisciplinary collaboration and improving data quality. However, recur ring challenges such as inconsistent data structures, unclear contractual require ments, and limited interoperability continue to hinder efficient project delivery. To address these issues, the Information Delivery Specification (IDS) was devel oped within the buildingSMART ecosystem as a computer-interpretable standard for defining and validating information requirements. Officially approved in June 2024, IDS bridges human-readable requirements with machine-interpretable val idation rules, positioning itself as both a contractual instrument and a technical validation tool. This study synthesizes insights from official IDS documentation and academic literature to provide a comprehensive evaluation of IDS’s role in the construction sector. The systematic literature review categorizes contributions into five the matic domains: standardization, application scenarios, systematic reviews, coun try and domain-specific studies, and methodological innovations. Findings high light IDS’s versatility across diverse applications, including acoustic assessment, accessibility compliance, railway projects, and energy simulation. At the same time, research gaps remain in areas such as national adaptation strategies, auto mated compliance checking through CI/CD pipelines, and methodological devel opment via linkage with the Level of Information Needs (LoIN). By integrating theoretical perspectives with practical case studies, this research demonstrates how IDS functions as both a technical standard and a methodolog ical framework. The study concludes that IDS has the potential to become a cor nerstone of digital construction practices, bridging regulatory requirements with automated validation in BIM workflows.
Authors - Anuja Kelkar, Pradnya Kardile, Aditi Dudhe, Prajakta Chaudhari, Meenal Kamlakar Abstract - In this paper we derive a new estimate of the channel bit rate. The estimates is a special transformation of the main EVT theorem that is particularly designed for use in telecommunication automated systesm meaning it’s robust to noise, computationally cheep, needs very few data points and no manual validation. Due to the EVT methodology we can evaluate if the bit rate can keep dropping indefinitely or if it has a guaranteed minimum value. The method is relatively fast because it uses Newton’s interpolation instead of hypothesis testing or regression.
Authors - Ankur Maurya, Shaurya Oberoi, Madhav Malhotra, Rakesh Chandra Joshi, Garima Aggarwal, Malay Kishore Dutta Abstract - Remote sensing imagery plays an important role in applications such as environmental monitoring, disaster management, urban planning and agricultural analysis. However, the spatial resolution of such imagery is often limited by sensor constraints, revisit frequency and acquisition cost. To address this challenge, this paper presents RCAN-RS, an enhanced Residual Channel Attention Network for remote sensing image super-resolution. The proposed model extends the RCAN framework through three targeted modifications: a dual-pooling channel attention mechanism, a spectral attention module and an edge enhancement module. These components are designed to improve detail reconstruction while preserving inter-channel consistency and sharp structural boundaries in remote sensing imagery. The model was trained and evaluated on the DOTA dataset un-der a 2× super-resolution setting from 256 × 256 to 512 × 512 pixels. Quantitative evaluation using both conventional image-quality metrics and remote-sensing-oriented measures shows that RCAN-RS achieves a mean PSNR of 34.42 dB, SSIM of 0.9398, Edge Preservation Index of 0.9524, ERGAS of 6.68 and UQI of 0.9846 on the test set. These results demonstrate the effectiveness of integrating attention-guided and edge-aware mechanisms for remote sensing image super-resolution.
Authors - Inuka Gajanayake, Gagani Kulathilaka, Guhanathan Poravi, Saadh Jawwadh Abstract - The swift growth of digital interfaces has facilitated manipulative design practices called dark patterns, which take advantage of cognitive biases to manipulate users and subvert informed decision-making. Though widespread across e-commerce, social media, and other areas, automated identification and empirical knowledge of user vulnerability are still in their infancy. This work introduces an end-to-end framework integrating a GenAI-augmented browser add-on for real-time detection of dark patterns with systematic estimation of user awareness and behavioral reactions. A new Pattern Vulnerability Index (PVI) measures the threat from individual patterns according to frequency, unawareness among users, and potential damage. Cross-platform analysis identified high-risk patterns like Discount Anchoring, Urgency, and cost-related manipulations to be frequently overlooked by users. Clustering identifies scenarios in which several deceptive patterns occur in co-presence, including checkout processes, promotional displays, and subscription pitfalls. The results highlight the moral significance of manipulative interface design and establish the capability of machine-based tools to promote user safeguard, sensitize, and guide regulation and design efforts. This study provides a basis for consumer-oriented solutions and future research towards more transparent and ethical online encounters.
Authors - Hiep. L. Thi Abstract - As a core pillar industry in China's economic transformation toward a service-oriented economy, the tourism industry plays an irreplaceable role in boosting domestic demand growth, optimizing regional industrial structures, and advancing high-quality economic development. The Dazu Rock Carvings in Chongqing, holding the dual top-tier qualifications of a World Cultural Heritage site and a National 5A Scenic Area, embody over 1,300 years of historical accumulation. With their unique cultural core of ‘Confucian-Buddhist-Taoist Syncretism’ and top-tier high-relief artistic craftsmanship, they stand as the pinnacle of Chinese stone carving art, boasting remarkable cultural tourism economic value and cultural inheritance value. However, for a long time, the Dazu Rock Carvings have been trapped in the dilemma of ‘high cultural value but low market recognition’—acclaimed but underrecognized in the market. Their visibility enhancement relies excessively on short-term hotspots, lacking a long-term support mechanism. Based on theories of culture-tourism integration, brand communication, and sustainable cultural heritage development, this paper employs literature review, data analysis, case comparison, and field research to accurately identify core pain points. It constructs a scientific and feasible new marketing path from six dimensions: innovative resource transformation, precise audience cultivation, diversified channel expansion, upgraded cross-border linkage, breakthrough international communication, and long-term institutional safeguards. This path aims to help the Dazu Rock Carvings transition from traffic-dependent development to value-driven development and, at the same time, provide practical references for similar cultural heritage scenic spots in China.
Authors - Deepak sharma, Pankajkumar Anawade, Anurag Luharia, Gaurav Mishra Abstract - The rapid digital transformation of modern society has significantly increased the complexity of network infrastructures and the sophistication of cyber threats. Traditional rule-based and signature-based security systems are increasingly ineffective against advanced persistent threats, zero-day vulnera bilities, and AI-driven cyberattacks. Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative technologies that enhance net work security through intelligent threat detection, automated response, and pre dictive analytics. However, the integration of AI and ML also introduces new vulnerabilities, including adversarial attacks, model poisoning, privacy con cerns, and algorithmic bias. This paper critically examines the evolution of net work security through AI and ML, analyzing both the technological advance ments and the emerging risks associated with their deployment. The study ar gues that while AI-driven security systems represent a significant improvement over traditional mechanisms, careful governance, transparency, and robust model protection are essential to mitigate new threats introduced by intelligent systems.
Authors - Isha Bhagat, Rishita Chourey, Anjali Kurhade, Vedika Desai, Meenal Kamalakar, Vishal Goswami, Nayan Wagh Abstract - In the shadow of overlooked safety violations, different factories have lost thousands, in terms of capital as well as lives. Which is especially harrowing as these were caused due to easily preventable work accidents or easily noticeable defective machinery. Our paper dives into how artificial intelligence based methodologies, particularly, would help in mitigating these risks based on past and present research. We also recommend a potential prototype system according to the findings from the literature we reviewed, for Real-Time worker safety check and automated industrial machine quality inspection system. We have reviewed four major topics pertaining to our system: [1] Personal Protective Equipment (PPE) compliance detection through CCTV monitoring as opposed to manual monitoring, [2] industrial machine quality inspection for automatic defect identification [3] evaluation of previously used object detection models and their performance for industry applications, and [4] system level considerations for practical deployment of the said systems on a large scale. We have compared methods, deployment strategies and results from existing studies to identify key criteria like scalable architectures as well as low latency processing. We are highlighting challenges such as insufficient annotated data for rare machinery defects, good accuracy in harsh industrial conditions that might hinder detection of safety violations, and ethical issues with worker monitoring as well in this paper.
Authors - P Subhash, P. Abhi Varshini, V. Udai Sree, P. Praneeth Reddy, Sai Mahitha Abstract - The recognition of transaction fraud in credit cards is a major problem that is still faced. It is mainly because of the gap between real and fraud transaction. In traditional methods, evaluations are mainly done with the main eye on accuracy, but it is sometimes inadequate and indecisive because the fraud occurrence is only 1% of all the data. Many studies in this field that have been done lately have focused on deep learning and machine learning structures. A very less number of works really stress on relatively simpler structures that can go well with imbalance and variance in class without the need of any complicated frameworks. A dataset that is publicly accessible has been used here for comparative study and has 284,807 transaction data. For classification, three learning algorithms like Logistic Regression, Random Forest, and XGBoost have been used. Precision-Recall AUC (PR-AUC), Matthews Correlation Coefficient (MCC), precision, and recall have been used to assess the model performance and not just accuracy. Random forest shows a steady outcome with a strong variance between false positive control and detection capability. The analysis also reveals that naive class-weighting strategies can significantly increase recall while producing impractically high false positive rates. Feature importance analysis further enhances interpretability and provides insight into influential transaction components.
Authors - Tintu Pious, Adon Hale J Payyapilly, Akshit Charan, Amal Suresh, Ashwin Babu Mampilly Abstract - The shift toward decentralized energy grids has established Vehicle-to-Vehicle (V2V) power transfer as a cornerstone of modern EV infrastructure. Central to this exchange is the Dual Active Bridge (DAB) converter, a bidirectional DC-DC topology prized for its high power density and galvanic isolation. The DAB utilizes two symmetrical H-bridges linked by a high-frequency transformer; one bridge acts as an inverter while the other performs synchronous rectification, depending on the power flow direction. Managing energy between independent batteries is challenging due to fluctuating voltage levels that create "moving targets" for control systems. Traditional PID loops often struggle with the instability caused by sudden voltage shifts in dynamic V2V scenarios. This project implements a Fuzzy Logic Controller (FLC) based on a voltage mapping principle. By comparing real-time voltage profiles of donor and receiver batteries, the FLC automatically determines the current direction and optimal phase shift angle without requiring complex mathematical modelling. Beyond emergency charging, this technology enables EVs to function as a mobile, distributed energy storage system within Smart Grids. It optimizes microgrid management in commercial hubs by sharing power autonomously, preventing transformer overload during peak demand. This approach ensures that decentralized energy sharing is both reliable and commercially viable.
Authors - Vladislav Vasilev, Georgi Iliev Abstract - In this paper we derive a new estimate of the channel bit rate. The estimates is a special transformation of the main EVT theorem that is particularly designed for use in telecommunication automated systesm meaning it’s robust to noise, computationally cheep, needs very few data points and no manual validation. Due to the EVT methodology we can evaluate if the bit rate can keep dropping indefinitely or if it has a guaranteed minimum value. The method is relatively fast because it uses Newton’s interpolation instead of hypothesis testing or regression.
Authors - Deepak sharma, Pankajkumar Anawade, Anurag Luharia, Gaurav Mishra, Akshit Yadav Abstract - The exponential growth of cybercrime, cloud-native infrastructures, Internet of Things (IoT) ecosystems, encrypted communications, and AI enabled adversarial techniques has fundamentally challenged traditional digital forensic methodologies. Conventional forensic frameworks developed for static systems cannot scale to high-velocity, heterogeneous data environments. This study proposes and empirically evaluates a lifecycle-oriented AI-enhanced digi tal forensic architecture integrating machine learning (ML), deep learning (DL), graph analytics, and explainable AI (XAI). Across benchmark datasets in intru sion detection, malware classification, multimedia authentication, and textual intelligence extraction, AI-enhanced systems significantly improved detection accuracy (up to 98.3%) and reduced analyst workload (40–60%). However, ad versarial robustness testing and explainability evaluation reveal governance and admissibility challenges. The findings demonstrate that while AI enhances scalability and zero-day detection, its responsible adoption requires reproduci bility controls, interpretability safeguards, and alignment with legal standards such as Daubert.
Authors - Netochukwu Onyiaji, Lukas Cironis, Leonid Bogachev, Liqun Liu, Janos Gyarmati-Szabo, Roy A. Ruddle Abstract - This study examines the adoption of AI-enabled hotel chatbots by investigating the role of technology readiness and consumer perceptions in shaping guests’ attitudes and behavioral intentions. Drawing upon the Technology Acceptance Model (TAM) and the Technology Readiness Index (TRI 2.0), the research integrates technological and psychological determinants of AI service adoption in hospitality settings. Data were collected from 270 hotel guests who had previously interacted with chatbots in four-star hotels in Jakarta and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that technology readiness, perceived convenience, and perceived information quality significantly influence guests’ attitudes toward AI hotel chatbots. However, attitude and perceived convenience do not directly translate into adoption intention, revealing an attitude–intention gap. The model explains 61% of the variance in attitude and 38% in behavioral intention. These findings extend technology adoption literature by highlighting the role of psychological readiness and service perceptions in shaping guest adoption of AI-enabled hospitality technologies.
Authors - Aung Nyein Chan Paing, Sudhir Kumar Sharma Abstract - This paper presents a semantic video search system that supports natural lan guage querying over video content using vision–language models and vector similarity search. The proposed system processes videos offline by extract ing representative frames through similarity-based filtering, generating textual descriptions using a pre-trained BLIP (Bootstrapping Language–Image Pre training) image captioning model, and encoding the captions into dense vector embeddings. These embeddings are indexed in a vector database to enable effi cient retrieval of relevant video segments based on textual queries. The system architecture comprises a Python-based backend with GPU acceleration for video processing and a web-based interface for query interaction. Experimental obser vations indicate that similarity-based frame filtering reduces redundant frames by approximately 50–70% while preserving semantic information. Qualitative eval uation demonstrates that the system effectively retrieves semantically relevant video timestamps in response to natural language queries. The proposed frame work serves as a modular prototype for content-based video retrieval and semantic video analysis applications.
Authors - Qing Li Abstract - Intrusion Detection Systems (IDS) are critical for cybersecurity, yet conventional approaches based on machine learning often suffer from limited explainability, high computational cost, and scalability issues. We introduce Recommendation-Driven IDS (RD-IDS), a novel framework that models security events and detection rules as a hypergraph, reformulating intrusion detection as a structured recommendation problem. Detection is achieved through the computation of minimal transversals, identifying minimal and actionable sets of security measures. RD-IDS is formally defined with hypergraph representations, recommendation semantics, and UML-based architecture, ensuring traceability and modularity. Algorithmically, we leverage minimal transversal enumeration, including the Fredman–Khachiyan dualization method, and analyze temporal and spatial complexity, demonstrating that structural reductions and active set optimizations mitigate overhead. RD-IDS offers deterministic, explainable, and scalable detection by construction, providing a principled alternative to machine learning-centric IDS. This work establishes the formal and algorithmic foundations of RD-IDS, laying the groundwork for practical implementation and experimental validation in a companion study.
Authors - Ahmed Alansary, Molham Mohamed, Ali Hamdi Abstract - Quantum secret sharing (QSS) scheme is a cryptographic protocol for sharing a secret among parties in a secure way, such that only the set of all authorized parties can reconstruct the secret using the quantum information. In this manuscript, a multi-secret sharing scheme (namely, qMSS) is proposed and analyzed utilizing a quantum error-correcting code (CSS code) for generating and reconstructing shares. qMSS generates n quantum shares of an m(≤ k)-bit classical secret using [[n,k,d]]q CSS code and distributes shares among n participants. This work generalizes the sharing of one-bit classical secret, utilizing CSS codes, proposed by Sarvepalli and Klappenecker. The set of all authorized parties is identified by minimal codewords associated with the classical code underlying the CSS code. The proposed qMSS is a perfect multi-secret sharing scheme due to the set of all unauthorized parties is unable to obtain any information about the secret.
Authors - Hasan Ahmed, Ram Singh Abstract - The growth of digital media platforms has resulted in more disseminated falsehoods which now include elaborate AI-generated syn thetic text instead of manually created false information. The develop ments create major obstacles which disrupt both information trustwor thiness and public confidence. The research presents a High-Accuracy Misinformation Detection Hybrid Transformer Framework which uses BERT and RoBERTa models within an ensemble learning system. The system undergoes initial training on WELFake dataset which serves as a standard benchmark collection that contains equal proportions of au thentic and fraudulent news articles derived from both verified and un verified sources. The framework achieves adaptability through its in cremental updating process which incorporates contemporary headlines and machine-generated content. The weighted fusion mechanism merges probability results from both transformer models to decrease model spe cific bias while strengthening the system’s classification ability. The sys tem shows better results than single transformer setups and operates through a web-based system which provides immediate misinformation assessment. The study results show that using ensemble modeling to gether with scheduled model updates creates an efficient method for tackling the ongoing emergence of synthetic misinformation.
Authors - Gagani Kulathilaka, Inuka Gajanayake, Guhanathan Poravi, Saadh Jawwadh Abstract - In modern digital environments, organizations require intelligent sys tems to manage complex workflows and decision-making. Unlike most of the task management systems that are manual and give no feedback and even lack competence; this leads to poor prioritization, deadline been missed and poor com munication between teams. Thus, IntelliTask is an intelligent system of dealing with tasks, which is AI-powered and, consequently, is context-aware, giving it an edge to enhance the quality of the working processes of the people using the system (both individuals and businesses), enhancing the prioritization, and im proving the productivity. The IntelliTask platform is machine-learning models, predictive analytics, and dynamic scheduling based on identifying key tasks to balance the workloads and the cognitive load on users without the user having to engage in the task. The solution will enhance the rate at which the tasks are ac complished, making informed decisions and will bring flexibility on what task management systems will be established in the future in enterprises.
Authors - Umar Ali R, Payas Khan H, Nouriensha N, Nithish Kumar S, Nisha M Abstract - An effort to calculate the infinite value of circumference ratio is made in this paper. Instead of being made of countless infinitesimals, a given circle is parts of an new defined infinity that is single magnitude continuum derived from the change in direction that indicates that there is a jumping from finiteness to infinity .This single magnitude continuum is the accumulations of infinitely many finite magnitudes and can never be achieved by forever extending continuously finite magnitudes.The change in direction implies that infinite length (i.e. infinite distance) can be defined as two parallel lines that never intersect ,which denotes that only the terminal end of the first straight line is meaningful when extending towards infinite distance, and this terminal end is defined as infinite length, which is a magnitude that cannot be discussed any magnitudes outside of it. When the first straight line extends to infinite distance, its one-dimensional feature will be lost and become an infinite dimensional magnitude, which is determined by the change in direction.The infinite value of circumference ratio is this new defined infinity.
Authors - Sarah Rahim, Guhanathan Poravi Abstract - In mobile networks without fixed base stations (MANETs), finding the best path for data is difficult when devices are constantly moving. Traditional methods often lead to dropped data and wasted battery. This study introduces a smarter approach by combining the standard routing protocol with a "Dolphin Partner Optimization" (DPO) algorithm. Much like how dolphins coordinate, this system picks the best path by looking at battery life, connection stability, and speed all at once. Testing shows this new method keeps the network running longer and sends data much more reliably than older systems.
Authors - Md. Mehedi Rahman Rana, Md. Anisur Rahman, Kamrul Hasan Talukder, Syed Md. Galib Abstract - The adoption of AI in the law sphere on a larger scale has left new opportunities of case analysis and verdict prediction as well as legal texts interpretation with the help of the robot. However, the existing Legal Judgment Prediction (LJP) systems are submissible to implicit data bias, which contains adult information on such delicate aspects as gender, caste, occupation, and socio-economic status. These biases may result in ethically unsound and unreliable forecasting, which is a vital issue in high stakes judicial settings. This work provides a Bias-Aware Legal Case Classification and Judgment Interpretation architecture that enables improved levels of fairness, interpretability and contextual reliability in legal decision support systems. The bias-sensitive preprocessing pipeline proposed combines the Named Entity Recognition and zero-shot and legal-specific bias-tagging. These two types of vocabularies are used with a dual-encoder framework based on LegalBERT on bias-masked data and BERT on unmasked data in order to trade-off legal reasoning with controlled demographic awareness. Representations in a gating-based fusion mechanism are combined in advance to make final classification. The system is set to work on the real case documents of the Indian laws based on the publicly available repositories. Instead of substituting the jurisdictional powers, the framework is intended to deliver ethical, transparent, and contextually sensitive support to the legal practitioners. The research is relevant in the history of responsible AI, as it focuses on the issues of fairness and interpretability in the field of automated legal analytics.
Authors - Leonardo Juan Ramirez Lopez, Cristian Santiago Cruz Jimenez, Johan Sebastian Ayala Gaitan Abstract - Ongoing technological progress has significantly increased global energy demand, particularly in rapidly developing economies, a trend further intensified by continuous population growth. Although improving energy efficiency is a universal objective, it remains an unresolved challenge. Advances in science and engineering have enabled the creation of diverse energy-harvesting technologies that utilize established non-conventional sources— such as solar, wind, thermal, hydro, piezoelectric, electromagnetic, and bio-battery systems—as well as emerging concepts like rectenna-based collection. This study aims to present a comprehensive evaluation and comparison of these technologies by examining their energy sources, availability, conversion principles, infrastructure needs, production costs, performance outputs, application domains, overall efficiency, harvesting capacity, constraints, resource characteristics, and commercial feasibility. By offering a systematic comparison, the authors seek to clarify the strengths of each approach while also highlighting the practical challenges involved in applying them to meet present and future global energy demands through both existing and prospective alternative energy solutions. The main objective of this paper is to systematically evaluate and compare a wide range of energy harvesting technologies—spanning established non-conventional sources and emerging concepts—by analyzing their operating principles, resource availability, infrastructure requirements, cost, efficiency, performance, limitations, and practical applicability, with the aim of identifying their strengths, challenges, and potential contributions toward meeting current and future global energy demands through sustainable alternative solutions.
Authors - Asmit U. Patil, Sneha Jadhav Mane, Swati Suryawanshi, Prerana Mahajan, Priya Sharma, Smita Shedbale, Dhanaraj S. Jadhav, Supriya Mane Abstract - Inference latency remains a critical bottleneck in deploying large language models, for real-time and resource-constrained environments. Prior work has proposed latency formulations that express latency as a function of key parameters. However, they often assume a linear dependence on sequence length, which fails to generalize to tasks involving significantly longer sequences, such as document-level language modeling, long-context retrieval, or time-series forecasting, where latency scales nonlinearly and unpredictably. This paper addresses the limitations of existing latency formulations by proposing three complementary enhancements to improve generalization across varying sequence lengths. First, we introduce a nonlinear term for sequence length, capturing the superlinear growth in latency observed in transformer-based architectures due to quadratic attention mechanisms and memory overhead. Second, we propose a sequence-length-dependent scaling factor for the sequence length parameter itself, allowing the model to adaptively adjust its sensitivity based on empirical latency profiles across different tasks and hardware configurations. Third, we incorporate an empirical correction term enabling calibration of the latency model to account for hardware-specific and implementation-level nuances. By explicitly modeling the nonlinear and context-sensitive behavior of sequence length, our approach offers a more faithful representation of latency dynamics. This work lays the foundation for more adaptive and hardware-aware latency estimation frameworks, with implications for model deployment, scheduling, and cost optimization in production systems. We conclude by discussing future directions for integrating dynamic profiling and reinforcement learning to further refine latency predictions in evolving runtime environments.
Authors - Felipe M. Coelho, Margarida N. P. dos Santos, Jeziel M. Pessoa, William A. P. de Melo, Joel C. do Nascimento, Carlos A. O. de Freitas , Debora R. Raimundo, Vandermi J. da Silva Abstract - The transition from 4G to 5G networks, particularly in Non Standalone (NSA) deployments, introduces new challenges for the energy effi ciency of mobile devices, as they must maintain simultaneous connectivity with LTE for signaling while using 5G NR for high-speed data transmission. To ad dress this issue, this work proposes a hybrid artificial intelligence approach for predicting current consumption that combines conventional deep learning with neuromorphic computing principles. Real-world telemetry data are first pro cessed using convolutional layers and bidirectional LSTM units to capture spa tial and temporal patterns, and the resulting representations are then converted through rate coding and provided to a Spiking Neural Network (SNN). The model is trained using a hybrid strategy that integrates Spike-Timing Dependent Plasticity (STDP) with surrogate gradients, together with a custom loss function designed to emphasize prediction accuracy during high-demand periods. Experimental results show that the proposed model achieves an RMSE of 0.1164 mA, representing a 6.3% improvement compared to standard Recur rent Spiking Neural Network (RSNN) approaches, indicating its ability to cap ture abrupt variations in power consumption typical of 5G NSA environments.
Authors - Udayamoorthy Venkateshkumar Abstract - This paper focus on dual axis solar panel tracking system using Brushless Direct Current motor (BLDC), in which rotor position estimation along azimuthal angle and elevation angle is predicted using incremental en coder. The physical kinematics and dynamics parameters which are non-linear in nature is converted to linear form and processed in conventional estimated kalman filter (EKF) algorithm. The physical process noise covariance value Qk and measured noise covariance value Rk is estimated from conventional EKF predicted value, using sliding window method. Smoothing factor λ is used for quick convergence and tuning factor to estimate the process noise covariance. The simulation is performed using Python and results shows rotor position es timation along azimuthal angle is improved by 50% and 55% along elevation angle. Dual axis estimation error convergence during dynamic tracking along azimuthal angle is reduced by 66% and along elevation angle is reduced by 70% when compared to conventional EKF algorithm.
Authors - Ananya Kale, Aditi Jaikar, Shravika Hamjade, Neeta Maitre, Rashmi Apte, Mangesh Bedekar Abstract - Singer identification is a challenging task because of pitch and me lodic variations, tempo, vibrato, and adaptive singing styles. This paper propos es a novel approach towards singer identification and classification by adapting a model originally meant for speaker recognition. Specifically, this work utiliz es vector representations extracted from a pretrained Speech Brain Emphasized Channel Attention, Propagation and Aggregation in Time Delay Neural Net work (ECAPA-TDNN) model. The research pipeline processes a custom curated dataset of four prominent Indian playback singers into fixed, 8 second audio clips, with mono channel sampled at 16 kHz and exported as wav files. The Speech Brain Emphasized Channel Attention, Propagation and Aggrega tion (ECAPA) encoder transforms these labelled clips into fixed embeddings which are unique vector representations of voice characteristics of each audio clips. A suite of classical machine learning classifiers is trained on these em beddings. The study evaluates four of them namely, Logistic Regression, Sup port Vector Machines, Random Forests, and a Multi-Layer Perceptron (MLP). The MLP achieved the highest accuracy of 99.38% on held-out test data. Sup porting this result, both confusion matrix analysis and t-SNE projection clearly demonstrate clear cluster separation based on individual singer identities. These findings thus collectively validate that ECAPA embeddings contain sufficient identity-bearing structure on a singing voice. This analysis thus concludes that adaptation of speaker recognition models with appropriate classifiers is a great ly effective and efficient approach for singer identification.
Authors - Mohammad Kaif, Anshika Banyal, Rohitashwa Dey, Shashi Mehrotra Abstract - A Natural Language Interface (NLI) lets users ask questions to get data from a database without having to learn a new language like Structured Query Language. Structured data with text is needed for many applications in many fields, such as search engines, customer service, and healthcare. There are many problems that have been studied, such as the popularity of relational databases, the complexity of configuration, and the processing needs of algorithms. Translating plain language into database queries is only one of these problems. The resurgence of natural language to database queries research is driven by the increasing prevalence of querying systems and speech-enabled interfaces. The last poll on this topic was done six years ago, in 2013. As far as we know, there hasn't been any recent research that looks at the best natural language translation frameworks for both structured and unstructured query languages. We examined 47 frameworks from 2008 to 2018 in this report. 35 of the 47 were very useful for what we do. There are three kinds of SQL-based frameworks: connectionist, symbolic, and statistical. There are two types of NoSQL-based frameworks: semantic matching and pattern matching. After that, these frameworks are judged based on their language support, heuristic rule sys-tem, interoperability support, dataset scope, and overall performance. The results showed that 70% of the work to make natural language queries work with databases has been done for SQL. NoSQL languages like SPAROL, CYPHER, and GREMLIN only account for 15%, 10%, and 5% of the work, respectively. It has also been found that most of the frame-works only work with English.
Authors - Avisek Sharma, Arpita Dey, Buddhadeb Sau Abstract - The increasing adoption of intelligent transportation systems has high lighted the importance of preventive vehicle safety mechanisms that address critical human factors such as unauthorized access, alcohol impairment, and driver fatigue. This review presents a structured analysis of recent research on automated vehicle access and driver alert systems that integrate biometric au thentication, alcohol sensing, and vision-based drowsiness detection. Embedded platforms, particularly Raspberry Pi– based implementations, are examined alongside computer vision techniques for facial and eye-state analysis and MQ series sensors for alcohol detection. The study reviews and compares commonly used algorithms, including classical feature-based methods and deep learning ap proaches, in terms of detection accuracy, computational requirements, and real time suitability for embedded environments. Communication strategies for alert generation and remote notification are also discussed. The review identifies key challenges related to multi-module system integration, robustness under varying illumination conditions, and long-term sensor calibration. It concludes that an integrated, low-cost, and real-time embedded framework offers a practical and scalable approach to improving vehicular security and reducing road accidents by ensuring that only authorized, sober, and alert drivers operate vehicles.
Authors - Alena Rodicheva, Svetlana S. Bodrunova, Zaeem Yasin, Ivan S. Blekanov, Nikita Tarasov Abstract - Polycystic ovary syndrome (PCOS) is a complex of symptoms that affects many women and is estimated to affect 6 to 12% of women of childbearing age. This commonality makes it hard for healthcare professionals to give an accurate diagnosis of PCOS and thereby received adequate treatment. We created a computer system that converses with users and guides their understanding of PCOS. This system uses a language model called Ollama, which is similar to the LLaMA model. We also added a vast detailed database about PCOS categorized into 12 sections. It analyzes user questions to ensure that the responses are relevant and correct. The system was trialed with positive performance. It accurately detected PCOS related queries and formulated appropriate responses. Well, the system is very smart and can go through a huge amount of data to find for each question three most relevant answers. The most common application is augmenting LLM with scraping & performing other programming operations over the LLM to give more accurate answers than just a language model. We developed a computer program that can help PCOS patients without compromising their privacy. This system even has benefits for healthcare providers as it provides information that aids them in such treatments for women with PCOS. This project is a great example of using computer programs to help humans with PCOS and other similar things.
Authors - Lakshmi Priya G G, Gokulakrishnan. V, Nithin Joel. J, Padmalakshmi Govindarajan Abstract - Potatoes are among the most widely farmed crops globally. Healthy potato plants are crucial for the large-scale production of potato-derived foods. However, a vari ety of leaf diseases can harm potato plants, with Early Blight and Late Blight being the most prevalent. In this investigation, we employed a dataset of 1500 photos comprising healthy, early, and late blight leaves. For the diagnosis of leaf diseases, we used a transfer learning-based Ensemble Modeling. We selected Effi cientNetB0, ResNet50, MobileNetv2, and VGG16 as transfer learning models, integrating logistic regression as a meta-classifier within the Ensemble Model. We have attained 99.4% accuracy in distinguishing disease-affected leaves from healthy potato leaves, which is better than most of the recent works. For the per formance measurements, we employed accuracy, precision, recall, and F1-score. To ensure the credibility of our technique, we have integrated explainable AI (Grad-CAM) for our models, which indicates which parts of the image play a vital role in our model’s performance.
Authors - Muhamad Surya Nugraha, Dedy Rahman Wijaya, Tuntun Aditara Maharta Abstract - The widespread adoption of Kubernetes for orchestrating micro services has heightened monitoring complexity if we focus on identifying per formance degradation not visible at the level of infrastructure resource utiliza tion. In this paper, we present an application-centric AIOps framework that can be leveraged to detect “high-latency, low-resource” anomalies in Kubernetes microservices. Traditional autoscaling mechanisms that only rely on resource metrics (CPU and memory) fail to provide optimum response time with the emergence of reactive applications. The model for anomaly detection is trained using machine learning classifiers such as Random Forest, LightGBM, and Lo gistic Regression. This approach leads to a weak supervision-based approach to label datasets, with respect to Service Level Objective (SLO) violations. A course registration system is proposed to validate the application of this frame work under conditions of high concurrency and parallel simulation traffic. Ex perimental results show that the established machine learning model exhibits marked performance compared with normal threshold methods, leading to im proved operational steadiness and service robustness.
Authors - Y. Rama Devi, Panigrahi Srikanth, Devansh Makam Abstract - Large language models have shown strong potential for Arabic medical text generation; however, traditional fine-tuning objectives treat all medical cases uniformly, ignoring differences in clinical severity. This limitation is particularly critical in healthcare settings, where errors in severe cases contain higher clinical risk. In this work, we propose a severity-aware weighted loss for fine-tuning Arabic language models on medical complaint–response data. The method depends on soft severity probabilities to dynamically scale token-level loss contributions during optimization, thereby prioritizing clinically critical interactions without modifying model architectures. Experiments are conducted using the MAQA dataset, which provides Arabic medical complaints and trusted human responses. Severity labels and probabilistic scores are automatically derived using a fine-tuned AraBERT-based classifier and incorporated exclusively at the loss level. The proposed approach is evaluated across ten Arabic large language models of varying architectures and parameter scales. While standard cross-entropy fine-tuning yields only modest improvements, severity-aware optimization consistently achieves larger gains. Using a balanced weighting configuration, performance improves from 54.04% to 66.14% for AraGPT2-Base, from 59.16% to 67.18% for AraGPT2-Medium, and from 57.83% to 66.86% for Qwen2.5-0.5B, with peak performance reaching 67.18%. Overall, severityaware fine-tuning delivers improvements of up to 12.10% over non-finetuned baselines, demonstrating robust and architecture-consistent gains.
Authors - Siddharth Jha, Mayur Jaiswal, Ajay Deshmukh, Kajal Joseph Abstract - The importance of agriculture for the survival of humans and the economic stability of the world continues to grow; however, at the same time, it has also come to face many severe problems due to increasing population figures, climate change, and the loss of natural resources. The traditional techniques for crop monitoring are mostly based on manual surveys and the use of vision for inspecting crops; thus, they are regarded as too labor-intensive, time-consuming, and passive in nature, thereby becoming ineffective for managing modern large-scale farming techniques. The avail-ability of the latest technological features, such as remote sensing, Internet of Things (IoT) devices, unmanned aerial vehicles (UAVs), artificial intelligence (AI) devices, and block chain technology, has transformed crop monitoring techniques into an intelligent and proactive process for farmers to monitor crops in an efficient and precise manner. Drones play an important role in crop monitoring by using high-resolution imaging devices for detecting early crop problems, such as crop stress, pest infestations, or nutrient deficiencies, whereas IoT devices are utilized for real-time monitoring of fluctuating environment parameters, such as soil, in an intelligent manner. All these innovations help towards a high and efficient agricultural system within a sustainable environment. Hence, there are still certain limitations and hindrances faced by these advanced techniques, including high initial cost, complexity, infrastructural constraints, and data privacy, limiting these techniques for small and marginal farmers. Hence, in this review paper, a detailed review of advanced crop monitoring techniques used in agriculture is discussed; further, a critical analysis of these techniques for achieving these requirements with efficiency and standards, and an understanding of these techniques for achieving a sustainable and robust ecosystem in an agricultural system is discussed.
Authors - Neha Kriti, Arti Devi, Sarthak Srivastava, Varun Dutt Abstract - Localization in Autonomous Underwater Vehicles (AUVs) continues to be a major challenge in GPS-denied environments, where inertial drift, low visibility and uncertain motion models frequently un dermine state estimation. In this paper, we present our visual-inertial odometry framework A-KIT VIO specifically designed for resilient pose tracking underwater. The system employs tightly coupled monocular camera observations with IMU data using an Extended Kalman Filter to maintain high-rate inertial propagation along with feature-based vi sual updates to avoid drift. To address the frequent covariance mismatch during non-stationary maneuvers, we added a transformer-based module to adaptively adjust IMU process noise based on the vehicle’s immediate motion context. This method of uncertainty modeling ensures filter sta bility in scenarios where standard, fixed-noise configurations typically diverge. Evaluated within a Gazebo-based underwater simulation, the framework demonstrated consistent state estimation and bounded drift over long-range trajectories, highlighting the efficacy of adaptive covari ance for reliable underwater localization.
Authors - Fatima Batool, Farzana Jabeen, Tahira Anwar Lashari, Mehvish Rashid Abstract - Autism Spectrum Disorder (ASD) is an invisible disorder that is of ten misdiagnosed in Pakistan due to unawareness and social stigma. There ex ist multiple technological digital interventions for children with autism designed to target their social, emotional or cognitive skills. However, recent studies have overlooked on the intervention integrating the psycho-social and behavioral skills of children with autism. This mixed-method study evaluates the effectiveness of a multi-modal learning framework, SHAAZ, for psycho-social and behavioral skills enhancement of children with ASD. Employing the proposed research design, the 7 week intervention was tested on autistic children with different severity level of disorder, aged 4 to 12 years. The results revealed that across observations, there is an improvement in users performance scores. The System Usability Scale (SUS)andAppQualityandImpactEvaluationbasedonMobileAppRatingScale (MARS) scores for the designed product was 89.16 and 4.27 respectively, imply ing high usability, user engagement and a positive impact on the targeted skills of the users.
Authors - Onkar Yende, Nayan Bhutada, Mohit Thakre, Sai Khadse, Mridula Korde Abstract -Reliable stock price forecasting remains challenging due to the noisy, nonlinear, and non-stationary characteristics of financial time-series data. Traditional statistical methods and deep learning models that rely solely on raw price data often struggle to capture short-term fluctuations and evolving market dynamics. To address these limitations, this study proposes a hybrid forecasting framework that integrates causal time-domain filtering, time–frequency feature extraction, and deep learning–based temporal modeling. The proposed approach employs Savitzky–Golay and Kalman filters to sup press high-frequency market noise while preserving important price trends in a causality-aware manner suitable for real-time forecasting. Localized spectral fea tures representing transient and time-varying market behavior are then extracted using the Short-Time Fourier Transform (STFT). These enhanced time-domain and frequency-domain features are combined and modeled using a Long Short Term Memory (LSTM) network, which effectively captures long-range depend encies and nonlinear temporal patterns in financial data. The framework is evaluated using standard performance metrics, including RMSE, MAPE, and R². Experimental results demonstrate that integrating causal filtering with STFT-based features significantly improves forecasting accuracy and robustness compared to baseline models, providing a reliable and practical solution for short-term and multi-step stock price prediction.
Authors - Meixin Hu, Chuanchen BI Abstract - Speech synthesis is an important tool for improving human-computer interac tion, accessibility, and other multimedia applications. Traditional Text-to-Speech (TTS) systems have issues related to robotic tone, slow inference and lack of expressiveness. This current study presented a realization of the effectiveness of the neural TTS system using Fast Speech 2 as the underlying neural TTS sys tem. The system used in the current study was a combination of Fast Speech 2 as the underlying neural system in generating high-quality utterances and HiFi-GAN as the underlying neural vocoder. The process involves reconstructing natural-sounding text utterances in terms of mel-spectrograms by Fast Speech 2 that incorporate the use of variance adaptation in terms of pitch, duration, and energy. The implementation of natural-sounding utterances in terms of mel spectrograms is done in real-time using HiFi-GAN. The implementation of the available studies provided insights into Fast Speech 2’s effectiveness in generating mel-spectrograms in real-time and faster. The use of HiFi-GAN provided insights in generating natural-sounding utterances in real-time. The effectiveness of Fast Speech 2 in generating high-quality utterances has further stretched the poten tial use of Fast Speech 2 in virtual assistant applications, audiobooks, accessible text services, further highlighting its significance in advanced human–computer interaction systems.
Authors - Cheng Cheng, Chuanchen BI Abstract - In recent years, there has been an increase in AI - generated images. This poses a major challenge in distinguishing fabricated images from real ones. This distinction is valuable for discovering misinformation and preserving digital trust. Some deep learning models, particularly large Convolutional Neu ral Networks (CNNs), have demonstrated high accuracy on benchmark datasets like CIFAKE, but their computational requirements often in clude specialised hardware like powerful Graphics Processing Units (GPUs), which ultimately limit practical deployment. This paper explores an alternative approach that focuses on efficiency and interpretability. The CIFAKE dataset is used, but a significantly lighter CNN architecture, ResNet18 is deployed which does not require high end local GPU hardware. Furthermore, the paper applied Gradient - weighted Class Activation Mapping (Grad - CAM) not just for visu alization, but also to validate that the model learns meaningful visual features that are relevant to the classification task. This work highlights a practical method to interpret AI - generated images.
Authors - Jiayan Peng, Chuanchen Bi Abstract - With the continued growth of digital education (and multiple platforms providing education/courses), students have many things to deal with in terms of finding useful content (e.g., Lecture videos; audio files; PDF's; slides, etc) and as a result, it may be difficult to efficiently scan and gather all of this information. AutoNoteX is a tool that automatically creates notes from your spoken word using speech-to-text technology (e.g. Whisper), Natural Language Processing, and various AI agents. AutoNoteX will provide accurate transcriptions, along with structured summaries that highlight key points and provide diagrams when appropriate in order to create good, clear notes for students. AutoNoteX can support collaborative and independent learning by allowing the user to merge their notes with Google Docs or download them as PDF's. AutoNoteX also includes interactive knowledge checks that have multiple levels of difficulty (easy, medium, difficult) when answering questions and also provide a means for the student to receive instant feedback on their progress. AutoNoteX was developed using React.js for the front end and Python Flask for the backend, and is cloud-enabled (scalable; accessible via many devices; and easy to integrate into a variety of subjects) giving students the tools they need to create better notes. Overall, AutoNoteX provides a new avenue for multi-modal, AI-assisted, and personalized digital note-taking, while reducing the amount of time needed to make notes and improving student comprehension by encouraging students to participate in their learning process actively.
Authors - Qixuan Geng, Chuanchen BI Abstract - Efficient nutrient management is vital in a sugarcane cultivation to sustain the crop yields. But, the conventional practices are still reactive and imprecise often leading to improper nutrient management and yield loss. To overcome this issue, the study utilizes a multimodal AI driven framework by integrating drone-based canopy imaging and in-field soil sensors to aid in real-time nutrient deficiency detection and precise recommendation of fertilizers. UAV images are analysed using a transfer learning based Convolutional Neural Network (CNN) to locate visible deficiency symptoms and determine its severity. In order to forecast impending nutrient deficiencies, significant soil parameters (NPK, moisture, pH, electrical conductivity and temperature) are monitored continuously and processed using GRU/ LSTM- based models. The data and information from sensor networks, images and environmental context are then integrated through a fusion architecture to produce a nutrient deficiency label, severity score, and confidence measure. To ensure interpretability and agronomic safety, predictions are incorporated with crop growth stage- specific nutrient gap model that convert deficiencies into dosages of fertilizers, with alerts given on high-risk conditions and optionally permissioned fertigation control. The proposed system allows proactive, data-driven nutrient management, mitigates the risk of over fertilization, and supports scalable precision agriculture.
Authors - Md. Riaz Mahmud, Kazi Asif Ahmed, Md. Rafiqul Islam, Kabya Guha Abstract - Modeling multi-scale spatial dependencies is essential in histopathology image analysis, where diagnostically relevant patterns span cellular textures and tissue-level structures. While convolutional neural networks effectively capture local features, they struggle to model long-range interactions, and transformer-based approaches address this limitation at the cost of quadratic computational complexity with respect to spatial resolution. In this work, we propose HiSS-Fuse, a linear-time hierarchical state-space fusion framework that integrates multi-scale fea ture representations using Mamba-based selective state-space modules. The proposed architecture progressively fuses local and global contex tual information across network depths while maintaining O(L) com putational complexity, where L denotes the number of spatial tokens. Experimental evaluation on the PathMNIST benchmark demonstrates that HiSS-Fuse achieves 97.0% classification accuracy with an AUC of 0.997 while maintaining strong computational efficiency. Ablation stud ies further confirm that hierarchical fusion systematically enhances rep resentation learning. Overall, HiSS-Fuse provides a scalable and compu tationally efficient alternative to quadratic attention-based architectures for multi-scale histopathology image analysis.
Authors - Cheng Cheng, Chuanchen BI Abstract - The increasing reliance on Information and Communication Technology (ICT)-driven intelligent systems has transformed organizational decision-making processes, enabling more efficient, data-driven, and adaptive strategies. These systems, which encompass artificial intelligence, machine learning, and decision support tools, have revolutionized how businesses process and analyze vast amounts of data to inform strategic decisions (Cheng et al., 2017; Yoo & Lee, 2020). This paper presents a strategic framework for integrating ICT-driven intelligent systems into organizational decision-making, addressing key challenges such as technological compatibility, organizational resistance, and alignment with strategic goals (Patel & Sharma, 2019; López et al., 2019). The main objective of this study is to develop a comprehensive and practical framework that organizations can adopt for successfully integrating intelligent systems into their decision-making processes. The research aims to bridge the gap between existing theoretical models and practical applications by proposing a step-by-step process that involves assessing organizational readiness, selecting appropriate systems, ensuring seamless integration, and fostering continuous improvement (Ahmad et al., 2021; Pereira et al., 2021). The methodology employed includes qualitative case studies from diverse industries, supplemented with a review of relevant literature and theoretical models such as the Technology-Organization-Environment (TOE) framework (Tor-natzky & Fleischer, 1990) and the Resource-Based View (Barney, 1991). The findings suggest that successful ICT integration is contingent upon a well-planned, strategic approach that aligns technological capabilities with organizational goals and promotes an adaptive organizational culture (Brinkman & Möller, 2018). The implications of this study are far-reaching, offering valuable insights for managers and policymakers to overcome integration barriers and optimize decision-making using intelligent systems (Hossain & Kaur, 2021). This research contributes to the growing body of knowledge on ICT integration in decision-making, offering both theoretical advancements and practical guidelines for successful implementation.
Authors - Tajamul Islam, Ruby Chanda Abstract - The present study explores the online privacy concerns of young Indian consumers. Using the segmentation approach popularized by Dr Alan Wes-tin in the U.S., this study identifies the segments within Indian youth. This study is based on a survey conducted on a sample of Indian university students. Hierarchical and non-hierarchical cluster analysis techniques were applied to identify segments within young Indian consumers based on their privacy concerns. The study identified three consumer segments: highly concerned, moderately concerned, and less concerned based on online privacy concerns. The findings also reveal important differences among the three segments in terms of out-come variables such as perceived effectiveness of legal/regulatory policy, fabricating personal information, and software usage for protection. The results indicate an overall increased level of concern for online privacy among young Indian consumers. The results suggest similarities and dissimilarities with Westin’s approach. While previous research on online privacy has been chiefly based on the Western context, this study offers a window to look at the Eastern context by examining the privacy concerns of young Indian consumers, who have not been studied, and hence provides an important contribution to the existing literature.
Authors - Meixin Hu, Chuanchen BI Abstract - Secret-sharing schemes are fundamental cryptographic primitives en- abling secure distribution of sensitive information among multiple parties. Orig- inally introduced to protect cryptographic keys, they have evolved into power- ful tools underpinning modern secure multiparty computation, distributed stor- age, blockchain systems, and privacy-preserving machine learning. This review presents a systematic overview of threshold secret-sharing schemes, ramp con- structions, and secret-sharing schemes for arbitrary access structures. We discuss information-theoretic foundations, lower bounds, structural generalizations, and recent advances. Furthermore, we highlight emerging applications in distributed computing, post-quantum cryptography, and secure AI systems.
Authors - Ying Tang, Chuanchen BI Abstract - This article presents a comprehensive analysis of methods and recent research in the sentiment analysis of Uzbek-language social media posts. A balanced corpus of 100,000 posts from Telegram, Instagram, Twitter, and Facebook was constructed as the object of study, in which positive, neutral, and negative classes are equally represented. The data were subjected to thorough preprocessing steps including cleaning, normalization, tokenization, removal of stop words, stemming, and lemmatization. The evaluated models include Naive Bayes, Support Vector Machines (SVM), Conditional Random Fields (CRF), Long Short-Term Memory networks (LSTM), and transformer-based architectures such as BERT and RoBERTa. The accuracy, F1-score, and runtime performance of each model were compared. Experimental results indicate that transformer-based models achieved the highest accuracy (~92%), followed by LSTM (~90%) and SVM (~88%). Despite being a simple method, Naive Bayes served as a baseline (~78% accuracy). The literature review highlights prior research conducted in Uzbek sentiment analysis, emphasizing the importance of corpus creation and accounting for language-specific features. The results indicate that transformer models provide the highest accuracy, whereas classical methods remain competitive even in low-resource settings. The article concludes with a discussion of promising directions and potential practical applications in the field of Uzbek-language sentiment analysis.
Authors - Lankalapalli Vamsi Krishna, Santanu Mandal Abstract - The rapid advancement of generative and agentic artificial intelligence (AI) is significantly transforming research in operations management and supply chain systems. Despite the substantial increase in scholarly output in recent years, the structural evolution and thematic consolidation of this interdisciplinary field remain insufficiently mapped. This study presents a bibliometric analysis of 116 Scopus-indexed articles published between 2015 and 2025 to examine publication trends, knowledge concentration, intellectual structure, and longitudinal thematic transitions. Utilizing the Bibliometrix R package, the analysis employs performance metrics, Bradford’s Law, keyword co-occurrence mapping, thematic centrality–density analysis, and temporal evolution modeling. The results indicate accelerating research growth and increasing consolidation within core engineering-oriented journals. Intellectual clustering reveals strong integration between computational modeling, reinforcement learning, and supply chain decision systems. Thematic mapping identifies computational methods and autonomous agents as central themes, while generative AI emerges as a developing yet increasingly interconnected trajectory. Longitudinal analysis reveals a clear shift from agent-based simulation frameworks toward adaptive, autonomous, and AI-integrated operational ecosystems. The findings suggest that generative and agentic AI are becoming foundational elements of next-generation operational intelligence systems. This study provides structured insights into the maturation of AI-enabled operational research and offers guidance for future interdisciplinary investigations in autonomous supply chain intelligence.
Authors - Sohesh Gandhe, Aditya Shirwalkar, Prathmesh Jomde, Shreyash Dhavale, Anil M. Bhadgale Abstract - Automatically generating Unified Modeling Language (UML) diagrams from unstructured software requirements remains one of the persistent challenges in modern software engineering. This paper introduces an intelligent project management framework that transforms client-provided requirement documents into accurate UML diagrams with minimal human intervention. Our system leverages Optical Character Recognition (OCR) to extract text from various document formats, employs a fine-tuned model for intelligent prompt synthesis, and utilizes a fine-tuned CodeLLaMA 7B model trained on prompt-to-MermaidJS code mappings. The generated diagrams—including sequence diagrams, flow charts, and Gantt charts—are rendered in real time through an integrated Mermaid Live Editor, providing immediate visual feedback within the project management interface. The experimental evaluation demonstrates substantial improvements in automation efficiency, reduced manual modeling effort, and improved consistency in UML generation. Our approach bridges the gap between natural language requirements and formal system design artifacts, offering a practical solution for automated software documentation and project planning at scale.
Authors - Trupti Shripad Tagare, K.L.Sudha, Nagendra Kumble, Sanketh T S, Belliappa M Abstract - The current developments in the design of aircraft have remarkably improved their overall performance. The parameter Rate of Climb (RoC) plays a very vital role in planning the trajectory of the flight, optimum fuel utilization and flight safety and is of significance for both technicians and pilots. The factors affecting RoC are weight of the aircraft, its design, and the atmospheric state. In this study, the estimation of real time RoC using predictive AI and deep learning is presented. The model is trained on real time flight data collected from Radome Technologies, Bengaluru. The parameters like drag, thrust, weight, climb angle and airspeed are provided as inputs to the model after preprocessing. The results show that the system achieves an enhanced predication accuracy with R2 of 0.9396, Root Mean Squared Error (RMSE) of 861.69 feet per minute and Mean Absolute Error (MAE) of 659 feet per minute. The efficiency and capability of several aircrafts can be measured and analysed using the rate of climb. The work greatly finds its important role in ground-based flight planning tools and in onboard decision-support systems. The fuel requirements for the aircraft can be reduced significantly by setting an optimum ROC. This will result in reduced costs and sustainable solutions. This work contributes to overall performance and safety, as the aircraft will maintain the optimal ascent using AI driven climb profile optimization.
Authors - Glenn Erick Zambrano Estupinan, Maria Genoveva Moreira Santos Abstract - Virtual Reality (VR) has gradually become an increasingly relevant technological tool in higher education, not only because of its innovative nature, but also due to its ability to create immersive experiences capable of capturing students’ attention and generating meaningful emotional responses. In this con-text, the aim of this study was to analyze the immediate emotional impact produced by a virtual reality experience on university students, using data mining techniques to identify patterns within the collected responses. The research followed a quantitative approach, with a descriptive–correlational and cross-sectional design, and included the participation of 305 students from the Faculty of Computer Sciences at the Technical University of Manabí. Each participant engaged in an immersive experience lasting approximately five minutes using the Meta Quest 2 device. After the activity, a Likert-type questionnaire, with a scale ranging from 1 to 5, was applied in order to evaluate variables such as perceived immersion, realism of the environment, level of attention, emotional interaction, empathy, and enthusiasm before and after the experience. The collected data were subsequently analyzed through exploratory and correlational analysis, as well as through several data mining techniques, including Principal Component Analysis (PCA), k-means clustering, and Apriori association rules. Overall, the results suggest that the virtual reality experience generated predominantly positive emotional responses among the students.
Authors - N. Revathy, V. Latha Sivasankari, Nikileshwar V, Surendhiran G, Abijith M, Sheik Mohamed S Abstract - Enterprise networks face escalating cyber threats as cloud, IoT, and remote work adoption expand attack surfaces. Traditional signature-based detection and manual response suffer average breach detection intervals of 287 days, failing to scale against rising alert volumes [9]. CyberSentinel addresses this through an autonomous pipeline processing Windows Security Event Logs: Isolation Forest anomaly detection on engineered behavioral features, large language model (LLM) threat explanations via local Ollama inference, and automated remediation including account deactivation, process termination, and firewall adjustment. A Flask web dashboard provides real-time threat visualization. Evaluation across 72 hours on a controlled Windows 10 Enterprise testbed with 28 injected anomalies confirms an F1-score of 0.78, 84.2% remediation success, and mean end-to-end latency of 24.7 seconds. The modular Python architecture enables fully autonomous operation on standard Windows hosts without dedicated SOC infrastructure.
Authors - Palgulla Rangaswami Reddy, Palla Maheswara Rao, Gogineni Hari Prasad, Guthikonda Akhila, T.V. Sai Krishna Abstract - The implementation and design of a covert communication channel that embeds hidden information within TCP/IP packet headers rather than within the actual payload of the packets is presented as a project. This is different than traditional embedding methods (steganography), which typically embed data into multime dia files, in that steganography in this case utilizes header fields that are not cur rently in use or can be modified so that TCP/IP packets can transmit hidden data. The fields that are used to transmit hidden data are the IP Identification Field, TCP Sequence Number, TCP Acknowledgment Number, and TCP Window Size. The sender module encodes and generates packets, and the receiver retrieves packets, extracts encoded bits, and reassembles data from the encoded bits found in the packets. The integrity of the data is verified using a checksum (SHA-256) and packet loss is reported. The lack of a payload will further enhance the stealth various data transmission methods may enjoy as it will circumvent conventional intrusion detection techniques (which primarily examine the payload data within packets). This project will demonstrate the ability to use this or similar covert communication channels to implement covert communication systems. In addi tion, covert communication channels can be used for different types of files and demonstrate the security and educational value of covert channel research in net work security.
Authors - Busrat Jahan, Kevin Osei-Onomah, Mansi Bhavsar, Hermela Dessie, Apu Chandra Bhowmik Abstract - In the global health sector, Diabetes is a major concern which needs accurate and effective models for early prediction. This work is quantitative re-search work. The dataset was collected from CDC Diabetes Health Indicators, and we used Light Gradient Boosting Machine (LightGBM) model for predicting diabetes. Since this research work is binary classification-based work, in our data preprocessing stage, we used Synthetic Minority Oversampling Technique (SMOTE) for controlling class imbalance and for feature selection we used Chi-square test to improve the model performance. The proposed LightGBM model showed its ability to recognize complex correlation between diabetes-related health indicators with the training accuracy of 92% and a ROC-AUC score of 0.97 on the test dataset. Overall, the findings highlight that predictive accuracy is significantly improved after applying both imbalance data controlling and most correlated feature selection techniques.
Authors - Ruby Bisht, Amit Kumar Uniyal Abstract - Digital transformation is reshaping education systems worldwide, with significant implications for rural and underserved regions. In India, initiatives aligned with the National Education Policy (2020) have promoted online learning platforms, digital classrooms, and technology-enabled teacher training to enhance access, equity, and quality in education. However, rural schools continue to face structural challenges such as limited infrastructure, digital divides, and inadequate teacher preparedness, which influence the effectiveness of digital integration.This conceptual paper examines the transformation of rural education in India from traditional teacher-centred classrooms to digitally enabled learning ecosystems. Grounded in Constructivist Learning Theory, the Technology Acceptance Model (TAM), Diffusion of Innovation Theory, and the TPACK framework, the study proposes an integrated conceptual model linking digital infrastructure, pedagogical innovation, and teacher competence to improved access, engagement, and learning outcomes. The paper argues that digital transformation represents a systemic pedagogical and institutional reform rather than a mere technological shift. Its success depends on inclusive infrastructure development, sustained teacher capacity building, and context-sensitive implementation in rural settings.
Authors - Ruby Bisht, Amit Kumar Uniyal Abstract - The rapid growth of Information and Communication Technologies (ICT) has profoundly altered educational systems by redefining teaching practices, institutional processes, and professional expectations. Within the broader context of sustainable development and smart education, ICT has emerged as an important facilitator of efficiency, accessibility, and innovation. This paper presents a conceptual analysis of how ICT can contribute to sustainable development through its influence on teachers’ work–life balance and job satisfaction in ICT-enabled learning environments. While ICT adoption has the potential to enhance instructional flexibility, autonomy, and efficiency, excessive digital connectivity, intensified workload, and blurred work–life boundaries may adversely affect teachers’ well-being. The paper identifies work life balance as a key mediating factor linking ICT use to job satisfaction and long term professional sustainability. Furthermore, the study situates teachers’ well being within the broader framework of sustainable development, emphasizing its relevance to Sustainable Development Goals such as SDG 3 (Good Health and Well-Being), SDG 4 (Quality Education), and SDG 8 (Decent Work and Economic Growth). The analysis underscores the need for human-centred, policy-driven, and ethically oriented ICT integration strategies that prioritize teacher well-being alongside technological advancement. The paper contributes to the discourse on sustainable and intelligent education systems by highlighting that the long-term effectiveness of ICT-driven educational transformation depends on balanced digital practices that support teachers’ work–life balance and job satisfaction.
Authors - Vasumathi R, Kalpana Y Abstract - Graduate communication competency gaps represent a critical barrier to the workforce readiness in the Indian higher education, yet existing assessment infrastructure measures a credential completion rather than the skill trajectories over time. This paper presents a LSTM-CDSF (Long Short-Term Memory Communication Demand and Skill Forecasting), a temporal deep learning based framework that predicts the future communication skill demand from the sequential monthly assessment records and also quantifies per skill gaps against the industry benchmarks. The framework operates on a synthetic dataset of 240 students observed over a period of 18 months calibrated to published NASSCOM and India Skills Report statistics. LSTM-CDSF achieves a Mean Absolute Error of 1.468, RMSE of 1.837, MAPE of 2.61%, and R² of 0.9249 on a held-out test set of 480 sequences, demonstrating consistent performance improvements over the Linear Regression, ARIMA, and a naïve baseline across all the evaluated metrics. Gap analysis reveals that the Digital Communication (gap: 25.4 points) and the Intercultural Communication (gap: 23.5 points) requires the most urgent curriculum interventions.
Authors - M. Kamaraju, B. Rajasekhar, V.N.V.R. Karthik, V.N.L. Mahima, Y.H.V. Satya Narayana, R. Pujitha Abstract - This manuscript presents a dedicated Application-Specific Integrated Circuit (ASIC) architecture purpose-designed for computing eigenvalues of two-dimensional square matrices in resource-constrained embedded systems. The fundamental challenge motivating this work stems from the computational intensity of eigenvalue decomposition in digital signal processing, robotics control systems, and embedded analytics, where conventional software implementations incur unacceptable latency and power overhead. The proposed solution lever-ages the closed-form algebraic solution inherent to 2×2 matrices, eliminating iterative numerical methods and their associated performance penalties. Our design employs a direct characteristic-equation approach mapped onto dedicated arithmetic circuits including parallel multipliers, adders, and a specialized square-root computation unit implementing the non-restoring digit-re-currence algorithm. The Verilog RTL synthesized using Cadence Genus in a 180 nm CMOS standard cell library yields a compact silicon footprint of 1,703 square micrometers utilizing 196 standard cells, with measured power dissipation of 0.5738 milliwatts at 100-megahertz operation. Timing closure is achieved with positive slack under worst-case process-voltage-temperature conditions. The high dynamic-to-static power ratio of 98.66 percent to 1.34 percent indicates activity-dominated power behavior, confirming successful implementation of low-leakage design principles. These metrics demonstrate that the proposed architecture constitutes an effective hardware acceleration solution for eigenvalue computation in battery-powered and always-on applications where conventional approaches prove infeasible.
Authors - Humma Ghaffar, Usman Ali, Muhammad Arfan, Sajid, Muhmmad Mujeeb Akbar Abstract - The growing mental health challenges around the globe need access to scalable, available, and safety conscious digital interventions. The paper describes a mental health support platform, based on AI, which combines conversational intelligence, multi-therapeutic persona modeling, structured mood analytics, proactive crisis identification, multi-lingual interaction, and voice-based access in a secure full stack design. The system, which runs on the Google Gemini AI, provides context-sensitive therapeutic dialogue and performs four-dimensional mood analysis of anxiety, stress, depression, and wellbeing, allowing longitudinal assessment by providing interactive dashboards and automated reporting. A safety-first crisis override system offers validated emergency capacity in the high-risk situations. The platform also includes multilingual voice feedback to facilitate inclusion of the visually impaired users and non-English speaking communities in providing inclusive digital mental health care. The proposed system is capable of changing the prevalent perception that AI and its applications may never be responsible and scalable because it integrates therapeutic diversity, structured analytics, accessibility features, and proactive safety controls into a single framework.
Authors - Pranay Kavthankar, Rutuj Koli, Ronit Ghadi, Yug Mora, Abhijit Joshi Abstract - Speech-to-Speech Translation (S2ST) has evolved from cas caded pipelines into end-to-end neural architectures. However, preserv ing emotion, prosody, and speaker identity across languages remains challenging. This survey examines state-of-the-art emotion and identity preserving S2ST and neural TTS systems, covering discrete-representation models, end-to-end systems, and cascaded pipelines. We analyze architec tures including Translatotron, VQ-Translatotron, SeamlessM4T, VALL E, VALL-E X, VITS, YourTTS, StyleTTS2, and XTTSv2. The survey discusses speaker identity preservation (x-vectors, d-vectors, codec repre sentations), prosody modeling (pitch, duration, energy), emotion reten tion (categorical, dimensional, embeddings), datasets, evaluation met rics, and challenges including data scarcity, cross-lingual emotion trans fer, and computational costs. We propose future directions toward large scale expressive datasets, improved cross-lingual modeling, and respon sible AI practices.
Authors - Maykin Warasart, Pallop Piriyasurawong, Panita Wannapiroon, Prachyanun Nilsook Abstract - This paper introduces an AI-based investment assistant that helps users to understand the fundamental principles of the financial markets. This work is mainly focused on stock market data to provide accurate insights and helps in various decision-making purposes. The rising volatility in the financial markets, massive data set, and the complexity of financial instruments, makes decision-making in financial sectors more difficult to individual investors.In order to cope with this problem, our model integrates time series forecasts, large language model intelligence with real-time financial information with interactive visualizations and personalized insights. The suggested system will interpret user queries in natural language with the help of a Large Language Model (Gemini 2.5 Flash) and extracts the corresponding stock tickers and financial objects and transforms them into structured inputs to be used in predictive analysis. Past and current stock market data are retrieved with the help of yfinance API and fed into an LSTM-based time-series predictive model that predicts future price fluctuations.The results predicted are presented in interactive charts created with Plotly, which users can analyze trends easily and compare several stocks. The system can also give personalized recommendations, textual summaries of stock movements (moving up or down), multi-turn chatbot conversations, portfolio, wishlist and real time price moves besides forecasting. The proposed investment assistant improves the gap between complicated financial information and practical results by incorporating natural language comprehension, deep learningbased prediction, and intuitive visualization etc. The system promotes user knowledge and helps them in effective decision making .
Authors - Gabriel M. da Silva, Nicolas O. da Rocha, Heloise V. C. Brito, Joao V. N. M. da Silva, Sergio A. S. da Silva, Anderson R. de Souza, Carlos A. O. de Freitas, Vandermi Joao da Silva Abstract - Spiking Neural Networks (SNNs) have been investigated as a biologically inspired alternative for efficient information processing, particularly in energy-sensitive applications. This work presents a comparative evaluation of the energy efficiency of different SNN techniques, including Liquid State Machines (LSM), Recurrent Spiking Neural Networks (RSNN), Spiking Convolutional Neural Networks (SCNN), and learning based on Spike-Timing Dependent Plasticity (STDP). The experiments were conducted on conventional hardware plat-forms, namely an Android smartphone and a notebook, using simulated implementations of SNNs without dedicated neuromorphic acceleration. The analysis considered different network scales by varying the number of neurons and was based on neural activity metrics, particularly the total number of generated spikes, employed as a proxy for the indirect estimation of energy consumption during audio signal processing. The results demonstrate a consistent relationship between neural activity and estimated energy consumption, as well as an energy saturation behavior as network complexity increases. Differences among the an-alyzed techniques are more pronounced in small-scale configurations, whereas larger networks exhibit convergent patterns of neural activity and energy consumption. Although conducted in a digital simulation environment, this study highlights the limitations of conventional platforms for the efficient execution of SNNs and reinforces the potential of dedicated neuromorphic hardware for embedded and low-power applications.
Authors - Maykin Warasart, Veerasith Wongkarn, Phonesavanh Nammakone, Duangtavanh Thatsaphone Abstract - Manual correction of written examination scripts is still the default practice in many institutions, but it is slow, tiring for evaluators, and not always consistent, especially when large numbers of papers must be graded in a short time. In this work we look at how recent advances in optical character recognition (OCR), machine learning (ML), and natural language processing (NLP) can be used together to support automatic evaluation of both objective and descriptive answers. In this paper We study a two–stage system: first, a handwriting recognizer based on convolutional and recurrent neural networks (CRNN) is used to read handwritten responses from scanned answer sheets; next, the recognized text is scored using semantic and syntactic similarity measures driven by transformer-based language models. By training the recognizer on a mixture of public handwriting corpora and locally collected scripts, and by combining keyword features with sentence-level embeddings, the system is able to approximate faculty grading patterns with good accuracy. This study examines the way that real tests are administered, including variations in writing styles, background noise in scans, the arrangement of answers on paper, and terms related to specific subjects. We clearly address each of those factors in our approach. Teachers won’t vanish because of this setup; instead, it aims to ease their ongoing tasks while offering fairness and consistency across student results.
Authors - Hai D. Nguyen, Nguyen Ngoc Quan, Viet H. Le, Mai T. Nguyen, Nguyen Huy Trung, Le Duc Huy, Nhu Son Nguyen Abstract - Military forces launch offensive operations to defeat and destroy enemy. Battlefield surveillance enables provisioning of timely and correct battle space information to commanders, both prior and during the launch of offensive operations. Static battlefield surveillance devices have certain limitations which restrict their usage during offensive operations. In the current paper, we review the requirement of surveillance devices during various periods of offensive operations, the limitations of static surveillance devices and efficacy of Unmanned Aerial Vehicles (UAVs) as prime battlefield surveillance device for offensive operations. We then explore the possibility of connecting UAVs with existing cellular base stations and with vehicle mounted cellular base stations which can be moved into enemy territory with the progress of offensive operations. Furthermore, a UAV communication model for enhanced battlefield surveillance during offensive operations is presented after analyzing various antenna techniques utilized to achieve desired data rates for UAV operations.
Authors - Quan Nguyen, Chau Vo, Phung Nguyen Abstract - In order to create reliable connectivity where there is no direct line-of-sight (LOS) path between ground terminals, this study provides the design and performance evaluation of a dual-hop Unmanned Aerial Vehicle (UAV) assisted free space optical communication system. The proposed ground–UAV–UAV–ground architecture enables non-LOS communication by employing aerial relays to bypass physical obstructions and extend transmission coverage. Three modulation formats—Non-Return to Zero (NRZ), Return to Zero (RZ), and Carrier-Suppressed Return to Zero (CSRZ)—under various weather conditions and turbulence regimes are used to assess the system performance. While all modulation schemes perform closely for different attenuation level, differences in performance is prominent under turbulence, CSRZ demonstrates superior robustness, followed by NRZ and RZ.
Authors - Rajesh Kapoor, Vishal Goyal, Aasheesh Shukla Abstract - This paper presents a systematic review of visual sarcasm detection research with a focus on learning-based approaches. The review examines input representations, feature extraction methods, model architectures, datasets, and evaluation practices reported in the literature. Studies are analyzed with respect to the use of visual information, including images and image–text pairs, along with associated deep learning frameworks such as convolutional, transformer-based, and hybrid models. A structured search strategy, defined inclusion criteria, and an analytical framework are employed to ensure consistency and reproducibility of the review process. The findings are synthesized to identify prevailing research patterns, methodological limitations, and gaps related to visual feature representation, model design, and experimental consistency. By organizing and comparing existing approaches, this systematic review provides a consolidated reference and supports future research in visual sarcasm detection.
Authors - G. Sabera, Kanajam Murali Krishna, N. Sabitha, Tummala Purnima, A. Naresh, Shaik Janbhasha Abstract - Complementing the continuous deep integration of culture and tour-ism, the tourism market environment and visitor consumption demand are constantly evolving, with cultural theme attractions playing an increasingly prominent role in tourism industry development. Tourism resources constitute the basic foundation of scenic destination development, while scientific and effective tour-ism marketing provide a key factor in enhancing market competitiveness and achieving sustainable development. Relying on the cultural resources of the Song Dynasty and martial arts culture, The Song Dynasty of Kungfu City has formed a distinctive thematic identity against the background of cultural–tourism integration and has gained a particular level of market attention. However, its tourism marketing practices still face practical challenges such as brand strengthening, intensified market competition, and changing visitor expectations. This study takes The Song Dynasty of Kungfu City as the research object and analyzes the current status of its tourism marketing, exploring the developmental foundation and practical challenges faced by the scenic area under the contemporary tourism market environment. A qualitative research approach is adopted. Relevant data were collected through field observation and in-depth interviews to review the scenic area’s tourism marketing activities. Based on this, the SWOT analytical framework was applied to systematically examine the strengths, weaknesses, opportunities, and threats associated with the tourism marketing status of The Song Dynasty of Kungfu City.
Authors - Sambhram Pattanayak, Akankasha Kathuria, Shreesha Mairaru Abstract - Reliable prediction of rare critical events is a key enabler for modern risk management, civil protection, and decision support sys tems, yet it remains challenging due to extreme class imbalance and strict requirements on false alarm rates. We present an ensemble learn ing framework that combines a deep feed-forward neural network with a Random Forest classifier, complemented by temporal feature engineering and precision-oriented optimization. The approach addresses three ob jectives: extracting informative temporal and regional patterns from raw event logs, learning calibrated probabilistic scores under severe imbalance using focal loss, and tuning per-region decision thresholds to achieve high precision while preserving acceptable recall. As a case study we apply the framework to air alert prediction over 25 administrative regions across 38 months, totalling 774,125 hourly observations. The system attains 96.13% accuracy, 75.1% precision, and 77.9% recall, demonstrating that high-precision early warning is feasible in strongly imbalanced settings. The framework is applicable to a wide range of safety-critical rare event prediction tasks.
Authors - Sanchit Prashant Joshi, Vedant Vipin Joshi, Aditya Arun Mangalekar, G.S.Mundada Abstract - Malware classification is essential in cyber-security. It en ables prevention of threats by identifying and accurately classifying ma licious software. It also helps in understanding attacker behavior, enhanc ing threat intelligence, and improving the overall effectiveness of security systems. It is increasingly critical as adversaries now employ obfuscation techniques to avoid detection. Traditional models such as Convolutional Neural Networks (CNN) often struggle with such obfuscated malware samples. In this paper, we propose MalViT, a Vision Transformer (ViT) based framework for robust malware classification using grayscale image representations of malware binaries. The ViT is fine-tuned on a prepro cessed Malimg dataset. To evaluate the robustness of the model, real world obfuscation techniques such as Encryption, Dead code insertion, Random masking and Junk Padding are simulated. ViT model is initially f ine-tuned on the clean samples and later on a combination of the clean and obfuscated samples. Both models are evaluated on the clean and obfuscated test sets to highlight the robustness of the model. The final model achieved a combined accuracy of 94.52 % on both the clean and obfuscated samples. The results demonstrate that MalViT maintains a competitive performance under obfuscation. This project highlights the potential of ViTs in building resilient malware classification systems and provides a foundation for future work in transformer based architecture for malware analysis.
Authors - Samiksha Ganesh Zagade, Arya Mahesh Parkar, Suman Madan Abstract - Advances in Artificial Intelligence, Machine Learning and Internet of Things technologies have enabled wearable devices to sense as well as process and respond to human behaviour in real time. While most wearable devices today are used for health and fitness tracking. Many people face communication challenges such as language barriers, difficulty understanding emotions or social cues, social anxiety and accessibility issues for individuals with hearing or speech impairments. Existing systems often collect data but fail to provide meaningful, real-time assistance during actual human interactions. This research paper presents a literature-based study on AI powered wearable devices designed to support and enhance human communication. The research papers are focusing on intelligent wearables that use multimodal sensors such as microphones, cameras and sensors. These systems apply AI techniques to interpret speech, gestures, facial expressions and emotional signals in real time. The wearable devices considered include everyday consumer-oriented systems such as smart eyewear that provides audio visual assistance and wrist worn wearables that offer haptic feedback. The key focus of this study is to examine how such devices can deliver subtle, real-time support through visual prompts, audio cues or vibrations to improve conversational awareness and user confidence. The expected outcome is to identify current capabilities, practical limitations and design considerations for developing human centric wearable technologies that move beyond passive tracking toward meaningful communication support.
Authors - Adnan Hasan, Ishaan Mishra, Jyotiska Bose, Jada Viswa Chaitanya Sai, Jai Kumar, Kaif Akhter, Ranjita Kumari Dash Abstract - In the present-day context, presentations and computer-based interac tion play a crucial role in various domains, particularly in education and business. Traditionally, users have to rely on physical devices such as mouses, keyboards, or laser. Although these devices meet the basic requirements, they still reveal many limitations regarding mobility, continuity, and dependence on battery life. To address these limitations, hand gesture-based presentation control systems have emerged as a promising solution due to their intuitive, natural, and engaging interaction style. This paper proposes a touchless system that enables users to control common desktop operations as well as presentations in a natural manner using hand gestures captured via a standard webcam. The proposed system lev erages OpenCV for real-time video acquisition and preprocessing, while Medi aPipe framework is employed for hand tracking and landmark extraction. From the experiments, our system can process in real-time with the accuracy of approx imately 92%. As a result, users can seamlessly control slides, use virtual mouse operations, annotate presentation content, and engage with the audience in a more interactive and natural way without physical contact.
Authors - Jyoti Chandel, Meenakshi Mittal Abstract - Internet of Things (IoT) devices are growing in domains because of their reliability and efficiency in monitoring, real-time detection and automated support. However, these IoT systems have also introduced security challenges. These devices are vulnerable to cyber threats, where attackers exploit weak points in the system to steal sensitive information. One of the attacks is the Distributed Denial of Service (DDoS) attack, which disrupts services by overwhelming systems and making them inaccessible to legitimate users. IoT devices are resource-constrained, so reducing feature dimensionality is essential to lower computational overhead and complexity. IoT devices generate data for detecting cyber-attacks, but sharing such data across organizations raises privacy concerns. To address these challenges, the proposed approach is designed in two phases. In the first phase, a hybrid feature selection technique using mutual information, permutation feature importance, and Greedy wrapper-based feature selection with cross-validation is applied to extract relevant features. In the second phase, Federated Learning (FL) is applied to train the model without sharing raw data among clients. Within the FL framework, Random Forest (RF) algorithm is utilized for training due to its robustness and classification capability. The proposed model is evaluated under two data distribution scenarios: mildly non-IID and strongly non-IID conditions. Experimental results demonstrate that the model achieved an accuracy of 99.69% in a mildly non-IID scenario and 98.36% under strongly non-IID conditions, highlighting the effectiveness and reliability of the proposed framework for secure IoT-based DDoS attack detection.
Authors - P.N. Deorukhakar, V.B. Waghmare, I.K. Mujawar, R.Y. Patil Abstract - Convolutional Neural Networks (CNNs) have been widely and successfully applied to bioacoustic and passive acoustic monitoring tasks, including soundscape classification. However, the high dimension ality of CNN-derived embeddings often results in increased computa tional cost and reduced efficiency, particularly in iterative learning frame works such as Active Learning (AL) and in scenarios with limited labeled data. This work addresses these limitations by proposing a method for adapting CNN architectures to generate compact and discriminative em beddings tailored to soundscape data classification. The proposed ap proach leverages transfer learning and incorporates three progressively reduced dense layers (512, 256, and 128 neurons), enabling dimensional ity reduction to be learned intrinsically during network training rather than applied as a post-processing step. Experimental evaluations con ducted across multiple soundscapes datasets under the Active Learning paradigm demonstrate that the proposed embeddings consistently out perform conventional CNN embeddings (CNNE) in terms of classification performance and the efficient use of labeled data. These results indicate that integrating dimensionality reduction directly into CNN training en hances representation quality and robustness, offering an effective solu tion for soundscape data classification in labeling-constrained environ ments.
Authors - Domenico D’Uva Abstract - Indoor air quality (IAQ) is a frequently overlooked determinant of health in rural villages, where the extensive use of solid fuels for cooking and space-heating generates elevated concentrations of airborne pollutants. This study presents an integrated, low-cost protocol for improving IAQ in rural dwellings, combining real-time environmental monitoring, simplified digital modelling and passive strategies of ventilation and biophilic design. The methodology can be structured into three steps: Conceptual digital twin, feedback interface, ventilation strategies, biophilic integration. Conceptual digital twin is based on the mapping of each dwelling linked to Arduino low-cost, stand-alone sensors (CO₂, PM₂.₅, temperature and relative humidity) that collect data at temporal resolution of one minute. An immediate feedback interface based on visual and/or acoustic indicators that prompt residents to take corrective actions (selective opening of windows, activation of cross-breezes), when exposure thresholds - derived from WHO Air Quality Guidelines - are exceeded. Data-driven natural-ventilation strategies – optimal ventilation windows identified through time-series analysis of sensor data, calibrated to local weather conditions and occupancy profiles to maximise air exchange while minimising heat losses. Biophilic integration implies the introduction of resilient plant species with proven phytoremediation capacity, as Epipremnum aureum) which could reduce CO₂ level, with quantitative guidance on density (two to three plants per main room) and optimal placement. Using low-cost IoT sensors, the protocol monitors environmental parameters and pollutant concentrations in real time. The system targets specific safety and comfort thresholds, aiming to maintain CO₂ levels below 700 ppm and PM₂.₅ below 50 μg/m³ to optimize occupant health (Wu et al, 2021). These thresholds, derived from World Health Organization (WHO) guidelines, are essential to ensure occupant satisfaction and well-being. The ultimate objective is to define a scalable and replicable intervention model capable of combining digital technologies and natural solutions for the sustainable regeneration of fragile territories.
Authors - Kritika Singhal, Khushi Madeshiya, Utkarsh Upadhyay, Siser Pratap Singh, Surendra Kr. Keshari, Veepin Kumar Abstract - The integration of artificial intelligence in the academic en vironment has been rapidly growing since late 2022. One of the most widely adopted artificial intelligence tools in engineering is the large lan guage model. By using large language models, the engineering students can generate assignment answers, solve problems through code, and ex plain engineering concepts. Unlike traditional approaches, the large lan guage models can reduce time and simplify the students’ work. Many researchers have worked on artificial intelligence tools, most specifically large language models for engineers. This paper reviews the literature on the application of artificial intelligence tools in the following five areas of engineering education, which include programming, problem-solving in the core subjects, intelligent tutoring, technical writing, and simula tion support. Further, this paper discusses the main challenges of large language models in engineering education. Finally, this article concludes by outlining the future scope of large language models in engineering.
Authors - S.Venkata Rakesh, K.Tarun Kumar, A.Lohith, M.Nirupama Bhatt Abstract - One of the world's most destructive types of malware is ransomware, which results in huge financial and data loss around the globe. Current signature-based detection methodologies do not work for the detection of these types of ransomware because they have no way to identify them prior to their creation (zero-day) or when a variant of the ransomware is created (polymorphic). A behaviour-based ransomware detection methodology that involves the use of CPU Hardware Performance Counters (HPC) in combination with machine learning models for the purpose of detecting ransomware activity is the focus of this project. The following HPC metrics will be used to monitor the execution of a program or application while it is executing: instruction count; cache references; cache hits; branch instructions; and CPU cycles. These low-level architectural events will provide information on the unique behaviour characteristics of a ransomware program or application based on the types of behaviours exhibited by the encryption pro-cesses of a ransomware program or application. A labelled dataset of HPC traces of typical programs/applications will be developed by running both standard pro-grams/applications and ransomware in a controlled testing environment. Several supervised learning models such as Random Forest, Support Vector Machines, and Logistic Regression will be trained and validated on the labelled dataset. The experimental results show that ransomware activity causes significantly different HPC metrics, thereby allowing the correct identification of ransomware. The pro-posed methodology will offer a real-time, graphical user interface for real-time monitoring and graphical representation of the detected ransomware program or application.
Authors - Vasavi Ravuri, S. Lalitha Geetanjali, T. Bhavana Sri, V. Praveen, M. Mokshgna Teja Abstract - Unstructured vehicle traffic (i.e. those containing multiple users such as automobile drivers, pedestrians, cyclists, and even animals) creates a significant challenge for road safety. This work presents the development of a real-time road risk assessment (RRA) system for analyzing dashcam video that combines several computer vision techniques: object detection, semantic segmentation, multi-object tracking, and alert classification, into a unified, integrated processing pipeline. Object detection and multi-object tracking are accomplished using the YOLOv8m and ByteTrack with Kalman Filter algorithms. Additionally, semantic segmentation of the road scene is achieved using a SegFormer-B2. Finally, a segmentation-assisted fusion filter and perspective-aware danger zone are applied (to define each point in the field of view as belonging to a zone with certain levels of risk). The Road Intrusion Risk Score (RIRS) is a composite score that quantifies the severity of intrusion accumulated over time, and provides graduated alert levels. Testing of the system on COCO val2017 and four dashcam videos produced reliable object detections with significantly fewer false positives and very close to real-time performance, demonstrating the potential of the system to improve driver assistance systems in unstructured road environments.
Authors - Nathula Dayarathne, Guhanathan Poravi Abstract - This paper presents a novel methodology for predicting bug severity and priority in software development using machine learning models. The approach involves leveraging a manually curated dataset labelled with the support of industry experts, enabling the incorporation of domainspecific knowledge into feature selection and classification. A K-Means clustering method is initially employed to label the collected data, ensuring accurate grouping and feature extraction. The study identifies and utilizes 16 key features for classification and develops separate models for severity and priority prediction. These models, trained on the expertly labelled dataset, achieve high performance with accuracy metrics above 90%. This study uniquely combines K-Means pre-labelling with expert validation to reduce manual annotation while maintaining model accuracy. The proposed method demonstrates the effectiveness of combining clustering techniques with expert-driven labelling for improving bug management processes. By automating severity and priority classification, this research contributes to enhancing the efficiency and reliability of software development workflows.
