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.
