Authors - Thanh-Phuong Ngo, Van-Thanh Huynh, Thai-Son Nguyen Abstract - This paper presents a novel Reversible Data Hiding (RDH) method for dual images. First, secret data is converted into a binary sequence of equal length and then divided into shorter segments to control the amount of data embedded into each pixel. The embedding process uses two copies of the original image to distribute the data, reducing the impact on each image while maintaining overall image quality. During recovery, the original image is restored by averaging the pixel values at corresponding locations in the two stego images, while the embedded data is recovered through a reverse process. Experimental results on grayscale images demonstrate that the method maintains good image quality, achieving a high Peak Signal-to-Noise Ratio (PSNR) across different embedding levels while ensuring accurate recovery of both the secret data and the original image.
Authors - Yasir Abdullah R, Lakshmana Kumar T, Vijaykumar M, Thirunavukkarasu C, Saravanagukhan P, Hariharasuthan M Abstract - In the recent past, vehicle theft in India has increasing nearly 2.5 times, with more than 2 lakh vehicles stolen annually. The Delhi NCR region alone accounts for over 30% of reported cases, and in Delhi, a vehicle is reportedly stolen approximately every 14 minutes. These alarming trends highlight the ur-gent need for stronger and smarter vehicle security mechanisms. Traditionally, vehicle anti-theft technologies have relied largely on non-biometric approaches such as GPS–GSM tracking modules. Thus, biometric authentication is an emerging security approach that limits vehicle access to authorized individuals by verifying unique biological traits such as fingerprints, facial features, iris pat-terns, or voice. Although this technology significantly strengthens vehicle security, its widespread deployment still faces certain technical and social constraints. Thus in this paper, an IoT enabled biometric ignition system with security alerts is proposed. The proposed model makes use of an ESP32 micro controller and fingerprint sensor to replace traditional keys. The system operates in two stages: first secure door access and secondly engine ignition authorization. Any unauthorized attempts trigger real-time alerts with GPS location via IoT protocols like MQTT or HTTP. Further, cloud integration enables remote monitoring, data storage, and scalability, making suitable for modern intelligent transport systems. In the same way, the fingerprint-based vehicle starter grants the privilege of starting the vehicle only to the registered users, thus deterring theft and ensuring safety. Over all, biometric vehicle ignition is a dependable, economical, and hassle-free solution to access control as well as theft prevention.
Authors - Amol Dhumane, Jitendra Chavan, Arijit Dutta, Priyanka Paygude, Aditi Sharma, Datta Takale, Yashwant Dongre Abstract - Depression is a psychiatric condition that is largely common all over the world and greatly influences the emotional stability, cognitive performance and behavior functioning. Computational techniques that can detect the condition early can help to prevent psychological dangers in the long term and ensure timely treatment of the disease. This paper refers to a complete machine learning architecture of automated depression recognition of textual information based on hybrid feature engineering and ensemble learning approaches. The suggested methodology is a combination of text preprocessing, Term Frequency / Inverse Document Frequency (TF -IDF) vectorization, unigram and bigram features, hand-crafted statistics and sentiment-based indicators, and several classification models such as Logistic Regression, Random Forest, XGBoost, and LightGBM. The issue of class imbalance is tackled using Synthetic Minority Over-sampling Technique (SMOTE) and compared. The original dataset of 7,489 samples was cleaned and narrowed down to 7,486 valid cases. Accuracy, Precision, Recall, F1 score, ROC-AUC and 5-fold cross-validation were used to evaluate the performance. There are experimental results to show that ensemble models are more effective compared to traditional linear classifiers. XGBoost performed best in the overall performance of 94.59% accuracy and F1-score of 0.8323. The hybrid-based feature fusion technique has a considerable improvement on the classification performance and does not sacrifice the level of interpretability and computational efficiency, which is why the framework is applicable to scalable mental health analytics services.
Authors - Armie E. Pakzad, Nathanael Adrian T. Cua, Louie T. Que, Alvin Josh T. Valenciano, Jana Johannes Valenzuela, Abbasali Pakzad Abstract - Emotional Support Conversation (ESC) seeks to lessen users’ emotional dis tress through sympathetic communication. Current approaches concentrate on comprehending present emotional states and combining support techniques to generate responses. But they fail to take into account an important factor: emotional trajectories (how users’ feelings change over time). Two people expe riencing the same feeling may need essentially different answers depending on whether they are in a therapeutic window (gradually improving), a depressed spiral (continuous hopelessness), or a crisis escalation (rapidly worsening). We propose TRAGEDY (TRAjectory-Guided Emotional Dialogue System), a sys tem that explicitly models clinical patterns and emotional trajectories in order to direct response creation. We present: (1) a trajectory encoder that records the temporal dynamics of emotion and intensity sequences; (2) a clinical pat tern detector that recognizes five psychologically grounded patterns (normal progression, therapeutic window, resistance pattern, depressed spiral, and crisis escalation); and (3) pattern-aware generation that bases responses on trajectories found. Experiments on the ESConv benchmark show that TRAGEDY provides interpretable trajectory insights while outperforming robust baselines, across standard generation metrics. Our approach opens new avenues for trajectory aware conversational AI and emphasizes the significance of temporal dynamics in emotional support.
Authors - Akhil P, Mallikharjuna Rao K. Abstract - Cloud storage platforms support diverse multimedia and col laborative workloads across organizations, yet conventional methods ne glect user behavior’s role in shaping access patterns. Privacy regulations prohibit centralized aggregation of interaction traces, while standard fed erated learning algorithms like FedAvg fail under statistical heterogene ity from varied user roles. This paper introduces FedPAE (Federated Per sonalized AutoEncoder), an unsupervised framework for behavior-aware user profiling in federated settings. FedPAE employs a shared global encoder for common patterns and private local decoders for individual adaptation, augmented by an Adaptive Fine-Tuning (AF) mechanism to mitigate encoder drift and preserve global semantics, without sharing any raw user data with the server. Evaluated on the CMU CERT benchmark and anonymized cloud storage logs, FedPAE surpasses FedAvg, FedProx, and FedPer in anomaly detection accuracy across all thresholds (e.g., F1 gains of 5–13% points over FedAvg across all precision thresholds), con f irming that the approach holds across heterogeneous client populations.
Authors - Yarragunta Babu , Challa Yuva Prasanthi, Vadapalli Sparjan, Sanagapati Venkata Siva Naga Sai Jayanth Abstract - Distributed systems rely on data replication across multiple nodes to ensure high availability, fault tolerance, and scalability. While replication improves system reliability, it also introduces temporary inconsistencies between primary and replica nodes during data propagation. This phenomenon, commonly referred to as consistency drift, occurs when distributed nodes maintain slightly different states before synchronization is completed. As distributed infrastructures grow in scale and complexity, consistency drift becomes increasingly significant due to network latency, workload variability, and communication overhead between nodes. Traditional synchronization mechanisms typically rely on static replication intervals or fixed update propagation strategies that do not adapt effectively to dynamic system conditions. Such approaches may allow drift to accumulate before synchronization occurs, resulting in delayed consistency and inefficient resource utilization. Managing consistency drift therefore becomes a critical challenge in distributed computing environments where maintaining accurate and synchronized data states is essential. This research addresses the problem of consistency drift in distributed systems by examining the factors that contribute to state divergence among nodes and exploring mechanisms for dynamic drift management. The proposed framework focuses on monitoring system behavior, including workload intensity, network latency, and node communication patterns, to regulate synchronization behavior more effectively. By enabling adaptive synchronization strategies that respond to real time system conditions, the framework aims to reduce drift accumulation and improve overall data consistency across distributed clusters. Effective management of consistency drift ultimately enhances system reliability, operational stability, and performance in modern distributed computing platforms operating under dynamic workloads.
Authors - Olutayo V. A., Agbele K. K., Ogundimu O. E., Dudu M. T. Abstract - As online shopping has become increasingly popular, companies must utilize social media to develop and improve customer experience. This study examined customer interaction sentiment regarding online shopping through automated systems to classify comments on social media sites like Twitter, Facebook, and Instagram. This research study compared three machine learning and natural language processing (NLP) techniques: Bidirectional Gated Recurrent Units (GRUs), Random Forests, and Naïve Bayes. Customer reviews were classified as positive, negative, and neutral, as well as analyzed for time-related patterns. The classification framework was constructed by using sentiment analysis, feature extraction, and data preprocessing techniques. Furthermore, model training and performance assessment were executed through Naïve Bayes and Support Vector Machines. Of all the models studied, the Bidirectional GRU had the best performance with an accuracy of 88.08 %. The results of this study help companies understand customer preferences better, and thereby refine their products, services, and marketing techniques.
Authors - Akbar Kushanoor, Sanjay K. Sahay Abstract - Traditional tree classification methods are inefficient, requiring tremendous effort, time, and labor. To address this, the primary objective of this research was to develop and implement a machine learning model that utilizes 3D Light Detection and Ranging (LiDAR) data, acquired via an unmanned aerial vehicle (UAV), for the accurate classification of tree species in the Philippines. Then, the collected data was pre-processed in preparation for the next portions of the methodology. Once completed, the features used in preparation for machine learning were extracted for the creation and training of the model. Ground truth data, validated by two licensed foresters, were used to ensure species accuracy, focusing on the five most abundant tree species in the dataset. Several machine learning algorithms were evaluated, with the XGBoost model achieving the best performance, reaching an overall accuracy of 85.63%, a mean class accuracy of 84.98%, and a Kappa accuracy of 81.57%. All producers’ accuracy exceeded 70%, indicating robust model reliability. Additionally, a user interface was developed to visualize the LiDAR data, tree attributes, and classification results. The findings demonstrate that LiDAR data obtained from UAVs can effectively be used for tree species classification in the Philippines, supporting forest inventory initiatives and reforestation efforts. Future work may include expanding the dataset, incorporating more species, and testing additional machine learning algorithms to further enhance classification accuracy.
Authors - Monir El Mounaoui, Kunale Kudagba, Mohamed Yassin Chkouri 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 - Tiurida Lily Anita, Siti Nahdiah, Muslikhin Muslikhin, Mohd. Nor Shahizan Ali Abstract - Despite the importance of Allied Healthcare professionals in healthcare service delivery, low professional development opportunities, a high turnover rate, and a shortage of workers in India are some of the challenges that are affecting Allied Healthcare professionals’ retention. The purpose of this research is to explore the po tential of Internet of Things (IoT) solutions and Big Data analytics, coupled with infor mation and communication technology (ICT) as a solution to Allied Healthcare profes sionals’ retention strategies. The purpose of this paper is to propose a conceptual frame work that can be achieved by utilizing Internet of Things solutions coupled with Big Data analytics as a solution to Allied Healthcare professionals’ retention strategies by utilizing theories such as Technology Acceptance Model theory, Job Demands-Re sources theory, Social Exchange Theory, among others. The paper concludes that ICT is a resource that can be utilized to reduce job stress, enhance effective communication, and provide career opportunities for Allied Healthcare professionals; whereas Big Data analytics coupled with Internet of Things solutions can be utilized to predict potential risks that may affect Allied Healthcare professionals’ retention. The proposed concep tual framework offers a theoretical understanding of the digital revolution of human resource management practices in healthcare services.
