Person Re-Identification (ReID) remains a pivotal challenge in intelligent surveillance, constantly tested by dramatic intra-class variations, pose changes, lighting shifts, occlusions and distracting backgrounds. This paper proposes a self-attention based hybrid framework (VSPeR) designed to improve data diversity and visual quality for person re-identification in real-world scenarios. At the heart of VSPeR lies a powerful synergy: the robust, identity preserving feature extraction of Variational Autoencoders (VAEs) meets the precise, context-aware focus of Self-Attention Generative Adversarial Networks (GANs). This hybrid architecture captures rich, semantically meaningful latent representations while zeroing in on the most discriminative visual cues necessary for accurate cross-view matching. The outcome is high-fidelity, identity-consistent synthetic images that significantly amplify the training dataset enabling smarter, more effective learning. Put to the test across four leading ReID benchmarks, VSPeR achieves competitive performance compared to existing approaches in terms of mean Average Precision (mAP) and Rank-1 accuracy.
In this study, EEGNet-CA, a lightweight deep learning model with an integrated attention mechanism, is proposed to diagnose Attention Deficit and Hyperactivity Disorder (ADHD) in children via EEG data. The proposed model is able to learn the contribution of each EEG channel in the classification and focus on important signals by integrating the channel attention (CA) mechanism into the classical EEGNet architecture. In this context, 19-channel EEG recordings from 121 children aged 7–12 years, obtained from the IEEE dataset, were used. The raw EEG signals were scaled with z-score normalization to improve the stability of the learning process and segmented using the 50 % overlap sliding window method. The EEGNet-CA model, developed using the obtained EEG segments, was utilised for classification purposes. Experimental results show that the proposed EEGNet-CA model achieved a 99.79 % F1-score and exhibited statistically significantly superior performance compared to standard EEGNet, 1D CNN, 2D CNN, and artificial neural network-based methods. In addition, high accuracy even with only frontal region channels supports the neurophysiological importance of this region in ADHD diagnosis. The results show that the CA mechanism is an effective method to increase the classification success in EEG-based diagnostic systems.
Recommendation systems built on ratings often fail to fully exploit the rich information contained in textual reviews and social-platform content. Text provides not only an additional signal for ranking, but also a natural basis for generating explanations. This study introduces XTRA, a rating-free, text-driven recommendation system that derives user relationships from shared review content and transforms sentiment expressed in text into personalized recommendation decisions. The proposed approach also incorporates explainability mechanisms to clarify both why users are considered similar and how textual sentiment contributes to the final recommendation. Experimental results on Amazon-Beauty, Yelp-Illinois, and IMDB-20K show that XTRA outperforms strong rating-based baselines on Yelp and IMDB, while remaining competitive on Amazon. These findings indicate that textual signals can serve as an effective alternative to ratings while also supporting transparent and interpretable recommendations. Implementation details are provided to support reproducibility and future extension.
This study investigates adaptive synthesis of intelligent monitoring (IM) for applications modified by large language models (LLMs). A multi-objective, property-driven genetic approach with a tensor of chromosome representation is proposed to generate monitoring configurations under architectural changes. Using migration from Oracle to PostgreSQL as a controlled case, the study shows that functional equivalence can be maintained while monitoring-related properties drift, supporting adaptive archi tecture-aware monitoring over static settings.
Fire and smoke detection in image data is a crucial application of computer vision; however, practical deployment in real-world environments remains challenging. While previous research has made significant progress; however, detection systems often struggle to maintain stability when faced with changing lighting conditions, partial occlusions, and the complex visual characteristics of indoor spaces. This study proposes an automated fire and smoke detection system based on the Real- Time Detection Transformer (RT-DETR) architecture. Unlike traditional models that focus solely on accuracy, this system is engineered to address the practical need for early warning, achieving a high recall of 91.6 % to minimise missed fire events. The system is designed for versatile integration into existing CCTV surveillance, Smart Building ecosystems, and IoT/Edge. Evaluated on the Home-Fire dataset using a five-fold cross-validation strategy, the model achieves an mAP@0.5 of 94.5 %. These results demonstrate that the proposed system offers a robust, scalable, and reliable solution for real-world fire safety monitoring, providing a robust foundation for autonomous fire safety monitoring and rapid emergency response.
