Editorial

Dear Readers, Welcome to another J.UCS regular issue covering 5 articles on topical research areas in computer science. As part of our continuous improvement process, we have decided to provide more information about the accepted papers in the editorials starting with this issue. I would like to thank all the authors for their sound research and the editorial board and guest reviewers for their extremely valuable review effort and suggestions for improvement. These contributions, together with the generous support of the KOALA initiative, contribute to the quality of our journal. In an ongoing effort to further strengthen our journal, I am continuously looking for new editorial board members: If you are a tenured associate professor or higher with a good publication record, you are welcome to apply to join our editorial board. We are also interested in high-quality proposals for special issues on new topics and trends. It gives me great pleasure to announce the eleventh J.UCS issue of 2025. In this issue, 5 papers by 14 authors from 5 countries - India, Malaysia, Sweden, Taiwan, Türkiye - cover various topical aspects of computer science. Timothy Louis Scott, Wei Wei Goh, and Navid Ali Khan from Malaysia introduce their research on aspect-based sentiment analysis for product reviews. To address the problem of information overload on e-commerce platforms, a hybrid machine learning classification algorithm that employs aspect-based sentiment analysis and soft voting, was developed to detect the polarity and key aspects mentioned in Amazon product reviews. Based on the experiments conducted, SVHA attained higher accuracies and macro F1-scores compared to four other algorithms, showing its suitability in conducting aspect-based sentiment analysis. Emine Cengiz and Murat Gök from Türkiye propose in their study an enhanced approach for detecting money laundering in blockchain networks by representing transaction graphs as chaotic time series, extracting Lyapunov Exponents through phase space reconstruction, and classifying them with Graph Convolutional Networks. The main contribution is a feature expansion and chaotic analysis framework that improves blockchain transaction representation and enables more effective detection of illicit activities. In a collaborative effort, researchers from India and Sweden, Ashish Ranjan Mishra, Rakesh Kumar, and Rajkumar Saini introduce a deep learning technique for multimodal biometric authentication. More specifically, the article proposes DeepV-Net, a multimodal biometric authentication system that fuses EEG signals with handwritten signatures using V-net integrated with squeeze-excitation and attention modules. The model outperforms unimodal and state-of-the-art methods, demonstrating high accuracy, robustness, and significant contributions from its fusion and attention mechanisms. Ming-Lung Hsu, Yu-Wei Liu, and Sheng Tun Li from Taiwan address in their study the limitations of existing monotonic classification models in one-class classification by proposing a monotonicity-constrained support vector domain description – the MC-SVDD model, which integrates monotonicity constraints into the SVDD framework using quadratic programming and visualization techniques. Experimental results show that MC-SVDD outperforms conventional SVDD in prediction performance, contributing to the advancement of domain-driven data mining. Jafseer KT, Shailesh S, and Sreekumar A from India address in their research a feature evolution aware classification framework for streaming data using dynamic autoencoder and ensembled learning. The proposed research focuses on handling dynamically evolving features by introducing an enhanced solution that leverages a Dynamic Autoencoder DAE and ensemble learning. The ensemble technique used in the proposed classification framework demonstrates promising performances in diverse datasets, achieving accuracies of 86%, 94%, and 95% in the Weather, Electricity and Forest Cover Type datasets. Enjoy Reading! Kind regards, Christian Gütl, Managing Editor-in-Chief