The Quantum Annealer built by D-Wave, known as Advantage, is currently the largest quantum computer in the world, featuring a topology called "Pegasus." This groundbreaking system opens new possibilities for solving highly complex problems. The advancement of quantum annealers has spurred experimental demonstrations and intensified research interest, particularly in quantum machine learning. However, the application of quantum annealing in machine learning remains limited due to the lack of conclusive performance evaluations on real-world datasets. There is still no clear consensus on the efficacy of these models in practical scenarios. This work focuses on experimentally evaluating quantum annealing machine learning (QAML) classification methods, specifically Quantum Support Vector Machines (QSVM) and QBoost, on ADMET datasets—one of the most important datasets in the drug discovery domain. We compare QAML with classical machine learning to evaluate their relative performance. This study seeks to address this gap by rigorously comparing the performance of QBoost and QSVM models using ADMET datasets, employing D-Wave’s Quantum Annealer. This work provides a comprehensive analysis of the potential and limitations of quantum annealers in quantum machine learning, with a focus on their application to real-world data in the ADMET domain. The findings offer critical insights into the nuanced advantages and challenges of quantum annealers in advancing machine learning methodologies.

This paper presents a novel solution aimed at enhancing news web pages for seamless integration into the Semantic Web. By utilizing advanced pattern mining techniques alongside OpenAI’s GPT-3, we rewrite news articles to improve their readability and accessibility for Google News aggregators. Our approach is characterized by its methodological rigour and is evaluated through quantitative metrics, validated using Google’s Rich Results Test API to confirm adherence to Google’s structured data guidelines. In this process, a “Pass” in the Rich Results Test is taken as an indication of eligibility for rich results, demonstrating the effectiveness of our generated structured data. The impact of our work is threefold: it advances the technological integration of a substantial segment of the web into the Semantic Web, promotes the adoption of Semantic Web technologies within the news sector, and significantly enhances the discoverability of news articles in aggregator platforms. Furthermore, our solution facilitates the broader dissemination of news content to diverse audiences. This submission introduces an innovative solution substantiated by empirical evidence of its impact and methodological soundness, thereby making a significant contribution to the field of Semantic Web research, particularly in the context of news and media articles.

Human Activity Recognition (HAR) plays a vital role in healthcare, smart home, and security applications. Recent advances in sensor technology and machine learning have substantially enhanced HAR capabilities, particularly for continuous monitoring of elderly individuals. Time-series imaging and convolutional neural networks (CNNs) have been effectively applied to this task. In this study, we adapt these techniques to the underexplored and challenging GOTOV dataset and further develop a novel framework, PWMF-ResMiniNet, a lightweight yet highly accurate solution forHARoptimized for resource-constrained edge computing environments. The framework introduces two key innovations. First, the Precision-Weighted Model Fusion (PWMF) algorithm performs decision-level fusion of multiple sensor-specific CNNs by assigning adaptive per-class weights based on each model’s validation precision, achieving state-of-the-art accuracy with zero additional computational cost while remaining interpretable and theoretically sound. Second, the ResMiniNet CNN architecture, inspired by the ResNet model, reduces trainable parameters by approximately 75% while maintaining competitive accuracy. Combined with Gramian angular field imaging, PWMF-ResMiniNet achieves over 90% F1-score on the GOTOV dataset—the highest reported accuracy on this dataset to date—and demonstrates consistent superiority over state-of-the-art methods such as HARDenseCNN and HART on the PAMAP2 and WISDM 2019 datasets. Moreover, the study discusses the limitations of the proposed framework and current HAR approaches, identifies open research challenges, and highlights promising directions for future investigation.