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PneuX-Net: An Enhanced Feature Extraction and Transformation Approach for Pneumonia Detection in X-Ray Images

IEEE Access 2025年 13卷 84024-84037页

作者： Munir, Kashif Usama Tanveer, Muhammad Alyamani, Hasan J. Bermak, Amine Ur Rehman, Atiq Khwaja Fareed University of Engineering and Information Technology Institute of Information Technology Rahim Yar Khan 64200 Pakistan King Abdulaziz University Faculty of Computing and Information Technology in Rabigh Department of Information Systems Jeddah 21589 Saudi Arabia Hamad Bin Khalifa University Division of Information and Computing Technology College of Science and Engineering Doha Qatar

Pneumonia is a critical respiratory condition characterized by inflammation in the alveoli, leading to impaired gas exchange and severe respiratory distress. It poses a significant threat to high-risk populations, including neonates, geriatric patients, and immunocompromised individuals. Early and precise detection is crucial to optimizing treatment strategies and reducing morbidity and mortality rates. To address this problem, we propose PneuX-Net, an ensemble-based feature extraction framework that integrates multiple machine learning (ML) models Random Forest (RF), Gaussian Naïve Bayes (GNB) and K-Nearest Centroid (KNC). Random Forest (RF) builds multiple decision trees to identify complex, non-linear patterns within the feature space. Gaussian Naïve Bayes (GNB) utilizes a probabilistic framework based on Bayes’ theorem to manage uncertainty in feature distributions. K-Nearest Centroid (KNC) improves class distinction by clustering feature vectors according to their proximity to centroids. The ensemble methodology harnesses the complementary strengths of these models, improving feature representation and mitigating overfitting, a prevalent issue in white-box models. To validate the effectiveness of PneuX-Net, we conduct extensive experimentation using a 10-fold cross-validation approach. Our results demonstrate that the PneuX-Net+KNC model achieves a remarkable 99.91% accuracy, highlighting its robustness and reliability in pneumonia classification tasks. This ensemble-driven methodology underscores the potential of machine learning in augmenting clinical decision-making by providing an accurate and automated pneumonia detection framework. © 2013 IEEE.

关键词： healthcare AI machine learning medical diagnosis pneumonia detection PneuX-Net

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Secure and privacy-preserving federated learning-based resource allocation for next generation networks

Secure and privacy-preserving federated learning-based resou...

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International Conference on Innovation and Technological Advances for Sustainability, ITAS 2023

作者： Dandoush, Abdulhalim Gouissem, Ala Ciftler, Bekir Sait Division of Information Technology College of Computing and Information Technology University of Doha for Science and Technology Doha Qatar ESME Sduria/L2TI at Sorbonne University Paris Nord France Division of Data Science and Artificial Intelligence College of Computing and Information Technology University of Doha for Science and Technology Doha Qatar

ISBN: (纸本)9781032803722

This paper surveys recent research on federated learning-based resource allocation for next-generation networks in order to identify research gaps and potential future directions. We start by outlining the main challenges and requirements for secure and privacy-preserving resource allocation in these networks. The existing solutions and techniques proposed in the literature are then reviewed. The strengths and limitations of these solutions are analyzed, and trade-offs in terms of security, privacy, efficiency, and scalability are discussed. The research gaps and promising directions for future research are identified, such as integrating multiple solutions (e.g., game theory or optimization techniques with federated learning) and developing new models and algorithms for AI-based resource allocation that address specific challenges and requirements of next-generation networks. The goal is to inspire further research in this critical and rapidly evolving field. © 2025 The Author(s).

关键词： Federated learning

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Modeling the Hallmarks of Avascular Tumors

Modeling the Hallmarks of Avascular Tumors

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European Conference on Numerical Mathematics and Advanced Applications, ENUMATH 2023

作者： Blom, Erik Engblom, Stefan Menz, Gesina Division of Scientific Computing Department of Information Technology Uppsala University Uppsala Sweden

ISBN: (纸本)9783031861727

We present a stochastic computational model of avascular tumors, emphasizing the detailed implementation of the first four so-called hallmarks of cancer: self-sufficiency in growth factors, resistance to growth inhibitors, avoidance of apoptosis, and unlimited growth potential. Our goal is to provide a foundational understanding of the first steps of cancer malignancy while addressing modeling uncertainties, thus bringing us closer to a first-principles grasp of this process. Preliminary numerical simulations illustrate the comprehensiveness of our perspective. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Stochastic models

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Classification of nuclei in whole slide imaging based on ensemble deep learning

Classification of nuclei in whole slide imaging based on ens...

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International Conference on Innovation and Technological Advances for Sustainability, ITAS 2023

作者： Al-Thelaya, Khaled Alzubaidi, Mahmood Majeed, Fahad Ullah, Gilal Nauman Agus, Marco Schneider, Jens Division of Information and Computing Technology College of Science and Engineering Hamad Bin Khalifa University Qatar Foundation Doha Qatar

ISBN: (纸本)9781032803722

Recently, several innovative digital imaging technologies led to the development of several medical imaging data representations. Whole slide imaging (WSI) refers to capturing and storing tissue glass slides and biopsy samples in a digital form. This newly emerging modality is increasingly gaining huge interest in pathology departments worldwide due to the unique advantages introduced by this medical imaging modality for diagnostic, educational, and research purposes. This work provides a comparative analysis of multiple classification approaches of nuclei in WSI where multiple nuclei context-aware manipulation strategies are evaluated by our nuclei classification experiments. We propose a contextaware deep ensemble approach based on ResNet deep architecture using different forms of input images pre-processed using multiple manual feature extraction techniques. Results show that our approach attains higher accuracy performance compared to traditional context-ware strategies in the literature. © 2025 The Author(s).

关键词： Medical imaging

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Simultaneous Multi-Adulterant Detection in Milk Using an In-house Developed Multiplexed Microfluidic Paper-Based Analytical Device (M-µPAD)

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Sensing and Imaging 2025年第1期26卷 1-20页

作者： Ahsan, Jannat Riaz, Kashif Samad, Hudair Mehmood, Muhammad Qasim Khelif, Abdelkrim Lahore54600 Pakistan Division of Information and Computing Technology College of Science and Engineering Hamad Bin Khalifa University Doha Qatar Advanced Manufacturing Research Centre University of Sheffield Rotherham United Kingdom

Milk adulteration poses a significant challenge in developing countries, impacting food safety and public health. This study introduces a multiplexed microfluidic paper-based analytical device (M-µPAD) for rapid and simultaneous detection of multiple milk adulterants. The M-µPAD, fabricated using readily available materials like filter paper and wax crayons, eliminates the need for specialized equipment. The device demonstrates linear relationships between colorimetric response and adulterant concentration for urea (10–200 mg/dL), starch (20-1000 mg/dL), and detergent (100–3000 mg/dL), with detection limits of 11.1, 22.4, and 120.8 mg/dL, respectively. Requiring only 40 µL of reagents and 80 µL of milk sample, the M-µPAD achieves over 95% accuracy and reproducibility with RSD values below 4%. This affordable, in-house fabricated platform offers a promising solution for milk quality assessment in resource-limited settings, addressing the need for accessible and efficient adulterant detection methods. © The Author(s) 2025.

关键词： Colorimetric analysis

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Communication Modeling Approaches in Energy System Applications: A Systematic Overview

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IEEE Access 2025年 13卷 80329-80339页

作者： Radtke, Malin Frost, Emilie Niebe, Astrid Lehnhoff, Sebastian Carl von Ossietzky Universität Department of Computing Science Oldenburg26129 Germany OFFIS—Institute for Information Technology Energy Division Oldenburg26121 Germany

The integration and consideration of communication networks in energy system studies is of critical importance due to the mutual dependencies that exist between the systems. These interdependencies have an impact on the overall performance, stability, and robustness of the energy system. There is no comprehensive guide or overview for integrating communication into power system applications, despite its recognized importance and the wide variety of existing solutions that take into account interdependencies. By providing a detailed overview, characterization, and classification of existing approaches to communication modeling, this paper contributes to filling this gap in existing guidelines or general descriptions for the integration of communication in power system applications. In addition to discussing the multiple objectives associated with different focus for modeling communication, we consider the required data and system perspectives. Therefore, this paper provides guidance on the selection of a suitable and applicable communication modeling approach, given the specific objective and prerequisite. © 2013 IEEE.

关键词： Data communication systems

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Optimal Stopping Theory Based Online Node Selection in IoT Networks for Multi-Parameter Federated Learning

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2025年 3卷 659-676页

作者： Dogan-Tusha, Seda Bouanani, Faissal El Qaraqe, Marwa Hamad Bin Khalifa University Qatar Foundation Division of Information and Computing Technology College of Science and Engineering Doha Qatar Mohammed V University Department of Computer Science Rabat10000 Morocco

Federated Learning (FL) has attracted the interest of researchers since it hinders inefficient resource utilization by developing a global learning model based on local model parameters (LMP). This study introduces a novel optimal stopping theory (OST) based online node selection scheme for low complex and multi-parameter FL procedure in IoT networks. Global model accuracy (GMA) in FL depends on the accuracy of the LMP received by the central entity (CE). It is therefore essential to choose trusty nodes to guarantee a certain level of global model accuracy without inducing additional system complexity. For this reason, the proposed technique in this study utilizes the secretary problem (SP) approach as an OST to perform node selection considering both received signal strength (RSS) and local model accuracy (LMA) of available nodes. By leveraging the SP, the proposed technique employs a stopping rule that maximizes the probability of selecting the node with the best quality, and thereby avoids testing all candidate nodes. To this end, this work provides a mathematical framework for maximizing the selection probability of the best node amongst candidate nodes. Specifically, the developed framework has been used to calculate the weighting coefficients of the RSS and LMA to define the node quality. Comprehensive analysis and simulation results illustrate that the OST based proposed technique outperforms state-of-the-art methods including the random node selection and the offline node selection (exhaustive search methods) in terms of GMA and computational complexity, respectively. © 2023 CCBY.

关键词： Federated learning

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An anisotropic nonlinear stabilization for finite element approximation of Vlasov–Poisson equations

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Journal of Computational Physics 2025年 536卷

作者： Wen, Junjie Nazarov, Murtazo Department of Information Technology Division of Scientific Computing Uppsala University Sweden

We introduce a high-order finite element method for approximating the Vlasov–Poisson equations. This approach employs continuous Lagrange polynomials in space and explicit Runge–Kutta schemes for time discretization. To stabilize the numerical oscillations inherent in the scheme, a new anisotropic nonlinear artificial viscosity method is introduced. Numerical results demonstrate that this method achieves optimal convergence order with respect to both the polynomial space and time integration. The method is validated using classic benchmark problems for the Vlasov–Poisson equations, including Landau damping, two-stream instability, and bump-on-tail instability in a two-dimensional phase space. © 2025 The Author(s)

关键词： Vlasov equation

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An Enhancement of Object Detection Using YOLO V8 and Mobile Net in Challenging Conditions

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SN Computer Science 2025年第4期6卷 1-20页

作者： Pasupuleti, Shailaja Ramalakshmi, K. Gunasekaran, Hemalatha Arokiaraj, Rex Macedo Debnath, Saswati Jebaseeli, T. Jemima Department of Computer Science and Engineering Alliance University Karnataka Anekal Bangalore India AU-Centre of Excellence Department of Computer Vision Alliance University Karnataka Anekal Bangalore India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman Department of Information Technology College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Oman Department of Computer Science and Engineering Alliance University Karnataka Bangalore India Division of Artificial Intelligence and Machine Learning Karunya Institute of Technology and Sciences Coimbatore India

Autonomous drones and deep learning neural networks are becoming popular tools to revolutionize aircraft operations by partially automating visual inspection processes in aircraft maintenance. The research aims to provide a trustworthy method for locating and classifying surface flaws on aircraft, such as dents, fractures, corrosion, and a few other anomalies. To assess the model performance with and without a lightweight network integration, the implementation merges YOLOv8 and Mobile Net architectures, using dropout regularization and data augmentation approaches. To guarantee the model's performance in low-light situations, the dataset has been carefully selected to contain images replicating such settings. Annotation, environment setup, model training, and online dataset scraping are all covered by the thorough technique. A web-scraped dataset is used to train and validate two popular semantic segmentation algorithms, YOLOv8 and Mobile Net, which show enough performance for segmenting and grading aircraft surface problems in practical settings. The results show that precision and recall values are improved using a hybrid technique that ensembles trained Mobile Net and YOLOv8 models. Using COCO metrics, in the proposed approach an assessment of Ensemble WBF, Mobile Net-YOLO, and YOLOv8 outcomes on test and validation sets, guaranteed reliable object recognition and classification abilities. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Aircraft Classification Mobile-Net Object detection Weighted Box Fusion YOLOv8

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An anisotropic nonlinear stabilization for finite element approximation of Vlasov-Poisson equations

arXiv

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arXiv 2025年

作者： Wen, Junjie Nazarov, Murtazo Department of Information Technology Division of Scientific Computing Uppsala University Sweden

We introduce a high-order finite element method for approximating the Vlasov-Poisson equations. This approach employs continuous Lagrange polynomials in space and explicit Runge-Kutta schemes for time discretization. To stabilize the numerical oscillations inherent in the scheme, a new anisotropic nonlinear artificial viscosity method is introduced. Numerical results demonstrate that this method achieves optimal convergence order with respect to both the polynomial space and time integration. The method is validated using classic benchmark problems for the Vlasov-Poisson equations, including Landau damping, two-stream instability, and bump-on-tail instability in a two-dimensional phase space. © 2025, CC BY.

关键词： Poisson equation

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