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ADConv-Net: Advanced Deep Convolution Neural Network for COVID-19 Diagnostics Using Chest X-Ray and CT Images

SN computer science 2025年第5期6卷 1-22页

作者： Kumar, Sunil Bhowmik, Biswajit Maharshi Patanjali CPS Lab BRICS Laboratory Department of Computer Science Engineering National Institute of Technology Karnataka Surathkal Karnataka Mangalore 575025 India

The worldwide COVID-19 epidemic has emerged as a significant concern, affecting daily lives and underscoring the importance of early diagnosis for effective treatment in medical and healthcare settings. Current diagnostic testing for COVID-19 is sluggish, typically requiring hours to get results. Detection of COVID-19 from medical imaging presents a challenging task that has gained substantial interest from experts worldwide. Essential imaging modalities for diagnosing COVID-19 include chest X-rays and computed tomography (CT) scans. By contrast, most of the chest radiography can be completed in within fifteen minutes. Thus, employing chest radiography gives a possibility for early and reliable diagnosis of COVID-19, intending to relieve therapeutic obstacles for patients and speed up the diagnostic process. Recently, deep learning (DL) techniques have been shown to be effective in image-based diagnostics. This paper proposed an advanced deep convolution neural network (ADConv-Net) for COVID-19 detection and categorization using chest X-ray and CT images. The proposed technique is not only capable of recognizing critical connections and similarities in image classification, but also leads to improved diagnostic accuracy. The proposed model undergoes thorough evaluation for standard performance metrics. After evaluation, the ADConv-Net model achieves high accuracies of 98.84% and 97.25% in training and testing for X-ray images and 99.41% and 98.87% in training and testing for CT images, respectively. Additionally, the proposed model demonstrates strong performance, with AUC values of 0.993 and 0.996 for X-ray and CT images, respectively. Further, the model also introduces a heatmap approach for displaying COVID-19 disease areas. Subsequently, radiologists can find COVID-19 disorders in chest X-ray and CT images with this approach. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Artificial Intelligence Classification COVID-19 CT Images Deep Learning X-Ray Images

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Aerial Reliable Collaborative Communications for Terrestrial Mobile Users via Evolutionary Multi-Objective Deep Reinforcement Learning

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IEEE Transactions on Mobile Computing 2025年第7期24卷 5731-5748页

作者： Sun, Geng Xiao, Jian Li, Jiahui Wang, Jiacheng Kang, Jiawen Niyato, Dusit Mao, Shiwen the College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China the College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore the School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore the School of Automation Guangdong University of Technology Guangzhou510641 China the Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States

—Unmanned aerial vehicles (UAVs) have emerged as the potential aerial base stations (BSs) to improve terrestrial communications. However, the limited onboard energy and antenna power of a UAV restrict its communication range and transmission capability. To address these limitations, this work employs collaborative beamforming through a UAV-enabled virtual antenna array to improve transmission performance from the UAV to terrestrial mobile users, under interference from non-associated BSs and dynamic channel conditions. Specifically, we introduce a memory-based random walk model to more accurately depict the mobility patterns of terrestrial mobile users. Following this, we formulate a multi-objective optimization problem (MOP) focused on maximizing the transmission rate while minimizing the flight energy consumption of the UAV swarm. Given the NP-hard nature of the formulated MOP and the highly dynamic environment, we transform this problem into a multi-objective Markov decision process and propose an improved evolutionary multi-objective reinforcement learning algorithm. Specifically, this algorithm introduces an evolutionary learning approach to obtain the approximate Pareto set for the formulated MOP. Moreover, the algorithm incorporates a long short-term memory network and hyper-sphere-based task selection method to discern the movement patterns of terrestrial mobile users and improve the diversity of the obtained Pareto set. Simulation results demonstrate that the proposed method effectively generates a diverse range of non-dominated policies and outperforms existing methods. Additional simulations demonstrate the scalability and robustness of the proposed CB-based method under different system parameters and various unexpected circumstances. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Markov processes

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Detection and Mitigation of Subtle Feature-map Attacks in Pseudo Parallel Collaborative CNN Models for Distributed Edge Intelligence 100

Detection and Mitigation of Subtle Feature-map Attacks in Ps...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Patel, Parth Hasan, Syed Rafay Rahman, Mohammad Ashiqur Homsi, Soamar Tennessee Tech University Department of Electrical and Computer Engineering United States Florida International University Department of Computer Science United States AirForce Research Laboratory Information Warfare Division United States

ISBN: (纸本)9798331517786

Although Collaborative Deep Neural Network (CDNN) promises to be an alternative mechanism to mitigate the effects of the untrusted cloud, this approach is susceptible to other kinds of adversarial attacks, which arise from one or more untrusted devices in CDNN acting maliciously. However, since each untrusted node in the CDNN contains only partial information of the complete DNN model, it is worth investigating whether the attacker can still muster a viable threat to CDNN or not. This led to the investigation of attack scenarios and their effects on convolutional neural networks (CNN) used for image classification in the CDNN environment. In this research, we are investigating the shortcomings of existing attacks on CDNN, that lead to non-subtle attack to the defender who is on look out against such attacks. Our research showed that sparse nature of feature maps (FMs) due to the ReLU function lead to many existing attacks more obvious to the attacker. Next, we investigated how one can detect the existing attacks if the defender has some previous knowledge of the complete CNN's FMs. Our results show minimal detection overhead of about 2%, with an accuracy of 95% and F1 score of above 0.97. © 2024 IEEE.

关键词： Convolutional neural networks

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An overview on IRS-enabled sensing and communications for 6G: architectures, fundamental limits, and joint beamforming designs

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science China(Information sciences) 2025年第5期68卷 170-193页

作者： Xianxin SONG Yuan FANG Feng WANG Zixiang REN Xianghao YU Ye ZHANG Fan LIU Jie XU Derrick Wing Kwan NG Rui ZHANG Shuguang CUI School of Science and Engineering (SSE) Shenzhen Future Network of Intelligence Institute (FNii-Shenzhen)and Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Department of Electrical Engineering City University of Hong Kong School of Information Engineering Guangdong University of Technology Key Laboratory of Wireless-Optical Communications Chinese Academy of Sciences School of Information Science and Technology University of Science and Technology of China Computer School Beijing Information Science and Technology University National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University School of Electrical Engineering and Telecommunications University of New South Wales School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Department of Electrical and Computer Engineering National University of Singapore

This study presents an overview on intelligent reflecting surface(IRS)-enabled sensing and communication for the forthcoming sixth-generation(6G) wireless networks, in which IRSs are strategically deployed to proactively reconfigure wireless environments to improve both sensing and communication(S&C) performance. First, we exploit a single IRS to enable wireless sensing in the base station's(BS's) non-line-of-sight(NLoS) area. In particular, we present three IRS-enabled NLoS target sensing architectures with fully-passive, semi-passive, and active IRSs, respectively. We compare their pros and cons by analyzing the fundamental sensing performance limits for target detection and parameter estimation. Next, we consider a single IRS to facilitate integrated sensing and communication(ISAC), in which the transmit signals at the BS are used for achieving both S&C functionalities, aided by the IRS through reflective beamforming. We present joint transmit signal and receiver processing designs for realizing efficient ISAC, and jointly optimize the transmit beamforming at the BS and reflective beamforming at the IRS to balance the fundamental performance tradeoff between S&C. Furthermore, we discuss multi-IRS networked ISAC, by particularly focusing on multi-IRS-enabled multi-link ISAC, multi-region ISAC, and ISAC signal routing, respectively. Finally, we highlight various promising research topics in this area to motivate future work.

关键词： integrated sensing and communication (ISAC) intelligent reflecting surface (IRS) non-line-of-sight (NLoS) sensing sensing and communication tradeoff

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Enhancing NSGA-II with a Knee Point for Constrained Multi-objective Optimization 13th

Enhancing NSGA-II with a Knee Point for Constrained Multi...

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13th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2025

作者： Pang, Lie Meng Ishibuchi, Hisao Nan, Yang Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)9789819635054

To handle constrained multi-objective optimization problems (CMOPs), many constrained multi-objective evolutionary algorithms (CMOEAs) have been proposed. However, a recent study has shown that many of these CMOEAs do not perform well on real-world CMOPs. In contrast, NSGA-II, proposed over 20 years ago with a simple constrained dominance principle, has demonstrated better performance than recent CMOEAs on many real-world CMOPs. Motivated by NSGA-II’s promising results, this paper aims to further enhance its performance. We explore the idea of enhancing NSGA-II with a knee point for solving CMOPs. Specifically, a knee point is identified using the minimum distance from the estimated ideal point, and always included in the next population of NSGA-II. Experimental results show that this simple idea improves the performance of NSGA-II on real-world CMOPs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Optimization algorithms

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A variable structure model of neurons’ application in computer vision

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Multimedia Tools and Applications 2025年 1-11页

作者： Sridi, Alaeddine Attia, Mouna Bouallegue, Kais Electronics and Micro-Electronics Laboratory Faculty of Sciences of Monastir University of Monastir Monastir Tunisia The Higher Institute of Computer Science and Communication Technologies of Hammam Sousse University of Sousse Sousse Tunisia Laboratory of Computer Science Parallelism and Intelligent Communicating Systems Faculty of Sciences of Tunisia University of Tunis ELManar Tunis Tunisia Department of Electrical Engineering Higher Institute of Applied Sciences and Technology of Sousse University of Sousse Sousse Tunisia

The neuron doctrine defines the neuron as the basic unit of the nervous system, which drives the dynamic behavior of our organs. This has led to neurons becoming the focus of modern neuroscience research and to the rise of neurocomputing, in which we try to digitally simulate human behavior. The goal being to better understand the human nervous system and gain more control over neuron-related diseases, helping doctors establish faster and more reliable medical diagnoses. In this context, we aim to simulate eye diseases and vision problems by convolving input images using a new class of neural networks based on a variable structure model of neurons presented in [1]. In this work, we focus on recreating the common convolution filters as a first step. This is the first time in neurocomputing literature that dendritic neurons have been used for dynamic convolution kernels generation and eye diseases simulation. We start with a presentation of our upgrades to the model’s flexibility and accuracy. Then, we propose a novel kernel generator algorithm to convolve the input images. Finally, we show its promising results in creating the outline, pixelization, and night convolution kernels, as well as control some metrics, including brightness and data retention. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Neurons

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T³Planner: Multi-Phase Planning Across Structure-Constrained Optical, IP, and Routing Topologies

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IEEE Journal on Selected Areas in Communications 2025年第5期43卷 1823-1839页

作者： Hao, Yijun Yang, Shusen Li, Fang Zhang, Yifan Zhao, Cong Ren, Xuebin Zhao, Peng Xu, Chenren Wang, Shibo Xi’an Jiaotong University National Engineering Laboratory for Big Data Analytics Xi’an710049 China Xi’an Jiaotong University National Engineering Laboratory for Big Data Analytics Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi’an710049 China Peking University School of Computer Science Beijing100871 China The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong National Engineering Laboratory for Big Data Analytics Xi'an Jiaotong University Xi'an710049 China

Network topology planning is an essential multi-phase process to build and jointly optimize the multi-layer network topologies in wide-area networks (WANs). Most existing practices target single-phase/layer planning, and are incapable of satisfying all rigorous topological structure constraints (e.g., dual-homing rings) defined by network standards and operators, especially in large-scale networks. These significantly limit their usability and performance in production networks. We consider a general topology planning problem with typical structure constraints over three essential phases (greenfield, reconfiguration, and site expansion) and topological layers (optical, IP, and routing topologies). We present, T3Planner, a novel practical solver to this problem in production. Specifically, we develop a structure-driven encoder based on graph neural network (GNN) for concise structure encoding, and design a new learning framework with optical-centric layer compression/reconstruction and rule-aided reinforcement learning (RL) for fast convergence and high performance. Extensive experiments on nine real topologies demonstrate that T3Planner scales to large optical networks with hundreds of sites, saves 46.6% cost, and supports 3.12x more demand when compared to related existing approaches. © 1983-2012 IEEE.

关键词： Network topology

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Next location prediction using transformers

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International Journal of Business Intelligence and Data Mining 2022年第2期21卷 247-263页

作者： Henouda, Salah Eddine Laallam, Fatima Zohra Kazar, Okba Sassi, Abdessamed LINATI Laboratory Department of Computer Science Kasdi Merbah University Ouargla Algeria Computer Science Department University of Biskra Algeria Department of Information Systems and Security College of Information Technology United Arab Emirate University United Arab Emirates Department of Computer Science Mohamed Khider University Biskra Algeria Department of computer Science L’arbi Ben Mhidi University Oum El Bouaghi Algeria

This work seeks to solve the next location prediction problem of mobile users. Chiefly, we focus on ROBERTA architecture (robustly optimised BERT approach) in order to build a next location prediction model through the use of a subset of a large real mobility trace database. The latter was made available to the public through the CRAWDAD project. ROBERTA, which is a well-known model in natural language processing (NLP), works intentionally on predicting hidden sections of text based on language masking strategy. The current paper follows a similar architecture as ROBERTA and proposes a new combination of Bertwordpiece tokeniser and ROBERTA for location prediction that we call WP-BERTA. The results demonstrated that our proposed model WP-BERTA outperformed the state-of-the-art models. They also indicated that the proposed model provided a significant improvement in the next location prediction accuracy compared to the state-of-the-art models. We particularly revealed that WP-BERTA outperformed Markovian models, support vector machine (SVM), convolutional neural networks (CNNs), and long short-term memory networks (LSTMs). Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Big data

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MAGICIAN: Malware classification Approach through Generation Image using a Conditional and wassersteIn generative Adversarial Network variants 4

MAGICIAN: Malware classification Approach through Generation...

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4th IEEE International Conference on Artificial Intelligence in Cybersecurity, ICAIC 2025

作者： Galantucci, Stefano Iannacone, Andrea Pirlo, Giuseppe Sarcinella, Lucia Stamerra, Alessandro University of Bari Aldo Moro Computer Science Department Bari Italy Cybersecurity Laboratory BV TECH SpA Milan Italy

ISBN: (纸本)9798331518882

One of the main challenges of cybersecurity is the detection and classification of malware to prevent damage to systems by both companies and private users. Identifying the specific type of malware is critical to performing targeted actions. This study proposes a classification approach that generates synthetic images of malware using Conditional Generative Adversarial Networks (cGAN) and Wasserstein Generative Adversarial Networks (WGAN). Using the Malimg dataset, consisting of 25 malware classes, the ResNet50 model shows an overall accuracy of 91.4% and an F1-score of 90.8% for synthetic images generated with WGAN. Resizing and resampling were employed as preprocessing strategies to obtain images of size 48 × 48;resampling has been shown to be more effective. Thus, the proposed methodology allows malware to be classified quickly and efficiently, and, on the other hand, unbalanced datasets can be enriched to aid classification performance. © 2025 IEEE.

关键词： Generative adversarial networks

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Poster Abstract: Enhancing Human Motion Sensing with synthesized Millimeter-Waves 23

Poster Abstract: Enhancing Human Motion Sensing with synthes...

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23rd ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2024

作者： Zhang, Xiaotong Wang, Kun Li, Zhenjiang Zhang, Jin City University of Hong Kong Department of Computer Science Hong Kong Hong Kong Southern University of Science and Technology Res. Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering Shenzhen China Peng Cheng Laboratory Shenzhen China

ISBN: (纸本)9798350362015

This poster introduces SynMotion, a novel mmWave-based human motion sensing system addressing the scarcity of training datasets. By synthesizing mmWave signals using existing vision-based human motion datasets, this system overcomes the challenge of collecting and labeling mmWave data, facilitating wider adoption of mmWave technology for applications like activity recognition, skeleton tracking and radar placement recommendation. © 2024 IEEE.

关键词： Millimeter waves

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