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CNN Ensemble Approach to Detect COVID-19 from Computed Tomography Chest Images

computers, Materials & Continua 2021年第6期67卷 3581-3599页

作者： Haikel Alhichri Department Computer Engineering Advanced Lab for Intelligent SystemsCollege of Computer and Information SciencesKing Saud ArabiaRiyadh11543Saudi Arabia

In January 2020,the World Health Organization declared a global health emergency concerning the spread of a new coronavirus disease,which was later named *** and fast diagnosis and isolation of COVID-19 patients have proven to be instrumental in limiting the spread of the *** tomography(CT)is a promising imaging method for fast diagnosis of *** this study,we develop a unique preprocessing step to resize CT chest images to a fixed size(256×256 pixels)that preserves the aspect ratio and reduces image ***,we present a deep learning(DL)method to classify CT chest images based on the light-weight pre-trained EfficientNet-B3 CNN model and ensemble *** proposed method,which we refer to as EfficientNet-B3-GAP-Ensemble,comprises an ensemble of a modified version of the *** build the ensemble using multiple runs and multiple training *** test the EfficientNet-B3-GAPEnsemble on two common benchmark datasets,i.e.,the COVID19-CT and SARS-CoV-2-CT *** proposed method has outperformed state-ofthe-art methods for both *** the COVID19-CT dataset,it achieved 88.18%sensitivity,88.29%precision,88.18%accuracy,an F1-score of 88.15%,and AUC of 92.10%.With the SARS-CoV-2-CT dataset,we tested the proposed method under different train-test splits,i.e.,20%–80%,50%–50%,and 80%–20%.For the latter split,the proposed method achieved 99.72%accuracy,99.80%sensitivity,precision,and F1-scores,and an AUC score of 99.99%.

关键词： COVID-19 detection computed tomography deep learning convolutional neural network EffecientNet-B3 ensemble techniques

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Machine Learning-Integrated Modeling of Thermal Properties and Relaxation Dynamics in Metallic Glasses

arXiv

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arXiv 2025年

作者： Que, Ngo T. Phan, Anh D. Tran, Truyen Huy, Pham T. Trang, Mai X. Luong, Thien V. Phenikaa Institute for Advanced Study Phenikaa University Hanoi12116 Viet Nam Faculty of Materials Science and Engineering Phenikaa University Hanoi12116 Viet Nam Deakin University Australia Faculty of Computer Science Phenikaa University Hanoi12116 Viet Nam Business AI Lab Faculty of Data Science and Artificial Intelligence National Economics University Hanoi10000 Viet Nam

Metallic glasses are a promising class of materials celebrated for their exceptional thermal and mechanical properties. However, accurately predicting and understanding the melting temperature (Tm) and glass transition temperature (Tg) remains a significant challenge. In this study, we present a comprehensive approach that integrates machine learning (ML) models with theoretical methods to predict and analyze these key thermal properties in metallic glasses. Our ML models using distributional data derived from elemental composition-based features obtain high accuracy while minimizing data preprocessing complexity. Furthermore, we explore the correlation between Tm and Tg to elucidate their dependence on alloy composition and thermodynamic behavior. When the Tgvalue of metallic glasses is known, further analysis using the Elastically Collective Nonlinear Langevin Equation (ECNLE) theory provides a deeper understanding of structural relaxation dynamics. This integrated framework establishes a quantitative description consistent with experimental data and previous works and paves the way for the efficient design and discovery of advanced materials with tailored thermal properties. Copyright © 2025, The Authors. All rights reserved.

关键词： Amorphous alloys

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Multiple additively constrained path selection.

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IEE PROCEEDINGS-COMMUNICATIONS 2002年第5-6期149卷 237-241页

作者： Cheng, G Ansari, N Advanced Networking Lab Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark USA

Finding a feasible path subject to multiple constraints in a network is an NP-complete problem and has been extensively studied. However, current algorithms suffer either high computational complexity or low success ratio in finding feasible paths. The authors propose a novel extended Bellman–Ford algorithm (EB), from which they present a high-performance algorithm with low computational complexity in finding feasible paths with multiple additive constraints. Through analysis and simulations, it is shown that the algorithm outperforms its contenders in the success rate of finding a feasible path as well as in terms of scalability; the proposed algorithm can achieve almost 100% success ratio as long as a feasible path exists. Furthermore, the worst case computational complexity is only twice that of the Bellman–Ford algorithm.

关键词： telecommunication network routing NP-complete problem multiple additively constrained path selection quality of service computational complexity feasible path packet switching Communication network design, planning and routing high-performance algorithm EB Communication switching extended Bellman-Ford algorithm

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Observation of charge to spin conversion in p-orbital InBi alloy

arXiv

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arXiv 2025年

作者： Li, Gen Zhang, Ying Xu, Xiaoguang Shen, Lei Feng, Zheng Meng, Kangkang Li, Ang Cheng, Lu He, Kang Tan, Wei Wu, Yong Wu, Yihong Jiang, Yong Key Laboratory of Advanced Materials and Devices for Post-Moore Chips Ministry of Education School of Materials Science and Engineering University of Science and Technology Beijing Beijing100083 China Department of Electrical and Computer Engineering National University of Singapore 117576 Singapore Research Institute Chongqing Liang Jiang New Area Chongqing401123 China Department of Mechanical Engineering National University of Singapore 117575 Singapore Microsystem & Terahertz Research Center CAEP Chengdu610200 China Faculty of Materials and Manufacturing Beijing Key Lab of Microstructure and Properties of Advanced Materials Beijing University of Technology Beijing100124 China Institute of Quantum Materials and Devices School of Electronic and Information Engineering State Key Laboratory of Separation Membranes and Membrane Processes Tiangong University Tianjin300387 China

High density data storage and spin-logic devices require highly efficient all-electric control of spin moment. So far, charge-to-spin conversion through the spin Hall effect limits to d-orbital materials associated with strong spin-orbit coupling, especially heavy metals. However, d-orbital heavy metals with strong spin-orbit coupling results in a short spin diffusion length, which restricts the spin transport and accumulation in spintronic devices. Therefore, the conflict between charge-to-spin conversion efficiency and spin transport ability is irreconcilable in d-orbital materials. Here, we report a large charge to spin conversion experimentally observed in the p-orbital In2Bi alloy, exhibiting the coexistence of a large spin Hall angle comparable to heave metal Pt and a long spin diffusion length (4 times that of Pt). First-principles calculations reveal that topological symmetry-protected gap opening near the Fermi level results in large Berry curvature-related spin Hall conductivity. Due to the delocalized nature of p-orbitals and semimetal properties of In2Bi, its spin current can overcome the physical barriers between spin Hall angle and spin diffusion length in d-orbital metals, thereby advancing the development of high-performance spintronic devices. Copyright © 2025, The Authors. All rights reserved.

关键词： Heavy metals

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Practical security analysis and attack strategies on permutation functions used in IoT supply chain systems

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Scientific Reports 2025年第1期15卷 1-15页

作者： Mokhtari, Narges Safari, Amirhossein Sadeghi, Sadegh Bagheri, Nasour Rostampour, Samad Bendavid, Ygal CPS2 Lab Department of Communication Faculty of Electrical Engineering Shahid Rajaee Teacher Training University Tehran 16788-15811 Iran Department of Computer Science and Information Technology Institute for Advanced Studies in Basic Sciences (IASBS) Zanjan 45137-66731 Iran Department of Mathematics Institute for Advanced Studies in Basic Sciences (IASBS) Zanjan 45137-66731 Iran School of Computer Science Institute for Research in Fundamental Sciences (IPM) Tehran 19538-33511 Iran Department of Computer Science Vanier College Montreal Canada Department of Analytics Operations and Technology School of Management Université du Québec à Montréal (UQAM) Montreal Canada

The widespread adoption of IoT devices has made the production of low-cost systems a priority. Since construction costs are generally directly related to the complexity of security methods, researchers are exploring methods that provide acceptable security with minimal hardware complexity. One such method is the use of permutation functions in ultra-lightweight authentication protocols that employ simple operators such as XOR and Shift. This paper demonstrates the critical importance of the internal structure of a permutation function in ensuring system security. This implies that even if a protocol is designed securely and efficiently, structural weaknesses in the function can render the protocol vulnerable. To illustrate this, we examine a recently published protocol named ULBRAP for supply chain management systems and reveal its security flaws, including secret disclosure and traceability attacks. We also demonstrate the attack step-by-step on Raspberry Pi devices, publishing the details on GitHub and presenting them in a video. The attack method requires 1,710,947 hash calculations, which takes approximately 5 min in our experiments. Finally, we propose a solution to address the issues associated with these functions. © The Author(s) 2025.

关键词： 5G mobile edge computing RFID authentication protocol Security analysis Supply chain management Ultra-lightweight blockchain

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Memory that never forgets:emerging nonvolatile memory and the implication for architecture design

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National Science Review 2018年第4期5卷 577-592页

作者： Guangyu Sun Jishen Zhao Matt Poremba Cong Xu Yuan Xie Center for Energy-Efficient Computing and Applications School of Electronics Engineering and Computer Science Peking University Department of Computer Engineering University of California at Santa Cruz Advanced Micro Devices Inc. Hewlett Packard Lab Department of Electrical and Computer Engineering University of California at Santa Barbara

In order to mitigate the problem of the ‘memory wall', various emerging nonvolatile memory(NVM)technologies have been proposed to replace the traditional ones. These emerging NVMs include spin torque transfer random access memory, PCRAM, resistive random access memory, racetrack memory, *** to traditional memory technologies, they have the advantages of near-zero standby power, high storage density and nonvolatility, which make them competitive for future memory hierarchy ***, it is inefficient to directly apply these NVMs in existing memory architectures. On the one hand,these NVMs have their own limitations, such as long write latency, high write energy, limited write numbers, etc. Thus, proper architecture modification is required to adopt them into the traditional memory hierarchy. On the other hand, the unique features of these NVMs allow new memory architectures in memory subsystems and also introduce new challenges to be solved at the same time. In this paper, we not only review various emerging NVMs but also study typical related work on these topics to investigate their implications for memory architecture design.

关键词： non-volatile memory cache main memory energy efficiency

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Using artificial intelligence to predict the next deceptive movement based on video sequence analysis: A case study on a professional cricket player's movements

Journal of Engineering Research (Kuwait)

引用

Journal of engineering Research (Kuwait) 2025年

作者： Mutawa, A.M. Rajesh Kumar, Korupalli V. K, Hemachandran Murugappan, M. Computer Engineering Department College of Engineering and Petroleum Kuwait University Safat 5969 13060 Kuwait CoE Sports Analytics and Management and AI Research Centre School of Business Woxsen University Hyderabad India Intelligent Signal Processing (ISP) Research Lab Department of Electronics and Communication Engineering Kuwait College of Science and Technology Block 4 Doha 13133 Kuwait Department of Electronics and Communication Engineering School of Engineering Vels Institute of Sciences Technology and Advanced Studies Tamilnadu Chennai India Center of Excellence for Unmanned Aerial Systems (CoEUAS) Universiti Malaysia Perlis Perlis 02600 Malaysia

This research develops an artificial intelligence-based model to predict the next deceptive movement of athletes by analyzing video sequences of previous movements. Utilizing advanced deep neural network models, we analyze deceptive movements to forecast the next move, with a practical application on the deceptive movements of a professional cricket player. The model employs machine learning techniques such as Random Forest (RF), Decision Trees (DT), and K-Nearest Neighbor (KNN) to enhance prediction accuracy. Achieving up to 70 % accuracy, this model rivals human capability, as even highly skilled players can easily fall for deceptive actions. The ability to predict deceptive movements sets humans apart from many intelligent creatures, allowing athletes to avoid predictable actions and gain an edge in various sports. This study applies this concept to cricket, leveraging video data to improve training methods. The results highlight the potential of artificial intelligence in revolutionizing training and performance optimization in sports. © 2025 The Authors

关键词： Artificial intelligence Cricket Deceptive movements Machine learning Performance optimization

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High-resolution simulations of nonlinear electromagnetic turbulence in tokamak devices

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The European Physical Journal Special Topics 2025年 1-14页

作者： Liu, Yanjun Liu, Zixi Liu, Jian Gao, Baofeng Zhang, Wei Xia, Tianyang Liu, Haiqing Zhuang, Ge Gao, Xiang Institute of Plasma Physics Chinese Academy of Sciences Anhui China KTX Laboratory and Department of Engineering and Applied Physics University of Science and Technology of China Anhui China Weihai Institute for Interdisciplinary Research Shandong University Shandong China SDU-ANU Joint Science College Shandong University Shandong China Shandong Computer Science Center (National Supercomputing Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Shandong China Advanced Algorithm Joint Lab National Supercomputing Center in Jinan Shandong China

To achieve long-pulse steady operation, the physical mechanisms of boundary turbulence need further investigation. We employ the two-fluid model with flute reduction on BOUT++ to simulate the boundary plasma in Tokamaks. The space and time scales of turbulence reproduced by our simulations closely relate to the spatial mesh size and time step size, respectively. As an inherent time scale, the Alfven time is sufficient to resolve MHD instabilities. The spatial scale can be refined by increasing mesh resolutions, which necessitates larger scale parallel computing resources. We have conducted nonlinear simulations using more than 33 million spatial meshes with 16,384 CPU processors in parallel. The results indicate that while the decrease in parallel efficiency with an increase in core numbers does not necessarily lead to shorter runtimes, higher computational complexity improves parallel efficiency for the same number of cores. In addition, the mesh resolution required for convergence conditions differs between linear and nonlinear simulations, with nonlinear simulations demanding higher resolution. Besides finer structure obtained, the fluctuation characteristic of density similar to WCM, which is more consistent with the experimental observation, also shows the requirement for high-resolution meshes and large-scale computing in the future.

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Communication Strategy on Macro-and-Micro Traffic State in Cooperative Deep Reinforcement Learning for Regional Traffic Signal Control

arXiv

引用

arXiv 2025年

作者： Gu, Hankang Wang, Shangbo Jia, Dongyao Zhang, Yuli Luo, Yanrong Mao, Guoqiang Wang, Jianping Lim, Eng Gee School of Advanced Technology Xi’an Jiaotong-Liverpool University Suzhou215123 China Department of Computer Science University of Liverpool LiverpoolL69 3GJ United Kingdom Department of Engineering and Design University of Sussex BrightonBN1 9RH United Kingdom School of Advanced Technology China International Business School Suzhou Xi’an Jiaotong-Liverpool University Suzhou215123 China ISN State Key Lab Xidian University Xi’an710126 China Department of Computer Science City University of Hong Kong Kowloon Hong Kong City University of Hong Kong Shenzhen Research Institute China

Adaptive Traffic Signal Control (ATSC) has become a popular research topic in intelligent transportation systems. Regional Traffic Signal Control (RTSC) using the Multi-agent Deep Reinforcement Learning (MADRL) technique has become a promising approach for ATSC due to its ability to achieve the optimum trade-off between scalability and optimality. Most existing RTSC approaches partition a traffic network into several disjoint regions, followed by applying centralized reinforcement learning techniques to each region. However, the pursuit of cooperation among RTSC agents still remains an open issue and no communication strategy for RTSC agents has been investigated. In this paper, we propose communication strategies to capture the correlation of micro-traffic states among lanes and the correlation of macro-traffic states among intersections. We first justify the evolution equation of the RTSC process is Markovian via a system of store-and-forward queues. Next, based on the evolution equation, we propose two GAT-Aggregated (GA2) communication modules—GA2-Naive and GA2-Aug to extract both intra-region and inter-region correlations between macro and micro traffic states. While GA2-Naive only considers the movements at each intersection, GA2-Aug also considers the lane-changing behavior of vehicles. Two proposed communication modules are then aggregated into two existing novel RTSC frameworks—RegionLight and Regional-DRL. Experimental results demonstrate that both GA2-Naive and GA2-Aug effectively improve the performance of existing RTSC frameworks under both real and synthetic scenarios. Hyperparameter testing also reveals the robustness and potential of our communication modules in large-scale traffic networks. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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Integrating a weighted-average method into the random walk framework to generate individual friend recommendations

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Science China(Information Sciences) 2017年第11期60卷 43-64页

作者： Jibing GONG Xiaoxia GAO Hong CHENG Jihui LIU Yanqing SONG Mantang ZHANG Yi ZHAO School of Information Science and Engineering Yanshan University The Key Lab for Computer Virtual Technology and System Integration Yanshan University State Key Lab of Mathematical Engineering and Advanced Computing Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong

Friend recommendation is a fundamental service in both social networks and practical applications,and is influenced by user behaviors such as interactions, interests, and activities. In this study, we first conduct in-depth investigations on factors that affect recommendation results. Next, we design Friend++, a hybrid multi-individual recommendation model that integrates a weighted average method(WAM) into the random walk(RW) framework by seamlessly employing social ties, behavior context, and personal information. In Friend++, the first plus signifies recommending a new friend through network features, while the second plus stands for using node features. To verify our method, we conduct experiments on three social datasets crawled from the Sina microblog system(Weibo). Experimental results show that the proposed method significantly outperforms six baseline methods in terms of recall, precision, F1-measure, and MAP. As a final step, we describe a case study that demonstrates the scalability and universality of our method. Through discussion, we reach a meaningful conclusion: although common interests are more important than user activities in making recommendations, user interactions may be the most important factor in finding the most appropriate potential friends.

关键词： multi-individual friend recommendation architecture behavior context analysis Intimacy degree random walk framework social networks

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