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SSRN 2023年

作者： Abdelwahed, Ahmed Helmi, Ahmed M. Issa, Mohamed Computer and Systems Department Faculty of Engineering Zagazig University Egypt Computer Engineering Dept. Engineering and Information Technology College Buraydah Private Colleges Saudi Arabia Faculty of Computer Science and Information Technology Egypt−Japan University for Science and Technology Egypt

Deep learning proved effective in detecting COVID-19 from chest X-ray images. Although computing is the current bottleneck in training and deploying models, few studies focus on developing efficient frameworks while many aim only at performance. We propose a novel framework for detecting COVID-19 cases from chest X-ray images efficiently. Our study utilizes four high-performing architectures, including MobileNetV3, RegNetX, NasNetMobile, and EfficientNetV2, which use minimal resources, to extract features from X-ray images and classify COVID-19 cases using various statistical models and machine learning algorithms. We evaluated our proposed framework using metrics such as accuracy, precision, recall, specificity, f1-score, and AUC scores. The false negative rate for each feature extractor and classifier pair is reported, and the average inference time, to measure resource efficiency, is calculated. The results show that EfficientNetV2 was the best-performing feature extractor, while MobileNetV3 had the best average inference time and the lowest false negative rate. The Neural Network classifier performed the best, with state-of-the-art accuracy reaching 99.28%, followed by both RBF-SVM and XGBoost. The proposed framework is more robust, reliable, and resource-efficient for COVID-19 detection compared to existing literature. Our findings indicate the prospects of using the proposed framework in efficient clinical diagnosis. © 2023, The Authors. All rights reserved.

关键词： COVID-19

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A Quantum Q-Learning Model for the Capacitated Vehicle Routing Problem

A Quantum Q-Learning Model for the Capacitated Vehicle Routi...

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IEEE International Symposium on Embedded Multicore Socs (MCSoC)

作者： Jiahua Xu Ningyi Xie Xinwei Lee Yoshiyuki Saito Nobuyoshi Asai Dongsheng Cai Graduate School of Science and Technology University of Tsukuba Japan School of Computing and Information Systems Singapore Management University Singapore Graduate School of Computer Science and Engineering University of Aizu Japan School of Computer Science and Engineering University of Aizu Japan Faculty of Engineering Information and Systems University of Tsukuba Japan

ISBN: (数字)9798331530471

ISBN: (纸本)9798331530488

The research on Variational Quantum Algorithms (VQAs) has gained significant momentum because of their promising practicality in the noisy intermediate-scale quantum (NISQ) era. Recent studies highlight the potential of VQAs in reinforcement learning (RL) by replacing classical components with parameterized quantum circuits (PQCs). Building on this, we propose a quantum Q-learning model for the Capacitated Vehicle Routing Problem (CVRP), combining a PQC with an RL training process. We present a PQC for embedding both static and dynamic states in RL environments. The proposed PQC approximates the Q-value function by using the expectation values of different observables. Experimental results demonstrate that the proposed PQC outperforms the existing PQC for the CVRP with fewer quantum resources. Furthermore, our method shows competitive performance compared to the classical counterpart.

关键词： Training Q-learning Quantum algorithm Multicore processing Vehicle routing Buildings Noise measurement Integrated circuit modeling Vehicle dynamics Quantum circuit

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Light Cone Cancellation for Variational Quantum Eigensolver Ansatz

arXiv

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arXiv 2024年

作者： Yan, Xinjian Lee, Xinwei Xie, Ningyi Saito, Yoshiyuki Kurosawa, Leo Asai, Nobuyoshi Cai, Dongsheng Lau, Hoong Chuin Graduate School of Science and Technology University of Tsukuba Japan School of Computing and Information Systems Singapore Management University Singapore Graduate School of Computer Science and Engineering University of Aizu Japan School of Computer Science and Engineering University of Aizu Japan Faculty of Engineering Information and Systems University of Tsukuba Japan

Variational Quantum Algorithms (VQAs) represent a class of algorithms that utilize a hybrid approach, combining classical and quantum computing techniques. In this approach, classical computers serve as optimizers that update circuit parameters to find approximate solutions to complex problems. In this study, we apply a method known as Light Cone Cancellation (LCC) to optimize variational circuits, effectively reducing the required number of qubits and gates for circuit simulation. We then evaluate the performance of LCC one of the VQAs—the Variational Quantum Eigensolver (VQE)—to address the Max-Cut problem. Compared with the Quantum Approximate Optimization Algorithm (QAOA), VQE offers greater degrees of freedom and promising results at lower circuit depths. By applying LCC to VQE, we can shift the complexity of circuit simulation from the number of qubits to the number of edges in the graph, i.e., from exponential time to polynomial time. This enables us to solve large problems up to 100 vertices, without actually simulating the entire circuit. From our simulation in a 7-qubit and a 27-qubit fake backends, we show that LCC yields higher approximation ratios than those cases without LCC, implying that the effect of noise is reduced when LCC is applied. © 2024, CC BY.

关键词： Qubits

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Cluster Availability Model with information Update Following Node Recovery

Cluster Availability Model with Information Update Following...

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Wave Electronics and its Application in information and Telecommunication systems (WECONF)

作者： V. A. Bogatyrev S. V. Bogatyrev V. V. Sivov Department Information Systems Security Saint-Petersburg State University of Aerospace Instrumentation Saint Petersburg Russia Faculty of Software Engineering and Computer Technology ITMO University Saint-Petersburg Russia

A model for evaluating the availability of a fault-tolerant cluster with a constraint on the maximum query service time is proposed. Node recovery in the cluster involves replacing the entire computational node with the latest backup results before the failure. information update after the backup is achieved through the replication of this information from the working nodes of the cluster. The purpose of the article is to assess the reliability of the cluster and its readiness to timely service requests critical to the waiting time. Restoring up-to-date data in a cluster node after previously entering the data saved during backup into it requires the use of resources of the node in which the data is being restored and the node containing up-to-date data. The specified pair of nodes is excluded from the request servicing process, which reduces the likelihood of timely maintenance. information recovery in case of failures of all nodes containing replicas of up-to-date data requires more time compared to replication from the memory of a functioning node.

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Time-Varying Optimal Sliding-Mode Lag Formation Control for High-Order Nonlinear Multiagent systems Based on Reinforcement Learning

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IEEE Transactions on Automation Science and engineering 2025年 22卷 16864-16876页

作者： Qinhong Fu Jin-Liang Wang Tingwen Huang School of Electronics and Information Engineering Tiangong University Tianjin China Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems School of Computer Science and Technology Tiangong University Tianjin China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen China

This paper considers a type of high-order nonlinear multiagent systems (MASs), and the time-varying optimal lag formation control problem for such MAS is addressed. To eliminate the influences of the unknown system dynamics and external disturbance on single agent, a neural network (NN) identifier and disturbance observer based compensation controller is developed. Then, an optimal control law to ensure the practical time-varying lag formation for the MAS is introduced by exploiting the sliding-mode control (SMC) method and reinforcement learning (RL) technique. Finally, a numerical example is given to validate the effectiveness of the proposed optimal control law. Note to Practitioners—Recently, leader-following approach has been widely utilized to investigate the time-varying formation (TVF) control problem for various MASs. In fact, time delay is unavoidable during the process of the follower tracking the leader, it is more meaningful and reasonable to address the time-varying lag formation control problem of MASs. Moreover, most of the existing TVF control results ignore the optimal performance. Considering that a balance between system performance and control resources, it is also very significant and challenging to further tackle the optimal TVF control problem of MASs. In this paper, an RL-based time-varying optimal sliding-mode lag formation control strategy for high-order nonlinear MASs is developed, and an MAS consisting of four unmanned surface vehicles (USVs) is utilized to validate the effectiveness of the presented control strategy.

关键词： Formation control Topology Artificial neural networks Symmetric matrices Protocols Automation Vectors Time-varying systems State feedback Reinforcement learning

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Data Fusion Architecture Empowered with Deep Learning for Breast Cancer Classification

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computers, Materials & Continua 2023年第12期77卷 2813-2831页

作者： Sahar Arooj Muhammad Farhan Khan Tariq Shahzad Muhammad Adnan Khan Muhammad Umar Nasir Muhammad Zubair Atta-ur-Rahman Khmaies Ouahada Faculty of Computing Riphah International UniversityIslamabad45000Pakistan Department of Forensic Sciences University of Health SciencesLahore54000Pakistan Department of Electrical and Electronic Engineering Science University of JohannesburgP.O.Box 524Johannesburg2006South Africa School of Computing Skyline University CollegeUniversity City SharjahSharjah1797United Arab Emirates Riphah School of Computing and Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department of Software Faculty of Artificial Intelligence&SoftwareGachon UniversitySeongnamGyeonggido13120Korea Department of Computer Science Bahria UniversityLahore CampusLahore54000Pakistan Department of Computer Science College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)P.O.Box 1982Dammam31441Saudi Arabia

Breast cancer(BC)is the most widespread tumor in females worldwide and is a severe public health *** is the leading reason of death affecting females between the ages of 20 to 59 around the *** detection and therapy can help women receive effective treatment and,as a result,decrease the rate of breast cancer *** cancer tumor develops when cells grow improperly and attack the healthy tissue in the human *** are classified as benign or malignant,and the absence of cancer in the breast is considered *** learning,machine learning,and transfer learning models are applied to detect and identify cancerous tissue like *** research assists in the identification and classification of *** implemented the pre-trained model AlexNet and proposed model Breast cancer identification and classification(BCIC),which are machine learning-based models,by evaluating them in the form of comparative *** used 3 datasets,A,B,and *** fuzzed these datasets and got 2 datasets,A2C and *** A2C is the fusion of A,B,and C with 2 classes categorized as benign and *** B3C is the fusion of datasets A,B,and C with 3 classes classified as benign,malignant,and *** used customized AlexNet according to our datasets and BCIC in our proposed *** achieved an accuracy of 86.5%on Dataset B3C and 76.8%on Dataset A2C by using AlexNet,and we achieved the optimum accuracy of 94.5%on Dataset B3C and 94.9%on Dataset A2C by using proposed model BCIC at 40 epochs with 0.00008 learning *** proposed fuzzed dataset model using transfer *** fuzzed three datasets to get more accurate results and the proposed model achieved the highest prediction accuracy using fuzzed dataset transfer learning technique.

关键词： Breast cancer classification deep learning machine learning transfer learning learning rate

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Reliability of a Reconfigurable Network with Segment Switching

Reliability of a Reconfigurable Network with Segment Switchi...

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Wave Electronics and its Application in information and Telecommunication systems (WECONF)

作者： Le Anh Tu V. A. Bogatyrev E. A. Abramova Faculty of Software Engineering and Computer Technology ITMO University Saint Peterburg Russia Department Information Systems Security Saint-Petersburg State University of Aerospace Instrumentation Saint Petersburg Russia

The article proposes models of the structural reliability of a multipath routing network with the possibility of its reconfiguration when switching path segments. The models are focused on optimizing the placement of segment switching nodes, aimed at achieving maximum network performance with the option of connecting request sources to the available communication paths with servers. The study considers the options for connecting each source of requests with one of the paths or with all paths for connecting to servers. The paper shows the existence of an optimal placement of path switching nodes, in which the maximum reliability of the system is achieved.

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Improved Archimedes Optimization Algorithm with Deep Learning Empowered Fall Detection System

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computers, Materials & Continua 2022年第8期72卷 2713-2727页

作者： Ala Saleh Alluhaidan Masoud Alajmi Fahd N.Al-Wesabi Anwer Mustafa Hilal Manar Ahmed Hamza Abdelwahed Motwakel Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Computer Science College of Science&Arts at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’a61101Yemen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

Human fall detection(FD)acts as an important part in creating sensor based alarm system,enabling physical therapists to minimize the effect of fall events and save human ***,elderly people suffer from several diseases,and fall action is a common situation which can occur at any *** this view,this paper presents an Improved Archimedes Optimization Algorithm with Deep Learning Empowered Fall Detection(IAOA-DLFD)model to identify the fall/non-fall *** proposed IAOA-DLFD technique comprises different levels of pre-processing to improve the input image ***,the IAOA with Capsule Network based feature extractor is derived to produce an optimal set of feature *** addition,the IAOA uses to significantly boost the overall FD performance by optimal choice of CapsNet ***,radial basis function(RBF)network is applied for determining the proper class labels of the test *** showcase the enhanced performance of the IAOA-DLFD technique,a wide range of experiments are executed and the outcomes stated the enhanced detection outcome of the IAOA-DLFD approach over the recent methods with the accuracy of 0.997.

关键词： Fall detection intelligent model deep learning archimedes optimization algorithm capsule network

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BD-ECG: Identification of Myocardial Infarction in ECG via Behavior Coupling 22

BD-ECG: Identification of Myocardial Infarction in ECG via B...

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6th International Conference on information System and Data Mining, ICISDM 2022

作者： Iqbal, Uzair Wah, Teh Ying Ur Rehman, Muhammad Habib Bilal, Muhammad Ahmed, Adeel Department of Computer Science National University of Computer and Emerging Sciences Pakistan Department of Information Systems Faculty of Computer Science and Information Technology University of Malaya Malaysia Division of Imaging Sciences and Biomedical Engineering Kings College London United Kingdom Department of Computer Science Quaid-e-Azam University Pakistan

ISBN: (纸本)9781450396257

In the cardiovascular diseases, early detection and identification of the relationship between different diseases are still open problems for cardiologists. In this paper, we propose a novel scheme for behavioral detection in electrocardiography data named as Behavioral detection-Electrography. The Behavioral detection-Electrography is used for early detection of abnormalities in electrography especially myocardial infarction. The Behavioral detection-Electrography embeds the two-tier architecture in which we integrate the behavioral relationship concepts with myocardial detection algorithm. In future the highlighted integral scheme will help us to identify the nature of cardiovascular diseases either it's normal or abnormal © 2022 ACM.

关键词： Diseases

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A Compact 28 GHz Millimeter Wave Antenna for Future Wireless Communication

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computers, Materials & Continua 2022年第7期72卷 301-314页

作者： Shahid Khan Adil Bashir Haider Ali Abdul Rauf Mohamed Marey Hala Mostafa Ikram Syed LCOMS ASECUniversity de LorraineMetz57070France Department of Electrical and Computer Engineering Comsats University IslamabadAbbottabad Campus22060Pakistan Electrical Engineering Department Bahauddin Zakariya UniversityMultan60800Pakistan Department of Electronics Technology University of TechnologyNowshera24100Pakistan Department of Electrical Engineering National University of Sciences and TechnologyH-12Islamabad44000Pakistan Smart Systems Engineering Laboratory College of EngineeringPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Computer Software Engineering Military College of SignalsNational University of Sciences and TechnologyIslamabad44000Pakistan

This article presents a novel modified chuck wagon dinner bell shaped millimeter wave(mm-wave)antenna at 28 *** proposed design has ultra-thin Rogers 5880 substrate with relative permittivity of *** design consists of T shaped resonating elements and two open ended side *** desired 28 GHz frequency response is achieved by careful parametric modeling of the proposed *** maximum achieved single element gain at the desired resonance frequency is 3.45 *** efficiency of the proposed design over the operating band is more than 88%.The impedance bandwidth achieved for−10 dB reference value is nearly 2.9 *** proposed antenna is transformed into four element linear array which increases the gain up to 10.5 *** fabricated prototype is tested for the measured *** is observed that measured results closely match the simulated *** considering its simple structure and focused radiation patterns,the proposed design is well suited for IoT(Internet of Things),mmWave microwave sensing,5G and future RF(Radio Frequency)frontends.

关键词： RF front-ends mmWave partial ground plane 28 GHz IoT 5G antenna

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