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DTQFL: A Digital Twin-Assisted quantum Federated Learning Algorithm for Intelligent Diagnosis in 5G Mobile Network

IEEE Journal of Biomedical and Health Informatics 2023年第5期29卷 1-10页

作者： Qu, Zhiguo Li, Yang Liu, Bo Gupta, Deepak Tiwari, Prayag Engineering Research Center of Digital Foren-sics Ministry of Education Jiangsu Collaborative Innovation Center of Atmospheric Environment the Equipment Technology and the School of Computer Science Nanjing University of Information Science and Technology Nanjing China School of Computer Science Nanjing University of Information Science and Technology Nanjing China Hubei University of Science and Technology Xianning China Department of Computer Science & Engi-neering Maharaja Agrasen Institute of Technology Delhi India School of Information Technology Halmstad University Sweden

Smart healthcare aims to revolutionize med-ical services by integrating artificial intelligence (AI). The limitations of classical machine learning include privacy concerns that prevent direct data sharing among medical institutions, untimely updates, and long training times. To address these issues, this study proposes a digital twin-assisted quantum federated learning algorithm (DTQFL). By leveraging the 5G mobile network, digital twins (DT) of patients can be created instantly using data from various Internet of Medical Things (IoMT) devices and simultane-ously reduce communication time in federated learning (FL) at the same time. DTQFL generates DT for patients with specific diseases, allowing for synchronous training and updating of the variational quantum neural network (VQNN) without disrupting the VQNN in the real world. This study utilized DTQFL to train its own personalized VQNN for each hospital, considering privacy security and training speed. Simultaneously, the personalized VQNN of each hospital was obtained through further local iterations of the final global parameters. The results indicate that DTQFL can train a good VQNN without collecting local data while achieving accuracy comparable to that of data-centralized algorithms. In addition, after personalized train-ing, the VQNN can achieve higher accuracy than that with-out personalized training. IEEE

关键词： Wireless networks

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Genetic Algorithm Based Deterministic Workflow Scheduling with Load Balancing 2

Genetic Algorithm Based Deterministic Workflow Scheduling wi...

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2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Mittal, Punit Kumar, Satender Sharma, Swati Department of Computer Science and Engineering Quantum University Roorkee India Department of Information Technology Meerut Institute of Engineering and Technology Meerut India

ISBN: (纸本)9798331530389

A crucial problem in cloud computing is load balancing, which makes it challenging to guarantee that services operate as intended in accordance with quality of service (QoS), performance reviews, and service contracts that cloud service providers (CSPs) demand of businesses. The main goal is to distribute workloads among computer resources in order to improve performance. Load balancing is categorized as an "NP-hard" problem due to the ample solution space and the time needed to identify the optimal solution. While only a few approaches may provide an exact answer in polynomial time, prior research shows that metaheuristic-based tactics can find correct solutions in a fair amount of time. We use a framework that integrates Neural Network Task Classification (N2TC) and Genetic Algorithm Task Assignment (GATA) for load balancing. By taking load balancing and task completion time into account, this approach seeks to maximize load distribution and reduce process execution time. Extensive simulations of application operations were carried out to evaluate the effectiveness of the suggested load-balancing strategy. Results have shown that the proposed framework outperforms tournament selection genetic algorithm (TSGA), Heterogeneous Earliest Finish Time (HEFT), and Performance Effective Task Scheduling (PETS) heuristics in terms of load balancing up to 4.52% and makespan by 6.47%. © 2025 IEEE.

关键词： Genetic algorithms

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Neural Network-Based State Estimation for Nonlinear Systems With Denial-of-Service Attack Under Try-Once-Discard Protocol

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IEEE/CAA Journal of Automatica Sinica 2024年第10期11卷 2182-2184页

作者： Xueli Wang Shangwei Zhao Ming Yang Xin Wang Xiaoming Wu the Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science CenterQilu University of Technology(Shandong Academy of Sciences)Jinan 250014 Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer ScienceJinan 250014China Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China

Dear Editor,This letter deals with state estimation issues of discrete-time nonlinear systems subject to denial-of-service(DoS)attacks under the try-once-discard(TOD)*** specifically,to reduce the communication burden,a TOD protocol with novel update rules on protocol weights is designed for scheduling measurement *** addition,unknown nonlinear functions vulnerable to DoS attacks are considered due to the openness and vulnerability of the network.

关键词： Service DoS service

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Ultra-fast interpretable machine-learning potentials

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npj Computational Materials 2023年第1期9卷 649-657页

作者： Stephen R.Xie Matthias Rupp Richard G.Hennig Department of Materials Science and Engineering University of FloridaGainesvilleFLUSA Quantum Theory Project University of FloridaGainesvilleFLUSA Department of Computer and Information Science University of KonstanzKonstanzGermany Materials Research and Technology Department Luxembourg Institute of Science and Technology(LIST)Esch-sur-AlzetteLuxembourg

All-atom dynamics simulations are an indispensable quantitative tool in physics,chemistry,and materials science,but large systems and long simulation times remain challenging due to the trade-off between computational efficiency and predictive *** address this challenge,we combine effective two-and three-body potentials in a cubic B-spline basis with regularized linear regression to obtain machine-learning potentials that are physically interpretable,sufficiently accurate for applications,as fast as the fastest traditional empirical potentials,and two to four orders of magnitude faster than state-of-the-art machine-learning *** data from empirical potentials,we demonstrate the exact retrieval of the *** data from density functional theory,the predicted energies,forces,and derived properties,including phonon spectra,elastic constants,and melting points,closely match those of the reference *** introduced potentials might contribute towards accurate all-atom dynamics simulations of large atomistic systems over long-time scales.

关键词： method spline faster

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Thrips Detection and Monitoring System in Mango Greenhouses Based on Deep Learning Image Recognition

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IEEE Transactions on Instrumentation and Measurement 2024年 73卷 1-18页

作者： Wu, Ge-Han Huang, Nen-Fu Huang, Yu-Hsiang Chan, Pin-Cheng Bhat, Showkat Ahmad National Tsing Hua University Department Of Computer Science Hsinchu1300044 Taiwan National Tsing Hua University Institute Of Information Systems And Applications Hsinchu1300044 Taiwan National Tsing Hua University Center Of Innovative Incubator Department Of Computer Science Hsinchu1300044 Taiwan

The ability of thrips to infect plants with viruses, oviposition, and feeding makes it one of the most damaging agricultural pests in the world. Due to their invasiveness, many pest species pose serious challenges to growers of a wide variety of crops especially Mango in both open fields and greenhouses globally. For detecting various pests, sticky paper traps have been widely used. It is essential to inspect sticky paper traps as part of an integrated pest management (IPM) process. Identification and counting the pests caught in the traps is a laborious process. A timely and effective strategy is therefore required to resolve the issue. This study proposes and develops a complete and feasible integrated pest detection and monitoring system (IPDMS) by combining an ultrahigh resolution pest monitoring device coupled with a deep learning-based object detection model. The developed system was able to collect sticky paper images with a resolution of 64 megapixels and an AWS database for analysis. Test results from two Mango greenhouses show that the model can detect thrips with 90% accuracy and an F1 -score of 0.95. Additionally, a web-based user interface was developed to provide visual image recognition results and thrip population analysis. Thus, the proposed IPDMS addresses the significant problems with pest detection systems (PDSs) by providing real-time information on pest occurrences in greenhouses. Thus, it makes pest detection and monitoring in agriculture efficient. © 1963-2012 IEEE.

关键词： Image recognition

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Boosted Spider Wasp Optimizer for High-dimensional Feature Selection

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Journal of Bionic Engineering 2024年第5期21卷 2424-2459页

作者： Elfadil A.Mohamed Malik Sh.Braik Mohammed Azmi Al-Betar Mohammed A.Awadallah Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman University346AjmanUnited Arab Emirates Department of Computer Science Al-Balqa Applied UniversityAl-Salt19117Jordan Department of Information Technology Al-Balqa Applied UniversityIrbid21510Jordan Department of Computer Science Al-Aqsa UniversityGaza4051Palestine Artificial Intelligence Research Center(AIRC) Ajman University346AjmanUnited Arab Emirates

With the increasing dimensionality of the data,High-dimensional Feature Selection(HFS)becomes an increasingly dif-ficult *** is not simple to find the best subset of features due to the breadth of the search space and the intricacy of the interactions between *** of the Feature Selection(FS)approaches now in use for these problems perform sig-nificantly less well when faced with such intricate situations involving high-dimensional search *** is demonstrated that meta-heuristic algorithms can provide sub-optimal results in an acceptable amount of *** paper presents a new binary Boosted version of the Spider Wasp Optimizer(BSWO)called Binary Boosted SWO(BBSWO),which combines a number of successful and promising strategies,in order to deal with *** shortcomings of the original BSWO,including early convergence,settling into local optimums,limited exploration and exploitation,and lack of population diversity,were addressed by the proposal of this new variant of *** concept of chaos optimization is introduced in BSWO,where initialization is consistently produced by utilizing the properties of sine chaos mapping.A new convergence parameter was then incorporated into BSWO to achieve a promising balance between exploration and *** exploration mechanisms were then applied in conjunction with several exploitation strategies to effectively enrich the search process of BSWO within the search ***,quantum-based optimization was added to enhance the diversity of the search agents in *** proposed BBSWO not only offers the most suitable subset of features located,but it also lessens the data's redundancy *** was evaluated using the k-Nearest Neighbor(k-NN)classifier on 23 HFS problems from the biomedical domain taken from the UCI *** results were compared with those of traditional BSWO and other well-known meta-heuristics-based *** findings indicate that,in comparison to other competing techn

关键词： High-dimensional features SWO algorithm Feature selection Optimization Machine learning

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PVDM-YOLOv8l: a solution for reliable pedestrian and vehicle detection in autonomous vehicles under adverse weather conditions

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Multimedia Tools and Applications 2024年 1-26页

作者： Tahir, Noor Ul Ain Zhang, Zuping Asim, Muhammad Iftikhar, Sundas A. Abd El-Latif, Ahmed School of Computer Science and Engineering Central South University Changsha410083 China EIAS Data Science Lab College of Computer and Information Sciences and Center of Excellence in Quantum and Intelligent Computing Prince Sultan University Riyadh11586 Saudi Arabia Gianforte School of Computing Montana State University Bozeman59717 United States Department of Mathematics and Computer Science Faculty of Science Menoufia University Shebin El-Koom32511 Egypt

Ensuring the safe navigation of autonomous vehicles in intelligent transportation system depends on their ability to detect pedestrians and vehicles. While transformer-based models for object detection have shown remarkable advancements, accurately identifying pedestrians and vehicles in adverse weather conditions remains a challenging task. Adverse weather introduces image quality degradation, leading to issues such as low contrast, reduced visibility, blurred edges, false detection, misdetection of tiny objects, and other impediments that further complicate the accuracy of detection. This paper introduces a novel Pedestrian and Vehicle Detection Model under adverse weather conditions, denoted as PVDM-YOLOv8l. In our proposed model, we first incorporate the Swin-Transformer method, which is designed for global extraction of feature of small objects to identify in poor visibility, into the YOLOv8l backbone structure. To enhance detection accuracy and address the impact of inaccurate features on recognition performance, CBAM is integrated between the neck and head networks of YOLOv8l, aiming to gather crucial information and obtain essential data. Finally, we adopted the loss function Wise-IOU v3. This function was implemented to mitigate the adverse effects of low-quality instances by minimizing negative gradients. Additionally, we enhanced and augmented the DAWN dataset and created a custom dataset, named DAWN2024, to cater to the specific requirements of our study. To verify the superiority of PVDM-YOLOV8l, its performance was compared against several commonly used object detectors, including YOLOv3, YOLOv3-tiny, YOLOv3-spp, YOLOv5, YOLOv6, and all the versions of YOLOv8 (n, m, s, l, and x) and some traditional models. The experimental results demonstrate that our proposed model achieved a 6.6%, 5.4%, 6%, and 5.1% improvement in precision, recall, F1-score and mean Average Precision (mAP) on the custom DAWN2024 dataset. This substantial improvement in accuracy ind

关键词： Vehicle detection

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A globally shared resource paradigm for encoded storage systems in the public cloud

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Fundamental Research 2024年第3期4卷 642-650页

作者： Zhiyue Li Guangyan Zhang Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China Beijing National Research Center for Information Science and Technology Tsinghua UniversityBeijing 100084China

Public clouds favor sharing of storage resources,in which many tenants acquire bandwidth and storage capacity from a shared storage *** provide high availability,data are often encoded to provide fault tolerance with low storage *** this,efficiently organizing an encoded storage system for shared I/Os is critical for application *** is usually hard to achieve as different applications have different stripe configurations and fault tolerance *** this paper,we first study the block trace from the Alibaba cloud,and find that I/O patterns of modern applications prefer the resource sharing *** on this,we propose a globally shared resource paradigm for encoded storage system in the public *** globally shared resource paradigm can provide balanced load and fault tolerance for numerous disk pool sizes and arbitrary application stripe ***,we demonstrate with two case studies that our theory can help address the device-specific problems of HDD and SSD RAID arrays with slight modifications:comparing the existing resource partition and resource sharing methods,our theory can promote the rebuild speed of the HDD RAID arrays by 2.5,and reduce the P99 tail latency of the SSD arrays by up to two orders of magnitude.

关键词： Public cloud Encoded storage Load balancing RAID reconstruction Tail latency

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Deep-Learning-Based COVID-19 Detection: Challenges and Future Directions

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第2期4卷 210-228页

作者： Arshad, Qurat-Ul-Ain Khan, Wazir Zada Azam, Faisal Khan, Muhammad Khurram Comsats University Department of Computer Science Islamabad47040 Pakistan University of Wah Department of Computer Science Wah Cantt47040 Pakistan King Saud University Center of Excellence in Information Assurance Riyadh11653 Saudi Arabia

Coronavirus disease 2019 (COVID-19) is an ecumenical pandemic that has affected the whole world drastically by raising a global calamitous situation. Owing to this pernicious disease, millions of people have lost their lives. The scientists are still far from knowing how to tackle the coronavirus due to its multiple mutations found around the globe. The standard testing technique called polymerase chain reaction for the clinical diagnosis of COVID-19 is expensive and time consuming. However, to assist specialists and radiologists in COVID-19 detection and diagnosis, deep learning plays an important role. Many research efforts have been done that leverage deep learning techniques and technologies for the identification or categorization of COVID-19-positive patients, and these techniques are proved to be a powerful tool that can automatically detect or diagnose COVID-19 cases. In this article, we identify significant challenges regarding deep-learning-based systems and techniques that use different medical imaging modalities, including cough and breadth, chest X-ray, and computed tomography, to combat COVID-19 outbreak. We also pinpoint important research questions for each category of challenges. © 2020 IEEE.

关键词： Coronavirus

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A Framework of Deep Optimal Features Selection for Apple Leaf Diseases Recognition

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computers, Materials & Continua 2023年第4期75卷 697-714页

作者： Samra Rehman Muhammad Attique Khan Majed Alhaisoni Ammar Armghan Usman Tariq Fayadh Alenezi Ye Jin Kim Byoungchol Chang Department of Computer Science HITEC UniversityTaxilaPakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Electrical Engineering College of EngineeringJouf UniversitySakakahSaudi Arabia Department of Management Information Systems CoBAPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Computer Science Hanyang UniversitySeoul04763Korea Center for Computational Social Science Hanyang UniversitySeoul04763Korea

Identifying fruit disease manually is time-consuming, expertrequired,and expensive;thus, a computer-based automated system is widelyrequired. Fruit diseases affect not only the quality but also the *** a result, it is possible to detect the disease early on and cure the fruitsusing computer-based techniques. However, computer-based methods faceseveral challenges, including low contrast, a lack of dataset for training amodel, and inappropriate feature extraction for final classification. In thispaper, we proposed an automated framework for detecting apple fruit leafdiseases usingCNNand a hybrid optimization algorithm. Data augmentationis performed initially to balance the selected apple dataset. After that, twopre-trained deep models are fine-tuning and trained using transfer ***, a fusion technique is proposed named Parallel Correlation Threshold(PCT). The fused feature vector is optimized in the next step using a hybridoptimization algorithm. The selected features are finally classified usingmachine learning algorithms. Four different experiments have been carriedout on the augmented Plant Village dataset and yielded the best accuracy of99.8%. The accuracy of the proposed framework is also compared to that ofseveral neural nets, and it outperforms them all.

关键词： Convolutional neural networks deep learning features fusion features optimization classification

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