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TRS Scheduling for Improved QoS Performance in Cloud System

computers, Materials & Continua 2023年第4期75卷 1547-1559页

作者： G.John Samuel Babu M.Baskar Department of Computer Science and Engineering School of ComputingCollege of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathurChengalpattuTamilnadu603203India Department of Computing Technologies School of ComputingCollege of Engineering andTechnologySRM Institute of Science and TechnologyKattankulathurChengalpattuTamilnadu603203India

Numerous methods are analysed in detail to improve task schedulingand data security performance in the cloud environment. The methodsinvolve scheduling according to the factors like makespan, waiting time,cost, deadline, and popularity. However, the methods are inappropriate forachieving higher scheduling performance. Regarding data security, existingmethods use various encryption schemes but introduce significant serviceinterruption. This article sketches a practical Real-time Application CentricTRS (Throughput-Resource utilization–Success) Scheduling with Data Security(RATRSDS) model by considering all these issues in task scheduling anddata security. The method identifies the required resource and their claim timeby receiving the service requests. Further, for the list of resources as services,the method computes throughput support (Thrs) according to the number ofstatements executed and the complete statements of the service. Similarly, themethod computes Resource utilization support (Ruts) according to the idletime on any duty cycle and total servicing time. Also, the method computesthe value of Success support (Sus) according to the number of completions forthe number of allocations. The method estimates the TRS score (ThroughputResource utilization Success) for different resources using all these supportmeasures. According to the value of the TRS score, the services are rankedand scheduled. On the other side, based on the requirement of service requests,the method computes Requirement Support (RS). The selection of service isperformed and allocated. Similarly, choosing the route according to the RouteSupport Measure (RSM) enforced route security. Finally, data security hasgets implemented with a service-based encryption technique. The RATRSDSscheme has claimed higher performance in data security and scheduling.

关键词： Cloud task scheduling TRS quality of service RSM route security data security SDE RATRSDS

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Adaptive Particle Swarm Optimization of U-net Parameters for Marine Aquaculture Image Segmentation 13

Adaptive Particle Swarm Optimization of U-net Parameters for...

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13th International Conference on Intelligent Control and Information Processing, ICICIP 2025

作者： Zhao, Jin Wang, Jun Han, Min Fan, Jianchao School of Computer Science and Engineering Chongqing Three Gorges University Chongqing China Department of Computer Science Department of Data Science City University of HongKong Hong Kong School of Control Science and Engineering Dalian University of Technology Dalian China

ISBN: (纸本)9798331516147

Marine aquaculture image segmentation plays a crucial role in managing aquatic resources and environmental protection. Traditional deep learning models rely on manual parameter tuning for image segmentation, which limits their efficiency and accuracy. This paper proposes an adaptive Particle Swarm Optimization (PSO) algorithm to optimize the parameters of the U-net model automatically. The algorithm dynamically adjusts the PSO parameters based on the population’s entropy and clustering metrics and employs a hill-climbing algorithm to address the issue of the PSO easily falling into local optima, enhancing the algorithm’s adaptability. The proposed algorithm updates and iterates particles to automatically find suitable model parameters. The remote sensing data used in this experiment were from the Yellow Sea aquaculture area near Dalian, China, with a segmentation accuracy of 91.8%. This method improves segmentation accuracy, reduces the burden of manual parameter tuning, and provides an effective solution for optimizing deep learning models. © 2025 IEEE.

关键词： Aquaculture

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Space/Air Covert Communications:Potentials,Scenarios,and Key Technologies

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China Communications 2024年第3期21卷 1-18页

作者： Mao Haobin Liu Yanming Zhu Lipeng Mao Tianqi Xiao Zhenyu Zhang Rui Han Zhu Xia Xianggen School of Electronic and Information Engineering Beihang UniversityBeijing 100191China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore School of Science and Engineering Shenzhen Research Institute of Big Datathe Chinese University of Hong KongShenzhenGuangdong 518172China Department of Electrical and Computer Engineering University of HoustonHoustonTX 77004USA Department of Computer Science and Engineering Kyung Hee UniversitySeoulSouth Korea446-701 Department of Electrical and Computer Engineering University of DelawareNewarkDE 19716USA

Space/air communications have been envisioned as an essential part of the next-generation mobile communication networks for providing highquality global connectivity. However, the inherent broadcasting nature of wireless propagation environment and the broad coverage pose severe threats to the protection of private data. Emerging covert communications provides a promising solution to achieve robust communication security. Aiming at facilitating the practical implementation of covert communications in space/air networks, we present a tutorial overview of its potentials, scenarios, and key technologies. Specifically, first, the commonly used covertness constraint model, covert performance metrics, and potential application scenarios are briefly introduced. Then, several efficient methods that introduce uncertainty into the covert system are thoroughly summarized, followed by several critical enabling technologies, including joint resource allocation and deployment/trajectory design, multi-antenna and beamforming techniques, reconfigurable intelligent surface(RIS), and artificial intelligence algorithms. Finally, we highlight some open issues for future investigation.

关键词： artificial intelligence(AI) sixth generation(6G) space-air-ground integrated networks(SAGINs) space/air covert communications

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Adaptive Hypergraph Network for Trust Prediction 40

Adaptive Hypergraph Network for Trust Prediction

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Xu, Rongwei Liu, Guanfeng Wang, Yan Zhang, Xuyun Zheng, Kai Zhou, Xiaofang School of Computing Macquarie University Australia School of Computer Science and Engineering University of Electronic Science and Technology of China China Hong Kong University of Science and Technology Department of Computer Science and Engineering China

ISBN: (纸本)9798350317152

Trust plays an essential role in an individual's decision-making. Traditional trust prediction models rely on pairwise correlations to infer potential relationships between users. However, in the real world, interactions between users are usually complicated rather than pairwise only. Hypergraphs offer a flexible approach to modeling these complex high-order correlations (not just pairwise connections), since hypergraphs can leverage hyperedeges to link more than two nodes. However, most hypergraph-based methods are generic and cannot be well applied to the trust prediction task. In this paper, we propose an Adaptive Hypergraph Network for Trust Prediction (AHNTP), a novel approach that improves trust prediction accuracy by using higher-order correlations. AHNTP utilizes Motif-based PageRank to capture high-order social influence information. In addition, it constructs hypergroups from both node-level and structure-level attributes to incorporate complex correlation information. Furthermore, AHNTP leverages adaptive hypergraph Graph Convolutional Network (GCN) layers and multilayer perceptrons (MLPs) to generate comprehensive user embeddings, facilitating trust relationship prediction. To enhance model generalization and robustness, we introduce a novel supervised contrastive learning loss for optimization. Extensive experiments demonstrate the superiority of our model over the state-of-the-art approaches in terms of trust prediction accuracy. © 2024 IEEE.

关键词： Multilayer neural networks

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QCVNET - Leveraging Quantum Technology in Convolutional Neural Networks for Improved Cardiovascular Disease Prediction 5

QCVNET - Leveraging Quantum Technology in Convolutional Neur...

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5th International Conference on Circuits, Control, Communication and Computing, I4C 2024

作者： Karthigeyan, S. Diviya, M. Bhuvaneswari, R. Sabhanayagam, T. Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Chennai India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India School of Computer Science and Engineering Galgotias University UP Greater Noida India

ISBN: (纸本)9798331528539

The primary issue of global health today is Cardio-vascular diseases (CVDs) and thus requires accurate predictive models for detecting it early. Quantum technology combined with regular deep learning techniques are used by researchers here to improve the accuracy of forecast on heart diseases. While using an expanded dataset with clinical and demographic records, an innovative approach was found to use Quantum Convolutional Neural Network (QCVNET) structure that merged quantum layers into classical CNN framework with Principal Component Analysis (PCA) that allowed for consistency of features with respect to the quantum bits' number. The performance of the QCVNET was evaluated by comparing it to Quantum LSTM, Quantum DNN, Quantum GRU, and Quantum RNN, which are other quantum models enhanced with conventional neural networks, and other traditional non-quantum models like SVM, RF, RNN and DNN, using different indicators of quality like f1-score or recall. In early CVD prediction, the QCVNET model produced an impressive accuracy of 91.73%, beating all other models, showcasing the potential impact of quantum-enhanced deep learning for timely diagnosis and personalized treatment strategies while providing means to reduce the global burden of cardiovascular diseases. © 2024 IEEE.

关键词： Convolutional neural networks

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Threats to medical diagnosis systems: analyzing targeted adversarial attacks in deep learning-based COVID-19 diagnosis

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Soft Computing 2025年第3期29卷 1879-1896页

作者： Haque, Sheikh Burhan Ul Zafar, Aasim Haq, Sheikh Riyaz Ul Haque, Sheikh Moeen Ul Ahmad, Mohassin Roshan, Khushnaseeb School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Greater Noida India Department of Computer Science Aligarh Muslim University Uttar Pradesh Aligarh202002 India Department of Computer Science Engineering Guru Nanak Institution Ibrahimpatnam Telangana Hyderabad501506 India Department of Electronics and Communication Guru Nanak Institution Ibrahimpatnam Telangana Hyderabad501506 India

Deep and machine learning models have become pivotal in medical image analysis, especially for diagnosing COVID-19 using X-rays and CT scans. While these models, including transfer learning-based approaches, have achieved high accuracy, they remain highly vulnerable to adversarial attacks, which can manipulate input data and cause misclassification, posing critical risks in clinical applications. This study introduces a novel approach to addressing this issue by systematically evaluating the impact of adversarial attacks on COVID-19 diagnosis models built with two leading architectures, VGG-16 and DenseNet-121, using the Fast Gradient Sign Method (FGSM). The FGSM attack causes a dramatic drop in accuracy, reducing VGG-16’s accuracy from 95.12 to 9.97% and DenseNet-121’s from 96.51 to 10.13%. To counter these vulnerabilities, we propose a novel defense mechanism that combines adversarial training with Gaussian noise data augmentation, a dynamic approach that generates perturbations across various epsilon values during the training phase. This innovative method significantly enhances model robustness, restoring accuracy to over 92% on adversarial examples. These findings emphasize the need for strong defense mechanisms in deep learning models for COVID-19 diagnosis, ensuring reliability and security against adversarial threats in clinical environments. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Adversarial machine learning

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Integrating Brainwave Analysis and Facial Detection for Enhanced Security in Transactional Systems 8

Integrating Brainwave Analysis and Facial Detection for Enha...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Talasila, Smeeth Vadlamudi, Gnana Abhinay Vadlamudi, Anvitha School of Computer Science and Engineering Plano West Senior High School TX United States Department of Computer Science and Engineering New York United States School of Computer Science and Engineering Vit Ap University Amaravati India

ISBN: (纸本)9798331505462

This paper proposes an innovative user authentication system tailored for high-value asset transactions, leveraging advancements in brainwave analysis and emotional state detection. Traditional authentication methods are increasingly vulnerable to sophisticated attacks, necessitating novel approaches to bolster security in transactional systems. Our research explores the untapped potential of Electroencephalography (EEG) data to establish a unique user identifier based on distinctive brainwave patterns, including alpha, beta, gamma, delta, and theta waves. We identify specific patterns associated with the user's relaxed state through rigorous analysis, presenting a robust and bypass-resistant authentication factor. Furthermore, our system integrates facial expression recognition technology to detect negative emotional states, such as stress or panic, during transactional interactions. By combining brainwave analysis with facial recognition, our multi-layered approach enhances security by considering both physiological and emotional indicators. If the brainwave analysis identifies a relaxed state while facial recognition detects negative emotions, the system identifies potential coercion attempts and restricts transaction completion, thus safeguarding against fraudulent activities. © 2024 IEEE.

关键词： Face recognition

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Weighted Average Ensembled Probability Pipeline of Pre-trained CNN Models for Hand Gesture Classification 15

Weighted Average Ensembled Probability Pipeline of Pre-train...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Gunti, Rahul Rohan Reddy, B. Swathi, Gunti Sharath Kumar Reddy, G. Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Chennai601103 India Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798350370249

The automatic detection of sign language from hand gesture images is fundamental for effective human-computer interaction, especially for individuals with hearing and speech disorders. Achieving accurate detection and classification of sign language gestures is paramount. However, accurately recognizing these gestures frequently presents challenges due to dull backgrounds, variations in gesture orientation, and fluctuations in lighting conditions. Previous approaches tried to overcome these challenges but still the model performance lacks recognition accuracy and visual quality. To address this need for accuracy, we proposed a Weighted Average Ensembled Probability Pipeline composed of EfficientNetB7, ResNet-50, and Xception models. Leveraging the Extensive Hand Gesture Recognition Database (HGRD), comprising 20,000 images, our individual models achieved notable accuracies: 96.498 % for EfficientNetB7, 93.2 % for ResNet-50, and 90.175 % for Xception. Through ensemble learning, we integrate these models to attain an impressive overall accuracy of 98.95 %. This research contributes to robust gesture recognition system and underscores the significance of ensemble techniques in enhancing performance for Gesture recognition systems. © 2024 IEEE.

关键词： Gesture recognition

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Blockchain Breakthrough: A Review of its Role in Modern Technology 2

Blockchain Breakthrough: A Review of its Role in Modern Tech...

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Asthana, Sameer Chaturvedi, Ravi Prakash Mishra, Annu Nayyar, Poorva Shaliyar, Md. Greater Noida Institute of Technology Department of Computer Science and Engineering Greater Noida India Sharda School of Engineering and Technology Department of Computer Science and Applications Greater Noida India Sharda School of Engineering and Technology Department of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9798350368109

This article summarizes the Blockchain technology along with the artificial intelligence. The cutting edge technology is described in a elaborated manner along with its advantages, disadvantages and its impact with the artificial intelligence. There are various sectors in which it is used and the outcomes are discussed. The proficiency of blockchain has proved its reliability and threshold in different domains. © 2024 IEEE.

关键词： artificial intelligence blockchain ledger security

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A Software Defect Prediction Method Using a Multivariate Heterogeneous Hybrid Deep Learning Algorithm

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computers, Materials & Continua 2025年第2期82卷 3251-3279页

作者： Qi Fei Haojun Hu Guisheng Yin Zhian Sun College of Computer Science and Technology Harbin Engineering UniversityHarbin150001China Information Technology Research Department Jiangsu Automation Research InstituteLianyungang222062China School of Computer Science and Technology Harbin Institute of TechnologyShenzhen518055China

Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy and generalization performance of prediction results. To address this issue, this study proposes a multivariate heterogeneous hybrid deep learning algorithm for defect prediction (DP-MHHDL). Initially, Abstract Syntax Tree (AST), Code Dependency Network (CDN), and code static quality metrics are extracted from source code files and used as inputs to ensure data diversity. Subsequently, for the three types of heterogeneous data, the study employs a graph convolutional network optimization model based on adjacency and spatial topologies, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) hybrid neural network model, and a TabNet model to extract data features. These features are then concatenated and processed through a fully connected neural network for defect prediction. Finally, the proposed framework is evaluated using ten promise defect repository projects, and performance is assessed with three metrics: F1, Area under the curve (AUC), and Matthews correlation coefficient (MCC). The experimental results demonstrate that the proposed algorithm outperforms existing methods, offering a novel solution for software defect prediction.

关键词： Software defect prediction multiple heterogeneous data graph convolutional network models based on adjacency and spatial topologies CNN-BiLSTM TabNet

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