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7th International Conference on Computing Methodologies and Communication, ICCMC 2023

作者： Lekha, D. Ahsan Shariff, M. Lakshmi, S. Venkata Anwar Basha, H. Department of Computer Science and Engineering RMK College of Engineering and Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Aalim Muhammed Salegh College of Engineering Tamil Nadu Chennai India Artificial Intelligence and Data Science Sri Krishna College of Engineering and Technology Tamil Nadu Coimbatore India School of Computer Science and Engineering REVA University Karnataka Bengaluru India

ISBN: (纸本)9781665464086

Wireless Sensor Network (WSN) suffers from different malware attacks. Several traditional approaches are proposed for detecting fault nodes in WSN. It is necessary to redistribute the nodes and detect the fault nodes while modifying the WSN parameters. Faults can occur in the nodes interrupting the continuous communication process of the nodes in the WSN. The main reason for a fault is also the induction of the energy drain in a node to the maximum possible level, failure of links in communicating nodes due to bandwidth constraints, and attacks induced by the malicious nodes. To solve this problems, Extended Finite State Machine based Fault Tolerance (EFSM_FT) in the WSN is used to detect the fault sensor nodes in the WSN. In this approach, called the finite state machine (FSM), state prompted is modified while a set of situations are true. Every node can be absolutely to be faulty or not establishing on the sensor node states. Using sensor energy, drop rate, forward sensor rate, sensor obtained rate, bandwidth, and transmission delay, this method finds the node with the fault. As a result, this approach accurately detects the faulty nodes in the WSN. The simulation outcomes illustrate the proposed method has a better fault detection ratio and minimizes the false negative ratio in the WSN. © 2023 IEEE.

关键词： Finite automata

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A disk I/O optimized system for concurrent graph processing jobs

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Frontiers of computer science 2024年第3期18卷 13-29页

作者： Xianghao XU Fang WANG Hong JIANG Yongli CHENG Dan FENG Peng FANG School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and TechnologyWuhan 430074China Department of Computer Science&Engineering University of Texas at ArlingtonArlingtonTX 76019USA College of Computer and Data Science Fuzhou UniversityFuzhou 350108China Zhejiang Lab Hangzhou 311121China

In order to analyze and process the large graphs with high cost efficiency,researchers have developed a number of out-of-core graph processing systems in recent years based on just one commodity *** the other hand,with the rapidly growing need of analyzing graphs in the real-world,graph processing systems have to efficiently handle massive concurrent graph processing(CGP)***,due to the inherent design for single graph processing job,existing out-of-core graph processing systems usually incur unnecessary data accesses and severe competition of I/O bandwidth when handling the CGP *** this paper,we propose GraphCP,a disk I/O optimized out-of-core graph processing system that efficiently supports the processing of CGP *** proposes a benefit-aware sharing execution model to share the I/O access and processing of graph data among the CGP jobs and adaptively schedule the graph data loading based on the states of vertices,which efficiently overcomes above challenges faced by existing out-of-core graph processing ***,GraphCP adopts a dependency-based future-vertex updating model so as to reduce disk I/Os in the future *** addition,GraphCP organizes the graph data with a Source-Sorted Sub-Block graph representation for better processing capacity and I/O access *** evaluation results show that GraphCP is 20.5×and 8.9×faster than two out-of-core graph processing systems GridGraph and GraphZ,and 3.5×and 1.7×faster than two state-of-art concurrent graph processing systems Seraph and GraphSO.

关键词： graph processing disk I/O concurrent jobs

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An improved branching algorithm for the proper interval edge deletion problem

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Frontiers of computer science 2022年第2期16卷 178-180页

作者： Wenjun LI Xiaojing TANG Yongjie YANG School of Computer and Communication Engineering Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on TransportationChangsha University of Science and TechnologyChangsha 410015China School of Computer Science and Engineering Central South UniversityChangsha 410083China

1 Introduction For a graph class G,the G-EDGE DELETION problem is to determine whether a given graph can be transformed into a graph in G by deleting at most k *** G-EDGE DELETION problem for a large body of graph cla... 详细信息

关键词： branching problem transformed

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Sustainable Diffusion-Based Incentive Mechanism for Generative AI-Driven Digital Twins in Industrial Cyber-Physical Systems

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 139-149页

作者： Wen, Jinbo Kang, Jiawen Niyato, Dusit Zhang, Yang Mao, Shiwen Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing210016 China Guangdong University of Technology School of Automation Guangzhou510006 China Nanyang Technological University College of Computing and Data Science Singapore639798 Singapore Auburn University Department of Electrical and Computer Engineering AuburnAL36849 United States

Industrial Cyber-Physical Systems (ICPSs) are an integral component of modern manufacturing and industries. By digitizing data throughout product life cycles, Digital Twins (DTs) in ICPSs enable a shift from current industrial infrastructures to intelligent and adaptive infrastructures. Thanks to data process capability, Generative Artificial Intelligence (GenAI) can drive the construction and update of DTs to improve predictive accuracy and prepare for diverse smart manufacturing. However, mechanisms that leverage Industrial Internet of Things (IIoT) devices to share sensing data for DT construction are susceptible to adverse selection problems. In this paper, we first develop a GenAI-driven DT architecture in ICPSs. To address the adverse selection problem caused by information asymmetry, we propose a contract theory model and develop a sustainable diffusion-based soft actor-critic algorithm to identify the optimal feasible contract. Specifically, we leverage dynamic structured pruning techniques to reduce parameter numbers of actor networks, allowing sustainability and efficient implementation of the proposed algorithm. Numerical results demonstrate the effectiveness of the proposed scheme and the algorithm, enabling efficient DT construction and updates to monitor and manage ICPSs. © 2023 IEEE.

关键词： Smart manufacturing

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Revolutionizing COVID-19 Diagnosis with Radiographic Images and Deep Learning: A Paradigm Shift in Healthcare

Revolutionizing COVID-19 Diagnosis with Radiographic Images ...

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2023 IEEE International Conference on Next Generation Electronics, NEleX 2023

作者： Mohanaprakash, T.A. Saranya, S. Abinaya, M. Rajheshwari, K.C. Elangovan, D. Gobinath, M. Panimalar Engineeering College Department of Computer Science and Engineering Chennai India Rajalakshmi Institute of Technology Department of Artificial Intelligence and Data Science Chennai India Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India Sona College of Technology Department of Computer Science and Engineering TamilNadu Salem India Sri Manakula Vinayagar Engineering College Departement of Computer and Communication Engineering Puducherry India

ISBN: (纸本)9798350319088

Amid the relentless battle against the COVID-19 pandemic, a cadre of researchers and scientists is diligently crafting innovative tools to expedite diagnosis and treatment. One standout innovation is the integration of radiographic images and deep convolutional neuronal networks (CNN), offering deeper insights into COVID-19. Early, accurate diagnosis is paramount in managing the COVID-19 crisis. It not only facilitates timely treatment but also curbs the virus's spread. The amalgamation of radiographic images and advanced machine learning techniques emerges as a transformative force. Traditional COVID-19 diagnostics rely heavily on molecular tests, like Polymerase Chain Reaction (PCR) tests. While highly accurate, these tests often involve protracted processing times and substantial resource demands. They may also produce false negatives in early infections. Radiographic imaging, especially chest X-rays, addresses these limitations. Researchers have harnessed deep convolutional neuronal networks, specialized for image analysis, to create an innovative diagnostic platform. This platform meticulously scrutinizes chest X-ray images, distinguishing COVID-19 pneumonia from other forms with exceptional precision. The deep CNN model undergoes intensive training on a diverse dataset, including chest X-rays from both COVID-19-positive and COVID-19-negative patients. It employs intricate processes of feature extraction and pattern recognition, discerning subtle nuances indicative of COVID-19-related lung abnormalities. This proficiency empowers the model to categorize new chest X-ray images, assisting radiologists in decision-making. The merits of this groundbreaking approach are multifaceted. Foremost is the substantial reduction in diagnosis time. Unlike molecular tests, which can take hours, chest X-ray analysis yields results within minutes, facilitating swift patient triage. It holds particular promise in resource-constrained settings where molecular testing may be scarc

关键词： COVID-19

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FedDGA: Federated Multitask Learning Based on Dynamic Guided Attention

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第2期6卷 268-280页

作者： Sun, Haoyun Zhao, Hongwei Xu, Liang Zhang, Weishan Guan, Hongqing Yang, Su China University of Petroleum Qingdao266580 China Shandong Data Open Innovative Application Laboratory Qingdao266580 China WINDAKA Technology Company Ltd. Qingdao266580 China Beijing University of Science and Technology Beijing100083 China Swansea University Department of Computer Science Faculty of Science and Engineering SwanseaSA2 8PP United Kingdom

The proliferation of privacy-sensitive data has spurred the development of federated learning (FL), which is an important technology for state-of-the-art machine learning and responsible AI. However, most existing FL methods are constrained in their applicability and generalizability due to their narrow focus on specific tasks. This article presents a novel federated multitask learning (FMTL) framework that is capable of acquiring knowledge across multiple tasks. To address the challenges posed by non-IID data and task imbalance in FMTL, this study proposes a federated fusion strategy based on dynamic guided attention (FedDGA), which adaptively fine-tunes local models for multiple tasks with personalized attention. In addition, this article designed dynamic batch weight (DBW) to balance the task losses and improve the convergence speed. Extensive experiments were conducted on various datasets, tasks, and settings, and the proposed method was compared with state-of-the-art methods such as FedAvg, FedProx, and SCAFFOLD. The results show that our method achieves significant performance gains, with up to 11.1% increase in accuracy over the baselines. © 2020 IEEE.

关键词： Scaffolds

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Exploring nonintrusive measurements of spatio-temporal portrait of microservices

Exploring nonintrusive measurements of spatio-temporal portr...

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作者： Zeng, Tao Xu, Zichen Wu, Dan Li, Xiaoling Liu, Biyong Hu, Haichuan Tan, Shuang Tan, Yusong Xu, Chenren Stewart, Christopher Zhou, Qihe Cao, Ye School of Mathematics and Computer Sciences Nanchang University Nanchang China College of Computer National University of Defense Technology Hunan China School of Computer Science Peking University Beijing China Computer Science and Engineering The Ohio State University ColumbusOH United States Faculty of Data Science City University of Macau China

As cloud native technology advances, the scale and complexity of applications built on microservice architecture continue to expand, leading to increasingly intricate differences between software within the same application. Microservice applications, offering high flexibility, are deployed in data centers as black boxes from the users' perspective, leaving them with no insight into the orchestration of cloud service providers. Consequently, users face challenges in promptly recognizing performance imbalances within their deployed applications. Meanwhile, cloud service providers may cut costs by offering a mix of qualified and unqualified services, potentially deceiving users. To enhance the understanding of microservice application organization, we propose a non-intrusive measurement framework, termed NMPI. NMPI facilitates rapid identification of microservice application defects, offering insights into cloud services and detecting fraudulent behavior in microservice-based applications. We model microservice applications using a queue analysis-based approach and filter the dominant frequency components of average response time signals by employing k-means on the fast fourier transform (FFT). Our model constructs a library of performance portraits for various software, with these portraits resembling human fingerprints that carry and mark the software's internal information. Utilizing a two-tier microservices-based application incorporating a database as a case study allows us to demonstrate the effectiveness of NMPI. Our experimental results show that NMPI can produce differentiable profiles of data service performance portraits across a diverse and extensive range of workloads, enabling the identification of software types and the analysis of performance conditions. © 2023 John Wiley & Sons, Ltd.

关键词： Fast Fourier transforms

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Emergency Care Patient Prediction using Electronic Health Records (EHR) data: An End-to-End Machine Learning Pipeline 10

Emergency Care Patient Prediction using Electronic Health Re...

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10th International Conference on Future Internet of Things and Cloud, FiCloud 2023

作者： Sumon, Maruful Abedin Kwemebe, Tor A. Melapu, Venkata K. Hasan, Md Mohiuddin Jackson State University Computational Data-Enabled Science and Engineering Jackson United States Jackson State University Department of Mathematics and Computer Science Jackson United States Jackson State University Computer Science and Engineering Jackson United States

ISBN: (纸本)9798350316353

Using electronic health records (EHR) data for predicting the condition of patients who are in need of emergency care is a promising application of machine learning. With the help of machine learning, complex problems of identification of patterns can be solved with ease and accuracy when proper machine learning model is implemented. This paper uses EHR data to build an end-to-end machine learning pipeline for predicting the outcome of emergency care patients. As a machine learning process, the pipeline includes data preprocessing, feature engineering, model selection, data set training and testing, evaluation, and deployment. By using EHR data in this pipeline, the model has been trained and tested on a large dataset of many patients information with different types of machine learning algorithms to improve and get the best prediction accuracy. This pipeline can aid in managing primary care patients efficiently and effectively with the implementation of best suitable machine learning algorithm at the time of urgency. By implementing the model on the EHR dataset, an accuracy of more than 75\% is obtained which indicates the efficiency of the machine learning process in the process of identifying patients who need the emergency care more than the general patients when taken to the primary care or emergency. © 2023 IEEE.

关键词： Forecasting

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Generative Multimodal data Augmentation for Low-Resource Multimodal Named Entity Recognition 24

Generative Multimodal Data Augmentation for Low-Resource Mul...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Li, Ziyan Yu, Jianfei Yang, Jia Wang, Wenya Yang, Li Xia, Rui School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China College of Computing and Data Science Nanyang Technological University Singapore Wee Kim Wee School of Communication and Information Nanyang Technological University Singapore

ISBN: (纸本)9798400706868

As an important task in multimodal information extraction, Multimodal Named Entity Recognition (MNER) has recently attracted considerable attention. One key challenge of MNER lies in the lack of sufficient fine-grained annotated data, especially in low-resource scenarios. Although data augmentation is a widely used technique to tackle the above issue, it is challenging to simultaneously generate synthetic text-image pairs and their corresponding high-quality entity annotations. In this work, we propose a novel Generative Multimodal data Augmentation (GMDA) framework for MNER, which contains two stages: Multimodal Text Generation and Multimodal Image Generation. Specifically, we first transform each annotated sentence into a linearized labeled sequence, and then train a Label-aware Multimodal Large Language Model (LMLLM) to generate the labeled sequence based on a label-aware prompt and its associated image. We further employ a Stable Diffusion model to generate the synthetic images that are semantically related to these sentences. Experimental results on three benchmark datasets demonstrate the effectiveness of the proposed GMDA framework, which consistently boosts the performance of several competitive methods for two subtasks of MNER in both full-supervision and low-resource settings. The low-resource dataset and source code are released at https://***/NUSTM/GMDA. © 2024 ACM.

关键词： data augmentation

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Performance analysis of various classification algorithms for providing competency training to workplace risk prevention

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Multimedia Tools and Applications 2025年第15期84卷 15123-15149页

作者： Garg, Shruti Murugan, Prabhu Manimaran, A. Sundar, Ramesh Dakshinamoorthy, Prabakar Rajaram, Gnanajeyaraman Birla Institute of Technology Ranchi Mesra India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai602105 India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai602105 India Department of Networking and Communication School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur India Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur India

In the workplace, risk prevention helps detect the risks and prevent accidents. To achieve this, workers' mental and physical parameters related to their health should be focused on and analyzed. It helps improve the workers' mental health and thus improves organizational performance. There are a lot of mental illnesses due to excess work and a lack of professional knowledge. It affects the performance of the worker. So, the organization needs to conduct competency training programs to improve their professional skills and the mindset to tackle the work pressure. There are several competency training programs given in various organizations to mold the workers, and it considerably reduces workplaces risks. Competency training in the workplace teaches integrity, honesty, managerial skills, and other workers. Developing organizational skills is the primary concern in this competency training. This paper enabled one of the best machine learning algorithms to be chosen to predict the hazards in the workplace and provide a healthy environment to the workers. For that, several classification methods such as Logistic Regression, Decision trees, AdaBoost classifier, Random Forest, Gradient Boosting classifier, and XGBoost classifier are implemented experimented with, and the performance is verified with python software. From the comparison, it has been found that the XGBoost classifier is selected as the best classifier and identified that it is highly suitable for analyzing and predicting workplace risks data. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Occupational risks

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