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18th International Conference on Intelligent Systems and Knowledge engineering, ISKE 2023

作者： Xu, Lixian Su, Rui Ali, Aftab Liu, Jun Liu, Xinfeng Yu, Dehu School of Computing Ulster University Northern Ireland United Kingdom School of Computer Science and Technology Shandong Jianzhu University Jinan China School of Computer Shandong Institute of Commerce and Technology Jinan China School of Civil Engineering Shandong Jianzhu University Jinan China

ISBN: (纸本)9798350318401

The failure of a sucker pump in the sucker rod pumping system significantly affects the process of oil production process. To address this issue, this paper proposes a machine learning diagnosis model based on the numerical information fusion of electrical parameters and indicator diagram. The study evaluated the performance of XGBoost and Random Forest models trained on the datasets, with the multi-order constraint conditions set, and the parameters optimized via Adaptive Genetic Algorithm. The experimental results demonstrate that the proposed method achieves accurate and efficient diagnosis of working conditions in pumping oil wells, which significantly improves the working efficiency of pumping oil wells. © 2023 IEEE.

关键词： Failure analysis

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Multi-stage generative adversarial network model for segmenting retinal vascular structures in eye disease prediction

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Journal of medical engineering & technology 2025年 1-26页

作者： Roshan S Bhanuse Ganesh Yenurkar Kavita R Singh Sandip Mal Sulakshana B Mane Rahul Kachhwah Neeraj Rajbhar Saksham Take Tejas Thakre Department of Computer Technology Yeshwantrao Chavan College of Engineering Nagpur Maharashtra India. School of Computing Science and Engineering VIT Bhopal University Bhopal India. Department of Computer Engineering Bharati Vidyapeeth College of Engineering Navi Mumbai Maharashtra IndiaMumbai.

Retinal vessel segmentation is essential for precise ophthalmological diagnoses, particularly in the prediction of retinal degenerative diseases. However, existing methods usually lack robustness and accuracy, especially in segmentation of thin or overlapping vessels. To face these challenges, this study introduces an enhanced retina-RV-Gain segmentation model, which employs an architecture of various stages to refine the results of segmentation iteratively. The model integrates attention mechanisms to better capture complex vessel structures and employs an adaptive loss function to manage class imbalance. In addition, a specially designed discriminator enhances the model's ability to distinguish fine details from background noise vessels. The proposed RV-Gan is trained in comprehensive data sets that comprise retinal images, segmentation masks and noted labels, including Stare-DB, Chase-DB1 and Drive, using the Python platform. Experimental results demonstrate a segmentation accuracy of up to 99% in normal, abnormal and base vessels. These findings highlight the potential of the model to significantly improve diagnostic accuracy and support early prediction of disease in clinical ophthalmology. Overall, the enhanced RV-Gan architecture offers a robust solution to the limitations of current approaches, providing segmentation of high fidelity retinal vessels and advancing the predictive analysis of retinal degenerative conditions.

关键词： Retinal vessel segmentation generative adversarial networks ophthalmology segmentation

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Diabetes Prediction Model for Better Clarification by using Machine Learning 6

Diabetes Prediction Model for Better Clarification by using ...

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Eben, J. Lysa Jayasudha, R. Ramya, S. Kaliappan, S. Aswal, Shobha Al-Salehi, Khalid Ali Salem Department of Computer Applications School of Computing Sciences Vels Institute of Science Technology and Advanced Studies Tamilnadu Chennai India Department of Mathematics Dr.N.G.P.Institute of Technology Coimbatore India Department of Computer Science and Engineering Kongu Engineering College Tamilnadu Erode India Division of Research and Development Lovely Professional University Punjab Phagwara144411 India School of Computing Graphic Era Hill University Dehradun India Biomedical Engineering Study Program Faculty of Science and Technology Airlangga University Surabaya Indonesia

ISBN: (纸本)9798350398496

Diabetes mellitus is one of the most pressing health concerns because so many people are afflicted by its disabling symptoms. Factors such as age, excess body fat, insufficient physical activity, a history of diabetes in one's family, a sedentary lifestyle, an unhealthy diet, hypertension, etc., all increase the likelihood of developing diabetes mellitus. Health complications are more common in people with diabetes, including cardiovascular disease, renal failure, stroke, blindness, and nerve injury. To validate a diagnosis of diabetes, hospitals typically perform a battery of procedures on the patient. Big data analytics has many vital applications in the healthcare sector. Numerous large computer systems are used in the healthcare sector. With the help of big data analytics, researchers can sift through mountains of data in search of previously unseen patterns and insights. Current techniques have a poor degree of precision in classification and forecast. While previous research has focused on factors such as glucose, body mass index, age, insulin, etc., the proposed model takes these into account and also the other factors that may be more relevant to the development of diabetes. The newer sample is superior to the older one based on categorization accuracy. A workflow algorithm for diabetes prognosis is also required to improve the accuracy. © 2023 IEEE.

关键词： Forecasting

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A Survey of Applications of Vision Transformer and its Variants

A Survey of Applications of Vision Transformer and its Varia...

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Intelligent Data and Security (IDS), IEEE International Conference on

作者： Chuang Wu Tingqin He Hunan Key Laboratory for Service computing and Novel Software Technology School of Computer Science and Engineering Hunan University of Science and Technology

ISBN: (数字)9798350389487

ISBN: (纸本)9798350389494

The Transformer architecture, renowned for its efficacy in natural language processing, encounters unique hur-dles when applied to computer vision. In response, the Vision Transformer (Vi $T$ ) emerges as a successful adaptation for image classification tasks. While ViT exhibits tremendous potential in revolutionizing computer vision, addressing its inherent chal-lenges and limitations stands as a critical endeavor. This compre-hensive survey meticulously scrutinizes the drawbacks associated with ViT, proposing bespoke adaptations tailored to specific applications while showcasing their remarkable performance across diverse visual tasks. Moreover, it delves into the evolution of ViT adaptations across various visual domains, elucidating four promising directions for future research and development in this dynamic field.

关键词： Surveys computer vision Visualization Computational modeling Transformers Real-time systems Data models

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A Hybrid Model Combined with SVM and CNN for Community Content Classification 23

A Hybrid Model Combined with SVM and CNN for Community Conte...

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23rd IEEE International Conference on High Performance computing and Communications, 7th IEEE International Conference on Data science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Ye, Yukui Xie, Xia Jin, Hai Wang, Duoqiang National Engineering Research Center for Big Data Technology and System School of Computer Science and Technology Huazhong University of Science and Technology Services Computing Technology and System Lab Cluster and Grid Computing Lab Wuhan China School of Computer Science and Technology Hainan University Haikou China

ISBN: (纸本)9781665494571

Community websites bring many conveniences to people, and the classification of community content is playing an important role in website management and information searching. As the carrier of community content, posts are difficult to classify manually. According to the characteristics of community content, a hybrid classification model for machine learning is proposed. This model consists of three steps. Firstly, aiming at the problem of fewer features of posts, a weighted word vector is proposed to enrich the features of posts. Secondly, since the single kernel function of SVM can not completely match all data distributions, a mixed kernel function is employed to improve the model. Finally, in order to fully utilize the powerful feature extraction ability of Convolutional Neural Network as well as the classification ability of SVM, a hybrid model is designed and implemented by replacing softmax layer with SVM classifier. The corresponding experiment results indicate that compared with traditional Convolutional Neural Network, the proposed hybrid model has better performance and stability with classification accuracy improved from 0.9% to 1.4% in general. © 2021 IEEE.

关键词： Support vector machines Smart cities Support vector machine classification Machine learning Feature extraction Stability analysis Data models

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IoT - based Weighted K-means Clustering with Decision Tree for Sedentary Behavior Analysis in Smart Healthcare Industry 2

IoT - based Weighted K-means Clustering with Decision Tree f...

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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Gudivaka, Rajya Lakshmi Alabdeli, Haider Sunil Kumar, V. Sushama, C. Muthu, BalaAnand Wipro Andhra Pradesh India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute Of Technology Department Of Aiml Bengaluru India Mohan Babu University School Of Computing Dept. Of Cse Tirupati India Tagore Institute Of Engineering And Technology Department Of Computer Science And Engineering Tamil Nadu India

ISBN: (纸本)9798350381665

Sedentary lifestyle has become a very prevalent feature of the modern living brought in different metabolic and cardiovascular problems. A high level of inactivity accompanied by physically unhealthy habits like drinking alcohol, smoking and bad diets may cause some deadly ailments in addition. Thus, it is necessary to identify health, behavior, and environmental anomalies to know the degree of health problems in people suffering from sedentary behavior. In particular, this study distinguishes itself from others with the proposed e-healthcare framework which involves merits of the Internet of Things (IoT) and fog technology for detecting health, behavioral, physical posture and environmental abnormalities of the individual. The 1st stage of data processing steps, weighted K-Mean clustering, is able to identify the faults of fog layer which will assist the network to keep the sensitivity of healthcare enterprise. In addition, the weighted k-mean clustering technique- decision tree (WKMC-DT) assisted hybrid methodology is recommended on the cloud layer for early health severity prediction. Using a multi-tiered decision process besides that will be an added advantage for better targeting of this resource allocation. Performance of irregularity determination will be calculated by tracking 15 individuals in the age group of 32-45 years continuously for 30 days. It can be done with the help of a framework deployed in an indoor laboratory. The experiment outcomes of the framework show that the method is effective to identify the health inconsistencies because it has 98.43% of accuracy, 94.56 % of sensitivity, 96.75% of specificity 96.58%, precision rate and 97.38% of f-measure. © 2024 IEEE.

关键词： Medical applications

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Key Management and Authentication Scheme Forward Trusted Pervasive Container computing

Key Management and Authentication Scheme Forward Trusted Per...

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Big Data and Privacy computing (BDPC), International Conference on

作者： XinHong Hei YuZhan Xue YiChuan Wang YiFan Liu Chen Peng Xiang Sun School of computer science and engineering Xi'an University of Technology Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technology School of computer science and engineering Xi'an University of Technology Xi’an China

ISBN: (数字)9798350307597

ISBN: (纸本)9798350307603

Cloud computing technology is an internet-based computing model that provides high reliability and security measures, including data backup, secure access control, etc. As Docker containers play a increasingly important role in traditional cloud computing architectures, the communication needs between containers become prominent. However, existing container to container communication mainly relies on network protocols for file transfer, making it difficult to achieve the expected results in cloud cluster security. Therefore, this paper primarily focuses on container based keys and proposes a container identity trusted key management method based on Pervasive container computing architecture. The experimental results demonstrate that the container identity trusted key management method not only meets the security transmission characteristics required in the container cloud architecture, but also adopts a strategy combined with trusted hardware, providing security guarantees. In addition, the method proposed in this article that combines trusted hardware for signature verification can defend against multiple network attacks compared to traditional container architectures without using security policies.

关键词： computers Cloud computing Handwriting recognition Data privacy Protocols computer architecture Containers

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Advanced Persistent Threats (APT): evolution, anatomy, attribution and countermeasures

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Journal of Ambient Intelligence and Humanized computing 2023年第7期14卷 9355-9381页

作者： Sharma, Amit Gupta, Brij B. Singh, Awadhesh Kumar Saraswat, V.K. Department of Computer Engineering National Institute of Technology Kurukshetra Kurukshetra136119 India International Center for AI and Cyber Security Research and Innovations Department of Computer Science and Information Engineering Asia University Taichung Taiwan NITI Aayog New Delhi110001 India Lebanese American University Beirut Lebanon School of Computing Skyline University College Sharjah United Arab Emirates Department of Computer Engineering National Institute of Technology Kurukshetra Kurukshetra India Birkbeck University of London London United Kingdom

In today’s cyber warfare realm, every stakeholder in cyberspace is becoming more potent by developing advanced cyber weapons. They have equipped with the most advanced malware and maintain a hidden attribution. The precocious cyber weapons, targeted and motivated with some specific intention are called as Advanced Persistent Threats (APT). Developing defense mechanisms and performing attribution analysis of such advanced attacks are extremely difficult due to the intricate design of attack vector and sophisticated malware employed with high stealth and evasive techniques. These attacks also include advanced zero-day and negative-day exploits and payloads. This paper provides a comprehensive survey on the evolution of advanced malware design paradigms, APT attack vector and its anatomy, APT attack Tactics, Techniques, and Procedures (TTP) and specific case studies on open-ended APT attacks. The survey covers a detailed discussion on APT attack phases and comparative study on threat life-cycle specification by various organizations. This work also addresses the APT attack attribution and countermeasures against these attacks from classical signature and heuristic based detection to modern machine learning and genetics based detection mechanisms along with sophisticated zero-day and negative day malware countermeasure by various techniques like monitoring of network traffic and DNS logs, moving target based defense, and attack graph based defenses. Furthermore, the survey addresses various research scopes in the domain of APT cyber-attacks. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Life cycle

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Hybrid Parallel Inference for Large Model on Heterogeneous Clusters for High Throughput 29

Hybrid Parallel Inference for Large Model on Heterogeneous C...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Xu, Chaonong Kong, Weiming Liu, Min Zhang, Mingming Li, Chao Gong, Luqi Beijing Key Lab of Petroleum Data Mining Beijing China Institute of Intelligent Computing Zhejiang Lab Hangzhou China Bupt School of Computer Science National Pilot Software Engineering School Beijing China

ISBN: (纸本)9798350330717

In high-throughput intelligent computing scenarios, multi-device parallelism strategies based on data parallelism or pipeline parallelism have been extensively utilized to accelerate large deep neural network model inference. Data parallelism offers nearly linear improvement in inference speed, but it is limited by the memory capacity of a single device which constrains the model size. On the other hand, pipeline parallelism can support larger models, but the total communication of the activations among devices is high, which limits the improvement of the inference speed. To address the demand for efficient model inference in high-throughput heterogeneous scenarios, we proposes a hybrid parallelism strategy that combines data parallelism and pipeline parallelism. The strategy involves grouping heterogeneous device clusters and then employing inter-group data parallelism along with intra-group pipeline parallelism. Moreover, we propose an algorithm to find an optimal hybrid parallel inference strategy with maximum throughput. The control variables of the strategy includes the number of groups, group-device assignments and model partition ratios. Our experimental evaluation demonstrates that compared to PipeEdge, a pipeline parallel inference framework for heterogeneous cluster, our strategy can achieve 1.7× -3.4× acceleration in an 8-device heterogeneous cluster without loss of accuracy. © 2023 IEEE.

关键词： data parallelism heterogeneous cluster hybrid parallelism model inference pipeline parallelism

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Evaluating Datagram Congestion Control Protocol (DCCP) for Real-Time Applications: A Comparative Study with TCP in Multi-Node Networks 10

Evaluating Datagram Congestion Control Protocol (DCCP) for R...

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10th International Conference on engineering and Emerging Technologies, ICEET 2024

作者： Hassan, Aqib Memon, Vengas Shaikh, Farhan Bashir Alahmari, Saad Iqbal, Mansoor Azam, Farooq School of Computing and Mathematical Sciences University of Greenwich London United Kingdom Universiti Tunku Faculty of Information and Communication Tech. Abdul Rahman Perak Malaysia Northern Border University Department of Computer Science Arar Saudi Arabia University of Wah Department of Computer Science Wah Cantt Pakistan Nust Dept. of Computer Software Engineering Islamabad Pakistan

ISBN: (纸本)9798331532895

The rise in demand for real-time applications, such as live streaming, online gaming, and Internet telephony, has highlighted the necessity for transport protocols that offer low latency and network stability. Traditional protocols, namely Transmission Control Protocol (TCP) and User Datagram Protocol (UDP), have inherent limitations. TCP ensures reliable data delivery but suffers from latency, making it unsuitable for real-time needs, while UDP provides fast data transmission but lacks congestion control, which can lead to network instability. This gap underscores the need for a protocol that combines high-speed data transfer with effective congestion management. To address this issue, the Internet engineering Task Force (IETF) introduced the Datagram Congestion Control Protocol (DCCP), designed to optimize data delivery speed while managing congestion. This study evaluates DCCP's performance against TCP in network scenarios of 8 and 16 nodes using the NS2 simulation environment. By comparing throughput and delay across various bandwidth and latency settings, the study finds that DCCP offers up to 35% higher throughput in 8-node setups and reduces packet loss by 20% in high-bandwidth scenarios. These results suggest that DCCP is well-suited for real-time applications, achieving a crucial balance between fast data transmission and network stability. © 2024 IEEE.

关键词： Congestion control (communication)

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