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2023 International Conference on computer application and Information Security, ICCAIS 2023

作者： Wang, Deguang Zhang, Ting Li, Fengqi Tong, Ning Xu, Fengqiang School of Software Dalian Jiaotong University Liaoning Dalian116028 China School of Computer Communication and Engineering Dalian Jiaotong University Liaoning Dalian116028 China The Dalian Key Laboratory of Blockchain Technology and Application Liaoning Dalian116028 China

ISBN: (纸本)9781510675032

Automobile insurance fraud causes huge economic losses to insurance companies, detection technology has become an urgent research topic. However, feature selection manually is prone to subjective judgment, which results in a decline in recognition accuracy. To settle this problem, a refined multi-channel Inception network for automobile insurance fraud identification based on an attention mechanism and dense connection architecture is proposed. Firstly, we redesign the backbone structure of inception network to strengthen feature extraction ability. Secondly, attention mechanism is fused to inception module, enabling network to pay attention to important features. Furthermore, a dense connection structure is introduced to Inception network, which strengthens the transmission of features. Finally, this paper carries out mass experiments on a real-world "***" dataset. Experimental results reveal that our proposed approach achieves impressive performance on automobile insurance fraud identification tasks and can reach 98.12% accuracy, 98.19% precision and 98.12% recall and 98.12% f1_score, respectively. © 2024 SPIE.

关键词： Feature extraction

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Enabling Clinical Decision-Making in Smart Healthcare through AI/ML Algorithms 15

Enabling Clinical Decision-Making in Smart Healthcare throug...

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

作者： Kharbas, Vikas Kumar Meena, Y.R. Thakur, Praveen Kumar Vivekananda Global University Department of Computer Science & Application Jaipur India School of Engineering and Technology Department of Civil Engineering Karnataka Bangalore India Arka Jain University Department of Law Jharkhand Jamshedpur India

ISBN: (纸本)9798350370249

Using AI/ML algorithms in clever healthcare has emerged as a promising improvement to facilitate green and effective medical choice-making. This novel era uses gadgets mastering and predictive analytics to extract deep insights from numerous assets of healthcare statistics, together with clinical statistics, development, imaging, and laboratory results. through leveraging huge datasets and effective algorithms, AI/ML-primarily based structures are capable of extra as it should be and suddenly become aware of correlations between elements that contribute to the development of diverse sicknesses. it can permit the implementation of more evidence-based interventions and customized treatments tailored to an individual-affected character's health profile. moreover, AI/ML algorithms can also robotize the scientific workflow, contemplating the automated detection and prognosis of sure illnesses and medical conditions. As a result, AI/ML structures can seriously lessen healthcare prices and enhance patient consequences. in the end, using AI/ML algorithms in smart healthcare can empower clinical vendors to make more informed decisions approximately the analysis and treatment of their patients. © 2024 IEEE.

关键词： Consisting Customized Healthcare Implementation Improvement Interventions Resources Structures

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Correlation Between 3D Magnetic Resonance Imaging and Clinical Outcomes in Traumatic Brain Injury 15

Correlation Between 3D Magnetic Resonance Imaging and Clinic...

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

作者： Srikantha, H. Joseph, Ginu Anie Yadav, Surendra School of Engineering and Technology Department of Civil Engineering Karnataka Bangalore India Arka Jain University Department of Nursing Jharkhand Jamshedpur India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350370249

This article identifies a correlation between the 3D Magnetic Resonance Imaging (MR) of patients with worrying brain harm (TBI) and the patient's medical results. The research team compares 3-D MR imaging outcomes with the patient's observe-up go-to facts, including neurologic assessments, Glasgow Coma Scale rating, and other parameters. The effects confirmed that patients who had a better degree of injury discovered from three-D MR scans had worse scientific effects in comparison to sufferers who had milder harm. This correlation became great for acute and subacute TBI patients. The authors endorse that 3-D MR scans can be used to predict the clinical results of TBI patients and can have the potential to inform treatment selections. © 2024 IEEE.

关键词： Article Correlation Glasgow Identifies Magnetic Resonance

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Research on Coding and Decoding Scheme for 5G Terminal Protocol Conformance Test Based on TTCN-3 6th

Research on Coding and Decoding Scheme for 5G Terminal Proto...

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6th International Conference on Innovative Computing and Communication, ICICC 2023

作者： Jingyao, Cao Yadav, Amit Khan, Asif Ansar, Sharmin School of Computer and Software Chengdu Neusoft University Chengdu China College of Engineering IT and Environment Charles Darwin University NT Australia Department of Computer Application Integral University Lucknow India Department of Biological Sciences and Bioengineering Indian Institute of Technology Kanpur India

ISBN: (纸本)9789819940707

Protocol conformance testing is an indispensable part of the marketization of commercial terminals. This paper elaborates and studies the architecture, test model, and design scheme of the 5G terminal protocol conformance testing system and proposes a 5G terminal protocol based on TTCN-3 Conformance test codec solution. The outcome of research shows that this solution has a positive role in promoting the industrialization of 5G. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： 5G mobile communication systems

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Research on Tensor Multi-Clustering Distributed Incremental Updating Method for Big Data

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computers, Materials & Continua 2024年第10期81卷 1409-1432页

作者： Hongjun Zhang Zeyu Zhang Yilong Ruan Hao Ye Peng Li Desheng Shi School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing210023China Ministry of Science and Technology Innovation China Communications Services Corporation LimitedBeijing100071China School of Artificial Intelligence The University of ManchesterManchesterM139PLUK Ministry of Technology Innovation China Telecom Artificial Intelligence Technology(Beijing)Corporation LimitedBeijing100032China Ministry of Science and Technology Innovation Zhongbo Information Technology Research Institute Corporation LimitedNanjing210012China Jiangsu Postal Big Data Technology and Application Engineering Research Center Nanjing University of Posts and TelecommunicationsNanjing210003China

The scale and complexity of big data are growing continuously,posing severe challenges to traditional data processing methods,especially in the field of clustering *** address this issue,this paper introduces a new method named Big Data Tensor Multi-Cluster Distributed Incremental Update(BDTMCDIncreUpdate),which combines distributed computing,storage technology,and incremental update techniques to provide an efficient and effective means for clustering ***,the original dataset is divided into multiple subblocks,and distributed computing resources are utilized to process the sub-blocks in parallel,enhancing ***,initial clustering is performed on each sub-block using tensor-based multi-clustering techniques to obtain preliminary *** new data arrives,incremental update technology is employed to update the core tensor and factor matrix,ensuring that the clustering model can adapt to changes in ***,by combining the updated core tensor and factor matrix with historical computational results,refined clustering results are obtained,achieving real-time adaptation to dynamic *** experimental simulation on the Aminer dataset,the BDTMCDIncreUpdate method has demonstrated outstanding performance in terms of accuracy(ACC)and normalized mutual information(NMI)metrics,achieving an accuracy rate of 90%and an NMI score of 0.85,which outperforms existing methods such as TClusInitUpdate and TKLClusUpdate in most ***,the BDTMCDIncreUpdate method offers an innovative solution to the field of big data analysis,integrating distributed computing,incremental updates,and tensor-based multi-clustering *** not only improves the efficiency and scalability in processing large-scale high-dimensional datasets but also has been validated for its effectiveness and accuracy through *** method shows great potential in real-world applications where dynamic data growth is common,and it is of significant imp

关键词： Tensor incremental update distributed clustering processing big data

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IntML-KNN: A Few-Shot Radio Frequency Fingerprint Identification Scheme for Lora Devices

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IEEE Signal Processing Letters 2025年 32卷 2259-2263页

作者： Cao, Xin Tan, Weijie Gao, Qiangqiang Hu, Zhilong Li, Chunguo Guizhou University College of Computer Science and Technology Guiyang550025 China Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610025 China Ministry of Industry and Information Technology Laboratory of Cryptograph Application Technology Innovation and Testing Verification Beijing100015 China Southeast University School of Information Science and Engineering Nanjing210096 China

Deep learning (DL) is widely used in radio frequency fingerprint identification (RFFI). However, in few-shot case, traditional DL-based RFFI need to construct auxiliary dataset to realize radio frequency fingerprint identification. To address this issue, we propose a few-shot RFFI (FS-RFFI) method based on interpolating metric learning and KNN (IntML-KNN). Specifically, the method first extends the dataset with data augmentation, and CutMix interpolation. Secondly, combining with metric learning to enhance the generalization capacity of the model. Finally, KNN algorithm is designed to realize device classification and detection. The proposed IntML-KNN method is verified on the commercial available LoRa dataset. The experimental results indicate that the proposed scheme exhibits strong classification and generalization performance in FS-RFFI. Meanwhile, the recognition rate of the proposed IntML-KNN reaches 97.00% with only 10% samples. © 1994-2012 IEEE.

关键词： Deep learning

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Enhanced License Plate Detection using YOLOv8n: An Approach for Efficient Vehicle Identification 3

Enhanced License Plate Detection using YOLOv8n: An Approach ...

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3rd IEEE World Conference on Applied Intelligence and Computing, AIC 2024

作者： Chandok, Vishesh Maurya, Sudhanshu Mirza, Kashish Kumar Yadav, Ajay Thomas Varghese, Sneha Garg, Rachit Symbiosis Institute of Technology Nagpur Campus Pune India Graphic Era Hill University Department of Computer Science & Engineering Uttarakhand Bhimtal Campus India United Institute of Management Department of Computer Application Prayagraj India Lovely Professional University School of Computer Science and Engineering Phagwara India

ISBN: (纸本)9798350384598

An essential feature of the intelligent transportation system is the automatic detection and recognition of license plate information. The e-payment systems for parking and toll collection in traffic control security could be utilized with this application. Numerous algorithms have been created for License Plate Recognition (LPR) and Identification, each with pros and cons depending on the circumstances. computer vision has increased in terms of techniques and developments since deep learning emerged and became popular in several artificial intelligence domains. It is one of the most efficient ways to monitor traffic signals, tax paid for parking, and police monitoring through Automatic LPR. Although it, as a technology, is widely used and developed, much more must be done to improve the system's accuracy. The current science community has excelled in efficiency and problem-solving in recent years. Along with the You Only Look Once (YOLO), targeted at the object detection area and applied to this study, our primary goal is to summarize and analyze several methods and advancements in LPR in the deep learning age. © 2024 IEEE.

关键词： Traffic signals

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A Novel Deep Federated Learning-Based and Profit-Driven Service Caching Method 19th

A Novel Deep Federated Learning-Based and Profit-Driven Ser...

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19th EAI International Conference on Collaborative Computing: Networking, applications and Worksharing, CollaborateCom 2023

作者： Ouyang, Zhaobin Xia, Yunni Peng, Qinglan Li, Yin Chen, Peng Wang, Xu College of Computer Science Chongqing University Chongqing China School of Artificial Intelligence Henan University Zhengzhou China Guangzhou Institute of Software Application Technology Guangzhou China School of Computer and Software Engineering Xihua University Chengdu China College of Mechanical and Vehicle Engineering Chongqing University Chongqing China

ISBN: (纸本)9783031545306

Service caching is an emerging solution to addressing massive service request in a distributed environment for supporting rapidly growing services and applications. With the explosive increases in global mobile data traffic, service caching over the edge computing architecture, Mobile edge computing (MEC), emerges for alleviating traffic congestion as well as for optimizing the efficiency of task processing. In this manuscript, we propose a novel profit-driven service caching method based on a federated learning model for service prediction and a deep reinforcement learning mode for yielding caching decisions (FPDRD) in an edge environment. The proposed method is temporal service popularity and user preference-aware. It aims to ensure quality of service (QoS) of delivery of cached service while maximizing the profits of network service providers. Experimental results clearly demonstrate that the FPDRD method outperforms traditional methods in multiple aspects. © ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Traffic congestion

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Improving Cancer Prediction in Recommender Systems through Gradient Boosting 15

Improving Cancer Prediction in Recommender Systems through G...

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

作者： Sengupta, Pompi Das Kharbas, Vikas Kumar Adarsha, H. Arka Jain University Department of Management Jharkhand Jamshedpur India Vivekananda Global University Department of Computer Science & Application Jaipur India School of Engineering and Technology Department of Mechanical Engineering Karnataka Bangalore India

ISBN: (纸本)9798350370249

The analysis of various styles of most cancers is frequently reliant on accurate prediction models. Recommender systems are powerful equipment that allows the prediction of cancer development and recurrence based totally on information accumulated from affected person information. But, standard feed-forward neural networks (FFNNs) utilized in current cancer prediction fashions are limited via their potential to capture subtle nonlinear relationships among affected person variables. Gradient boosting is a device gaining knowledge of set of rules that makes use of an ensemble of weak selection bushes to produce an effective predictive version. This have a look at examines the capability for boosting algorithms to enhance the predictive ability of the FFNN fashions currently used in recommender structures for predicting most cancers progression and recurrence. The outcomes of our test reveal that boosting algorithms are able to boom model accuracy over FFNNs whilst additionally lowering training time. Moreover, nonlinear relationships between affected person variables are captured extra successfully by means of boosting than with FFNNs. those findings keep crucial implications for the development of powerful predictive models for cancer progression and recurrence. © 2024 IEEE.

关键词： Cancer Implications Networks Neural Predictive Produce Recurrence Relationships

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Deep U-Net Architecture with Curriculum Learning for Left Atrial Segmentation 1

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1st Left Atrial and Scar Quantification and Segmentation Challenge, LAScarQS 2022, held in Conjunction with the 25th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2022

作者： Jiang, Lei Li, Yan Wang, Yifan Cui, Hengfei Xia, Yong Zhang, Yanning National Engineering Laboratory for Integrated Aero -Space -Ground -Ocean Big Data Application Technology School of Computer Science Northwestern Polytechnical University Xi’an710072 China School of Computer Science Northwestern Polytechnical University Xi’an710072 China

ISBN: (数字)9783031317781

ISBN: (纸本)9783031317774

Segmentation of the late-stage gadolinium-enhanced magnetic resonance imaging (LGE-MRI) is a critical step in the ablation therapy for atrial fibrillation (AF). In this work, we propose an end-to-end deep learning-based segmentation method for delineating 3D left atrial (LA) structures in multiple domains. The proposed method uses the 6 layers deep U-Net architecture as the segmentation backbone. Curriculum learning is integrated into the deep U-Net architecture, helping the network learn step by step from easy to difficult scene. We have tested normal and strong version of data augmentation methods, to verify the effect of reducing domain shifts. Other techniques like Fourier-based data augmentation and Swin Transformer Block have also been explored to further improve the segmentation performance. The experimental results demonstrate that the strong version of data augmentation method can reduce the domain shifts and achieve more accurate result, with mean Dice score of 0.881 on the validation set of LAScarQS 2022 challenge. The evaluation results demonstrate our method’s effectiveness on left atrial segmentation in multi-sequence cardiac magnetic resonance (CMR) data. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Curricula

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