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4th EAI international conference on Big data Innovation for Sustainable Cognitive Computing, BDCC 2021

4th EAI International Conference on Big Data Innovation for ...

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4th EAI international conference on Big data Innovation for Sustainable Cognitive Computing, BDCC 2021

ISBN: (纸本)9783031076534

the proceedings contain 18 papers. the special focus in this conference is on Big data Innovation for Sustainable Cognitive Computing. the topics include: Solar Radiation Prediction Using the Random Forest Regression Algorithm;online Covid-19 Risk Analysis System for Early Detection of Possible Infection;finger Knuckle Print recognition Using Complex Conjugate Feature Vector;a Systematic Literature Review on data Freshness for Reinforcing Mutual Authentication in Wireless Body Area Networks;vehicular Support System for User and Vehicle Accident Prevention;application of Multi-Focused and Multimodal Image Fusion Using Guided Filter on Biomedical Images;Modelling Cognitive Scores for Alzheimer’s Disease Progression Prediction Using Longitudinal MRI data;an Automated Cervical Cancer Detection Mechanism Using Pap Smear Images;Experimental Comparative Analysis on Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) on Aspect-Level Sentiment Analysis;AI-Enabled Pregnancy Risk Monitoring and Prediction: A Review;detection of Non-Technical Losses in Power Utilities Using machine learning;data Hiding in Binary Images for Secret and Secure Communication Using Decision Tree;private and Secure Blockchain-Based Mechanism for an Online Voting System;A Novel Real-Time 3D Object Detection Network in Autonomous Driving Using Reformed RS-ResNet network;various Physiological Methods to Identify Sleep Onset;Internet of things (IoT) Adoption: Challenges Among Small and Medium Scale Enterprises (SMEs).

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Comparison of Multi-Modal Federated learning Framework and SPSS in the Evaluation of Lymph Node Metastasis Probability in Gynecological Malignancies

Comparison of Multi-Modal Federated Learning Framework and S...

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pattern recognition and machine learning (PRML), IEEE international conference on

作者： Zhijun. Hu Ling. Ma Ding Yue Guangyu Wu Xiaohua Shi Shalayiding Sirejiding Kaijiang. Liu Department of Gynecologic Oncology Renji Hospital School of Medicine Shanghai Jiao Tong University Shanghai China Library Shanghai Jiao Tong University Shanghai China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Department of Radiology Renji Hospital School of Medicine Shanghai Jiao Tong University Shanghai China

this study aimed to evaluate the diagnostic efficacy of CT, MRI, and PET/CT in detecting lymph node metastasis (LNM) in patients with gynecologic malignancies. A total of 567 patients diagnosed with cervical carcinoma (CC) and endometrial carcinoma (EC) were included in this study. the results of this study indicated that PET/CT demonstrated higher levels of sensitivity but lower levels of specificity compared to CT and MRI. Additionally, a deep learning-based multimodal federated learning model was implemented, which integrated non-image and MRI data, to improve diagnostic accuracy in the detection of LNM. the model exhibited heightened sensitivity and specificity in comparison with individual imaging modalities. Overall, the findings of this study underscore the potential of employing multimodal federated learning models to accurately detect LNM in patients with gynecological malignancies, which in turn emphasizes the necessity for further research and validation.

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Performance optimization algorithm for CRF layer of Chinese sequence labeling model based on deep learning 4

Performance optimization algorithm for CRF layer of Chinese ...

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4th international conference on machine learning, Big data and Business Intelligence, MLBDBI 2022

作者： Zhu, Li Zhang, Yue Dong, Yu Cui, Chengyu Engineering University of Pap Xi'an China Shijiazhuang Division of Plaa Infantry College Shijiazhuang China National Defense University Joint Operations College Shijiazhuang China China Mobile Chengdu Institute of Research and Development Chengdu China

ISBN: (纸本)9798350333947

the 'deep learning model + CRF' model architecture such as Bi-LSTM-CRF has outstanding performance| in the tasks of part-of-speech tagging, named entity recognition, and semantic role tagging in the field of natural language processing, and has become the most popular algorithm for sequence tagging tasks. However, in practical applications, due to the strong fitting ability of the feature extractor in the deep learning model, the use efficiency of the CRF layer is relatively low;at the same time, the calculation cost of the CRF layer is relatively large. this paper proposes a performance optimization algorithm for the CRF layer in the sequence annotation task based on the deep learning model. the algorithm introduces five rules. First of all, the tag result of the feature extractor is checked by the five rules, if the adjacent tag is found to be logically incorrect, then use the CRF layer for logic correction. the experimental results show that the algorithm shows great advantages in the process of word segmentation, part-of-speech tagging and named entity recognition, a lot of computing time has been saved. © 2022 IEEE.

关键词： Semantics

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Research on intelligent customs declaration generation in Guangdong-Hong Kong cross-border road cargo clearance 4

Research on intelligent customs declaration generation in Gu...

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4th international conference on Computer Science and Communication Technology, ICCSCT 2023

作者： Han, Chengguo Wang, Bin School of Management Guangdong University of Science and Technology Guangdong Dongguan523083 China Shenzhen Taizhou Technology Co. Ltd. Guangdong Shenzhen518031 China

ISBN: (纸本)9781510671232

Due to the uniqueness of "one country, two systems" and "two customs territories" in Guangdong (GD) and Hong Kong (HK), cross-border road cargo clearance between the two regions requires the creation of several documents. these include the Road Cargo Manifest, Vehicle Declaration, Customs Declaration, HK Seamless, HK Bundle, and HK Customs Declaration, as well as additional documents such as Packing List, Contract, and more. the use of Artificial intelligence (AI) can streamline the process of generating these documents and ensure accuracy and efficiency in cross-border road cargo clearance operations. this paper focuses on four aspects: text scanning and recognition, extraction of commodity classification declaration elements, conversion of customs declaration documents, and intelligent commodity classification. these areas of research aim to address the time-consuming and labour-intensive process of document creation and verification. By leveraging AI technologies, such as optical character recognition (OCR), data extraction algorithms, and machine learning (ML), it is possible to automate and optimize the production and review of these documents, thereby reducing time and manpower costs in cross-border road cargo clearance operations between GD and HK. Based on big data analysis of customs declaration documents from both regions, AI technologies such as feature extraction and machine learning can be utilized to achieve automation in the identification of documents like road cargo manifests and customs declarations. this research applied to the China international Trade Single Window (Shenzhen) and significantly improved the efficiency and accuracy of customs declaration in GD and HK. © 2023 SPIE.

关键词： international trade

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Early recognition of Sudden Cardiac Death Combined with Deep Transfer learning and Superficial ECG Features

Early Recognition of Sudden Cardiac Death Combined with Deep...

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pattern recognition and machine learning (PRML), IEEE international conference on

作者： Yue Zhang Xingzeng Cha Bingqian Cui Dakun Lai School of Electronic Science and Engineering University of Electronic Science and Technology of China Chengdu China

Sudden cardiac death (SCD) is a global public safety issue that poses a serious threat to human life and health. Moreover, the clinical records of SCD data are extremely limited. this paper proposed an early prediction algorithm for SCD based on deep transfer learning. the algorithm used a two-stage training approach, which included pre-training and fine-tuning of convolutional neural networks (CNNs) to automatically capture the deep heart rate features (HRFs) in high-risk electrocardiogram (ECG) signals before SCDs. the aim is to explore automatic early prediction of SCD under limited ECG data training. this paper extracted three HRFs as input sets from 30 second single lead ECG signals of 18 patients with malignant arrhythmia and 18 healthy subjects, including RR interval, dRR interval, and turbulence onset (TO). then, the ten-fold crossover method was used to evaluate the classification performance of this model. Finally, the results showed that the average accuracy (Acc), sensitivity (Se), and specificity (Sp) of predicting SCD within 30 minutes before onset were 91.79%, 87.00%, and 96.63%, respectively. the transfer learning algorithm proposed in this paper effectively addresses the issue of insufficient data in the early prediction models of SCD. It also achieves early and accurate prediction of high-risk signs of SCD by using ECG. this is expected to form an intelligent and effective monitoring and early warning technology for high-risk patients of SCD.

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4th international conference on Communication, Computing and Electronics Systems, ICCCES 2022

4th International Conference on Communication, Computing and...

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4th international conference on Communication, Computing and Electronics Systems, ICCCES 2022

ISBN: (纸本)9789811977527

the proceedings contain 80 papers. the special focus in this conference is on Communication, Computing and Electronics Systems. the topics include: Smart Door Unlocking System;An Insight into AI and ICT Towards Sustainable Manufacturing;FPGA Implementation of Efficient 32-Bit 3-Operand Addition Using Kogge–Stone (KS) Parallel Prefix Adder;automatic Mulching machine;multi-user Hybrid Beamforming for mmWave Systems Using learning-Aided Link Adaptation;prediction of Disease Using Retinal Image in Deep learning;plant Health Analyzer Using Convolutional Neural Networks;behaviors of Modern Game Non-playable Characters;Low-Noise Amplifier with Co-designed Microstrip Antenna for 60 GHz Wireless Communications;Impact of High Dimensionality Reduction in Financial datasets of SMEs with Feature Pre-processing in data Mining;generation of Counters and Compressors Using Sorting Network;deep learning-Based Triphase Community Detection for Multimedia data;Design of a High-Speed and Low-Power AES Architecture;improving Sleep Apnea Screening with Variational Mode Decomposition and Deep learning Techniques;effect of Selectively-Filled-Ethanol on Dispersion Characteristics of Circular Shaped Hollow Core Photonic Crystal Fiber;a Demand Management Planning System for a Meat Factory Based on the Predicted Market Price Under Indian Market Scenario;Performance Comparison of MCML, PFSCL, and Dynamic CML Gates with Parametric Analysis in 45 nm CMOS Technology;VANET Authentication with Privacy-Preserving Schemes—A Survey;An Effective Protection Approach for Deceive Attacker in AES Attack;Effective EMI Reductıon in Medical Devices and Automotive Power Converters;conformal Antenna with Bow and Arrow Shaped Radiator for Wireless Capsule Endoscopy;gujarati Language Automatic Speech recognition Using Integrated Feature Extraction and Hybrid Acoustic Model;A CNN-Based Underage Driver Detection System.

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Construction of Security Risk Prediction Model for Wireless Transmission of Multi Axis NC Machining data 4th

Construction of Security Risk Prediction Model for Wireless ...

引用

4th international conference on machine learning for Cyber Security, ML4CS 2022

作者： Zhao, Guoqiang Zhang, Meitao Wu, Yingying Shandong Vocational College of Science and Technology Weifang261053 China Weichai Heavy Machinery Co. Ltd. Weifang261108 China Weichai Power Co. Ltd. Weifang261061 China

ISBN: (纸本)9783031200953

In multi axis NC machining, there is a certain security risk in wireless data transmission. A security risk prediction model for wireless data transmission in multi axis NC machining is designed. through frequent pattern mining, multi axis NC machining data wireless transmission security risk data mining is implemented. Based on machine learning, the security risk prediction model of data wireless transmission is constructed. the model is mainly divided into four parts: data collection and processing module, grey synthesis and Markov prediction module, Bayesian network and evidence theory module and risk quantification calculation module. AHP method is used to analyze the weight of model indexes. Test the prediction performance of the model. the test results show that the accuracy of risk prediction of the model in different scenarios is high, higher than 88%, and the prediction time is short, less than 6 s, which proves the prediction performance of the model. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： data mining

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Mixed Attention Interleaved Execution Cascade Architecture for Fittings Instance Segmentation in Overhead Transmission Line Images

Mixed Attention Interleaved Execution Cascade Architecture f...

引用

pattern recognition and machine learning (PRML), IEEE international conference on

作者： Yilong Yang Shangping Zhong Kaizhi Chen College of Computer and Data Science Fuzhou University Fuzhou China

recognition and localization of the fittings in overhead transmission line (OTL) images are the basis of fittings status detection and fault diagnosis. Most of the current recognition and localization of the fittings are based on object detection methods, which cannot accurately locate the fittings, and existing instance segmentation methods have limited accuracy for instance segmentation of fittings in complex OTL scenarios. To solve these problems, in the interleaved execution part, we inherit the idea of Hybrid Task Cascade (HTC) and add a direct information path to the same-stage box and mask branches to reinforce the coupled relationship between detection and segmentation; in the mask branch part, we sequentially apply the efficient channel attention (ECA) module and the dilated spatial attention (DSA) module and then insert them into the mask branch to improve the cross-stage information communication in the cascade architecture and mask prediction. Combining them results in Mixed Attention Interleaved Execution Cascade (MAIEC), a new cascade architecture for instance segmentation. Extensive experiments on the OTL fittings dataset reveal the effectiveness of the proposed method. the proposed MAIEC improves the AP of the box and mask predictions by respectively 2.0% and 1.5% compared to the strong HTC baseline.

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machine learning Based Feature Selection for DDoS Detection in Named data Networking 4

Machine Learning Based Feature Selection for DDoS Detection ...

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4th international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

作者： Subasri, I. Emil Selvan, G.S.R. Ramkumar, M.P. Thiagarajar College of Engineering Department of Computer Science and Engineering Madurai India

ISBN: (纸本)9781665474368

With the growing recognition that current Internet protocols have significant security flaws;several ongoing research projects are attempting to design potential next-generation Internet architectures to eliminate flaws made in the past. these projects are attempting to address privacy and security as their essential parameters. NDN (Named data Networking) is a new networking paradigm that is being investigated as a potential alternative for the present host-centric IP-based Internet architecture. It concentrates on content delivery, which is probably underserved by IP, and it prioritizes security and privacy. NDN must be resistant to present and upcoming threats in order to become a feasible Internet framework. DDoS (Distributed Denial of Service) attacks are serious attacks that have the potential to interrupt servers, systems, or application layers. Due to the probability of this attack, the network security environment is made susceptible. the resilience of any new architecture against the DDoS attacks which afflict today's Internet is a critical concern that demands comprehensive consideration. As a result, research on feature selection approaches was conducted in order to use machine learning techniques to identify DDoS attacks in NDN. In this research, features were chosen using the Information Gain and data Reduction approach with the aid of the WEKA machine learning tool to identify DDoS attacks. the dataset was tested using K-Nearest Neighbor (KNN), Decision Table, and Artificial Neural Network (ANN) algorithms to categorize the selected features. Experimental results shows that Decision Table classifier outperforms well when compared to other classification algorithms with the with the accuracy of 85.42% and obtained highest precision and recall score with 0.876 and 0.854 respectively when compared to the other classification techniques. © 2022 IEEE.

关键词： data reduction

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A Framework for Cognitive Load recognition Based on machine learning and Multimodal Physiological Signals by Wearable Sensors

A Framework for Cognitive Load Recognition Based on Machine ...

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pattern recognition and machine learning (PRML), IEEE international conference on

作者： ChenWei Wang XinYu Li JinYang Huang Peng Zhao Meng Wang GuoHang Zhuang Xiao Sun School of Computer and Information Hefei University of Technology Hefei China

Cognitive load recognition is of great significance to the study of emotional health, as excessive cognitive load not only impairs work and learning efficiency but also leads to mental pressure and fatigue, affecting overall well-being. Advances in wearable technology and understanding the role of physiological signals in assessing human health have enabled the possibility of cognitive load recognition using these signals. this work proposes a framework that combines multiple physiological signals (ECG, EDA, and PPG) collected through wearable sensors for cognitive load recognition. Heart rate variability (HRV) features are extracted from ECG signals, while statistical characteristics are extracted from EDA and PPG signals. the RFECV feature selection algorithm is utilized in the feature selection module to achieve optimal feature subset selection and redundant feature removal. the classification of cognitive load states is implemented based on the ASAPSO-LightGBM(Adaptive Simulated Annealing Particle Swarm Optimization-Light Gradient Boosting machine) machine learning model. We validated the effectiveness of the framework on two publicly available datasets (MAUS and WESAD). Experimental comparisons of feature combinations and machine learning models demonstrate that the proposed framework achieves superior performance, with recognition accuracy of 98.52% and 88.38% on the two datasets, respectively.

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