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International Conference on Communication and Electronics Systems (ICCES)

作者： P Ananthi K Nirmala Devi Pradisa. T Department of Computer Technology Kongu Engineering College Perundurai TamilNadu Department of Computer Science(PG) Kristu Jayanti College Bangaluru MSc Software Systems Kongu Engineering College Perundurai TamilNadu

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

In today's world, cardiovascular disease is still a major concern. The electrocardiogram (ECG) is one of the most trustworthy non-invasive tools for identifying heart problems. ECG interpretation, however, takes a lot of effort and specialized knowledge. The quality and effectiveness of medical care are enhanced by the development of a revolutionary technique for early disease detection. Using a public dataset of ECG scans from cardiac patients, this study leveraged deep learning algorithms to predict two primary cardiac abnormalities: abnormal heartbeat and normal person classes. The low-scale pre-trained deep neural Alex Net was used to study the transfer learning process. To forecast cardiac issues, a novel Convolutional Neural Network (CNN) architecture was put out. We used the conventional deep learning methods, VGG-16, Resnet50, RNN, and LSTM, to feature-extracted data utilizing the previously mentioned pre-trained models and our suggested LSTM model. The experimental results show that the LSTM model outperforms the previous attempts in terms of performance measures.

关键词： Deep learning Transfer learning Medical services Electrocardiography Prediction algorithms Data models Convolutional neural networks Cardiovascular diseases Long short term memory Residual neural networks

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Smart Diagnostic System for Early Detection and Prediction of Polycystic Ovary Syndrome

Smart Diagnostic System for Early Detection and Prediction o...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Harish, K.P. Dhivyanchali, M.N. Devi, K.Nirmala Krishnamoorthy, N. Dhana Sree, R. Dharanidharan, R. Kongu Engineering College Department of Computer Science & Engineering Erode India Swamy Vivekanandha Medical College Hospital and Research Institute Elayampalayam Tamilnadu Tiruchengode637 205 India Vellore Institute of Technology Vellore Campus School of Information Technology and Engineering Department of Software Systems and Engineering Tamilnadu 632014 India

ISBN: (纸本)9798350348217

PCOS are the serious condition which affects female ovaries during their reproductive age of 15 to 45. This disease affects 5 to 10% of reproductive-age females. Although it is difficult to fully resolve this issue,the PCOS affected women can be mitigated through proper exercise, by taking proper nutritious diet and maintaining the healthy BMI. Until they take a pregnancy test, the majority of women are unaware of the disease. The clinical dataset has the 541 instances and 45 attributes of unbalanced classes of 0 and 1 (no and yes) which has 364 instances of 0 (no) class and 177 instances of 1 (yes) class. Preprocessing is done for the unbalanced dataset by filling the null values and changing the datatype of all attributes to numeric datatypes. The unbalanced dataset is balanced by the balancing techniques of SMOTE and Random Over Sampling. Comparing the both balanced techniques through the accuracy the random oversampling gives the *** supervised learning algorithms are Decision tree, KNN,Random Forest,AdaBoost, Logistic regression,Gradient boosting, cat boosting, XGBoosting, Linear SVM, Radial SVM, Linear discriminant analysis and Quadratic discriminant analysis are *** supervised learning algorithms are trained and tested by splitting the dataset to 70% for training and 30% for testing. The ensemble stacking techniques are used by implementing the all models at the cross validation of *** xgboost gives the accuracy of 96% for the balanced dataset. © 2023 IEEE.

关键词： Adaptive boosting

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A Simulation Method for Industrial Control Honeynet Based on Time Series Prediction 5

A Simulation Method for Industrial Control Honeynet Based on...

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5th International Conference on Information science, Parallel and Distributed Systems, ISPDS 2024

作者： Jiang, Tianqi Yao, Yu Xu, Chao Yang, Wei Zhou, Xiaoming He, Miao Zhang, Wenjie Wang, Qianyi Meng, Meng Wang, Rui Northeastern University School of Computer Science and Engineering Shenyang China State Grid Liaoning Xintong Company Shenyang China Software College Shenyang China State Grid Liaoning Electric Power Co. Ltd. Shenyang China

ISBN: (纸本)9798350352719

This paper proposes a time series prediction based industrial control honey network simulation method to address the issue that many current industrial control honey network solutions only consider the response interaction simulation of attackers within a certain period of time and cannot effectively achieve communication simulation and collaborative work between honeypots. Firstly, the deployment and basic interaction simulation of the industrial control honeynet have been achieved;Then, a communication simulation method between devices within the industrial control honey network was studied and implemented to simulate collaborative work scenarios between devices;Secondly, using time series prediction algorithms to generate simulation data for various devices within the industrial control honey network, improving the sustained response capability of the industrial control honey network;Finally, comparative experiments have shown that the method proposed in this article can improve the ability to trap attackers, provide more data for security analysis, and help users establish a comprehensive industrial control network security protection system. © 2024 IEEE.

关键词： Prediction models

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Multi-Server Cooperative Video Caching Strategy Based on Deep Reinforcement Learning in Cloud-Edge Computing 10

Multi-Server Cooperative Video Caching Strategy Based on Dee...

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10th International Conference on Big Data Computing and Communications, BIGCOM 2024

作者： Li, Guanyu Mao, Yingchi Peng, Xinxin Zheng, Haotian Wang, Zibo College of Computer Science and Software Engineering Hohai University Nanjing China Hohai University Key Laboratory of Water Big Data Technology of Ministry of Water Resources Nanjing China Huaneng Hydro Lancang Xiaowan Hydropower Plant Dali China

ISBN: (纸本)9798331509538

The soaring number of mobile devices has led the innovation of the edge caching in edge computing, which relieves the heavy pressure of cloud computing. However, with the escalating popularity of short video services, the prevalence of video content in edge server caches has seen a marked increase. Previous work ignored the impact of edge server performance on video caching and failed to automatically adjust the optimal video bit rate in response to users' demand. To improve users' evaluation for watching videos on devices with different display sizes, we propose a novel edge caching strategy called Gate Self-attention Recurrent Network-Multi Agent Reinforcement Learning(GSRN-MARL). GSRN-MARL utilizes leverages Gate Recurrent Unit(GRU) and attention mechanisms to predict cache content based on user historical request information and device details, consequently improving edge server caching performance and users' evaluation of video. Experiment demonstrates that GSRN-MARL surpasses LRU, DRL, and MAAC across two critical metrics: video cache hit rate and users' QoE (Quality of Experience). © 2024 IEEE.

关键词： Deep reinforcement learning

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EFFECTS OF GEOMETRY PARAMETERS ON AERODYNAMIC CHARACTERISTICS OF ROTOR AIRFOIL IN REVERSE FLOW 34th

EFFECTS OF GEOMETRY PARAMETERS ON AERODYNAMIC CHARACTERISTIC...

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34th Congress of the International Council of the Aeronautical sciences, ICAS 2024

作者： Liu, Cheng-Peng Song, Wen-Ping Han, Shao-Qiang Han, Zhong-Hua School of Aeronautics Northwestern Polytechnical University Xi’an710072 China Tianfu Engineering-oriented Numerical Software Innovation Center College of Computer Science Sichuan University China National Key Laboratory of Aircraft Configuration Design Xi’an710072 China

When rotorcrafts fly at high speeds, the retreating blades experience reverse flow due to their high advance ratio. Flow separates even at small angle of attack in reverse flow, which leads to negative lift, drag and pitching moment penalties. The aerodynamic characteristics of an airfoil are closely related to its geometric shape. Therefore, to mitigate aerodynamic deterioration caused by reverse flow and enhance the flight performance of rotorcrafts at high speeds, it is crucial to study the effects of geometric parameters on the aerodynamic characteristics of rotor airfoils in reverse flow. Given that conventional URANS method with low-precision scheme cannot adequately capture the vortex-induced large separation flow in reverse flow conditions, an improved high-order WENO scheme and IDDES method are used in airfoil reverse flow simulation. Initially, the simulation method is validated against experiment data of NACA 0012 airfoil in reverse flow. Subsequently, the OA312 airfoil is selected as a baseline and morphed by leading-edge radius, camber, camber crest position, thickness, thickness crest position and shape of trailing-edge. The aerodynamic coefficients of these modified airfoils are simulated and compared to analyze the influence of different geometry parameters. The results can be used to guide the design of new rotor airfoil with high performance. © 2024, International Council of the Aeronautical sciences. All rights reserved.

关键词： Cambers

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Exploring Natural Language Processing Model Acceleration in Molecular Dynamics Simulation Using High-Performance Computing and Machine Learning

Exploring Natural Language Processing Model Acceleration in ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Du, Qi Wang, Feng Huang, Hui Wan, Heng Wu, Xiaoyu Wu, Chengkun Hunan University College of Computer Science and Electronic Engineering Hunan Changsha China Hunan Institute of Scientific and Technical Information Hunan Changsha China National University of Defense Technology Laboratory of Digitizing Software for Frontier Equipment Hunan Changsha China

ISBN: (纸本)9798350386226

In molecular dynamics simulations, the integral methods used to calculate molecular trajectories, such as the Verlet integral method, involve significant computational costs. On the premise of ensuring the accuracy of simulation, how to effectively reduce the amount of computation is always a challenging problem. This paper applies Natural Language Processing models to simulate molecular motion trajectories in molecular dynamics, integrating the MPI programming model and machine learning on a high-performance computing platform to achieve MPI parallelization during model training and inference. The results indicate that different types of neural network architectures have a significant impact on inference performance and accuracy. When using the deep spatio-temporal networks model, the mean absolute error is 0.0032. At an atomic scale of 3M, the parallel efficiency with 1024 computational nodes exceeds 90%, demonstrating excellent parallel performance. © 2024 IEEE.

关键词： Natural language processing systems

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A Study on the Construction and Practical Application of the Smart Education Data Platform at Guizhou Traffic Technician and Transportation college 14

A Study on the Construction and Practical Application of the...

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14th International Conference on Information Technology in Medicine and Education, ITME 2024

作者： Li, Hong Zhang, Hong Deng, Shaoxun Li, Bo Guizhou Bit Software Co. Ltd. Research and Development Department Guiyang China Guizhou Traffic Technician and Transportation College Department of Management Engineering Guiyang China Guizhou University School of Computer Science and Technology Guiyang China

ISBN: (纸本)9798350356670

This paper investigates the digital transformation at Guizhou Traffic Technician and Transportation college, aligned with the 'China Education Modernization 2035' agenda. It introduces a microservices-based data platform aimed at upgrading educational IT systems, enhancing instructional quality, and informing decisions through smarter data use. The approach integrates microservices, cloud, big data, and AI to achieve efficient data handling and analytics, leading to improved management and decision-making processes. The study documents the platform's architecture, security, challenges, and solutions, along with successes like data visualization and automated reporting. Findings emphasize the project's relevance for vocational education's digital transition, pointing out future challenges, including data security and technology updates. The research concludes with key takeaways for institutions undergoing digitalization, underscoring the paramountcy of data security and tech optimization. © 2024 IEEE.

关键词： Big data

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Enhancing Pulmonary Nodule Classification performance with TriCaps-RL: A Capsule Network Reinforcement Learning Approach

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第3期9卷 1441-1459页

作者： Zheng, Guang-Yuan Zhang, Fu-Quan Cheng, Chen Shan, Hong-Tao Soomro, Nouman Qadeer Liu, Yi College of Information Technology Shanghai Jian Qiao University Shanghai201306 China School of Mathematics and Computer Science Yanan University Shaanxi 716000 China School of Computer and Data Science Minjiang University No.200 Xiyuangong Road Fuzhou University Town Fuzhou350108 China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu610021 China Fuzhou Technology Innovation Center of intelligent Manufacturing information System Minjiang University Fuzhou350108 China Fujian Province University Fuzhou350300 China College of Business Shanghai Jian Qiao University Shanghai201306 China College of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai201620 China Software Department Mehran University of Engineering & Technology Sindh 76062 Pakistan

Distinguishing the manifestations of pulmonary nodules poses a significant challenge in the medical field, demanding the expertise of experienced radiologists. This complexity results in the high cost and inadequacy of sample labeling. In response, this study introduces a reinforcement learning approach, denoted as TriCaps-RL, for the classification of pulmonary nodules based on CT signs. This approach initially employs the Q-value loss to train a single CapsNet. When the performance of the CapsNet plateaus and further enhancement becomes challenging, the loss function is progressively replaced by a triplet metric to augment performance. Consequently, a more nuanced differentiation among various types of pulmonary nodules is achieved. The proposed method can improve its performance through interactions with radiologists during their CT image reading process, thereby mitigating the shortage of radiologists and addressing the time-consuming issue of training sample labeling in medical image research. This paper delves into the fine-grained classification of pulmonary nodules using deep reinforcement learning based on CT imaging signs. In comparison to previous studies that primarily concentrate on the benign/malignant classification of lung nodules, this fine-grained classification of nodules founded on CT imaging signs proves to be more valuable for medical practitioners in making accurate diagnostic decisions. © 2024, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Lung cancer

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Efficient High-Precision Classification Algorithm for Radar Deceptive Jamming via Array Detection

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Progress in Electromagnetics Research C 2025年 155卷 29-35页

作者： Liu, Wenjie Qin, Wenfang Shen, Mingwei Han, Guodong College of Information Science and Engineering Hohai University Jiangsu Changzhou213200 China College of Computer Science and Software Engineering Hohai University Jiangsu Nanjing211106 China The 54th Research Institute of CETC Hebei Shijiazhuang050081 China

In complex electromagnetic scenarios where multiple deceptive jamming signals are simultaneously aliased in the time frequency domain, conventional single-channel electronic detection systems struggle to effectively separate and classify overlapping jamming sources. To address this limitation, this paper investigates an array detection-based classification scheme for multi-source jamming. First, due to the spatial degrees of freedom offered by array systems, the Direction-of-Arrival (DOA) of each jamming source is precisely estimated using the MUSIC algorithm. Then, the adaptive digital beamforming (ADBF) filters are designed based on the estimated DOAparameters, enabling spatial-domain extraction of individual jamming signals. Finally, the DOA information is integrated into the Pulse Description Word (PDW) of each separated jamming measurement, which can facilitate adaptive K-Means clustering with enhanced class separability. Simulation results demonstrate that the proposed method achieves a 22.4% improvement in classification accuracy compared to existing single-channel detection approaches, while maintaining computational efficiency. © 2025, Electromagnetics Academy. All rights reserved.

关键词： Frequency response

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Research on prediction model of carbon trading price based on BAS-BP neural network - Take Shenzhen, Guangdong Province, China as an example 5

Research on prediction model of carbon trading price based o...

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5th International Conference on computer Information science and Application Technology, CISAT 2022

作者： Deng, Zihan Wang, Yeying Nian, Ling Software Engineering Harbin Engineering University Heilongjiang Harbin China Computer Science and Technology Nankai University Binhai College Binhai Tianjin China Computer Science and Technology Zhejiang Sci-Tech University Zhejiang Hangzhou China

ISBN: (纸本)9781510660076

To the relevant policies of the carbon trading market in China, this paper adds the optimization algorithm that longicorn must use based on the traditional BP neural network, which increases the convergence speed and global optimization ability of the original BP neural network. The empirical study takes the carbon trading price of Shenzhen city, Guangdong Province, China, from 2015-to 2022 as an example. The results show that the base-BP neural network can significantly reduce the probability of local convergence in the iterative process and reduce the mean square error of prediction by 3.08% compared with the traditional BP neural network. © 2022 SPIE.

关键词： Global optimization

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