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检索条件"任意字段=5th Seminar on Neural Network Applications in Electrical Engineering"

共 2379 条记录，以下是21-30 订阅

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Improving gesture recognition accuracy with multimodal signals based on fusion of surface EMG and acceleration signals 24

Improving gesture recognition accuracy with multimodal signa...

引用

5th International Symposium on Artificial Intelligence for Medicine Science, ISAIMS 2024

作者： Zhang, Shenke Chen, Wenjie Sun, Xiantao Zhang, Cheng College of Electrical Engineering and Automation Anhui University Anhui Hefei China

ISBN: (纸本)9798400717826

Human-computer interaction technology exhibits a broad prospect in the field of rehabilitation assistance, with gesture recognition garnering significant attention as a vital interaction method. the traditional approach to gesture recognition based on surface electromyographic (sEMG) signals encounters challenges such as signal singularity and difficult feature extraction, thereby limiting recognition accuracy. In this paper, we propose a method of combining surface point signals with acceleration signals and additional spatial attention mechanism convolutional neural network (SA-CSAM) to improve the recognition accuracy. Experiments conducted on the NinaPro DB2 dataset demonstrate that our method significantly improves the average accuracy by approximately 19.70%, from 71.96% to 91.66%, when compared to gesture recognition using single sEMG signals. It shows that the SA-CSAM method proposed in this paper is conducive to improving the accuracy of gesture recognition and has potential applications in assisting mobility-impaired elderly people to accomplish daily life tasks. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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2002 6th seminar on neural network applications in electrical engineering, NEUREL 2002 - Proceedings: Foreword

2002 6th Seminar on Neural Network Applications in Electrica...

引用

2002 6th seminar on neural network applications in electrical engineering, NEUREL 2002 - Proceedings 2002年

作者： Reljin, Branimir Stankovi, Srdan Faculty of Electrical Engineering University of Belgrade Serbia

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8th seminar on neural network applications in electrical engineering, Neurel-2006 Proceedings: Foreword

8th Seminar on Neural Network Applications in Electrical Eng...

引用

8th seminar on neural network applications in electrical engineering, Neurel-2006 Proceedings 2006年

作者： Reljin, Branimir Stankovic, Srdan

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ISBN: (纸本)1424404339

No abstract available

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An Analysis of 3D Magnetic Resonance Imaging Data for Diagnosing Neurodegenerative Diseases 5th

An Analysis of 3D Magnetic Resonance Imaging Data for Diagno...

引用

5th International Conference on Data Science, Machine Learning and applications, ICDSMLA 2023

作者： Ojha, Ananta Yadav, Dhananjay Kumar Sharma, Neeraj Salahuddin Karnataka Bangalore India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Electrical Engineering Vivekananda Global University Jaipur India Uttar Pradesh Greater Noida India

ISBN: (纸本)9789819780426

this take a look at affords an evaluation of three-dimensional (3-d) magnetic resonance imaging (MRI) statistics with the goal of diagnosing neurodegenerative sicknesses. A Convolutional neural network (CNN) model was utilized to extract quantitative capabilities from three-D MRI facts to be expecting the presence and/or severity of these conditions. the extracted functions have been used to classify sufferers into healthful, CNN model achieved a sensitivity of ninety seven.43%, 1.27% specificity, and an AUC of *** four. the approach is likewise shown to be sturdy in phrases of variance over distinctive hyperparameter values. the have a look at offers insights into growing 3-D MRI-based totally early prognosis and detection of neurodegenerative sicknesses and may be similarly investigated with larger datasets for extra accurate outcomes. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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A CNN-Random Forest Hybrid Model for network Traffic Classification 5

A CNN-Random Forest Hybrid Model for Network Traffic Classif...

引用

5th IEEE Middle East and North Africa COMMunications Conference, MENACOMM 2025

作者： Chaiban, Jean Paul Elhajj, Imad H Kayssi, Ayman Tabet, Salam American University of Beirut Department of Electrical and Computer Engineering Beirut Lebanon

ISBN: (纸本)9798331519957

Internet service providers (ISPs) have been using Machine Learning (ML)-based network traffic classification in recent years primarily to dynamically adjust their networks to the growing needs of their customers. Even though ML models have several advantages in classifying network traffic, especially in their ability to automatically adapt to ever-changing network traffic patterns, our understanding of their accuracy and generalizability remains limited. In this paper, we evaluate the generalizability of the best performing state-of-the-Art machine learning models used for network classification on a Gaming Traffic Dataset and suggest an architecture for enhanced classification of a large number of classes. Initially, we evaluate Random Forest (RF) utilizing pre-engineered variables including packet length, inter-Arrival periods, and other flow data. Additionally, we evaluate another state-of-the-Art model, the Convolutional neural network (CNN), which receives raw network data that has been transformed into either an RGB or gray-scale image. Furthermore, we propose a classification system that consists of two phases: An unsupervised phase governed by Density-Based Spatial Clustering of applications with Noise (DBSCAN) clustering, followed by a supervised phase that consists of a stacked arrangement of sub CNN base models and a final Random Forest meta estimator. the evaluation of this semi-supervised stacked CNN-RF model shows that it outperforms the state-of-the-Art RF and CNN models, achieving a 7.186% improvement in the testing accuracy in the dataset, and an average 1.7% improvement in the testing accuracy for other datasets. © 2025 IEEE.

关键词： Convolutional neural networks

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Comparison of Different neural network Architectures for Cardiac Arrest Risk Prediction 5th

Comparison of Different Neural Network Architectures for Car...

引用

5th International Conference on Data Science, Machine Learning and applications, ICDSMLA 2023

作者： Rahman, Mohammed Ziaur Mishra, Awakash Ojha, Ananta Goswami, Shubhashish School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India Department of Computer Science & Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India

ISBN: (纸本)9789819780426

Using a realistic dataset based on the United Kingdom clinical exercise studies Datalink, various architectures are evolved and evaluated to decide the simplest for predicting the risk of cardiac arrest. the architectures examined include several modified lengthy short-term memory networks, multi-input/multi-output architectures, Convolutional neural networks (CNNs), and Residual neural networks. Moreover, the consequences of the architectures on the predictions are compared while the statistics are supplemented with external capabilities. Furthermore, the results recommend increasing the facts with external functions, enhancing prediction accuracy for several architectures. Via this observation, conclusions may be drawn about which architectures are satisfactory-desirable for predicting cardiac arrest chance, as well as the consequences of external capabilities on the effectiveness of the architectures. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Stock Price Forecasting in Finance with Artificial neural networks 5

Stock Price Forecasting in Finance with Artificial Neural Ne...

引用

5th Asia Conference on Information engineering, ACIE 2025

作者： Daloglu, Turgut Osi, Garibe Aslan, Murat Beskirli, Mehmet Cizre District Directorate of National Education Şirnak University Şirnak Turkey Şirnak University Department of Computer Engineering Şirnak Turkey Karamanoǧlu Mehmetbey University Faculty of Medicine Departmen of Medical Education and Informatics Karaman Turkey

ISBN: (纸本)9798331528409

this paper examines the applications of Artificial neural networks (ANNs) in the field of finance, particularly in stock price forecasting. Artificial neural networks are mathematical models that mimic biological nervous systems and are used to solve complex problems such as financial data analysis. Stock price forecasting is an important topic for investors and finance professionals. Artificial neural networks have been widely used in this field due to their ability to process large amounts of financial data, learn complex relationships and predict future price movements. this study deals with stock price forecasting with artificial neural networks using historical stock prices, economic indicators and other financial data. neural networks attempt to predict future price movements by learning the complex relationships between these data. Stock price forecasting with artificial neural networks can offer a more flexible and adaptive approach than traditional statistical methods. However, the success of the model depends on factors such as the quality of the data set used, network architecture and training parameters. this study was conducted to understand the potential of artificial neural networks in finance and to evaluate their effectiveness in stock price forecasting. the purpose of this study is to explore how artificial neural networks can be used in financial data analysis, and in particular their potential in stock price forecasting. the study is designed to understand the advantages of applying ANN in finance, to evaluate the stock price forecasting performance of this technology and to provide a new perspective that can influence future investment strategies. © 2025 IEEE.

关键词： neural network models

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Exploring the Efficiency of Clustered neural network Aggregators for Time Series 5th

Exploring the Efficiency of Clustered Neural Network Aggrega...

引用

5th International Conference on Data Science, Machine Learning and applications, ICDSMLA 2023

作者： Sivaraman, Himani Tembhre, Sh obhit Mishra, Awakash Gobi, N. Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Karnataka Bangalore India

ISBN: (纸本)9789819780426

this paper explores the performance of clustered neural community aggregators (CNNA) for time series. through the years, time collection evaluation has allowed for insights into numerous phenomena, ranging from low cost to environmental and organic and clustered neural networks’ purpose to provide higher outcomes via mixing a couple of neural networks. However, their effectiveness has yet to be thoroughly studied in time collection. In this paper, several CNNA architectures are compared against their non-clustered equivalents, and the results are evaluated across an expansion of time collection datasets. Additionally, the overall performance of different clustering algorithms is studied in terms of clustering first-class and time cost. the outcomes show that, compared to non-clustered fashions, CNNA can substantially enhance the prediction performance of time collection and that the effectiveness of various types of clustering algorithms depends substantially on the kind of time collection analyzed. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： effectiveness fashions substantially

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Investigating the Use of Hierarchical Recurrent neural networks for Medical Image Segmentation 5th

Investigating the Use of Hierarchical Recurrent Neural Netwo...

引用

5th International Conference on Data Science, Machine Learning and applications, ICDSMLA 2023

作者： Suneetha, K. Dobriyal, Rohit Elangovan, Vetrimani Savita Karnataka Bangalore India Department of Computer Science and Engineering School of Engineering and Computing Dev Bhoomi Uttarakhand University Navgaon Manduwala Dehradun India School of Computer Science and Engineering Presidency University Karnataka Bengaluru India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

this paper investigates the usage of hierarchical recurrent neural networks (HRNNs) for clinical photo segmentation. HRNNs are a neural network structure combining more than one layer of recurrent cells and layers of convolution cells. the intention of this paper changed to evaluate the effectiveness of HRNNs for medical picture segmentation and how well it can be applied for this reason. the paper evaluates a present HRNN implementation for medical picture segmentation and shows its accuracy primarily based on the dice coefficient (DC). the authors additionally proposed a changed HRNN architecture to improve the segmentation performance: the architecture integrated residual connections, multi-scale inputs, multi-resolution outputs, and an utterly convolution structure. the proposed DT-SL loss was potent in improving the segmentation accuracy. Typically, the outcomes reveal that HRNNs are promising devices for clinical photograph segmentation tasks and can effectively facilitate faster and extra correct segmentation. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Recurrent neural networks

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An Effectıve Svm-Based Performance Model for the Optımızed neural network Intended for Classıfyıng Breast Cancer Dısease 5th

An Effectıve Svm-Based Performance Model for the Optımıze...

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5th International Conference on Data Science, Machine Learning and applications, ICDSMLA 2023

作者： Jyothi, Modugula Siva Sanaboina, S. V. S. V. Prasad Kumar, Voruganti Naresh Babu, P. Raveendra Shaik, Abdul Subhani Reddy, L. Chandrasekhar Department of IT CMR Technical Campus Telangana Hyderabad India Department of CSE CMR Technical Campus Telangana Hyderabad India Department of ECE CMR College of Engineering & Technology Telangana Hyderabad501401 India Department of CSD CMR College of Engineering & Technology Telangana Hyderabad501401 India

ISBN: (纸本)9789819780426

In this study, we present a novel Convolutional neural network (CNN)-based method for classifying breast detection cells. Tumors from breast cancer can be benign or malignant. the correct classification of a breast cancer tumor is essential for medical diagnosis. In this paper, a convolutional neural network (CNN) approach is suggested for the detection of breast cancer. It looks into the suggested method for automatically detecting breast cancer using several convolutional neural network (CNN) architectures [1]. It is a difficult task to diagnose breast cancer in order to improve patient treatment. A recent study suggests using CNN to find and get accurate findings, which may lessen human error in the process of diagnosis and lower the cost of cancer detection [2]. Our algorithm locates regions of bulk and categorizes them according to benign or cancerous [3]. On the evaluated dataset, the algorithm demonstrated 95% accuracy in detecting and 94% AUC-ROC. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Lung cancer

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