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检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

共 4166 条记录，以下是171-180 订阅

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Using Deep learning to Study Key Electrode Points in AIEEG 24

Using Deep Learning to Study Key Electrode Points in AIEEG

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proceedings of the 2024 4th international conference on Big Data, Artificial Intelligence and Risk Management

作者： Guoxia Zou Guangdong Open University (Guangdong Polytechnic Institute) Guangzhou Guangdong China

ISBN: (纸本)9798400709753

Over 1 billion people worldwide are living with the inconvenience caused by disabilities. With the rapid development of artificial intelligence technology, human-machine interface technology can lead to a qualitative breakthrough in helping people with mobility difficulties. Although the related research aims to understand the users’ action needs through an electroencephalogram (EEG), too many electrode points are currently used, limiting this technology's application. Indeed, finding the key electrode points will ease the transformation of EEG research into a real-world application. Studying the key electrode points in AIEEG (Action Idea Electroencephalogram) is significant in achieving this goal. Hence, this study used the LSR (least squares regression) algorithm to calculate the feature coefficients of 16-channel data and conducted grouping experiments based on the characteristics of the feature coefficients. then, used the Conv1d-GRU model to train and verify the collected data of the electrode points corresponding to each group of characteristic coefficients. the verification results demonstrate that four electrode points are required in the AIEEG to collect data and identify five action ideas. Relying on these four points provides a testing accuracy of 99%.

关键词： Action Idea Electroencephalogram

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Integrating Optimized CNN and Deep CNN Model for Enhanced Maize Plant Leaf Disease Classification and Prediction Systems 4th

Integrating Optimized CNN and Deep CNN Model for Enhanced Ma...

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4th international conference on Advanced Network Technologies and Intelligent Computing, ANTIC 2024

作者： Deepan, P. Vidya, R. Kalaivani, K. Ganesh, J. Dhiravidaselvi, S. St. Peter’s Engineering College Hyderabad500043 India School of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of CSE Srinivasa Ramanujan Centre SASTRA Deemed to Be University Kumbakonam India Roever Engineering College Perambalur621212 India

ISBN: (纸本)9783031837890

India’s economy is supported by agriculture crops, which is one of the reasons why it is considered to be one of the developing nations in the globe. the detection of crop diseases is still a challenging process, despite the fact that crop diseases pose a significant threat to the nation’s food supply. ANN, SVM, k-NN, and CNN are only some of the classic classification methods that can be utilized in this context. Both machine learning and Deep learning are also available for application. As a consequence of this, the accuracy of such systems has reached its maximum level because they are dependent on a feature extraction process that is built by hand. For the purpose of improving the accuracy of our classification method, it is essential to merge more than one deep learning model for the classification of maize leaf disease. therefore, in order to effectively address this matter, we have developed a hybrid plant disease recognition model in order to enhance the learning outcomes of Deep CNN. this was accomplished by combining two models, namely VGG-16 and optimized CNN model, into a single model. For the goal of evaluating the effectiveness of the maize crop in the experiments, 4,400 images of maize crop leaves were taken from the Village Net dataset. these images included Common_rust, Gray_leaf_spot, Healthy and Northern_leaf_blight. According to the findings of our investigation, we have shown that a hybrid maize disease recognition model (VGG-16 Net model and Optimized CNN model) performs better than separate deep learning models (VGG-16 Net model and Optimized CNN model). © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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False Data Injection Attack Prediction Using Federated Deep learning Approach 4th

False Data Injection Attack Prediction Using Federated Deep ...

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4th international Research conference on machine learning, Deep learning, and Blockchain, IRCICD 2023

作者： Mouli, K. Arun, P. Vaitheeswari, R. Sridhar, S. Department of CSE SRM Institute of Science and Technology Vadapalani Chennai India

ISBN: (纸本)9783031882364

A unique data-driven methodology for system state estimation against attacks from bogus data injection that cannot be observed is provided in this study. the proposed system continuously detects and classifies attacks involving false data injection. Using the knowledge that has been learned, the control signal is then retrieved. this method is carried out by three main modules that implement state-of-the-art detection categorization and retrieval of control signals. the detecting module keeps track of any deviation pattern brought on by a complicated plane strike by tracking historical phasor measurement changes. the direction, amount, and ratio of the fake data injection are among the assault elements revealed by this method. With the use of this knowledge, the signal recovery module can swiftly obtain the actual control signal and get rid of the false data that was purposefully added. the classifier module can be used to learn more details about the attack type. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image reconstruction

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Streamlining Financial Predictions: A Web application for Stock, Cryptocurrency, and Market Sentiment Analysis

Streamlining Financial Predictions: A Web Application for St...

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Sentiment Analysis and Deep learning (ICSADL), international conference on

作者： Chettiyar Vani Vivekanand P. Kumar A. Amizhthini Computer Science and Engineering Rajalakshmi Engineering College Chennai India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Among the high-degree lines of study in this area, one of the most interesting ones is stock price prediction, particularly in the context of machine learning and time series forecasting models. the traditional past ones include ARIMA, and SARIMAX, while deep learning models have been recently applied for predicting the stock prices based upon historical data. However, these models do hold much promise and also come with volatility and non-linearity in financial markets. Causes for such inaccuracies in predictions include sudden market shifts, external economic events, and investor sentiment. All these factors are difficult to model well. this study will propose a web application that uses advanced time series models to try and overcome such challenges. the system is implemented with Streamlit and Flask, so it gives users a smooth interface. It has five critical modules: Nifty 50, which deals with the analysis and forecasting of stocks using ARIMA and SARIMAX models; Global Market Data, which fetches real-time data from the Yahoo Finance API for granular analysis; Cryptocurrency, which relies heavily on Ethereum data with historical analysis and price forecasting; Twitter Sentiment Analysis, which focusses on real-time analysis of market sentiment through tweets; and the prediction module, which evaluates the accuracy of the models using Root Mean Square Error to measure prediction quality. this solution will provide a straightforward yet potent platform for analyzing stocks, cryptocurrency, and sentiment in real-time. the goal is to raise precision, reliability, and overall user experience concerning financial prediction, while more valuable insights become available to the investor and the researcher, having multiple sources integrated into the model.

关键词： Sentiment analysis Analytical models Social networking (online) Biological system modeling Time series analysis Predictive models Data models Real-time systems Cryptocurrency Forecasting

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Automatic Extraction of PDF Table Data Based on Deep learning 25

Automatic Extraction of PDF Table Data Based on Deep Learnin...

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proceedings of the 2025 4th international conference on Big Data, Information and computer Network

作者： Wen Yang Min Yu Shandong Yingcai University Jinan Shandong China Shandong Key Laboratory of Electric Power Big Data Processing and Security Jinan Shandong China

ISBN: (纸本)9798400712425

this article delves into the automatic extraction technology of PDF table data based on deep learning, with a particular focus on the application and performance of the PaddleOCR framework in this field. With the advent of the information age, PDF documents have become one of the primary formats for data exchange and information storage, and table data, as an important component thereof, renders its automatic extraction technology particularly crucial. Subsequently, the article highlights the key features and applications of the PaddleOCR framework in extracting PDF table data. As a powerful OCR tool, PaddleOCR supports multi-task learning, including text detection, text recognition, and layout analysis, among others, enabling it to comprehensively understand the structure of PDF documents, especially those containing tables. the article conducts a detailed evaluation of PaddleOCR's performance in extracting PDF table data. By comparing the recognition results with the original tables, it is found that PaddleOCR performs exceptionally well when processing most tables, with a recognition accuracy rate of up to 93%. However, when dealing with complex tables containing merged cells and watermarks, issues such as redundant characters and recognition errors still exist. In response to these issues, the article proposes optimization solutions, such as utilizing the PP-PicoDet model for layout analysis and incorporating a watermark removal step, ultimately increasing the recognition accuracy rate to 96.4%. Finally, the article summarizes the advantages and disadvantages of PaddleOCR in extracting PDF table data and proposes future research directions.

关键词： Deep learning

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5th international conference on Innovative Intelligent Industrial Production and Logistics, IN4PL 2024

5th International Conference on Innovative Intelligent Indus...

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5th international conference on Innovative Intelligent Industrial Production and Logistics, IN4PL 2024

ISBN: (纸本)9783031807596

the proceedings contain 61 papers. the special focus in this conference is on Innovative Intelligent Industrial Production and Logistics. the topics include: An Out-of-Sample Clustering Ensemble Method for Defect Detection and Classification in Metal Additive Manufacturing;Advanced Process Monitoring and OEE Metrics: Leveraging AASs for Efficiency;obsolescence Forecasting of Intel Processors: A Transformers Approach;machine learning Tool for Yield Maximization in Cream Cheese Production;comparative Evaluation of Irregular Shape Strip-Packing Algorithms;an Inventory Management Support Tool through Indirect Q-Value Estimation: A Combined Optimization and Forecasting Approach;multiscale Clustering to Improve Anomaly Detection in Nuclear Equipments;clustering Analysis for Forecasting Medicine Consumption;a Decision Support System of the Configuration of a Supermarket in a Components Company for the Automotive Industry;a Distributed Framework for Cooperative Scheduling of Production, Transportation, and Maintenance Using Multi-Agent Systems;the Role of Actor’s Creative Self-efficacy in AI Enabled Value Creation;building Realistic Environment from computer Vision Approach Applied to Manufacturing Simulation in the Digital Twin Context;towards a Process-Based Industry 5.0 Maturity Model: A Feasibility Study in Supply Chain;a Multi-objective Genetic Algorithm Approach for Multi-component Products Recovery and Remanufacturing Planning;reinforcement learning for Optimizing Routing in the Production Supply of Matrix Production Systems;an Innovative Fault Detection Robotic Tool for Overhead Cranes in Industries: Magnetic Wheel Modelling and Experimental Validation;current Trends and Future Challenges to Put Circular Manufacturing in Practice;enhancing Human – Robot Collaboration in the Industry 5.0 Framework with Physics-Informed Neural Networks: application to Collision Detection;AI-Driven Smart Air Conditioning System for a Sustainable and Energy-Efficient Industrial Future.

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5th international conference on Innovative Intelligent Industrial Production and Logistics, IN4PL 2024

5th International Conference on Innovative Intelligent Indus...

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5th international conference on Innovative Intelligent Industrial Production and Logistics, IN4PL 2024

ISBN: (纸本)9783031807749

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Research on Autonomous Vehicle Trajectory Planning for Complex Urban Environments 25

Research on Autonomous Vehicle Trajectory Planning for Compl...

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proceedings of the 2025 4th international conference on Big Data, Information and computer Network

作者： Bingbing Long Sixuan Leng Xingzhi Lin GuangXi Vocational Normal University Nanning Guangxi China Guangxi Universities Key Laboratory of Application Technology of Intelligent Connected Vehicle Nanning Guangxi China GuangXi Vocational Normal University Nanning Guangxi China

ISBN: (纸本)9798400712425

In terms of the impact of complex urban environments on the trajectory planning of self-driving vehicles, there are issues such as the complexity of the urban traffic environment, the diversity of traffic participants, and the suddenness of traffic events. A solution for trajectory planning of self-driving vehicles is proposed in terms of improving travelling safety and enhancing traffic efficiency. In order to solve this problem, integrated environment sensing and path planning techniques are used to construct a comprehensive framework for autonomous driving trajectory planning. Using proximal policy optimisation and reinforcement learning methods to train the dynamic scenario trajectory planning strategy for self-driving cars, we obtain an economical trajectory planning strategy network that can take into account a variety of constraints, combine various sensors to obtain the information of the surrounding environment and combine with high-precision maps and advanced algorithms to sense and understand the road situation in real time, and make the corresponding decisions and control. the results of the study show that the research can improve the ability of self-driving vehicles to cope with complex urban environments and the safety and stability of driving.

关键词： Autonomous driving

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4th international Workshop on Human Brain and Artificial Intelligence, HBAI 2024

4th International Workshop on Human Brain and Artificial Int...

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4th international Workshop on Human Brain and Artificial Intelligence, HBAI 2024

ISBN: (纸本)9789819640003

the proceedings contain 28 papers. the special focus in this conference is on Human Brain and Artificial Intelligence. the topics include: Uncovering Cognitive Taskonomy through Transfer learning in Masked Autoencoder-Based fMRI Reconstruction;Interpersonal Relationship Analysis with Dyadic EEG Signals via learning Spatial-Temporal Patterns;Effect of Music Training in Neural Responses to Emotional Speech Prosody: Insights from EEG and Brain Network Analysis;potential Indicator for Continuous Emotion Arousal by Dynamic Neural Synchrony;Exploring EEG-Based Neural Correlates of Multivariate Ordinal Emotion Representations;the Co-varying Multimodal Pattern in Treatment-Resistant and Non-treatment-Resistant Schizophrenia;investigating the Dynamics of Seizure Neuroactivities Using Hidden Markov Model;suppressing Seizure via Optimal Electrical Stimulation to the Hub of Epileptic Brain Network;SVFormer: A Direct Training Spiking Transformer for Efficient Video Action Recognition;BL-BERT: Extracting Body Language from Behavior Sequences in Freely Moving Mice;Benchmarking Neural Decoding Backbones Towards Enhanced On-Edge iBCI applications;Enhanced Local Attention with Deep Neural Networks for EEG Decoding;Mirror Contrastive Loss Based Sliding Window Transformer for Subject-Independent Motor Imagery Based EEG Signal Recognition;Active Urination Detection Using EEG Based on FBCNet;D2CAN: Domain-Guided Contrastive Adversarial Network for EEG-Based Cross-Subject Cognitive Workload Decoding;group-Specific Fusion Model and Its application in Identifying Multimodal Co-varying Diagnostic Patterns for Psychiatric Disorders;multi-category Brain Tumor Segmentation via Multi-scale and Cross-category Relation Modeling;consistent Brain Age Difference in Childhood Autism Spectrum Disorder and its Subtypes;Brain-Aware Readout Layers in GNNs: Advancing Alzheimer’s Early Detection and Neuroimaging;TSICNet: Importance of Connectome Information for Epilepsy Classification;a Brain-Inspired Distr

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Deep learning Approach to Indian Sign Language Recognition 4th

Deep Learning Approach to Indian Sign Language Recognition

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4th international Research conference on machine learning, Deep learning, and Blockchain, IRCICD 2023

作者： Vilashni, N. Anju Uthika, B. Sanjana, A. Yegu Prabha, B. Department of Computer Science and Engineering SRM Institute of Science and Technology Vadapalani Chennai India

ISBN: (纸本)9783031882364

Communication is one of civilization’s fundamental demands. About 20% of the global population has disabling hearing loss. the deaf and hard-of-hearing people mostly use sign language. It may also be used by individuals who can listen but cannot speak and by those who have deaf family members. Our proposed method aims to convert real-time data of Indian Sign Language (ISL) into text and audio data. the equivalent American Sign Language (ASL) field has seen many studies. However, some obstacles hinder further analysis of ISL. the main obstacle preventing further ISL research has been the lack of standard datasets, hidden characteristics, and local language variance. the majority of the work is based on handcrafted facets. Indian Sign Language (ISL) has static and dynamic signs and various gestures for the same expression depending on the area of India. It makes constructing a standardized dataset very challenging. Furthermore, there is no standard collection accessible. Hence, we were forced to make our dataset which consists of alphabets (A-Z) and numbers (0–9). the proposed method uses a custom-made Convolution Neural Network (CNN) model to classify 36 signs. Once the CNN model recognizes the sign, the output label is printed as text. this proposed model achieves a validation accuracy of 99.4%. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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