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检索条件"任意字段=3rd International Conference on Signal Processing and Machine Learning, CONF-SPML 2023"

共 147 条记录，以下是81-90 订阅

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machine learning Model Based on Deep Neural Networks for Emotion Detection Using Audio-Visual Modalities 3rd

Machine Learning Model Based on Deep Neural Networks for Emo...

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3rd international conference on signal and Data processing, ICSDP 2023

作者： Jeevakumari, S. A. Amutha Mohanty, Gyanadipta Vellore Institute of Technology Chennai India

ISBN: (纸本)9789819795772

Emotion identification is a challenging endeavor since emotions can manifest in so many different ways. Recent advances have been made in the field, particularly with the development of deep neural networks, which have shown to be astonishingly good at recognizing emotional states. This work set out to investigate the difficulties of model-level fusion in order to develop a sophisticated multimodal model capable of simultaneously assessing both video and audio inputs for Emotion Recognition. To rigorously evaluate the performance of this novel approach, the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) dataset was chosen as the ideal testing ground. This diverse dataset encompasses a wide range of emotional expressions, making it an excellent benchmark for assessing the proposed model’s accuracy and robustness. By combining cutting-edge deep learning techniques with a multimodal approach, this study aims to contribute to the evolving landscape of Emotion Recognition, with the potential to enhance applications in fields such as affective computing, human–computer interaction, and emotional well-being assessment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

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A Comparative Analysis of Denoising Techniques in Image processing 3rd

A Comparative Analysis of Denoising Techniques in Image Proc...

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3rd international conference on machine Vision and Augmented Intelligence, MAI 2023

作者： Jaiswal, Nitesh Kumar Srivastava, Subodh Mahto, Manipuran Department of Electronics and Communication Engineering NIT Patna Bihar Patna India

ISBN: (纸本)9789819743582

This paper presents the results obtained from an experimental study on the effectiveness of a noise reduction technique applied to medical images. This study compares the effectiveness of various noise-removal filters in order to determine which is the most efficient. Mode, median, Gaussian, adaptive median, non-local mean, wavelet, and deep learning-based denoising convolutional neural network (CNN) filters were used. This paper seeks to demonstrate experimental results on two types of noisy input images used: impulse noise and Gaussian noise, and compare them to the denoising effect of various methods. Among all the methods evaluated, the deep learning-based Convolutional Neural Network (CNN) approach demonstrates superior performance compared to the other techniques. While maintaining the edges and fine structures in the images, this method was able to eliminate both additive and multiplicative noise. The evaluation of these methods performance primarily relied on several metrics, including visual assessment, Mean Square Error (MSE), Peak signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Normalized Absolute Mean (NAM), and Cross-Correlation (CC). These metrics provided a comprehensive analysis of the effectiveness and quality of the methods in reducing noise and preserving image details. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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machine learning based wordle difficulty analysis 3

Machine learning based wordle difficulty analysis

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3rd international conference on Advanced Algorithms and signal Image processing, AASIP 2023

作者： Su, Pujun Wang, Shanqi Wang, Ting Department of Information Science and Engineering Ocean University of Chin 238 Miaoling Road Laoshan District Qingdao China Department of Marine Technology Ocean University of China 238 Miaoling Road Laoshan District Qingdao China

ISBN: (纸本)9781510668522

Wordle is a popular game, and it is necessary to analyze the massive amount of data to help improve the game. For the prediction problem of the number of reports, the three-Times exponential smoothing prediction model with better prediction results was selected. This study collected the relationship between word attributes and the percentage of the number of reports in the difficult mode. After data preprocessing, the RMAE test and o '.2 test were performed on the prediction effects of the two models;For the problem of predicting the percentage of attempts (1, 2, 3, 4, 5, 6, X), a BP neural network model optimized using a genetic algorithm was built and the results showed that the confidence level of the model was 0.742, which tested by prediction of the percentage distribution of words corresponding to the number of attempts per day;For the word difficulty prediction grading problem, this study built a hierarchical clustering model and solved the optimal number of clusters K=4 for word classification according to the elbow rule, and obtained the classification results using the improved BP neural network model to train the data. The results showed that the accuracy of the model was 0.831. This study can improve Wordle, enhance the user's sense of game experience, and attract more people to participate in the game. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Forecasting

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3rd international Workshop on New Approaches for Multidimensional signal processing, NAMSP 2022

3rd International Workshop on New Approaches for Multidimens...

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3rd international Workshop on New Approaches for Multidimensional signal processing, NAMSP 2022

ISBN: (纸本)9789811978418

The proceedings contain 23 papers. The special focus in this conference is on New Approaches for Multidimensional signal processing. The topics include: Development of IoT Indoor Monitoring System for Independent Elderly;improving the Process of Evaluating User Stories Using the Paraconsistent Annotated Evidential Logic Eτ;neural Network Algorithm Applied in Electrical Engineering Automation;Truss Structure Optimization Design Based on FE-PSO-SQP Algorithm;practice System of Ant Colony Optimization Algorithm in Business Administration;literature Review of Smart Contracts Using Blockchain Technology;a Comprehensive Study of 5th Generation Scheduling Algorithms;a Comparative Analysis of Homogeneous and Heterogeneous Protocols to Maximize the Lifetime of Wireless Sensor Network for Precision Agriculture;the Integration Development and Upgrading Path of Industry 4.0 Architecture Industrial Engineering Network Driven by Big Data;video Tracing of Moving Objects by Fusing Three-Term Decompositions;SAAS Application Prospect Analysis in Hrm and Methods to Upgrade the Contemporary System;All Digital Phase Locked Loop (ADPLL) and Its Blocks—A Comprehensive Knowledge;on Realization of Smart Logistic Warehouse Management with Internet of Things;development and Design of Intelligent Traction System for Bulk Cargo Terminal;deep learning Approaches for Classroom Audio Classification Using Mel Spectrograms;Tensor Spectral Pyramid for Color Video Sequences Representation, Based on 3D FO-AHKLT;electromyography signal Acquisition, processing, Optimization and Its Applications;research on the Radar signal Classification Method Based on the Deep Faith Network Model;measuring machine Intelligence Using Black-Box-Based Universal Intelligence Metrics;COVID Detection Using ECG Image Reports: A Survey;disease Detection Techniques in Plants: Transition from Manual to Automation.

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EEG-Based PD classification model coupled with machine learning

EEG-Based PD classification model coupled with machine learn...

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3rd international conference on Biomedicine and Bioinformatics Engineering, ICBBE 2023

作者： Bian, Zhen Department of Microelectronics Science and Engineering Sun Yat-sen University Guangdong China

ISBN: (纸本)9781643684321

One of the most prevalent neurological brain diseases is Parkinson's disease, which can be diagnosed a long time ago with a variety of clinical methods. In recent years, it has been common practice to use Electroencephalography (EEG) signal analysis to identify dementia in its early stages because of its high speed, low cost, and accessibility. Many novel methods which apply EEG to the diagnosis of Parkinson's disease are shown to be simple and effective. Recent years have seen the development of EEG signal processing as a key technique for researchers to gather appropriate features for Parkinson's disease diagnosis. In this study, a novel system was created for computer-Aided diagnosis that is capable of extracting features from EEG signals and discriminating patients affected by Parkinson's disease. After per processing the EEG data, the Butterworth filter has been used to decompose the signals into four frequency sub-bands. Welch's PSD features were then extracted as the input of supervised machine learning methods-The k-Nearest Neighbor (KNN) to classify EEG features into Parkinson's disease (PD) and healthy controls (HC). The 10-fold cross-validation has been employed to validate the performance of this model. The results achieve 98.82% accuracy, 99.19% sensitivity, and 91.77% specificity, respectively. The acquired findings demonstrate the validity of our strategy and that our diagnosis method is improved when compared to earlier research. At last, this novel method may be a supplementary tool for the clinical diagnosis of Parkinson's disease. © 2023 IOS Press. All rights reserved.

关键词： Electroencephalography

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3rd EAI international conference on Cognitive Computing and Cyber Physical Systems, IC4S 2022

3rd EAI International Conference on Cognitive Computing and ...

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3rd EAI international conference on Cognitive Computing and Cyber Physical Systems, IC4S 2022

ISBN: (纸本)9783031289743

The proceedings contain 22 papers. The special focus in this conference is on Cognitive Computing and Cyber Physical Systems. The topics include: Performance Evaluation of Fast DCP Algorithm for Single Image Dehazing;Non Destructive Analysis of Crack Using Image processing, Ultrasonic and IRT: A Critical Review and Analysis;ioT Enabled Driver Compatible Cost-Effective System for Drowsiness Detection with Optimized Response Time;Voice Based Objects Detection for Visually Challenged Using Active RFID Technology;depth Estimation and Navigation Route Planning for Mobile Robots Based on Stereo Camera;Water Level Forecasting in Reservoirs Using Time Series Analysis – Auto ARIMA Model;water Quality Monitoring Using Remote Control Boat;study of Smart City Compatible Monolithic Quantum Well Photodetector;frequency Reconfigurable Antenna for 5G Applications at n77 and n78 Bands;solar Energy Prediction using machine learning with Support Vector Regression Algorithm;analysis of Acoustic Channel Model Characteristics in Deep-Sea Water;A Comprehensive Review on Channel Estimation Methods for Millimeter Wave MIMO Systems;comparison of Acoustic Channel Characteristics in Shallow and Deep-Sea Water;price Estimation of Used Cars Using machine learning Algorithms;machine learning Framework for Identification of Abnormal EEG signal;alzheimer’s Disease Detection Using Ensemble of Classifiers;publishing Data Objects in Data Aware Networking;youTube Comment Analysis Using Lexicon Based Techniques;Information Theoretic Heuristics to Find the Minimal SOP Expression Considering Don’t Care Using Binary Decision Diagrams;single Image Dehazing Through Feed Forward Artificial Neural Network.

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Design of a Free Trade Zone Data Management and Analysis Platform Based on Computer machine learning Technology

Design of a Free Trade Zone Data Management and Analysis Pla...

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signal processing, Communications and Engineering Management conference (ISPCEM), international

作者： Chunyang Liu Yi Liu Shuli Wang School of Economics Wuhan University of Technology Wuhan China

This study is dedicated to architecting a data management and analysis platform, integrating advanced computer technologies and machine learning algorithms. Traditional approaches often fall short in handling and analyzing vast data sets. Thus, this paper proposes an innovative solution: enhancing data processing efficiency and precision through machine learning technologies. The research thoroughly examines the application of machine learning in data management, including the selection of appropriate algorithms, effective model training and optimization, and their integration into a user-friendly platform. The platform's design aims to deliver more accurate data analysis, supporting informed decision-making.

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FC-NT: A crime prediction algorithm incorporating formal context and neural networks 3

FC-NT: A crime prediction algorithm incorporating formal con...

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3rd international conference on Advanced Algorithms and signal Image processing, AASIP 2023

作者： Zao, Feifei Lv, Lingling Ding, Feng Jin, Shixian Guo, Xiaoding Henan Province Engineering Research Center of Spatial Information Processing Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Henan University Zhengzhou450046 China School of Electrical Engineering North China University of Water Resources and Electric Power Zhengzhou450046 China School of Internet of Things Engineering Jiangnan University Wuxi214122 China Henan Provincial Educational Resources Guarantee Center Zhengzhou450003 China

ISBN: (纸本)9781510668522

The task of crime prediction is to predict the charges of a case based on the given description of the case, which has become a research hot spot. Most of the existing methods use neural networks and machine learning algorithms to predict. The prediction results have high accuracy, but the prediction results are often not well interpretable. In addition, some early traditional methods can explain the prediction results, but their accuracy is often very low. Formal concept analysis is an effective data extraction tool. Therefore, this paper combines formal concept analysis with neural network to improve the accuracy of the algorithm. The experimental results show that our method has higher prediction results and can better explain the prediction results than the original neural network model. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Forecasting

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Research on Digital Creative Data processing System Based on Computer machine learning Model

Research on Digital Creative Data Processing System Based on...

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signal processing, Communications and Engineering Management conference (ISPCEM), international

作者： Lei Zheng Mingchao Li School of Business Shenzhen Institute of Technology Shenzhen China

This paper proposes the design and construction method of mobile phone digital creative creation system based on machine learning. In this paper, its architecture, design and implementation are described in detail. The machine learning method based on gradient lifting regression tree is used to obtain the interaction between the communication of each process. Through the statistical analysis of the relevant data, the correlation degree of each digital creative creation is obtained. The analysis of experimental data. Further reduce the technical barriers of mobile terminal digital creativity, and improve the commercial content innovation and service efficiency of mobile terminal digital creativity.

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A TCAD Augmented machine learning Based Drain Current Model for InGaAs FinFETs

A TCAD Augmented Machine Learning Based Drain Current Model ...

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international conference on Applied Electromagnetics, signal processing and Communication (AESPC)

作者： Arya Maity Subir Kumar Maity School of Electronics Engineering Kalinga Institute of Industrial Technology (KIIT) Bhubaneswar Odisha India

In this paper, we describe a framework for model development of InGaAs FinFET based on machine learning. An artificial neural network is used to create the model, and TCAD simulation data was used to train it. The model accurately and efficiently captures changes in gate length, drain, and gate bias voltages. In order to enable circuit simulation utilizing the SPICE environment, the trained model is exported to the Verilog-A language, and circuit simulation is demonstrated.

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