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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41-50 订阅

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ESSCIRC 2022 - IEEE 48th European Solid State Circuits conference, Proceedings

ESSCIRC 2022 - IEEE 48th European Solid State Circuits Confe...

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3rd international conference on Artificial Intelligence and signal processing, AISP 2023

ISBN: (纸本)9798350320749

The proceedings contain 128 papers. The topics discussed include: brain tumor segmentation using deep learning techniques: comparative analysis;imperceptibility and robustness study in different transform domain for copyright protection in digital image watermarking in hybrid SVD-domain;optimized detection of continuous gravitational-wave signals using convolutional neural network;automated method for calibration findings of auto slide scanner using automated digital microscopy;implementation of perovskite solar cells using GPVDM;mangoes taste detection system using deep learning;bird species recognition using deep learning;optimization of power and delay in VLSI circuits using hybrid flip-flop circuit;ellipse shaped graphene metamaterial absorber for terahertz applications;design and development of a microgripper for use in pipeline inspection robot;and screening of citrus diseases using deep learning embedders and machine learning techniques.

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Convolutional neural network-based signal discrimination for P300 brain-computer interface 8

Convolutional neural network-based signal discrimination for...

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8th international conference on Intelligent Computing and signal processing, ICSP 2023

作者： Liao, Yongzhi Zhang, Liumei Wang, Yaoxin Xi'an Shiyou University School of Computer Science Xi'an China

ISBN: (纸本)9798350302455

The P300-based brain-machine interface can achieve efficient communication between human brain and computer, and help physically disabled people achieve autonomous control of external devices. In this paper, we propose a convolutional neural network-based signal detection method, which includes signal pre-processing, construction and training of convolutional neural network, and testing and performance evaluation of the model. This paper conducts experimental validation on dataset II of the 3rd BCI competition and compares it with some traditional machine learning methods and deep learning methods. The test accuracy of the experimental results reaches 0.85 and the AUC reaches 0.93, which indicates that the method has significantly improved in classification accuracy and model performance, and has high practicality and application prospects. © 2023 IEEE.

关键词： Convolutional neural networks

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

3rd International Conference on Machine Vision and Augmented...

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

ISBN: (纸本)9789819743582

The proceedings contain 76 papers. The special focus in this conference is on machine Vision and Augmented Intelligence. The topics include: Survey on Robustness of Deep learning Techniques on Adversarial Attacks in WBAN;synergizing Collaborative and Content-Based Filtering for Enhanced Movie Recommendations;exploring Transformer-Based Approaches for Hyperspectral Image Classification: A Comparative Analysis;deep learning for Cognitive Task and Seizure Classification with Hilbert–Huang Transform and Variational Mode Decomposition;tracking of Ship and Plane in Satellite Videos Using a Convolutional Regression Network with Deep Features;Tumor Detection and Analysis from Brain MRI Images Using Deep learning;software Maintenance Prediction Using Stack Ensemble Deep learning Algorithms;resource Allocation in 6G Network for High-Speed Train Using D2D Outband Communication;controlling the Band-to-Band Tunneling Effect in Charge Plasma Based Dopingless Transistor;Comparison of Different CIC Filter Architectures on the Basis of a Novel Parameter Called Noise Factor for Sigma-Delta Based ADCs;the Scientific Analysis on Effective Yoga Posture Recognition Techniques;impact of Gamma Rays on Emerging Devices for Photonic Applications;shaft Rotation Monitoring Using Radar signal processing and Wavelet Transform;gysel Power Divider Miniaturization Using an Inter-Digital Capacitor-Based Slow-Wave Structure;noise Estimation and Removal in Fundus Images Using Pyramid Real Image Denoising Network;evaluation of Hybrid Encryption Method to Secure Healthcare Data;multimodal Face Recognition System Using Hybrid Deep learning Feature;Classification of Copy and Move Image by Using HELM-FSK Method: An Efficient Approach;analysis of Energy Efficient Smart Home Based on IoT System;role of Explainable Artificial Intelligence Approaches in Cybersecurity.

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Efficient Hardware and Software Co-design for EEG signal Classification based on Extreme learning machine 3

Efficient Hardware and Software Co-design for EEG Signal Cla...

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3rd international conference on Electrical, Computer and Communication Engineering, ECCE 2023

作者： Lyu, Songyang Chowdhury, Mehdi Hasan Cheung, Ray C.C. City University of Hong Kong Department of Electrical Engineering Hong Kong Chittagong University of Engineering and Technology Department of Eee Chittagong Bangladesh

ISBN: (纸本)9798350345360

ElectroEncephaloGram (EEG) is associated with multiple functions, including communications with neurons, organic monitoring, and interactions with external stimuli. By decoding EEG signals, certain human activities such as sleeping, brain diseases, motor imagery, movement of limbs, and others can be observed and controlled through the brain-computer interface (BCI). Therefore, it is vital to efficiently process EEG signals with a robust and accurate system to build BCI systems with powerful applications. However, as a weak biosignal, EEG demands a fast-reaction system signal processing with high accuracy and sensitivity. In this work, a hardware/software co-design network based on Extreme learning machine (ELM) is introduced for the classification of certain actions, motor imagery of the human brain. This system is based on an optimized Hierarchical Extreme learning machine (H-ELM) on the software layer. The proposed method has advantages over previous designs with an accuracy of 90.3%. It also improves the training speed by around 25X compared to conventional methods. The software model is also translated into efficient FPGA hardware design to maintain high computation efficiency and reduce power consumption for biomedical applications. © 2023 IEEE.

关键词： Electroencephalography

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EEG mental arithmetic task levels classification using machine learning and deep learning algorithms 3

EEG mental arithmetic task levels classification using machi...

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3rd international conference on Artificial Intelligence and signal processing, AISP 2023

作者： Nirde, Krashana Gunda, Manasa Manthalkar, Ramchandra Gajre, Suhas SRTMU Vishnupuri Nanded Electronics and Telecommunication Engg.SGGSIE&T Nanded India

ISBN: (纸本)9798350320749

EEG signals are often-used modalities for monitoring mental arithmetic performance. In order to learn robust characteristics and predict the level of classification from EEG, we provide a deep neural networks and machine learning architecture. In this research, we considered online public dataset related to mental arithmetic task. EEG dataset recording are stored last 3 minutes of resting period (180 sec before task) and first 1 minute of the mental arithmetic task period (60 sec-while doing task). The task involves series of subtraction performed by 36 participants. The participants were divided among Good (1) and Bad (0) based on task level. We used machine learning and deep learning methods to classify three different task levels. (i.e., no task, normal task, and difficult task). In algorithm 1 we extracted five features like skewness, kurtosis, mean, standard deviation and variance from the EEG signals and applied as input to the classifiers. Here three machine learning classifiers used for three task levels classification like SVM, KNN and Decision Tree. KNN obtained high classification with 91% whereas SVM obtained S9% and Decision Tree obtained 65%. Algorithm 2 uses ANN and LSTM, two deep learning algorithms, to learn directly from raw EEG data. For better performance we used optimization techniques. ANN used rmsprop and obtained the classification accuracy of 96.80% whereas LSTM uses Adam and SGD and obtained the classification accuracy of 94%. Therefore, when raw data applied to deep learning algorithms can perform well when grouping of handmade features given to a machine learning algorithm. © 2023 IEEE.

关键词： learning algorithms

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3rd international conference on Optimization, learning Algorithms and Applications, OL2A 2023

3rd International Conference on Optimization, Learning Algor...

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3rd international conference on Optimization, learning Algorithms and Applications, OL2A 2023

ISBN: (纸本)9783031530357

The proceedings contain 26 papers. The special focus in this conference is on Optimization, learning Algorithms and Applications. The topics include: Pest Detection in Olive Groves Using YOLOv7 and YOLOv8 Models;Using LiDAR Data as Image for AI to Recognize Objects in the Mobile Robot Operational Environment;an Evaluation of Image Preprocessing in Skin Lesions Detection;an Artificial Intelligence-Based Method to Identify the Stage of Maturation in Olive Oil Mills;vehicle Industry Big Data Analysis Using Clustering Approaches;enhancing Forest Fire Detection and Monitoring Through Satellite Image Recognition: A Comparative Analysis of Classification Algorithms Using Sentinel-2 Data;An Efficient GPU Parallelization of the Jaya Optimization Algorithm and Its Application for Solving Large Systems of Nonlinear Equations;Multi-objective Optimal Sizing of an AC/DC Grid Connected Microgrid System;sub-system Integration and Health Dashboard for Autonomous Mobile Robots;movement Pattern Recognition in Boxing Using Raw Inertial Measurements;optimization Models for Hydrokinetic Energy Generated Downstream of Hydropower Plants;deep Conditional Measure Quantization;fault Classification of Wind Turbine: A Comparison of Hyperparameter Optimization Methods;Assessing the Reliability of AI-Based Angle Detection for Shoulder and Elbow Rehabilitation;deep learning and machine learning Techniques Applied to Speaker Identification on Small Datasets;performance of Heuristics for Classifying Leftovers from Cutting Stock Problem;Deep learning-Based Classification and Quantification of Emulsion Droplets: A YOLOv7 Approach;identification of Late Blight in Potato Leaves Using Image processing and machine learning;adaptive Convolutional Neural Network for Predicting Steering Angle and Acceleration on Autonomous Driving Scenario;Impact of EMG signal Filters on machine learning Model Training: A Comparison with Clustering on Raw signal;assessing the 3D Position of a Car with a Single 2D Camera Usin

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3rd international conference on Optimization, learning Algorithms and Applications, OL2A 2023

3rd International Conference on Optimization, Learning Algor...

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3rd international conference on Optimization, learning Algorithms and Applications, OL2A 2023

ISBN: (纸本)9783031530241

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A machine learning Model on Healthcare Based Chatbot and Appointment System 3

A Machine Learning Model on Healthcare Based Chatbot and App...

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3rd international conference on Artificial Intelligence and signal processing, AISP 2023

作者： Srivastava, Kushagra Pandey, Trilok Nath Roy, Debaraj Sahoo, Sambit Vellore Institute of Technology School of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350320749

This paper discusses the clinical chatbot which could examine the contamination and deliver essential insights regarding the contamination previous to counseling a specialist. To lower the healthcare charges and similarly increase openness to clinical facts the clinical chatbot is constructed. Certain chatbots pass approximately as clinical reference books, which facilitates the affected person to discover approximately their contamination and assists with running on their fitness. The person can accomplish the real gain of a chatbot simply while it is able to examine all forms of infections and deliver essential data. In later sections of the paper, a doctor appointment system software is also discussed that is integrated with the chatbot so that the specialist which is recommended by medical chatbot can be easily visited at the mutually agreed time of both specialist and patient. Such an appointment system has its own advantages like it reduces the waiting time of the patient, patients can choose the appointment time in keeping with their desire additionally to be had and booked slots are proven in powerful graphical person interface. © 2023 IEEE.

关键词： Health care

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Anomalous Sound Pattern Detection for machine Health Monitoring 3rd

Anomalous Sound Pattern Detection for Machine Health Monitor...

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3rd international conference on Artificial Intelligence and Knowledge processing (AIKP)

作者： Dalmia, Shivali Rege, Manjeet Univ St Thomas St Paul MN 55105 USA

ISBN: (纸本)9783031686160;9783031686177

Anomaly detection using audio signals from industrial machines in the manufacturing industry has gained broad interest over the last few years. For example, predictive maintenance solutions utilize raw analog signals to identify trends and patterns. In a few scenarios, an engineer working in a factory setting can tell when a machine is behaving abnormally just by hearing unexpected sounds (e.g., the loudness of sound) that are well within the human perceivable frequency range (20 Hz - lowest pitch to 20 kHz - highest pitch) which are typically concentrated in a narrow range of frequencies and amplitudes. The human perception of the amplitude of a sound is its loudness. However, the audio signal in its raw form is not always the best representation of the important features (e.g., frequencies, amplitude, peaks). Additionally, the machine learning applications which rely on using traditional digital signal processing techniques (e.g., digital signal processors, chips) have a lot of dependency on subject matter experts to tune the system for a better performance. Thus, we investigate how the digital transformation of waveform signals from microphone sensors (e.g., Audio recordings of industrial pumps, valves, slide rails) into Spectrograms (A spectrogram is a voiceprint of a signal which expresses an audio signal as an image using different colors to indicate the amplitude or strength of each frequency.) can help to monitor machine health (e.g., anomaly classification). In the pre-processing phase, raw audio signals (.WAV format) from each machine are converted to Mel Spectrogram images using short-term Fourier transformation. Then, comparative study of image classification techniques using deep convolutional neural networks (CNN) with and without data augmentation, is conducted to classify images as normal or abnormal. The approach is evaluated using Malfunctioning industrial machine investigation and inspection dataset [1-3] (MIMII dataset). Results show that the neu

关键词： Anomaly detection machine health monitoring Predictive maintenance

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3rd international conference on Artificial Intelligence and Knowledge processing, AIKP 2023

3rd International Conference on Artificial Intelligence and ...

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3rd international conference on Artificial Intelligence and Knowledge processing, AIKP 2023

ISBN: (纸本)9783031686160

The proceedings contain 28 papers. The special focus in this conference is on Artificial Intelligence and Knowledge processing. The topics include: How Do Senior Secondary Level Students and Their Teachers Perceive Artificial Intelligence and Its Implementation? An Exploratory Study;anomalous Sound Pattern Detection for machine Health Monitoring;performance Evaluation of Various machine learning Algorithms for Lung Cancer Prediction Using Demographic Data;enhancing Stock Portfolio Optimization Based on a Hybrid Approach Using Artificial Bee Colony Optimization and Firefly Optimization;Enhancing Yarn Quality in the Cotton Industry: AI- Based Nep Detection for Improved Manufacturing Processes;breast Cancer Diagnosis from Ultrasonic Image and Histopathology Image Using Deep learning Approach;Advancing Time Series Forecasting: LSTM Networks with Multiple Attention Mechanisms;trajectory Tracking and Navigation Model for Autonomous Vehicles Using Reinforcement learning;quantum Graph Neural Networks Based Protein-Ligand Classification;comparative Analysis on Speech Driven Gesture Generation;enhancing Deep learning: Leveraging Skip Connections and Memory Efficiency;quality-Based Decision-Making Using Image processing for Supply Chain Management;enhancing Endometrial Tumor Detection: Early Diagnosis with Advanced Vision Transformer Architecture;sweetSight: A Deep Convolutional Neural Network Approach for Automatic Categorization of Bengal Sweets;a Systematic Review: How Computer Vision is Transforming Agriculture in Economic Growth;automatic Conversion of Broadcasted Football Match Recordings to Its 2D Top View;measuring the Vehicle-in-Motion, Density and Allocation of Traffic signal Using Transfer learning;Ensemble Model of VGG16, ResNet50, and DenseNet121 for Human Identification Through Gait Features;Performance of Sentiment Analysis APIs on Political Opinion Polling;summarization of Telugu Text Discourses;crowd-Sourced Supervisors for the Automatic Invigilation of Online

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