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检索条件"任意字段=5th International Conference on Signal Processing and Machine Learning, SPML 2022"

共 777 条记录，以下是1-10 订阅

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spml 2022 - Proceedings of 2022 5th international conference on signal processing and machine learning

SPML 2022 - Proceedings of 2022 5th International Conference...

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5th international conference on signal processing and machine learning, spml 2022

ISBN: (纸本)9781450396912

the proceedings contain 43 papers. the topics discussed include: performance comparison of seven pretrained models on a text classification task;decorrelation proportionate normalized least mean M-estimate algorithm with variable step-size;cluster analysis for cancer omics data using neural network with data augmentation;sentiment analysis for e-commerce reviews based on deep learning hybrid model;aspect-sited hybrid graph neural networks for sentiment classification;handling concept drift in financial time series data: recurrent Xavier on-line sequential extreme learning machine;an investigation of feature difference between child and adult voices using line spectral pairs;wideband beamspace robust capon beamformer;application of adaptive sub-band filters on active noise control;a unifying framework for blind source separation algorithms based on generalized eigen-value decomposition;research on visual recognition technology of damaged navigation mark based on improved Efficientnet;and human activity recognition based on convolutional neural network via smart-phone sensors.

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4th international conference on machine Intelligence and signal processing, MISP 2022

4th International Conference on Machine Intelligence and Sig...

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4th international conference on machine Intelligence and signal processing, MISP 2022

ISBN: (纸本)9789819900848

the proceedings contain 74 papers. the special focus in this conference is on machine Intelligence and signal processing. the topics include: A Hybrid Approach for Missing Data Imputation in Gene Expression Dataset Using Extra Tree Regressor and a Genetic Algorithm;A Clustering and TOPSIS-Based Developer Ranking Model for Decision-Making in Software Bug Triaging;GujAGra: An Acyclic Graph to Unify Semantic Knowledge, Antonyms, and Gujarati–English Translation of Input Text;attribute-Based Encryption Techniques: A Review Study on Secure Access to Cloud System;Fall Detection and Elderly Monitoring System Using the CNN;precise Stratification of Gastritis Associated Risk Factors by Handling Outliers with Feature Selection in Multilayer Perceptron Model;portfolio Selection Using Golden Eagle Optimizer in Bombay Stock Exchange;hybrid Moth Search and Dragonfly Algorithm for Energy-Efficient 5G Networks;identifying the Impact of Crime in Indian Jail Prison Strength with Statical Measures;automatic Cataract Detection Using Ensemble Model;Nepali Voice-Based Gender Classification Using MFCC and GMM;analysis of Convolutional Neural Network Architectures for the Classification of Lung and Colon Cancer;wireless String: machine learning-Based Estimation of Distance Between Two Bluetooth Devices;function Characterization of Unknown Protein Sequences Using One Hot Encoding and Convolutional Neural Network Based Model;prediction of Dementia Using Whale Optimization Algorithm Based Convolutional Neural Network;goodput Improvement with Low–Latency in Data Center Network;empirical Study of Image Captioning Models Using Various Deep learning Encoders;SMOTE Variants for Data Balancing in Intrusion Detection System Using machine learning;grey Wolf Based Portfolio Optimization Model Optimizing Sharpe Ratio in Bombay Stock Exchange.

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4th international conference on machine Intelligence and signal processing, MISP 2022

4th International Conference on Machine Intelligence and Sig...

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4th international conference on machine Intelligence and signal processing, MISP 2022

ISBN: (纸本)9789819900466

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MLNLP 2022 - 2022 5th international conference on machine learning and Natural Language processing, conference Proceedings

MLNLP 2022 - 2022 5th International Conference on Machine Le...

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5th international conference on machine learning and Natural Language processing, MLNLP 2022

ISBN: (纸本)9781450399067

the proceedings contain 66 papers. the topics discussed include: answering questions over tables based on TAPAS and graph attention model;prompt-based few-shot learning for table-based fact verification;human finger motion analysis based on machine learning;relevance calculation of users’ document sending behavior under the network social platform;dense attention memory network for multi-modal emotion recognition;a faster method for generating Chinese text summaries-combining extractive summarization and abstractive summarization;pedestrian trajectory prediction based on improved social spatio-temporal graph convolution neural network;long short-term dynamic graph neural networks: for short-term intense rainfall forecasting;an effective time-aware encoder for temporal knowledge graph reasoning;and keyword extractor for contrastive learning of unsupervised sentence embedding.

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the application of large-scale MIMO detection algorithm in 5G and beyond 5G 4

The application of large-scale MIMO detection algorithm in 5...

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4th international conference on signal processing and machine learning, CONF-spml 2024

作者： Tang, Qiteng Harbin Institute of Technology Shenzhen Electronics and Information Engineering Shenzhen China

ISBN: (纸本)9781510674721

In recent years, the rapid advancement of 5G technology has brought to the forefront the pivotal role of Multiple-Input Multiple-Output (MIMO) system algorithms. this paper delves into a comprehensive exploration of two distinct algorithmic approaches within the context of 5G applications for massive MIMO systems. these two approaches are matrix transformation and machine learning, and the following paragraphs will shed light on their respective attributes and intricacies. Matrix transformation is a fundamental technique in MIMO systems, which aims to optimize the transmission of signals by manipulating the channel matrices. this method, while established and reliable, exhibits certain limitations in accommodating the dynamic and complex nature of 5G environments. On the other hand, machine learning algorithms, with their adaptability and capacity for self-improvement, have gained prominence in recent years. they offer a promising avenue for addressing the challenges presented by 5G MIMO systems, such as handling interference and optimizing resource allocation. In this paper, we provide concrete examples to analyze the strengths and weaknesses of both matrix transformation and machine learning in the context of 5G applications. Furthermore, we explore potential directions for the application of these algorithms and propose areas for improvement, with the ultimate goal of enhancing the efficiency and performance of massive MIMO systems in the evolving landscape of 5G technology. © 2024 SPIE.

关键词： machine learning

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Performance Enhancement in machine learning Algorithm using FinFET Technology 5

Performance Enhancement in Machine learning Algorithm using ...

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5th international conference on Innovative Trends in Information Technology (ICITIIT)

作者： Haldorai, Anandakumar Lincy, Babitha R. Suriya, M. Balakrishnan, Minu Sri Eshwar Coll Engn Ctr Future Networks & Digital Twin Coimbatore Tamil Nadu India

ISBN: (纸本)9798350386813;9798350386820

the present machine learning classifiers utilizes digital MOSFETS circuits, which consumes lot of power for its processing. To overcome the mentioned issue, Mixed-signal machine learning classification has been harnessed effectively. In this work, FinFET based classifier has been developed for the enhancement of machine learning performance in terms of area, power and accuracy. Here, SG (Short-Gate) FinFET is used to generate high-resolution multiplication operation which plays a critical role for predictors in the classifier circuit. A classifier functions based on the output of each predictor, which is built using 45 nm FinFET technology and runs at 550 MHz with a mild 1 V power supply. the MNIST dataset is used to verify the proposed machine learning classifier. the suggested work was simulated using SPICE to examine its performance in terms of prediction accuracy, which was 90%, energy consumption, which was 25 pJ per classification, and area, which was 1,365 mu m(2).

关键词： machine learning classifier Predictor SG-FinFET high resolution Accuracy Power consumption Area consumption

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An Overview of Generative Models for EEG signal processing 5th

An Overview of Generative Models for EEG Signal Processing

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5th international conference on Communication and Intelligent Systems (ICCIS)

作者： Belkar, Tanisha Bajpai, Vikas LNM Inst Informat Technol Jaipur 302031 Rajasthan India

ISBN: (纸本)9789819720811;9789819720828

In medical science, EEG data is often analyzed by doctors to detect diseases and to observe brain activity under stimuli. Over the years, researchers have attempted to use machine learning algorithms for the classification and analysis of EEG signals. this paper is a study of different methods, ranging from machine learning to deep learning that have been used for EEG signal processing.

关键词： EEG signals Deep learning Classification processing Normalizing flows

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An Overview of Automatic Speech Recognition Based on Deep learning and Bio-signal Sensors 5th

An Overview of Automatic Speech Recognition Based on Deep Le...

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5th international conference on Data Science, machine learning and Applications

作者： Venkatesh, N. Krishna, K. Sai Geetha, M. P. Dave, Megha R. Kapila, Dhiraj SR Univ Sch CS & AI Warangal Telangana India Anurag Univ Elect & Commun Engn Hyderabad 500072 Telangana India Sri Eshwar Coll Engn Artificial Intelligence & Data Sci Coimbatore Tamil Nadu India Sankalchand Patel Univ Comp Engn Swami Sachchidanand Polytech Coll Visnagar 38431 Gujarat India Lovely Profess Univ Dept Comp Sci & Engn Phagwara Punjab India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

the process of producing speech is complex and includes a number of biosignals in addition to acoustics. In order to overcome the limitations of conventional speech processing in particular and to gain a better understanding of the process of speech creation in general, these biosignals can be used. they originate from the articulators, the movement of the articulator muscles, the connections within the brain, and the brain itself. With an emphasis on speech production, recognition, and volitional control, we discuss artificial mouth techniques in this review that make use of a variety of sensors, including gyros, images, 3-axial magnetic sensors, electromyography as EMG, electroencephalography as EEG, electropalatography as EPG, electromagnetic articulography as EMA, permanent magnet articulography as PMA, and articulator electromyography. Before classifying them into taxonomy, we evaluate the flow of several voice recognitionrelated deep learning technologies, including visual speech recognition and silent speech interface. We conclude by talking about ways to address the communication issues that persons with speech impairments face as well as upcoming deep learning research.

关键词： Bio signals deep learning speech recognition EMG EMA

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Experimental Study of Visible Light Localization Based on machine learning and Multiple Photo Diodes 5

Experimental Study of Visible Light Localization Based on Ma...

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5th IEEE international conference for Emerging Technology, INCET 2024

作者： Yang, Xiaoyun Jingchu University of Technology Hubei Province Jingmen City448000 China

ISBN: (纸本)9798350361155

In view of the limitations of the traditional Search for excellent algorithm in the research of System for locating visible light resources, a research scheme of localization experimental study based on machine learning together with multiple photo diodes was proposed. Firstly, the influencing factors is accurately located through signal processing theories, and the indicators is reasonably divided to reduce interference, and the machine learning together with multiple photo diodes is used to construct a research scheme for localization experimental study. Experimental results show that under certain evaluation criteria, the proposed scheme is superior to the traditional Search for excellent algorithm in terms of research accuracy and processing time of influencing factors of localization experimental study, and has obvious advantages. the research of localization experimental study plays an extremely important role in visible light, which can accurately predict and optimize the growth characteristics and product generation of visible light. However, the traditional Search for excellent algorithm has some limitations in solving the problem of positioning experiment simulation, especially when dealing with complex problems. In this paper, this paper proposes a research scheme of localization experimental study based on machine learning together with multiple photo diodes to better solve this problem. the scheme accurately locates the influencing factors through signal processing theories, so as to determine the division of indicators, and uses the machine learning together with multiple photo diodes to construct the scheme. Experimental results show that under certain evaluation criteria, the accuracy and speed of the scheme is significantly improved for different problems, and it has better performance. therefore, the simulation scheme based on machine learning together with multiple photo diodes in the research of System for locating visible light resources can bet

关键词： machine learning

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Application of signal Averaging CEEMDAN and Adaptive Wavelet threshold Denoising in signal processing 5

Application of Signal Averaging CEEMDAN and Adaptive Wavelet...

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5th international conference on machine learning and Computer Application, ICMLCA 2024

作者： Liu, Junbo Luan, Jingyao Tai, Yufeng Gao, Changlong Li, Shouxue Jiao, Lixin Jilin Electric Power Scientific Research Institute Co. Ltd. Jilin China Electric Power Research Institute of State Grid Jilin Electric Power Co. Ltd. Jilin China

ISBN: (纸本)9798331530334

Insulators play a vital role in the power system, and their safety and reliability are crucial for power transmission. However, the insulator ultrasonic detection echo signal contains a lot of noise, and a joint denoising method based on signal averaging, CCEMDAN and adaptive wavelet threshold is proposed to solve this problem. In this method, firstly, the preprocessing of the echo signal is used to mean the signal average, the noise is denoised and decomposed by CCEMDAN, the high-frequency components are denoised by the adaptive wavelet threshold, and finally the denoised signal is obtained through signal reconstruction. the experimental results show that the proposed method has a good denoising effect, effectively retains the real information in the echo signal, improves the signal-to-noise ratio of the ultrasonic echo signal, and reduces the mean square error of the echo signal. © 2024 IEEE.

关键词： signal denoising

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