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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191-200 订阅

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Fashion Images Classification using machine learning, Deep learning and Transfer learning Models 7

Fashion Images Classification using Machine Learning, Deep L...

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7th international conference on Image and signal processing and their Applications, ISPA 2022

作者： Samia, Bougareche Soraya, Zehani Malika, Mimi Biskra University Dept. of Electrical Engineering Biskra Algeria Mostaganem University Dept. of Electrical Engineering Mostaganem Algeria

ISBN: (纸本)9781665480420

Fashion is the way we present ourselves which mainly focuses on vision, has attracted great interest from computer vision researchers. It is generally used to search fashion products in online shopping malls to know the descriptive information of the product. the main objectives of our paper is to use deep learning (DL) and machine learning (ML) methods to correctly identify and categorize clothing images. In this work, we used ML algorithms (support vector machines (SVM), K-Nearest Neirghbors (KNN), Decision tree (DT), Random Forest (RF)), DL algorithms (Convolutionnal Neurals Network (CNN), AlexNet, GoogleNet, LeNet, LeNet5) and the transfer learning using a pretrained models (VGG16, MobileNet and RestNet50). We trained and tested our models online using google colaboratory with Tensorflow/Keras and Scikit-Learn libraries that support deep learning and machine learning in Python. the main metric used in our study to evaluate the performance of ML and DL algorithms is the accuracy and matrix confusion. the best result for the ML models is obtained with the use of ANN (88.71%) and for the DL models is obtained for the GoogleNet architecture (93.75%). the results obtained showed that the number of epochs and the depth of the network have an effect in obtaining the best results. © 2022 IEEE.

关键词： Image classification

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An Extreme learning machine-Based AutoEncoder (ELM-AE) for Denoising Knee X-ray Images and Grading Knee Osteoarthritis Severity 5th

An Extreme Learning Machine-Based AutoEncoder (ELM-AE) for D...

引用

5th international conference on Recent Trends in Image processing and Pattern Recognition (RTIP2R)

作者： Chaugule, Sushma Malemath, V. S. KLE DR MS Sheshgiri Coll Engn & Technol Dept CSE Belagavi India

ISBN: (纸本)9783031235986;9783031235993

Osteoarthritis (OA) is the most usual form of arthritis. Radiologists assess the OA severity by observing the pieces of evidence on both sides of knee bones, hinged on the Kellgren-Lawrence (KL) grading system. Computer-assisted diagnosis has been a prime field of research for the past few decades as it tends to provide highly accurate performance. In this work, we propose the Knee Osteoarthritis (KOA) classification problem to segregate the severity into five grades. the proposed work can be framed into two-stage, using X-ray images. Stage one dealswith preprocessing and denoising, while stage two dealswith classification. this work considers, a standard OAI dataset as well as locally collected images as input, and are fed to an Extreme learning machine-based AutoEncoder (ELM-AE) to get the denoised images, which are then used for training the Dense Neural Network model DenseNet201and are later classified, based on KL grades. In experimentation, evaluation of performance is carried out for the model with and without using autoencoders. It is observed that with autoencoders the overall performance is enhanced significantly for standard as well as the local dataset.

关键词： Extreme learning machine Denoising autoencoders Knee OA X-ray images DenseNet201 OAI

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A Sparse Recovery Method on EPC-MIMO Radar System 5

A Sparse Recovery Method on EPC-MIMO Radar System

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5th international conference on Information Communication and signal processing, ICICSP 2022

作者： Liu, Kun He, Xiongpeng Liao, Guisheng Xu, Jingwei Zhu, Shengqi Guo, Yifan Xidian University National Laboratory of Radar Signal Processing Xi'an China School of Marine Science and Technology Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9781665485890

Multiple-Input Multiple-Output Radar with Element-Pulse Coding (EPC) is a novel way to address the performance degradation caused by range ambiguity in space-time adaptive processing. In this paper, we use the sparse recovery method to solve the problem that EPC-MIMO has a large demand for independent and identically distributed (IID) samples. On the one hand, we use the Sparse Bayesian learning (SBL) to achieve space-time spectral estimation under the small sample condition, and on the other hand, the use of prior knowledge, reduce the redundancy of the sparse recovery dictionary and improve the computational efficiency of the algorithm. the simulation results demonstrate the effectiveness of the proposed method. © 2022 IEEE.

关键词： MIMO systems

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Comparison of Deep Neural Network learning Algorithms for Biomedical Image processing 5

Comparison of Deep Neural Network Learning Algorithms for Bi...

引用

5th international conference on Informatics and Data-Driven Medicine (IDDM)

作者： Berezsky, Oleh Liashchynskyi, Petro Pitsun, Oleh Liashchynskyi, Pavlo Berezkyy, Mykola West Ukrainian Natl Univ 11 Lvivska St UA-46009 Ternopol Ukraine

In recent years, the popularity of deep neural networks used for various problem-solving tasks has increased dramatically. the main tasks include image classification and synthesis using convolutional and generative-adversarial neural networks. these types of networks need large amounts of training data to achieve the required accuracy and performance. In addition, these networks have a long training time. the authors of the paper analyzed and compared the gradient-based neural network learning algorithms. the biomedical image classification with the use of a convolutional neural network of a given architecture was carried out. A comparison of learning algorithms (SGD, Adadelta, RMSProp, Adam, Adamax, Adagrad, and Nadam) was made according to the following parameters: training time, training loss, training accuracy, test loss, and test accuracy. For the experiments, the authors used the Python programming language, the Keras machine learning library, and the Google Colaboratory development environment, which provides free use of the Nvidia Tesla K80 graphics processor. For the experiments tracking and logging the authors used the Weights & Biases service.

关键词： machine learning CNN GAN optimization algorithms biomedical images

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Instrument Fault Diagnosis Method based on machine learning 5

Instrument Fault Diagnosis Method based on Machine Learning

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5th IEEE international conference on Automation, Electronics and Electrical Engineering, AUTEEE 2022

作者： Wang, Hainuo Liu, Yangsen Zhang, Yucong Wang, Zhiyuan Shao, Yingzhe Zhang, Xin Tang, Lin Chen, Jiayu Chongqing University of Posts and Telecommunications Chongqing China University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781665471978

this paper mainly through the analysis of instrument waveform signal data, extract important signal characteristics, and then achieve intelligent fault detection of equipment and instruments. According to the characteristics of signal data, we firstly draw boxplot, de-noise outliers, extract principal component features with principal component analysis (PCA), extract important features with random forest algorithm, and then use K-means clustering algorithm and Gaussian mixture distribution to classify equipment faults. Finally, Random forest and its optimization and Gauss-naive Bayes method are used to classify equipment faults. © 2022 IEEE.

关键词： Principal component analysis

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SSIP 2022 - 2022 5th international conference on Sensors, signal and Image processing

SSIP 2022 - 2022 5th International Conference on Sensors, Si...

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5th international conference on Sensors, signal and Image processing, SSIP 2022

ISBN: (纸本)9781450397124

the proceedings contain 14 papers. the topics discussed include: low-cost video system for the monitoring process in the detection of sleep apnea;pneumonia image classification method based on improved convolutional neural network;research on the application of Unet with convolutional block attention module to semantic segmentation task;unsupervised transfer learning for generative image inpainting with adversarial edge learning;research on the identification method of dangerous goods in security inspection images based on deep learning;feature analysis and automatic extraction for the 3D point cloud of the sanitary wares body;judgment model of cock reproductive performance based on vison transformer;methodology for interoperability between health information systems, for information management and decision-making;and particle swarm optimization and differential evolution hybrid algorithm applied to calibration of triaxial accelerometer.

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Radar Emitter Identification using signal Noise and Power Spectrum Analysis in Deep learning 5

Radar Emitter Identification using Signal Noise and Power Sp...

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5th IEEE international conference of Women in Data Science (WiDS-PSU)

作者： Madhu, Akhila Prajeesha, Prajeesha Kulkarni, Abhijit S. PES Univ Elect & Commun Engn Bangalore Karnataka India DRDO CASDIC Bangalore Karnataka India

ISBN: (纸本)9781665408127

Specific emitter identification (SEI) is a vital function of the electronic radar warfare support system. the challenge emphasizes recognizing and locating unique transmitters, avoiding potential threats, and preparing a countermeasure. Unlike the analog transient parameters, radar fingerprinting is effectively possible with digitized feature extraction and image processing methods. Our novel approach utilizes the power spectrum and signals noise to efficiently work on a large image dataset using deep learning techniques. the convolution neural network is tested on various time-frequency estimators to yield the most accurate results. Based on signal-to-noise ratios, radar emitters get distinguished using the ablest estimator.

关键词： Radar SEI radar fingerprinting image processing deep learning convolution neural network power spectrum signal noise

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Proceedings - 2022 5th international conference of Women in Data Science at Prince Sultan University, WiDS-PSU 2022

Proceedings - 2022 5th International Conference of Women in ...

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5th international conference of Women in Data Science at Prince Sultan University, WiDS-PSU 2022

ISBN: (纸本)9781665408127

the proceedings contain 33 papers. the topics discussed include: a predictive model for early diabetes detection;crowdsourcing, sentiment analysis and content analysis as an ethnographic reflection of social media human/climate interaction;analysis of the dimensionality issues in house price forecasting modeling;data science approach for crime analysis and prediction: Saudi Arabia use-case;a survey on resource management and security issues in IoT operating systems;organizational culture and its impact on the success of virtual software development projects;radar emitter identification using signal noise and power spectrum analysis in deep learning;and machine learning model for breast anticancer drug sensitivity prediction from gene expression.

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Real-Time Monitoring of Video Quality in a DASH-based Digital Video Broadcasting using Deep learning 5

Real-Time Monitoring of Video Quality in a DASH-based Digita...

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5th international conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (ICABCD)

作者： Motaung, William Ogudo, Kingsley A. Chabalala, Chabalala Univ Johannesburg Dept Elect & Elect Engn Johannesburg South Africa Univ Witwatersrand Sch Elect & Informat Engn Johannesburg South Africa

ISBN: (数字)9781665484220

ISBN: (纸本)9781665484220

Digital video processing and transmission can introduce numerous distortions while capturing signals from broadcasting stations. these distortions become a nightmare for multimedia companies, especially terrestrial broadcasting companies that have fully adopted the online video streaming service. While terrestrial broadcasting benefits from online streaming through over-the-top (OTT) channels, there is a potential setback to reducing the video quality due to preprocessing of signals. Video quality assessment (VQA) algorithms have been developed for analyzing the quality of videos in a database, but little attention has been paid to implementing such algorithms in a real-time situation. this paper develops a novel real-time VQA framework by integrating a deep learning technology into the broadcasting pipeline. Previous studies used objective metrics augmented with subjective values to validate techniques. However, this approach is not appropriate for real-time video evaluation. Our proposed framework uses objective metrics (devoid of subjective scores like mean opinion scores) but rather introduced a new metric to validate the framework. the whole framework is validated using compressed/uncompressed signals and varying devices to show the signal differences. Results show that the framework is a step toward feasible incorporation of a VQA tool in a digital terrestrial television model. Using 100 epochs for our simulated video stream, the restricted Boltzmann machine yields a root mean square and mean absolute of 3.6903 and 2.3861 respectively.

关键词： deep learning digital video broadcasting multimedia streaming restricted Boltzmann machine video quality assessment

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Comparison of AI-Based Algorithms for Low Energy Communication 1

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5th international conference on machine learning for Networking, MLN 2022

作者： Joly, Morgane Renault, Éric Rivière, Fabian NXP Semiconductors 2 Esplanade Antone Philips Caen14000 France LIGM Univ. Gustave Eiffel CNRS ESIEE Paris Noisy-le-Grand93162 France

ISBN: (数字)9783031361838

ISBN: (纸本)9783031361821

Spectrum environment is more and more crowded by the presences of wireless sensor network (WSN), but radios still present an irregular quality of service (QoS) with a strong signal disturbances in their vicinity. Here, the main disturbance factors at the analogue level restricting the performances of radios using the 802.15.1 protocol are presented. the variety of solutions existing today, dummy as well as artificial intelligent (AI) based, are compared. A discussion of their advantages, their limiting conditions and their evolution is proposed. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： machine learning

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