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检索条件"任意字段=6th International Conference on Signal Processing and Machine Learning, SPML 2023"

共 769 条记录，以下是351-360 订阅

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Turning Knife Health Monitoring Based on Vibration signal with machine learning Algorithms 6

Turning Knife Health Monitoring Based on Vibration Signal wi...

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2022 6th international conference on Artificial Intelligence, Automation and Control Technologies, AIACT 2022

作者： Jiayu, Liu Xiaomao, Xia Yuxi, Song Co. Ltd. China

the wear of the turning tool has a great impact on product quality. With the development of sensor technology and increasing computing power, more and more research focuses on online monitoring and prediction of tool condition using machine learning methods. Nevertheless, most research has been conducted in laboratories, making it difficult to apply the results to the shop floor under complex production conditions. the research is based on vibration data measured in a real production environment of a turning machine. machine learning models such as Random Forest, XGBoost, etc. were trained to predict the wear condition of the turning tool after features were extracted using digital signal processing techniques such as wavelet decomposition and empirical mode decomposition. A moving average-based method was proposed to convert the binary classification results into the probability of tool failure. On a real manufacturing data set, the online real-time monitoring is realized with an accuracy of 93.8%. It proves to be a good approach for managing spare parts in a real-time industrial environment with limited and isolated data sources. © Published under licence by IOP Publishing Ltd.

关键词： Wavelet decomposition

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Privacy-Preserving Scheme Against Collusion Attacks Based on Federated learning

Privacy-Preserving Scheme Against Collusion Attacks Based on...

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6th international conference on Intelligent Computing and signal processing (ICSP)

作者： Longxiang Qu School of Computer Science Wuhan Vocational College of Software and Engineering (Wuhan Open University) Wuhan China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

With the rapid growth and increasing complexity of data, there are significant challenges to security and efficiency when using the traditional centralized machine learning model to deal with large-scale data. However, as a distributed learning mechanism, federated learning also has potential threats to handle data applications. For example, attackers can attempt to infer sensitive information of data providers by analyzing model or gradient information. therefore, we propose a privacy-preserving scheme against collusion attacks based on federated learning to mitigate the above-mentioned threats. Specifically, combined with the federated learning mechanism, the local gradients are blinded by random numbers, and the local data security are ensured by aggregating the blinded gradients. through experimental analysis on real data sets, we validate the performance of our scheme, which incurring minimal computational overhead, effectively achieves resistance against collusion attacks.

关键词： Training Resistance Computer aided instruction Federated learning Distance learning Computational modeling signal processing Data models Performance analysis Cryptography

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Early Detection of Alzheimer's Disease Using VOT Mean Measure in New Tunisian Arabic Database 6

Early Detection of Alzheimer's Disease Using VOT Mean Measur...

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6th IEEE international conference on Advanced Systems and Emergent Technologies, IC_ASET 2023

作者： Dabbabi, Karim Kehili, Ahlem Cherif, Adnen Research Unite of Analysis and Processing of Electrical and Energitic Systems Faculty of Sciences of Tunis Tunis Tunisia

ISBN: (纸本)9798350321029

Alzheimer's disease (AD) is one of the neurodegenerative diseases, which first affects the function of speech in addition to a few other functions. this brings speech back to the top of promising biomarkers for the early detection of this disease before the appearance of the first preclinical symptoms. Despite the fact that there are many methods explored for early detection of Alzheimer's disease (AD), however the Voice Onset Time (VOT) values of stop consonants can be a good indicator of this disease by containing clues about the speaker's voice disorder in a language. In this study, we have proposed VOT-Mean measures to detect these deficiencies for the task of early detection of Alzheimer's disease (AD) in Arabic language using /pa/., /ta/., /ka/, and /pata/ syllables. An Arabic speech database of healthy control individuals (HC) and other with AD has been developed in association with the Alzheimer Family Assistance (AFA) center. Experimental tests were performed on this database for the didochakokinetik (DDK) task and showed that there is high significance for VOT-Mean with repect to syllables, while no significance with respect to gender. Additionnally, there is no statistical significance for the duration of words or for the duration of words in relation to sex. For the best results of the performances assessed using the VOT-Mean measurement, they were achieved with the XGBoost algorithm compared to those obtained by other machine learning algorithms for the AD and HC groups. © 2023 IEEE.

关键词： Neurodegenerative diseases

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An IoT-Enabled Smart Network Traffic signal Assistant System for Emergency Vehicles Using Computer Vision 6th

An IoT-Enabled Smart Network Traffic Signal Assistant System...

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6th international conference on Intelligent Sustainable Systems, ICISS 2023

作者： Senthil, G.A. Prabha, R. Suganthi, S. Sridevi, S. Shanthi, N. Department of Information Technology Agni College of Technology Chennai India Department of Electronics and Communication Engineering Sri Sai Ram Institute of Technology Chennai India Department of Artificial Intelligence and Data Science Sri Sai Ram Institute of Technology Chennai India Department of Computer Science and Engineering Sri Ramachandra Engineering and Technology Chennai India Department of Electrical and Electronics Engineering Sri Sai Ram Institute of Technology Chennai India

ISBN: (纸本)9789819917259

the increase in vehicles on the road causes an increase in traffic conditions, which induces difficulty in clearing the path for emergency vehicles such as ambulances, rescue fire engine and emergence vehicles. To avoid difficulty wait in traffic signal for the emergency situation, an IoT sensor smart-based system is developed that identifies the arrival of an ambulance to the hotspots through the signals received from the ambulance and maintains the signals accordingly. the regulation of the signals based on the location of the ambulance paves the way for the ambulance and rescue fire engine to urge its destination. this can be achieved via a device that incorporates a camera and sensor placed on the hotspots, as a Smart Network Traffic signal (SNTS) detects and locates the vehicle and performs formulations to regulate traffic before the traffic signal. machine learning algorithms are implicated to develop the model for the detection of the ambulance through the signals received. Convolutional Neural Network (CNN) and MobileNet algorithms employed yield 81 and 99% efficiency and are considered accurate for this system. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Neural networks

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machine learning Isolation Forest-Based Target Detection Algorithm for Airborne Radar

Machine Learning Isolation Forest-Based Target Detection Alg...

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6th international conference on Intelligent Computing and signal processing (ICSP)

作者： Jing Liu Cao Zeng Filbert H. Juwono Pengcheng Huang Haihong Tao National Key Laboratory of Radar Signal Processing Xidian University Xi’an China Department of Electrical and Electronic Engineering Xi'an Jiaotong-Liverpool University Suzhou China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

While the advantages of airborne radar are widely recognized, it is prone to target overwhelm by clutter during detection tasks, and most traditional detectors rely on clutter mathematical models to obtain detection thresholds. this paper proposes a new machine learning isolation forest-based airborne radar target detection algorithm, fully exploiting the advantages of ensemble decision trees. the proposed algorithm first designs new feature data with feature extraction and space-time weight vector, and the new data enable separate utilization of multidimensional features, serving as inputs for the target and clutter classifier to be constructed. then, the proposed algorithm designs a unique isolation forest-based target and clutter detector, constructing isolation forest anomaly scores for each designed feature data, and determining the presence of targets for airborne radar based on score discrimination criteria. Compared with various classical detectors, the proposed algorithm significantly enhances target detection performance, providing higher detection probabilities, and offering a new idea and direction for radar target detection. the proposed algorithm is confirmed by simulations to be effective and advantageous.

关键词： machine learning algorithms Airborne radar signal processing algorithms Forestry Object detection Detectors machine learning Feature extraction Classification algorithms Clutter

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MDVT: A Multi-modal Fake News Detection Framework based on Vision Transformer 6

MDVT: A Multi-modal Fake News Detection Framework based on V...

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6th international conference on machine learning and Natural Language processing, MLNLP 2023

作者： Wu, Yuexin Tang, Yi Fan, Chunxiao Liu, Yu School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing China Beijing-Dublin International College Beijing University of Technology Beijing China

ISBN: (纸本)9798400709241

the growth of social media in recent years has contributed to the spread of fake news on the Internet. Since multimodal contents such as pictures have a huge impact on the spread of news in social media, researchers are increasingly focusing on the automatic detection of multimodal false news. Existing multimodal approaches disregard the recent development results of vision pretraining, consequently, their visual feature extractor primarily uses early proposed convolutional neural networks, such as VGG and ResNet. Compared with vision Transformers, traditional convolution neural networks have insufficient ability to extract local semantics from images as well as capture the relationships between different regions in images, which may reduce the performance of multimodal methods. To address this issue, we propose a multimodal fake news detection framework based on vision Transformer(MDVT). By experimenting with different multi-modal combination methods and feature extractors on this framework, we get a top-performing which combined MacBERT text features and Swin Transformer image features through the maximum method, named MDVT-MS. Experimental studies on a widely used social media dataset Weibo demonstrate that MDVT-MS outperforms current state-of-the-Art multimodal detection methods by 4.4% in accuracy. this proves the effectiveness of the proposed method and shows that vision Transformer can be used as a new vision backbone for multimodal fake news detection. © 2023 ACM.

关键词： Modal analysis

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Evaluation of technology transfer capability based on machine learning clustering algorithm

Evaluation of technology transfer capability based on machin...

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6th international conference on Intelligent Computing and signal processing (ICSP)

作者： Kai Guo Yan Liang Business School Henan University of Science and Technology Luoyang Henan China Henan Collaborative Innovation Center of Nonferrous Metals Luoyang Henan China

Technology transfer is a crucial element behind the sustained growth of the country’s economy, and the close integration of technology and economy is the primary task for country to achieve strategic innovation. Based on the perspective of technology input and output, this paper takes high-tech industries in 30 provinces in China as examples, constructs evaluation indicators for technology transfer, and employs the K-means technique for cluster analysis while analyzing the state of technology transfer in each province’s high-tech sectors. the study found that there are big differences in the development level of technology transfer in China’s high-tech industries, pointed out the problems in China’s current technology transfer, and put forward corresponding countermeasures and suggestions.

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Unveiling Gender from Speech: An Investigation into Acoustic Features for Accurate Gender Detection 6

Unveiling Gender from Speech: An Investigation into Acoustic...

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6th international conference on Contemporary Computing and Informatics, IC3I 2023

作者： Samant, Sandhya Aditi Prajapati, Shobhit Chaudhary, Renu Rathi, Aaditya Arya, Swati COER University Computer Science & Egineering Roorkee India COER University Information Technology Roorkee India College of Smart Computing COER University Roorkee India School of Computing COER University Roorkee India

ISBN: (纸本)9798350304480

this paper focuses on the development of a classification model for gender recognition based on voice speech using various acoustic parameters. the aim is to predict whether a voice sample belongs to a male or female speaker. the initial step in the paper involves dividing the dataset into two parts: training data and test data, through a test-train data split. Following this, the study explores the performance of various machine learning algorithms for gender classification based on voice features. the algorithms under consideration include Support Vector machine (SVM), Decision Tree Classifier, k-Nearest Neighbors (KNN), Random Forest, and Logistic Regression. Each of these algorithms is individually trained on the training data and subsequently evaluated on the test data to gauge their effectiveness in gender classification. the paper then analyzes and compares the results obtained from each algorithm, assessing their accuracy, precision, recall, and F1-score to identify the model that performs best in predicting gender based on voice features. the performance of every model is assessed using a confusion matrix and classification report, offering valuable information about accuracy, precision, recall, and F1-score for each gender class. the model with the highest accuracy is determined as the best performer among them. By employing these classification algorithms and analysing the acoustic parameters of voice speech, this paper aims to provide a reliable method for gender recognition. the results obtained from this study can have potential applications in various fields, including speech processing, voice-based user interfaces, and automatic gender identification systems. © 2023 IEEE.

关键词： Decision Tree K Nearest Neighbors(KNN) Random Forest Support Vector machine (SVM)

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Low Light Image Enhancement Algorithm Based on Retinex Model learning 6

Low Light Image Enhancement Algorithm Based on Retinex Model...

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6th international conference on Artificial Intelligence and Pattern Recognition, AIPR 2023

作者： Liu, Weihua Zhang, Xuhui Yi, Chen School of Communication and Information Engineering Xi'an University of Posts and Telecommunications Shaanxi Provincial Forensic Science Electronic Information Research Center Shaanxi Prov. Intl. Joint Research Center for Wireless Communication and Information Processing China School of Communication and Information Engineering Xi'an University of Posts and Telecommunications China

ISBN: (纸本)9798400707674

Low-light images have low contrast and unclear details, resulting in the reduction of available information for human vision. the current mainstream enhancement algorithms have problems such as noise amplification, color distortion, and dependence on data sets during the enhancement process. therefore, a low-light image enhancement algorithm based on Retinex model and deep learning is proposed. First, the proposed residual network cascade is applied to the decomposition network based on Retinex theory to improve the gradient disappearance problem of the deep neural network, and at the same time obtain the illumination and reflection components of the image. Secondly, an adaptive gamma transformation function is designed to enhance the illumination component, which can effectively improve the low contrast problem of the image;since the noise in the dark area is amplified during the enhancement process, a full-scale learning network is designed to improve the image quality. the reflection component is denoised. Finally, the enhanced illumination component and reflection component are fused to obtain the final enhanced image. In the data set test results, the peak signal-to-noise ratio of the proposed algorithm is improved by an average of 0.33dB compared with the mainstream algorithm Zero-DCE, and by an average of 0.48dB compared with the URetinex-Net algorithm. the experimental results show that the algorithm can effectively improve the brightness of the image while reducing the image noise and accurately restore the texture information of the image. © 2023 ACM.

关键词： Image enhancement

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Gaze estimation based on swin transformer 6

Gaze estimation based on swin transformer

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6th international conference on Advanced Electronic Materials, Computers, and Software Engineering, AEMCSE 2023

作者： Chen, Jiahui Ma, Jiaxin Wang, Xiwen Huang, Longzhao Li, Yujie School of Artificial Intelligence Guilin University of Electronic Technology Jinji Road Guilin541004 China Guangxi Colleges and Universities Key Laboratory of AI Algorithm Engineering School of Artificial Intelligence Guilin University of Electronic Technology Guilin541004 China

ISBN: (纸本)9781510668249

the direction of human eye gaze is an important human behavior information that reflects the level of attention and cognitive state of the gazer towards various visual information in the environment. Eye gaze estimation has wide application value in multiple fields such as medical care, market research, and human-computer interaction. In recent years, some studies have introduced Transformer into the task of eye gaze estimation and achieved advanced performance. Although Transformer has better global modeling ability, its structural characteristics are not suitable for multi-scale feature learning in visual tasks. In addition, the global self-Attention calculation for images has high complexity. this paper introduces Swin Transformer into the field of eye gaze estimation, using self-Attention mechanism to perform more flexible and effective global modeling of images. the self-Attention calculation uses Windows Multi-head Self-Attention(W-MSA) and Shifted Windows Multi-head Self-Attention (SW-MSA), which greatly reduces the calculation of image self-Attention. the experimental results demonstrate that the Swin Transformer can obtain good results in the task of eye gaze estimation © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： machine learning

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