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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371-380 订阅

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Golub Kahan Based Methods for Partial Multidimensional Singular Value Decomposition 13

Golub Kahan Based Methods for Partial Multidimensional Singu...

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13th international conference on Dependable Systems, Services and Technologies, DESSERT 2023

作者： Hachimi, Anas El Hached, Mustapha Jbilou, Khalide Ratnani, Ahmed Mohammed VI Polytechnic University UM6P Vanguard Center Green City Morocco Université du Littoral ULCO Calais France Laboratoire P. Painlevé Université de Lille Villeneuve d'Ascq France Université du Littoral ULCO Laboratoire LMPA Calais France Mohammed VI Polytechnic University Green City Morocco

ISBN: (纸本)9798350396119

Many applications of machine learning, data, image and signal processing require the design of algorithms capable of dealing with very large datasets. Extracting the most meaningful features out of these large datasets is often a computationally challenging task. In the matrix case, the Singular Value Decomposition (SVD) has shown to be a very efficient tool to do so. In recent years there has been a very significant increase in the amount of available data and the datasets are often formatted as tensors, for which some generalisations of the SVD have already been proposed for some specific choices of tensor-tensor products, well adapted to tensors of order 3. In this work, we propose a new generalisation, associated with the well-known Einstein product, regardless of the order of the tensors. As in the matrix case, only a few singular elements need to be computed and we address this problem by proposing an iterative approach based on the tensor Lanczos bidiagonalization applied to the Einstein product. We also propose a refinement to this method in order to increase its accuracy. We give numerical results for both synthetic and real data to illustrate the efficiency of these methods. © 2023 IEEE.

关键词： Tensors

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Identification and Classification of Human Mental Stress using Physiological Data: A Low -Power Hybrid Approach 6

Identification and Classification of Human Mental Stress usi...

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IEEE 6th international conference on Condition Assessment Techniques in Electrical Systems (IEEE CATCON)

作者： Roy, Ayush Acharya, Arkaprovo Biswas, Sabarno Ray, Susanta Ganguly, Biswarup Jadavpur Univ Dept Elect Engn Kolkata India Meghnad Saha Inst Technol Dept Elect Engn Kolkata India

ISBN: (纸本)9781665473804

this paper proposes a framework using artificial intelligence for recognizing human mental stress based on physiological data. It comprises three stages;at first, emotion detection has been carried out from the facial image using a deep learning-based framework employing convolutional neural networks (CNN). Secondly, electrocardiogram (ECG) signals have been acquired with the help of the developed sensor -based system integrating Arduino UNO with AD8232 sensor. Finally, preprocessing has been performed and extracted features from the ECG signals are fed to support vector machines (SVM) for identification of arrhythmia and hence diagnosis of mental stress. All three aspects are connected together via the overall output calculation. A score is added in accordance to the output for each of these three stages. the range of the final score (i.e., the overall output) decides the level of mental stress suffered by the person. the proposed method has been trained on FER-2013 for emotion detection and on MIT-BIH for arrhythmia detection. It is found that the performance outperforms other machine learning and deep learning models without much compromise in accuracy. the proposed method can perform classification based on the patient's current condition and gives an insight of the degree and time duration of stress of the patient.

关键词： ECG Classification emotion detection mental stress detection signal processing

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A Classification Approach for Heart Disease Diagnosis using machine learning 6

A Classification Approach for Heart Disease Diagnosis using ...

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6th IEEE international conference on signal processing, Computing and Control, ISPCC 2021

作者： Jeny, J. Rethna Virgil Reddy, Nalla Sreeja Aishwarya, P. Samreen Vignan Institute Of Technology And Science Department Of CSE Telangana Deshmukhi India

ISBN: (纸本)9781665425520

Heart Disease (HD)is a critical condition that many people suffer from and it is the expression which is used to characterize different types of heart conditions. Many people suffer with heart issues Heart disease remains the number one killer disease, especially in the United States. the main symptoms of heart disease are smoking, obesity, alcohol, high blood cholesterol level, hypertension etc. there are many algorithms that are used to improve the performance in health care system through information gathering in the form of datasets and it is used for various applications that are advanced in healthcare industry. In our paper, we used four machine learning classification techniques. Firstly, we included Support Vector Classifier (SVC). Is used to predict the model based on parameters considered. second one is the Logistic regression(LR). It is used to describe the classification problems based on the input parameters. thirdly, Naive Bayes (NB) classifier and finally, the Decision tree (DT) Algorithm. We used different classifiers to boom the accuracy and overall performance. the principle intension of our proposal is to improve the accuracy rate and to identify heart disease by implementing the machine learning classifier techniques. © 2021 IEEE.

关键词： Cholesterol

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Current Topological and machine learning Applications for Bias Detection in Text

Current Topological and Machine Learning Applications for Bi...

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international conference on signal processing and Information Security (ICSPIS)

作者： Colleen Farrelly Yashbir Singh Quincy A. Hathaway Gunnar Carlsson Ashok Choudhary Rahul Paul Gianfranco Doretto Yassine Himeur Shadi Atalls Wathiq Mansoor Staticlysm LLC Miami FL USA Radiology Mayo Clinic Rochester MN West Virginia University Morgantown WV USA Stanford University California USA Department of Surgery Mayo Clinic Rochester MN USA Harvard University USA College of Engineering and Information Technology University of Dubai Dubai UAE

Institutional bias can impact patient outcomes, educational attainment, and legal system navigation. Written records often reflect bias, and once bias is identified; it is possible to refer individuals for training to reduce bias. Many machine learning tools exist to explore text data and create predictive models that can search written records to identify real-time bias. However, few previous studies investigate large language model embeddings and geometric models of biased text data to understand geometry’s impact on bias modeling accuracy. To overcome this issue, this study utilizes the RedditBias database to analyze textual biases. Four transformer models, including BERT and RoBERTa variants, were explored. Post-embedding, t-SNE allowed two-dimensional visualization of data. KNN classifiers differentiated bias types, with lower k-values proving more effective. Findings suggest BERT, particularly mini BERT, excels in bias classification, while multilingual models lag. the recommendation emphasizes refining monolingual models and exploring domain-specific biases.

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Adaptive Space-time Broadband Beamforming using machine learning approach 6

Adaptive Space-time Broadband Beamforming using Machine Lear...

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6th international conference on Communication, Image and signal processings, CCISP 2021

作者： Wu, Xun Zhang, Shurui Zhang, Renli Cui, Jie Guo, Shanhong School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9781665432795

Adaptive space-time broadband beamforming technology is a significant branch of array signal processing. For the conventional iterative algorithms of the broadband beamforming, the iteration step is always the fixed constant for each approach. In this paper, a variable step gradient algorithm based on machine learning is proposed for the adaptive space-time broadband beamforming, namely adaptive gradient Frost beamformer (AGFB) and adaptive gradient frequency constrained Frost beamformer (AGFCFB). Using the concept of adaptive gradient (AdaGrad) in machine learning (ML), the iterative formulas of adaptive broadband beamformers are ameliorated. the algorithm has the capability of adjusting the step of each approximation for different parameter values independently, making the entire process of iteration more flexible. Meanwhile, the convergence rates of the proposed beamformers are faster than the conventional algorithms. Simulation results highlight the effectiveness of the proposed methods. © 2021 IEEE

关键词： machine learning

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Application and Analysis of machine learning Based Rainfall Prediction

Application and Analysis of Machine Learning Based Rainfall ...

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

作者： Hongyi Duan Yuchen Li Qingyang Li Yiyi Wang Yuming Xie Haohui Peng Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an China Faculty of Electronic Science National University of Defense Technology Changsha China Astronomy and Space Science School Nanjing University Nanjing China

Rainfall predicting is closely related to people's lives, and improving the accuracy of rainfall prediction is of great importance for people’s life and scientific researches. In this paper we attempt to use several machine learning algorithms to analyse and predict rainfall from over 140,000 data recorded at 49 weather stations in Australia. the specific research in this paper is as follows.(1) Explain the limitations of using traditional empirical/physical statistical methods in rainfall predicting through reviewing, and explain the advantages and remaining limitations of current machine learning algorithms applied to rainfall predicting over traditional methods.(2) Different methods are applied to pre-process the data set for both typed and continuous variables to ensure the integrity of the information and to avoid the loss of excessive information in the data.(3)the K-nearest neighbour algorithm, random forest algorithm and support vector machine algorithm are used to model the data for prediction. By comparing the prediction results of each model, summarize the advantages and disadvantages of each *** prediction results show that the prediction accuracy of the above three algorithms are 82.4464%, 85.6416% and 81.3915% respectively, and the F1-score values are 52.2388%, 61.8736% and 65.1689% respectively. Results of each algorithm are similar in accuracy, and all shows good performance in both accuracy and F1-score.

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Soliton solutions of the fifth order Caudrey-Dodd-Gibbon equation via a linear and nonlinear separate convolutional neural network

Soliton solutions of the fifth order Caudrey-Dodd-Gibbon equ...

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Intelligent Control, Measurement and signal processing and Intelligent Oil Field (ICMSP), international conference on

作者： Pingru Xiang Xingxin Song Xiaoli Wang Dejun Zhao Kaipeng Sui Department of Mathematics and Artificial Intelligence Qilu University of Technology (Shandong Academy of Sciences) Jinan China

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

With the fast progress of information technology, machine learning is getting increasing attention. Among them, there are a growing body of researches on learning partial differential equations (PDEs) via Convolutional Neural Networks (CNNs). the linear and nonlinear separate network (LNSN) is a useful network for learning PDEs, and the Caudrey-Dodd-Gibbon (CDG) equation is an important PDE. As far as we know, the research on learning CDG equation via LNSN has never been documented. therefore, we learn the CDG equation via the LNSN. In this work, we use the LNSN to learn one-soliton solution of the CDG equation. We compare results of one-soliton solution for the CDG equation via the LNSN under different time step sizes, training epochs, number of neurons per layer, and activation functions. It is found that the relative mean error (RME) can reach $\mathcal{O}\left( {{th^{ - 6}}} \right)$. Our experiments show the applicability of the LNSN for learning one-soliton solution of the CDG equation.

关键词： Training Partial differential equations Neurons Solitons machine learning signal processing Time measurement Convolutional neural networks Information technology Intelligent control

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Comparative Analysis of Tomato Leaf Disease Detection Using machine learning

Comparative Analysis of Tomato Leaf Disease Detection Using ...

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international conference on Information Systems and Computer Networks (ISCON)

作者： Shruti Agnihotri Jaydeep Gupta Neha Garg Pallavi Khatri Department of CSA ITM University Gwalior India

Disease transmission from affected crops to unaffected crops is getting the most dangerous things to agriculture. Epidemics spread like wildfire can affect entire farms if not spotted early. the plant disease detection method helps to identify infected plants at a very early stage and also helps users to extend plant disease identification for a variety of plants in an efficient manner. the purpose for work is to test the data using different machine learning models, Convolutional Neural Networks (CNN), K Nearest Neighbors (KNN), Visual Geometry Group (VGG-19), and apply them to plant disease detection on tomato leaves. the results on the implementation of machine learning models show that the KNN model performs better on every evaluation indicator.

关键词： Plant diseases Visual analytics Image processing Crops Imaging machine learning Software

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A Novel Method for Concrete Crack Detection Using Image processing Technique 6

A Novel Method for Concrete Crack Detection Using Image Proc...

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6th international conference on Advanced Computing and Communication Technologies for High Performance Applications (ACCthPA)

作者： Davies, theres Kumar, R. Satheesh Antony, Anly M. Sahrdaya Coll Engn & Technol Dept Comp Sci & Engn Trichur India

ISBN: (纸本)9798350398441

Currently, the number of obsolete high-rise buildings around the world is increasing, and majority of them are built using concrete. Concrete can also lose strength due to constant pressure and environmental influences. Damage to the mold's exterior surface may ensue as a result (cracks and chips). Mold integrity may be jeopardised if these defects are left unidentified and untreated. the template must therefore be updated every day. the drone has been employed in some earlier studies, most of which were based on visions, to capture and chronicle how the country is currently shaped. To ascertain whether the application of classification, localisation, and segmentation techniques was damaging, the recorded videos and images were evaluated. In other research, a server receives the statistics that the drones have gathered overWi-Fi. However, sophisticated, time-consuming, and bandwidth-intensive advanced structures are a drawback. A Python-based real-time multiple picture identification engine is used to resolve this issue. the software platform for this machine is Jupyter, and it is tested against a dataset made up of 20000 (480 480 pixel) pictures of various damage patterns coming from various sources. Based on Python categorization, manually labelled photos are changed for learning and experience. In the future, this version can be used in combination with various modules to completely avoid life-related risks.

关键词： DCNN DDS DIP ResNet50 VGG16

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Weak signal Detection Method Based on Bayesian Neural Network

Weak Signal Detection Method Based on Bayesian Neural Networ...

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Pattern Recognition and Artificial Intelligence (PRAI), international conference on

作者： Xiao Zhao Lihui Wang Zhenjia Chen Po Shao Ran Chen School of Information and Communication Engineering Hainan University Haikou China Department of was the School of Information and Communication Engineering Collaborative Innovation Center of Information Technology Hainan University Haikou China

With the development of the Times, the high efficiency and privacy of modern communication become increasingly important. How to distinguish weak signal from noise in the case of insufficient prior information becomes particularly important. the traditional modulation recognition process requires manual extraction of signal features, and the process is complicated and the unstable factors are too large, which cannot meet the requirements of high precision and high time of modern communication. With the development of deep learning, machine learning has made great breakthroughs in the direction of radio signal processing due to its obvious classification and decision advantages, and signal recognition has a higher accuracy than before. More and more researches begin to use machine learning method to extract features directly from the original data of communication signals, which ensures the real-time and robustness of automatic modulation recognition of communication signals. Naive Bayes is one of the classical algorithms of machine learning and one of the few classification algorithms based on probability theory. this method is still effective in the case of less data and can deal with multiple kinds of problems. Based on Bayesian network algorithm, this paper studies the weak signal identification method of wireless telecommunication below 0db. Aiming at the problem that the generalization ability of traditional model is insufficient, this paper proposes a weak signal recognition method based on Bayesian network. the experimental results show that the recognition rate of this method is 40 percent higher than that of different Bayesian neural networks in the recognition of radio weak signals, which improves the recognition ability of the whole recognition model for weak signals not in training concentration.

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