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检索条件"任意字段=2022 International Conference on Machine Learning and Data Engineering, ICMLDE 2022"

共 33664 条记录，以下是4911-4920 订阅

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Research on mechanism-data dual-driven ship manoeuvring mathematical modeling 34th

Research on mechanism-data dual-driven ship manoeuvring math...

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34th international Ocean and Polar engineering conference, ISOPE 2024

作者： Shen, Wenhe Liu, Jialun Hu, Xinjue Li, Shijie Zhang, Song State Key Laboratory of Maritime Technology and Safety Wuhan University of Technology Hubei Wuhan China School of Naval Architecture Ocean and Energy Power Engineering Wuhan University of Technology Hubei Wuhan China Hubei Longzhong Laboratory Wuhan University of Technology Xiangyang Demonstration Zone Xiangyang China Intelligent Transportation Systems Research Center Wuhan University of Technology Hubei Wuhan China School of Transportation and Logistics Engineering Wuhan University of Technology Hubei Wuhan China

ISBN: (纸本)9781880653784

Ship dynamics models are the foundation to predict ship movement. Traditionally, mechanism-driven models have low accuracy and datadriven models have high data dependency. Thus, a novel mechanismdata dual-driven method is proposed to address the above problems. An MMG model is constructed to generate virtual data for the offline identification. An online model is established with reference to the offline model. The online model is trained by two state-of-the-art online algorithms, kernel adaptive projection and projection support vector machine. It has been verified that the proposed method can improve the prediction accuracy and reduce the data dependency. © 2024 by the international Society of Offshore and Polar Engineers (ISOPE).

关键词： Support vector machines

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A machine learning Model for Accurate Cardiopulmonary Resuscitation Chest Compression Feature Extraction 7

A Machine Learning Model for Accurate Cardiopulmonary Resusc...

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7th IEEE international Women in engineering (WIE) conference on Electrical and Computer engineering (WIECON-ECE)

作者： Lalitha, V Nithya, M. Samundeswari, S. Srinivasan, Srinidhi Sri Sairam Engn Coll Dept Comp Sci & Engn Chennai Tamil Nadu India

ISBN: (纸本)9781665478496

The National Centre for Biotechnology Information claims that 83% of in-out patient died due to complication and lack of proper CPR performance. Currently there are only subjective or qualitative CPR parameter measurements which lead to inaccuracy. Existing hardware solutions may lead to neurological deficits and other complications. The proposed solution develops an accurate machine learning model using k-means algorithm to extract the features of CPR bio signals for different health groups from infants to senior adults. When the model is passed on to Simulink interfaced with dolorimeter, it predicts the amount of loading (the pressure needed to be applied on the chest) and unloading (the depressure) that the clinician or the CPR metrician should follow to save the live.

关键词： Cardiopulmonary Resuscitation (CPR) machine learning (ML) Multisim Manikin Visualization

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2022 IEEE 2nd international conference on Computation, Communication and engineering, ICCCE 2022

2022 IEEE 2nd International Conference on Computation, Commu...

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2nd IEEE international conference on Computation, Communication and engineering, ICCCE 2022

ISBN: (纸本)9781665482486

The proceedings contain 29 papers. The topics discussed include: machine learning based approach to selective measurements of hydrogen for catalytic gas sensors;adaptive grey wolf optimizer for global numerical optimization;analysis of digital pattern generation technology based on fractal graph;applying particle swarm optimization in scheduling modular software testing projects;applying system dynamics approach for optimizing software release decisions;automatic method for red spider detection in images;machine learning framework for enterprise profits forecasting;machine learning to identify bitcoin mining by web browsers;picture in picture detection for mobile captured digital video;and study of grey relational generating and development of toolbox by c language.

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Application of machine learning models for partial remote work intension in provincial areas of Japan 12

Application of machine learning models for partial remote wo...

引用

Joint 12th international conference on Soft Computing and Intelligent Systems / 23rd international Symposium on Advanced Intelligent Systems (SCIS and ISIS)

作者： Okushima, Masashi Tokushima Univ Dept Civil & Environm Engn Tokushima Japan

ISBN: (纸本)9781665499248

The purpose of this study is to model the relationship between the intention of remote work and its factors in the provincial areas of Japan. Particularly, we focus on partial remote work, which is easy to implement. Therefore, using remote work intention data, we construct not only regular remote work models but also partial remote work models. On these models, we applied various machine learning models to compare the learning accuracy. As a result, we found shopping time as a crucial factor in regular remote work. we also showed working hours as a key factor in partial remote work. The decision tree model has the same or higher estimation accuracy with fewer variables than the logistic regression model. The random forest model and the SVM model were able to train accurately for learning data. Regarding the verification of remote work, there was no significant difference in each modeling.

关键词： machine learning decision tree remote work verification

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A Hybrid machine learning Method for Diabetes Detection based on Unsupervised Clustering 23

A Hybrid Machine Learning Method for Diabetes Detection base...

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7th international conference on machine learning and Soft Computing, ICMLSC 2023

作者： Liu, Junhong Peng, Bo Yin, Zezhao School of Computer and Artificial Intelligence Southwest Jiaotong University China

ISBN: (纸本)9781450398633

Diabetes is a common disease, and due to the increasing incidence year by year. But most diabetics can not be easily detected in the early stage, since the symptoms are not obvious. The objective of this study is to propose a machine-learning method based on unsupervised clustering to improve the accuracy of diabetes detection. Due to massive unlabeled data sets and the problems in the traditional K-means clustering algorithms, we adopt the Fuzzy c-means clustering algorithm with an improvement on the calculation of parameter m. Our method includes a combination of the principal component analysis(PCA), an improved Fuzzy c-means (FCM) clustering algorithm, and K-nearest neighbor(KNN) classification algorithm optimized with K value. After 10 times 10-fold cross-validation, the average accuracy of the proposed method reaches 99.31%, which is higher than that of other machine learning models. Therefore, our method is proven to be more suitable for detecting diabetes. At the same time, further experiments on a new data set validate the applicability of our method in a more practical way for the diabetes detection. © 2023 ACM.

关键词： Principal component analysis

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CMA: A Cross-Modal Attack on Radar Signal Recognition Model Based on Time-Frequency Analysis 59

CMA: A Cross-Modal Attack on Radar Signal Recognition Model ...

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59th Annual IEEE international conference on Communications (IEEE ICC)

作者： Wang, Mengchao Zhang, Sicheng Xuan, Qi Lin, Yun Harbin Engn Univ Coll Informat & Commun Engn Harbin Peoples R China Zhejiang Univ Technol Inst Cyberspace Secur Hangzhou 310023 Peoples R China

ISBN: (纸本)9781728190549

In recent years, with the rise of deep learning, it has become a hot research topic to combine time-frequency analysis technology with deep learning to recognize radar signals. For the application of deep learning in radar signal recognition, however, the discovery of adversarial examples poses a tremendous security risk. Based on experiments, it appears that the radar signal recognition model based on the time-frequency image have been shown to be less vulnerable to adversarial attack methods based on time domain. Therefore, we propose a cross-modal attack (CMA). Firstly, we establish a surrogate model architecture locally, including three parts: time-frequency analysis, data quantization, and classifier. Secondly, we train this architecture as a whole and generate adversarial examples utilizing the trained surrogate model architecture parameters and adversarial attack methods. Finally, we carry out the CMA on the radar signal recognition model based on the time-frequency image by adding adversarial perturbations to the original signal. According to experimental results, the CMA can reduce the model recognition accuracy by more than 30%, demonstrating good attack performance, when the perturbation strength is 0.1 and the signal-to-noise ratio is 0 dB.

关键词： Radar signal recognition deep learning adversarial attack time-frequency analysis cross-modal attack

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machine learning for Diabetes, Heart Disease, Pneumonia, and Covid-19 Diagnostics 4

Machine Learning for Diabetes, Heart Disease, Pneumonia, and...

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4th international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

作者： Malhotra, Harshit Arora, Kirti Gogia, Kritika Raj, Piyush Jain, Dhyanendra Singh, Prashant Dr. Akhilesh das Gupta Institute of Technology and Management Department of Information Technology Delhi India

ISBN: (纸本)9781665474368

machine learning, a part of artificial intelligence which is applied in numerous health-related sector which includes the development of innovative medical procedures, the treatment of chronic diseases and the management of medical data. If a patient can recognize the disease at an early stage from the ease of home, they can start their medication sooner and consult a doctor accordingly for their treatment. This paper attempts to detect various diseases in the healthcare field such as Covid-19 and Pneumonia using Image processing technique with the help of Convolutional Neural Network, and other diseases such as Heart Disease and Diabetes using Random Forest, XGBoost, Support Vector machine and K-Nearest Neighbour Classifiers. © 2022 IEEE.

关键词： Convolutional neural networks

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Automated Topic Categorisation of Citizens' Contributions: Reducing Manual Labelling Efforts Through Active learning 21st

Automated Topic Categorisation of Citizens' Contributions: R...

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21st IFIP WG 8.5 international conference on Electronic Government (EGOV) / conference on E-Democracy and Open Government conference (CeDEM) / IFIP WG 8.5 international conference on Electronic Participation (EPart)

作者： Romberg, Julia Escher, Tobias Heinrich Heine Univ Dusseldorf Germany

ISBN: (纸本)9783031150869;9783031150852

Political authorities in democratic countries regularly consult the public on specific issues but subsequently evaluating the contributions requires substantial human resources, often leading to inefficiencies and delays in the decision-making process. Among the solutions proposed is to support human analysts by thematically grouping the contributions through automated means. While supervised machine learning would naturally lend itself to the task of classifying citizens' proposal according to certain predefined topics, the amount of training data required is often prohibitive given the idiosyncratic nature of most public participation processes. One potential solution to minimise the amount of training data is the use of active learning. While this semi-supervised procedure has proliferated in recent years, these promising approaches have never been applied to the evaluation of participation contributions. Therefore we utilise data from online participation processes in three German cities, provide classification baselines and subsequently assess how different active learning strategies can reduce manual labelling efforts while maintaining a good model performance. Our results show not only that supervised machine learning models can reliably classify topic categories for public participation contributions, but that active learning significantly reduces the amount of training data required. This has important implications for the practice of public participation because it dramatically cuts the time required for evaluation from which in particular processes with a larger number of contributions benefit.

关键词： Topic classification Public participation Active learning Natural language processing

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machine learning Assisted Intelligent Reflecting Surface MIMO Communication-Gateway for 6G—A Review 4th

Machine Learning Assisted Intelligent Reflecting Surface MIM...

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4th international conference on Computing, Communications, and Cyber-Security, IC4S 2022

作者： Srivastava, Praveen Khera, Shelej School of Electronics and Electrical Engineering Lovely Professional University Punjab Phagwara144411 India Department of Electronics and Communication Engineering Pranveer Singh Institute of Technology Kanpur208001 India

ISBN: (纸本)9789819914784

Intelligent reflecting surfaces (IRSs) are gaining interest due of their potential coverage and spectral efficiency advantages. However, there are a number of issues that must be resolved in order to materialise these surfaces in practise. A collaborative design for the BS and IRS beamformers is necessary, and the IRS components must be easily modified accordingly. Channel information is crucial to the accuracy of beamformer design deep learning (DL) is a data-driven technique that is essential in overcoming these difficulties. Deep learning is resistant to data flaws and environmental changes because of its faster computation speeds and model-free structure. DL has been demonstrated to be effective at the physical layer for active/passive beamforming, channel estimation, and IRS signal recognition utilising supervised, unsupervised, and reinforcement learning architectures. The methods for creating deep learning-based IRS-assisted wireless systems are summarised in this article. It gives insight to the different deep learning techniques like reinforcement learning, supervised learning, and federated learning which could be used along with IRS. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Beamforming

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Analysis of Ensemble of machine learning Algorithms for Detection of Parkinson's Disease 1

Analysis of Ensemble of Machine Learning Algorithms for Dete...

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1st international conference on Applied Artificial Intelligence and Computing, ICAAIC 2022

作者： Tallapureddy, Gowthami Radha, D. Amrita School of Engineering Amrita Vishwa Vidyapeetham Dept of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781665497107

Parkinson's Disorder(PD) is a neurodegenerative motion disorder. This disorder can be identified by the signs and symptoms which may increase with a small tremor in right or left palms and a feeling of tension. It may be worse thereafter. In overall, six million people in the world are affected by this disease as per the analysis of researchers. The proposed concept is to search for a way to discover Parkinson's sickness at a preliminary level that can assist in supplying the required remedy at the best time and avoid the disorder. machine learning algorithms are implemented onto Parkinson's Disease Speech features data set to identify whether the individual is healthy or not. If affected, the severity can also be diagnosed and probably be taken care of appropriately. The proposed work uses various models like Naive Bayes, Random Forest classification, K Nearest Neighbour, XGBoost, Decision Tree, Support Vector machine. The number of features is also reduced by using Principal component analysis (PCA). The performance of these models are analysed and compared. These models show the accuracy in the range of 70% to 90% using Parkinson's Disease Speech Features dataset. The higher performance models are considered for ensemble and to improve the overall performance metrics. The ensemble of models are used to get the best accuracy of 91% and precise recognition of the disease. © 2022 IEEE.

关键词： Decision trees

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