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检索条件"任意字段=3rd International Conference on Machine Learning and Computer Application, ICMLCA 2022"

共 1897 条记录，以下是461-470 订阅

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Deep learning based defect detection for printed circuit boards 3

Deep learning based defect detection for printed circuit boa...

引用

3rd international conference on computer Vision and Pattern Analysis, ICCPA 2023

作者： Liu, Xuehua Wang, Jian Zhang, Runxu Ruan, Weixiang Software Engineering Institute of Guangzhou Guangzhou China

ISBN: (纸本)9781510667563

Printed circuit boards (PCB) are manufactured and transported and stored in such a way that many factors can lead to different types of defects. Currently, manual defect detection and machine vision-based defect detection methods have problems such as slow detection speed, high false detection rate and fewer types of defects that can be detected. In this paper, a modeling method for PCB defect detection model based on deep learning is proposed. First, to address the problem of difficult feature extraction due to the small size of PCB defects, the original image is first generated at super-resolution by generative adversarial networks, and then the original backbone network of FasterRCNN is replaced by Restnet101 for feature extraction, with a deeper network ensuring better feature extraction results. Experiments show that the model can effectively detect six types of PCB defects: missing holes, pseudo-copper, short circuit, stray, mouse bite and open circuit. © 2023 SPIE.

关键词： Printed circuit boards

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machine learning-based analysis of online course learning experience 3

Machine learning-based analysis of online course learning ex...

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3rd international conference on Big Data, Artificial Intelligence and Internet of Things Engineering, ICBAIE 2022

作者： Yang, Wei Pan, Yong Wuhan Qingchuan University School of Business Wuhan China Huazhong University of Science and Technology School of Computer Science and Technology Wuhan China

ISBN: (数字)9781665451604

ISBN: (纸本)9781665451604

In order to timely and accurately reflect the learning experience in online learning and improve the course quality of online learning platform, the author selected the representative *** as the research object, and proposed a method based on machine learning for analyzing online course evaluation text. This method uses Scrapy framework of web crawler, Jieba Chinese word segmentation tool, Word2vec word vector conversion model, LDA topic model and other research tools, and uses TF-IDF algorithm. This analysis illustrate some problems existing in *** from three aspects of TF-IDF characteristic word frequency analysis, topic-keyword matrix and the proportion of negative evaluation topics. The results prove that the method proposed in this paper is of practical value. This method is expected to contribute to the development of online education. © 2022 IEEE.

关键词： Web crawler

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applications of ML/AI for Decision-Intensive Tasks in Production Planning and Control 3

Applications of ML/AI for Decision-Intensive Tasks in Produc...

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3rd international conference on Industry 4.0 and Smart Manufacturing (ISM)

作者： Elbasheer, Mohaiad Longo, Francesco Nicoletti, Letizia Padovano, Antonio Solina, Vittorio Vetrano, Marco Univ Calabria Dept Mech Energy & Management Engn DIMEG Arcavacata Di Rende Italy CAL TEK Srl Arcavacata Di Rende Italy

The fast-growing literature corpus of industry 4.0 in the last years also reflects the various applications of Artificial Intelligence (AI) and machine learning (ML) in the manufacturing domain. This paper conducts a literature survey to infer patterns and correlations within this scientific domain. This analysis aims to identify the knowledge gaps within the literature of ML/AI-enabled manufacturing systems. Therefore, the objective of this study is twofold;first, we aim to identify commonalities and general trends in AI/ML-enabled manufacturing systems. Secondly, we map the various application areas of AI/ML-based Production Planning and Control (PPC). To this end, an overall analysis of the keywords highlighted four major scientific scopes for ML/AI in manufacturing systems. Moreover, further investigation of this literature corpus was made to identify the different applications for AI/ML to enhance decision-making in PPC. Based on this analysis, three major pillars were identified as potential areas for AI/ML-enabled PPC for decision intensive tasks: dynamic scheduling, performance evaluation and monitoring, and automated process control. (C) 2022 The Authors. Published by Elsevier B.V.

关键词： Artificial Intelligence machine learning Production Planning and Control Dynamic Scheduling Performance Evaluation and Monitoring Process Automation

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Multiclass Support Vector Data Description in Extreme learning machine Feature Space 3

Multiclass Support Vector Data Description in Extreme Learni...

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3rd international conference on computer, Big Data and Artificial Intelligence, ICCBDAI 2022

作者： Lei, Yaolin Ding, Wenrui Wang, Yufeng Zhang, Wenbo Chai, Xinghua Electronic Information Engineering College Beihang University Beijing100000 China 54th Research Institute of China Electronics Technology Group Corporation Shijiazhuang050081 China School of Electronic Engineering Xidian University Xi'an710071 China

Support vector data description (SVDD) method aims to address the one-class classification (OCC) problem to find a hypersphere-shaped description of target data set. For extending SVDD to multiclass classification while remaining the ability of detecting outliers, we propose a novel multiclass SVDD scheme which can be used in effective feature mapping and meta-class separation based on the extreme learning machine algorithm (ELM-MSVDD). Accordingly, the imprecise data difficult to distinguish in specific classes is classified to a meta-class,the meta-class is defined by the disjunction of these specific classes, this operation can reduce the error rate effectively. Experimental results of our ELM-MSVDD method show well performance on the datasets from the UCI machine learning library and radar signal source recognition. Meanwhile, our proposed method provide a theoretical and practical support for other relevant pattern recognition field. © Published under licence by IOP Publishing Ltd.

关键词： machine learning

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machine learning-based solution of Kepler's equation 3

Machine learning-based solution of Kepler's equation

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3rd international conference on computer Science and Communication Technology, ICCSCT 2022

作者： Zheng, Maozhang Luo, Jianjun Dang, Zhaohui School of Astronautics Northwestern Polytechnical University Shaanxi Xi'an710072 China

ISBN: (纸本)9781510661240

In this study, an analytical solution of elliptical Kepler's equation, which gives the position of a celestial body moving in orbit as a function of time, is designed by using artificial intelligence techniques. For the eccentric anomaly, Kepler's equation is a transcendental equation with no precise analytical solution. In this paper, a high precision approximate analytical solution is presented to determine eccentric anomaly. The proposed method is based on machine learning where a non-iterative accurate solution is learned from training data. The solution to Kepler's solution is created using an artificial neural network based on the universal approximation theorem. Simulation results show that this solution is computationally efficient and has a constant complexity. © 2022 SPIE.

关键词： machine learning

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Medicine assistance application for visually impaired people 3

Medicine assistance application for visually impaired people

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3rd international conference on Intelligent Computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Mishra, Anubhav Vedhapriyavadhana, R. Vellore Institute of Technology Chennai School of Computer Science and Engineering Chennai India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

Visual written information nowadays is the basis for most of the tasks but for visually impaired people reading printed text is a challenging task. Nowadays smartphones are very common and accessible to each and everyone. The objective of this project is to assist visually challenged elderly people in taking correct and timely doses of medicines without being dependent on others using their smartphones. Users need to take pictures of the backside of medicine strips with the help of their mobile camera in the app. The application will scan the text written on it with the help of optical character recognition (OCR) and with the help of text localization techniques it will extract medicine details from the wrapper of medicine. App also allows users to set reminders to take dosage of their medicine on time. This project is proposed to help visually challenged people with the help of Artificial intelligence, machine learning, image to text recognition and voice assistance. © 2022 IEEE.

关键词： Optical character recognition

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Corona Virus Outbreak Prediction Using machine learning 3

Corona Virus Outbreak Prediction Using Machine Learning

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3rd international conference on Intelligent Engineering and Management, ICIEM 2022

作者： Kumar, Aman Kumar, Vivek Gupta, Vaishali Galgotias University School of Computer Science Greater Noida India

ISBN: (数字)9781665467568

ISBN: (纸本)9781665467568

Predicting the coronavirus is divided into several parts in this research report, including a state-by-state analysis [2], that includes active, total, cured, and death cases, as well as a daily increase in cases that includes India confirmed, death, and recovered cases. This also includes a thread of new coronavirus cases and forecasts how an outbreak will play out in the next days. This model is implemented using Anaconda navigator and Kaggle, an open-source platform. We utilize Kaggle to forecast time series data in order to predict the virus outbreak. Anaconda Navigator is a free online cloud that gives us with a Jupyter notebook environment that is suited for machine learning ideas. Support machine learning concepts such as Vector machine, Regression, and Data Visualization are applied to improve the outcomes of the research. This model includes all available data on the virus's transmission, including total, new, and active cases, as well as forecasting future outbreaks and a weekly epidemic research. © 2022 IEEE.

关键词： Coronavirus

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Online learning effect analysis based on machine learning 3

Online learning effect analysis based on machine learning

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3rd international conference on computer Science and Communication Technology, ICCSCT 2022

作者： Su, Junyu Wang, Fengling Liu, Wenjian Shi, Chiyu Institute of Data Science City University of Macau 999078 China School of Artificial Intelligence Hezhou University Guangxi Hezhou542899 China

ISBN: (纸本)9781510661240

Online learning has grown in popularity due to the advancement of Internet technology, and how to accurately assess its efficacy has emerged as a major challenge. Online learning provides advantages and benefits in terms of enhancing people's learning effectiveness and cognitive approach, but it also faces numerous obstacles as a new learning technique and educational model. To increase the efficacy of online learning, we assess and investigate the existing state, shortcomings, learning effects, and behavioral evaluation of online learning in this research. © 2022 SPIE.

关键词： machine learning

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3rd international conference on Image Processing and Vision Engineering, IMPROVE 2023

3rd International Conference on Image Processing and Vision ...

引用

3rd international conference on Image Processing and Vision Engineering, IMPROVE 2023

ISBN: (纸本)9789897586422

The proceedings contain 23 papers. The special focus in this conference is on Image Processing and Vision Engineering. The topics include: Multi-Scale Surface Normal Estimation from Depth Maps;intrinsic Image Decomposition: Challenges and New Perspectives;vegetation Coverage and Urban Amenity Mapping Using computer Vision and machine learning;a Deep learning Approach for Estimating the Rind Thickness of Trentingrana Cheese from Images;application of Particle Detection Methods to Solve Particle Overlapping Problems;an Anisotropic and Asymmetric Causal Filtering Based Corner Detection Method;layer-wise External Attention for Efficient Deep Anomaly Detection;emotion Based Music Visualization with Fractal Arts;handling Data Heterogeneity in Federated learning with Global Data Distribution;climbing with Virtual Mentor by Means of Video-Based Motion Analysis;normalised Color Distances;fuzzy Inference System in a Local Eigenvector Based Color Image Smoothing Framework;3D Reference-Based Skeletal Movement Evaluation;FUB-Clustering: Fully Unsupervised Batch Clustering;from Depth Sensing to Deep Depth Estimation for 3D Reconstruction: Open Challenges;deep learning and Medical Image Analysis: Epistemology and Ethical Issues;an Integrated Mobile Vision System for Enhancing the Interaction of Blind and Low Vision Users with Their Surroundings;automatic Defect Detection in Sewer Network Using Deep learning Based Object Detector;facial Expression Recognition with Quarantine Face Masks Using a Synthetic Dataset Generator;A Global Multi-Temporal Dataset with STGAN Baseline for Cloud and Cloud Shadow Removal.

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Face-To-camera distance estimation using machine learning 3

Face-To-camera distance estimation using machine learning

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3rd international conference on Innovations in computer Science and Software Engineering, ICONICS 2022

作者： Ausaf Hussain, Syed Waseemullah Ahmed Khan, Najeed NED University of Engineering and Technology Department of Computer Science and Information Technology Karachi Pakistan

ISBN: (纸本)9781665461412

Distance estimation of moving objects from the camera with accuracy is a challenging task in the digital era where human interaction increases with smart systems and state-of-The-Art applications. The wide-ranging applications of distance estimation include the 'zooming' effect in a document reader and finding the power of the correction lens for eyesight (eyesight testing). A lot of research has already been done on different methods of distance estimation like the most widely-used methods of "mono-vision"and "stereo-vision". The purpose of this study is to introduce a novel approach to finding the distance between the face and the camera with a high degree of accuracy and speed. The proposed method is based on detecting, measuring, and calculating the size of irises on an image of a human face obtained from a single camera, and a Supervised machine learning algorithm. The open-source Mediapipe Python package has been employed to extract the irises from the images. The proposed method has given the results of the estimated distance with an average accuracy of 95.6%. © 2022 IEEE.

关键词： Cameras

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