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检索条件"任意字段=2021 2nd International Conference on Machine Learning and Computer Application, ICMLCA 2021"

共 1034 条记录，以下是271-280 订阅

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Foreign matter detection of coal conveying belt based on machine vision 2

Foreign matter detection of coal conveying belt based on mac...

引用

2nd international conference on computer Science and Management Technology, ICCSMT 2021

作者： Xie, Yehui Yu, Sun Huang, Ziyang Xi'an University of Science and Technology Xi'an China

ISBN: (纸本)9781665420631

Because of its complex working environment, most coal mines take belt conveyor as the main transportation equipment. However, in the process of transportation, due to long-time and high-intensity operation, the belt is very easy to be damaged by gangue, iron and other foreign matters doped in coal, resulting in unnecessary losses. Foreign objects in the process of belt transportation can be effectively identified by machine vision. This is of great significance to ensure coal mine safety production, improve transportation efficiency and increase enterprise income. © 2021 IEEE.

关键词： Deep learning

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Meta-learning Based Breast Abnormality Classification on Screening Mammograms 2

Meta-learning Based Breast Abnormality Classification on Scr...

引用

2nd international conference on computer Engineering and application, ICCEA 2021

作者： Wang, Yu Song, Mingjie Tian, Xinyu Shandong Weihai264209 China Ohio State University ColumbusOH43210 United States Duke Kunshan University Jiangsu Kunshan215316 China

ISBN: (纸本)9781665426169

General breast cancer detection contains two steps, the breast abnormality classification, and the diagnostic classification. The determination of the abnormality contributes further to the following steps, and computational technologies can aid in the process. A lot of machine learning methods have been applied to automate the detection. However, most of them focus on the diagnostic classification and the breast abnormality classification only attracts little attention. The insufficient size of public mammogram datasets also limits the performance of many machine learning algorithms. Considering the importance of breast abnormality classification and the shortage of public large-scale medical datasets, we proposed a meta-learning-based breast abnormality classification method. Our model referred to the latest work of meta-learning-based image classifier and modified it. Specifically, we applied the idea of meta-learning to retrain a pretrained embedding neural network in order to adapt its feature extraction ability to the CBIS-DDSM dataset [1]. The dataset contains two types of abnormal breast mammograms, mass and calcification, and each type is made of two categories of medical images, full mammograms, and ROI [2]. The application of the data augmentation techniques and the idea of meta-learning helped to deal with the insufficient training sample problem and showed a final accuracy of 76%, which beat the 71% accuracy reached by a neural network baseline model. © 2021 IEEE.

关键词： Classification (of information)

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Vision-Based Object Classification Using Deep learning for Mixed Palletizing Operation in an Automated Warehouse Environment 2nd

Vision-Based Object Classification Using Deep Learning for M...

引用

2nd international conference on Recent Advances in Manufacturing, RAM 2021

作者： Patel, Anubhav Dinesh Chowdhury, Abhra Roy Department of Computer Science and Engineering Indian Institute of Information Technology Kalyani West Bengal Kalyani India Division of Mechanical Sciences Centre for Product Development and Manufacturing Indian Institute of Science Bangalore India

ISBN: (纸本)9789811677861

Present object identification and classification methods in warehouse automation either require a tedious calibration process or run at a speed that is slow for real time application. This research paper proposes a computer vision-based method using a deep convolutional neural learning network to calculate dimensions of 5 distinct objects found in an automated robot warehouse and classify them using images taken from a camera for the purpose of mixed palletizing inside warehouses. It achieved a classification accuracy of 98.94% on the machine parts images dataset created by the authors comprising more than 1450 images by utilizing transfer learning and data augmentation. The employed method ResNet-50 uses a combination of residual learning and deep architecture parsing called residual network architecture. The proposed method for extracting object dimensions has an error rate of 6.492% on average and it runs at 250 frames per second, therefore it is also suitable for applications in automated warehouses. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： computer vision Convolutional neural networks Deep learning Object classification Palletization ResNet-50 Warehouse automation

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Detecting Fake News via Deep learning Techniques

Detecting Fake News via Deep Learning Techniques

引用

icmlca 2021;2nd international conference on machine learning and computer application

作者： Yu Chang Xiaolong Wang

The problem caused by the rapid growth of news information is that it brings too much fake news. Fake news negatively impacts both economy and society, even causes public panic, and threatens public security. And the huge amount of data makes it more difficult to identify fake news manually. Hence, how to automatically detect false news from both the internet and the traditional public media while preventing the spread of fake news in time attracts research in various fields. In this paper, we revise some state-of-the-art work on fake news detection. Specifically, we first describe and compare the definition of both fake news and fake news detection. Secondly, an introduction of the metrics and datasets of fake news detection is shown. And then, we revise some deep learning-based fake news detection. These works are divided into two categories following the modality, namely, single-modal methods and multi-modal methods. Finally, we analyse some potential and important challenges of fake news detection. The review cannot provide an introduction for researchers but also a good reference for rookies.

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Artificially intelligent health chatbot using deep learning 2

Artificially intelligent health chatbot using deep learning

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2nd international conference for Emerging Technology, INCET 2021

作者： Bagwan, Faraz Phalnikar, Rashmi Desai, Sharmista D. Y. Patil School of Engineering Department of Computer Engineering Pune India Maharashtra Institute of Technology-World Peace University School of Computer Engineering and Technology Pune India

ISBN: (纸本)9781728170299

Chatbot is a popular application used to converse with a computer-based system in an interactive and amicable manner. It enables a person to interact with the vast amount of knowledge in an easier and cost-effective way. Chatbots are able to answer the questions using its knowledge base just like a human expert, the only exception is that it takes mere minutes or even seconds for a chatbot to learn as opposed to the human's ability to acquire the same knowledge over a period of months or years. The learning rate depends on the algorithm applied, processing speed of the system and the amount of data to train the model. This particular characteristic of quick learning proves valuable in all application domains and more so in the health services. In this paper, we propose a health chatbot developed using deep learning techniques of complex neural networks. The model is trained on a Knowledge Base which consists of health queries and its answers. These question-answer pairs were obtained manually from WebMD, which is one of the leading health service websites on the internet. An accuracy of 0.97% and loss of 0.02%, in terms of correct answer prediction for a given query, was obtained while training the deep learning model. © 2021 IEEE.

关键词： Health

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Research on the scheduling strategy of intelligent manufacturing workshop based on machine learning 2

Research on the scheduling strategy of intelligent manufactu...

引用

2nd international conference on Artificial Intelligence and computer Engineering, ICAICE 2021

作者： Guan, Haonan Jiangsu University of Science and Technology China

ISBN: (纸本)9781665421867

In the context of industry 4.0, this paper proposes an improved genetic algorithm for optimizing the scheduling operation of smart manufacturing shops. Coding matrixes are separately developed for the manufacturing processes and machines and a retention operator is added to retain the optimal individuals in each generation of the population. After the approximate global optimal solution is obtained, the chromosomes are decoded by the adoption of an insertion greedy decoding algorithm. Simulation results have demonstrated the effectiveness of the algorithm. © 2021 IEEE.

关键词： Job shop scheduling

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Proceedings - 2021 2nd international conference on Intelligent Computing and Human-computer Interaction, ICHCI 2021

Proceedings - 2021 2nd International Conference on Intellige...

引用

2nd international conference on Intelligent Computing and Human-computer Interaction, ICHCI 2021

ISBN: (纸本)9781665407649

The proceedings contain 70 papers. The topics discussed include: mid-term and long-term prediction of carbon emissions in Jiangsu province based on PCA-STIRPAT improved GA-BP;prediction of PM2.5 concentration in Guangzhou based on LSTM neural network;research on the application of mathematical methods in image denoising technology;research on intelligent terminal authentication strategy based on block chain;complex network variability analysis with impact of major events on aviation networks;influence and effect of fitness applet on college students' physical health;research on the technology of generating single-table SQL query sentences in Chinese natural language;a video scene segmentation optimization algorithm based on convolutional neural network;a Roberta-based model for identifying non-substantive factual elements of the case;and research on air quality prediction based on machine learning.

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A Real Time application for Lung Cancer Prediction using machine learning 2

A Real Time Application for Lung Cancer Prediction using Mac...

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2nd international conference on Communication, Computing and Industry 4.0, C2I4 2021

作者： Mahadevaswamy, U.B. Siddhanti, Sharayu R. Bhumika, S.M. Mounashree, B. SJCE Department of ECE Mysore India

ISBN: (纸本)9781665420136

Disease detection is one of the significant areas of research in medical science. So far, there is no automation technique proposed for lung cancer prediction. Existing methods employ manual procedure which is less efficient, time-consuming, and expensive. This paper proposes an automated system to predict the level of lung cancer. The proposed method uses KNN algorithm to predict the level of Lung cancer in terms of high, medium and low. The results obtained shows an accuracy of 95.8%. © 2021 IEEE.

关键词： machine learning

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Anonymization as homeomorphic data space transformation for privacy-preserving deep learning 2

Anonymization as homeomorphic data space transformation for ...

引用

2nd international conference on Industry 4.0 and Smart Manufacturing (ISM)

作者： Girka, Anastasiia Terziyan, Vagan Gavriushenko, Mariia Gontarenko, Andrii Univ Jyvaskyla Fac Informat Technol Jyvaskyla 40100 Finland Qvantel Finland Oy Piippukatu 11 Jyvaskyla 40100 Finland

Industry 4.0 is largely data-driven nowadays. Owners of the data, on the one hand, want to get added value from the data by using remote artificial intelligence tools as services, on the other hand, they concern on privacy of their data within external premises. Ideal solution for this challenge would be such anonymization of the data, which makes the data safe in remote servers and, at the same time, leaves the opportunity for the machine learning algorithms to capture useful patterns from the data. In this paper, we take the problem of supervised machine learning with deep feedforward neural nets and provide an anonymization algorithm (based on the homeomorphic data space transformation), which guarantees privacy of the data and allows neural networks to learn successfully. We made several experiments to show how much the performance of the trained neural nets will suffer from the deepening of the anonymization power. (C) 2021 The Authors. Published by Elsevier B.V.

关键词： Industry 4.0 Privacy Neural Network Deep learning Topology

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An Analytical Approach of Crime Prediction Using machine learning 2nd

An Analytical Approach of Crime Prediction Using Machine Lea...

引用

2nd international conference on Mechanical and Energy Technologies , ICMET 2021

作者： Kapoor, Punya Vellore Campus Tiruvalam Rd Tamil Nadu Katpadi Vellore632014 India

ISBN: (纸本)9789811901072

Crime is considered regarded as our society’s maximum critical and serious and growing problem, and stopping it would be an essential duty. On a daily basis, a great number of crimes are perpetrated. This necessitates maintaining note of all crimes and retaining a database for them to be referenced in the destiny. The gift issue is maintaining a dependable crime dataset and analyzing these statistics to aid inside the prediction and backbone of destiny crimes. The goal of this task is to observe a collection including a selection of crimes and forecast the type of crime that could arise within the future based on a diffusion of factors. The science of creating computer systems that make judgments without human intervention is called device getting to know. machine gaining knowledge has currently been used within the improvement of self-driving vehicles, speech identity, web website seek, and a higher understanding of the human genome. It has also made it feasible to perceive crime using referenced data. This research appears into crime prediction the use of gadget studying. This look examines crime data from India over the last 15 years using critical information-processing methodologies. When predicting crime in India, gadget-mastering predictive models including K-nearest-neighbor and boosted choice tree are used, and crime prediction accuracy levels from thirty-nine % to forty-four%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Forecasting

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