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检索条件"任意字段=3rd International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 3281 条记录，以下是1341-1350 订阅

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machine learning Model Based Expert System for Pig Disease Diagnosis 3rd

Machine Learning Model Based Expert System for Pig Disease D...

引用

3rd international conference on Recent Trends in Image Processing and pattern recognition, RTIP2R 2020

作者： Robindro, Khumukcham Singh, Ksh. Nilakanta Singh, Leishangthem Sashikumar Department of Computer Science Manipur University Imphal795003 India

ISBN: (纸本)9789811604928

This paper describes the importance of machine learning (ML) algorithms in the design and development of diagnostic expert systems. The designed system is intended on how ML techniques can be used in the prediction of pig diseases through visible symptoms and their past behaviours. These types of systems are very useful in situations where the domain experts in the field are not readily available. It is important to make an accurate diagnosis of pig diseases to provide control strategies for any symptom shows in a pig to prevent related health issues. The main concerned in the study begins with the collection of disease symptoms and related information of pigs during their life span from the domain experts and literatures in the domain. The acquired knowledge and information are then pre-processed to be used for being trained different ML algorithms. The system development involves the training and analysis of different ML classifier models and their performances. It has thus been found in the study that SVM classifier can produce more accurate results in disease prediction. The paper finally presents the design and development of an expert system for the diagnosis of Pig diseases using SVM classifier which is trained with pig disease dataset. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Expert systems

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Using Deep learning and Image recognition to Realize The Electronic Management of Paper Original Records

Using Deep Learning and Image Recognition to Realize The Ele...

引用

international conference on Applied machine learning (ICAML)

作者： Yujun Shan Fengwei Wang Chunting Wang Liaoning Inspection Examination & Certification Centre Shenyang China

ISBN: (纸本)9781665421263

In this paper, a large number of paper-based original records generated in the detection process, which are cumbersome in circulation and difficult in management, are proposed to realize the electronic management of original records by using image recognition. Through the learning and recognition of original record format, simulated original record and handwriting, the key data area of paper original record can be recognized.

关键词： Deep learning Handwriting recognition Image recognition Market research Personnel Testing Quality management

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An Optimized Seven-Layer Convolutional Neural Network with data Augmentation for Classification of Chinese Fingerspelling Sign Language 1

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3rd EAI international conference on Multimedia Technology and Enhanced learning, ICMTEL 2021

作者： Gao, Yalan Zhu, Rongxin Gao, Ruina Weng, Yuxiang Jiang, Xianwei Nanjing Normal University of Special Education Nanjing210038 China

ISBN: (数字)9783030825652

ISBN: (纸本)9783030825645

Sign language recognition especially finger language recognition facilitates the life of deaf people in China. It overcomes many difficulties and provides convenience for deaf people’s life. In this paper, we used the advanced convolutional neural network to extract the different characteristics of the input. We created an optimized seven-layer CNN, including five convolution layers for feature extraction and two fully connected layers for classification to enhance the original signal function and reduce noise after operation. Some advanced techniques such as batch normalization, ReLu and dropout were employed to optimize the neural network. Meanwhile, we adopted data augmentation technology, which not only expanded the data set and improve the performance of machine learning algorithm, but also avoided the over-fitting problem. The experimental results show that the average recognition accuracy reaches 91.99 ± 1.21%, which indicate an excellent property. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Convolutional neural networks

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Visual recognition and Recommendation System for Cultural Tourism Attractions Based on Deep learning 1

引用

1st international conference on pattern Analysis and machine Intelligence, ICPAMI 2024

作者： Li, Si Yang, Xiaoqi Guangzhou College of Commerce Guangzhou China

ISBN: (数字)9789819633494

ISBN: (纸本)9789819633487

This article designs and constructs a deep prediction model that combines deep neural networks and Transformer encoders. Build a prediction model using deep neural networks and Transformer encoders. Due to the powerful feature extraction ability of deep neural networks and their ability to fuse heterogeneous data from multiple sources, they are used to process basic information about users and scenic spots. The Transformer encoder is used to process user comment data, modeling long-distance dependencies between words in sentences through position encoding and self attention at the encoding end, thereby fully mining the comprehensive features of comment data and obtaining more potential user interest features. The features of the last three networks are input into the factorization machine to further learn their potential interaction relationships, thereby improving the prediction accuracy of the model and alleviating the problem of data sparsity. Finally, the effectiveness and accuracy of this recommendation model were verified through experimental comparison with traditional recommendation models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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Detection and learning of Color Objects Using Kernelized Support Correlation Filter 3rd

Detection and Learning of Color Objects Using Kernelized Sup...

引用

3rd international conference on Recent Trends in Image Processing and pattern recognition, RTIP2R 2020

作者： Jeve, Kishor S. Gaikwad, Ashok T. College of Computer Science and Information Technology Latur Maharashtra India Vivekanand College Aurangabad Maharashtra India

ISBN: (纸本)9789811605062

Moving Object detection and tracking are important for many computer vision applications. The support vector machines (SVMs) are used to reduce the cost of computation and to increase the efficiency of computation. Kernelized correlation filters are used for efficient object tracking. In this method, the patches of translated images are used through dense sampling to form the circulant matrix. Support correlation filtering is an approach which is composed using combination features of the support vector machine and circulant matrix. We use a discrete Fourier transform to pose the tracking problem. We further use kernel function with support correlation filters to improve the performance of the object tracking algorithm. Kernelized support correlation filter is a good alternative to improve the accuracy and speed of the object tracking process. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Support vector machines

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Ensembling learning Based Melanoma Classification Using Gradient Boosting Decision Trees 20

Ensembling Learning Based Melanoma Classification Using Grad...

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3rd international conference on Artificial Intelligence and pattern recognition (AIPR)

作者： Han, Yipeng Zheng, Xiaolu Univ New South Wales Sydney NSW Australia Beihang Univ Beijing Peoples R China

ISBN: (纸本)9781450375511

Melanoma has been regarded as one of the fatal skin cancer diseases all around the world. Early detection on melanoma can be quite helpful in the clinical treatment, to prevent the deterioration of the deadly diseases. Handcrafted-feature extraction and shallow architecture-based classifier (such as k-nearest neighbors algorithm, random forest, support vector machine) worked as the basis of the previous attempts in detecting process. During the recent years, the new approach named deep convolutional neural network (CNN) was used for the detecting task. Although the persistent progress and efforts have been achieved, the classification methods desire to go a further step in pursuing further improvement on its performance. The goal of this paper is to improve the detection performance using an ensemble learning framework. Both the personal information (such as the age, gender information of the patients) and latest deep learning approaches are applied in this paper. The two approaches have provided the mutual complements for each other, which demonstrated enormous advantages for the ensemble learning framework in detecting task. We conducted extensive experiments that provide a large dataset for detecting melanoma, which illustrates that our ensemble learning can provide superior performance with high accuracy.

关键词： medical image classification deep learning convolutional neural network ensemble learning

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Enhancing Enterprise Business Processes Through AI Based Approach for Entity Extraction – An Overview of an Application 3rd

Enhancing Enterprise Business Processes Through AI Based App...

引用

3rd international conference on Recent Trends in Image Processing and pattern recognition, RTIP2R 2020

作者： Dwivedi, Ankit Vijayan, Praveen Gupta, Rishi Ramdasi, Preeti TCS Data Office Analytics & Insights Tata Consultancy Services Pune India

ISBN: (纸本)9789811605062

While Industries are growing strong with their digital transformation, advanced analytics are making them stronger through data driven decisions. At the same time traditional automation is getting matured and emerging as cognitive automation. In the era of Industry 4.0, handshake of business process automation, advance analytics and cognitive services have laid down a strong platform for ‘Cognitive Bots’. Enterprises can leverage the advent of powerful technologies and approaches, anticipating ultimate goal of the business through more adaptive, self-learning, and contextual applications. This paper explains one of such cognitive bot for finance department where invoices are of utmost importance for the function. The mentioned bot is intended for amount detection and verification;additionally, it can also extract various entities like organization name, location and date which contributes to perform analytics to a great extent. The application reward business in reducing turnaround time and human errors. The accuracy of specially customized trained neural model has achieved state of the art results on the current set of learning data. The proposed framework makes use of Optical Character recognition and PDFMiner for text extraction from scanned invoices. A Quality Classifier that will reject hand written invoices for any further processing. A spaCy’s Name-Entity-recognition predicts the amount, date, organization name and location from extracted unstructured text. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Optical character recognition

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Detecting Fraudulent Transactions Using a machine learning Algorithm 1

引用

3rd international conference on Futuristic Trends in Network and Communication Technologies, FTNCT 2020

作者： Belyakov, S.L. Karpov, S.M. Zalilov, E.F. Southern Federal University Nekrasovsky. 44 Rostov-on-Don347928 Russia

ISBN: (数字)9789811614804

ISBN: (纸本)9789811614798

This study is dedicated to the problem of rapid detection of fraudulent financial transactions. Current approaches to monitoring and detection of fraud in banking transactions were analyzed. The problem of the most reliable recognition of classes of financial transactions using unbalanced data was considered. The problem of choosing the best classifier among ensemble algorithms was investigated. Specifically, these are algorithms of a Random Forest, Adaptive Boosting, and Decision Trees bagging. Methods of solving the problem of unbalanced data samples were analyzed. It was suggested to use the random undersampling algorithm to create balanced subsets of ensemble algorithm estimators. The results of experimental comparison of the selected methods are presented. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Decision trees

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Pre-trained CNN and SVM Based Hybrid Model for Apple Crops Phenological Phase recognition

Pre-trained CNN and SVM Based Hybrid Model for Apple Crops P...

引用

Computing, Analytics and Networks (Indo-Taiwan ICAN), Indo – Taiwan 2nd international conference on

作者： Rakesh Mohan Datt Vinay Kukreja Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9781665499453

This study suggests a hybrid approach for identifying different phenological phases of apple crop development. This is one of the key research areas in agriculture. The proposed hybrid model combines a custom pre-trained convolutional neural network (CNN) and a supervised machine learning algorithm support vector machine (SVM). The custom pre-trained CNN model extracts the critical features from the images. Whereas, the SVM performs the task of classification. The hybrid model was trained on a custom dataset of RGB images of various principal phonological stages of apples from an orchard situated close to Srinagar airport, JK (UT), India. Images of the dataset were expanded using data augmentation methods. The expanded training dataset has more than 5000 images, the validation dataset has around 1400 images and the testing dataset has around 1750 images. The performance of the new hybrid model with other famous models like VGG16, VGG19, ResNet50, and Inception-v3 was compared. The suggested hybrid model has a better f1-score of 0.97.

关键词： Support vector machines Analytical models Computational modeling Atmospheric modeling Transfer learning Crops Feature extraction

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A Review of Research on Pulsar Candidate recognition Based on machine learning 3

A Review of Research on Pulsar Candidate Recognition Based o...

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3rd international conference on Mechatronics and Intelligent Robotics (ICMIR)

作者： Zhang, Cheng Jun Shang, Zhen Hong Chen, Wan Min Xie, Liu Miao, Xiang Hua Kunming Univ Sci & Technol Fac Informat Engn & Automat Kunming 650500 Yunnan Peoples R China

The pulsar is a highly magnetized rotating neutron star that provides the first indirect evidence for the existence of gravitational waves and also provides the possibility to reveal extreme phenomena in neutron star astrophysics. Therefore, the identification of pulsars in the universe is a prerequisite for the study of pulsars and gravitational waves. At present, a large number of pulsar searches have produced millions of pulsar candidates. In the face of these large-scale data, if only relying on manual visual classification by experts in related fields, it will be a huge project. Since the emergence of machine learning, its theory and technology have become increasingly mature, and has been successfully applied to astronomical research fields such as pulsar candidate screening. This paper introduces the related machine learning theory of pulsar candidate recognition firstly, and then reviews the research status of pulsar candidate recognition based on machine learning in recent years. Finally, we discuss and prospect the identification of pulsars in the future. (C) 2020 The Authors. Published by Elsevier B.V.

关键词： Pulsar machine learning Candidate identification

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