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检索条件"任意字段=5th International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 3137 条记录，以下是371-380 订阅

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Alignment Offset Based Adaptive Training for Simultaneous machine Translation 5

Alignment Offset Based Adaptive Training for Simultaneous Ma...

引用

5th international conference on Natural Language Processing, ICNLP 2023

作者： Liang, Qiqi Liu, Yijin Meng, Fandong Xu, Jinan Chen, Yufeng Zhou, Jie Beijing Jiaotong University Beijing China Pattern Recognition Center WeChat Ai Tencent Inc Beijing China

ISBN: (纸本)9798350302219

Given incomplete source sentences as inputs, it is generally difficult for Simultaneous machine Translation (SiMT) models to generate a target token once its aligned source tokens are absent. How to measure such difficulty and further conduct adaptive training for SiMT models are not sufficiently studied. In this paper, we propose a new metric named alignment offset (AO) to quantify the learning difficulty of target tokens for SiMT models. Given a target token, its AO is calculated by the offset between its aligned source tokens and the already received source tokens. Furthermore, we design two AO-based adaptive training methods to improve the training of SiMT models. Firstly, we introduce token-level curriculum learning based on AO, which progressively switches the training process from easy target tokens to difficult ones. Secondly, we assign an appropriate weight to the training loss of each target token according to its AO. Experimental results on four datasets demonstrate that our methods significantly and consistently outperform all the strong baselines. © 2023 IEEE.

关键词： Curricula

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machine learning-based classification study of rice blast disease 5

Machine learning-based classification study of rice blast di...

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5th international conference on Computer Vision and data mining, ICCVDM 2024

作者： Yang, Xu Liu, Fangyuan Song, Shaozhong An, Xiaofeng Wu, Yonghao School of Electronic Information Engineering Changchun University of Science and Technology Changchun China School of Data Science and Artificial Intelligence JiLin Engineering Normal University Changchun China Education Quality Monitoring Center JiLin Engineering Normal University Changchun China

ISBN: (纸本)9781510682863

Begin Rice, as the largest food crop grown in China, has shown a high incidence of rice blast disease, which seriously threatens the sustainability of rice production. A UAV multispectral remote sensing platform is used to realize the identification and monitoring of the incidence of rice blast disease and early warning at the field level, which assists variable fertilizer and drug application by plant protection UAVs. this research is based on a rice blast experiment in a large field in Wanchang Town, Yongji County, Jilin City, based on a malefactor, using a UAV multispectral remote sensing platform to obtain images of two fertility stages, namely, the nodulation stage(NS) and the spike stage(SS) of rice, and using six different vegetation index parameters as inputs to establish a DBO-XGBoost classification model, with an accuracy rate of more than 90% for both fertility stages. © 2024 SPIE.

关键词： Vegetation

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Medical Image Analysis Using Deep learning Technnique 5

Medical Image Analysis Using Deep Learning Technnique

引用

5th international conference on Information Management and machine Intelligence, ICIMMI 2023

作者： Saxena, Surabhi Parveen, Nikhat Agarwal, Devendra Department of Artificial Intelligence and Data Science Poornima Institute of Engineering & Technology Rajasthan Jaipur India Department of Computer Science and Engineering Konneru Lakshmaiah Education Foundation Vijayawada Guntur India Goel Institute of Technology and Management Uttar Pradesh Lucknow India

ISBN: (纸本)9798400709418

Clinical picture classification, pattern recognition, and quantification have seen significant advancements with the help of artificial intelligence, particularly through deep learning techniques. Deep learning has rapidly emerged as the most rapidly evolving field within AI, and its applications have been successfully demonstrated across various domains, including medicine. this review briefly examines recent applied research in several medical fields, such as neurology, brain imaging, retinal analysis, pneumonias, computerized pathology, breast imaging, cardiovascular studies, musculoskeletal imaging, and gastrointestinal imaging. Deep learning networks prove to be highly effective when dealing with large scale medical datasets, enabling information discovery, knowledge dissemination, and knowledge-based prediction. this research aims to present both foundational knowledge and state-of-the-art deep learning techniques to facilitate the interpretation and analysis of medical images. the primary objectives of this work are to explore advancements in medical image processing research and implement the identified and addressed key criteria in practical applications. © 2023 ACM.

关键词： Deep learning

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An Extreme learning machine-Based AutoEncoder (ELM-AE) for Denoising Knee X-ray Images and Grading Knee Osteoarthritis Severity 5th

An Extreme Learning Machine-Based AutoEncoder (ELM-AE) for D...

引用

5th international conference on Recent Trends in Image Processing and pattern recognition (RTIP2R)

作者： Chaugule, Sushma Malemath, V. S. KLE DR MS Sheshgiri Coll Engn & Technol Dept CSE Belagavi India

ISBN: (纸本)9783031235986;9783031235993

Osteoarthritis (OA) is the most usual form of arthritis. Radiologists assess the OA severity by observing the pieces of evidence on both sides of knee bones, hinged on the Kellgren-Lawrence (KL) grading system. Computer-assisted diagnosis has been a prime field of research for the past few decades as it tends to provide highly accurate performance. In this work, we propose the Knee Osteoarthritis (KOA) classification problem to segregate the severity into five grades. the proposed work can be framed into two-stage, using X-ray images. Stage one dealswith preprocessing and denoising, while stage two dealswith classification. this work considers, a standard OAI dataset as well as locally collected images as input, and are fed to an Extreme learning machine-based AutoEncoder (ELM-AE) to get the denoised images, which are then used for training the Dense Neural Network model DenseNet201and are later classified, based on KL grades. In experimentation, evaluation of performance is carried out for the model with and without using autoencoders. It is observed that with autoencoders the overall performance is enhanced significantly for standard as well as the local dataset.

关键词： Extreme learning machine Denoising autoencoders Knee OA X-ray images DenseNet201 OAI

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Sentiment Analysis Perspective using Supervised machine learning Method 5

Sentiment Analysis Perspective using Supervised Machine Lear...

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5th IEEE international conference on Electrical, Computer and Communication Technologies, ICECCT 2023

作者： Patrick, Harold Andrew Praveen Gujjar, J. Sharief, M.H. Mukherjee, Ujjal Faculty of Management Studies Bengaluru India

ISBN: (纸本)9781665493604

Customer feedback play an important role in assisting the organization. Sentiment analysis of the customer feedback takes the business to next level of profit making. Based on the sentiment or the opinion of the customer organization can identify the needs of the customer. Sentiment analysis is applicable not only for the customer feedback analysis, it can also be used employee review system. To create the sentiment analysis model supervised machine learning algorithm can be used extensively. Supervised learning algorithm is used for training the model for classification and regression problem. In this article various learning algorithm like linear regression, KNN, SVM, Random Forest, Bagging and Gradient Boosting are considered for developing and testing the model. the dataset was divided into training and test dataset. the model was developed using the training dataset and tested on the test dataset. the data was collected from 884 employees working in different organizations. the reliability control has shown that 1.1 % of respondents were unreliable, as some questions were left unattended. the final data of 802 employees was taken for the study. the different lexical resources like Afinn, VADER and sentiment from textblob were considered for determining the sentiment related to all the three dimensions of positive leadership those are strength, perspective and recognition. the result shows that random forest supervised machine learning algorithm gives the highest accuracy of 0.711 whereas the KNN algorithm gives the accuracy of 0.515. © 2023 IEEE.

关键词： Sentiment analysis

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CapsNet-based drift detection in data stream mining 23

CapsNet-based drift detection in data stream mining

引用

5th international conference on pattern recognition and Intelligent Systems, PRIS 2023

作者： Lin, Borong Jin, Nanlin School of Advanced Technology Xi an Jiaotong-liverpool University China

ISBN: (纸本)9781450399968

For data streams, drift detection methods warn and detect the changes in patterns over time. For example, in smart manufacturing, many data streams are generated from sensors that monitor the real-time operation of manufacturing. Drift detection can be used to discover if and how the operation status changes. At present, there have been three main approaches in drift detection: error rate-based, distribution-based, and hypothesis-based. However, these approaches bear an impractical limitation: delays due to the demand for computational time. In a large-scale and high-speed data stream, a time-efficient detector is vital. To address this, this paper proposes a CapsNet-based drift detection algorithm (CapsNet-DDM). Our experimental results and comparative studies have found that CapsNet-DDM demonstrates a distinguishing advantage on computational time, with no compromise on accuracy, F1 score, and effective drift detection rates. © 2023 ACM.

关键词： data mining

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Wheat Disease recognition Based on a Fusion Model with Improved Spatial and Channel Reconstruction Convolution Combined with Contextual Transformer 5

Wheat Disease Recognition Based on a Fusion Model with Impro...

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5th international conference on Computer Vision, Image and Deep learning, CVIDL 2024

作者： Yang, Zicong Jin, Aitong Wang, Junxi Xie, Haifan Zhan, Xin Li, Qiaolin Zhuhai College of Science and Technology Department of Computer Zhuhai China Zhuhai College of Science and Technology Department of Statistics and Data Science Zhuhai China Zhuhai College of Science and Technology Department of Mechanical Engineering Zhuhai China Fujian University of Traditional Chinese Medicine School of Traditional Chinese Medicine Fuzhou China

ISBN: (纸本)9798350373820

Crop diseases cause loss of agricultural products and have a critical impact on society, the economy, and the environment, so it is crucial for early defending the wheat disease. However, seeking an expert diagnosis can contribute to delays and higher costs. therefore, this paper proposes a deep learning neural network with improved spatial and channel reconstruction convolution combined with CoT Attention module, named SCCoT-WheatNet. this innovated neural network can recognize wheat disease. the experimental dataset was obtained from the Internet and some publicly available datasets using web crawler was technology. this modification is based on the VGG13 model. In the final two sections of the VGG13 model, feature extraction replaces conventional convolutional layers with Contextual Transformer module and spatial and channel reconstruction convolution block. the experiment adopts a cross-fold approach. In the first fold test, the results show that the average recognition accuracy of the SCCoT-WheatNet network is 9 6. 9 4%, which is higher than the classical VGG network and the Vision Transformer models of the last two years. © 2024 IEEE.

关键词： Convolution

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Proceedings - 2024 international conference on Advances in Electrical Engineering and Computer Applications, AEECA 2024

Proceedings - 2024 International Conference on Advances in E...

引用

5th international conference on Advances in Electrical Engineering and Computer Applications, AEECA 2024

ISBN: (纸本)9798350355253

the proceedings contain 127 papers. the topics discussed include: Advanced data storage and processing technologies in a next-generation electric information acquisition system;analyzing file access characteristics for deep learning workloads on mobile devices;optimal scheduling of distributed energy storage for electric vehicles based on evolutionary dissipation theory;a novel semi-supervised learning approach for referring expression comprehension;research and implementation of material image subject segmentation method based on machine vision;application of image recognition and 3D reconstruction technology in virtual museum system;knowledge graph technology-based active research and judgment technology for electric power customer complaint risk;and path planning for unmanned underwater vehicles based on improved ant colony algorithm.

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AI Tools for Media data Governance in the Post-Truth Era: from Abnormal data recognition to Intelligent Opinion Monitoring Algorithm 5

AI Tools for Media Data Governance in the Post-Truth Era: fr...

引用

5th international conference on Inventive Computation Technologies (ICICT)

作者： He, Yonghua Peng, Debin Guangzhou Coll Technol & Business Sch Management Guangzhou 510850 Peoples R China Jinan Univ Student Affairs Off Guangzhou 510632 Peoples R China

ISBN: (数字)9781665408370

ISBN: (纸本)9781665408387;9781665408370

AI tools for media data governance in the post-truth era from abnormal data recognition to intelligent opinion monitoring algorithm is studied in the paper. the decisions and rules made by artificial intelligence are not necessarily superior to human beings. In terms of fairness, machine learning identifies patterns from past data and makes new decisions based on these patterns. therefore, AI systems may consolidate or amplify historical bias. In the analysis of network public opinion, the first thing is to then grab the network text, and analyze the behavior characteristics, classify different behavior characteristics, so as to detect network public opinion according to some different behavior types and with this inspiration. In our designed model, the data mining algorithm is designed for the modelling. through the comparison analysis, the performance is then validated.

关键词： AI Tools Media data Governance Post-Truth Era data recognition Intelligent Opinion Monitoring Algorithm

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Application of data mining Technology in Optimal Configuration of Enterprise Resource 5

Application of Data Mining Technology in Optimal Configurati...

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5th international conference on pattern recognition and Artificial Intelligence, PRAI 2022

作者： Yan, Xiaofeng Chongqing Medical and Pharmaceutical College Chongqing China

ISBN: (数字)9781665499163

ISBN: (纸本)9781665499163

the efficiency of a business is determined by the resources it has and how those resources are allocated, which has an impact on enterprise performance. therefore, there is a complex relationship between resource allocation and performance. therefore, it is particularly important to study the resource allocation of innovative enterprises under multi-project management and the relationship between resource allocation and enterprise efficiency. data mining technology can seek the best in the complex enterprise resource allocation and achieve the optimal allocation of resources. this paper adopts the data mining technology based on multi-sensing neural network and genetic algorithm to study the resource allocation optimization method of multi-project management of innovative enterprises. through analysis, demonstration and simulation, it provides a certain reference for the resource allocation of innovative enterprises in project management. © 2022 IEEE.

关键词： Genetic algorithms

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