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

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2022 3rd international conference on Computer Vision, Image and Deep learning and international conference on Computer Engineering and Applications, CVIDL and ICCEA 2022

2022 3rd International Conference on Computer Vision, Image ...

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3rd international conference on Computer Vision, Image and Deep learning and international conference on Computer Engineering and Applications, CVIDL and ICCEA 2022

ISBN: (纸本)9781665459112

The proceedings contain 253 papers. The topics discussed include: testing the feasibility of quantizing the progress of stroke patients’ rehabilitation with a computer vision method;recognition of surface defects of aluminum profiles based on convolutional neural network;a novel generative adversarial network simulating the complementary structure of DNA genetic information;an improved lightweight method of convolutional neural network for face recognition;a calculation method for line-of-sight stable attitude of networked optical transceiver based on depth feedforward neural network;a grapevine virus disease detection method based on convolution neural network;a predict method of measuring equipment operation performance based on improved local weighted partial least squares;research on multi-scale network computer modeling based on mobile vendor management;and identification method of dress pattern drawing based on machine vision algorithm.

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3rd international conference on Mobile Radio Communications and 5G Networks, MRCN 2022

3rd International Conference on Mobile Radio Communications ...

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3rd international conference on Mobile Radio Communications and 5G Networks, MRCN 2022

ISBN: (纸本)9789811979811

The proceedings contain 56 papers. The special focus in this conference is on Mobile Radio Communications and 5G Networks. The topics include: Predictive Analysis of Air Pollutants Using machine learning;machine learning Techniques Applied of Land Use—Land Cover (LULC) Image Classification: Research Avenues Challenges with Issues;crime Analysis Using Computer Vision Approach with machine learning;natural Language Processing Implementation for Sentiment Analysis on Tweets;VLSI Implementation of BCH Encoder with Triple DES Encryption for Baseband Transceiver;design and Implementation of Image De-hazing Using Histogram Equalization;Improved Hybrid Unified Power Flow Controller Using Fractional Order PID Controlled Systems;Optimized Activation Function-Based SAR Ship Detection;framework for Implementation of Smart Driver Assistance System Using Augmented Reality;Power-Efficient Hardware Design of ECC Algorithm on High Performance FPGA;A Creative Domain of Blockchain Application: NFTs;sign Language recognition Using machine learning;an IoT-Based Health Monitoring System for Stress Detection in Human Beings;ioT-Based Driving pattern Analysis and Engine Sensor Damage Prediction Using Onboard Diagnostics;Solving the Element Detecting Problem in Graphs via Quantum Walk Search Algorithm (QWSA);critical Analysis of Secure Strategies Against Threats on Cloud Platform;pareto Optimal Solution for Fully Fuzzy Bi-criteria Multi-index Bulk Transportation Problem;automatic Candidature Selection by Artificial Natural Language Processing;elimination and Restoring Deduplicated Storage for Multilevel Integrated Approach with Cost Estimation;vulnerability Assessment of Cryptocurrency Wallet and Exchange Websites;greedy Theory Using Improved Performance Prim’s Algorithm, Big Bang Speedup of the Bellman–Ford Algorithm;performance Analysis of High-Speed Optical Communication Systems Under the Impact of Four Wave Mixing.

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An Asynchronous Robust Federated learning Approach for data Value in IoT 3rd

An Asynchronous Robust Federated Learning Approach for Data ...

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3rd international conference on machine learning, Cloud Computing and Intelligent mining, MLCCIM 2024

作者： Liang, Hua Jin, Min Yan, Hua Han, Shihai Li, Wei Yang, Zhigang Wang, Zhuotong State Grid Chongqing Electric Power Company Electric Power Research Institute Chongqing401123 China Digitization Department State Grid Chongqing Electric Power Company Chongqing400015 China School of Cyber Security and Information Law Chongqing University of Posts and Telecommunications Chongqing Chongqing400065 China School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing Chongqing400065 China

ISBN: (纸本)9789819624676

The heterogeneity of IoT devices, resource bottlenecks, and security risks present challenges to the robustness and privacy of federated learning. The difference in the value of IoT data further weakens the robustness of the model. Additionally, attack methods such as Byzantine attacks and privacy inference attacks escalate the risks and vulnerabilities of federated learning models. To address these issues, we propose an asynchronous robust federated learning method based on data value. Firstly, an asynchronous grouping robust aggregation (AGRA) algorithm is designed and combined with a cosine anomaly filter to eliminate abnormal gradients. Simultaneously, an inter-group delay aggregation strategy oriented towards data value is proposed. Secondly, an efficient and robust aggregation protocol supporting asynchronous grouping is constructed to significantly reduce communication overhead while safeguarding data privacy. Finally, through data grading and secure storage adjustment using differential privacy, high-value data can be maximized, thereby enhancing the efficiency and performance of federated learning while protecting privacy. Results show that the algorithm can significantly improve accuracy by 0.9% to 65.8% in the presence of stragglers, while also reducing communication overhead by 4.2% to 5.4%. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Deep learning-based Sentiment Analysis of Movie Reviews 3

Deep Learning-based Sentiment Analysis of Movie Reviews

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3rd international conference on machine learning and Computer Application, ICMLCA 2022

作者： Lou, Yuqi School of Engineering The Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)9781510664814

Sentiment analysis, sometimes regarded as sentiment classification or opinion mining, can be utilized to assess public perceptions of certain products, occasions, people or ideas. There is a wide range of applications for sentiment analysis, including detecting the overall sentiment of text documents, identifying positive and negative opinions expressed in texts, and predicting the future stock market trend based on social media sentiment. In this study, the convolutional neural networks are employed to categorize the sentiment of English movie reviews, i.e., the CNNs are utilized to detect whether a review is positive or negative. Two text representation techniques, the bag-of-words model and TF-IDF, are applied to convert the movie reviews into numerical representation before transmitting the processed data to the CNN model. According to the model's prediction results on the testing set after training, the text processed with the bag-of-word model achieves a higher classification accuracy of 84.84% in the proposed CNN framework, while the TF-IDF for text processing has better precision of 88.96%. © 2023 SPIE.

关键词： Sentiment analysis

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Improved Prediction of Electric Vehicle User Behavior with machine learning based Analysis 3

Improved Prediction of Electric Vehicle User Behavior with M...

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3rd IEEE international conference on Smart Technologies for Power, Energy and Control, STPEC 2023

作者： Prasad, Kavita Kiran Mali, Akshay Agarwal, Vivek Indian Institute of Technology Dept. of Electrical Engineering Bombay Mumbai India

ISBN: (纸本)9798350304732

With the rapid growth of Electric Vehicles (EVs), charging load demand is increasing significantly. Energy providers need to analyze the load requirements and meet them efficiently. Due to the highly nonlinear characteristics of EV user behavior, researchers are turning to machine learning (ML) models suited for nonlinear data. In the proposed study, the Adaptive Charging Network (ACN)'s public charging dataset is utilized to predict EV user behavior. New features like traffic, charging currents, and number of connection-disconnection events are integrated along with the weather data to improve prediction accuracy. Also, the features are included by observing the pattern in one-hour slots, which results in substantial improvements compared to previous work. While existing research mainly focuses on session duration and energy consumption, this work also predicts the charging duration, which is useful to get an idea of when the EV is ready to use. It is shown that ensemble model performs the best among various ML models, achieving a Mean Absolute Error (MAE) of 56 minutes for session duration and 43.2 minutes for charging duration. Also, the energy consumption is best predicted with Random Forest (RF) regression with MAE of 1.36 kWh. All the key results of this work are presented. © 2023 IEEE.

关键词： Electric Vehicles (EVs) machine learning (ML) Public Charging User Behavior Prediction

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1st international Virtual conference on Visual pattern Extraction and recognition for Cultural Heritage Understanding (VIPERC 2022) 1

1st International Virtual Conference on Visual Pattern Extra...

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1st international Virtual conference on Visual pattern Extraction and recognition for Cultural Heritage Understanding, VIPERC 2022

作者： Amelio, Alessia Montelpare, Sergio Ursino, Domenico InGeo University "G. d’Annunzio" Chieti-Pescara Viale Pindaro 42 Pescara65127 Italy DII Marche Polytechnic University Via Brecce Bianche 12 Ancona60131 Italy

This document introduces the 1st international Virtual conference on Visual pattern Extraction and recognition for Cultural Heritage Understanding (VIPERC 2022), a premier forum for presenting the state-of-the-art, new research, ongoing work, academic and project reports in advanced statistics and machine learning, 3D modelling and simulation, knowledge representation, intelligent systems, information retrieval and software engineering, for visual pattern extraction, analysis and recognition to preserve the cultural heritage. © 2022 Copyright for this paper by its authors.

关键词： 3D modeling

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

Proceedings of 3rd International Conference on Intelligent E...

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

ISBN: (纸本)9781665467568

The proceedings contain 168 papers. The topics discussed include: design for ergonomics: application of single- passenger electric car via design of experiments;detection of breast cancer using machine learning and deep learning methods;automobile insurance fraud detection: an overview;a survey on detection of lung cancer using different image processing techniques;deep learning methods for automatic number plate recognition system: a review;skin cancer detection using machine learning: a survey;transforming education system through artificial intelligence and machine learning;model for retailers with price dependent demand rates;neuromarketing: an emerging domain in the formal education system;design of dual slot microstrip antenna for wireless application;analysis of ancillary services provided by distributed energy resources in smart distribution grid;and grey scale image skeletonization of vein pattern using Hausdroff image binarization technique in biometric security system.

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machine learning Techniques For Software Component Reusability 3

Machine Learning Techniques For Software Component Reusabili...

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3rd international conference for Emerging Technology, INCET 2022

作者： Khan, Feroz Lingala, Gowtham Koneru Lakshmaiah Education Foundation Vaddeswaram Department of Computer Science and Engineering Andhra Pradesh Guntur522502 India

ISBN: (纸本)9781665494991

Software reuse has considerably reduced the products time to market with increased reliability and productivity. Widespread use of software reuse, agile software development, User stories and component-based development methods can be observed nowadays. To reap the advantages of software reuse developers can access it from the repositories. The repositories are generated through different techniques used in data mining. In order to predict the reusability of a component machine learning techniques is used. This article provides a method of integration of agile, user stories and component-based developments methods using artificial neural network. The factors that determine the reusability of software are studied and corresponding software metrics are input to the proposed classifier. The metrics used for the study are Cyclomatic Complexity (CC), Design Structure Quality Index (DSQI), Cohesion, weighted method per class (WMP), Bugs, Coupling. Depth Inheritance Tree (DIT), Number of children (NOC). The classifier was tested with some standard classifiers namely Random Forest, Logit Boost, J48 and ESRT used in machine learning and the accuracy achieved is 91.8%. © 2022 IEEE.

关键词： Computer software reusability

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Comparative Analysis of Different machine learning Methods for Hate Speech recognition in Twitter Text data 3

Comparative Analysis of Different Machine Learning Methods f...

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

作者： Tiwari, Abhay Agrawal, Anupam Indian Institute of Information Technology Allahabad Department of Information Technology Prayagraj India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

The purpose of this work is to solve the challenges faced by us in the field of hate speech recognition on various social media platforms, that is to get a better machine learning model that can detect hate speech with greater accuracy. As the reach of the internet and mobile phones has extended abruptly in the past few years, everyone has the power to share their opinions, but some use it as an opportunity to spread hate among one another. In this paper, we used the Davidson [10] dataset which is the most popular Twitter dataset for hate speech detection, and further we implemented various machine learning-based algorithms and compared them on the basis of various parameters such as accuracy, precision score, recall and F1 scores. After the study, we found out that XGBoost when used with TF-IDF transformer embedding gave us an accuracy of 94.43%, which is the maximum among these three models for the given benchmark dataset. © 2022 IEEE.

关键词： Speech recognition

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Dynamic Hand Gesture Classification Based on Attention Mechanism and Transformer 3

Dynamic Hand Gesture Classification Based on Attention Mecha...

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3rd international conference on Computer Vision and pattern Analysis, ICCPA 2023

作者： Ren, Zhen He, Haonan Huang, Zhengyi School of Big Data & Software Engineering Chongqing University Chongqing401331 China School of Mechanical & Automotive Engineering South China University of Technology Guangdong Guangzhou510641 China School of Electromechanical Engineering Guangdong University of Technology Guangdong Guangzhou510006 China

ISBN: (纸本)9781510667563

In the era of rapid development of deep learning, the popular transformer in recent years has played an outstanding role in many fields, but most of it focuses on the development of language models, and is still in the development stage in computer vision. Gesture recognition is a relatively popular module in computer vision, but the mainstream method still uses CNN for gesture recognition, and there are few articles combining gesture recognition with attention mechanism and transformer. In this paper, we propose a dynamic gesture recognition model based on attention mechanism and Transformer. In order to extract the valid information in each frame, we add the attention mechanism to the feature extraction network, followed by passing it into the transformer to predict the hand gesture through the self-attention mechanism. The model has been tested on the IsoGD dataset, achiving good experimental results. Not only do we confirm that the attention mechanism can improve the recognition accuracy through ablation study, but also prove that transformer is feasible to process and identify the temporal information among frames in dynamic gestures and perform recognition. © 2023 SPIE.

关键词： Gesture recognition

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