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检索条件"任意字段=2022 International Conference on Machine Learning and Data Engineering, ICMLDE 2022"

共 33943 条记录，以下是801-810 订阅

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Stratification of Depressed and Non-Depressed Texts from Social Media using LSTM and its Variants 2

Stratification of Depressed and Non-Depressed Texts from Soc...

引用

2nd international conference on machine learning and data engineering, icmlde 2023

作者： Kumar, Keerthan G.T. Anoop, R. Koolagudi, Shashidhar G. Rao, Trupthi Kodipalli, Ashwini Department of Information Science and Engineering Siddaganga Institute of Technology Tumkur India Department of Artificial Intelligence and Data Science Global academy of Technology Bengaluru India Department of Computer Science and Engineering National Institute of Technology Karnataka Surathkal India

This work examines the performance of various LSTM (long short-term memory) variants on social media text data. This study evaluates the performance of LSTM models with different architectures, namely, classic LSTM, Bidirectional LSTM, Stacked LSTM, gated recurrent unit (GRU), and bidirectional GRU, on a social network dataset comprising texts extracted from multiple social media platforms. We aim to identify the most effective LSTM variant of the five considered LSTM models for text analysis through a comparative study of the models' precision, recall, F1-score, and accuracy. The research findings show that the Classic LSTM and the GRU model perform better than the other models in accuracy. In contrast, the bidirectional models (Bidirectional LSTM and Bidirectional GRU) provide better precision scores than their respective primitive models. This research has significant implications for developing more efficient models for natural language processing applications. It offers beneficial insights into the implications involving the scrutiny of depression on social media platforms through text data analysis. © 2024 Elsevier B.V.. All rights reserved.

关键词： Long short-term memory

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Incremental machine learning: Incremental Classification 7

Incremental Machine Learning: Incremental Classification

引用

7th international conference on Computer Science and engineering, UBMK 2022

作者： Baysal, Engin Bayilmis, Cuneyt Ardahan Universitesi Uzaktan Eǧitim Uygulama Ve Araştirma Merkezi Ardahan Turkey Sakarya Üniversitesi Bilgisayar Ve Bilişim Mühendisliǧi Fakültesi Sakarya Turkey

ISBN: (数字)9781665470100

ISBN: (纸本)9781665470100

One of the research topics in machine learning is incremental machine learning. The ever-increasing data size and variety in response to the limited memory and processing power make incremental learning approaches mandatory. In this study, focal changes in the data are determined for incremental classification algorithms by defining the general framework of the incremental machine learning approach. In addition, along with the theoretical definition of incremental machine learning, the existing machine learning algorithms that are suitable for incremental machine learning are defined. It is mentioned that in terms of which features of these algorithms are suitable for incremental learning. This study provides a detailed definition for researchers who will work on incremental machine learning issues, as well as defines the incremental classification characteristics and gives information about the focus changes in the data. © 2022 IEEE.

关键词： Classification (of information)

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Comparative Analysis of Different machine learning Algorithms in Classification

Comparative Analysis of Different Machine Learning Algorithm...

引用

international conference on Big data, Information and Computer Network (BDICN)

作者： Wang, Lincong Xu, Weiwen Zhu, Zhenghao Univ Calif Irvine Phys Sci Irvine CA 92697 USA Zhejiang Univ Finance & Econ Informat Management & Artificial Intelligence Hangzhou 310018 Zhejiang Peoples R China North China Elect Power Univ Inst Math Baoding 071003 Hebei Peoples R China

ISBN: (纸本)9781665484763

This paper aims to find a relatively better method to deal with the classification problems of different data sets by exploring other different machine learning methods. The main research direction is to analyze the performance difference of the machine learning method in the data classification of Gaussian Distribution, Images, Voice, Text, and Excel to find the better classification method for the corresponding data set. This study mainly used three machine learning methods: k-nearest neighbors algorithm (KNN), Support-vector machine (SVM), and neural network. The final test results show that for relatively simple discrete data, such as Excel, KNN has the best effect. In addition, the SVM and KNN methods for Images data are inferior to neural networks. For the remaining two data classifications, these three machine learning methods have reasonable accuracy rates.

关键词： KNN SVM neural network machine leraning deep learning

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A Digital Twin-Based Production-Maintenance Joint Scheduling Framework with Reinforcement learning 8

A Digital Twin-Based Production-Maintenance Joint Scheduling...

引用

8th international conference on Control and Robotics engineering (ICCRE)

作者： Hao, Qinglong Lv, Yaqiong Wuhan Univ Technol Sch Transportat & Logist Engn Wuhan Peoples R China Sourthern Marine Sci & Engn Guangdong Lab Zhuhai Wuhan Peoples R China

ISBN: (纸本)9798350345650

The bridge of job scheduling and production equipment maintenance is usually the main joint scheduling task of a production system. However, the predicament of data acquisition in real systems leads to the difficulty of verifying the effectiveness of scheduling algorithms. In order to make joint scheduling work easier to implement in real production systems, this paper presents a joint scheduling framework for production systems based on digital twin and reinforcement learning. Firstly, the virtual mapping of physical production system, namely digital twin system, is established by using AnyLogic software and multiagent modeling technology. Then, a joint scheduling agent is trained by Deep Q Network (DQN) algorithm and the virtual data generated by the twinning system. And the experimental results demonstrate the effectiveness of proposed framework in production systems with uncertainties, and it has higher production efficiency and lower machine failure frequency compared with a scheduling scheme based on common-used heuristic rules.

关键词： production system joint scheduling digital twin reinforcement learning

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machine learning based Automatic Hate Speech Recognition System

Machine Learning based Automatic Hate Speech Recognition Sys...

引用

2022 international conference on Sustainable Computing and data Communication Systems, ICSCDS 2022

作者： William, P. Gade, Ritik Chaudhari, Rup Esh Pawar, A.B. Jawale, M.A. Sanjivani College of Engineering Savitribai Phule Pune University Department of Information Technology Pune India Sanjivani College of Engineering Savitribai Phule Pune University Department of Computer Engineering Pune India

ISBN: (纸本)9781665478847

Social media and knowledge sharing have had a positive impact on humanity. However, this has also led to a number of issues, such as the dissemination and dissemination of hate speech. This new problem of hate speech on social media has been addressed by recent studies that utilized a number of feature engineering techniques and machine learning algorithms. It's not clear if there is a study that compares different methods for generating features and machine learning algorithms in order to determine which one is better for a standard publicly available dataset. With the support vector machine technique, the testing findings showed that bigram features performed best with 79 percent overall accuracy when utilized with the bigram feature set. Detecting automated hate speech messages can be made easier with the findings of our investigation. It will also be used as a benchmark for future research into existing automatic text classification algorithms, based on the results of the various comparisons. The use of natural language processing to classify text and hate speech are all examples of machine learning © 2022 IEEE.

关键词： Support vector machines

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EEG-based Multimodal Representation learning for Emotion Recognition 13

EEG-based Multimodal Representation Learning for Emotion Rec...

引用

13th international Winter conference on Brain-Computer Interface, BCI 2025

作者： Yin, Kang Shin, Hye-Bin Li, Dan Lee, Seong-Whan Dept. of Artificial Intelligence Korea University Seoul Korea Republic of Dept. of Brain and Cognitive Engineering Korea University Seoul Korea Republic of

ISBN: (纸本)9798331521929

Multimodal learning has been a popular area of research, yet integrating electroencephalogram (EEG) data poses unique challenges due to its inherent variability and limited availability. In this paper, we introduce a novel multimodal framework that accommodates not only conventional modalities such as video, images, and audio, but also incorporates EEG data. Our framework is designed to flexibly handle varying input sizes, while dynamically adjusting attention to account for feature importance across modalities. We evaluate our approach on a recently introduced emotion recognition dataset that combines data from three modalities, making it an ideal testbed for multimodal learning. The experimental results provide a benchmark for the dataset and demonstrate the effectiveness of the proposed framework. This work highlights the potential of integrating EEG into multimodal systems, paving the way for more robust and comprehensive applications in emotion recognition and beyond. © 2025 IEEE.

关键词： Emotion Recognition

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Security Threats to machine learning Systems

Security Threats to Machine Learning Systems

引用

2022 IEEE international Students' conference on Electrical, Electronics and Computer Science, SCEECS 2022

作者： Soni, Rohit Paliya, Sparsh Gupta, Lalita Computer Science Engineering Lnct Group of Colleges Madhya Pradesh Bhopal462021 India Computer Science Engineering Vit Bhopal Madhya Pradesh Bhopal466114 India Maulana Azad National Institute of Technology Electronics and Communication Engineering Madhya Pradesh Bhopal462003 India

ISBN: (纸本)9781665414180

machine learning not only promises to solve issues, but it also has the potential to assist businesses in formulating forecasts and so enhancing decision-making. machine learning, on the other hand, has security difficulties. This study examines the many security concerns that machine learning faces and how they might be mitigated. There are two types of security risks to ML systems: those that occur before model training and those that occur after model training. The document also offers information on a variety of additional threats that fall under these categories and target other sorts of data while posing varied hazards. © 2022 IEEE. All rights reserved

关键词： machine learning

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machine learning Application in Enhancing Drilling Performance

Machine Learning Application in Enhancing Drilling Performan...

引用

2022 international conference on machine learning and data engineering, icmlde 2022

作者： Nautiyal, Aditi Mishra, Amit Kumar School of Computing DIT University Uttarakhand Dehradun248001 India

Drilling Oil and Gas wells is an expensive operation, where the cost for drilling a single shallow offshore HP-UHT well may exceed 30 million USD easily. Due to the huge cost involved, companies are eager to accomplish the drilling operations in a minimal time frame, by increasing the drilling rate or rate of penetration (ROP) and reducing the downhole tool failures. The Mechanical Specific Energy (MSE) concept addresses these two drilling performance measures in totality as it provides maximum ROP which can be achieved with the existing drilling system (i.e., Drill Bits, Drilling Tubular, Well Profile, and Mud weight) without causing downhole tool failures. ROP is a function of Weight on Bit, Rotation per Minute, and Flow rate for an existing drilling system, optimization of these parameters results in increased ROP, whereas unharmonious values lead to shock and vibration in drilling assembly. machine learning algorithms facilitate this optimization of drilling parameters for enhancing drilling performance, by the development of an accurate ROP prediction and optimization model. The ROP prediction model was developed using Artificial Neural Network and Random Forest, and the optimization model was developed using evolutionary optimization algorithms like particle swarm optimization and genetic algorithms. The result of the optimization model depends upon the accuracy of the ROP prediction model and the upper and lower limits of drilling parameters defined in the optimization model. machine learning applications in improving the Drilling Performance can be easily quantified in terms of saving days, where one day of operational cost for the company is approximately 0.2 million USD per Offshore - HP-UHT well. © 2022 Elsevier B.V.. All rights reserved.

关键词： Genetic algorithms

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Research on Identification Algorithm of Pollutant Excessive Emission Users Based on Power Big data and Artificial Intelligence 6

Research on Identification Algorithm of Pollutant Excessive ...

引用

6th international conference on Power and Energy engineering (ICPEE)

作者： Qin, Yu Zhang, Xiaohang State Grid Informat & Telecommun Grp Acad Informat & Commun Res Beijing Peoples R China

ISBN: (纸本)9781665464758

With the rapid development of industrial technology, environmental pollution has attracted social attention. Traditional pollution prevention and control work mainly depends on on-site inspection, treatment and prevention, with high human and material costs and low efficiency. In order to solve this problem, we propose a scheme to identify pollutant excessive emission users using power big data. The algorithm combining machine learning and deep learning is exploited to deeply excavate the characteristic data such as daily energy consumption data and peak valley characteristics of users in recent one year, so as to identify and screen out the users with pollutant excessive emission behavior. Specifically, we first preprocesses different types of data, uses feature engineering to divide the data into different subsets, selects features from classification variables such as user information through machine learning algorithm, extracts features from energy consumption data through deep learning algorithm, and finally fuses different algorithms to generate the final user identification result of pollutant excessive emission. The scheme is tested on the test set and shows high prediction accuracy and good generalization ability.

关键词： artificial intelligence feature engineering power big data air pollution prevention

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Research on Insulator Self-Explosion Fault Identification of UAV Inspection Based on machine learning 3

Research on Insulator Self-Explosion Fault Identification of...

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

作者： Wang, Sen Yu, Wanhong Wang, Wenshuo Qu, Haoming Chen, Nan Zhao, Bingnan Zhao, Dianzhe Leng, Yueyan Wu, Jiaze College of Automation Shenyang Institute of Engineering Liaoning Shenyang110136 China

ISBN: (纸本)9781510664814

In the study of UAV detection of transmission line defects, in order to improve the accuracy of detecting insulator defects, a self-explosion insulator detection method based on optimized YOLOv5 is proposed. In the training, the activation function is optimized, and the original ReLU activation function is optimized to SiLU activation function. Through the training and verification of a large number of transmission line image data collected by UAV inspection, the experimental results show that this method can effectively detect the self-explosion and falling defects of glass insulators under various complex background conditions. The mean average accuracy is used to evaluate the method. The detection rate of insulator self-explosion and flake defect is 94.7 %. © 2023 SPIE.

关键词： Inspection

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