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检索条件"任意字段=Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application"

共 4179 条记录，以下是821-830 订阅

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Leveraging the Strengths of Long Short-Term Memory and Gated Recurrent Unit Architectures for Indian Stock Market Prediction 4

Leveraging the Strengths of Long Short-Term Memory and Gated...

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4th international conference on Emerging Systems and Intelligent Computing, ESIC 2024

作者： Jena, Prem Purusottam Das, Kaberi Mishra, Debahuti Lenka, Arundhati Mishra, Sashikala Mallick, Pradeep Kumar Siksha 'o' Anusandhan [Deemed to Be University Department of Computer Science and Engineering Bhubaneswar India Siksha 'o' Anusandhan [Deemed to Be University Department of Computer Application Bhubaneswar India Pune India Kiit Deemed to Be University School of Computer Engineering Bhubaneswar India

ISBN: (纸本)9798350349856

the study recognizes the multifaceted nature of the stock market and the challenges inherent in predicting market movements accurately. Focused on the Indian stock market, the research harnesses the capabilities of deep learning, specifically the long short-term memory (LSTM) and gated recurrent unit (GRU) architectures, known for their prowess in processing sequential data with predictions over a 10-day horizon. the models are systematically evaluated, considering parameters such as root mean square error (RMSE), mean absolute error (MAE), regression variance, and R2-score, with a comparative analysis highlighting the effectiveness of the proposed hybrid models - LSTM-GRU and GRU-LSTM. the experimental analysis reveals that the GRU-LSTM model demonstrates superior performance on the HDFC bank Ltd. dataset, while the LSTM-GRU model excels on the TATA Motors Ltd. dataset, aligning with the predictive performance and accuracy metrics observed in earlier experiments. © 2024 IEEE.

关键词： Long short-term memory

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proceedings of 2023 IEEE 9th international Women in Engineering (WIE) conference on Electrical and computer Engineering, WIECON-ECE 2023

Proceedings of 2023 IEEE 9th International Women in Engineer...

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9th IEEE international Women in Engineering (WIE) conference on Electrical and computer Engineering, WIECON-ECE 2023

ISBN: (纸本)9798350319651

the proceedings contain 85 papers. the topics discussed include: review on machine learning-based change detection and pan sharpening algorithms for remote sensing datasets;design of 4-stage single-ended ring oscillators with quadrature-phase output;brain tumor classification on MRI images with big transfer and vision transformer: comparative study;detection of heart disease using association pattern mining;design and performance analysis of gold-coated photonic crystal fiber plasmonic biosensor;sentiment analysis on Bangla and phonetic Bangla reviews: a product rating procedure using NLP and machine learning;whale optimization algorithm for renewable energy sources integration considering solar-to-vehicle technology;and ANTIBIOLOG: an advanced tool for combat antibiotic resistance with enhanced multinomial logistic regression.

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4th international conference on Recent Trends in Advanced Computing - computer Vision and machine Intelligence Paradigms for Sustainable Development Goals, ICRTAC-CVMIP 2021

4th International Conference on Recent Trends in Advanced Co...

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4th international conference on Recent Trends in Advanced Computing - computer Vision and machine Intelligence Paradigms for Sustainable Development Goals, ICRTAC-CVMIP 2021

ISBN: (纸本)9789811971686

the proceedings contain 30 papers. the special focus in this conference is on Recent Trends in Advanced Computing - computer Vision and machine Intelligence Paradigms for Sustainable Development Goals. the topics include: Advanced Algorithmic Techniques for Topic Prediction and Recommendation—An Analysis;Implementation of an Automatic EEG Feature Extraction with Gated Recurrent Neural Network for Emotion Recognition;high Performance Classifier for Brain Tumor Detection Using Capsule Neural Network;Mining Suitable Symptoms to Identify Disease Using Apriori and NBC;background Features-Based Novel Visual Ego-Motion Estimation;livspecs: Design and Implementation of Smart Specs for Hearing and Visually Challenged Persons;Self-balancing Robot Using Arduino and PID Controller;a Survey Based on Online Voting System Using Blockchain Technology;convergence Perceptual Model for Computing Time Series Data on Fog Environment;survey on Collaborative Filtering Technique for Recommender System Using Deep learning;a Survey on Power Consumption Indicator Using machine learning-Based Approach;a Novel Hand Gesture Recognition for Aphonic People Using Convolutional Neural Network;comprehensive Analysis of Defect Detection through Image Processing and machine learning for Photovoltaic Panels;covid Analysis Prediction Using Densenet Method in Deep learning;Feature Extraction Based on GLCM and GLRM Methods on COVID-19 Dataset;memory Augmented Distributed Monte Carlo Tree Search Algorithm-Based Content Popularity Aware Content Recommendation Using Content Centric Networks;Enhancing Protection Against Scalper Bots with ML;An Enhanced Optimized Abstractive Text Summarization Traditional Approach Employing Multi-layered Attentional Stacked LSTM with the Attention RNN;implementation of machine learning Methods on Data to Analyze Emotional Health;SMS Spam Classification Using PSO-C4.5.

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machine learning-based crack prediction modeling of laser cladding coatings 4

Machine learning-based crack prediction modeling of laser cl...

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2024 4th international conference on Industrial Manufacturing and Structural Materials, IMSM 2024

作者： Cui, Jiahuan Zhang, Yanhui Lv, Weiyan Department of Materials Science and Engineering Northeastern University Shenyang110819 China Shenyang National Laboratory for Materials Science Institute of Metal Research Shenyang110016 China

Because of its flexibility, precision, and strong generalization capacity, machine learning offers a whole new research viewpoint to the fields of materials science and engineering when compared to traditional experimental and computer simulation methods. this paper adopts laser cladding cracking research as the application background. A prediction model for small datasets is established using well-developed prediction algorithms, and a crack prediction model with superior generalizability, accuracy, and efficiency for cladding is proposed. the findings indicate that the tendency to crack increases with scanning speed, but it decreases with laser power;the random forest-based crack density prediction model has an accuracy of 90.1% and a coefficient of determination of R2 = 0.874, which can better realize the prediction of the density and enhance some engineering practice guidelines. © Published under licence by IOP Publishing Ltd.

关键词： Prediction models

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Feature learning model based on Feature space constraints and Self-Attention learning 4

Feature learning model based on Feature space constraints an...

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4th international conference on machine learning and computer application, ICMLCA 2023

作者： Xu, Changming Wang, Yichuan Zhou, Qilei Wang, Dongnian Dong, Hechao Wang, Junwei Northeastern University at Qinhuangdao Haigang district Hebei province Qinghuangdao China

ISBN: (纸本)9798400709449

Supervised time series anomaly detection is of significant importance in both machine learning research and industrial applications. Autoencoder-based methods have achieved outstanding performance in this field. However, these methods often suffer from the challenge of limited separation between low-dimensional representations of normal and anomalous data. To address this issue, this paper introduces a model called FFSA, which utilizes feature space constraints and self-attention. the FFSA model is built on the foundation of a temporal network-based autoencoder, featuring an information entropy feature learning module and a self-attention feature learning module. the information entropy feature learning module comprises an MLP mapping layer, layer normalization, and an entropy analysis layer, while the self-attention feature learning module includes layer-wise parameter extraction and concatenation, layer normalization, and a self-attention mechanism. these modules work together to increase the differentiation between normal and anomalous data and to extract more informative low-dimensional representations. Experimental results on multiple public datasets demonstrate that the FFSA model outperforms mainstream anomaly detection models, achieving an impressive F1 score of up to 97% on the KDDCUP dataset. © 2023 ACM.

关键词： Anomaly detection

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Performance Analysis of machine learning and Deep learning Architecture for Earthquake Magnitude Prediction 9

Performance Analysis of Machine Learning and Deep Learning A...

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9th IEEE international Women in Engineering (WIE) conference on Electrical and computer Engineering, WIECON-ECE 2023

作者： Feroz, Shejuti Binte Sevas, Muhammad Samee Sharmin, Nusrat Chatterjee, Arindam Department of Computer Science and Engineering Dhaka1216 Bangladesh

ISBN: (纸本)9798350319651

Earthquakes possess immense destructive power, capable of causing significant harm to buildings and the natural surroundings. Accurate earthquake magnitude prediction is crucial for determining seismic risks and implementing appropriate mitigation measures. Although deep learning (DL) and machine learning (ML) algorithms have been utilized extensively in earth-quake prediction research, a thorough DL vs. ML comparison is absent. the comparison is broadened in this research by concentrating on earthquake magnitude prediction. Moreover, we employ a set of innovative algorithms that have had limited application within this domain. the optimal algorithm to estimate the magnitude of an earthquake has not been explicitly discussed in any of the papers. this study aims to bridge this gap by employing a diverse range of twelve algorithms, i.e. Decision Tree Regressor, Random Forest Regressor, SVR, Linear Regressor, Gradient Boosting, XGBoost, LightGBM, CatBoost, ANN, RNN, LSTM, and MLP, encompassing both machine learning and deep learning techniques, to predict earthquake magnitudes. through comprehensive experimentation and comparative research, we evaluate the performance of the ensemble-based models in terms of different evaluation metrics, such as R2 Scores, MAE, MSE and RMSE. © 2023 IEEE.

关键词： Earthquakes

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8th international conference on computer Science and Computational Intelligence, ICCSCI 2023

8th International Conference on Computer Science and Computa...

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8th international conference on computer Science and Computational Intelligence, ICCSCI 2023

the proceedings contain 130 papers. the topics discussed include: path planning using the bee colony algorithm to find routes for a coffee plantation using drones;machine learning approach for single nucleotide polymorphism selection in genetic testing results;a modified beluga whale optimization for optimizing energy-efficient no-idle permutation flow shop scheduling problem;determining the objective weight of green manufacturing indicators: a case study of the Indonesian manufacturing company;guidance in designing a smart campus: a systematic literature review;study of cross-site request forgery on web-based application: exploitations and preventions;the utilization of mobile communication on marketing: a systematic review;and technique of mental health issues classification based on machine learning: systematic literature review.

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Surface garbage target detection technology based on deep learning 4

Surface garbage target detection technology based on deep le...

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4th international conference on Signal Processing and computer Science, SPCS 2023

作者： Peng, Shurong Chen, Huixia Guo, Lijuan He, Jieni Peng, Jiayi Changsha University of Science and Technology Hunan Changsha410114 China

ISBN: (纸本)9781510672505

Aiming at the problem that a large amount of water surface garbage causes serious pollution to the environment and is difficult to clean up in time, artificial intelligence equipment and machine vision target detection are combined. A deep learning-based lightweight floating object detection algorithm MobileNetV3-YOLOv4. MobileNetV3-YOLOv4 proposes to use a depthwise separable convolution method to replace the 3 × 3 conventional convolution in the network, replaces the original CSPDarknet53 backbone feature extraction network with MobileNetV3 network, and proposes to use a synthetic fusion pyramid network (SFPN) in the neck network of the model. A synthetic fusion Pyramid Network can reduce the gap between the detection accuracy and the original model while reducing the number of parameters and calculations. the number of parameters and calculations is only 20% of the original model, and the detection speed reaches 96 frames per second. Compared with the original YOLOv4 model, it is 69 frames per second, an increase of 39.1%. © 2023 SPIE.

关键词： Object detection

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machine learning Based Security Situation Awareness Method for Network Data Transmission Process 4th

Machine Learning Based Security Situation Awareness Method f...

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4th international conference on machine learning for Cyber Security, ML4CS 2022

作者： Du, Hui Beijing Polytechnic Beijing100016 China

ISBN: (纸本)9783031200953

With the popularization and development of computer technology, network has become an essential element in people's daily production and life. Due to the openness of the network and the large number of users, the network data transmission process is vulnerable to attacks, resulting in data loss, tampering and other phenomena. therefore, this research designs a security situation awareness method for network data transmission process based on machine learning. Firstly, a three-level model of security situation awareness is built, based on which security situation data are collected. then the sparse self encoder is used to reduce the dimension of security situation data and extract its features. Based on this, the security situation is evaluated based on machine learning support vector machine, and the hierarchical genetic algorithm is used to predict the security situation of data transmission process. the experimental results show that the delay and error of security situational awareness are less than the given maximum limit, and the security situational awareness results are consistent with the actual results, which fully proves that the application effect of this method is good. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Support vector machines

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Unmanned Aerial Vehicle Data Uploading Using Parking Resources After Disaster Rescue 4

Unmanned Aerial Vehicle Data Uploading Using Parking Resourc...

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4th international conference on Information Science, Parallel and Distributed Systems, ISPDS 2023

作者： Wei, Yanmin Wang, Jinao Wang, Luyao School of Computer and Information Engineering Tianjin Normal University Tianjin China

ISBN: (纸本)9798350337181

As an important device for disaster rescue, unmanned aerial vehicles(UAVs) are usually limited by their batteries and computational power to perform complex computational tasks during the rescue process. In response to the above situation, it is proposed that the disaster rescue makes parked vehicles collaboratively perform the application tasks generated by UAVs. We organize parked vehicles into parking clusters.. then, a task scheduling model of deep reinforcement learning(DRL) is built, in which multiple vehicles in the parking cluster are chosen to execute task data together to ensure the quality of experience(QoE) of UAVs. the experimental results demonstrate that the proposed strategy achieves a lower execution cost and higher completion rate compared with other offloading strategies. © 2023 IEEE.

关键词： Reinforcement learning

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