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machine learning Based Method for Predicting the Loading Weight of Loaders 4

Machine Learning Based Method for Predicting the Loading Wei...

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4th international conference on computer Science and Blockchain, CCSB 2024

作者： Chen, Xuejiao Xie, Jiabin Chen, Yanhui Tan, Cheng Guangxi Vocational College of Water Resources and Electric Power Nanning China

ISBN: (纸本)9798350356502

the loading weight of a single loader shoveling operation is one of the important factors in evaluating the operational efficiency of the loader. It is significant to accurately predict the loading weight for planning the shoveling trajectory and improving the operational efficiency of the loader. this paper analyzes the influence of the displacement of each mechanical structure on the loading weight of the loader in the shoveling process. A Support Vector machine (SVM) loading weight prediction model is constructed by taking the displacement of each mechanical structure in the process of shoveling as an input feature. Meanwhile, to improve the prediction accuracy of the model, this paper proposes an Adaptive Particle Swarm Optimization (APSO) to optimize the model. the APSO-SVM model is tested in two working conditions shoveling gravel and sand. the results show that the model has good prediction performance, generalization ability, and stability. the Mean Absolute Percentage Error (MAPE) of the predicted values for the gravel sample is 1.95%, and the MAPE of the predicted values for the sand sample is 2.10%. © 2024 IEEE.

关键词： Support vector machines

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A Comprehensive Survey on Deep learning for Enhanced Node Position Prediction in Vehicular Ad-Hoc Networks 4

A Comprehensive Survey on Deep Learning for Enhanced Node Po...

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4th international conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Nayomi, B.Deena Divya Jyothsna, V. Department of Computer Science and Engineering Andhra Pradesh Tirupati India Department of Data Science Andhra Pradesh Tirupati India

ISBN: (纸本)9798331529635

this research study presents a thorough examination of the application of deep learning techniques in enhancing node position prediction within Vehicular Ad-Hoc Networks (VANETs). As VANETs become increasingly integral to the development of intelligent transportation systems, accurate and efficient node position prediction emerges as a critical component for ensuring optimal network functionality, safety, and traffic management. this paper explores the performance and adaptability of multiple deep learning models in the dynamic and complicated VANET environment, such as Graph Neural Networks (GNNs), Recurrent Neural Networks (RNNs), and Convolutional Neural Networks (CNNs). We provide a detailed analysis of the existing literature, highlighting the strengths and limitations of different deep learning approaches in handling the high mobility, variable density, and recurrently changing topology of vehicular networks. Furthermore, the survey discusses the integration of contextual information, such as environmental factors and road conditions, into the prediction models, enhancing their accuracy and reliability. We also explore the implications of advanced methodologies like Transfer learning and Federated learning in this domain. In addition to providing insights into future research paths, the paper finishes with an assessment of the difficulties encountered in the practical application of these models, including data privacy and computing limits. the goal of this thorough investigation is to provide a roadmap for future developments in node location prediction in VANETs, making it an invaluable tool for scholars and professionals working in the subject of intelligent transportation systems. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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Multi-label text classification method based on ReBERTa-TextCNN 4

Multi-label text classification method based on ReBERTa-Text...

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4th international conference on computer Vision, application, and Algorithm, CVAA 2024

作者： Zhang, Shengnan Xu, Huiyang Shenyang University of Technology School of Software Liaoning Shenyang China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

Multi Label Text Classification (MLTC) is an important research problem widely used in the field of natural language processing (NLP) text classification. Aiming at the problem of small sample data sets and unbalanced labels, this paper proposes a multi label text classification method based on prompt learning and RoBERTA Te People's Republic of China NN model. First, use the RoBERta model to obtain the label word vector in the text, and obtain the global features containing the text context information. the dynamic mask can improve the efficiency of the work. then use the Te People's Republic of China NN model to extract the local features of the text, and use the feature fusion method to fuse the global features and local features, so as to improve the classification accuracy of the model. Among them, prompt learning is used to process small sample data, and loss function is used to solve the problem of label imbalance and long tail distribution. the experiment on the actual dataset shows that the accuracy rate of multi label text classification reaches 96%, which reflects the advantage of this model in effectively improving the effect of multi label text classification. © 2025 SPIE.

关键词： Contrastive learning

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An Intelligent Approach to Segment the Liver Cancer using machine learning Method 4

An Intelligent Approach to Segment the Liver Cancer using Ma...

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4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Anand, L. Maurya, Mahesh Seetha, J. Nagaraju, D. Ravuri, Ananda Vidhya, R.G. School of Computing Srm Institute of Science and Technology Department of Networking and Communications India University of Mumbai K. C. College of Engineering Management and Research Mith Bunder Road Near Hume Pipe Maharashtra Thane400603 India Panimalar Engineering College Department of Computer Science and Business Systems Chennai India Sri Venkatesa Perumal College of Engineering and Technology Department of CSE Andhra Pradesh Puttur India Senior Software Engineer Intel Corporation HillsboroOR97124 United States Hkbk College of Engineering Department of Ece Bangalore India

ISBN: (纸本)9798350300093

Cancer Liver cancer is difficult to detect and diagnose early. machine learning has improved liver cancer diagnosis in recent years. this abstract describes a novel liver cancer detection and classification method using Autoencoder-Extreme learning machine (AE-ELM) and Convolutional Neural Network (CNN) technology. AE-ELM models extract features in the suggested method. the autoencoder-extreme learning machine AE-ELM technique learns high-level abstract characteristics from incoming data. AE-ELM extracts liver cancer-specific features by reducing the dimensionality of input data. A CNN model classifies the extracted features. CNNs are good at detecting spatial patterns in medical pictures like CT and MRI scans. the CNN model learns hierarchical representations of input information and classifies them into liver cancer subtypes or stages using various convolutional and pooling processes. AE-ELM and CNN are advantageous. First, AE-ELM decreases input data dimensionality to extract critical features without manual feature engineering. this simplifies feature selection and improves the model's capacity to handle big data. Second, the CNN model uses hierarchical convolutional processes to capture subtle patterns and spatial correlations in input data, enhancing liver cancer classification accuracy. the proposed liver cancer diagnostic method works in experiments. AE-ELM and CNN models outperform classic machine learning approaches and standalone CNN models. Improved liver cancer detection accuracy, sensitivity, and specificity help healthcare practitioners make quick and precise diagnosis. Finally, AE-ELM and CNN technologies can identify and classify liver cancer. this unique strategy uses both models to extract relevant characteristics and capture complicated patterns, improving liver cancer diagnosis accuracy and efficiency. To evaluate this approach's therapeutic promise and application in real-world healthcare settings, additional datasets are needed. © 2023 IEEE.

关键词： Convolutional neural networks

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Musical Genres Classification Utilizing the Pre-trained ResNet50 CNN Model and Deep learning Techniques 4

Musical Genres Classification Utilizing the Pre-trained ResN...

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4th international conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Mittal, Khushi Gill, Kanwarpartap Singh Kumar, Mukesh Rawat, Ruchira Chythanya, Kanegonda Ravi Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science & Engineering Uttarakhand Dehradun India Graphic Era Deemed to Be University Computer Science & Engineering Uttarakhand Dehradun India Sr University School of Computer Science & Artificial Intelligence Warangal India

ISBN: (纸本)9798350307757

this research investigates the application of the ResNet50 Convolutional Neural Network (CNN) within a ResNet50 model framework for the purpose of classifying musical genres. the objective is to enhance the accuracy and efficiency of automated music genre categorization systems through the utilization of deep learning techniques. the proposed model employs a methodology that processes raw audio data, involving the extraction of relevant innovative features through convolutional layers. these layers are designed to capture hierarchical patterns inherent to specific genres. the incorporation of the ResNet50 architecture in machine learning facilitates the capture of temporal relationships, allowing the model to recognize subtle nuances and variations in musical compositions. the study utilizes a diverse dataset encompassing multiple genres to enhance the robustness and adaptability of the model. the primary goal is to validate the effectiveness of the CNN ResNet50 Model in accurately classifying musical genres. through rigorous experimentation and assessment, this research aims to contribute significantly to the advancement of automated music analysis and classification systems. the findings of this study have noteworthy implications for various applications, including music recommendation systems, content tagging, and music streaming services. © 2024 IEEE.

关键词： Neural network models

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Different Respiratory Lung Sounds Prediction using Deep learning 4

Different Respiratory Lung Sounds Prediction using Deep Lear...

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4th international conference on Electronics and Sustainable Communication Systems, ICESC 2023

作者： Vasava, Rajeshree Parsingbhai Joshiara, Hetal A. L D College of Engineering Computer Engineering Department Gujarat Ahmedabad India

ISBN: (纸本)9798350300093

the sounds produced by the lungs when breathing might provide important information to physicians. Based on the findings, a deep learning-based approach is recommended for the prediction of breathing-related lung sounds. the Proposed model was trained in lung sounds collected from people suffering from a broad variety of respiratory conditions. the research improves classifying lung sounds, by audio to image spectrogram features is taken and used to train a deep convolutional neural network. the proposed technique accurately predicts many different types of respiratory lung sounds, demonstrating the promise of deep learning in this domain. this research results have important implications for the development of automated diagnostic tools that might help doctors make correct diagnoses of respiratory disorders more quickly and accurately. © 2023 IEEE.

关键词： Convolution

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machine learning, Deep learning Models for Agro-Meteorology applications 4

Machine Learning, Deep Learning Models for Agro-Meteorology ...

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4th international conference on Signal Processing and Communication, ICSPC 2023

作者： Jala, Prakash Kumar Meenal, R. Nagabushanam, P. Selvakumar, A. Immanuel Jude Hemanth, D. Rajasekaran, E. Electrical and Electronics Engineering Karunya Institute of Technology and Sciences Coimbatore India V.S.B. College of Engineering Department of Science and Humanities Karur India

ISBN: (纸本)9798350300772

Increasing water, energy, and food demand from the rising population of India needs organized water management using clean and sustainable energy with improved crop yield. Agricultural Meteorology is the application of meteorology to the measurement and analysis of the physical environment in the agricultural field. It is the performance of the weather and climate factors that has direct relevance to agriculture and their outcome in crop production. Over the present period, there have been massive improvements in weather prediction systems;yet, their direct applications have not been completely explored in effective crop water management. the aim of this paper is to review the machine learning (ML) and Deep learning (DL) models for agro-meteorological applications. ML and DL-based Climate-smart agriculture approaches are discussed in this paper to maximize crop yield and minimize water use. this review study can be used as an effective platform for irrigation planning, design, and management in smart farming and to address the current challenges in agro-meteorological applications. © 2023 IEEE.

关键词： Meteorology

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Dataset Collecting for UWB NLOS Identification Based on machine learning 6

Dataset Collecting for UWB NLOS Identification Based on Mach...

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6th international conference on computer Information Science and application Technology, CISAT 2023

作者： Zhang, Yifang Liu, Yiming Hao, Li School of Information Science and Technology Southwest Jiaotong University Sichuan Chengdu611756 China

ISBN: (纸本)9781510668546

the non-line-of-sight (NLOS) environment significantly reduces the accuracy of ultra-wideband (UWB) ranging and positioning in the UWB positioning system based on time of arrival. the mainstream method is to identify the line-of-sight (LOS) and NLOS environment through machine learning and make corresponding corrections to improve the accuracy of the positioning system. However, the existing research and application of machine learning methods do not fully consider the situation that the test set exceeds the training set, which is inconsistent with the actual scenario. Firstly, in this paper, we show through experimental results that when the test set and the training set are independent, different training set acquisition methods will directly affect the accuracy of machine learning. the denser the training set, the higher the precision and the greater the workload. therefore, aiming at how to choose the training set collection scheme, we put forward an evaluation index to determine the best training set collection scheme. Commonly used machine learning algorithms, such as random forest (RF), and XGBoost, are used here to explore the parameter configuration of this index. the research results show that when using machine learning method to identify ultra-wideband NLOS scenes, the acquisition step size of training set is 1.6 meters, which can make the identification accuracy high and the acquisition workload small. © 2023 SPIE.

关键词： machine learning

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Research on Financial Fraud Identification Model Based on Privacy-Preserving machine learning 4

Research on Financial Fraud Identification Model Based on Pr...

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

作者： Jiao, Li Li, Hui Shandong Institute of Commerce and Technology Jinan250103 China

ISBN: (纸本)9798400709449

Financial fraud will not only mislead investors and reduce investor confidence, but also disrupt market order and hinder the orderly development of the capital market. In order to solve the problem of financial fraud, based on the data mining process, a process framework of the financial fraud identification method based on machine learning is proposed, which includes three steps: sample and feature selection, data preparation, and model construction and analysis. Based on the process framework of the financial fraud identification method based on machine learning, GBDT methods are used to build models respectively, and the model effect is evaluated based on indicators such as recall rate. Due to the particularity of financial reporting fraud, the model should aim to identify fraud samples as much as possible, so the recall rate is a major evaluation index in this article. the recall rate of the GBDT model is better when based on fewer indicators. therefore, after comprehensively considering factors such as data acquisition costs, this article recommends using the GBDT model to identify financial fraud. © 2023 ACM.

关键词： machine learning

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2023 4th international Symposium on computer Engineering and Intelligent Communications, ISCEIC 2023

2023 4th International Symposium on Computer Engineering and...

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4th international Symposium on computer Engineering and Intelligent Communications, ISCEIC 2023

ISBN: (纸本)9798350306323

the proceedings contain 126 papers. the topics discussed include: calculation-based beam training scheme for massive MIMO systems;research on the technology framework of the manufacturing execution system with high reusability for flexible manufacturing;personalized learning resource recommendation using differential evolution-based graph neural network: a GraphSAGE approach;development and application of NPP cold source integrated information management system;video content understanding based on spatio-temporal feature extraction and pruning network;analysis of the near-field response of the dual-channel monopulse system and its applications in phase calibration;and research of time-frequency synchronization technology based on OFDM system.

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