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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"

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Analysis of Friction and Dry Sliding Wear Characteristics of Nano-Aluminium Metal Composite Using machine learning through Linear Regression Algorithm 4

Analysis of Friction and Dry Sliding Wear Characteristics of...

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4th international conference on Smart Electronics and Communication, ICOSEC 2023

作者： Basri, Syeda Jeelani Mamidala, Kishore Kumar Balakrishnan, N. Geetha, C. Kiranmai, V.L. Jayalakshmi, D.S. G.Pullaiah College of Engineering and Technology Department of Chemistry Andhra Pradesh Kurnool India Telangana Hyderabad501401 India Sona College of Technology Department of Mca Tamilnadu Salem India Rmk Engineering College Department of Computer Science and Engineering Tamilnadu Chennai India KKR and KSR Institute of Technology and Sciences Department of Physics Andhra Pradesh Guntur India Department of Physics Tamilnadu Chennai India

ISBN: (纸本)9798350300888

the aim of this research is to study the friction and dry sliding wear characteristics of nano-aluminium metal composite using machine learning through linear regression algorithm. Al 5182 alloy was used as a base material and SiC nano-particles were dispersed into the alloy through stir casting and ultra-sonic dispersion at different compositions (0.2, 0.4, 0.6, 0.8, and 1%) in order to analyze the effect of composition on friction and wear characteristics. L 25 alloy was used as a counter material in the experiments. the response variables, wear loss (Wl), were measured and the input parameters, composition, rotational speed, and sliding distance, were varied in the experiments. Signal to noise ratio was used to determine the best grouping of parameters that reduced wear loss. the results were then predicted using artificial neural network (ANN) and regression analysis. the derived equation for predicting the responses showed better accuracy in predicting the wear loss values. the study provides important insights into the resistance and wear characteristics of nano-aluminium composites, and the results can be used to improve the performance and life of these materials in various applications. Overall, this research provides a comprehensive analysis of the friction and wear behavior of nano-aluminium metal composites using machine learning through linear regression algorithm. the results obtained in this study can be used to design and optimize the production process of nano-aluminium metal composites for specific applications, leading to improved performance and longer life of the materials. © 2023 IEEE.

关键词： machine learning

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Automated Malware Detection Based on a machine learning Algorithm 10

Automated Malware Detection Based on a Machine Learning Algo...

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10th IEEE international conference on Communications and Networking, ComNet 2023

作者： Almuqren, Almaha Frikha, Mounir Albuali, Abdullah King Faisal University Department of Computer Networks and Communications Al Hofuf Saudi Arabia

ISBN: (纸本)9798350381719

Malware detection relies on the discriminative power of machine learning to identify new variants of malware samples. Automated malware detection, driven by machine learning algorithms, has garnered significant recognition for its capability to detect previously unknown malware. In recent years, various machine learning techniques have exhibited promise in enhancing malware detection. these techniques facilitate the analysis of substantial datasets, the identification of intricate patterns, and the detection of emergent threats, surpassing conventional signature-based methods. this paper offers an overview of these machine learning techniques and their potential to enhance the precision and efficiency of malware detection systems. the objective is to conduct a comprehensive literature review, analyze selected research papers, and present a taxonomy of machine learning methods for malware detection. the study delves into the intersection of malware and machine learning within the cybersecurity domain, encompassing the taxonomy of malware detection and the classification of machine learning algorithms. Moreover, the taxonomy is employed to evaluate the latest algorithms and perform an exhaustive analysis of machine learning approaches. Additionally, this paper discusses the challenges related to the application of machine learning in malware detection. machine learning techniques provide a robust toolkit for the development of more effective and efficient malware detection systems. Continued research in this field is imperative to mitigate the ever-growing malware threat. © 2023 IEEE.

关键词： Taxonomies

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application of System Operation and Maintenance Knowledge Base based on machine learning in Automobile Industry 8

Application of System Operation and Maintenance Knowledge Ba...

引用

8th international conference on Information Systems Engineering, ICISE 2023

作者： Cheng, Bin Hao, Qiong Chen, Zhiwen Wuhan Railway Vocational College of Technology College of Computer and Information Engineering Wuhan430000 China

ISBN: (纸本)9798350307009

With the increasingly fierce competition in the automobile industry, the demand for digital transformation is increasingly strong, and the requirements for system operation and maintenance are also increasingly high. In the face of the current situation of system operation and maintenance faced by the automobile industry, such as passive fault detection, repetitive work, and reliance on experience, this paper innovatively proposes a system operation and maintenance knowledge base based on machine learning applicable to the automobile industry. It uses the Scrap crawler to obtain the system history data, uses the decision tree algorithm to extract the value contained in the data, and finally visualizes the results to improve the service quality of operation and maintenance and optimize the operation and maintenance management process. © 2023 IEEE.

关键词： Automotive industry

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2023 4th international conference on Big Data and Artificial Intelligence and Software Engineering, ICBASE 2023

2023 4th International Conference on Big Data and Artificial...

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4th international conference on Big Data and Artificial Intelligence and Software Engineering, ICBASE 2023

ISBN: (纸本)9798350329490

the proceedings contain 85 papers. the topics discussed include: tuberculosis screening with cough sounds using the deep learning models;ship trajectory compression in fish net area based on improved sliding window algorithm;critical system design based on high availability cluster technology;monthly rainfall prediction based on VMD-GRA-Elman model;light information access when operating a small drone;a convolutional neural network based method for masked face detection;control over distributed topology of wire-less sensor network based on power optimization;deep CNN-RNN with self-attention model for electric IoT traffic classification;machine learning modeling on acoustic emission leak detection of metal-sealed pressure vessel;a restricted embedding transfer model for hyperspectral anomaly detection;and text labels classification model based on BERT algorithm.

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A Piecewise Continuous learning Method Based On A Spatial-spectral Relational Neural Networks 4

A Piecewise Continuous Learning Method Based On A Spatial-sp...

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

作者： Rao, Mengbin Tang, Ping Ge, Jianjun Information Science Academy China Electronics Technology Group Corporation Beijing100086 China Beijing100101 China

ISBN: (纸本)9781510672505

In recent years, with the development of hyperspectral sensor technology, how to accurately capture different classes of ground objects from hyperspectral images in a large area is an urgent problem. this paper proposes a piecewise continuous learning method based on a spatial-spectral relational network, a circular classification process, including image cutting, image classification, and result map mosaic. this method can directly fine-tune each sub-region without modifying the network structure and can infer the whole region's figure class information. Taking Houston dataset as the experimental dataset, this paper verifies the effectiveness of the proposed classification process. Further, it reveals the application value of the proposed method in large-area hyperspectral image classification. © 2023 SPIE.

关键词： Image classification

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2023 4th international conference on Computing and Communication Systems, I3CS 2023

2023 4th International Conference on Computing and Communica...

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4th international conference on Computing and Communication Systems, I3CS 2023

ISBN: (纸本)9798350323771

the proceedings contain 83 papers. the topics discussed include: random forest regression based water quality prediction for smart aquaculture;image representation of numerical data-points for classification using convolutional neural network;homomorphic encryption based federated learning for financial data security;a transfer learning based approach for human foot image classification;design and development of a wearable wristband for measuring pulse transit time (PTT) using pressure sensors;landslide susceptibility mapping using support vector machine for Meghalaya, India;a lightweight semantic model for IoT architecture: smart water meter usecase;validating user-centric business process based service composition;identification and analysis of Assamese vowel speech signal using formant feature-fusion and feed-forward neural network model;and real-time classification of haze and non-haze images on Arduino nano BLE using edge impulse.

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Research on multi-model ensemble machine learning methods for temperature forecasting 4

Research on multi-model ensemble machine learning methods fo...

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4th international conference on computers, Information Processing and Advanced Education, CIPAE 2023

作者： Lei, Ting Dong, Yan Jin, Chenxi Min, Jingjing Han, Chao Beijing Meteorological Service Centre Beijing China

ISBN: (纸本)9798350342710

To improve the accuracy of short-term and imminent surface air temperature forecast in Beijing, a multi-model ensemble rolling temperature forecasting model was developed using two machine learning methods: ridge regression and support vector machines. the model was based on 2 m temperature numerical forecast products from four weather forecast models with 24-hour lead times from the European Centre for Medium-Range Weather Forecasts (EC), the US National Centers for Environmental Prediction in the United States (GFS), the Japan Meteorological Agency (JMA), and the Beijing Meteorological Bureau Rapid-refresh Multi-scale Analysis and Prediction System (RMAPS), as well as observations from 20 national-level stations in Beijing. the results showed that the machine learning-based multi-model ensemble effectively corrected the systematic bias in the model forecasts. the absolute mean bias of temperature forecasts was less than 0.3 °C for the 20 stations, and the root mean square error of the forecasts was smaller than that of any individual model and its equally weighted ensemble mean. the accuracy of the forecasts was significantly improved. Compared to the equal-weight ensemble mean results, the multi-model ensemble method based on ridge regression and support vector machines significantly improved the skill of temperature forecasts in the mountainous areas of Beijing. During the operation period, the forecast accuracy of temperature in FoYeDing is improved by over 30%, which has better operational application value. © 2023 IEEE.

关键词： Atmospheric temperature

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Activation Function Conundrums in the Modern machine learning Paradigm 5

Activation Function Conundrums in the Modern Machine Learnin...

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5th international conference on computer and applications, ICCA 2023

作者： Janjua, Jamshaid Iqbal Zulfiqar, Sidra Khan, Tahir Abbas Ramay, Sadaqat Ali Lahore Pakistan National College of Business Administration & Economics School of Computer Sciences Lahore Pakistan Okara University Department of Computer Science Okara Pakistan The Times Institute Department of Computer Science Multan Pakistan

ISBN: (纸本)9798350303254

the decision-making components of main neural networks are activation functions. Furthermore, they evaluate the output of the network's neural node;consequently, they are critical to the overall network's performance. As a result, selecting the most appropriate activation function in neural network calculation is crucial. these functions have various features that are thought to be necessary for successful learning. these features include their methods to ensure individual derivatives and finite range. the generally used cumulative functions, such as Sigmoid, Leaky ReLU, softmax, TanH and ReLU, and so on, will be evaluated in this study article. this will be followed by their qualities, individual disadvantages and advantages, and specific formula application advice. © 2023 IEEE.

关键词： Decision making

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A Reliable Apple Leaf Disease Identification Using a Deep learning-Based MobileNetV2 to Safeguard Apple Fruit Safety 4

A Reliable Apple Leaf Disease Identification Using a Deep Le...

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4th international conference on Embedded and Distributed Systems, EDiS 2024

作者： Belmir, Meroua Difallah, Wafa Ghazli, Abdelkader Faculty of Exact Sciences Department of mathematics and computer science Bechar Algeria Department of mathematics and computer science Bechar Algeria

ISBN: (纸本)9798331519872

Apples rank as the fourth most produced fruit globally. Nevertheless, substantial annual losses in apple yields are attributed to various leaf diseases. Early detection of these diseases is crucial for enhancing apple production. this research demonstrates the application of deep learning in accurately diagnosing apple leaf diseases from images without human intervention. We utilized the Apple Leaf Disease Dataset, which includes images of three categories of apple leaves: scab, rust, and healthy. the study introduces a customized five-layer head into the MobileNetV2 architecture. this modified version, termed TL-MobileNetV2, replaces the original classification layer with the new customized head. the TL-MobileNetV2 model achieved outstanding results, with an overall accuracy of 98.27%, a precision of 98.36%, a recall of 98.27%, and an F1 score of 98.28%. Additionally, Class Activation Maps were employed to generate heatmaps for disease detection, visually highlighting the regions that influenced the model's predictions. © 2024 IEEE.

关键词： Apple leaf disease identification Class Activation Map deep learning MobileNetV2 transfer learning

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Research on Key Technologies for Bird's Nest Detection in High-Speed Railway Catenary Systems Based on Deep learning 4

Research on Key Technologies for Bird's Nest Detection in Hi...

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4th international conference on Electronic Information Engineering and computer Technology, EIECT 2024

作者： Wang, Rong Chen, Shenglan Le, Qiqing Wang, Jun Hunan Automotive Engineering Vocational University Zhuzhou China The College of Electrical Engineering and Automation Fuzhou University Fuzhou China The College of Automation Central South University Changsha China Crrc Times Co. Ltd Zhuzhou China

ISBN: (纸本)9798331528850

With the rapid development of high-speed railway technology, ensuring its operational safety has become a critical issue. In particular, real-time monitoring of the high-speed railway pantograph system is of utmost importance for preventing failures and reducing the incidence of accidents. the aim of this research is to enhance the intelligent detection performance of the high-speed railway pantograph network status, especially in the detection of bird nests, through an improved MobileNetV3 algorithm. the research methodology includes the application of deep learning and image processing technologies, focusing on optimizing the MobileNetV3 algorithm to increase its detection accuracy in complex environments, particularly its ability to identify bird nests in complex scenes and adapt to dynamic environments. It is expected that these improvements will enable more accurate and efficient monitoring of the status of high-speed railway pantographs, thereby enhancing the safe operation level of high-speed railways. © 2024 IEEE.

关键词： Birds

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