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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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application Of Genetic Programming Algorithm in Flow Control 4

Application Of Genetic Programming Algorithm in Flow Control

引用

4th international conference on Artificial Intelligence and Electromechanical Automation, AIEA 2023

作者： Sun, Jiyuan International College of Engineering Changsha University of Science & Technology Changsha410000 China

ISBN: (纸本)9781510666412

As a major discipline in the field of fluid mechanics, flow control is commonly used in academic research and engineering applications. However, because the flow problem is more complex as a nonlinear problem, it has been very difficult to carry out closed-loop control in its research process. machine learning has developed rapidly in recent years, where genetic programming provides an optimization method for solving flow control problems. this paper reviews the successful cases of combining genetic planning with flow control in the experimental field by showing the application methods of genetic planning and flow control, and analyzes the advantages and disadvantages of genetic planning in flow control applications. the article describes the overall overview of the application of genetic programming in flow control, but because the application of genetic programming in flow control is still in its infancy, through this paper we also hope to promote the application of genetic programming in the field of flow control engineering. © 2023 SPIE.

关键词： Genetic programming

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Performance Evaluation of Deep learning Models for Leaf Disease Detection: A Comparative Study 4

Performance Evaluation of Deep Learning Models for Leaf Dise...

引用

4th international conference on Computing, Mathematics and Engineering Technologies, iCoMET 2023

作者： Akbar, Wajahat Soomro, Abdullah Ullah, Mohib Inam Ul Haq, Muhammad Ullah Khan, Sana Ali Shah, Tahir Khushal Khan Khattak University Dept. of Computer Science & Bi Karak Pakistan The Islamia University of Bahawalpur Dept. of Computer Science Bahawalpur Pakistan Kohat University of Science and Technology Dept. of Computer Science Kohat Pakistan Shaanxi Normal University Dept. of Computer Science Shaanxi China

ISBN: (纸本)9798350335316

Early detection of plant diseases is crucial before plant growth is affected. Plant diseases have been detected and classified using a variety of machine learning (ML) models in the past. Deep learning (DL) appears to have great potential in terms of increased accuracy;however, in agricultural applications of Convolutional Neural Networks (CNN) has widely been utilised by researchers. CNNs are so effective at identifying plant species, managing yields, detecting weeds, managing soil, and water, counting fruits, detecting diseases and pests, and evaluating plant nutrient status. A farmer can diagnose plant diseases quickly and accurately with an automated disease detection system. To speed up crop diagnosis, plant leaf disease detection systems must be automated. In this paper, we evaluated twelve different models on a new plant diseases dataset and demonstrated that the most accurate model was Densenet169. In training and validation, the accuracy was 97.2% and 97.8%, respectively. © 2023 IEEE.

关键词： Convolutional neural networks

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Fault Diagnosis and Region Detection of CCD Image in Double Delay three-Step Autocorrelator 4

Fault Diagnosis and Region Detection of CCD Image in Double ...

引用

4th IEEE international conference on Pattern Recognition and machine learning, PRML 2023

作者： Li, Suicheng He, Zizhou Shen, Wenwen Xia, Yanwen Ni, Zhigao Zhou, Xiaowei Institute of Computer Application CAEP Department of Computer Engineering and Science Sichuan Province China Research Center of Laser Fusion CAEP Department of Optical Instrument Sichuan Province China

ISBN: (纸本)9798350324303

With the development of national optical experimental facilities, countries worldwide have invested significant amounts of funding into constructing various facilities. Among them, the double delay three-step autocorrelator is an important instrument for measuring the ultrashort pulse laser waveform and is frequently used in laser experiments. Because electronic instruments are too slow to measure directly, we can only use CCD images to reconstruct the laser waveform. therefore, CCD, in particular, plays a vital role in the closed-loop control system of double delay three-step autocorrelator, and CCD image quality and processing can directly affect the measurement results of the pulse laser. In this paper, we propose an image feature-based CCD fault diagnosis and effective region detection method for the double delay three-step autocorrelator. this fault diagnosis method first calculates the pixel sum of each row and analyzes the primary pixel distribution of each frame which could represents the main features of CCD image. Next, we design a fault diagnosis model to classify those features and pick out the suitable image. Finally, we analyze and process the above image and design a boundary detection algorithm to detect the effective region for further waveform recovery algorithm. In addition, to verify performance of the proposed method, we conduct a significant number of experiments. through qualitative and quantitative analyses of the experimental results, we verify the effectiveness and robustness of the proposed method for CCD fault diagnosis. © 2023 IEEE.

关键词： Failure analysis

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An Effective Cartoonifying of an Image using machine learning 4

An Effective Cartoonifying of an Image using Machine Learnin...

引用

4th IEEE Annual Flagship India Council international Subsections conference, INDISCON 2023

作者： Tomar, Suryansh Upadhyay, Nandini Guru Prasad, M.S. Divyashree, N. Mamgain, Vinayak Ram, Mangey Dept. of Computer Science and Engg. Dehradun India School of Computing and Information Technology Reva University Bengaluru India

ISBN: (纸本)9798350333558

Cartoonifying an image is the process of transforming a regular photograph into a cartoon-style image. this research paper proposes a method to cartoonify images using OpenCV, a popular open-source computer vision library using Python. the proposed method involves several steps, including edge detection, color quantization, and image smoothing. the edge detection step is used to extract edges from the input image. then, in the color quantization step, the image palette is reduced to a fixed number of colors using the k-means clustering algorithm. Finally, the image is smoothed using a bilateral filter to create a cartoon-like effect. the proposed method is evaluated on several images, and the results show that the proposed method produces high-quality cartoon images with reduced noise and better visual appeal compared to existing methods. this method has potential applications in various fields, such as entertainment, advertising, and digital art, and can be easily integrated into existing applications. © 2023 IEEE.

关键词： Color

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Supervised machine learning Models for the Prediction of Renal Failure in Senegal 4

Supervised Machine Learning Models for the Prediction of Ren...

引用

4th international conference on Control, Artificial Intelligence, Robotics and Optimization, ICCAIRO 2023

作者： Seck, Djamal Abdoul Nasser Diakite, Fatima Bintou Rassoul Cheikh Anta Diop University of Dakar Faculty of Science and Technology Dakar-Fann BP 5005 Senegal

ISBN: (纸本)9798350300918

the objective of this paper goal is to propose models to predict the diagnosis of acute, chronic or terminal chronic renal failure using supervised machine learning methods. We used medical data from the Nephrology Service of the Aristide Le Dantec Hospital in Dakar to build prediction models with supervised learning methods: Multilayer Perceptron, Support Vector machines, Random Forest and Extreme Gradient Boosting. the model built with Extreme Gradient Boosting proved to be the best according to the performance measures we used, namely precision, recall, F1-score and accuracy. Follow, in order, the models built with Random Forest, Logistic Regression, Support Vector machines and Multilayer Perceptron. © 2023 IEEE.

关键词： Forecasting

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A Robust Framework for Fixing the Vulnerability of Compressed Distributed learning 26

A Robust Framework for Fixing The Vulnerability of Compresse...

引用

26th international conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Chen, Yueyao Wang, Beilun Zhang, Yan Kuang, Jingyu Southeast University School of Computer Science and Engineering Nanjing211102 China

ISBN: (纸本)9798350331684

Nowadays, as a prevailing paradigm for large-scale machine learning, distributed learning has been faced with two challenges, communication bottleneck and limited robustness. For the communication challenge, compression is widely-used as a solution. For the robustness challenge, some robust defence methods have been proposed. However, previous works that simultaneously consider these two challenges are limited. through an experiment on the w8a dataset, we found that compressed distributed learning with rand-K is vulnerable to poisoning attacks. therefore, in this paper, we propose a robust compressed distributed framework for distributed learning settings. Experimental evaluations on a9a and w8a datasets have shown the effectiveness of our proposed framework, which markedly decreases the average optimality gap from 1.47E -2 and 2.15E -2 to 3.98E -4 and 4.33E -4 respectively. © 2023 IEEE.

关键词： machine learning

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Enhancing Sustainable Supply Chain Forecasting Using machine learning for Sales Prediction 4

Enhancing Sustainable Supply Chain Forecasting Using Machine...

引用

4th international conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Rahman Mahin, Md. Parvezur Shahriar, Munem Das, Ritu Rani Roy, Anuradha Reza, Ahmed Wasif Department of Computer Science East West University Dhaka1219 Bangladesh

Managing the supply chain is crucial to success in the competitive business sector. Demand forecasting using sales data is one of the major things in supply chain management because it is directly connected to profit margins, inventory levels, sales, and customer satisfaction. this research tried to provide an innovative approach to sales prediction using advanced machine learning methods to enhance supply chain operations and boost the predictive accuracy of supply chain models after analyzing historical sales data and considering different factors like seasonality, trends, and stock. Various machine learning algorithms were applied, including Linear Regression, Elastic Net Regression, KNN, Random Forest, and the ensemble Voting Regressor. the performance of Random Forest and KNN is very well but the Voting Regressor is better than other models for its strength of multiple algorithms. the Voting Regressor provides the lowest RMSE of 1.54 and the highest R2 of 0.9999. this ensemble method improves sales forecasting accuracy by reducing errors and ensuring computational efficiency. It also provides more reliable tools to manage inventory, prevent overstocks, and minimize holding costs. this research presents the importance of machine learning integration in supply chain management. It shows the Voting Regressor as the most effective approach for demand forecast. Future research could explore the model's application in broader markets, integrating other key factors and deep learning algorithms to refine predictive capabilities later. © 2025 the Authors.

关键词： Adversarial machine learning

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Detection of Pneumonia from Chest X-Ray Images Using Transfer learning on Deep CNN 4

Detection of Pneumonia from Chest X-Ray Images Using Transfe...

引用

4th international conference for Emerging Technology, INCET 2023

作者： Gupta, Advait Padsala, Manan Saikia, Pallabi Rajiv Gandhi Institute of Petroleum Technology Dept. of CSE Uttar Pradesh Jais India

ISBN: (纸本)9798350335750

Pneumonia is a serious lung infection that can be life-threatening if not diagnosed and treated on time. Accurately diagnosing pneumonia from X-ray images is essential for timely treatment and improved patient outcomes. In this research paper, we propose an approach for diagnosing pneumonia using X-ray images by leveraging the power of convolutional neural networks (CNNs) and transfer learning. the proposed approach utilizes a pre-trained MobileNet model, which is a popular CNN architecture, and adds additional layers on top to fine-tune the model for the specific task of diagnosing pneumonia. this approach was more effective than training a CNN model from scratch or using traditional machine-learning methods. Our proposed approach demonstrated superior performance when compared to previously utilized models and techniques as evidenced by the results obtained. the use of CNNs and transfer learning in medical imaging is a growing area of research, and our proposed approach is a significant advancement in the field of pneumonia diagnosis. the proposed approach has the potential to improve the accuracy and efficiency of pneumonia diagnosis and ultimately save lives. © 2023 IEEE.

关键词： computer aided diagnosis

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Research on the application of deep learning in autonomous vehicle 4

Research on the application of deep learning in autonomous v...

引用

4th international conference on Advanced Algorithms and Neural Networks, AANN 2024

作者： Yan, Qiyan Chang, Zhoulin Liu, Xia School of mechanical and electrical engineering Guangdong university of science and technology Dongguan523083 China

ISBN: (纸本)9781510686106

the rapid development of artificial intelligence and new generation information technology is driving the intelligence and networking of automotive products. Currently, driven by the comprehensive development of technology and industrial exploration practices, autonomous driving of automobiles has become a research focus and hotspot in the field of automotive technology. Based on this, this paper analyzes the development and research status of key technologies of auto driving, such as environmental awareness, planning and decision-making, and control execution, and focuses on the application of in-depth learning in the planning and decision-making of autonomous vehicle, providing some reference for the planning and decision-making of autonomous vehicle. © 2024 SPIE.

关键词： Autonomous driving Deep learning Decision making Autonomous vehicles Unmanned vehicles Environmental sensing Data modeling LIDAR Neural networks Target recognition

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A Framework Leveraging Robotics and machine learning Technologies for Early Disease and Pest Detection in Greenhouse Tomato Crops 4

A Framework Leveraging Robotics and Machine Learning Technol...

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4th international conference on Control, Artificial Intelligence, Robotics and Optimization, ICCAIRO 2023

作者： Kounalakis, Nikolaos Kalykakis, Emmanouil Kosmopoulos, Dimitrios Fasoulas, John Sfakiotakis, Michael Heraklion Greece University of Patras Dept. of Computer Engineering and Informatics Patras Greece

ISBN: (纸本)9798350300918

We present an integrated framework for intelligent automated plant monitoring, towards early disease and pest detection in greenhouse tomato crops. the framework combines the use of a robotic mobile platform to autonomously collect multi-spectral images of the plants, with a tool that utilizes Faster R-CNN to detect regions that signify the presence of a disease or pest. the robot is based on a modified mobile vertical mast lift platform, and integrates a 6-dof robotic arm that is used to position the plant imaging multi-spectral camera. the robot can navigate autonomously inside the greenhouse via a magnetic guidance sensor. Results from a series of experiments demonstrate the validity and effectiveness of the implemented framework. © 2023 IEEE.

关键词： machine learning

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