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检索条件"任意字段=3rd International Conference on Machine Learning and Computer Application, ICMLCA 2022"

共 1863 条记录，以下是271-280 订阅

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A Comparison between Using Fuzzy Cognitive Mapping and machine learning to Predict Students' Performance in Higher Education 3

A Comparison between Using Fuzzy Cognitive Mapping and Machi...

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3rd IEEE international conference on Electronics, Control, Optimization and computer Science, ICECOCS 2022

作者： Oqaidi, Khalid Aouhassi, Sarah Mansouri, Khalifa ENSET of Mohammedia University Hassan II of Casablanca Laboratory SSDIA Mohammedia Morocco

ISBN: (数字)9781665457231

ISBN: (纸本)9781665457231

Higher education quality (HEQ) stakeholders increasingly see the student as the core of the teaching operation. To improve HEQ a good prediction of students' performance is a considerable step. In this paper we discuss the use of two approaches used in the literature in students' performance (SP) prediction: machine learning (ML) techniques and Fuzzy Cognitive Mapping (FCM). Our main contribution is a comparative analysis of these approaches and recommendations in context of the Moroccan Higher Education institutions. © 2022 IEEE.

关键词： machine learning

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Prediction of O3 Concentration Based on CEEMDAN-RCMSE-LSTM Model 4

Prediction of O3 Concentration Based on CEEMDAN-RCMSE-LSTM M...

引用

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

作者： Yang, Lan Li, Shen Liu, Wenqi Kunming University of Science and Technology Kunming China

ISBN: (纸本)9798400709449

In response to the problem that the current ozone forecasting methods fail to consider the characteristic recognition of ozone concentration sequences, a combined forecasting model CEEMDAN-RCMSE-LSTM is proposed. Model extracts the feature distribution of the time series through the frequency-domain processing method and entropy value classification, and predicts daily ozone concentrations at time 0 and time 12 through the LSTM model. The model's performance is validated using the atmospheric ozone concentrations in Beijing's Xicheng district time 0 from January 1, 2020, to August 27, 2022, and the atmospheric ozone concentrations in Beijing at time 12 from January 1, 2020, to August 27, 2022. The results show the combined forecasting model CEEMDAN-RCMSE-LSTM has significantly improved accuracy and can improve the predictive performance of the single LSTM model by 12.3% and 12.6%, respectively. Despite some errors in meteorological forecasts, the proposed still achieves high-precision forecasting results and has good robustness. © 2023 ACM.

关键词： Ozone

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Hounan: An Intelligent Mobile application for Brain Cancer Classification 3

Hounan: An Intelligent Mobile Application for Brain Cancer C...

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3rd international conference on Creative Communication and Innovative Technology, ICCIT 2024

作者： Al-Shahrani, Amal Al-Amoudi, Wusaylah Bazaraah, Raghad Farouquee, Hanan Al-Sharief, Atheer Umm Al-Qura University College of Computing Makkah Saudi Arabia

ISBN: (纸本)9798350367492

Diagnosing brain tumors is an important consideration, especially when given the significant increase in cancer prevalence globally. Hounan is an innovative application designed for diagnosing cancer patients, capable of diagnosing 14 different types of cancer. The application features a chatbot to address all user inquiries, and reminders for appointments to help users keep track of their scheduled appointments. Conventional cancer diagnosis methods often entail delays and can be cumbersome for patients. To address these challenges, "Hounan"leverages advanced machine learning algorithms and computer vision techniques to provide efficient and accurate cancer diagnosis remotely. The mobile application was developed using Flutter and integrated with a model that classifies and diagnoses brain tumors. Additionally, the chatbot feature offers personalized support and information on various aspects of cancer diagnosis and treatment. The integration of appointment reminders ensures that users stay informed and organized throughout their medical journey. Hounan represents a significant advancement in cancer diagnosis, offering convenience, accuracy, and personalized support to patients seeking timely medical assistance. The purpose of this research is to enhance the efficiency and accessibility of cancer diagnosis, ultimately improving patient outcomes and providing timely medical support. © 2024 IEEE.

关键词： Lung cancer

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Use of transfer learning for detection of structural alterations 3

Use of transfer learning for detection of structural alterat...

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3rd international conference on Industry 4.0 and Smart Manufacturing (ISM)

作者： Reyes-Carmenaty, Guillermo Perez, Marco A. Univ Ramon Llull IQS Sch Engn Via Augusta 390 Barcelona 08017 Spain

Structural health monitoring is a discipline dedicated to the detection, identification, location and quantification of damage in structures based on performance indicators. Given the aim of the discipline, non-destructive method for data acquisition are always preferred. One such method is vibration-based testing, with which this work concerns itself There has been widespread use of both machine learning applications when dealing with vibration data and use of computer vision-oriented machine learning models with pictures of the studied structure in order to address the concerns of structural health monitoring applications;this work propose a combination of the two. Since there are many pre-trained models for computer vision-oriented applications, this work successfully proposes a method for harnessing such models for processing of vibrational data through the use of transfer learning methodologies and finite element models. This can be achieved thanks to the visual nature of the Complex Frequency Domain Assurance Criterion (CFDAC) matrix, which can be obtained from vibrational data. (C) 2022 The Authors. Published by Elsevier B.V.

关键词： transfer learning structural health monitoring machine learning convolutional neural networks frequency response function

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Real Time Object Distance and Dimension Measurement using Deep learning and OpenCV 3

Real Time Object Distance and Dimension Measurement using De...

引用

3rd international conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Basavaraj, M.U. Raghuram, H. Mohana Rv College of Engineering® Electronics and Telecommunication Engineering Karnataka Bengaluru India Karnataka Bengaluru India

ISBN: (纸本)9781665462167

Deep learning is a subset of machine learning based on artificial neural networks in which multiple layers of processing are used to extract progressively higher-level features from data. Deep learning is focused on improving the AI process of having machines learning new things on its own. In this paper, proposed real time object dimension detection and dimension analysis using python. For ML and AI-based papers, python programming language offers consistency, simplicity, and access to excellent libraries and frameworks. It also offers platform freedom, flexibility, and a large developer community. Proposed work, explorations were done on some methodologies, including You Only Look Once (YOLO), for object identification that is meant for speed and real-time application use, and Region-Based Convolutional Neural Networks (R-CNNs) built for model performance and analysis. To make it better, a canny edge detection algorithm is being used. Canny edge detector is a multistage algorithm-based on edge detection operator that can identify a variety of edges in image. © 2023 IEEE.

关键词： computer vision

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Bandgap Prediction of Two-Dimensional Transition Metal Layered Binary Compounds Based on Regression Forest machine learning Method 3

Bandgap Prediction of Two-Dimensional Transition Metal Layer...

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3rd international conference on Electronic Information Engineering and computer Science, EIECS 2023

作者： Li, Cheng Wu, Jiayin Macau University of Science and Technology Faculty of Innovation Engineering Avenida Wai Long China Guangdong Vocational College of Post and Telecom School of Computer 191 West Zhongshan Avenue Guangdong China

ISBN: (纸本)9798350316674

Two-dimensional (2D) materials, renowned for their exceptional electronic properties, have found widespread applications in various domains. Accurate prediction of the bandgap width, a critical characteristic of 2D materials, is a challenging task due to bandgap underestimating associated with the PBE calculation method and the prohibitively high computational costs of the HSE approach. In this study, we propose the application of machine learning methodologies - Regression Forest, Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Regression (SVR), Ridge Regression (RIDGE), Elastic networks (Elastic Net), Adaptive Boosting Regression (AdaBoost Regression), Regression tree, and Gradient Boosting Regression (GBR) - to train a regression model. This regression model is designed to capture the 16 essential characteristics of 2D transition metal layered binary compounds and the corresponding HSE bandgap theory values. To assess the model's performance, we employ the 10-fold cross-validation technique. The experimental findings demonstrate that machine learning-based bandgap prediction for 2D transition metal layered binary compounds not only achieves remarkable accuracy but also significantly reduces the computational requirements. Consequently, this research offers a promising and efficient approach for the precise characterization of various 2D material properties. Code available: https://***/lyc209059/BandReF (Password: BandReF3358code) © 2023 IEEE.

关键词： 2D materials bandgap HSE machine learning Random Forest transition metal layered binary compounds

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Stress Prediction in Indian School Students Using machine learning 3

Stress Prediction in Indian School Students Using Machine Le...

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3rd international conference on Intelligent Engineering and Management, ICIEM 2022

作者： Bisht, Aanchal Vashisth, Shreya Gupta, Muskan Jain, Ena DIT University School of Computing Dehradun India

ISBN: (数字)9781665467568

ISBN: (纸本)9781665467568

Today, kids from all over the world are under a tremendous amount of stress, which can have serious effects if not addressed promptly. Anxiety, frustration, anger, or uneasiness can all cause stress. computer science has advanced significantly as the digital world has progressed, particularly in the field of health care with the application of machine learning. We have presented a methodology that will assist schools, parents, and students in predicting the level of stress a student is experiencing. We developed a real-time database in which we surveyed over 190 school children aged 14 to 18 years old with a variety of 26 questions to analyze their stress and cure them early using machine learning Algorithms. Different types of machine learning algorithms are used, including Decision Tree, Logistic Regression, K Nearest Neighbors, and Random Forest, with a maximum accuracy of 88 per cent using K Nearest Neighbors. © 2022 IEEE.

关键词： Students

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Advancing Drug Discovery through a Deep learning-Based Protein-Ligand Interaction Prediction Tool 3

Advancing Drug Discovery through a Deep Learning-Based Prote...

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3rd international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Hunge, Sudhir S. Yadao, Aman Rajendra Shewate, Varsha Tekdas Lalsare, Prakash Namdeorao Prasannan, Shiji Ramulu, Kothuri Parashu Chintamani College of Science Pg Dept. of Chemistry Maharashtra Pombhurna India Shriram Arts And Science Junior College Dept. Of Chemistry Maharashtra Ramtek India Chintamani College of Science Dept. of Chemistry Maharashtra Pombhurna India Shri Prabhu Ramchandra Junior College Dept. of Chemistry Nanda Tah Korpana Maharashtra India Faculty of Management Studies Parul Institute of Management and Research Parul University Gujarat Vadodara India Indur Institute Of Engineering And Technology Dept. of Computer Science and Engineering India

ISBN: (纸本)9798331515911

In recent years, deep learning has emerged as a promising approach to advance drug discovery by accurately predicting interactions between proteins and small molecules, or ligands. The identification of these interactions is essential in developing drugs, as it informs us about a molecule's potential therapeutic efficacy. Traditional methods, like molecular docking, often struggle with accuracy and computational efficiency, limiting their application in high-throughput settings. In this paper, we propose a novel deep learning-based tool designed to predict protein-ligand interactions with enhanced precision and scalability. Our model leverages convolutional and graph neural networks to capture the complex, three-dimensional nature of these interactions. Using a large, curated dataset, we train and test the model to optimize binding affinity prediction, employing metrics such as mean squared error and accuracy to validate performance. The results show that our approach outperforms conventional techniques and recent machine learning models, demonstrating a significant improvement in predictive reliability. This tool has potential applications in screening vast compound libraries in early-stage drug discovery, reducing time and costs associated with experimental trials. By providing more accurate insights into protein-ligand affinities, our method represents a step forward in computational drug design, with implications for developing targeted therapies across various disease areas. © 2025 IEEE.

关键词： Graph neural networks

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Research and design of machine vision-based defect detection method for 3C digital screens with Multi-feature fusion 3

Research and design of machine vision-based defect detection...

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3rd international conference on Image Processing and Media Computing (ICIPMC)

作者： Zhao Jianjun Dong Xiang Zhu Zhenggen Zheng Zhong Anhui Univ Sch Elect Engn & Automat Hefei 230601 Peoples R China Chinese Acad Sci Inst Intelligent Machines Hefei Inst Phys Sci Hefei 230031 Peoples R China

ISBN: (数字)9798350386660

ISBN: (纸本)9798350386660;9798350386677

This paper aims at the current digital screen defect detection in the field of the feature region is not clear, a single screen samples on the screen and the line part of the complexity of the problem of simultaneous detection. It proposes an inspection system design method that realizes digital screen defect detection and classification by superimposing the sample bright field and dark field images, complementing traditional machine vision and computer vision solutions, and adopting a multi-feature fusion method to detect the rows of wires that cannot be detected due to overexposure in the bright field by using dark field images;and detecting the main part of the screen with lower contrast in the dark field by using bright field images. The algorithm adopts the improved YOLOv5 deep learning model, and the feature extraction network selects the lightweight GhostNet, and the feature information extracted from the screen pictures is fused, defect recognition and classification through the bidirectional multi-scale fusion network. Comparison with the manual detection results shows that: the detection rate accuracy of the combined bright and dark field defect detection device using the improved YOLOv5 algorithm is 6.8% higher than that of the original YOLOv5 algorithm device;and the detection time of a single piece of defect detection is reduced by 67.4% compared with that of the manual defect detection. The experimental results prove that the defect detection device has high value and research significance.

关键词： Multi-feature YOLOv5 Defect Detection

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A learning Sentiment Database for machine learning 3

A Learning Sentiment Database for Machine Learning

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3rd international conference on computer, Big Data and Artificial Intelligence, ICCBDAI 2022

作者： Zhang, Jing Hu, Siquan Shi, Zhiguo Han, Shumin School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Shunde Innovation School University of Science and Technology Beijing Foshan China

Learners' affective states play a crucial role in learning evaluation, and the external expressions that can directly reflect affect are facial expressions. However, the sample size of the database for the learning process of learners is limited, and most of the existing expression databases are the expressions of ordinary people, which is difficult to support an in-depth study of the emotional states of learners in learning scenarios. In order to better study the influence of learners' emotion on the learning state, we built a learning expression database for machine learning, and at the same time labeled the data with expression-emotion state, and finally formed a database of 8,000 expression images containing 36 learners, which is important for studying learners' emotion state under learning scenarios. © Published under licence by IOP Publishing Ltd.

关键词： Database systems

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