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检索条件"任意字段=4th International Conference on Optimization Learning Algorithms and Applications"

共 13501 条记录，以下是351-360 订阅

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Tool Wear State Recognition Based on Multi-source Feature Fusion and Deep learning 4

Tool Wear State Recognition Based on Multi-source Feature Fu...

引用

4th international conference on Machine learning and Intelligent Systems Engineering (MLISE)

作者： Song, Ning Yu, Yuna Han, Tongtongn Xie, Guihua Mo, Desheng Li, Na Qilu Inst Technol Jinan Peoples R China

ISBN: (纸本)9798350375084;9798350375077

Aiming at the problems of single feature dimension and low accuracy in condition monitoring only from vibration characteristics and tool wear characteristics. In this paper, a tool wear state recognition method based on multi-source feature fusion and deep learning is proposed. the main innovation of our work lies in two aspects: firstly, the tool wear data is processed by multi-source feature fusion method, which effectively fuses multiple sensor signals and improves the efficiency and accuracy of data processing. Secondly, we optimize GRU (Gated Recursive Unit) model by combining whale algorithm for parameter optimization and add XGBoost to improve the prediction performance and robustness. Different milling wear experimental data sets are used to verify the recognition performance of the training model. the experimental results show that compared with the traditional methods, our proposed method has obvious advantages in tool wear state recognition.

关键词： tool wear multi-source feature fusion deep learning GRU whale algorithm

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A Data-Driven Prediction Method of Short-Term Cooling Load Based on Machine learning and Regression Analysis 4

A Data-Driven Prediction Method of Short-Term Cooling Load B...

引用

4th international conference on Energy Engineering and Power Systems, EEPS 2024

作者： Guo, Jie Zhao, Qiang University of Jinan School of Physics and Technology Jinan China

ISBN: (纸本)9798350366914

the modeling and prediction of air conditioning load in residential buildings has been an indispensable step in energy conservation and cost reduction. this paper proposes a data-driven load prediction model based on machine learning technology. Firstly, the input data is processed by feature engineering according to the Pearson Correlation Coefficient (PCC) between load and features. Secondly, three machine learning algorithms (SVR, MLP, and RF) and Pearson Correlation Coefficient (GTO) optimization algorithm are used for prediction. Finally, the predicted load is output by linear analysis and weighted regression. the model is verified by using residential building simulation data generated by Dest software. the influence of model integration on the overall prediction results and the error distribution of predicted load in different periods are analyzed and discussed. the Mean Absolute Error (EMAE) of predicted load is 1.52kW, and the Mean Absolute Percentage Error (EMAPE) is 10.7%. © 2024 IEEE.

关键词： Prediction models

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Improving Optical Character Recognition in License Plate Images Using Reinforcement learning 7

Improving Optical Character Recognition in License Plate Ima...

引用

7th international conference on Machine Vision and applications (ICMVA)

作者： Gnanavelu, Aakash Huang, Ching Yee Yee, Tze Swen Wang, Hongyi Oh, Oliver Aik Dee Chang, Kai Rong Wong, Siaw-Lang HELP Int Sch Kuala Lumpur Malaysia

ISBN: (纸本)9798400716553

Optical character recognition (OCR) is an essential component of computer vision systems, especially for tasks like license plate recognition. Problems arise when OCR is used on smaller pictures, which results in a significant loss in accuracy. Although modern technologies have attempted to overcome this obstacle, they face practical challenges, chief among which is the need for large datasets containing high-quality and low-quality pictures. this work presents a unique technique that uses reinforcement learning models to solve the challenge of improving OCR accuracy in lower-resolution pictures. Our methodology is motivated by the need to reduce reliance on large datasets using reinforcement learning. To stimulate this, we train our models using a small sample of license plates for training. this research paper compares the performance of different reinforcement learning models. Proximal Policy optimization is identified as the best among the algorithms we tested and is on par with other research methods, even with smaller training data.

关键词： Artificial Intelligence Optical Character Recognition Reinforcement learning

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Competition-Based Stochastic learning Particle Swarm optimization Algorithm for Large-Scale optimization Problem 11

Competition-Based Stochastic Learning Particle Swarm Optimiz...

引用

11th international conference on Machine Intelligence theory and applications, MiTA 2024

作者： Zheng, Xin Li, Jian-Yu Yang, Qiang Duan, Dan-Ting Zhan, Zhi-Hui Henan Normal University College of Computer and Information Engineering Xinxiang453000 China Nankai University College of Artificial Intelligence Tianjin300350 China Nanjing University of Information Science and Technology School of Artificial Intelligence Jiangsu China Beijing China

ISBN: (纸本)9798350385670

With the development of information technology, large-scale optimization problem is the core issue in various applications. Due to the high dimension characteristics, large-scale optimization problems are challenging to solve. therefore, this paper proposes a competition-based stochastic learning particle swarm optimization algorithm (CSLPSO) for solving large-scale optimization problems efficiently, which has two novel designs. First, a competition-based stochastic learning strategy is proposed to improve the exploration ability of PSO. Second, a parameter control mechanism suitable for the CSLPSO algorithm is proposed, so that the algorithm does not require manual parameter selection. In the experiment, the CSLPSO algorithm is applied to 12 large-scale problems with state-of-the-art algorithms as competitors. the experimental results show the effectiveness of the proposed CSLPSO. © 2024 IEEE.

关键词： optimization algorithms

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Credit Default Prediction with Machine learning: A Comparative Study and Interpretability Insights 4

Credit Default Prediction with Machine Learning: A Comparati...

引用

4th international conference on Communication Technology and Information Technology, ICCTIT 2024

作者： Wang, Yuhan Xu, Zhen Ma, Kunyuan Chen, Yuan Liu, Jinsong Columbia University New YorkNY United States New York University New YorkNY United States Rice University HoustonTX United States University at Buffalo BuffaloNY United States Shanghai China

ISBN: (纸本)9798331528973

this study built an efficient credit default prediction framework based on the LightGBM model, aiming to improve the prediction performance and enhance the interpretability of the model. through experiments on the public Home Credit Default Risk dataset, this paper comprehensively compared LightGBM with SVM, XGBoost, GBDT, and random forest models, and verified the superiority of LightGBM from multiple indicators such as accuracy, recall, and F1-score. the experimental results show that LightGBM performs best in processing complex high-dimensional data with its efficient feature selection and optimization capabilities. At the same time, combined with SHAP value analysis, it reveals the impact of features on model decisions, providing transparent and reliable decision support for credit risk assessment. this study not only provides an effective technical solution for financial institutions to improve risk management efficiency but also lays the foundation for the application of machine learning algorithms in the financial field. In the future, multimodal data and deep learning technology will be further integrated to build a more powerful prediction model. © 2024 IEEE.

关键词： Prediction models

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Scheduling optimization of Multiple Radars for Space Object Detection Mission 4

Scheduling Optimization of Multiple Radars for Space Object ...

引用

4th international conference on Control theory and applications, ICoCTA 2024

作者： Cai, Weiwei Yin, Qian Tian, Jingwen Li, Guosheng National University of Defense Technology College of Aerospace Science and Engineering Changsha China Central South University School of Traffic and Transportation Engineering Changsha China

ISBN: (纸本)9798331506155

With the increasing frequency of human activities, more and more spacecraft are launched into orbit, making the space environment increasingly crowded. In order to ensure the safe operation of spacecraft in orbit, people usually use ground-based radar and telescopes to detect the space objects and determine their motion status. However, the rapidly increased amount of resident space objects makes the detection mission challenging for the existing ground-based sensor network, requiring the scheduling optimization of the detection plan. In this paper, the scheduling optimization procedure of space objection detection is first analyzed, and the spatiotemporal constraints among the ground-based sensors and space objects are precomputed by introducing the concept of detectable arc segment. then the scheduling optimization model is developed under the framework of constraint satisfaction problem, where both the tracking requirements of space objects and the detection capabilities of ground-based radars are reformulated as constraints. An adaptive simulated annealing algorithm is proposed, which include a fitness-based initial solution generation strategy and some adaptive adjustment mechanisms like temperature adaptive update and neighborhood structure adaptive selection. Numerical simulations are conducted alongside the existing algorithms like differential evolution algorithm and ant colony optimization, validating the validity and performance of the scheduling optimization algorithm herein. © 2024 IEEE.

关键词： Constraint satisfaction problems

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Predicting Career Transitions through Insights from Academic Background and Workforce Dynamics Using Machine learning 4

Predicting Career Transitions Through Insights from Academic...

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4th international conference on Technological Advancements in Computational Sciences, ICTACS 2024

作者： Kler, Rajneesh Singh, Gurinder Chaudhary, Naina Abdullaev, Bobur Bhandwal, Mohit Ather, Danish Amity University Tashkent Uzbekistan Amity University Noida India

ISBN: (纸本)9798350387490

the increased dynamism in modern employment landscapes has emanated to the frequent career changes and, therefore, the importance of accustoming to the factors explaining occupational change is growing. the paper discusses the various machine learning models in the prediction of career changes with the help of the data set that contains record number of 38444 and has 22 features including the academic background of the person, job satisfaction, skills gap, and the growth of the industry. therefore, the investigations conducted regarding the connections between these features and career mobility offer practical recommendations for various applications of HR analytics, career guidance, and workforce planning. Logistic regression, decision tree and random forests algorithms were used from the family of machine learning algorithms. Of the models presented, the random forest model was found to be most effective with an accuracy of 85%. In the feature correlation analysis we determined other factors that deem to influence this factors for example field of study, levels of job satisfaction and growth in the industry. Demographic factors are also identified as critical drivers of career choices in the study. the research is useful to identify opportunities for the support of predictive analytics in context to current issues pertaining to talent management, talent development, and the overall talent retention. this research extends prior literature in the field of HR analytics by providing a quantitative approach for analyzing career mobility. © 2024 IEEE.

关键词： Forestry

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applications of Artificial Intelligence in Conversational Agents: A Systematic Literature Review of AI in Chatbots 4

Applications of Artificial Intelligence in Conversational Ag...

引用

4th IEEE international conference on Software Engineering and Artificial Intelligence (SEAI)

作者： Jane Samonte, Mary Arlando, Raphaela Dominique C. Joquino, Nicholas Andrew P. Manongas, Jomari B. Poblete, Jared O. Mapua Univ Sch Informat Technol Makati Philippines

ISBN: (纸本)9798350374353;9798350374346

Artificial Intelligence in Chatbots has been growing interest to a lot of people in different industries and fields. A software application that could hold a conversation with a human agent through text or text-to-speech. However there has been no clear definition of how AI is applied to these chatbots. this study aims to analyze on the applications of AI in Chatbots and understands its influence on diverse fields. the goal is to gather data on the fields in which AI chatbots are mostly utilized and understand the techniques, methods, and relevancy in applying AI chatbots in that field. the methods used in the study is the Systematic Literature Review (SLR) approach to address the proposed research question and the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) approach to help in the selection and acquisition of studies and articles. Findings show that the most common application of AI is Natural Language Processing (NLP), Machine learning (ML), and AI algorithms. the study also discovered a significant increase in published research about AI Chatbots every year. In the different fields, results show that Customer Support, Finance, and Education are the three fields that had the most studies. Chatbots have a better opportunity of being incorporated into other fields, as chatbots improve the workload and offer a more efficient method for handling the large workload. For future research, it is recommended to find more studies on the topic to validate and check the variety of applications of AI chatbots in various fields.

关键词： Machine learning Cognitive Science Neural networks Decision tree

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Identifying and predicting key factors of customer satisfaction based on machine learning 24

Identifying and predicting key factors of customer satisfact...

引用

4th international conference on Internet of things and Machine learning

作者： Li, Beiling Lai, Yuxuan Ling, Yangbo Li, Ganxiang Shanghai Univ Sch Management Shanghai Peoples R China Hunan Univ Business Sch Changsha Hunan Peoples R China

ISBN: (纸本)9798400710353

With the advancement of mobile communication technology and the continuous development of network infrastructure, people increasingly enjoy the convenience brought by mobile communication and the internet. the user's network experience is also increasingly valued by mobile operators. To help further enhance the quality of network services and market operations, the current study focuses on customer satisfaction as an important indicator. Firstly, feature engineering is used to better anchor the key factors and improve the performance of the model. Secondly, various models such as Tree-based models and Support Vector Machine (SVM) model were used for data processing, and Bayesian parameter tuning methods for model optimization. Finally, a predictive model is established using the Stacking ensemble method to integrate the models. It turns out that the ensemble model has a better performance to predict customers' satisfaction than any single model.

关键词： Feature engineering XGBoost AdaBoost LightGBM SVM Stacking Customer satisfaction

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Automatic Detection of learning Styles on OpenEdx Using Trace Analysis 4

Automatic Detection of Learning Styles on OpenEdx Using Trac...

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4th international conference on Emerging Smart Technologies and applications (eSmarTA)

作者： Chaabi, Youness Lekdioui, Khadija Al-Ashmoery, Yahya Royal Inst Amazigh Culture IRCAM Ctr Comp Studies Informat & Commun Syst CEISIC Rabat Morocco Ibn Tofail Univ SETIME Lab Fac Sci ENSC Kenitra Morocco Al Razi Univ Dept Informat Technol Sanaa Yemen Sanaa Univ Dept Math & Comp Fac Sci Sanaa Yemen

ISBN: (纸本)9798350354140;9798350354133

In a learning management system (LMS), the tutor does not interact with the students in person to interpret their reactions and grimaces or to determine the level of course assimilation. As a result, analyzing learners' behavior and traces in an e-learning environment has become necessary for tutors or teachers to assist their students. Many researchers argue that learners' learning styles are an important factor to consider in LMS. these learning styles enable students to tailor their learning to their specific environment. Every learner has a unique learning style and way of perceiving, processing, retaining, and comprehending new information. this approach, which is based on the analysis of learners' interaction traces in LMS, gives teachers a sense of their students' behavior and identifies their learning styles. Our method has demonstrated an accuracy rate of 80% in detecting learning styles, significantly enhancing engagement and motivation. By tailoring content, activities, and assessments to match each student's preferences, we can adapt interventions dynamically.

关键词： learning Styles learning Analytics Trace Analysis learning Management System.

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