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检索条件"任意字段=4th Conference on Learning and Intelligent Optimization"

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Research on Electric Power Dialogue System Based on Deep Reinforcement learning 4

Research on Electric Power Dialogue System Based on Deep Rei...

引用

4th International Signal Processing, Communications and Engineering Management conference, ISPCEM 2024

作者： Geng, Chao Wei, Bengang Zhang, Sujie Zheng, Long Du, Congjin Tan, Kaixiang State Grid Shanghai Electric Power Company Equipment Department Shanghai China State Grid Shanghai Electric Power Company Shanghai China Jiangsu Titan Wisdom Technology Co. LTD Wuxi China

ISBN: (纸本)9798331528676

this paper proposes an innovative deep reinforcement learning framework for building a power dialogue system, which aims to optimize the interactive experience and efficiency of power services. therefore, this paper designs a hybrid reinforcement learning architecture combining Deep Q-Network (DQN) and Proximal Policy optimization (PPO) to solve the complex and variable dialogue scenarios in the power field. DQN is responsible for the preliminary value evaluation and action selection due to its efficient exploration ability in discrete action space. However, PPO, with its robust policy optimization mechanism, focuses on the fine adjustment of policies to promote the continuous improvement and optimization of dialogue strategies. the method designed in this paper not only improves the response quality and user satisfaction of the dialogue system, but also enhances the robustness and adaptability of the system to the dynamic environment of the power system. It shows a significant performance improvement in the power dialogue task, and can well handle unstructured user requests and perform precise power service operations. And we can maintain high performance and high reliability in complex and changing environments. ©2024 IEEE.

关键词： Deep reinforcement learning

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the Impact of Metacognitive Strategy-Supported intelligent Agents on the Quality of Collaborative learning from the Perspective of the Community of Inquiry 4

The Impact of Metacognitive Strategy-Supported Intelligent A...

引用

4th International conference on Educational Technology, ICET 2024

作者： Chen, Mengfan Wu, Linjing Liu, Zhang Ma, Xinqian Central China Normal University Faculty of Artificial Intelligence in Education Hubei Wuhan China

ISBN: (纸本)9798350376944

this study investigates how students interact with intelligent agents based on metacognitive strategies, and the changes in metacognitive levels within AI-supported collaborative learning environments. It was conducted in naturalistic learning settings, involving 24 college students and lasting approximately two months. the students' metacognitive survey data and their reflective essays on interactions with AI agents were analyzed using descriptive statistical analysis and epistemic network analysis, based on the community of inquiry framework and metacognitive theory. the results show that AI agents providing metacognitive strategy support effectively improve learners' metacognitive levels. the change in metacognitive level of learners is related to their perceived teaching and cognitive existence. this study contributes to and has implications for the educational implementation of intelligent agents, as well as the facilitation and graphical representation of learner-AI interactions in educational settings. © 2024 IEEE.

关键词： Collaborative learning

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Proceedings of 2023 4th International conference on Computer Science and Management Technology, ICCSMT 2023

Proceedings of 2023 4th International Conference on Computer...

引用

4th International conference on Computer Science and Management Technology, ICCSMT 2023

ISBN: (纸本)9798400709517

the proceedings contain 181 papers. the topics discussed include: construction of highway electromechanical automation operation and maintenance system based on computer network;carbon emission reduction decision and optimization of blockchain-based supply cold chain;design and implementation of e-commerce big data analysis system based on Hadoop;design and research on supervision project management system based on system engineering;research on a mine safety intelligent protection system based on an embedded platform and exponential smoothing algorithm;digital management system for power grid devices: design and implementation;design of a university book push system based on big data;optimization strategies for testing environments in large-scale distributed systems based on Java-Agent;and a deep learning framework: prediction of the number of participants in wordle and the distribution of game outcomes.

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Enhancing Deep learning with Optimized Gradient Descent: Bridging Numerical Methods and Neural Network Training 4

Enhancing Deep Learning with Optimized Gradient Descent: Bri...

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4th International conference on Computer Science and Blockchain, CCSB 2024

作者： Ma, Yuhan Sun, Dan Gao, Erdi Sang, Ningjing Li, Iris Huang, Guanming Johns Hopkins University Baltimore United States Washington University in St. Louis St. Louis United States New York University New York United States Columbia University New York United States University of Chicago Chicago United States

ISBN: (纸本)9798350356502

optimization theory serves as a pivotal scientific instrument for achieving optimal system performance, with its origins in economic applications to identify the best investment strategies for maximizing benefits. Over the centuries, from the geometric inquiries of ancient Greece to the calculus contributions by Newton and Leibniz, optimization theory has significantly advanced. the persistent work of scientists like Lagrange, Cauchy, and von Neumann has fortified its progress. the modern era has seen an unprecedented expansion of optimization theory applications, particularly with the growth of computer science, enabling more sophisticated computational practices and widespread utilization across engineering, decision analysis, and operations research. this paper delves into the profound relationship between optimization theory and deep learning, highlighting the omnipresence of optimization problems in the latter. We explore the gradient descent algorithm and its variants, which are the cornerstone of optimizing neural networks. the chapter introduces an enhancement to the SGD optimizer, drawing inspiration from numerical optimization methods, aiming to enhance interpretability and accuracy. Our experiments on diverse deep learning tasks substantiate the improved algorithm's efficacy. the paper concludes by emphasizing the continuous development of optimization theory and its expanding role in solving intricate problems, enhancing computational capabilities, and informing better policy decisions. © 2024 IEEE.

关键词： optimization algorithms

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EVAL IQ: intelligent Educational Assessment and Recommendation Platform 4

EVAL IQ: Intelligent Educational Assessment and Recommendati...

引用

4th IEEE International conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Vandana, C.P. Poornachandra, Poornachandra Shetty, Rohan New Horizon College of Engineering Department of Information Science and Engineering Bangalore India

ISBN: (纸本)9798331518981

EVAL IQ is an innovative educational assessment platform designed to simplify the evaluation process for both educators and learners. It offers separate portals tailored to each group's needs. Teachers can manage student data, create, and organize courses, design questions, and assess performance using automated scoring and in-depth analytics. the platform also includes a feedback system to facilitate continuous improvement. For students, EVAL IQ provides an easy-to-use interface for completing assessments, meeting deadlines, and providing feedback. Built with modern technology, EVAL IQ ensures efficient, transparent, and effective educational assessments, enhancing the overall teaching and learning experience. © 2024 IEEE.

关键词： Contrastive learning

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Credit Scoring for Loan Applicants Using Machine learning Models 4

Credit Scoring for Loan Applicants Using Machine Learning Mo...

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4th International conference on intelligent Technologies, CONIT 2024

作者： Krishna, Kaivaram Sivarama Kumar, Bommala Deepak Reddy, Morumpalli Deekshith Saketh, Ch Venugopalan, Manju Amrita School of Computing Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798350349900

this study presents a comprehensive framework for credit scoring in loan application processes. the research presents a framework that uses data analysis and machine learning techniques to evaluate creditworthiness, in loan applications. By incorporating cutting-edge algorithms like Naive Bayes, SVM, Decision Tree, Multi Layer Perceptron (MLP), and Gradient Booster (GB) the goal is to revolutionize the decision-making process for loan approvals by using analytics. the model utilizes a dataset that includes payment statements enabling a thorough and randomized credit scoring system for each individual applying for a loan. By leveraging machine learning algorithms, the system examines past payment behaviors to extract patterns and information. these findings are then used to generate a credit score that is further classified as good, bad, or moderate. this approach provides an assessment of an applicant's reliability. To improve the model's accuracy, feature selection techniques and Synthetic Minority Oversampling Techniques (SMOTE) are utilized to compare models' effectiveness, in predicting creditworthiness. the integration of data and machine learning not only enhances the efficiency of loan approval processes but also facilitates more informed financial decisions based on data analysis. On all the machine learning models which were performed, SVM performed best among the five models with an F-measure value of 0.86 incorporating feature selection and SMOTE. © 2024 IEEE.

关键词： Feature Selection

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Machine learning and Artificial Intelligence for Enhanced Alzheimer's Disease Detection: A State-of-the-Art Review 4

Machine Learning and Artificial Intelligence for Enhanced Al...

引用

4th International conference on Ubiquitous Computing and intelligent Information Systems, ICUIS 2024

作者： Mohammed, Shehu Malhotra, Neha Lovely Professional University Department of Computer Applications Phagwara India

ISBN: (纸本)9798331529635

Alzheimer's disease (AD) is a substantial public health challenge due to its rising prevalence on a global scale. Timely identification and tailored treatment are essential for enhancing patient results and maximizing healthcare resources. this paper offers a thorough examination of the latest progress in applying machine learning (ML) and artificial intelligence (AI) methods to improve Alzheimer's disease (AD) detection and customize care tactics. We will first discuss the etiology of Alzheimer's disease and emphasize the need of early identification. We will explore different machine learning and artificial intelligence techniques such as deep learning, support vector machines, and ensemble approaches used for Alzheimer's disease detection and prognosis. We investigate combining many types of data sources, including neuroimaging, genetic markers, and clinical assessments, to improve predictive models. We also examine how AI-driven decision support systems help create tailored care plans and optimize treatment for people with Alzheimer's disease. We discuss present obstacles and prospective paths in the area, such as data diversity, model explainability, and ethical concerns. this study seeks to offer insights into the current state of machine learning (ML) and artificial intelligence (AI) applications in Alzheimer's disease (AD) research and clinical practice, with the goal of advancing early diagnosis and individualized management strategies for this severe neurological ailment. © 2024 IEEE.

关键词： Clinical research

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4-Way Ka-Band Power Combiner Using Rectangular Waveguide 4

4-Way Ka-Band Power Combiner Using Rectangular Waveguide

引用

4th International conference on intelligent Technologies, CONIT 2024

作者： Vani, R. Meenakshi, S. Shree Sahitya, B. Srm Institute of Science and Technology Department of Electronics and Communication Engineering Ramapuram Chennai India

ISBN: (纸本)9798350349900

the design and analysis of a 4:1 WR28 waveguide power combiner developed for Ka-band frequency microwaves has been presented and designed using the electromagnetic simulation platform, CST Studio Suite. Within the specified 32 to 38 GHz frequency range, this proposed power combiner works seamlessly with Ka-band systems. Combining rigorous electromagnetic simulation with optimization techniques, the design achieves combining gain of 5.5 dB, high isolation, good return loss and impedance matching. the combiner was simulated in high-frequency problem type and the assigned material was silver. the VSWR is obtained between 1 and 2 making it ideal for a combiner to combine waves with minimum reflection and return loss is obtained below - 10 dB. Experimental validation further highlights the suitability of the designed power combiner for practical deployment in Ka-band systems. © 2024 IEEE.

关键词： Rectangular waveguides

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PRP protocol optimization method based on Q-learning under the Linux operating system 4

PRP protocol optimization method based on Q-learning under t...

引用

2024 4th International conference on Artificial Intelligence and Industrial Technology Applications, AIITA 2024

作者： Wang, Ruini Hu, Xianlang Kong, Xiangying Jiangsu Automation Research Institute Lianyungang China

In the rapid development of industrial networks, in the face of complex and changeable network environments, the demand for reliable and efficient communication systems is becoming increasingly prominent. In this paper, a PRP protocol optimization method based on Q-learning under the Linux operating system is proposed. By introducing a Q-learning algorithm, the adaptive learning of a dynamic network environment is realized through Q-value update rules, to adjust PRP protocol parameters. the experimental results show that the optimization of PRP protocol based on Q-learning can improve the performance of PRP protocol under various network conditions and load changes, thus improving communication reliability. © Published under licence by IOP Publishing Ltd.

关键词： Linux

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Convolutional Neural Networks and Raman Spectroscopy for Semi-Supervised Dataset Construction and Transfer learning Applications in Real-Time Quantitative Detection 4

Convolutional Neural Networks and Raman Spectroscopy for Sem...

引用

4th International conference on Bioinformatics and intelligent Computing (BIC)

作者： Liu, Yuan Zhou, Xiaotian Zhang, Xiaopeng Wang, Jian Wen, Zhenguo Beijing Inst Petrochem Technol Beijing Key Lab Enze Biomass & Fine Chem Beijing Peoples R China Beijing Shengsheng Biomed Res Co Beijing Peoples R China Aoxing Hengxun Life Technol Shanghai Co Shanghai Peoples R China Beijing Inst Petrochem Technol Dept Pharmaceut Engn 19 Qingyuan North Rd Beijing Peoples R China

ISBN: (纸本)9798400716645

Online Raman spectroscopy technology uses frequency shift of scattered light to obtain real-time characteristic peak information of the test substance, combined with machine learning regression models for real-time concentration prediction. However, in the microbial cultivation process, due to the complex and variable composition of the cultivation system substances, this poses a great challenge to the performance of the model. In addition, model training requires a large amount of data, and the process of collecting a large number of samples for offline detection is very difficult. In this study, a convolutional neural network was introduced to replace the partial least squares model in chemometrics to improve the accuracy of model prediction, and a non-offline sampling labeling technique was proposed to obtain a semi-supervised training set with sufficient data. For multiple detection substances, each substance's feature peak position was optimized using a genetic algorithm, and transfer learning was implemented through fine-tuning model structure and hyperparameters to retrain difficult-to-sample substance correlation models. Introduction of transfer learning also adapts to errors in the production scaling-up process without the need for completely retraining the model. With its unique convolutional kernel structure, the convolutional neural network replaces the tedious sliding window filtering and calibration processes in spectral pre-processing, simplifying the spectral processing workflow. After multiple batches of data cross-validation training, the model exhibits excellent robustness and accuracy, and is suitable for predicting substances in microbial cultivation process.

关键词： Genetic algorithm Semi-supervised dataset Convolutional Neural Network Online Raman spectroscopy Transfer learning Microbial cultivation

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