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

共 11519 条记录，以下是31-40 订阅

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optimization Design of Power Grid intelligent Monitoring System Based on Deep learning 4

Optimization Design of Power Grid Intelligent Monitoring Sys...

引用

4th International conference on Electronic Information Engineering and Computer Communication, EIECC 2024

作者： Hao, Zhixu Shenyang Urban Construction University Liaoning Shenyang110167 China

ISBN: (纸本)9798331534622

the intelligent monitoring system of the power grid is a critical support for ensuring the safe and stable operation of the grid, and enhancing its intelligence level is of great significance. Addressing issues such as low fault recognition accuracy and slow response speed in traditional monitoring systems, this paper designs an intelligent monitoring system for power grids based on deep learning. A layered architecture is adopted to design the system framework, and a CNN-LSTM hybrid network is constructed for fault diagnosis. Additionally, a distributed data processing platform is built on the Spark framework. Experimental results demonstrate that the proposed system achieves high fault recognition accuracy and data processing capabilities, significantly shortens warning times, reduces deployment and maintenance costs, and provides a feasible technical solution for smart grid development. © 2024 IEEE.

关键词： Smart power grids

来源：评论

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Design and optimization of Central Air Conditioning Systems Based on Digital Twin Technology 24

Design and Optimization of Central Air Conditioning Systems ...

引用

4th International conference on Internet of things and Machine learning

作者： Peng, Fan Hebei Univ Technol Internet Things Engn Tianjin Peoples R China

ISBN: (纸本)9798400710353

With the rapid development of Internet technologies, the Internet of things (IoT) has become a crucial bridge connecting the physical and digital worlds. As a significant branch of IoT applications, smart homes focus on automating and intelligently controlling the home environment through the interconnection and data exchange of smart devices. However, as the complexity of smart home systems increases, ensuring their efficient operation and meeting users' personalized needs has become a focal point of research. Digital twin technology, as an innovative solution, involves creating virtual replicas of physical entities to simulate, analyze, and optimize systems without disrupting their operations. this paper proposes a smart home control system design based on digital twins, aiming to achieve precise monitoring and intelligent control of the smart home environment through a highly integrated system architecture and modular design. this system not only improves energy efficiency but also autonomously learns from user behavior and preferences, thus offering more personalized services.

关键词： Digital twin smart home Central Air Conditioning System Design Energy Efficiency optimization User Behavior Analysis

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Research on intelligent monitoring and optimization control system model of greenhouse environment based on deep learning 4

Research on intelligent monitoring and optimization control ...

引用

4th International conference on Electronic Information Engineering and Computer Communication, EIECC 2024

作者： Shao, Xue Chen, Yangfeiyi Chen, Xiwei Zhu, Fengzhe JiLin Agricultural Science and Technology University Electrical and Information Engineering College Jilin Jilin China

ISBN: (纸本)9798331534622

this paper presents an improved intelligent control model for greenhouse environments, integrating environmental monitoring data with a deep learning approach combining multilayer CNN and LSTM networks with model predictive control (MPC). By optimizing model parameters and utilizing data fusion techniques, the system's prediction accuracy and response speed are enhanced. Experimental results from a real greenhouse monitoring system show that the improved model significantly outperforms traditional models and other machine learning algorithms, with a mean absolute error (MAE) of 2.342, mean square error (MSE) of 6.749, and a coefficient of determination (R2) of 0.931, demonstrating its efficiency and reliability in controlling key environmental indicators like temperature, humidity, and CO2 concentration. © 2024 IEEE.

关键词： Environmental monitoring

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Multi-Behavioral Recommender System Based on Multi-View Contrastive learning 4

Multi-Behavioral Recommender System Based on Multi-View Cont...

引用

4th International conference on Machine learning and intelligent Systems Engineering (MLISE)

作者： Zhang, Haiyang Gao, Rong Liu, Donghua Wan, Xiang Hubei Univ Technol Sch Comp Sci Wuhan Peoples R China China Waterborne Transport Res Inst Beijing Peoples R China Wuhan Second Ship Design & Res Inst Wuhan Peoples R China

ISBN: (纸本)9798350375084;9798350375077

In modern recommendation systems, diverse user behavior data such as browsing, clicking, and purchasing provide rich information for personalized recommendations. However, effectively integrating and utilizing these varied behavioral data remains a challenge. this paper proposes a multi-behavior recommendation approach based on multi-behavior and contrastive learning. Firstly, multiple user and item views are generated through different masking mechanisms to capture diverse user behavioral characteristics. Subsequently, the LightGCN model is employed to generate embedding representations for users and items, effectively learning the interaction information between them. Next, leveraging contrastive learning methods for the same behaviors across different views involves pulling embedding vectors of similar behaviors closer while pushing those of dissimilar behaviors farther apart, thereby enhancing the model's discriminative power. Finally, aggregation of multiple behavior views and optimization using the Bayesian Personalized Ranking (BPR) loss function aim to maximize ranking differences, further improving recommendation accuracy. Experimental results demonstrate that the proposed approach effectively leverages diverse user behavior data, significantly outperforming traditional single-view and non-contrastive learning-based recommendation methods in terms of recommendation precision and user satisfaction.

关键词： multi-behavior view generation contrastive learning recommendation systems

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optimization of the YOLOv5 deep learning model for peripheral blood cell detection and recognition 4

Optimization of the YOLOv5 deep learning model for periphera...

引用

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

作者： Gao, Yunfei Liu, Yanhong Zhao, Shilei Ma, Xingxing Zhou, Yun Inner Mongolia Univ Sci & Technol Baotou Med Coll Baotou City Inner Mongolia Peoples R China

ISBN: (纸本)9798400716645

In recent years, as neural networks continue to evolve, the use of YOLO deep learning algorithms in medical imaging and diagnosis has become increasingly prevalent. the detection and recognition of blood cells are crucial aspects of medical diagnosis. While deep learning-based object detection and recognition are garnering increasing interest, detecting and counting blood cells in medical imaging remains an essential and challenging task. In this paper, we optimize and improve the YOLOv5 algorithm to achieve efficient detection and recognition of peripheral blood cells. We begin by outlining the architecture and the training procedures of YOLOv5 and discuss its potential applications in medical imaging. Building on the characteristics of blood cell images, we introduce an optimization method using DenseNet based on YOLOv5. Enhancements in data preprocessing, network structuring, and training parameters have improved the accuracy and efficiency of blood cell recognition and counting. Experiments conducted on our proprietary dataset and subsequent comparisons with the base algorithm affirm our claims. Results suggest that our proposed method demonstrates strong performance in detecting and identifying blood cells, offering high practicality for applications.

关键词： peripheral blood cells detection and recognition YOLOv5 deep learning DenseNet

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Diversified Ensemble learning: Integrating Bagging and Teaching-learning-Based optimization with a Pairwise Dissimilarity Measure 1

引用

4th International conference on Advanced Engineering optimization through intelligent Techniques

作者： Arukonda, Srinivas Cheruku, Ramalingaswamy VIT AP Univ Amaravathi Andhra Pradesh India Natl Inst Technol Warangal Telangana India

ISBN: (数字)9789819746545

ISBN: (纸本)9789819796618;9789819746545;9789819746538

Ensemble learning has emerged as a powerful technique for improving classification accuracy by combining multiple base models. this study presents an innovative approach to enhance ensemble learning through diversification. the proposed method integrates bagging, a resampling technique, with teaching-learning-based optimization (TLBO), and incorporates a pairwise dissimilarity measure to promote diversity within the ensemble. the TLBO algorithm optimizes the composition of the ensemble by iteratively selecting optimal bags of instances from the training data. the diversity measure quantifies the dissimilarity between bags, ensuring that the ensemble consists of diverse and complementary models. Our proposed model experimented on four benchmarked disease datasets and experimental results demonstrate that the proposed approach achieves superior performance compared to traditional ensemble methods. the ensemble models generated through this approach exhibit improved performance. the proposed model is statistically evaluated using the statistically paired T-test, and the results show our proposed model differs from base models.

关键词： Ensemble learning Bagging Teaching-learning-based optimization (TLBO) Diversity measure Classification Base models Instance selection optimization Pairwise dissimilarity Performance improvement

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A new attacks intrusion detection model based on deep learning in Software-Defined Networking Environments 4

A new attacks intrusion detection model based on deep learni...

引用

4th International conference on Machine learning and intelligent Systems Engineering (MLISE)

作者： Yang, Jikun Univ Melbourne Sch Comp & Informat Syst Melbourne Vic Australia

ISBN: (纸本)9798350375084;9798350375077

Software-defined networking (SDN) has transformed the landscape of network communication. SDN separates the control plane from the data plane, offering a centralized management system and dynamic resource allocation. Nevertheless, SDN is susceptible to security risks, necessitating the deployment of sophisticated Intrusion Detection Systems (IDS). Several researchers have recently employed machine learning and other cutting-edge technologies to analyze and identify rapidly growing attacks and anomalies. However, the majority of these techniques exhibit low accuracy and poor scalability. In response to this challenge, this paper proposes an Intrusion Detection System (IDS) framework based on the Convolutional Neural Network-Gated Recurrent Unit (CNN- GRU) network. this framework leverages Convolutional Neural Networks (CNNs) and Gated Recurrent Units (GRUs) to identify real-time network intrusions. the framework was trained and evaluated on the UNSW-NB15 and InSDN datasets using Bayesian optimization (BO), achieving exceptional accuracy and F1 scores exceeding 99.93% on the UNSW-NB15 dataset. Similarly, on the InSDN dataset, the framework achieved an accuracy of 99.93%, with precision, recall, and F1 score values of 99.89%, 99.97%, and 99.93%, respectively. these demonstrate the framework's effectiveness in discerning between normal and malicious network behavior.

关键词： SDN IDS CNN-GRU

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Foreign Exchange Market Analysis based on Novel Grey Wolf based Extreme learning Machine Predictor 4

Foreign Exchange Market Analysis based on Novel Grey Wolf ba...

引用

4th International conference on Emerging Systems and intelligent Computing, ESIC 2024

作者： Dash, Swaty Sahu, Pradip Kumar Mishra, Debahuti Veer Surendra Sai University of Technology Burla Dept. of Information Technology Odisha Sambalpur India University Dept. of Computer Science & Engineering Odisha Bhubaneswar India

ISBN: (纸本)9798350349856

Foreign exchange trading basically bridges a gap between buyer and seller to transact at a set of prices of the currencies to make profit out of it by the traders and investors. In this paper, foreign exchange predictive models are explored based on basic Extreme learning Machine (ELM) along with optimized version of ELM. Initially, this work tried to obtain the best optimized ELM network based upon the four nature-inspired optimization methodologies such as;particle swarm, ant colony, moth flame and grey wolf optimization (GWO). From this phase, it has been observed that the ELM network optimized with GWO outperforms rest of other experimented optimized ELM networks with respect to error graphs and performance metrics. In order to evaluate the performance, the ELM-GWO currency exchange predictor, the recognition performance metrics such as;EVS, MAE, MSE and R2 Score are computed, the error graphs are obtained and the average computational time taken are compared. © 2024 IEEE.

关键词： Ant colony optimization

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Research on Energy Efficiency optimization of IRS-NOMA Wireless Communication System Based on Deep Reinforcement learning 4

Research on Energy Efficiency Optimization of IRS-NOMA Wirel...

引用

4th IEEE International conference on Electronic Technology, Communication and Information, ICETCI 2024

作者： Liu, Jinyu Shen, Yangguang Wu, Tao Li, Liwei Shandong University of Science and Technology Department of Electrical Information Jinan China Jiangsu University School of Electrical and Information Engineering Zhenjiang China Xi'an University of Posts and Telecommunications School of Electronic Information Engineering Xi'an China University College London London United Kingdom

ISBN: (纸本)9798350361643

In this study, the design of an energy-efficient IRS-NOMA wireless communication system based on Non-Orthogonal Multiple Access (NOMA) and Simultaneous Transmitting and Reflecting Reconfigurable intelligent Surface (STAR-RIS) technologies is investigated. the study uses the Deep Deterministic Policy Gradient (DDPG) algorithm, a deep reinforcement learning approach, to jointly optimize the transmit beamforming vectors at the base station and the coefficient matrix at the STAR-RIS for maximizing Energy Efficiency (EE). this approach addresses the optimization challenges inherent in traditional methods. © 2024 IEEE.

关键词： Reinforcement learning

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Role of linear algebra and algorithms in machine learning 4th

Role of linear algebra and algorithms in machine learning

引用

4th International conference on Computational Methods in Science and Technology, ICCMST 2024

作者： Rohit Jhamb, Shelja Parmar, Mamta Chandigarh Engineering College-CGC Landran Punjab Mohali India

ISBN: (纸本)9781032911571

All machine learning procedures consume a mathematical foundation. the aforementioned is applicable to Deep learning, optimization, and any additional Statistics Science processes since Deep Knowledge is a subset of Machine learning. We are abetted by these algorithms in extracting information from the data. When we inscribe these procedures down in a software design language, the processor system that runs them on the dataset seems brainy, which is how artificial intelligence gets its name. A classical that dismiss learn since statistics and generate approximations expending an algorithm is produced via machine learning. It’s employed to determine the workings of a mechanism and the reasons for model superiority. A mathematical requirement is included into machine learning. In order to create more intelligent software, this discipline mixes computer science, probability, statistics, linear algebra, and algorithms. these programmers are capable of extracting insightful and relevant material since statistics to attain at business visions. © 2025 the Author(s).

关键词： Adversarial machine learning

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