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

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A Comparative Study on Enhancing Prediction in Social Network Advertisement through Data Augmentation 4

A Comparative Study on Enhancing Prediction in Social Networ...

引用

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

作者： Yang, Qikai Li, Panfeng Xu, Xinhe Ding, Zhicheng Zhou, Wenjing Nian, Yi Univ Illinois Dept Comp Sci Champaign IL 61820 USA Univ Michigan Elect Engn & Comp Sci Ann Arbor MI 48109 USA Columbia Univ Fu Fdn Sch Engn & Appl Sci New York NY USA Univ Michigan Dept Stat Ann Arbor MI 48109 USA Univ Chicago Dept Comp Sci Chicago IL 60637 USA

ISBN: (纸本)9798350375084;9798350375077

In the ever-evolving landscape of social network advertising, the volume and accuracy of data play a critical role in the performance of predictive models. However, the development of robust predictive algorithms is often hampered by the limited size and potential bias present in real-world datasets. this study presents and explores a generative augmentation framework of social network advertising data. Our framework explores three generative models for data augmentation - Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Gaussian Mixture Models (GMMs) - to enrich data availability and diversity in the context of social network advertising analytics effectiveness. By performing synthetic extensions of the feature space, we find that through data augmentation, the performance of various classifiers has been quantitatively improved. Furthermore, we compare the relative performance gains brought by each data augmentation technique, providing insights for practitioners to select appropriate techniques to enhance model performance. this paper contributes to the literature by showing that synthetic data augmentation alleviates the limitations imposed by small or imbalanced datasets in the field of social network advertising. At the same time, this article also provides a comparative perspective on the practicality of different data augmentation methods, thereby guiding practitioners to choose appropriate techniques to enhance model performance.

关键词： Social Network Data Augmentation Deep learning VAE GAN Gaussian Mixture Model

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Integrating Big Data, ML, and DL for Predictive Analytics in Healthcare: A Comprehensive Analysis and Hybrid Framework Proposal 4

Integrating Big Data, ML, and DL for Predictive Analytics in...

引用

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

作者： Kargotra, Poonam Parray, Irfan Ramzan School of Computer Science and Engineering Lovely Professional University Phagwara India

ISBN: (纸本)9798331529635

the paper presents a comprehensive in-depth analysis of big data, machine learning (ML), and deep learning (DL) methodologies in predictive healthcare analytics, with a focus on their comparative strengths, research gaps, and applications in real world. the study synthesizes findings from recent research papers related to same objective, highlighting the evolution of data-driven technologies in healthcare sector and the challenges which are associated with data integration, privacy, and real-time processing. the big data analytics provides foundational insights from vast healthcare datasets, while ML algorithms enhance predictive accuracy on structured data, while DL models achieve high precision even in complex tasks, such as medical imaging and real-time IoT embedded systems monitoring. this article introduces a novel hybrid approach that combines the strength of all these approaches, employing common data for heterogenous data integration, federated learning for privacy, and a feedback-driven adaptive system for continuous improvement. Analyzing quantitative comparisons, the effectiveness of each approach is illustrated: DL demonstrate high acuracy, and ML, in turn, flexibility with a moderate rate. Discussing the research gaps of current methodologies, the proposed framework discus the scalability, adaptability, and privacy enhancements of this new conceptual model to create more precise and proactive healthcare analytics. Possible future investigations indicate that it is necessary to enhance these adaptive learning solutions and to expand the arbitrary federative learning approaches to treat different interdisciplinary domains. © 2024 IEEE.

关键词： Federated learning

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Artificial Intelligence Analysis for PMSM Servo System Based on Dynamic Parameters 4

Artificial Intelligence Analysis for PMSM Servo System Based...

引用

4th International conference on Computer Science, Electronic Information Engineering and intelligent Control Technology, CEI 2024

作者： Wang, Shixing Tan, Kangbo Xu, Zhijiang Lu, Yingmei Chen, Long Zhao, Ziwen Huang, Xing Wang, Hao XiDian University School of Electric Engineering Xi'an China Co. Ltd Ningbo China China Electronics Technology Group Corporation 10th Research Institute Chengdu China

ISBN: (纸本)9798350378542

To improve the analyzing efficiency for PMSM servo system considering the dynamic parameters of the insulated-gate bipolar transistor, in this work, a novel approach of an artificial intelligence computational framework for system-level simulation project is proposed. An instance is investigated subsequently, meanwhile, the accuracy of the approach with five machine learning methods is compared. the time consumption and error in test sets prove the high efficiency of the approach to analyze the speed of permanent magnet synchronous motor. this work is helpful for the massive numerical analysis in the design process of electric vehicles. © 2024 IEEE.

关键词： Insulated gate bipolar transistors (IGBT)

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Research on Multi-modal Data Management System for intelligent Connected Vehicle 4

Research on Multi-modal Data Management System for Intellige...

引用

4th IEEE International conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2024

作者： Zhang, Lu Guangxi Vocational Normal University School of Computer and Information Engineering Guangxi China

ISBN: (纸本)9798350363586

the development of intelligent networked vehicle technology and the testing of related algorithms require a large number of datasets as the foundation. the existing datasets are mainly collected from foreign traffic scenarios, which are significantly different from the actual road traffic environment of China. therefore, it is particularly important to construct local datasets and data management systems. this paper describes in detail the design of multi-modal data management system for intelligent Connected Vehicle, covering the overall architecture of systems, hardware composition, the design of software function modules, database design, and key steps for data processing. Experiments and tests verified the reliability and effectiveness of the data management system. © 2024 IEEE.

关键词： Data assimilation

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A Hybrid Meta-heuristic optimization Algorithm for Solving the Multi-objective Crew Allocation Problem 4

A Hybrid Meta-heuristic Optimization Algorithm for Solving t...

引用

4th IEEE International conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2024

作者： Tang, Lin Li, Xun Zhou, Wanning Su, Si Unit 91977 of the Pla Beijing China

ISBN: (纸本)9798350363586

the ship crew scheduling problem is a critical challenge to improve the organizational efficiency and safety. this paper conducts a systematic study on this problem and achieves innovative results in both theoretical modeling and algorithm. Firstly, a novel multi-objective optimization model for crew scheduling is proposed, comprehensively considering multiple optimization objectives. Secondly, an efficient hybrid meta-heuristic optimization algorithm is designed for solving the model. the algorithm integrates intelligent optimization strategies and makes targeted improvements in the algorithm framework, initial solution generation, neighborhood structure design, etc., improving the solving performance. Extensive simulation experiments demonstrate the model's applicability and the algorithm's superiority in solution quality and computational efficiency. the findings provide insights for improving human resource allocation in ship crew scheduling and human resource allocation. Extensive simulation experiments demonstrate the model's applicability superiority over classic methods in solution quality and computational efficiency. © 2024 IEEE.

关键词： Human resource management

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Adaptive Anomaly Detection Using Deep Bi-LSTM and Self-Adaptive Pufferfish optimization 4

Adaptive Anomaly Detection Using Deep Bi-LSTM and Self-Adapt...

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

作者： Kartheesan, L. Sam Kumar, G.V. Krishnan, Sundara Rajulu Navaneetha Tamilvizhi, T. Surendran, R. Vel Tech Rangarajan Dr.Sangunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram India Dhanalakshmi Srinivasan University Department of Computer Science and Engineering Trichy Samayapuram Campus Tiruchirappalli India Panimalar Engineering College Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331529635

the task of anomaly detection has become critical in many applications, including network security, fraud detection, and fault diagnosis. Traditionally, most of the existing methods fail with high-dimensional data, making it difficult and challenging to get accurate anomaly detection with low false positives. the main techniques proposed so far may also face problems in being computationally costly and not adaptive to the variability of the data characteristics. therefore, there is an urgent need to design a well-built, efficient, and adaptive framework that properly identifies anomalies within large volumes of complex datasets while optimizing the selection of features. To solve these issues, this paper proposes a novel advanced preprocessing, feature extraction, feature selection, and classification methods integrated into the anomaly detection framework. Initially, the dataset undergoes data cleaning, normalization, and standardization to ensure uniformity in scale and distribution. then it will extract statistical features that encapsulate critical insights from the raw data. For the detection stage, we propose Deep BiLstm, a hybrid deep learning architecture that leverages the strengths of Bi-Lstm by using lightweight densely connected layers for efficient anomaly classification. the hyperparameter of the Deep Bi-Lstm model is fine-tuned using the Self-Adaptive Pufferfish optimization Algorithm (SAPFOA). the entire framework is implemented using the Python tool. Evaluate the effectiveness of our approach over the Ton-IoT dataset, which is the most frequently used benchmark of network intrusion and anomaly detection. © 2024 IEEE.

关键词： Network security

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Path Planning of intelligent Radar Anti-jamming Matrix based on Q-learning Algorithm 4

Path Planning of Intelligent Radar Anti-jamming Matrix based...

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4th International conference on Sensors and Information Technology, ICSI 2024

作者： Shi, Shasha Zhou, Qingsong Qian, Jialong Shi, Shujie College of Electronic Engineering National University of Defense Technology Hefei230037 China

ISBN: (数字)9781510678699

ISBN: (纸本)9781510678682

intelligent radar is an important topic in the field of cognitive electronic warfare, and the application of reinforcement learning in intelligent radar anti-jamming decision-making has been a recent research focus. this paper proposes an anti-jamming matrix model composed of multiple radar configurations and anti-jamming patterns, and analyzes the path planning problem in radar anti-jamming decision-making using the Q-learning algorithm. Simulation experiments demonstrate that using reinforcement learning allows the model to plan paths based on the environment autonomously and select the optimal path to achieve the best anti-jamming state ultimately. © 2024 SPIE.

关键词： Decision making

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Research of online learning status monitoring based on deep learning 4

Research of online learning status monitoring based on deep ...

引用

2023 4th International conference on Computer Engineering and intelligent Control, ICCEIC 2023

作者： Li, Zhi Wang, Jiaxin Wei, Jiahao Wang, Houbin South China Normal University Business School Guangdong China Guangdong University of Finance & Economics School of Economics Guangdong China Minzu University of China School of Economics Beijing China Ludong University School of Resources and Environmental Engineering Shandong China

ISBN: (纸本)9798350308877

In contrast to traditional classroom settings, online teaching leverages network technology to facilitate instruction. A pressing challenge in this domain is the effective monitoring of students' engagement levels to optimize learning outcomes. Addressing this, the present study introduces a deep learning-based online classroom system equipped with a learning status monitoring feature. this system employs image processing techniques and software development to capture and analyze students' postures during online sessions, thereby providing educators and guardians with comprehensive insights into students' engagement levels. © 2023 IEEE.

关键词： Object detection

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Research on mechanical structure optimization based on topology theory 4

Research on mechanical structure optimization based on topol...

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4th International conference on Mechanical Engineering, intelligent Manufacturing, and Automation Technology, MEMAT 2023

作者： Geng, Qingling GuangDong Mechanical & Electronic Polytechnic Guangzhou China

ISBN: (纸本)9781510674820

At present, structural topology optimization in mechanical equipment with excellent performance by optimizing the arrangement and distribution of materials in the design domain, which has important significance in the initial stage of mechanical material design. However, most of existing research on mechanical structure optimization concentrates on planar or simple three-dimensional structures, and can only solve some topology optimization problems with simple initial structure, optimized design domain rules, and single optimization constraints. In this paper, we extract a real mechanical bracket topology information and optimize the topology structure by utilizing the variable density method. After optimizing the topology, we compare our results with existing optimization methods and estimate the performance of different materials. From our extensive experiments, we can conclude that our proposed method can achieve the optimization process for mechanical components and outperform existing methods with acceptable stress and reasonable weights. © 2024 SPIE.

关键词： Shape optimization

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An Empirical Study on the Digital Twin System of intelligent Production Line 4

An Empirical Study on the Digital Twin System of Intelligent...

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

作者： Zhu, Xiuli Li, Chengwei Mai, Junjie Yang, Junsong Kuang, Ziyang School of Robotics Guangdong Polytechnic of Science and Technology Zhuhai China

ISBN: (纸本)9798350356502

this paper focuses on the exploration of the digital twin system for intelligent production lines to address the various challenges encountered during the operation of such lines. the digital twin technology connects the physical and digital worlds by constructing virtual models, optimizing manufacturing processes such as simulating production processes, monitoring equipment operation, and rationally allocating resources. the system's architecture includes physical entity layers, data transmission layers, data layers, tools and visualization layers, and application layers. Its implementation involves modeling, data processing and analysis, real-time monitoring and prediction, optimization and simulation, and system integration and testing. the system offers diverse robot control modes, and experimental verification shows that it has excellent real-time performance, meeting the requirements for synchronized tasks in virtual and physical environments. the system integrates various functions, which help improve production efficiency, enhance safety, and optimize management processes, making significant contributions to the virtual-real joint tuning of intelligent production lines and the intelligent development of the manufacturing industry. In the future, digital twin technology will deeply integrate with other technologies and be widely applied in more fields. © 2024 IEEE.

关键词： Smart manufacturing

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