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检索条件"任意字段=5th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2023"

共 150 条记录，以下是1-10 订阅

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2023 5th international conference on data-driven optimization of complex systems, docs 2023

2023 5th International Conference on Data-Driven Optimizatio...

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5th international conference on data-driven optimization of complex systems, docs 2023

ISBN: (纸本)9798350393521

the proceedings contain 65 papers. the topics discussed include: structural balance computation of signed hypergraphs via memetic algorithm;1DCNN-TRSNet: a hybrid end-to-end arrhythmia classification deep network based on transformer;data-driven regression modeling for measuring the influence of infodemic over social media;link constraint reliability learning for semi-supervised community detection;dynamic constrained multi-objective evolutionary optimization via adaptive two-stage archiving and autoencoder prediction;impact of spatial encoding methods on differential evolution: a case study on non-contact current measurement;multiobjective fuzzy competitive swarm optimization for high-dimensional feature selection;and Eghan: event graph enhanced hypergraph attention network for stock movement forecasting.

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Research on Target Detection optimization in Intelligent Solid Waste Classification Based on YOLOv8 Algorithm 24

Research on Target Detection Optimization in Intelligent Sol...

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5th international conference on Computer Science and Management Technology, ICCSMT 2024

作者： Wu, Zixuan Zhan, Xin Shen, Junwei Fu, Zuming Department of Mechanical and Electronic Engineering Zhuhai University of Science and Technology Guangdong Zhuhai China Department of Mechanical and Electronic Engineering Guangdong University of Technology Guangdong Zhuhai China

ISBN: (纸本)9798400709999

driven by globalization and urbanization, municipal solid waste has *** the front-end link of resource recovery and environmental protection, garbage classification is particularly urgent in order to improve its automation and intelligence *** on the YOLOv8 algorithm, this study proposes a series of optimization strategies such as using GhostBottleneck lightweight module, fusion improvement of CBAM attention mechanism, and using WIoU loss function for target detection tasks in intelligent garbage classification systems, aiming at improving detection accuracy and system *** study considered complex issues such as lighting conditions during garbage classification, the integrity of the garbage itself, the severity of the environment, and the difficulty of *** used multiple shared garbage image data sets on the network to test the garbage classification optimization *** average accuracy rate was significantly improved, reaching 96.12%. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Algorithm optimization

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A Comprehensive Survey on Enhancing Patient Care through Deep Learning and IoT-Enabled Healthcare Innovations 5th

A Comprehensive Survey on Enhancing Patient Care Through Dee...

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5th international conference on data Science, Machine Learning and Applications

作者： Valaboju, Sabitha Devi, T. Rupa Devi, D. Gayathri Sudheer, P. CVR Coll Engn Hyderabad Telangana India Keshav Mem Inst Technol Hyderabad Telangana India Vignan Bharathi Inst Technol Hyderabad Telangana India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

this study explores recent transformative advancements in deep learning-based healthcare technologies, revolutionizing patient care by enabling personalized precision medicine and extending access through IoT and cyberphysical systems. Diagnostic methodologies for critical conditions like cancer, diabetes, and heart failure are examined, utilizing computational intelligence to enhance patient identification and treatment. the focus lies on electronic health records (EHRs), investigating contemporary deep learning techniques for cancer prediction and framework-based mechanisms for healthcare optimization. Key algorithms like SVMs, Autoencoders, and CNNs are explored, showing applicability in clinical settings and genomic sequence-based diagnostics. Healthcare's unique processing techniques, spanning gene-based strategies, clinical tests, observation, and diagnostic models, are scrutinized for predictive and treatment potential. Integration of statistical and medical references is highlighted for efficient predictions alongside patient-specific data. the research advocates for AI-powered DSS integration, with CNNs as potent tools for customized medical interventions, marking a significant step towards elevated patient care and precision medicine realization.

关键词： Deep learning IoT-driven Healthcare Innovations Electronic Health Records (HER) Computational intelligence

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Enhancing QSAR Model Accuracy for Biodegradability Prediction Using Chaotic Adaptive Binary Manta Ray Foraging optimization

Enhancing QSAR Model Accuracy for Biodegradability Predictio...

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5th international conference on Emerging Electrical Energy, Electronics and Computing Technologies, ICE4CT 2024

作者： Aziz, Nor Fatin Nabila Yusof, Norfadzlia Mohd Kumar, Yogan Jaya Anuar, Siti Haryanti Hj Hairol Gharehchopogh, Farhad Soleimanian Fakulti Teknologi Maklumat dan Komunikasi Universiti Teknikal Malaysia Melaka Hang Tuah Jaya Durian Tunggal Melaka76100 Malaysia Fakulti Kecerdasan Buatan dan Keselamatan Siber Universiti Teknikal Malaysia Melaka Hang Tuah Jaya Durian Tunggal Melaka76100 Malaysia Fakulti Teknologi dan Kejuruteraan Elektronik dan Komputer Universiti Teknikal Malaysia Melaka Hang Tuah Jaya Durian Tunggal Melaka76100 Malaysia Department of Computer Engineering Urmia Branch Islamic Azad University Urmia Iran

Choosing descriptors is a crucial aspect of improving QSAR (Quantitative Structure-Activity Relationship) models, especially when it comes to precisely forecasting chemical biodegradability. As cheminformatics advances, the handling of large molecular datasets introduces challenges due to the high dimensionality created by numerous molecular descriptors. this study presents the Chaotic Adaptive Somersault Factor Binary Manta Ray Foraging optimization (CASF-BMRFO) algorithm, designed to optimize descriptor selection and boost QSAR model performance. By integrating innovative techniques such as the Piecewise map and a nonlinear time-varying Sigmoid transfer function, CASF-BMRFO achieves improved accuracy and efficiency, particularly for complex biodegradability datasets. the algorithms test on high-dimensional QSAR biodegradation data, the CASF-BMRFO6 variant showed substantial performance gains, achieving faster convergence while reducing selected descriptors by 75%. Additionally, it enhanced prediction accuracy by 11.59%, demonstrating its efficacy in selecting relevant descriptors and its potential for broader application in other classification tasks. these findings highlight CASF-BMRFO6’s effectiveness in feature selection and encourage further exploration of its adaptability across diverse data-driven domains. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Molecular graphics

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A data-driven Algorithm Based on Neural Network Prediction for Agent Trajectory Tracking 5

A Data-Driven Algorithm Based on Neural Network Prediction f...

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5th international conference on data-driven optimization of complex systems, docs 2023

作者： Gu, Junhua Liu, Wei Zheng, Xiaoyuan Liu, Hongpu School of Artificial Intelligence Hebei University of Technology Tianjin China

ISBN: (纸本)9798350393521

A distributed model-free adaptive control scheme for agent systems is presented in this paper, which solves the trajectory tracking problem in fixed communication topology. the unknown nonlinear function is used to model the dynamic equation of each agent. Furthermore, the desired trajectory required is acceptable to a portion of the following agent. the main feature of this method is that it can solve the trajectory tracking control problem only by relying on the I/O data of each agent. A neural net-work is used to estimate the unknown quantity in the adaptive control law. theoretical analysis shows that the tracking error is convergent and the output and control input sequences are bounded. Finally, a simulation is given to verify the effectiveness of the proposed design method. © 2023 IEEE.

关键词： Topology

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A New Expected Improvement Acquisition Function for Expensive Multi-Objective optimization 5

A New Expected Improvement Acquisition Function for Expensiv...

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5th international conference on data-driven optimization of complex systems, docs 2023

作者： Wang, Xilu Jin, Yaochu Bielefeld University Faculty of Technology Bielefeld Germany

ISBN: (纸本)9798350393521

While Bayesian optimization, a popular solution to black-box optimization problems, has been extended to handle expensive multi-objective optimization problems (MOPs), expected improvement (EI), a commonly used acquisition function, for MOPs is often either computationally expensive or inefficient to balance convergence and diversity. this paper proposes a new expected improvement acquisition function for MOPs, which can take advantage of both EI and multi-objective optimization. To extend the EI to MOPs, all Pareto optimal solutions obtained so far are used to calculate the EI values, resulting in an EI matrix. Instead of transferring the matrix into a scalar value, the proposed algorithm identifies the Pareto optimal solution with the minimum sum of EI values, and utilizes the corresponding EI vector from the matrix as the final value of the acquisition function. the efficiency of the proposed acquisition function is validated by comparing it with state-of-the-art multi-objective Bayesian approaches on two widely used test suites. © 2023 IEEE.

关键词： Multiobjective optimization

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Inventory Control with Lateral Transshipment Using Proximal Policy optimization 5

Inventory Control with Lateral Transshipment Using Proximal ...

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5th international conference on data-driven optimization of complex systems, docs 2023

作者： Liu, Ziang Nishi, Tatsushi Okayama University Faculty of Environmental Life Natural Science and Technology Okayama Japan

ISBN: (纸本)9798350393521

this paper presents the proximal policy optimization algorithm to find the optimal policy for the inventory control problem with lateral transshipment. the inventory control problem with lateral transshipment in a three-echelon supply chain is formulated as a Markov decision process. the supply chain consists of one supplier, two warehouses, and two retailers, where lateral transshipment is permitted between the warehouses. the demands at the retailers are independent and stochastic. Proximal policy optimization is used to find the optimal ordering policies and the optimal lateral transshipment policies for two warehouses to maximize the profit of the inventory system. To evaluate the performance of the proximal policy optimization algorithm, value iteration is also implemented to solve the inventory control problem. the simulation method is then used to evaluate the performance of both algorithms. the experimental results show that the policy obtained by the proximal policy optimization algorithm performs close to the optimal policy obtained by value iteration without knowing the transition probability of the system. © 2023 IEEE.

关键词： Supply chains

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Dual-State-driven Evolutionary optimization for Expensive optimization Problems with Continuous and Categorical Variables 5

Dual-State-Driven Evolutionary Optimization for Expensive Op...

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5th international conference on data-driven optimization of complex systems, docs 2023

作者： Xie, Lindong Li, Genghui Lin, Kangnian Wang, Zhenkun School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China School of System Design and Intelligent Manufacturing Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen China

ISBN: (纸本)9798350393521

the surrogate-assisted evolutionary algorithm (SAEA) is one of the most efficient approaches for addressing expensive continuous or combinatorial optimization problems. However, it encounters significant challenges in expensive mixed-variable optimization problems (EMVOPs). To overcome this limitation, a dual-state-driven evolutionary optimization (called DSDEO), integrating a surrogate-assisted mixed-variable evolutionary optimization stage (MVEOS) and a surrogate-assisted continuous-variable evolutionary optimization stage (CVEOS), is proposed in this paper. Specifically, MVEOS employs global and local search to enhance the exploration and exploitation of the mixed-variable space. Global and local searches are alternately executed if one search fails to yield a better solution. CVEOS utilizes a continuous-improvement strategy to refine the continuous variables of the best solution obtained so far. Experimental results demonstrate the advantages of DSDEO compared to some state-of-the-art SAEAs on many benchmark problems. © 2023 IEEE.

关键词： Evolutionary algorithms

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Impact of Spatial Encoding Methods on Differential Evolution: A Case Study on Non-Contact Current Measurement 5

Impact of Spatial Encoding Methods on Differential Evolution...

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5th international conference on data-driven optimization of complex systems, docs 2023

作者： He, Yonglin Wu, You He, Cheng Li, Hongbin Ma, Chaojun School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798350393521

Differential evolution (DE) algorithms have emerged as one of the most frequently used algorithms in engineering optimization problems. Considerable research has been made into effective offspring generation to improve their capability in global optimization. Nevertheless, offspring updating is also a crucial aspect of the DEs, and different encoding methods exhibit significant differences in offspring updating. this paper presents a case study on non-contact current measurement to quantify the influence of different spatial encoding methods on the performance of DE. the case study indicates that integrating spatial encoding methods allows for expanding the search space and enhancing the diversity of DE without compromising its superiority. Moreover, we aim to formulate versatile and effective optimization strategies based on spatial encoding methods for practical engineering problems through further experiments. © 2023 IEEE.

关键词： Global optimization

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Multiobjective Fuzzy Competitive Swarm optimization for High-Dimensional Feature Selection 5

Multiobjective Fuzzy Competitive Swarm Optimization for High...

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5th international conference on data-driven optimization of complex systems, docs 2023

作者： Li, Xiaomin Li, Bo Wang, Yunhe Hebei University of Technology School of Artificial Intelligence Tianjin China Tianjin Hezhong Technology Co. Ltd Tianjin China

ISBN: (纸本)9798350393521

Feature selection (FS) is a fundamental technique in machine learning and data mining that aims to choose the most relevant and important features from a high-dimensional feature space. this process can enhance the performance and generalization ability of the classification model. However, classifying high-dimensional datasets presents challenges including high computational cost and stagnation in local optima. Evolutionary algorithms (EAs) have been widely applied in FS to mitigate these issues given their global search capabilities. In this study, we propose a multiobjective fuzzy competitive swarm optimization (MOFCSO) algorithm for FS on high-dimensional data. First, a fuzzy logic-based approach is proposed to classify the competitive particles, enabling better exploration of the search space. then, a self-learning mechanism for failed particles is introduced to further enhance the global search. To demonstrate the proposed algorithm, we contrast it with multiple progressive algorithms. the experimental outcomes indicate the proposed algorithm is a valid method for choosing features in data with high dimensions. © 2023 IEEE.

关键词： Fuzzy logic

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