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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11-20 订阅

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Research on Digital Economy Data Processing Systems Based on Artificial Intelligence algorithms 24

Research on Digital Economy Data Processing Systems Based on...

引用

4th international conference on Internet of things and Machine learning

作者： Mao, Ganfeng EAC Emilio Aguinaldo Coll Manila Philippines

ISBN: (纸本)9798400710353

In the context of the rapid development of the digital economy, data processing technology has become a crucial means for enterprises and organizations to gain competitive advantages. this paper aims to study a digital economy data processing system based on artificial intelligence algorithms, exploring how advanced machine learning and deep learning technologies can efficiently process and analyze large volumes of heterogeneous data. By analyzing existing data processing challenges and designing and implementing system architecture and core algorithms, this paper proposes an innovative system solution. Experimental results show that the system excels in data preprocessing, analysis, and prediction, significantly improving the efficiency and accuracy of data processing. this research not only provides new insights into data processing in the context of the digital economy but also offers valuable references for academic research and practical applications in related fields.

关键词： Digital Economy Artificial Intelligence algorithms Data Processing Machine learning

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A Comprehensive Survey on Meta-heuristic algorithms for Feature Selection in High-Dimensional Data: Challenges, applications, and Future Directions 4

A Comprehensive Survey on Meta-heuristic Algorithms for Feat...

引用

4th international Mobile, Intelligent, and Ubiquitous Computing conference

作者： Kamal, Reham Amin, Eman AbdElminaam, Diaa Salama Ismail, Rasha Ain Shams Univ Fac Comp & Informat Sci Cairo Egypt Misr Int Univ Fac Comp Sci Cairo Egypt Benha Univ Fac Comp Sci & Artificial Intelligence Banha Egypt

ISBN: (纸本)9798350367782;9798350367775

Feature selection is crucial for improving machine learning models by reducing dimensionality and lowering computational costs. this survey paper provides an in-depth review on recent advancements in feature selection methods, with a focus on meta-heuristic algorithms. Known for their robustness and effectiveness, these algorithms have become key in tackling the combinatorial challenges in feature selection. the paper categorizes and evaluates various meta-heuristic approaches, including evolutionary algorithms, swarm intelligence, and hybrid techniques, highlighting their strengths, limitations, and applications across different fields. It also examines the integration of meta-heuristics with other optimization methods and machine learning frameworks, identifying current trends and challenges. the paper concludes by discussing future research directions, emphasizing the potential of meta-heuristic-based feature selection in handling high-dimensional data and complex real-world problems.

关键词： feature selection machine learning meta-heuristics

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XGBSO: An Employability Assessment Model 4

XGBSO: An Employability Assessment Model

引用

4th IEEE international conference on Power, Electronics and Computer applications, ICPECA 2024

作者： Tu, Xue Business School Hohai University Nanjing China

ISBN: (纸本)9798350359589

To meet the increasing competition in the marketplace during the post epidemic era, it is crucial for the IT industry to swiftly assess and select job seekers while identifying the enhancement skills required for individual job seekers. Current research lacks model optimization and predominantly uses a combination of machine learning methods. However, this study addresses this gap by employing multiple machine learning algorithms based on annual developer survey data from Stack Overflow to predict job seeker skill judgments and quantitative feature scores. the optimized XGBoost model based on the snake optimization algorithm (XGBSO) shows superior performance in accuracy and F1 score compared to other models, accurately predicting the job search ability of programmers. Feature weighting analysis reveals that computer skill is the most crucial feature. the XGBSO model is validated through ROC curves and AUC-ROC values, displaying an accuracy of 0.838 and an F1 score of 0.855, thus indicating excellent performance. the proposed XGBSO model serves as an effective tool for assessing programmers' job search ability and yields significant results. © 2024 IEEE.

关键词： Feature selection Machine learning Parameter optimization ROC curve Snake optimization

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Advanced applications of Machine learning Techniques in FOITS 4

Advanced Applications of Machine Learning Techniques in FOIT...

引用

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

作者： Davronbekov, Dilmurod Juraeva, Nafisa Boboev, Asilbek Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Department of Mobile Communication Technologies Tashkent Uzbekistan New Uzbekistan University Department of Computer Science Tashkent Uzbekistan

ISBN: (纸本)9798350387490

the use of machine learning methods in fiberoptic information transmission systems (FOITS) is considered. the article discusses the basic operating principles of fiber optic systems and the problems they face, such as noise, nonlinear effects, and degradation of transmitted information. Describes various machine learning techniques used in fiber optic communication systems to control and monitor performance, prevent intelligent decisions, and suppress nonlinear fiber optic noise. Approaches used in machine learning are presented, such as neural networks, classification and regression algorithms, their application in the analysis and optimization of fiber optic systems, such as neural networks, support vector machines, classification and regression algorithms, their application in the analysis and optimization of fiber optic systems. this paper proposes a method for monitoring performance and predicting failures in optical networks based on machine learning. © 2024 IEEE.

关键词： Adversarial machine learning

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A Review of Machine learning Methods to Solve Flight Scheduling Research 4

A Review of Machine Learning Methods to Solve Flight Schedul...

引用

4th international conference on Big Data Engineering and Education, BDEE 2024

作者： Pan, Yanbin Xu, Haiwen Qian, Jide Chu, Mengtao Yan, Hao Civil Aviation Flight University of China Faculty of Science Guanghan China Civil Aviation Flight University of China Key Laboratory of Civil Aviation Flight Technology and Flight Safety Guanghan China

ISBN: (纸本)9798350388268

the crew scheduling problem is a crucial component of airline operations planning. Using traditional operations research optimization methods to optimize the crew scheduling process can enhance the scientific and accurate nature of the plan, significantly impacting the airline's operational efficiency, cost savings, and ensuring the safety of flight operations. However, due to the increasing number and complexity of modern flight schedules, traditional combinatorial optimization algorithms face enormous computational pressure and cost. Additionally, the need for timely recovery of problem flights imposes limitations on traditional methods. therefore, by reviewing recent algorithms that use machine learning to address combinatorial optimization problems, this research explores and examines the integration methods, algorithmic logic, and areas for improvement. It also investigates the potential of these methods for solving flight scheduling issues, providing insights and references for future applications of machine learning in addressing flight scheduling problems. © 2024 IEEE.

关键词： Neural networks

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Multi-condition Multi-objective Airfoil Shape Optimisation Using Deep Reinforcement learning Compared to Genetic algorithms 1

引用

4th international conference on optimization learning algorithms and applications

作者： Balasooriya, Dasun Shalila Blair, Alan Wheeler, Craig Chalup, Stephan Univ Newcastle Callaghan NSW 2308 Australia Univ New South Wales Sydney NSW 2052 Australia

ISBN: (数字)9783031774324

ISBN: (纸本)9783031774317;9783031774324

this study investigates the potential of Deep Reinforcement learning (DRL) in the context of Multi-Condition Multi-Objective airfoil shape optimisation by benchmarking a customised DRL algorithm, namely Single-Step Proximal Policy Optimisation, against NSGA-II, a conventional genetic algorithm. We illustrate the capability of the DRL algorithm to effectively optimise across a continuous multi-condition plane, eliminating the need to discretise it into discrete points, a practice commonly employed in conventional Genetic algorithms. We further demonstrate that the DRL algorithm achieves hypervolume averages and convergence rates that are competitive when compared to NSGA-II. Analysis of Deep Neural Networks extracted from the training phase of the DRL algorithm indicates that almost complete knowledge of the Pareto front is retained by the network, which can be utilised to accelerate the discovery of the Pareto front in similar optimisation tasks via transfer learning.

关键词： Multi-Objective Optimisation Multi-Condition Multi-Objective Optimisation Deep Reinforcement learning Genetic algorithms Airfoil

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Harnessing Uncertainty: Integrating Fuzzy Logic into Machine learning algorithms 4

Harnessing Uncertainty: Integrating Fuzzy Logic into Machine...

引用

4th international conference on Pervasive Computing and Social Networking (ICPCSN)

作者： Imamguluyev, Rahib Hashim, Shaban Beshirov Hajiyev, Ilham Baku Business Univ IT Dept Baku Azerbaijan Western Caspian Univ Mech & Math Dept Baku Azerbaijan Aydinoglu Acad IT Dept Baku Azerbaijan

ISBN: (纸本)9798350386356;9798350386349

As machine learning algorithms continue to advance, the incorporation of uncertainty modeling becomes pivotal for robust and adaptable systems. this article explores the fusion of fuzzy logic with machine learning methodologies, present-ing a comprehensive approach to harnessing uncertainty in data-driven decision-making processes. the integration of fuzzy logic provides a nuanced framework that accommodates imprecise and ambiguous information, enhancing the algorithms' capacity to handle real-world complexities. through a detailed examination of the synergies between fuzzy logic and machine learning, this study contributes to the development of more resilient and versatile systems, demonstrating the efficacy of uncertainty-aware models in various applications. the findings underscore the potential for improved accuracy and interpretability in machine learning outcomes by embracing the inherent uncertainty in data.

关键词： Fuzzy Logic Machine learning algorithms Uncertainty Modeling Decision-Making Ambiguous Information Resilient Systems

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Intelligent Decision optimization Method for Water Information System Based on Artificial Intelligence 24

Intelligent Decision Optimization Method for Water Informati...

引用

4th international conference on Artificial Intelligence, Automation and algorithms

作者： Xian, Feng Yin, Yisu Shanghai Chengtou Water Grp Co Ltd Shanghai Peoples R China

ISBN: (纸本)9798400717840

the traditional water management method struggles with delayed information acquisition and low decision-making efficiency, making it difficult to meet complex water resource management needs. To address this, the paper proposes an AI-based optimization method for water information systems. It uses sensor networks and IoT to collect real-time data, including water quality, quantity, and pipe pressure. Machine learning models historical data to identify key factors and risks. An optimization algorithm simulates and selects the best scheduling schemes. A visualization platform presents analysis results, aiding managers in decision-making. Users rated the platform features between 7 and 9 in satisfaction. this method is general, scalable, and applicable to various water systems, offering a useful reference for other fields.

关键词： Artificial Intelligence Water Information System Intelligent Decision Making Scheduling Scheme

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Maximizing Wind Energy Efficiency through ACO and ANN-Based MPPT algorithms 4th

Maximizing Wind Energy Efficiency Through ACO and ANN-Based ...

引用

4th international conference on Digital Technologies and applications (ICDTA)

作者： Mardoude, Yahya Rahali, Abderrafii Hilali, Abdelilah Moulay Ismail Univ Fac Sci Meknes Morocco

ISBN: (纸本)9783031686528;9783031686535

thewind energy sector is experiencing continuous improvement aimed at increasing the exploitation of this type of energy by optimizing its energy efficiency as much as possible. the energy efficiency of wind power systems is study by evaluating two MPPT algorithms is presented. the objective focuses on two approaches: the use of artificial neural networks (ANN) and the ant colony optimization (ACO) algorithm. the adaptability and learning capabilities inherent in these methods and their characteristics is explained. this study aims to provide a performance analysis of these various MPPT algorithms and evaluate their accuracy in tracking the maximum power point, their effectiveness in reducing energy losses and their stability as well as improving energy efficiency.

关键词： Artificial neural network Wind turbine MPPT ACO optimization

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Artificial Bee Colony optimization Techniques' Utilization for Intrusion Detection Systems' Analysis 4

Artificial Bee Colony Optimization Techniques' Utilization f...

引用

4th IEEE international conference on Artificial Intelligence in Cybersecurity, ICAIC 2025

作者： Tokgoz, Emre State University of New York Computer Security Department FarmingdaleNY United States

ISBN: (纸本)9798331518882

A variety of nonlinear optimization techniques have been used for measuring effectiveness of Intrusion Detection Systems (IDS). the variability of these optimization techniques resulted in several different approaches for different systems that depended on the IDS selection. there is a tremendous increase in the number of nature-inspired optimization techniques introduced in recent years and the associated applications in IDS appear recently after their introductions. Artificial Bee Colony (ABC) optimization is one of the optimization techniques that attracted many researchers, and a comprehensive review of ABC is needed. While there are few other reviews of ABC algorithms in the literature, the combination of ABC with other optimization techniques and improvement opportunities with these optimization techniques are not covered in these articles. therefore, in this work, an extensive review of the ABC optimization techniques introduced for IDS analysis as well as the most recent techniques that are applied to IDS in combination with ABC will be covered. New research areas to be explored are recommended as a part of the conclusions and future works. this work demonstrates a clear overview of the IDS and nonlinear optimization applications along with the optimization applications and directions followed by the research community. © 2025 IEEE.

关键词： optimization algorithms

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