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learning and intelligent optimization - 4th International conference, LION 4, Selected Papers

4th International conference on learning and intelligent optimization, LION 4

ISBN: (纸本)3642137997

the proceedings contain 35 papers. the topics discussed include: a column generation heuristic for the general vehicle routing problem;a combination of evolutionary algorithm, mathematical programming, and a new local search procedure for the just-in-time job-shop scheduling problem;a math-heuristic algorithm for the DNA sequencing problem;a randomized iterated greedy algorithm for the founder sequence reconstruction problem;algorithm selection as a bandit problem with unbounded losses;bandit-based estimation of distribution algorithms for noisy optimization: rigorous runtime analysis;distance functions, clustering algorithms and microarray data analysis;Gaussian process assisted particle swarm optimization;learning of highly-filtered data manifold using spectral methods;multiclass visual classifier based on bipartite graph representation of decision tables;and a linear approximation of the value function of an approximate dynamic programming approach for the ship scheduling problem.

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intelligent model selection and hyperparameter optimization: MLOPS 4th

Intelligent model selection and hyperparameter optimization:...

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

作者： Singh, Ikjyot Puri, Digvijay Singh, Gursimar Singh, Monika Apex Institute of Technology Chandigarh University Mohali Gharuan India

ISBN: (纸本)9781032911571

Simplifying the deployment, management, and maintenance of gadget mastering fashions within the manufacturing environment is a key aim in the swiftly growing system gaining knowledge of environment (MLOps). intelligent version selection and hyperparameter tuning are also MLOps and are crucial to optimizing the effectiveness and performance of the model. In the framework of MLOps, this article offers a thorough analysis of recent studies and methods in intelligent model selection and hyperparameter optimization. We expound upon the benefits, constraints, and practical applications of various methodologies, including reinforcement learning, random search, grid search, and Model optimization. the objective of conducting this assessment is to assist scholars and professionals in deploying intelligent approaches to enhance machine learning models in MLOps workflows. © 2025 the Author(s).

关键词： Smart manufacturing

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Novel Abnormal Data Mining and Detection Algorithm for intelligent Systems 4

Novel Abnormal Data Mining and Detection Algorithm for Intel...

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4th International conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Xu, Shaomei Fan, Yanling Lin, Bing Continuing Education Service Center Shandong Institute of Commerce and Technology Shandong Jinan250103 China

ISBN: (纸本)9798331523923

In intelligent systems, as the amount of data increases, how to analyse data and extract abnormal information is an important task. Based on this, in order to improve the detection efficiency and accuracy of abnormal data in intelligent systems, this study proposes a new and efficient abnormal data mining and detection algorithm. In the initial modelling process, this study introduced the minimum modification cost principle based on cross entropy for modelling. this algorithm optimizes the time series data cleaning process, thereby removing noise data while maintaining the integrity of the data sequence. then, the extreme gradient boosting algorithm is combined with the Gaussian kernel function to achieve data dimension reduction. this operation can reduce data dimensions and feature redundancy. Finally, the overall anomaly detection operation by integrating the improved Ada boost algorithm with the sampling balance technology can improve the accuracy of the classifier while solving the problem of unbalanced data. the experimental results show that the proposed method is superior to the traditional algorithms in comprehensive indicators. © 2025 IEEE.

关键词： Abnormal Data Detection Data Mining Algorithms Efficiency optimization intelligent Systems

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Role of linear algebra and algorithms in machine learning 4th

Role of linear algebra and algorithms in machine learning

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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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Deep learning-Augmented Evolutionary Strategies for intelligent Global optimization

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IEEE Access 2025年 13卷 70138-70178页

作者： Ezugwu, Absalom El-Shamir Oyelade, Olaide Nathaniel Agushaka, Jeffrey Ovre Saha, Apu Kumar Unit for Data Science and Computing North-West University Potchefstroom2520 South Africa School of Electronics Electrical Engineering and Computer Science Queen’s University BelfastBT9 5BN United Kingdom Department of Computer Science Federal University of Lafia Lafia950101 Nigeria Department of Mathematics National Institute of Technology Agartala Tripura Agartala799046 India

this paper introduces the Susceptible-Infected-Removed Optimizer (SIRO), a novel learned heuristic inspired by biological systems and deep learning techniques. SIRO models its search process after the SIR epidemiological compartmental model, predicting the susceptibility, infection, and recovery dynamics of solutions. SIRO integrates deep learning into its initialization and parameter setting to enhance its efficiency, enabling intelligent and adaptive behavior. this hybridization improves solution quality, accelerates convergence, enhances robustness, and reduces computational costs. the algorithm’s performance was evaluated using CEC 2017 benchmark functions, demonstrating superior results in hybrid functions (C1-C28) despite moderate performance on traditional CEC1-CEC14 functions. Friedman’s test ranked SIRO 4th overall, with SSA as the top-performing algorithm. Additionally, SIRO was tested on real-world optimization problems, including mechanical engineering design, hyperparameter tuning, and feature selection for medical image classification. In the classification task, SIRO-enhanced CNN achieved an accuracy of 0.86 at the 5th epoch, outperforming CNN (0.66), CNN-GA (0.76), and CNN-WOA (0.75). Furthermore, SIRO reported a precision of 0.96, recall of 1.0, and F1-score of 0.98, highlighting its effectiveness. these results validate the benefits of integrating a learning mechanism into SIRO, yielding superior precision, computational efficiency, and performance over conventional optimization approaches. © 2013 IEEE.

关键词： Adversarial machine learning

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Application of intelligent Techniques in Smart Grid Automation 4

Application of Intelligent Techniques in Smart Grid Automati...

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4th International conference on Power, Control and Computing Technologies, ICPC2T 2025

作者： Babu, Manish Kumar Ray, Papia Swain, Anita Department of EE VSSUT Burla India Department of ECE BCE Khordha India

ISBN: (纸本)9798331530402

the smart grid is an advanced power system network that integrates cutting-edge technologies to enable efficient, reliable, and sustainable energy generation, distribution, and utilization. this paper provides a comprehensive survey of intelligent techniques, including machine learning for load forecasting and fault detection, optimization algorithms for demand-side management and renewable energy integration, and data analytics for real-time grid monitoring and decision-making. these approaches enhance grid performance by improving flexibility, operational efficiency, and resilience to disruptions. Specific examples, such as predictive maintenance using machine learning and optimization-based energy scheduling for microgrids, are discussed to illustrate their practical applications. the paper also explores challenges like cybersecurity and data quality, offering potential mitigation strategies. Additionally, successful implementations and their outcomes are highlighted, showcasing the transformative impact of intelligent techniques on smart grid automation. Finally, future research directions, such as leveraging blockchain for secure transactions and quantum computing for advanced optimizations, are identified to guide advancements in this field. © 2025 IEEE.

关键词： Qubits

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Cluster Head Selection Algorithm using Machine learning 4

Cluster Head Selection Algorithm using Machine Learning

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4th International conference on Sentiment Analysis and Deep learning, ICSADL 2025

作者： Nidhya, M.S. Gurumoorthi, E. Babu, M. Charumathi, A.C. Naved, Mohd Maram, Balajee School of Computer Applications Dayananda Sagar University Bangalore India School of Engineering Malla Reddy University India Chennai Institute of Technology College Dept of Ai & Ds Chennai India Sona College of Technology Dept of Information Technology India Jaipuria Institute of Management Dept of Business Analytics Noida India School of Computer Science and Artificial Intelligence Sr University Ananthasagar Hasanparthy Warangal India

ISBN: (纸本)9798331523923

Wireless Sensor Networks (WSNs) find extensive applications in environmental monitoring, healthcare, and smart cities. Energy efficiency, however, continues to be a significant challenge with the limited lifetime of sensor node batteries. the conventional heuristic-based cluster head (CH) selection techniques tend not to adapt dynamically to network changes, resulting in inefficient energy utilization and decreased network lifetime. this research discusses Machine learning (ML)-oriented CH selection approaches to improve energy efficiency, network lifetime, and data fusion. Using supervised and unsupervised learning methods, ML algorithms are able to learn optimal CHs dynamically based on residual energy, network structure, and data traffic. the comparative analysis show that ML-based CH selection enhances network stability by 95% and lowers energy consumption by 50% compared to traditional methods. the research points to the promise of ML in WSN performance optimization and opens the doors to intelligent, adaptive clustering technologies. © 2025 IEEE.

关键词： Self-supervised learning

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intelligent City Construction Resource optimization and Scheduling Algorithm Based on T-S Fuzzy Neural Network and Deep Data Mining 24

Intelligent City Construction Resource Optimization and Sche...

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4th International Joint conference on Robotics and Artificial Intelligence, JCRAI 2024

作者： Yu, Jiming Feng, Qiang Zhang, Liyuan Columbia University New York United States Changchun University of Technology Changchun China Changchun Institute of Technology Changchun China

ISBN: (纸本)9798400710100

the challenges of cost control in construction and installation projects have perennially been a significant concern for entities in the construction sector. the intricate interplay of various equipment, personnel, and tasks during the installation process often leads to an extension in the construction timeline, thereby escalating the overall cost of construction. To address this issue, this study introduces a heuristic two-phase optimization deployment approach. Furthermore, to facilitate efficient and intelligent resource management for urban construction projects, a resource optimization algorithm predicated on the T-S Fuzzy Neural Network (T-S FNN) and deep data mining is proposed. this algorithm employs the T-S FNN to conduct an in-depth analysis and selection of resource data, followed by the application of the Particle Swarm optimization algorithm to enhance the network. Experimental outcomes demonstrate that the proposed algorithm significantly curtails the overall construction duration and labor cost when juxtaposed with multiple standard allocation strategies. this underscores its efficacy and efficiency in reducing the time and cost associated with project construction. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Particle swarm optimization (PSO)

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Research on Software Development Methods for intelligent Mobile Robots 24

Research on Software Development Methods for Intelligent Mob...

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4th International Joint conference on Robotics and Artificial Intelligence, JCRAI 2024

作者： Shi, Jiatao Yang, Yihang Zhou, Xiao Lv, Pan Li, Hong Yang, Guoqing Weichai Power Co. Ltd Shandong China Zhejiang University Zhejiang China

ISBN: (纸本)9798400710100

With the widespread application of artificial intelligence technology in robotics, robots have become more and more intelligent. the development of intelligent robots and the continuous enhancement of learning capabilities generate new challenges for intelligent robot programming. the way in which the behavior of intelligent robots is defined has become more complex. In this paper, an ability -based intelligent robot development method is proposed, which provides a more effective means for the definition of intelligent robot behavior. the definition of intelligent robot behavior is divided into 4 steps, including function developing, ablility packaging, skill training and task execution. For ability encapsulation, the article proposes a set of ability definition, training and management methods. Finally, taking the food delivery robot as an example, the application process of the ability-based intelligent robot behavior definition method is explained in detail. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Microrobots

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Proceedings of the 2024 4th International Joint conference on Robotics and Artificial Intelligence, JCRAI 2024

Proceedings of the 2024 4th International Joint Conference o...

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4th International Joint conference on Robotics and Artificial Intelligence, JCRAI 2024

ISBN: (纸本)9798400710100

the proceedings contain 43 papers. the topics discussed include: intelligent city construction resource optimization and scheduling algorithm based on T-S fuzzy neural network and deep data mining;research on domain text classification algorithm based on multi-granularity fusion;the impact of body posture on 3D spatial survey knowledge in VR simulated microgravity environment;a personalized virtual training method for human-machine safety interaction oriented toward human-centric manufacturing;research on software development methods for intelligent mobile robots;overview and analysis of the application of digital twin-based human-machine collaboration in the product development process;and decision making for postgraduate training based on differential evolution algorithm.

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