Showing 1 - 7 results of 7 for search 'preference adjustment machine algorithm', query time: 0.25s Refine Results
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    Prediction of Rheological Parameters of Polymers by Machine Learning Methods by T. N. Kondratieva, A. S. Chepurnenko

    Published 2024-03-01
    “…Thus, the k-nearest neighbor algorithm and SVM can be used to predict the rheological parameters of polymers as an alternative to artificial neural networks and the CatBoost algorithm, requiring less effort to preset adjustment. …”
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    The human-on-the-loop evaluation of multiobjective optimization algorithms for solving a real-world problem that integrates the food-energy-water nexus security and climate change... by Isaac Okola, Daniel Orwa Ochieng, Evans Kirimi Miriti, Gilbert Ouma Ong'isa

    Published 2025-09-01
    “…Based on the identified knowledge gaps, the findings, and the limitations of this research, we propose future research areas that can be undertaken to improve the performance evaluation of algorithms. Such research areas include incorporating machine learning to predict, using performance data, the most suitable algorithm to solve a specific problem, and advancing interactive learning and user adjustments of the optimization process.…”
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    Full-chain comprehensive assessment and multi-scenario simulation of geological disaster vulnerability based on the VSD framework: a case study of Yunnan province in China by Li Xu, Shucheng Tan, Runyang Li

    Published 2025-06-01
    “…Furthermore, the Ordered Weighted Averaging (OWA) algorithm and the Partical Swarm Optimization-Support Vector Machine (PSO-SVM) model were combined to simulate future GDV scenarios for 2030–2050 under three development preferences: environment oriented, status quo, and economically oriented. …”
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    Integrated GBR–NSGA-II Optimization Framework for Sustainable Utilization of Steel Slag in Road Base Layers by Merve Akbas

    Published 2025-07-01
    “…To facilitate actionable decision-making, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method was applied, identifying an optimal balanced solution characterized by a transport distance of 47 km, energy intensity of 1.21 kWh/ton, moisture content of 6.2%, moderate gradation adjustment, and a grid CO<sub>2</sub> factor of 0.47 kg CO<sub>2</sub>/kWh. …”
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    Application of KTA-KELM in Fault Diagnosis of Rolling Bearing by Zhuo Wang, Wenjun Zhao, Tao Ma, Zhijun Li, Bo Qin

    Published 2019-06-01
    “…In the process of data-driven rolling bearing state identification model construction,the improper selection of the radial width parameter σ of the Gaussian kernel function in the Kernel Extreme Learning Machine(KELM)algorithm is very easy to cause poor classification accuracy. …”
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