Suchergebnisse - "LSSVM model"

Prediction of the anti-carbonation performance of concrete based on random forest – least squares support vector machine model

Autoren: Sivaraja M. Swaminathen A. N. Kuttimarks M. S. et al.

Quelle: Journal of Environmental Engineering and Landscape Management, Vol 33, Iss 2 (2025)

Schlagwörter: safety assessment, concrete, Environmental engineering, random forest (RF), least squares support vector machine (LSSVM) model, TA170-171, carbonation resistance

Zugangs-URL: https://doaj.org/article/e47a784fbbe94cc497e21569c98178b6

Intelligent algorithm-based model for predicting mass transfer performance in CO2 absorption within a rotating packed bed

Autoren: Wei Zhang Hao Chen Ke Huang et al.

Quelle: Case Studies in Thermal Engineering, Vol 73, Iss , Pp 106662- (2025)

Schlagwörter: CO2 absorption, Intelligent algorithms, LSSVM model, Rotating packed bed, Mass transfer, Engineering (General). Civil engineering (General), TA1-2040

Dateibeschreibung: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2214157X25009220; https://doaj.org/toc/2214-157X

Zugangs-URL: https://doaj.org/article/f8d016f72f8d48628f10c93006622afc

A Novel Prediction Method for the Remaining Life of Corrosion Damage in Oil and Gas Pipeline Systems

Autoren: Jian Cui Jie Wang Lei Dong

Quelle: IEEE Access, Vol 12, Pp 176104-176123 (2024)

Schlagwörter: Oil and gas pipeline systems, corrosion damage, deep learning algorithms, remaining life prediction, MFO-LSSVM model, Electrical engineering. Electronics. Nuclear engineering, TK1-9971

Zugangs-URL: https://doaj.org/article/10d85b1db65d4acd8533a70ca940fd5d

A new hybrid method with data-characteristic-driven analysis for artificial intelligence and robotics index return forecasting

Autoren: Yue-Jun Zhang Han Zhang Rangan Gupta

Quelle: Financial Innovation, Vol 9, Iss 1, Pp 1-23 (2023)

Schlagwörter: Artificial Intelligence and Robotics index return forecasting, PSO-LSSVM model, GARCH model, Decomposition and integration model, Combination model, Public finance, K4430-4675, Finance, HG1-9999

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/2199-4730

Zugangs-URL: https://doaj.org/article/93d2b3c7c8a740c9826b6cc6efb34ce9

Integration of advanced optimization algorithms into least-square support vector machine (LSSVM) for water quality index prediction

Autoren: See Leng Chia Min Yan Chia Chai Hoon Koo et al.

Quelle: Water Supply, Vol 22, Iss 2, Pp 1951-1963 (2022)

Schlagwörter: TC401-506, advanced optimization algorithm, Water supply for domestic and industrial purposes, water quality index, 0207 environmental engineering, 02 engineering and technology, 01 natural sciences, 6. Clean water, hybrid optimized lssvm model, River, lake, and water-supply engineering (General), kernel function, 14. Life underwater, TD201-500, river water quality parameter, 0105 earth and related environmental sciences

Zugangs-URL: https://iwaponline.com/ws/article-pdf/doi/10.2166/ws.2021.303/967469/ws2021303.pdf
https://doaj.org/article/3028d03203554d8bb5ee0e111f7985c9

Research on HC-LSSVM Model for Soft Soil Settlement Prediction Based on Homotopy Continuation Method

Autoren: Guangjun Cui Shenghua Xiong Cuiying Zhou et al.

Quelle: Applied Sciences, Vol 11, Iss 10666, p 10666 (2021)

Schlagwörter: soft soil settlement prediction, LSSVM model, model parameter solution, homotopy continuation method, HC-LSSVM model, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999

Relation: https://www.mdpi.com/2076-3417/11/22/10666; https://doaj.org/toc/2076-3417; https://doaj.org/article/a7197a401dd84e06b75733f81ac1c7da

Verfügbarkeit: https://doi.org/10.3390/app112210666
https://doaj.org/article/a7197a401dd84e06b75733f81ac1c7da

Rapid Detection of Physicochemical Indicators of Tobacco Flavorings Using Fourier-Transform Near Infrared Spectroscopy with Chemometrics and Machine Learning

Autoren: Qinlin Xiao Jian Zheng Jing Wen et al.

Schlagwörter: Physiology, Space Science, Environmental Sciences not elsewhere classified, Biological Sciences not elsewhere classified, Chemical Sciences not elsewhere classified, Information Systems not elsewhere classified, select characteristic wavelengths, encompassing 145 categories, actual industrial scenarios, 90 production batches, 608 flavoring samples, support vector machine, accurate quality management, accurate quality assessment, detecting physicochemical indicators, detect physicochemical indicators, machine learning timely, least angle regression, tobacco flavoring products, lssvm model achieved, 2 , dt ), least, nir spectroscopy combined, establish detection models, physicochemical indicators, machine learning, nir spectroscopy, lssvm ), partial least

Verfügbarkeit: https://doi.org/10.1021/acsomega.5c00225.s001
https://figshare.com/articles/dataset/Rapid_Detection_of_Physicochemical_Indicators_of_Tobacco_Flavorings_Using_Fourier-Transform_Near_Infrared_Spectroscopy_with_Chemometrics_and_Machine_Learning/28937845

Prediction of biodiesel iodine value from its fatty acids composition using a novel approach

Autoren: Fashe Li Guirong Bao Hua Wang et al.

Quelle: The Proceedings of the International Conference on Power Engineering (ICOPE). 2021, :2021-0243

Landslide Displacement Prediction of Shuping Landslide Combining PSO and LSSVM Model

Autoren: Wenjun Jia Tao Wen Decheng Li et al.

Quelle: Water
Volume 15
Issue 4
Pages: 612

Schlagwörter: TSM, 13. Climate action, landslide displacement prediction, 0211 other engineering and technologies, PSO-LSSVM model, 02 engineering and technology, Shuping landslide

Dateibeschreibung: application/pdf

On the assessment of pseudo steady-state horizontal well productivity: application in rectangular and square drainage areas

Autoren: Kamari, Arash Bahadori, Alireza Mohammadi, Amir H

Quelle: School of Environment, Science and Engineering Papers

Schlagwörter: Productivity performance, Pseudo skin factor, LSSVM model, Drainage area, Horizontal well, Environmental Sciences

Relation: https://epubs.scu.edu.au/esm_pubs/3654; https://www.vurup.sk/petroleum/2018/volume-60/

Verfügbarkeit: https://epubs.scu.edu.au/esm_pubs/3654
https://www.vurup.sk/petroleum/2018/volume-60/

Prediction of enhanced coagulation with varied pre-oxidations for seasonal variations of cyanobacteria-dominated algae-laden water quality.

Autoren: Ding L Zhou Z Yang Y et al.

Quelle: Journal of environmental management [J Environ Manage] 2025 Jun; Vol. 386, pp. 125697. Date of Electronic Publication: 2025 May 12.

Publikationsart: Journal Article

Info zur Zeitschrift: Publisher: Academic Press Country of Publication: England NLM ID: 0401664 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1095-8630 (Electronic) Linking ISSN: 03014797 NLM ISO Abbreviation: J Environ Manage Subsets: MEDLINE

MeSH-Schlagworte: Cyanobacteria* , Water Quality* , Water Purification*/methods, Seasons ; Potassium Permanganate/chemistry ; Oxidation-Reduction ; Potassium Compounds