Suchergebnisse - "dynamic arithmetic optimization algorithm"

Using a dynamic arithmetic optimization approach to improve ridgelet neural network performance in remote sensing scene classification

Autoren: Hui Zhang Jun Chen Hui Xie et al.

Quelle: Scientific Reports, Vol 15, Iss 1, Pp 1-19 (2025)

Schlagwörter: Ridgelet neural network, Remote sensing scene classification, Dynamic arithmetic optimization algorithm, Hyperparameter optimization, Deep learning, Computer vision, Medicine, Science

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/2045-2322

Zugangs-URL: https://doaj.org/article/cbb43c5ece7c478fac7c51be8be58adb

Assessment of Ultimate Bearing Capacity Using Hybrid Gaussian Process Regression Approach

Autoren: Zahra Entezami Human Mirzaali

Quelle: Advances in Engineering and Intelligence Systems, Vol 004, Iss 03, Pp 71-85 (2025)

Schlagwörter: ultimate bearing capacity, machine learning, gaussian process regression, prairie dog optimization, dynamic arithmetic optimization algorithm, Computer software, QA76.75-76.765, Mining engineering. Metallurgy, TN1-997

Dateibeschreibung: electronic resource

Relation: https://aeis.bilijipub.com/article_230930_2332985fc4be8a37d95e096452e2ad91.pdf; https://doaj.org/toc/2821-0263

Zugangs-URL: https://doaj.org/article/1452a9cc12014de9b31a067e5898576b

Predicting the California Bearing Ratio Applying the Automated Framework of Regression Model

Autoren: Pan Hu Jing Jin Yu Yun

Quelle: Journal of Applied Science and Engineering, Vol 28, Iss 7, Pp 1435-1447 (2024)

Schlagwörter: california bearing ratio, random forest, dynamic arithmetic optimization algorithm, slime mould algorithm, aquila optimizer, Engineering (General). Civil engineering (General), TA1-2040, Chemical engineering, TP155-156, Physics, QC1-999

Dateibeschreibung: electronic resource

Relation: http://jase.tku.edu.tw/articles/jase-202507-28-07-0005; https://doaj.org/toc/2708-9967; https://doaj.org/toc/2708-9975

Zugangs-URL: https://doaj.org/article/0ea16bf4e18f49f19734b247e4597c9f

Dynamic Arithmetic Optimization Algorithm for Truss Optimization Under Natural Frequency Constraints

Autoren: Seyedali Mirjalili

Schlagwörter: Satisfiability and optimisation, Arithmetic, Dynamic arithmetic optimization algorithm (DAOA), Frequency constraints, Genetic algorithms, Heuristic algorithms, Licenses, Metaheuristics, Optimal design, Search problems, Time-frequency analysis, Truss structures

Relation: https://figshare.com/articles/journal contribution/Dynamic_Arithmetic_Optimization_Algorithm_for_Truss_Optimization_Under_Natural_Frequency_Constraints/30144328

Verfügbarkeit: https://doi.org/10.1109/ACCESS.2022.3146374

Enhancing unconfined compressive strength of stabilized soil with lime and cement prediction through a robust hybrid machine learning approach utilizing Naive Bayes Algorithm

Autoren: Weiqing Wan

Quelle: Journal of Engineering and Applied Science, Vol 71, Iss 1, Pp 1-23 (2024)

Schlagwörter: Unconfined compressive strength, Machine learning, Naive Bayes, Smell agent optimization, Dynamic arithmetic optimization algorithm, Engineering (General). Civil engineering (General), TA1-2040

Dateibeschreibung: electronic resource

Relation: https://doaj.org/toc/1110-1903; https://doaj.org/toc/2536-9512

Zugangs-URL: https://doaj.org/article/61115fc329b7461a84a94cfbbdf2bcf0

ACBiGRU-DAO: Attention Convolutional Bidirectional Gated Recurrent Unit-based Dynamic Arithmetic Optimization for Air Quality Prediction.

Autoren: Panneerselvam V Thiagarajan R

Quelle: Environmental science and pollution research international [Environ Sci Pollut Res Int] 2023 Aug; Vol. 30 (37), pp. 86804-86820. Date of Electronic Publication: 2023 Jul 06.

Publikationsart: Journal Article

Info zur Zeitschrift: Publisher: Springer Country of Publication: Germany NLM ID: 9441769 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1614-7499 (Electronic) Linking ISSN: 09441344 NLM ISO Abbreviation: Environ Sci Pollut Res Int Subsets: MEDLINE

MeSH-Schlagworte: Air Pollutants*/analysis , Air Pollution*/analysis , Ozone*, Carbon Monoxide/analysis ; Particulate Matter/analysis ; Nitrogen Dioxide/analysis ; Sulfur Dioxide