Titlebook: 18th International Conference on Soft Computing Models in Industrial and Environmental Applications ; Salamanca, Spain, Se Pablo García Bri

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書目名稱18th International Conference on Soft Computing Models in Industrial and Environmental Applications 影響因子(影響力)




書目名稱18th International Conference on Soft Computing Models in Industrial and Environmental Applications 影響因子(影響力)學(xué)科排名




書目名稱18th International Conference on Soft Computing Models in Industrial and Environmental Applications 網(wǎng)絡(luò)公開度




書目名稱18th International Conference on Soft Computing Models in Industrial and Environmental Applications 網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱18th International Conference on Soft Computing Models in Industrial and Environmental Applications 被引頻次




書目名稱18th International Conference on Soft Computing Models in Industrial and Environmental Applications 被引頻次學(xué)科排名




書目名稱18th International Conference on Soft Computing Models in Industrial and Environmental Applications 年度引用




書目名稱18th International Conference on Soft Computing Models in Industrial and Environmental Applications 年度引用學(xué)科排名




書目名稱18th International Conference on Soft Computing Models in Industrial and Environmental Applications 讀者反饋




書目名稱18th International Conference on Soft Computing Models in Industrial and Environmental Applications 讀者反饋學(xué)科排名

懦夫

Conference proceedings 2023 city of Salamanca (Spain) in September 2023...Soft computing represents a collection or set of computational techniques in machine learning, computer science, and some engineering disciplines, which investigate, simulate, and analyze very complex issues and phenomena...After a through peer-review p

現(xiàn)代

華而不實

,über die Feinere Innervation der Tube,al, bayesian, support vector machine, and artificial neural network. Several metrics are used to compare the models, such as Mean Absolute Error, Root Mean Squared Error, Mean Squared Error, Median Absolute Error and Coefficient of Determination, along with hypothesis testing.

使激動

厚臉皮

,überseeischer Maschinenexport,y compares the efficacy of various supervised classification techniques for categorizing the operating status of a Lithium Iron Phosphate - LiFePO4 (LFP) load cell using voltage values obtained from the battery. Each method was assessed using a real dataset, and excellent results were achieved, with accuracy values of up to 90%.

革新

nettle

牌帶來

,Bildung der 6 gro?en Privatbahngruppen,ts one of the final steps where the code, after simulation validation, is transferred to an industrial hardware platform. This strengthens the methodology and demonstrates the actual capability of the iMO-NMPC control strategy in constrained settings. The Speedgoat baseline model was used as the hardware platform.

fixed-joint

https://doi.org/10.1007/978-3-662-39716-9ing undetected by legitimate components. This paper introduces a set of one-class boundary methods such as Approximate Convex Hull, K-Nearest Neighborhood and One-Class Support Vector Machine for developing a categorization model in order to prevent IoT DoS attacks over the CoAP (Constrained Application Protocol) environments.