Extreme Learning Machine(ELM)is popular in batch learning,sequential learning,and progressive learning,due to its speed,easy integration,and generalization ***,Traditional ELM cannot train massive data rapidly and eff...
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Extreme Learning Machine(ELM)is popular in batch learning,sequential learning,and progressive learning,due to its speed,easy integration,and generalization ***,Traditional ELM cannot train massive data rapidly and efficiently due to its memory residence,high time and space *** ELM,the hidden layer typically necessitates a huge number of ***,there is no certainty that the arrangement of weights and biases within the hidden layer is *** solve this problem,the traditional ELM has been hybridized with swarm intelligence optimization *** paper displays five proposed hybrid algorithms“Salp Swarm algorithm(SSA-ELM),Grasshopper algorithm(GOA-ELM),Grey Wolf algorithm(GWO-ELM),whaleoptimizationalgorithm(WOA-ELM)andMoth Flame optimization(MFO-ELM)”.These five optimizers are hybridized with standard ELM methodology for resolving the tumor type classification using gene expression *** proposed models applied to the predication of electricity loading data,that describes the energy use of a single residence over a fouryear *** the hidden layer,Swarm algorithms are used to pick a smaller number of nodes to speed up the execution of *** best weights and preferences were calculated by these algorithms for the hidden *** results demonstrated that the proposed MFO-ELM achieved 98.13%accuracy and this is the highest model in accuracy in tumor type classification gene expression *** in predication,the proposed GOA-ELM achieved 0.397which is least RMSE compared to the other models.
The turning operation is a traditional and conventional process for machining cylindrical materials to achieve a desired shape. During machining in the turning process, the accuracy and quality of the end product main...
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The turning operation is a traditional and conventional process for machining cylindrical materials to achieve a desired shape. During machining in the turning process, the accuracy and quality of the end product mainly depend on its process parameters. Therefore, this study attempts to achieve a quality product from a conventional lathe machine by optimizing its process parameters for En2-BS970 mild steel. The experiments were designed with the help of response surface methodology (RSM). The most influential turning parameters, such as feed rate, spindle speed, cutting fluid flow rate, and cutting angle, are investigated experimentally. In addition, the machining responses, namely material removal rate (MRR), surface roughness (SR), cutting force (CF), and cutting time (CT), have been optimized using the RSM numerical optimization method. Furthermore, a deep neural network (DNN) machine learning prediction model based on the whaleoptimizationalgorithm (WOA) is developed for this experiment in order to predict turning performances for En2-BS970 material. The predicted DNN results showed an accuracy of about 90% compared to experimental results, indicating that the implementation of WOA significantly optimized the DNN weights during training. The RSM optimized responses are obtained as 14.608 mm3/min for MRR, 0.7504 mu m for SR, 442.94 N for CF, and 2.48 seconds for CT during the turning operation with input settings of 0.24 mm/rev, 466.535 rpm, 0.075 ml/s and 60.18 degrees for feed rate, spindle speed, cutting fluid flow rate and cutting edge angle respectively.
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