Titlebook: Recent Advances on Soft Computing and Data Mining; The Second Internati Tutut Herawan,Rozaida Ghazali,Mustafa Mat Deris Conference proceedi

Ankylo-

FID

Optimizing Weights in Elman Recurrent Neural Networks with Wolf Search Algorithmiants on benchmark classification datasets. The simulation results show that the proposed Metahybrid WRNN algorithm has better performance in terms of CPU time, accuracy and MSE than the other algorithms.

rheumatism

A Fuzzy TOPSIS with Z-Numbers Approach for Evaluation on Accident at the Construction Siteis, it shows that the FTOPSIS with Z-numbers provides us with an another useful way to handle Fuzzy Multi-Criteria Decision Making (FMCDM) problems in a more intelligent and flexible manner due to the fact that it uses Z-numbers with FTOPSIS.

卷發(fā)

Modified Backpropagation Algorithm for Polycystic Ovary Syndrome Detection Based on Ultrasound Imageas a drawback of running time. The best accuracy of Levenberg - Marquardt is 93.925% which is gained from 33 neurons and 16 vector feature and Conjugate Gradient - Fletcher Reeves is 87.85% from 13 neurons and 16 vector feature.

內(nèi)部

NIP

Cluster Validation Analysis on Attribute Relative of Soft-Set Theoryalysis, the validity of the clusters produced by MTAR technique is evaluated by the entropy measure using two standards dataset: Soybean (Small) and Zoo from University California at Irvine (UCI) repository. Results show that the clusters produce by MTAR technique have better entropy and improved the clusters validity up?to 33%.

強化

Formation Control Optimization for Odor Localizationm robots in the process of odor localization. As the results found that the propose algorithm produce fast response and efficiently process than Fuzzy-PSO, they are able to locate the source of odor in a short time and capable for keeping in formation to find the target.

engrossed

Tartar

Improved Functional Link Neural Network Learning Using Modified Bee-Firefly Algorithm for Classificaperformance of FLNN. This paper discussed the implementation of modified Artificial Bee Colony with Firefly algorithm for training the FLNN network to overcome the drawback of BP-learning scheme. The aim is to introduce an alternative learning scheme that can provide a better solution for training the FLNN network for classification task.

osteoclasts

Artificial Neural Network with Hyperbolic Tangent Activation Function to Improve the Accuracy of COCocess. In the experiment, COCOMO II SDR dataset is used for training and testing the model. The result shows that eight out of twelve projects have a closer effort value of actual effort. It shows that the proposed model produces better performance comparing to sigmodal function.