Showing papers in "Applied Soft Computing in 2019"

TL;DR: Experimental results demonstrate that the proposed feature selection method effectively reduces the dimensions of the dataset and achieves superior classification accuracy using the selected features.

TL;DR: The results show that TQWT performs better or comparable to the state-of-the-art speech signal processing techniques used in PD classification, and Mel-frequency cepstral and the tunable-Q wavelet coefficients, which give the highest accuracies, contain complementary information inPD classification problem resulting in an improved system when combined using a filter feature selection technique.

TL;DR: An illuminating example to confirm the suggested approach for multi attribute decision making issues, ordering the alternatives based on the accuracy function, and a novel T2NN-TOPSIS strategy combining type 2 neutrosophic numbers and TOPSIS under group decision making as application of T1NN.

TL;DR: It can be observed on benchmark test functions that PFA is able to converge global optimum and avoid the local optima effectively and show that it can approximate to true Pareto optimal solutions.

TL;DR: A model for picture fuzzy Dombi aggregation operators to solve multiple attribute decision making (MADM) methods in an updated way is developed and at the end of the study a practical application of the deducted decision over investment alternatives is reported.

TL;DR: This is the first work to consider the divergence of PFSs for measuring the discrepancy of data from the perspective of the relative entropy, and a novel divergence measure is proposed by taking advantage of the Jensen–Shannon divergence, called as PFSJS distance.

TL;DR: An improved complete ensemble empirical mode decomposition with adaptive noise technology was applied to decompose the wind energy series for eliminating noise and extracting the main features of original data to enhance prediction accuracy.

TL;DR: A detailed, empirical comparison of 85 variants of minority oversampling techniques is presented and discussed involving 104 imbalanced datasets for evaluation, to set a new baseline in the field, determine the oversampler principles leading to the best results under general circumstances, and give guidance to practitioners on which techniques to use with certain types of datasets.

TL;DR: Experimental results show that the proposed algorithm can be used to quickly obtain suitable feature subsets and SVM parameters, thereby achieving a better classification result.

TL;DR: The proposed Multi-Objective Optimization by Ratio Analysis based on the Z-number theory (Z-MOORA) was implemented in the automotive spare parts industry, and the results indicate a full prioritization of the failures in comparison with other conventional methods.

TL;DR: The experimental results show that the proposed MEGWO, which integrate multiple improved search strategies, outperforms other variants of GWO and other algorithms in terms of accuracy and convergence speed.

TL;DR: Genetic Algorithm-based Optimized Clustering (GAOC) protocol is designed for optimized CH selection by integrating the parameters of residual energy, distance to the sink and node density in its formulated fitness function and outperform the state-of-the-art protocols on the benchmark of different performance metrics.

TL;DR: Deep learning-based approach is proposed for brain tumor image segmentation and proves that the proposed technique has outperformed SVM and CNN in terms of accuracy, PSNR, MSE and other performance parameters.

TL;DR: In a first stage of the proposed methodology, different feature selection techniques were evaluated in order to obtain the most relevant attributes for the predictions and the selected attributes achieved an improvement of more than 10%, in accuracy, for the price direction predictions, with respect to the state-of-the-art papers.

TL;DR: The aggregation of the different parts of MULTIMOORA which makes the technique more operational, especially in case of large-scale applications, and compared to those obtained by employing TOPSIS and VIKOR methods.

TL;DR: Uncertainty measures are introduced by using new defined divergence-based cross entropy measure of Atanassov's intuitionistic fuzzy sets and it is demonstrated by application examples that the proposed measures can get reasonable results coinciding with other existing methods.

TL;DR: A formulation and solution procedure for stochastic optimal reactive power dispatch (ORPD) problem with uncertainties in load demand, wind and solar power, and the effectiveness of a proper constraint handling technique is substantiated.

TL;DR: The results show that combining call-detail records with traditional data in credit scoring models significantly increases their performance when measured in AUC, and the calling behavior features are the most predictive in other models, both in terms of statistical and economic performance.

TL;DR: A new ELECTRE III method to address multiple criteria decision-making (MCDM) problems based on novel operations of PLTSs, which is implemented to solve a problem concerning the nurse–patient relationship with the modified Okatani Keyco nurse– patient relationship trust scale.

TL;DR: A new computing paradigm is presented for evaluation of dynamics of nonlinear prey–predator mathematical model by exploiting the strengths of integrated intelligent mechanism through artificial neural networks, genetic algorithms and interior-point algorithm.

TL;DR: The experimental results and analysis demonstrate that the proposed OBLGWO can significantly outperform GWO, previous enhanced GWO variants and some of the other well-established algorithms in terms of convergence speed and the quality of solutions.

TL;DR: A new perspective of a compromised solution, which can handle the decision maker’s psychological behavior by inducing TODIM (a Portuguese acronym meaning Interactive Multi-Criteria Decision Making), is provided.

TL;DR: The results show that the proposed algorithms are very effective and efficient to solve the problem under consideration as they outperform the existing methods by a significant margin.

TL;DR: A novel multi-objective cellular grey wolf optimizer (MOCGWO) is proposed to address the hybrid flowshop scheduling problem by integrating the merits of cellular automata for diversification and variable neighborhood search for intensification, which balances exploration and exploitation.

TL;DR: A new method for MAGDM with PLTSs is put forward and a Fintech example is analyzed to show the effectiveness of the proposed method.

TL;DR: A stacking-based ensemble learning method is proposed that simultaneously constructs the diagnostic model and extracts interpretable diagnostic rules from the constructed ensemble learning model, which outperforms that of several state-of-the-art methods in terms of the classification accuracy, specificity and specificity.

TL;DR: Experimental results demonstrate that the proposed approach achieves better false positive rate, accuracy of prediction, and reduced delay in comparison to the conventional techniques.

TL;DR: The results show that dispersion of the size and location of distributed renewable generation leads to a further reduction in losses and a better improvement of the reliability criterion, and it is clear that the multi-objective optimization is a more precise approach to network utilization taking into account all objective indices than the single objective method.

TL;DR: The simulation results show that the PIWP obtained by the Beta-PSO-LSTM model has higher reliability and narrower interval bandwidth, which can provide decision support for the safe and stable operation of power systems.

TL;DR: A novel multi-objective opposition based chaotic differential evolution (MOCDE) algorithm is proposed for solving the multi- objective problem in order to avoid premature convergence and is observed that the proposed algorithm can produce better results in terms of power loss and yearly economic loss minimization as well as improvement of voltage profile.