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The Cascade Correlation learning algorithm is a special supervised learning algorithm for artificial neural network architecture. The optimization algorithm in the traditional neural network has the disadvantages of a single optimization goal, slow convergence speed, and can easily fall into local area, which cannot fully meet the key elements in the cascade correlation learning algorithm. In comparison, the group intelligence optimization algorithm can take into account these key elements in the optimization process at the same time, and obtain better optimization results. In this paper, we propose the single-objective optimization algorithm jDE-B and the multi-objective optimization algorithm MOEA-T, and improve the network expansion mode in the learning process of Cascade Correlation neural networks. We investigate the effect of applying the group intelligent optimization algorithm in the Cascade Correlation learning algorithm. Experimental results show that our improved algorithm is able to enhance the ability of the Cascade Correlation neural network to fit problems, reduce the number of hidden units and the depth of the network, and optimize the network structure. [ABSTRACT FROM AUTHOR]

This paper presents a new approach for learning in structured domains (SDs) using a constructive neural network for graphs (NN4G). The new model allows the extension of the input domain for supervised neural networks to a general class of graphs including both acyclic/cyclic, directed/undirected labeled graphs. In particular, the model can realize adaptive contextual transductions, learning the mapping from graphs for both classification and regression tasks. In contrast to previous neural networks for structures that had a recursive dynamics, NN4G is based on a constructive feedforward architecture with state variables that uses neurons with no feedback connections. The neurons are applied to the input graphs by a general traversal process that relaxes the constraints of previous approaches derived by the causality assumption over hierarchical input data. Moreover, the incremental approach eliminates the need to introduce cyclic dependencies in the definition of the system state variables. In the traversal process, the NN4G units exploit (local) contextual information of the graphs vertices. In spite of the simplicity of the approach, we show that, through the compositionality of the contextual information developed by the learning, the model can deal with contextual information that is incrementally extended according to the graphs topology. The effectiveness and the generality of the new approach are investigated by analyzing its theoretical properties and providing experimental results. [ABSTRACT FROM AUTHOR]

In this paper, a proposed algorithm that dynamically changes the neural network structure is presented. The structure is changed based on some features in the cascade correlation algorithm. Cascade correlation is an important algorithm that is used to solve the actual problem by artificial neural networks as a new architecture and supervised learning algorithm. This process optimizes the architectures of the network which intends to accelerate the learning process and produce better performance in generalization. Many researchers have to date proposed several growing algorithms to optimize the feedforward neural network architectures. The proposed algorithm has been tested on various medical data sets. The results prove that the proposed algorithm is a better method to evaluate the accuracy and flexibility resulting from it. [ABSTRACT FROM AUTHOR]

The Cascade Correlation learning algorithm is a special supervised learning algorithm for artificial neural network architecture. The optimization algorithm in the traditional neural network has the disadvantages of a single optimization goal, slow convergence speed, and can easily fall into local area, which cannot fully meet the key elements in the cascade correlation learning algorithm. In comparison, the group intelligence optimization algorithm can take into account these key elements in the optimization process at the same time, and obtain better optimization results. In this paper, we propose the single-objective optimization algorithm jDE-B and the multi-objective optimization algorithm MOEA-T, and improve the network expansion mode in the learning process of Cascade Correlation neural networks. We investigate the effect of applying the group intelligent optimization algorithm in the Cascade Correlation learning algorithm. Experimental results show that our improved algorithm is able to enhance the ability of the Cascade Correlation neural network to fit problems, reduce the number of hidden units and the depth of the network, and optimize the network structure.

Finger-vein verification is a considerable problem to be addressed in a biometric system. A lot of research works have been intended for finger-vein authentication with aid of diverse data mining algorithms. But, the verification accuracy using conventional algorithms was minimal. Also, the time complexity involved during the finger-vein verification was maximal. To overcome the above drawbacks, Steepest Deep Bipolar Cascade Correlative Machine Learning (SDBCCML) technique is proposed. The proposed SDBCCML technique is designed to efficiently perform the finger-vein verification process when considering the large size of the dataset as input. The proposed SDBCCML technique contains three main components namely input, hidden, and output units for effective finger-vein authentication. The input unit in the proposed SDBCCML technique takes a number of finger vein images as input and then sent it to the hidden units. The proposed SDBCCML technique employs more numbers of hidden units with aiming to deeply learn the input finger vein images and thereby find the significant vein features by using the Gabor filter. Subsequently, the discovered vein features at hidden units are forwarded to the output unit. In the proposed SDBCCML technique, the output unit applies a bipolar activation function that compares the extracted vein features with pre-stored templates in the dataset. After that, the output unit gives the verification result. If the output unit result is +1, then the input finger vein image is classified as an authorized person. If the output unit result is −1, then the input finger vein image is classified as an unauthorized person. Thus, the main contribution of the proposed SDBCCML technique increases the authentication performance of finger-vein with higher accuracy and minimal time. The simulation of the proposed SDBCCML technique is conducted using metrics such as accuracy, time complexity, error rate, F-Score, and space complexity for a diverse number of finger-vein images. [ABSTRACT FROM AUTHOR]

Abstract: Missing data are omnipresent in forestry research, and this poses problems in the analysis of primary data. Many statistical problems have been viewed as missing data problems. To cope with incomplete data, several methods are currently being used. They are all based on assumptions some of which might not be valid in a particular case. The choice mostly depends on the objective of the study. Considerable mensuration research is motivated by the need for yield projections that can support forest management decisions. This paper is focused on a new approach for filling gaps in diameter measurements on standing tree boles. Dealing with this problem, an attempt was made to examine the applicability of artificial neural network models for missing data estimation and to use the estimated values in the subsequent analysis. The procedure that should be followed in the development of such models is outlined. The results show good performance of the examined ANN models compared to regression treatments for missing data and ANN models demonstrate their adequacy and potential for filling gaps in diameter measurements on standing tree boles. The ANN models applied in this study are sufficiently general and have great potential to be applicable for estimating the missing values of many variables in environmental applications. [Copyright &y& Elsevier]

In this paper, a proposed algorithm that dynamically changes the neural network structure is presented. The structure is changed based on some features in the cascade correlation algorithm. Cascade correlation is an important algorithm that is used to solve the actual problem by artificial neural networks as a new architecture and supervised learning algorithm. This process optimizes the architectures of the network which intends to accelerate the learning process and produce better performance in generalization. Many researchers have to date proposed several growing algorithms to optimize the feedforward neural network architectures. The proposed algorithm has been tested on various medical data sets. The results prove that the proposed algorithm is a better method to evaluate the accuracy and flexibility resulting from it.

This study evaluates standalone and hybrid soft computing models for predicting dissolved oxygen (DO) concentration by utilizing different water quality parameters. In the first stage, two standalone soft computing models, including multilayer perceptron (MLP) neural network and cascade correlation neural network (CCNN), were proposed for estimating the DO concentration in the St. Johns River, Florida, USA. The DO concentration and water quality parameters (e.g., chloride (Cl), nitrogen oxides (NOx), total dissolved solid (TDS), potential of hydrogen (pH), and water temperature (WT)) were used for developing the standalone models by defining six combinations of input parameters. Results were evaluated using five performance criteria metrics. Overall results revealed that the CCNN model with input combination III (CCNN-III) provided the most accurate predictions of DO concentration values (root mean square error (RMSE) = 1.261 mg/L, Nash-Sutcliffe coefficient (NSE) = 0.736, Willmott's index of agreement (WI) = 0.919, R2 = 0.801, and mean absolute error (MAE) = 0.989 mg/L) for the standalone model category. In the second stage, two decomposition approaches, including discrete wavelet transform (DWT) and variational mode decomposition (VMD), were employed to improve the accuracy of DO concentration using the MLP and CCNN models with input combination III (e.g., DWT-MLP-III, DWT-CCNN-III, VMD-MLP-III, and VMD-CCNN-III). From the results, the DWT-MLP-III and VMD-MLP-III models provided better accuracy than the standalone models (e.g., MLP-III and CCNN-III). Comparison of the best hybrid soft computing models showed that the VMD-MLP-III model with 4 intrinsic mode functions (IMFs) and 10 quadratic penalty factor (VMD-MLP-III (K = 4 and α = 10)) model yielded slightly better performance than the DWT-MLP-III with Daubechies-6 (D6) and Symmlet-6 (S6) (DWT-MLP-III (D6 and S6)) models. Unfortunately, the DWT-CCNN-III and VMD-CCNN-III models did not improve the performance of the CCNN-III model. It was found that the CCNN-III model cannot be used to apply the hybrid soft computing modeling for prediction of the DO concentration. Graphical comparisons (e.g., Taylor diagram and violin plot) were also utilized to examine the similarity between the observed and predicted DO concentration values. The DWT-MLP-III and VMD-MLP-III models can be an alternative tool for accurate prediction of the DO concentration values. [ABSTRACT FROM AUTHOR]