Towards a Comprehensive Assessment of Statistical Versus Soft Computing Models in Hydrology: Application to Monthly Pan Evaporation Prediction

This paper evaluates six soft computational models along with three statistical data-driven models for the prediction of pan evaporation (EP). Accordingly, improved kriging—as a novel statistical model—is proposed for accurate predictions of EP for two meteorological stations in Turkey. In the standard kriging model, the input data nonlinearity effects are increased by using a nonlinear map and transferring input data from a polynomial to an exponential basic function. The accuracy, precision, and over/under prediction tendencies of the response surface method, kriging, improved kriging, multilayer perceptron neural network using the Levenberg–Marquardt (MLP-LM) as well as a conjugate gradient (MLP-CG), radial basis function neural network (RBFNN), multivariate adaptive regression spline (MARS), M5Tree and support vector regression (SVR) were compared. Overall, all the applied models were highly capable of predicting monthly EP in both stations with a mean absolute error (MAE) < 0.77 mm and a Willmott index (d) > 0.95. Considering periodicity as an input parameter, the MLP-LM provided better results than the other methods among the soft computing models (MAE = 0.492 mm and d = 0.981). However, the improved kriging method surpassed all the other models based on the statistical measures (MAE = 0.471 mm and d = 0.983). Finally, the outcomes of the Mann–Whitney test indicated that the applied soft computational models do not have significant superiority over the statistical ones (p-value > 0.65 at α = 0.01 and α = 0.05).

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Analysis of the Nosema Cells Identification for Microscopic Images

Sensors ◽

10.3390/s21093068 ◽

2021 ◽

Vol 21 (9) ◽

pp. 3068

Author(s):

Soumaya Dghim ◽

Carlos M. Travieso-González ◽

Radim Burget

Keyword(s):

Neural Network ◽

Machine Learning ◽

Image Processing ◽

Deep Learning ◽

The Other ◽

Support Vector ◽

Learning Approaches ◽

Microscopic Images ◽

Trained Neural Network ◽

Nosema Disease

The use of image processing tools, machine learning, and deep learning approaches has become very useful and robust in recent years. This paper introduces the detection of the Nosema disease, which is considered to be one of the most economically significant diseases today. This work shows a solution for recognizing and identifying Nosema cells between the other existing objects in the microscopic image. Two main strategies are examined. The first strategy uses image processing tools to extract the most valuable information and features from the dataset of microscopic images. Then, machine learning methods are applied, such as a neural network (ANN) and support vector machine (SVM) for detecting and classifying the Nosema disease cells. The second strategy explores deep learning and transfers learning. Several approaches were examined, including a convolutional neural network (CNN) classifier and several methods of transfer learning (AlexNet, VGG-16 and VGG-19), which were fine-tuned and applied to the object sub-images in order to identify the Nosema images from the other object images. The best accuracy was reached by the VGG-16 pre-trained neural network with 96.25%.

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Soft Computing Tool Approach for Texture Classification Using Discrete Cosine Transform

International Journal of Electronics Signals and Systems ◽

10.47893/ijess.2011.1003 ◽

2011 ◽

pp. 12-16

Author(s):

Pankaj H. Chandankhede

Keyword(s):

Neural Network ◽

Soft Computing ◽

Learning Algorithm ◽

Texture Classification ◽

Fuzzy Model ◽

Performance Comparison ◽

Cosine Transform ◽

Neuro Fuzzy ◽

Soft Computing Techniques ◽

Computing Models

Texture can be considered as a repeating pattern of local variation of pixel intensities. Cosine Transform (DCT) coefficients of texture images. As DCT works on gray level images, the color scheme of each image is transformed into gray levels. For classifying the images using DCT, two popular soft computing techniques namely neurocomputing and neuro-fuzzy computing are used. A feedforward neural network is used to train the backpropagation learning algorithm and an evolving fuzzy neural network to classify the textures. The soft computing models were trained using 80% of the texture data and the remaining was used for testing and validation purposes. A performance comparison was made among the soft computing models for the texture classification problem. In texture classification the goal is to assign an unknown sample image to a set of known texture classes. It is observed that the proposed neuro-fuzzy model performed better than the neural network.

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A Neural Networks Pruning and Data Fusion Based Intrusion Detection Model

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.651-653.1772 ◽

2014 ◽

Vol 651-653 ◽

pp. 1772-1775

Author(s):

Wei Gong

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Networks ◽

Intrusion Detection ◽

Data Fusion ◽

Input Data ◽

The Other ◽

Pruning Algorithm ◽

Detection Model ◽

Process Ability

The abilities of summarization, learning and self-fitting and inner-parallel computing make artificial neural networks suitable for intrusion detection. On the other hand, data fusion based IDS has been used to solve the problem of distorting rate and failing-to-report rate and improve its performance. However, multi-sensor input-data makes the IDS lose its efficiency. The research of neural network based data fusion IDS tries to combine the strong process ability of neural network with the advantages of data fusion IDS. A neural network is designed to realize the data fusion and intrusion analysis and Pruning algorithm of neural networks is used for filtering information from multi-sensors. In the process of intrusion analysis pruning algorithm of neural networks is used for filtering information from multi-sensors so as to increase its performance and save the bandwidth of networks.

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A Comparative Study of Different Machine Learning Algorithms in Predicting the Content of Ilmenite in Titanium Placer

Applied Sciences ◽

10.3390/app10020635 ◽

2020 ◽

Vol 10 (2) ◽

pp. 635 ◽

Cited By ~ 5

Author(s):

Yingli LV ◽

Qui-Thao Le ◽

Hoang-Bac Bui ◽

Xuan-Nam Bui ◽

Hoang Nguyen ◽

...

Keyword(s):

Soft Computing ◽

Mean Squared Error ◽

Machine Learning Algorithms ◽

Classification And Regression Tree ◽

Gradient Boosting ◽

Support Vector ◽

Stochastic Gradient Boosting ◽

Soft Computing Techniques ◽

Residuals Analysis ◽

Computing Models

In this study, the ilmenite content in beach placer sand was estimated using seven soft computing techniques, namely random forest (RF), artificial neural network (ANN), k-nearest neighbors (kNN), cubist, support vector machine (SVM), stochastic gradient boosting (SGB), and classification and regression tree (CART). The 405 beach placer borehole samples were collected from Southern Suoi Nhum deposit, Binh Thuan province, Vietnam, to test the feasibility of these soft computing techniques in estimating ilmenite content. Heavy mineral analysis indicated that valuable minerals in the placer sand are zircon, ilmenite, leucoxene, rutile, anatase, and monazite. In this study, five materials, namely rutile, anatase, leucoxene, zircon, and monazite, were used as the input variables to estimate ilmenite content based on the above mentioned soft computing models. Of the whole dataset, 325 samples were used to build the regarded soft computing models; 80 remaining samples were used for the models’ verification. Root-mean-squared error (RMSE), determination coefficient (R2), a simple ranking method, and residuals analysis technique were used as the statistical criteria for assessing the model performances. The numerical experiments revealed that soft computing techniques are capable of estimating the content of ilmenite with high accuracy. The residuals analysis also indicated that the SGB model was the most suitable for determining the ilmenite content in the context of this research.

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A Comparative Study between Regression and Neural Networks for Modeling Al6082-T6 Alloy Drilling

Machines ◽

10.3390/machines7010013 ◽

2019 ◽

Vol 7 (1) ◽

pp. 13 ◽

Cited By ~ 3

Author(s):

Nikolaos Karkalos ◽

Nikolaos Efkolidis ◽

Panagiotis Kyratsis ◽

Angelos Markopoulos

Keyword(s):

Neural Network ◽

Neural Networks ◽

Regression Model ◽

Soft Computing ◽

Regression Models ◽

Fuzzy Inference ◽

Process Conditions ◽

Neural Network Models ◽

Inference System ◽

Computing Models

Apart from experimental research, the development of accurate and efficient models is considerably important in the field of manufacturing processes. Initially, regression models were significantly popular for this purpose, but later, the soft computing models were proven as a viable alternative to the established models. However, the effectiveness of soft computing models can be often dependent on the size of the experimental dataset, and it can be lower compared to that of the regression models for a small-sized dataset. In the present study, it is intended to conduct a comparison of the performance of various neural network models, such as the Multi-layer Perceptron (MLP), the Radial Basis Function Neural Network (RBF-NN), and the Adaptive Neuro-Fuzzy Inference System (ANFIS) models with the performance of a multiple regression model. For the development of the models, data from drilling experiments on an Al6082-T6 workpiece for various process conditions are employed, and the performance of models related to thrust force (Fz) and cutting torque (Mz) is assessed based on several criteria. From the analysis, it was found that the MLP models were superior to the other neural networks model and the regression model, as they were able to achieve a relatively lower prediction error for both models of Fz and Mz.

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Can Decomposition Approaches Always Enhance Soft Computing Models? Predicting the Dissolved Oxygen Concentration in the St. Johns River, Florida

Applied Sciences ◽

10.3390/app9122534 ◽

2019 ◽

Vol 9 (12) ◽

pp. 2534 ◽

Cited By ~ 14

Author(s):

Mohammad Zounemat-Kermani ◽

Youngmin Seo ◽

Sungwon Kim ◽

Mohammad Ali Ghorbani ◽

Saeed Samadianfard ◽

...

Keyword(s):

Neural Network ◽

Water Quality ◽

Dissolved Oxygen ◽

Soft Computing ◽

Quality Parameters ◽

Water Quality Parameters ◽

Performance Criteria ◽

Input Combination ◽

Do Concentration ◽

Computing Models

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.

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Structured Pyramidal Neural Networks

International Journal of Neural Systems ◽

10.1142/s0129065717500216 ◽

2018 ◽

Vol 28 (05) ◽

pp. 1750021 ◽

Cited By ~ 3

Author(s):

Alessandra M. Soares ◽

Bruno J. T. Fernandes ◽

Carmelo J. A. Bastos-Filho

Keyword(s):

Neural Network ◽

Neural Networks ◽

Processing Time ◽

Receptive Fields ◽

Memory Capacity ◽

The Other ◽

Support Vector ◽

Proposed Model ◽

Novel Method ◽

Original Approach

The Pyramidal Neural Networks (PNN) are an example of a successful recently proposed model inspired by the human visual system and deep learning theory. PNNs are applied to computer vision and based on the concept of receptive fields. This paper proposes a variation of PNN, named here as Structured Pyramidal Neural Network (SPNN). SPNN has self-adaptive variable receptive fields, while the original PNNs rely on the same size for the fields of all neurons, which limits the model since it is not possible to put more computing resources in a particular region of the image. Another limitation of the original approach is the need to define values for a reasonable number of parameters, which can turn difficult the application of PNNs in contexts in which the user does not have experience. On the other hand, SPNN has a fewer number of parameters. Its structure is determined using a novel method with Delaunay Triangulation and k-means clustering. SPNN achieved better results than PNNs and similar performance when compared to Convolutional Neural Network (CNN) and Support Vector Machine (SVM), but using lower memory capacity and processing time.

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Fuzzy Clustering Neural Networks for Real-Time Odor Recognition System

Journal of Automated Methods and Management in Chemistry ◽

10.1155/2007/38405 ◽

2007 ◽

Vol 2007 ◽

pp. 1-6 ◽

Cited By ~ 5

Author(s):

Bekir Karlık ◽

Kemal Yüksek

Keyword(s):

Neural Network ◽

Neural Networks ◽

Real Time ◽

Fuzzy Clustering ◽

Input Data ◽

Recognition System ◽

Neural System ◽

The Other ◽

Odor Recognition ◽

Recognition Probability

The aim of this study is to develop a novel fuzzy clustering neural network (FCNN) algorithm as pattern classifiers for real-time odor recognition system. In this type of FCNN, the input neurons activations are derived through fuzzy c mean clustering of the input data, so that the neural system could deal with the statistics of the measurement error directly. Then the performance of FCNN network is compared with the other network which is well-known algorithm, named multilayer perceptron (MLP), for the same odor recognition system. Experimental results show that both FCNN and MLP provided high recognition probability in determining various learn categories of odors, however, the FCNN neural system has better ability to recognize odors more than the MLP network.

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GIS-Based Soft Computing Models for Landslide Susceptibility Mapping: A Case Study of Pithoragarh District, Uttarakhand State, India

Mathematical Problems in Engineering ◽

10.1155/2021/9914650 ◽

2021 ◽

Vol 2021 ◽

pp. 1-19

Author(s):

Trung-Hieu Tran ◽

Nguyen Duc Dam ◽

Fazal E. Jalal ◽

Nadhir Al-Ansari ◽

Lanh Si Ho ◽

...

Keyword(s):

Soft Computing ◽

Landslide Susceptibility ◽

Google Earth ◽

Susceptibility Mapping ◽

Landslide Susceptibility Mapping ◽

Statistical Measures ◽

Susceptibility Maps ◽

Predicted Values ◽

Landslide Susceptibility Maps ◽

Computing Models

The main objective of the study was to investigate performance of three soft computing models: Naïve Bayes (NB), Multilayer Perceptron (MLP) neural network classifier, and Alternating Decision Tree (ADT) in landslide susceptibility mapping of Pithoragarh District of Uttarakhand State, India. For this purpose, data of 91 past landslide locations and ten landslide influencing factors, namely, slope degree, curvature, aspect, land cover, slope forming materials (SFM), elevation, distance to rivers, geomorphology, overburden depth, and distance to roads were considered in the models study. Thematic maps of the Geological Survey of India (GSI), Google Earth images, and Aster Digital Elevation Model (DEM) were used for the development of landslide susceptibility maps in the Geographic Information System (GIS) environment. Landslide locations data was divided into a 70 : 30 ratio for the training (70%) and testing/validation (30%) of the three models. Standard statistical measures, namely, Positive Predicted Values (PPV), Negative Predicted Values (NPV), Sensitivity, Specificity, Mean Absolute Error (MAE), Root Mean Squire Error (RMSE), and Area under the ROC Curve (AUC) were used for the evaluation of the models. All the three soft computing models used in this study have shown good performance in the accurate development of landslide susceptibility maps, but performance of the ADT and MLP is better than NB. Therefore, these models can be used for the construction of accurate landslide susceptibility maps in other landslide-prone areas also.

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Multi-Stage Feature Selection (MSFS) Algorithm for UWB-Based Early Breast Cancer Size Prediction

10.1101/2020.02.06.936831 ◽

2020 ◽

Author(s):

V. Vijayasarveswari ◽

A.M. Andrew ◽

M. Jusoh ◽

T. Sabapathy ◽

R.A.A. Raof ◽

...

Keyword(s):

Breast Cancer ◽

Neural Network ◽

Support Vector Machine ◽

Feature Selection ◽

Probabilistic Neural Network ◽

The Other ◽

Ultra Wideband ◽

Support Vector ◽

Data Normalization ◽

Multi Stage

AbstractBreast cancer is the most common cancer among women and it is one of the main causes of death for women worldwide. To attain an optimum medical treatment for breast cancer, an early breast cancer detection is crucial. This paper proposes a multistage feature selection method that extracts statistically significant features for breast cancer size detection using proposed data normalization techniques. Ultra-wideband (UWB) signals, controlled using microcontroller are transmitted via an antenna from one end of the breast phantom and are received on the other end. These ultra-wideband analogue signals are represented in both time and frequency domain. The preprocessed digital data is passed to the proposed multi-stage feature selection algorithm. This algorithm has four selection stages. It comprises of data normalization methods, feature extraction, data dimensional reduction and feature fusion. The output data is fused together to form the proposed datasets, namely, 8-HybridFeature, 9-HybridFeature and 10-HybridFeature datasets. The classification performance of these datasets is tested using the Support Vector Machine, Probabilistic Neural Network and Naïve Bayes classifiers for breast cancer size classification. The research findings indicate that the 8-HybridFeature dataset performs better in comparison to the other two datasets. For the 8-HybridFeature dataset, the Naïve Bayes classifier (91.98%) outperformed the Support Vector Machine (90.44%) and Probabilistic Neural Network (80.05%) classifiers in terms of classification accuracy. The finalized method is tested and visualized in the MATLAB based 2D and 3D environment.

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