In semi-arid regions, the deterioration in groundwater quality and drop water level upshots importance of resource management for drinking irrigation. Therefore geospatial techniques could be integrated with mathematical models accurate spatiotemporal mapping risk areas at village level. present study, changes level, patterns, future trends were analyzed using eight years (2012–2019) data 171 v...

The use of artificial neural networks (ANN) for nonlinear system modeling is a field where still there is much theoretical work to be done. A structured ANN which obtains neural models of nonlinear systems is presented. Those neural models are Fourier-series based. To check the goodness of the method, conventional difference equations are re-modeled via ANN and their respective input/outputs co...

In this paper an original method for integrating Artiicial Neural Networks (ANN) with Hidden Markov Models (HMM) is proposed. ANNs are suitable to perform phonetic classiication, whereas HMMs have been proven successful at modeling the temporal structure of the speech signal. In the approach described here, the ANN outputs constitute the sequence of observation vectors for the HMM. An algorithm...

In this paper, in 2012 and 2013 two of the Three-River Headwaters Region of soil total-nitrogen, data combined ASD FieldSpec 4 made by America spectroradiometer measured spectral reflectance of soil sample chamber data model MSLR and ANN methods modeling. The spectral data is mainly composed of original spectral reflectance(REF) through nine point weighted moving average obtaining four forms of...

Artificial neural networks (ANNs) have been successfully applied for solving a wide variety of problems. However, determining of ANN architectural and training parameter values still remains a difficult task. This paper is concerned with the usage of design of experiment (DOE) method in order to determine parameter settings of multilayer feedforward (MLFF) ANN trained with backpropagation (BP) ...

Catharina M. Alam, Jonas S. G. Silvander, Ebot N. Daniel, Guo-Zhong Tao, Sofie M. Kvarnström, Parvez Alam, M. Bishr Omary, Arno Hänninen and Diana M. Toivola* Department of Biosciences, Cell Biology, Åbo Akademi University, Tykistökatu 6A, FIN-20520 Turku, Finland Department of Surgery, Stanford University School of Medicine, Stanford, California, USA Centre for Functional Materials, Åbo Akadem...

This paper presents an application of design of experiments techniques to determine the optimized parameters of artificial neural network (ANN), which are used to estimate force from Electromyogram (sEMG) signals. The accuracy of ANN model is highly dependent on the network parameters settings. There are plenty of algorithms that are used to obtain the optimal ANN setting. However, to the best ...

The quantitative structure-retention relationship (QSRR) of nanoparticles in roadside atmosphere against the comprehensive two-dimensional gas chromatography which was coupled to high-resolution time-of-flight mass spectrometry was studied. The genetic algorithm (GA) was employed to select the variables that resulted in the best-fitted models. After the variables were selected, the linear multi...

The efficiency of a hybrid systems method which combined artificial neural networks (ANNs) as a modeling tool and genetic algorithms (GAs) as an optimizing method for input variables used in ANN modeling was assessed. Hence, as a new technique, it was applied for the prediction and optimization of the plant hormones concentrations and combinations for in vitro proliferation of Garnem (G × N15) ...