Learn Artificial Neural Networks with SN Sivanandam Neural Networks PDF 118: A Book for Undergraduat

Modeling of river pollution contributes to better management of water quality and this will lead to the improvement of human health. The advection dispersion equation (ADE) is the government equation on pollutant transmission in the river. Modeling the pollution transmission includes numerical solution of the ADE and estimating the longitudinal dispersion coefficient (LDC). In this paper, a novel approach is proposed for numerical modeling of the pollution transmission in rivers. It is related to use both finite volume method as numerical method and artificial neural network (ANN) as soft computing technique together in simulation. In this approach, the result of the ANN for predicting the LDC was considered as input parameter for the numerical solution of the ADE. To validate the model performance in real engineering problems, the pollutant transmission in Severn River has been simulated. Comparison of the final model results with measured data of the Severn River showed that the model has good performance. Predicting the LDC by ANN model significantly improved the accuracy of computer simulation of the pollution transmission in river.

Preparing the soft computing techniques are based on the dataset so for preparing the multilayer perceptron (MLP) neural network about 150 dataset related to the Eq. (10) was collected and these range is given in Table 2.

sn sivanandam neural networks pdf 118

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To use the final computer model for simulation of pollution transmission in the Severn River, first, the initial condition was defined. The properties of cross section and hydraulic conditions were given to MLP model as input parameters and LDC was predicted. Then, according to the LDC, a calibration was conducted to determine the computational concentration profile at the station A and then the concentration profile was simulated and derived to each sampling station. The results of computer modeling and observed data are shown in Figs. 13 and 14. As shown in Figs. 13 and 14, the final model has good ability to simulate the pollution transmission in Severn River and it is related to the predicting LDC by neural network.

Artificial neural networks (ANNs) were found to be utilized as the AI/ML algorithm in 13 studies, convolutional neural networks (CNNs) and support vector machine (SVM) in 9 studies and regression in 8 studies apart from 23 other algorithms utilized in various studies. Results classified as per the type of AI algorithm employed in the study are listed in Table 3.

This scoping review also tried to examine the types of AI algorithms commonly employed in various studies. The results reveal that artificial neural networks (ANNs) were the widely utilized AI/ML algorithm (10) followed by convolutional neural networks (CNNs), support vector machine (SVM)-8 studies and regression (logistic and linear) in 8 studies apart from 23 other algorithms utilized in various studies.

A group of researchers have specifically studied the applications of AI and ML for the detection of TMJ osteoarthritis [22, 47, 66, 67] and have concluded that deep learning neural network was the most accurate method for classification of TMJ-OA that allows disease staging of bony changes in TMJ-OA. The authors expected their efforts to boost future studies into early detection and osteoarthritis patient-specific therapeutic interventions, and thereby improve the health of articular joints.

- R2N2_ILP and R2N2_GA: In [5], amethod for ranking the sentences for MDS is proposed. Through aranking framework upon recursive neural networks (R2N2), based on ahierarchical regression process the most important sentences of eachdocument are selected.

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