Application of Artificial Neural Networks Modeling for Evaluation of E-Learning/Training Convergence Time

2017 ◽  
Vol 2 (2) ◽  
pp. 159-179
Author(s):  
Hassan Moustafa ◽  
Fadhel Ben Tourkia ◽  
Ramadan Mohamed Ramadan
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Convergence Time ◽  
E Learning ◽  
Artificial Neural

Literature Survey for Applications of Artificial Neural Networks

2022 ◽  
pp. 669-682
Author(s):  
Pooja Deepakbhai Pancholi ◽  
Sonal Jayantilal Patel
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Weather Forecasting ◽  
Handwriting Recognition ◽  
Complete Solution ◽  
Learning System ◽  
E Learning ◽  
Artificial Neural

The artificial neural network could probably be the complete solution in recent decades, widely used in many applications. This chapter is devoted to the major applications of artificial neural networks and the importance of the e-learning application. It is necessary to adapt to the new intelligent e-learning system to personalize each learner. The result focused on the importance of using neural networks in possible applications and its influence on the learner's progress with the personalization system. The number of ANN applications has considerably increased in recent years, fueled by theoretical and applied successes in various disciplines. This chapter presents an investigation into the explosive developments of many artificial neural network related applications. The ANN is gaining importance in various applications such as pattern recognition, weather forecasting, handwriting recognition, facial recognition, autopilot, etc. Artificial neural network belongs to the family of artificial intelligence with fuzzy logic, expert systems, vector support machines.

Literature Survey for Applications of Artificial Neural Networks

2021 ◽  
pp. 1-17
Author(s):  
Pooja Deepakbhai Pancholi ◽  
Sonal Jayantilal Patel
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Weather Forecasting ◽  
Handwriting Recognition ◽  
Complete Solution ◽  
Learning System ◽  
E Learning ◽  
Artificial Neural

The artificial neural network could probably be the complete solution in recent decades, widely used in many applications. This chapter is devoted to the major applications of artificial neural networks and the importance of the e-learning application. It is necessary to adapt to the new intelligent e-learning system to personalize each learner. The result focused on the importance of using neural networks in possible applications and its influence on the learner's progress with the personalization system. The number of ANN applications has considerably increased in recent years, fueled by theoretical and applied successes in various disciplines. This chapter presents an investigation into the explosive developments of many artificial neural network related applications. The ANN is gaining importance in various applications such as pattern recognition, weather forecasting, handwriting recognition, facial recognition, autopilot, etc. Artificial neural network belongs to the family of artificial intelligence with fuzzy logic, expert systems, vector support machines.

scholarly journals Results of Application of Artificial Neural Networks in Predicting Geo-Mechanical Properties of Stabilised Clays—A Review

Geotechnics ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. 147-171
Author(s):  
Jeremiah J. Jeremiah ◽  
Samuel J. Abbey ◽  
Colin A. Booth ◽  
Anil Kashyap
Keyword(s):  
Mechanical Properties ◽  
Neural Networks ◽  
Regression Analysis ◽  
Artificial Neural Networks ◽  
High Performance ◽  
Cementitious Materials ◽  
Convergence Time ◽  
Ann Model ◽  
Marquardt Algorithm ◽  
Artificial Neural

This study presents a literature review on the use of artificial neural networks in the prediction of geo-mechanical properties of stabilised clays. In this paper, the application of ANNs in a geotechnical analysis of clay stabilised with cement, lime, geopolymers and by-product cementitious materials has been evaluated. The chemical treatment of expansive clays will involve the development of optimum binder mix proportions or the improvement of a specific soil property using additives. These procedures often generate large data requiring regression analysis in order to correlate experimental data and model the performance of the soil in the field. These analyses involve large datasets and tedious mathematical procedures to correlate the variables and develop required models using traditional regression analysis. The findings from this study show that ANNs are becoming well known in dealing with the problem of mathematical modelling involving nonlinear functions due to their robust data analysis and correlation capabilities and have been successfully applied to the stabilisation of clays with high performance. The study also shows that the supervised ANN model is well adapted to dealing with stabilisation of clays with high performance as indicated by high R2 and low MAE, RMSE and MSE values. The Levenberg–Marquardt algorithm is effective in shortening the convergence time during model training.

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