scholarly journals Application of Multi Layer Artificial Neural Network in the Diagnosis System: A Systematic Review

2018 ◽  
Vol 7 (3) ◽  
pp. 138
Author(s):  
Arvind Singh Rawat ◽  
Arti Rana ◽  
Adesh Kumar ◽  
Ashish Bagwari
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Reconfigurable Computing ◽  
Research Papers ◽  
Computing Systems ◽  
System A ◽  
Major Scale ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Execution Technique

Basic hardware comprehension of an artificial neural network (ANN), to a major scale depends on the proficientrealization of a distinctneuron. For hardware execution of NNs, mostly FPGA-designed reconfigurable computing systems are favorable .FPGA comprehension of ANNs through a hugeamount of neurons is mainlyan exigentassignment. This workconverses the reviews on various research articles of neural networks whose concernsfocused in execution of more than one input neuron and multilayer with or without linearity property by using FPGA. An execution technique through reserve substitution isprojected to adjust signed decimal facts. A detailed review of many research papers have been done for the <br /> proposed work.

scholarly journals Application of artificial neural network in sizing a stand-alone photovoltaic system: a review

2020 ◽  
Vol 11 (1) ◽  
pp. 342 ◽  
Author(s):  
Ahmad Fateh Mohamad Nor ◽  
Suriana Salimin ◽  
Mohd Noor Abdullah ◽  
Muhammad Nafis Ismail
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Electrical Energy ◽  
Design Tool ◽  
Photovoltaic System ◽  
Pv System ◽  
System A ◽  
Load Demand ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

<span>Artificial Neural Network (ANN) techniques are becoming useful in the current era due to the vast development of the current computer technologies. ANN has been used in various fields especially in the field of science and technology. One of the advantage that makes ANN so interesting is the ANN’s ability to learn the input and output relationship even though the relationship is non-linear. In addition, ANN is also useful for modelling, optimization, prediction, forecasting, and controlling systems. The main objective of this paper is to present a review of the ANN techniques for sizing a stand-alone photovoltaic (PV) system. The review in this paper shows the potential of ANN as a design tool for a stand-alone PV. In addition, ANN is very useful to improve the sizing process of the stand-alone PV system. The sizing process is of paramount importance to a stand-alone PV system in order to make sure the system can generate ample electrical energy to supply the load demand.</span>

Application of the Artificial Neural Network (ANN) Method as MPPT Photovoltaic for DC Source Storage

2019 ◽  
Vol 12 (3) ◽  
pp. 145 ◽  
Author(s):  
Epyk Sunarno ◽  
Ramadhan Bilal Assidiq ◽  
Syechu Dwitya Nugraha ◽  
Indhana Sudiharto ◽  
Ony Asrarul Qudsi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

Detection of Biomedical Images by Using Bio-inspired Artificial Intelligent

2020 ◽  
Vol 38 (2A) ◽  
pp. 255-264
Author(s):  
Hanan A. R. Akkar ◽  
Sameem A. Salman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Square Error ◽  
Average Rate ◽  
Mean Square ◽  
Performance Measurements ◽  
Artificial Intelligent ◽  
Average Mean Square Error ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Computer vision and image processing are extremely necessary for medical pictures analysis. During this paper, a method of Bio-inspired Artificial Intelligent (AI) optimization supported by an artificial neural network (ANN) has been widely used to detect pictures of skin carcinoma. A Moth Flame Optimization (MFO) is utilized to educate the artificial neural network (ANN). A different feature is an extract to train the classifier. The comparison has been formed with the projected sample and two Artificial Intelligent optimizations, primarily based on classifier especially with, ANN-ACO (ANN training with Ant Colony Optimization (ACO)) and ANN-PSO (training ANN with Particle Swarm Optimization (PSO)). The results were assessed using a variety of overall performance measurements to measure indicators such as Average Rate of Detection (ARD), Average Mean Square error (AMSTR) obtained from training, Average Mean Square error (AMSTE) obtained for testing the trained network, the Average Effective Processing Time (AEPT) in seconds, and the Average Effective Iteration Number (AEIN). Experimental results clearly show the superiority of the proposed (ANN-MFO) model with different features.

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