Object detection in images using artificial neural network and improved binary gravitational search algorithm

2015 ◽  
Author(s):  
Farzaneh Azadi Pourghahestani ◽  
Esmat Rashedi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Object Detection ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Gravitational Search ◽  
Artificial Neural

RF MEMS for Reconfigurable Antenna Using Gravitational Search Optimization and Artificial Neural Network

2018 ◽  
Vol 27 (08) ◽  
pp. 1850132
Author(s):  
T. A. Balarajuswamy ◽  
R. Nakkeeran
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Rf Mems ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Beam Width ◽  
Optimization Method ◽  
Gravitational Search ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The projected method explains about the problems occurred in the combination of the MEMS switches and the complete scheme plan is resolved through choosing the finest devise limits for the plan. The devise limits, namely, length of beam, width of beam, torsion arm length, switch thickness, holes and gap were measured. At this point, the finest value of the devise limit is forecast by the aid of artificial neural network (ANN). Furthermore, the method contains the optimization method of Gravitational Search Algorithm (GSA) to optimize the input signal and so dropping the Mean Square Error (MSE). The complete scheme is executed in the operational platform of MATLAB and the outcomes were examined.

scholarly journals Kombinasi Algoritma Backpropagation Neural Network dengan Gravitational Search Algorithm Dalam Meningkatkan Akurasi

2021 ◽  
Vol 5 (1) ◽  
pp. 90
Author(s):  
Miftahul Falah ◽  
Dian Palupi Rini ◽  
Iwan Pahendra
Keyword(s):  
Neural Network ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Disease Prediction ◽  
Backpropagation Algorithm ◽  
Cancer Data ◽  
Long Time ◽  
Gravitational Search ◽  
Artificial Neural ◽  
Hidden Layer

Predicting disease is usually done based on the experience and knowledge of the doctor. Diagnosis of such a disease is traditionally less effective. The development of medical diagnosis based on machine learning in terms of disease prediction provides a more accurate diagnosis than the traditional way. In terms of predicting disease can use artificial neural networks. The artificial neural network consists of various algorithms, one of which is the Backpropagation Algorithm. In this paper it is proposed that disease prediction systems use the Backpropagation algorithm. Backpropagation algorithms are often used in disease prediction, but the Backpropagation algorithm has a slight drawback that tends to take a long time in obtaining optimum accuracy values. Therefore, a combination of algorithms can overcome the shortcomings of the Backpropagation algorithm by using the success of the Gravitational Search Algorithm (GSA) algorithm, which can overcome the slow convergence and local minimum problems contained in the Backpropagation algorithm. So the authors propose to combine the Backpropagation algorithm using the Gravitational Search Algorithm (GSA) in hopes of improving accuracy results better than using only the Backpropagation algorithm. The results resulted in a higher level of accuracy with the same number of iterations than using Backpropagation only. Can be seen in the first trial of breast cancer data with parameters namely hidden layer 5, learning rate of 2 and iteration as much as 5000 resulting in accuracy of 99.3 % with error 0.7% on Backpropagation Algorithm, while in combination BP & GSA got accuracy of 99.68 % with error of 0.32%.

Oppositional Gravitational Search Algorithm and Artificial Neural Network-based Classification of Kidney Images

2018 ◽  
Vol 29 (1) ◽  
pp. 485-496 ◽  
Author(s):  
S.M.K. Chaitanya ◽  
P. Rajesh Kumar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Region Of Interest ◽  
Principal Component ◽  
Gabor Feature ◽  
Artificial Neural ◽  
Gabor Feature Extraction ◽  
Probabilistic Principal Component Analysis

Abstract Ultrasound (US) imaging has been broadly utilized as part of kidney diagnosis because of its ability to show structural abnormalities like cysts, stones, and infections as well as information about kidney function. The main aim of this research is to effectively classify normal and abnormal kidney images through US based on the selection of relevant features. In this study, abnormal kidney images were classified through gray-scale conversion, region-of-interest generation, multi-scale wavelet-based Gabor feature extraction, probabilistic principal component analysis-based feature selection and adaptive artificial neural network technique. The anticipated method is executed in the working platform of MATLAB, and the results were analyzed and contrasted. Results show that the proposed approach had 94% accuracy and 100% specificity. In addition, its false-acceptance rate is 0%, whereas that of existing methods is not <27%. This shows the precise prediction level of the proposed approach, compared with that of existing methods.

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