scholarly journals Soft Computing: Optimization of Hybridized Neural Network Variant with Genetic Algorithm for Anomaly Detection

2019 ◽  
Vol 8 (9) ◽  
Author(s):  
Ismaila Wasiu Oladimeji ◽  
Ayodele Adefemi Lawrence ◽  
Omidiora E. Olusayo ◽  
Falohun Adeleye S
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Anomaly Detection ◽  
Soft Computing

scholarly journals Performance comparisons of particle swarm optimization, echo state neural network and genetic algorithm for vegetation segmentation

2017 ◽  
Vol 7 (1.1) ◽  
pp. 184
Author(s):  
Rincy Merlin Mathew ◽  
S. Purushothaman ◽  
P. Rajeswari
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Soft Computing ◽  
Particle Swarm ◽  
Band 3 ◽  
Landsat 8 ◽  
Swarm Optimization ◽  
Landsat 7 ◽  
Performance Comparisons

This article presents the implementation of vegetation segmentation by using soft computing methods: particle swarm optimization (PSO), echostate neural network(ESNN) and genetic algorithm (GA). Multispectral image with the required band from Landsat 8 (5, 4, 3) and Landsat 7 (4, 3, 2) are used. In this paper, images from ERDAS format acquired by Landsat 7 ‘Paris.lan’ (band 4, band 3, Band 2) and image acquired from Landsat 8 (band5, band 4, band 3) are used. The soft computing algorithms are used to segment the plane-1(Near infra-red spectra) and plane 2(RED spectra). The monochrome of the two segmented images is compared to present performance comparisons of the implemented algorithms.

scholarly journals Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Lukas Falat ◽  
Dusan Marcek ◽  
Maria Durisova
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Decision Making ◽  
Soft Computing ◽  
Clustering Algorithm ◽  
Prediction Models ◽  
Moving Average ◽  
Series Data ◽  
Decision Making Process ◽  
Hybrid Neural Network

This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined withK-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process.

scholarly journals A Survey of Soft Computing Approaches in Biomedical Imaging

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Manju Devi ◽  
Sukhdip Singh ◽  
Shailendra Tiwari ◽  
Subhash Chandra Patel ◽  
Melkamu Teshome Ayana
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Medical Imaging ◽  
Soft Computing ◽  
Short Review ◽  
Imaging Modalities ◽  
System Evaluation ◽  
Medical Applications ◽  
Research Strategies ◽  
Further Development

Medical imaging is an essential technique for the diagnosis and treatment of diseases in modern clinics. Soft computing plays a major role in the recent advances in medical imaging. It handles uncertainties and improves the qualities of an image. Until now, various soft computing approaches have been proposed for medical applications. This paper discusses various medical imaging modalities and presents a short review of soft computing approaches such as fuzzy logic, artificial neural network, genetic algorithm, machine learning, and deep learning. We also studied and compared each approach used for other imaging modalities based on the certain parameter used for the system evaluation. Finally, based on comparative analysis, the possible research strategies for further development are proposed. As far as we know, no previous work examined this issue.

scholarly journals Kansei Information Processing in Multimedia Applying Intelligent Soft Computing Techniques

2000 ◽  
Vol 4 (6) ◽  
pp. 428-436 ◽  
Author(s):  
Takehisa Onisawa ◽  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Information Processing ◽  
Soft Computing ◽  
Set Theory ◽  
Fuzzy Set Theory ◽  
Interactive Genetic Algorithm ◽  
Fuzzy Measures ◽  
Soft Computing Techniques ◽  
Genetic Algorithm Approach

This paper mentions the concept of Kansei information that must be dealt with in multimedia. Kansei information has subjectivity, ambiguity, vagueness and situation dependence. This piece of information is not dealt with by the conventional natural science techniques. This paper also introduces soft computing techniques such as a neural network model, fuzzy set theory, a fuzzy measures and fuzzy integrals model, and the interactive genetic algorithm approach that are applied to Kansei information processing or some related problems.

A neural network approach to anomaly detection in spectra

1997 ◽  
Author(s):  
Daniel Benzing ◽  
Kevin Whitaker ◽  
Dedra Moore ◽  
Daniel Benzing ◽  
Kevin Whitaker ◽  
...  
Keyword(s):  
Neural Network ◽  
Anomaly Detection ◽  
Network Approach ◽  
Neural Network Approach
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