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49 results for “Learning algorithm and training”
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Research ArticleOpen accessIn the abstractAn Efficient and Effective Immune Based ClassifierShahram Golzari, Shyamala Doraisamy, Md Nasir Sulaiman, Nur Izura UdzirProblem statement: Artificial Immune Recognition System (AIRS) is most popular and effective immune inspired classifier. Resource competition is one stage of AIRS. Resource competition is done based on the number of allocated resources. AIRS uses a linear method to allocate resources. The linear resource allocation increases the training time of classifier. Approach: In this study, a new nonlinear resource allocation … …Journal of Computer Science, Vol. 7, Issue 2 · 25 Feb 2011 · 7,768 accessesView PDFCite
Research ArticleOpen accessIn the abstractNeural Network Change Detection Model for Satellite Images Using Textural and Spectral CharacteristicsA. K. Helmy, Gh. S. El-TaweelProblem statement: Change detection is the process of identifying difference of the state of an object or phenomena by observing it at different time. Essentially, it involves the ability to quantify temporal effects using multi-temporal data sets. Information about change is necessary for evaluating land cover and the management of natural resources. Approach: A neural network model based on both … …American Journal of Engineering and Applied Sciences, Vol. 3, Issue 4 · 4 Nov 2010 · 8,156 accessesView PDFCite
Research ArticleOpen accessIn the abstractImproving the Performance of Backpropagation Neural Network Algorithm for Image Compression/Decompression SystemOmaima N.A. AL-AllafProblem statement: The problem inherent to any digital image is the large amount of bandwidth required for transmission or storage. This has driven the research area of image compression to develop algorithms that compress images to lower data rates with better quality. Artificial neural networks are becoming attractive in image processing where high computational performance and parallel architectures are required. … …Journal of Computer Science, Vol. 6, Issue 11 · 26 Oct 2010 · 8,370 accessesView PDFCite
Research ArticleOpen accessIn the abstractNew Constructive Neural Network Architecture for Pattern ClassificationS. S. Sridhar, M. PonnavaikkoProblem statement: Constructive neural network learning algorithms provide optimal ways to determine the architecture of a multi layer perceptron network along with learning algorithms for determining appropriate weights for pattern classification problems. These algorithms initially start with small network and dynamically allow the network to grow by adding and training neurons as needed until a satisfactory solution is found. The … …Journal of Computer Science, Vol. 5, Issue 11 · 30 Nov 2009 · 6,565 accessesView PDFCite
Research ArticleOpen accessIn the abstractBoosting Kernel Discriminative Common Vectors for Face RecognitionC. Lakshmi, M. PonnavaikkoProblem statement: Kernel discriminative common vector (KDCV) was one of the most effective non-linear techniques for feature extraction from high dimensional data including images and text data. Approach: This study presented a new algorithm called Boosting Kernel Discriminative Common Vector (BKDCV) to further improve the overall performance of KDCV by integrating the boosting and KDCV techniques. Results: In BKDCV, the … …Journal of Computer Science, Vol. 5, Issue 11 · 30 Nov 2009 · 6,571 accessesView PDFCite
Research ArticleOpen accessIn the abstractLizard Learning Algorithm for Invariant Pattern RecognitionG. M. Rao, G. R. Babu, G. V. KumariResearches are keen to know astonishing and intricate details of the nature. Each creature has its own admiring abilities and performs their routine task in more efficient manner. The bug navigation system has drawn keen attention among research community to know how they are able to perform their routine task in utmost skillful manner. The lizard is capable of identifying … …Journal of Computer Science, Vol. 3, Issue 2 · 28 Feb 2007 · 7,489 accessesView PDFCite
Research ArticleOpen accessIn the abstractEnergy and Carbon Flux Coupling: Multi-ecosystem Comparisons Using Artificial Neural NetworkAssefa M. Melesse, Rodney S. HanleyA multi-ecosystems carbon flux simulation from energy fluxes is presented. A new statistical learning technique based on Artificial Neural Network (ANN) back propagation algorithm and multi-layer perceptron architecture was used in the CO2 simulation. Four input layers (net radiation, soil heat flux, sensible and latent heat flux) were used for training (calibration) and testing (verification) of model outputs. The 15-days … …American Journal of Applied Sciences, Vol. 2, Issue 2 · 28 Feb 2005 · 7,347 accessesView PDFCite
Research ArticleOpen accessIn the abstractChange Detection Using Neural Network with Improvement Factor in Satellite ImagesM. A. Fkirin, S. M. Badwai, Sayed A. MohamedProblem statement: The aim of this study is to investigate the applicability of using the neural network techniques in change detection of remotely sensed data. Approach: In addition, the tuning parameters of the network, such as encoding the output classes, adding the momentum term and learning rate, are investigated in order to achieve best network performance. Results: Neural network-based change … …American Journal of Environmental Sciences, Vol. 5, Issue 6 · 31 Dec 2009 · 6,648 accessesView PDFCite
Research ArticleOpen accessIn the abstractSelf-Generation ART-1 Neural Network with Gradient-Descent Method Aid for Latin Alphabet RecognitionMbaïtiga ZacharieProblem statement: In this study a self-generation ART-1 neural network that is an efficient algorithm that emulates the self-organizing pattern recognition developed to avoid the stability-plasticity dilemma in competitive networks learning, is presented for Latin alphabet recognition to use in a vision system for road sings recognition. Approach: The first step of our approach deals with the training process where … …Journal of Computer Science, Vol. 4, Issue 8 · 31 Aug 2008 · 6,566 accessesView PDFCite
Research ArticleOpen accessIn the abstractForecasting River Flow in the USA: A Comparison between Auto-Regression and Neural Network Non-Parametric ModelsAbdel K.M. Baareh, Alaa F. Sheta, Khaled A. KhnaifesForecasting a time series became one of the most challenging tasks to variety of data sets. The existence of large number of parameters to be estimated and the effect of uncertainty and outliers in the measurements makes the time series modeling too complicated. Recently, Artificial Neural Network (ANN) became quite successful tool to handle time series modeling problem. This paper … …Journal of Computer Science, Vol. 2, Issue 10 · 31 Oct 2006 · 6,962 accessesView PDFCite