Training a Feed-forward Neural Network with Artificial Bee Colony Based Backpropagation Method

Back-propagation algorithm is one of the most widely used and popular techniques to optimize the feed forward neural network training. Nature inspired meta-heuristic algorithms also provide derivative-free solution to optimize complex problem. Artificial bee colony algorithm is a nature inspired meta-heuristic algorithm, mimicking the foraging or food source searching behaviour of bees in a bee colony and this algorithm is implemented in several applications for an improved optimized outcome. The proposed method in this paper includes an improved artificial bee colony algorithm based back-propagation neural network training method for fast and improved convergence rate of the hybrid neural network learning method. The result is analysed with the genetic algorithm based back-propagation method, and it is another hybridized procedure of its kind. Analysis is performed over standard data sets, reflecting the light of efficiency of proposed method in terms of convergence speed and rate.Comment: 14 Pages, 11 figure

An Improved Gauss-Newtons Method based Back-propagation Algorithm for Fast Convergence

The present work deals with an improved back-propagation algorithm based on Gauss-Newton numerical optimization method for fast convergence. The steepest descent method is used for the back-propagation. The algorithm is tested using various datasets and compared with the steepest descent back-propagation algorithm. In the system, optimization is carried out using multilayer neural network. The efficacy of the proposed method is observed during the training period as it converges quickly for the dataset used in test. The requirement of memory for computing the steps of algorithm is also analyzed.Comment: 7 pages, 6 figures,2 tables, Published with International Journal of Computer Applications (IJCA

Energy and decay width of the pi-K atom

The energy and decay width of the pi-K atom are evaluated in the framework of the quasipotential-constraint theory approach. The main electromagnetic and isospin symmetry breaking corrections to the lowest-order formulas for the energy shift from the Coulomb binding energy and for the decay width are calculated. They are estimated to be of the order of a few per cent. We display formulas to extract the strong interaction S-wave pi-K scattering lengths from future experimental data concerning the pi-K atom.Comment: 37 pages, 5 figures, uses Axodra

Analysis of Statistical Hypothesis based Learning Mechanism for Faster Crawling

The growth of world-wide-web (WWW) spreads its wings from an intangible quantities of web-pages to a gigantic hub of web information which gradually increases the complexity of crawling process in a search engine. A search engine handles a lot of queries from various parts of this world, and the answers of it solely depend on the knowledge that it gathers by means of crawling. The information sharing becomes a most common habit of the society, and it is done by means of publishing structured, semi-structured and unstructured resources on the web. This social practice leads to an exponential growth of web-resource, and hence it became essential to crawl for continuous updating of web-knowledge and modification of several existing resources in any situation. In this paper one statistical hypothesis based learning mechanism is incorporated for learning the behavior of crawling speed in different environment of network, and for intelligently control of the speed of crawler. The scaling technique is used to compare the performance proposed method with the standard crawler. The high speed performance is observed after scaling, and the retrieval of relevant web-resource in such a high speed is analyzed.Comment: 14 Pages, 7 Figures This paper has been withdrawn by the author due to a crucial sign error in page no. 3,4,7 and 11. The error is also observed with equation no in page 1

The role of case proximity in transmission of visceral leishmaniasis in a highly endemic village in Bangladesh

Data collection containing: [1] Odds ratios for VL and asymptomatic infection risk based on proximity to infected individuals (S1 Table); [2] Definitions of model likelihood and deviance information criterion, and details of MCMC algorithm (S1 Text); [3] Data on visceral leishmaniasis status and leishmanin skin test status of 2494 individuals in the study area, including dates of onset, diagnosis, treatment, and, where applicable, relapse and treatment for relapse for 183 VL cases (S1 Data); [4] Matrix of pairwise distances between all individuals in the study (S2 Data); [5] Metadata for S1 and S2 Data (S3 Data); [6] Locations of VL cases in para 2 by year of onset, 1999-2004 (S1 Fig); [7] Locations of VL cases in para 3 by year of onset, 1999-2004 (S2 Fig); [8] Deviance distributions for the different models (S3 Fig