Handwritten character recognition is one of the practically important issues in pattern recognition applications. The applications of digit recognition include in postal mail sorting, bank check processing, form data entry, etc. The main problem lies within the ability on developing an efficient algorithm that can recognize hand written digits, which is submitted by users by the way of a scanner, tablet, and other digital devices. This paper presents an approach to off-line handwritten digit recognition based on different machine learning techniques. The main objective of this paper is to ensure the effectiveness and reliability of the approached recognition of handwritten digits. Several machines learning algorithms (i.e. Multilayer Perceptron, Support Vector Machine, Naïve Bayes, Bayes Net, Random Forest, J48, and Random Tree) have been used for the recognition of digits using WEKA. The experimental results showed that the highest accuracy was obtained by Multilayer Perceptron with the value of 90.37%.
A comparative investigation in the cell performance of Copper Indium Gallium Selenide (CIGS) thin-film solar cell has been reported. The main objective behind our work is to present the effect of the doping concentration on each layer i.e. window layer (ZnO), buffer layer (CdS) and absorption layer (CIGS) in the CIGS solar cell to find out the optimum doping concentration using ADEPT 2.0, a 1D simulation software. The device parameters are optimized separately for each layer. Energy conversion efficiency is calculated from light J-V characteristics curve. A total-area efficiency of 19•75% for ZnO:Al/i-ZnO/CdS/CIGS based thin-film solar cells has been reported.
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