With the advancement of modern technology the necessity of pattern recognition has increased a lot. Character recognition it's part of pattern recognition. In last few decades there has been some researches on optical character recognition(OCR) for so many languages like -Roman, Japanese, African, Chinese, English and some researches of Indian language like -Tamil, Devanagari, Telugu, Gujratietc and so many other languages. There are very few works on handwritten Bangla character recognition. As it is tough to understand like Bangla language because of different people handwritten varies in fervidity or formation, stripe and angle. For this it's so much challenging to work in this field. In some researches SVM, MLP, ANN, HMM, HLP & CNN has been used for handwritten Bangla character recognition. In this paper an attempt is made to recognize handwritten Bangla character using Convolutional Neural Network along with the method of inception module without feature extraction. The feature extraction occurs during the training phase rather than the dataset preprocessing phase. As CNN can't take input data that varying in shape ,so had to rescaled the dataset images at fixed different size. In total final dataset contains 100000 images of dimension 28x28. 85000 images is used for training and 3000 images is used for testing. After analyzing the results a conclusion is derived on the proposed work and stated the future goals and plans to achieve highest success and accuracy rate.

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This article introduces Fuzzy Inspired Bat Algorithm (FIBA), which is an improved variant of the original Bat algorithm. The novelty of FIBA lies in the integration of a fuzzy controller with the basic Bat algorithm that tries to bring balance between the degree of explorations and exploitations during the mutation operation. Another novelty of FIBA is the introduction of a step size parameter, maintained separately for every candidate solution, to customize and control the mix of explorative and exploitative operations around each candidate solution. FIBA is tested on a standard benchmark set that includes 10 complex, scalable, high dimensional functions. The results on benchmark functions reveal that FIBA can perform sufficiently well, and often better than the original Bat algorithm and another recently proposed improved Bat variant. Such improvements on the experimental results imply that the fuzzy technique adopted by FIBA might be effective on other existing problems as well, and hence demand further research and investigation.

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