Implementation of Grey Scale Normalization in Machine Learning & Artificial Intelligence for Bioinformatics using Convolutional Neural Networks

Machine Learning is a trending field nowadays and is very well known as an application of Artificial Intelligence (AI). Machine learning makes use of secure arithmetical algorithms to construct computers assignment in a constructive way without being unequivocally programmed. The algorithms acquire freedom of a participated value and estimate output for this by utilizing definite arithmetical methods. The key function of machine learning is to create smart machines that can visualize and work related to human beings. Artificial Intelligence has been witnessing a massive improvement in bridging the gap among the capabilities of humans and technologies. Similarly, one of the characteristics of the field were tried to combine the outstanding effects. A Convolution Neural Network (CNN) is a sort of Deep Learning algorithm which can obtain an input image, assign consequence to different aspects in the image and capable to differentiate one from the other. The pre-processing requirement in a CNN is lesser as compared to other classification algorithms. CNN has the capability to find out these filter's characteristics. Bioinformatics is a term that is a mixture of two terms bio and informatics. Bio means associated with biology and informatics means in a sequence of information. Thus bioinformatics is an area that deals with handing out and accepting biological statistics using the computational and arithmetical approaches. Machine Learning has added up to of applications in the area of bioinformatics. Machine Learning finds its submission in the subsequent subfields of bioinformatics. The aim of this article is to perform a grayscale normalization of a selected image and thereafter to reduce the effect of illumination differences. Normalization is considered so that CNN works in a faster manner. Different models are available but Keras model is selected to perform this task. Keras supports the style of data preparation for image statistics via the Image Data Generator group and Application Programming Interface (API). The Image Data Generator group in Keras provides a matching set of techniques for scaling pixel standards in the image dataset subsequent to modeling. The Keras functional API provides an additional flexible approach for significant models. It particularly allows identifying several input or output models as well as models that allocate layers.