The automatic generation of image captions has received considerable attention. The problem of evaluating caption generation systems, though, has not been that much explored. We propose a novel evaluation approach based on comparing the underlying visual semantics of the candidate and ground-truth captions. With this goal in mind we have defined a semantic representation for visually descriptive language and have augmented a subset of the Flickr-8K dataset with semantic annotations. Our evaluation metric (BAST) can be used not only to compare systems but also to do error analysis and get a better understanding of the type of mistakes a system does. To compute BAST we need to predict the semantic representation for the automatically generated captions. We use the Flickr-ST dataset to train classifiers that predict STs so that evaluation can be fully automated 1 .

The medical image classification system is an important subject in the field of biotechnology. Here, the network is trained with a large amount of computation to obtain high accuracy rate [1]. Chest X-rays images (CXRs) are broadly used in identifying the abnormalities in the chest area. Automatic detecting techniques are used in most of the diagnosing process, to improve the accuracy rate of abnormality detection. The main objective of this work is to prove the range of error, loss and accuracy by using the convolution neural networks (CNNs) model for detecting tuberculosis in the chest images. Tremendous progress has been made in deep learning models for classifying medical images.

This paper proposes a method for the automatic detection of optic disc in retinal images. In the diagnosis and grading, the essential step is recognition of optic disk for diabetic retinopathy. The analysis of directional cross-section profile focused on the local maximum pixel of pre-processed image is realized by the proposed method using optic disc detection. Each profile is implemented by peak detection and property like shape, size, and height of the peak are estimated. The statistical measure of the estimated values for the attributes, where the orientation of the cross-section changes the constitute feature used in morphological classification to exclude encourages candidates. The result is to find the patient is affected by diabetics or not.

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The use of fuzzy logic has no limits in for its power to solve real life problems either for control or for information. In the realm of information transfer through coding schemes, there are certain probabilistic algorithms currently in use very much for digital communications. To examine the feasibility of rethinking one of these problems based on the fuzzy logic principle has been considered. For this purpose, the principles relating to the basic hidden Markov Model has been discussed. The model is explained with an example in a lucid manner and the three problems associated with the solution of such models is brought out, with an example on speech recognition as well, given in the appendix, as drawn from current recent literature. Speech recognition in a computer system domain may be defined as the ability of computer systems to accept spoken words in an audio format -such as wav or raw and then generate its content in text format. Speech recognition in a computer domain involves various steps with issues attached with them. In this paper, we focus on tackling these various issues while implementing a speaker-independent.