Artificial Intelligence (AI) has been applied successfully in many real-life domains for solving complex problems. With the invention of Machine Learning (ML) paradigms, it becomes convenient for researchers to predict the outcome based on past data. Nowadays, ML is acting as the biggest weapon against the COVID-19 pandemic by detecting symptomatic cases at an early stage and warning people about its futuristic effects. It is observed that COVID-19 has blown out globally so much in a short period because of the shortage of testing facilities and delays in test reports. To address this challenge, AI can be effectively applied to produce fast as well as cost-effective solutions. Plenty of researchers come up with AI-based solutions for preliminary diagnosis using chest CT Images, respiratory sound analysis, voice analysis of symptomatic persons with asymptomatic ones, and so forth. Some AI-based applications claim good accuracy in predicting the chances of being COVID-19-positive. Within a short period, plenty of research work is published regarding the identification of COVID-19. This paper has carefully examined and presented a comprehensive survey of more than 110 papers that came from various reputed sources, that is, Springer, IEEE, Elsevier, MDPI, arXiv, and medRxiv. Most of the papers selected for this survey presented candid work to detect and classify COVID-19, using deep-learning-based models from chest X-Rays and CT scan images. We hope that this survey covers most of the work and provides insights to the research community in proposing efficient as well as accurate solutions for fighting the pandemic.

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Aims: The manuscript aims at designing and developing a model for optimum contrast enhancement of an input image. The output image of model ensures the minimum noise, the maximum brightness and the maximum entropy preservation. Objectives: * To determine an optimal value of threshold by using the concept of entropy maximization for segmentation of all types of low contrast images. * To minimize the problem of over enhancement by using a combination of weighted distribution and weighted constrained model before applying histogram equalization process. * To provide an optimum contrast enhancement with minimum noise and undesirable visual artefacts. * To preserve the maximum entropy during the contrast enhancement process and providing detailed information recorded in an image. * To provide the maximum mean brightness preservation with better PSNR and contrast. * To effectively retain the natural appearance of an images. * To avoid all unnatural changes that occur in Cumulative Density Function. * To minimize the problems such as noise, blurring and intensity saturation artefacts. Methods: 1. Histogram Building. 2. Segmentation using Shannon’s Entropy Maximization. 3. Weighted Normalized Constrained Model. 4. Histogram Equalization. 5. Adaptive Gamma Correction Process. 6. Homomorphic Filtering. Results: Experimental results obtained by applying the proposed technique MEWCHE-AGC on the dataset of low contrast images, prove that MEWCHE-AGC preserves the maximum brightness, yields the maximum entropy, high value of PSNR and high contrast. This technique is also effective in retaining the natural appearance of an images. The comparative analysis of MEWCHE-AGC with existing techniques of contrast enhancement is an evidence for its better performance in both qualitative as well as quantitative aspects. Conclusion: The technique MEWCHE-AGC is suitable for enhancement of digital images with varying contrasts. Thus useful for extracting the detailed and precise information from an input image. Thus becomes useful in identification of a desired regions in an image.

Background: Diabetes is spreading in the entire world. In a survey, it is observed that every generation from child to old age people are suffering from diabetes. If diabetes is not identified in time, it may lead to deadliest disease. Prediction of diabetes is of the utmost challenging task by machines. In the human body, diabetes is one of the perilous maladies that creates depended disease such as kidney disease, heart attack, blindness etc. Thus it is very important to diagnose diabetes in time. Objective: Our target is to develop a system using Artificial Neural Network(ANN), with the ability to predict whether a patient suffers from diabetes or not. Method: This paper illustrates various machine learning techniques in form of literature review; such as Support Vector Machine, Naïve Bayes, K Nearest Neighbor, Decision Tree, Random Forest Etc. We applied ANN to predict diabetes. In this paper, the architecture of ANN consists of four hidden layers each of six neurons and one output layer with one neuron. Optimizer used for the architecture is ‘Adam’. Results: We have Pima Indian diabetes dataset of sufficient number of patients with nine different symptoms with respect to the patients and nine different features in connection with the mathematical computation/prediction. Hence we bifurcate the dataset into training and testing set in majority and minority ratio of 80:20 respectively. It facilitates us the majority patient’s data to be used as training set and minority data to be used as testing set. We train our network for multiple epoch with different activation function. We used four hidden layers with six neurons in each hidden layer and one output layer. On the hidden layer, we used multiple activation functions such as sigmoid, ReLU etc. and obtained beat accuracy (88.71%) in 600 epochs with ReLU activation function. On the output layer, we used only sigmoid activation function because we have only two classes in our dataset. Conclusion: Diabetes prediction by machine is a challenging task. So many machine learning algorithms exist to predict the diabetes such as Naïve Bayes, decision tree, K nearest neighbor, support vector machine etc. This paper presents a novel approach to predict whether a patient has diabetes or not based on Pima Indian diabetes dataset. In this paper, we used artificial neural network to train out network and it is observed that artificial neural network approach performs better than all other classifiers

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