Automatic Question Generation (AQG) systems are applied in a myriad of domains to generate questions from sources such as documents, images, knowledge graphs to name a few. With the rising interest in such AQG systems, it is equally important to recognize structured data like tables while generating questions from documents. In this paper, we propose a single model architecture for question generation from tables along with text using “Text-to-Text Transfer Transformer” (T5) - a fully end-to-end model which does not rely on any intermediate planning steps, delexicalization, or copy mechanisms. We also present our systematic approach in modifying the ToTTo dataset, release the augmented dataset as TabQGen along with the scores achieved using T5 as a baseline to aid further research.

Leishmaniasis is an endemic parasitic disease, predominantly found in the poor locality of Africa, Asia and Latin America. It is associated with malnutrition, weak immune system of people and their housing locality. At present, it is diagnosed by microscopic identification, molecular and biochemical characterisation or serum analysis for parasitic compounds. In this study, we present a new approach for diagnosing Leishmaniasis using cognitive computing. The Genetic datasets of leishmaniasis are collected from Gene Expression Omnibus database and it's then processed. The algorithm for training and developing a model, based on the data is prepared and coded using python. The algorithm and their corresponding datasets are integrated using TensorFlow dataframe. A feed forward Artificial Neural Network trained model with multi-layer perceptron is developed as a diagnosing model for Leishmaniasis, using genetic dataset. It is developed using recurrent neural network. The cognitive model of the trained network is interpreted using the maps and mathematical formula of the influencing parameters. The credit of the system is measured using the accuracy, loss and error of the system. This integrated system of the leishmaniasis genetic dataset and neural network proved to be the good choice for diagnosis with higher accuracy and lower error. Through this approach, all records of the data are effectively incorporated into the system. The experimental results of feed forward multilayer perceptron model after normalization; mean square error (219.84), loss function (1.94) and accuracy (85.71%) of the model, shows good fit of model with the process and it could possibly serve as a better solution for diagnosing Leishmaniasis in future, using genetic datasets.The code is available in Github repository: https://github.com/shailzasingh/Machine-Learning-code-for-analyzing-genetic-dataset-in-Leishmaniasis

The outbreak of the SARS CoV-2, referred to as COVID-19, was initially reported in 2019 and has swiftly spread around the world. The identification of COVID-19 cases is one of the key factors to inhibit the spread of the virus. While there are multiple ways to diagnose COVID-19, these techniques are often expensive, time-consuming, or not readily available. Detection of COVID-19 using a radiological examination of Chest X-Rays provides a more viable, rapid, and efficient solution as it is easily available in most countries. The paper outlines a method that employs an unsupervised convolutional filter learning using Convolutional Autoencoder (CAE) followed by applying it to COVID-19 classification as a downstream task. This shows that the proposed technique provides state-of-the-art results with an average accuracy of 99.7%, AUC of 99.7%, specificity of 99.8%, sensitivity of 99.6%, and F1-score of 99.6%. We release the data and code for this work to aid further research.