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Computer-assisted colour prediction and quality control have become increasingly important to the dyeing process in many consumer goods manufacturing industries, including textile and leather. The most challenging aspect concerns dye recipe prediction for the production of the required shade on a given substrate. Computer recipe prediction based on the conventional and widely used Kubelka-Munk model often fails under a variety of conditions. In the present investigation, an attempt has been made to develop an artificial neural network model to predict colour in terms of tristimulus values (X, Y, Z) given the concentration of dyes. An artificial neural network model was trained with 300 pairs of known input vectors, i.e. dye concentrations, and output vectors, i.e. colour parameters, using a backpropagation algorithm. The artificial neural network topology consists of three neurons in the input layer to represent the concentration of dyes, three neurons in the output layer to represent the tristimulus values X, Y, and Z, and five neurons in the hidden layer with a log-sigmoid transfer function. The artificial neural network results showed a good level of colour prediction during the training and testing phase. The results also indicate that the artificial neural network has the potential to give better predictive performance than the conventional Kubelka-Munk model.

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Diagnosis of COVID-19 pneumonia using patients’ chest X-Ray images is new but yet important task in the field of medicine. Researchers from different parts of the globe have developed many deep learning models to classify COVID-19. The performance of feature extraction and classifier plays a vital role in the recognizing the different patterns in the image. The pivotal process is the extraction of optimum features from the chest X-Ray images. The main goal of this study is to design an efficient hybrid algorithm that integrates the robustness of MobileNet (using transfer learning approach) to extract features and Support Vector Machine (SVM) to classify COVID-19. Experiments were conducted to test the proposed algorithm and it was found to have a high classification accuracy of 95%.

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Diagnosing COVID-19, current pandemic disease using Chest X-ray images is widely used to evaluate the lung disorders. As the spread of the disease is enormous many medical camps are being conducted to screen the patients and Chest X-ray is a simple imaging modality to detect presence of lung disorders. Manual lung disorder detection using Chest X-ray by radiologist is a tedious process and may lead to inter and intra-rate errors. Various deep convolution neural network techniques were tested for detecting COVID-19 abnormalities in lungs using Chest X-ray images. This paper proposes deep learning model to classify COVID-19 and normal chest X-ray images. Experiments are carried out for deep feature extraction, fine-tuning of convolutional neural networks (CNN) hyper parameters, and end-to-end training of four variants of the CNN model. The proposed CovMnet provide better classification accuracy of 97.4% for COVID-19 /normal than those reported in the previous studies. The proposed CovMnet model has potential to aid radiologist to monitor COVID-19 disease and proves to be an efficient non-invasive COVID-19 diagnostic tool for lung disorders.