A Novel Approach to detect COVID-19 from chest X-ray images using CNN

In light of the present COVID-19 pandemic, it is important to consider the worth of human life, prosperity, and quality of life while also realizing that it is difficult to restrict case spread and mortality. One of the most difficult challenges for practitioners is identifying individuals who are COVID19-infected and isolating patients to stop COVID transmission. Therefore, identifying the covid19 infection is important. For the detection of COVID-19, a 4-6-hour reverse transcriptase chain reaction is used. Chest X-rays provide us with a different method for detecting Coronavirus early in the disease phase. We detected properties from chest X-ray scans and divided them into three categories with VGG16 as well as ResNet50 deep learning algorithms: COVID-19, normal, and viral pneumonia. To test the model's accuracy in specialized cases, we injected them with 15153 scans. The average COVID-19 case detection accuracy for the ResNet50 model is 91.39%, compared to 89.34% for the VGG16 model. However, a larger dataset is required when using deep learning to identify COVID-19. It accurately detects situations, which is the desired outcome

A SimRank based Ensemble Method for Resolving Challenges of Partition Clustering Methods

323–327Traditional clustering techniques alone cannot resolve all challenges of partition-based clustering methods. In the partition based clustering, particularly in variants of K-means, initial cluster centre selection is a significant and crucial point. The dependency of final cluster is totally based on initial cluster centres; hence, this process is delineated to be most significant in the entire clustering operation. The random selection of initial cluster centres is unstable, since different cluster centre points are achieved during each run of the algorithm. Ensemble based clustering methods resolve challenges of partition-based methods. The clustering ensembles join several partitions generated by different clustering algorithms into a single clustering solution. The proposed ensemble methodology resolves initial centroid problems and improves the efficiency of cluster results. This method finds centroid selection through overall mean distance measure. The SimRank based similarity matrix find that the bipartite graph helps to ensemble

Blended Multi-Modal Deep ConvNet Features for Diabetic Retinopathy Severity Prediction

Diabetic Retinopathy (DR) is one of the major causes of visual impairment and blindness across the world. It is usually found in patients who suffer from diabetes for a long period. The major focus of this work is to derive optimal representation of retinal images that further helps to improve the performance of DR recognition models. To extract optimal representation, features extracted from multiple pre-trained ConvNet models are blended using proposed multi-modal fusion module. These final representations are used to train a Deep Neural Network (DNN) used for DR identification and severity level prediction. As each ConvNet extracts different features, fusing them using 1D pooling and cross pooling leads to better representation than using features extracted from a single ConvNet. Experimental studies on benchmark Kaggle APTOS 2019 contest dataset reveals that the model trained on proposed blended feature representations is superior to the existing methods. In addition, we notice that cross average pooling based fusion of features from Xception and VGG16 is the most appropriate for DR recognition. With the proposed model, we achieve an accuracy of 97.41%, and a kappa statistic of 94.82 for DR identification and an accuracy of 81.7% and a kappa statistic of 71.1% for severity level prediction. Another interesting observation is that DNN with dropout at input layer converges more quickly when trained using blended features, compared to the same model trained using uni-modal deep features.Comment: 18 pages, 8 figures, published in Electronics MDPI journa

Emission of Sulfur Dioxide from Sulfite-Treated Birch Leaves

In the present paper, we report the emission of S02 from 10 mM sulfite-treated birch ( Betula papyrifera Marsh) leaves both in light and in the dark. The emission reaches maximum at around 60 min during treatment. In some leaves, maximum emission is noticed even after 5 to 10 min of initiation of sulfitetreatment. It is also observed in the presence of 3{3,4-dichlorophenyl)-1-1 dimethylurea (DCMU). However, the emission rates are higher in light than in the dark or in the presence of DCMU. This emission is not observed with sulfate-treated leaves