COVID-19 syndrome has extensively escalated worldwide with the induction of the year 2020 and has resulted in the illness of millions of people. COVID-19 patients bear an elevated risk once the symptoms deteriorate. Hence, early recognition of diseased patients can facilitate early intervention and avoid disease succession. This article intends to develop a hybrid deep neural networks (HDNNs), using computed tomography (CT) and X-ray imaging, to predict the risk of the onset of disease in patients suffering from COVID-19. To be precise, the subjects were classified into 3 categories namely normal, Pneumonia, and COVID-19. Initially, the CT and chest X-ray images, denoted as ‘hybrid images’ (with resolution 1080 × 1080) were collected from different sources, including GitHub, COVID-19 radiography database, Kaggle, COVID-19 image data collection, and Actual Med COVID-19 Chest X-ray Dataset, which are open source and publicly available data repositories. The 80% hybrid images were used to train the hybrid deep neural network model and the remaining 20% were used for the testing purpose. The capability and prediction accuracy of the HDNNs were calculated using the confusion matrix. The hybrid deep neural network showed a 99% classification accuracy on the test set data.
The conventional and agile software development process models are proposed and used nowadays in software industry to meet emergent requirements of the customers. Conventional software development models such as Waterfall, V model and RUP have been predominant in industry until mid 1990s, but these models are mainly focused on extensive planning, heavy documentation and team expertise which suit only to medium and large scale projects. The Rational Unified Process is one of the widely used conventional models. Agile process models got attention of the software industry in last decade due to limitations of conventional models such as slow adaptation to rapidly changing business requirements and they overcome problems of schedule and cost. Extreme Programming is one of the most useful agile methods that provide best engineering practices for a good quality product at small scale. XP follows the iterative and incremental approach, but its key focus is on programming, and reusability becomes arduous. In this paper, we present characteristics, strengths, and weaknesses of RUP and XP process models, and propose a new hybrid software development model eXRUP(eXtreme Programming and Rational Unified Process), which integrates the strengths of RUP and XP while suppressing their weaknesses. The proposed process model is validated through a controlled case study.
In the domain of computer vision, the efficient representation of an image feature vector for the retrieval of images remains a significant problem. Extensive research has been undertaken on Content-Based Image Retrieval (CBIR) using various descriptors, and machine learning algorithms with certain descriptors have significantly improved the performance of these systems. In this proposed research, a new scheme for CBIR was implemented to address the semantic gap issue and to form an efficient feature vector. This technique was based on the histogram formation of query and dataset images. The auto-correlogram of the images was computed w.r.t RGB format, followed by a moment’s extraction. To form efficient feature vectors, Discrete Wavelet Transform (DWT) in a multi-resolution framework was applied. A codebook was formed using a density-based clustering approach known as Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The similarity index was computed using the Euclidean distance between the feature vector of the query image and the dataset images. Different classifiers, like Support Vector (SVM), K-Nearest Neighbor (KNN), and Decision Tree, were used for the classification of images. The set experiment was performed on three publicly available datasets, and the performance of the proposed framework was compared with another state of the proposed frameworks which have had a positive performance in terms of accuracy.
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