Multidimensional query processing algorithm by dimension transformation.

Multidimensional query processing is an important access pattern for multidimensional scientific data. We propose an in-memory multidimensional query processing algorithm for dense data using a higher-dimensional array. We developed a new array system namely a Converted two-dimensional Array (C2A) of a multidimensional array of dimension n ([Formula: see text]) where the n dimensions are transformed into 2 dimensions. Using the C2A, we design and analyze less complex algorithms that show improve performance for data locality and cache miss rate. Therefore, improved performance for data retrieval is achieved. We demonstrate algorithms for single key and range key queries for both Traditional Multidimensional Array(TMA) and C2A. We also compare the performance of both schemes. The cost of index computation gets high when the number of dimensions increases in a TMA but the proposed C2A based algorithm shows less computation cost. The cache miss rate is also lower for in C2A based algorithm than TMA based algorithm. Theoretical and experimental results show that the performance of C2A based algorithm outperforms the TMA-based algorithms.

A Transfer Learning-Based Approach with Deep CNN for COVID-19- and Pneumonia-Affected Chest X-ray Image Classification.

The COVID-19 pandemic creates a significant impact on everyone's life. One of the fundamental movements to cope with this challenge is identifying the COVID-19-affected patients as early as possible. In this paper, we classified COVID-19, Pneumonia, and Healthy cases from the chest X-ray images by applying the transfer learning approach on the pre-trained VGG-19 architecture. We use MongoDB as a database to store the original image and corresponding category. The analysis is performed on a public dataset of 3797 X-ray images, among them COVID-19 affected (1184 images), Pneumonia affected (1294 images), and Healthy (1319 images) (https://www.kaggle.com/tawsifurrahman/covid19-radiography-database/version/3). This research gained an accuracy of 97.11%, average precision of 97%, and average Recall of 97% on the test dataset.

HOG + CNN Net: Diagnosing COVID-19 and Pneumonia by Deep Neural Network from Chest X-Ray Images.

Coronavirus disease 2019 in short COVID-19 is a contagious disease caused by coronavirus SARS-CoV-2, which has caused a global pandemic and still infecting millions around the globe. COVID-19 has made an enormous impact on everybody's day-to-day life. One of the main strengths of COVID-19 is its extraordinary infectious capability. Early detection systems can thus play a big role in curbing the exponential growth of COVID-19. Some medical radiography techniques, such as chest X-rays and chest CT scans, are used for fast and reliable detection of coronavirus-induced pneumonia. In this paper, we propose a histogram of oriented gradients and deep convolutional network-based model that can find out the specific abnormality in frontal chest X-ray images and effectively classify the data into COVID-19 positive, pneumonia positive, and normal classes. The proposed system performed effectively in terms of various performance measures and proved capable as an effective early detection system.