An Efficient Deep Learning Model To Detect COVID-19 Using Chest X-Ray Images

The tragic pandemic of COVID-19, due to the Severe Acute Respiratory Syndrome coronavirus-2 or SARS-CoV-2, has shaken the entire world, and has significantly disrupted healthcare systems in many countries. Because of the existing challenges and controversies to testing for COVID-19, improved and cost-effective methods are needed to detect the disease. For this purpose, machine learning (ML) has emerged as a strong forecasting method for detecting COVID-19 from chest X-ray images. In this paper, we used a Deep Learning Method (DLM) to detect COVID-19 using chest X-ray (CXR) images. Radiographic images are readily available and can be used effectively for COVID-19 detection compared to other expensive and time-consuming pathological tests. We used a dataset of 10,040 samples, of which 2143 had COVID-19, 3674 had pneumonia (but not COVID-19), and 4223 were normal (not COVID-19 or pneumonia). Our model had a detection accuracy of 96.43% and a sensitivity of 93.68%. The area under the ROC curve was 99% for COVID-19, 97% for pneumonia (but not COVID-19 positive), and 98% for normal cases. In conclusion, ML approaches may be used for rapid analysis of CXR images and thus enable radiologists to filter potential candidates in a time-effective manner to detect COVID-19

A Fourier Transformation Based Method to Mine Peptide Space for Antimicrobial Activity

Background Naturally occurring antimicrobial peptides are currently being explored as potential candidate peptide drugs. Since antimicrobial peptides are part of the innate immune system of every living organism, it is possible to discover new candidate peptides using the available genomic and proteomic data. High throughput computational techniques could also be used to virtually scan the entire peptide space for discovering out new candidate antimicrobial peptides. Result We have identified a unique indexing method based on biologically distinct characteristic features of known antimicrobial peptides. Analysis of the entries in the antimicrobial peptide databases, based on our indexing method, using Fourier transformation technique revealed a distinct peak in their power spectrum. We have developed a method to mine the genomic and proteomic data, for the presence of peptides with potential antimicrobial activity, by looking for this distinct peak. We also used the Euclidean metric to rank the potential antimicrobial peptides activity. We have parallelized our method so that virtually any given protein space could be data mined, in search of antimicrobial peptides. Conclusion The results show that the Fourier transform based method with the property based coding strategy could be used to scan the peptide space for discovering new potential antimicrobial peptides

A Novel Medical Prognosis System for Breast Cancer

The availability of huge variety of medical dataset coming from different sources is boon for automatic medical prognosis system. Breast cancer or breast carcinoma is one of the deadly diseases of modern times. Here in this paper, we emphasize on exploring unsupervised learning techniques. The objectives of this paper are to analyze the breast cancer dataset using different clustering methods to understand the correlations of the attributes present in the dataset and then investigate with different algorithm like random forest (RF), support vector machine (SVM), multilayer perceptron (MLP) and J48 to get the best model for our prediction and classification. So, we proposed a novel medical prognosis system (NMPS) which is an ensemble learning model combines all these algorithms and gives all possible results stated above in the purview of unsupervised learning classification with different clustering techniques

MPI/FT: A Model-based Approach to Low-overhead Fault Tolerant Messagepassing

Fault tolerance in parallel systems has traditionally been achieved through a combination of redundancy and checkpointing methods. This notion has also been extended to message-passing systems with user-transparent process checkpointing and message logging. Furthermore, studies of multiple types of rollback and recovery have been reported in literature, ranging from communication-induced checkpointing to pessimistic and synchronous solutions. However, many of these solutions incorporate high overhead because of their inability to utilize application level information. This paper describes the design and implementation of MPI/FT, a highperformance MPI-1.2 implementation enhanced with low-overhead functionality to detect and recover from process failures. The strategy behind MPI/FT is that fault tolerance in message-passing middleware can be optimized based on an application’s execution model derived from its communication topology and parallel programming semantics. MPI/FT exploits the specific characteristics of two parallel application execution models in order to optimize performance. MPI/FT also introduces the self-checking thread tha

A Novel Distributed Database Architectural Model For Mobile Cloud Computing

Cloud computing is the way by which we connect to servers, large systems into a distributed secure manner without worrying about local memory limits. Here, in this paper, we proposed a Novel distributed database architectural model for mobile cloud computing (NDDAMMCC). Accelerating the exponential growth of wireless technologies and Internet which are following Nielsen’s Law of Internet Bandwidth, we are in the new era of cloud computing. In the recent technological era, smart mobile devices play a big role in all sort of day-by-day human needs. The applicability is so huge that the number of apps install on a mobile system becomes a hazard due to local memory limitations for mobile phone users and demands an alternative approach to solve this local memory problems. Mobile cloud computing (MCC) is the ultimate mechanism to this issue, and our model presents a promising path in this new kind of cloud computing technology

Image Restoration Based on the Fast Marching Method and Block Based Sampling

In this paper, we propose an efficient image inpainting algorithm by introducing important aspects and improvements corresponding to the filling order of the pixels in the target region and texture synthesis in a dynamic searching range. The algorithm consists of two parts. The first part decides the filling order of the pixels in the target regions based on the high accuracy fast marching method. The second part of the algorithm implicitly assumes a Markov random field model for textured image regions and computes blocks of texture using an efficient search process and the SSD (Sum of Squared Differences) measure. The algorithm is straightforward to implement and restores the target regions with visually plausible quality that is at par or better than several existing methods, with a lower execution cost. (C) 2010 Elsevier Inc. All rights reserved

VOLUMETRIC COLOR IMAGE COMPRESSION USING SET PARTITIONING METHODS

Abstract- In this work, we present the applications of three-dimensional set partitioning methods to the sequence of still color images. The set partitioning methods we use in this paper are SPIHT, a state-of-the-art encoder and SPECK, a more recently developed, low complexity encoder. The three-dimensional versions of these methods are based on the observation that the sequences of images are contiguous in the temporal axis and there is no motion between slices. Therefore, the 3D discrete wavelet transform can fully exploit the inter-slices correlations. The set partitioning techniques involve a progressive ”bitplane ” coding of the wavelet coefficients, where the SPECK uses a cube-splitting quantization structure and the SPIHT uses a zerotree-like quantization structure. We extend the 3D-SPECK and 3D-SPIHT to code the color image sequences and call these schemes 3D-CSPECK and 3D-CSPIHT. Rate-distortion (Peak Signal-to-Noise Ratio (PSNR) vs. bit rate) performances were presented by comparing 3D-CSPECK and 3D-CSPIHT on one sequence of Visible Human datasets. Results show that 3D-CSPECK is comparable to 3D-CSPIHT, which matches the published results of gray scale image sequence compression