/pdf/a-break-in-the-clouds-towards-a-cloud-definition-svovsw1r5a.pdf

A Break in the Clouds: Towards a Cloud Definition

A Combined Deep CNN-LSTM Network for the Detection of Novel Coronavirus (COVID-19) Using X-ray Images

Nowadays automatic disease detection has become a crucial issue in medical science with the rapid growth of population. Coronavirus (COVID-19) has become one of the most severe and acute diseases in very recent times that has been spread globally. Automatic disease detection framework assists the doctors in the diagnosis of disease and provides exact, consistent, and fast reply as well as reduces the death rate. Therefore, an automated detection system should be implemented as the fastest way of diagnostic option to impede COVID-19 from spreading. This paper aims to introduce a deep learning technique based on the combination of a convolutional neural network (CNN) and long short-term memory (LSTM) to diagnose COVID-19 automatically from X-ray images. In this system, CNN is used for deep feature extraction and LSTM is used for detection using the extracted feature. A collection of 421 X-ray images including 141 images of COVID-19 is used as a dataset in this system. The experimental results show that our proposed system has achieved 97% accuracy, 91% specificity, and 100% sensitivity. The system achieved desired results on a small dataset which can be further improved when more COVID-19 images become available. The proposed system can assist doctors to diagnose and treatment the COVID-19 patients easily.

https://medrxiv.org/content/10.1101/2020.06.18.20134718v1.full.pdf

Many health systems over the world have collapsed due to limited capacity and a dramatic increase of suspected COVID-19 cases. What has emerged is the need for finding an efficient, quick and accurate method to mitigate the overloading of radiologists' efforts to diagnose the suspected cases. This study presents the combination of deep learning of extracted features with the Q-deformed entropy handcrafted features for discriminating between COVID-19 coronavirus, pneumonia and healthy computed tomography (CT) lung scans. In this study, pre-processing is used to reduce the effect of intensity variations between CT slices. Then histogram thresholding is used to isolate the background of the CT lung scan. Each CT lung scan undergoes a feature extraction which involves deep learning and a Q-deformed entropy algorithm. The obtained features are classified using a long short-term memory (LSTM) neural network classifier. Subsequently, combining all extracted features significantly improves the performance of the LSTM network to precisely discriminate between COVID-19, pneumonia and healthy cases. The maximum achieved accuracy for classifying the collected dataset comprising 321 patients is 99.68%.

https://www.mdpi.com/1099-4300/22/5/517/pdf

Classification of Covid-19 coronavirus, pneumonia and healthy lungs in CT scans using Q-deformed entropy and deep learning features

As more and more mobile applications are developed, mobile app testing and quality assurance have become very important. Due to the diversity of mobile devices and platforms, compatibility testing for mobile apps has been identified as one urgent and challenging issue. There are two major reasons contributing to this issue. They are: a) the large number of mobile devices with diverse features and platforms which are upgraded frequently, b) a higher cost and complexity in mobile app compatibility testing. This paper proposes one optimized compatibility testing strategy using a statistical approach to reduce test costs, and improve engineer's operation efficiency. The paper provides a solution to generate an optimized compatibility test sequence for mobile apps using the K-Means statistical algorithm. A compatibility testing service has been proposed for mobile apps. Moreover, two case study results are reported to demonstrate its potential application and effectiveness.

Compatibility Testing Service for Mobile Applications

Progresses in the areas of artificial intelligence, machine learning, and medical imaging technologies have allowed the development of the medical image processing field with some astonishing results in the last two decades. These innovations enabled the clinicians to view the human body in high-resolution or three-dimensional cross-sectional slices, which resulted in an increase in the accuracy of the diagnosis and the examination of patients in a non-invasive manner. The fundamental step for magnetic resonance imaging (MRI) brain scans classifiers is their ability to extract meaningful features. As a result, many works have proposed different methods for features extraction to classify the abnormal growths in the brain MRI scans. More recently, the application of deep learning algorithms to medical imaging leads to impressive performance enhancements in classifying and diagnosing complicated pathologies, such as brain tumors. In this paper, a deep learning feature extraction algorithm is proposed to extract the relevant features from MRI brain scans. In parallel, handcrafted features are extracted using the modified gray level co-occurrence matrix (MGLCM) method. Subsequently, the extracted relevant features are combined with handcrafted features to improve the classification process of MRI brain scans with support vector machine (SVM) used as the classifier. The obtained results proved that the combination of the deep learning approach and the handcrafted features extracted by MGLCM improves the accuracy of classification of the SVM classifier up to 99.30%.

/pdf/combining-deep-and-handcrafted-image-features-for-mri-brain-1jolj7fbab.pdf

Combining Deep and Handcrafted Image Features for MRI Brain Scan Classification

Mobile cloud computing models security issues: A systematic review

Mobile cloud computing (MCC) enables mobile devices to exploit seamless cloud services via offloading, and has numerous advantages and increased security and complexity. Penetration testing of mobile applications has become more complex and expensive due to several parameters, such as the platform, device heterogeneity, context event types, and offloading. Numerous studies have been published in the MCC domain, whereas few studies have addressed the common issues and challenges of MCC testing. However, current studies do not address MCC and penetration testing. Therefore, revisiting MCC and penetration testing domains is essential to overcoming the inherent complexity and reducing costs. Motivated by the importance of revisiting these domains, this paper pursues two objectives: to provide a comprehensive systematic literature review (SLR) of the MCC, security and penetration testing domains and to establish the requirements for penetration testing of MCC applications. This paper has systematically reviewed previous penetration testing models and techniques based on the requirements in Kitchenham's SLR guidelines. The SLR outcome has indicated the following deficiencies: the offloading parameter is disregarded; studies that address mobile, cloud, and web vulnerabilities are lacking; and a MCC application penetration testing model has not been addressed by current studies. In particular, offloading and mobile state management are two new and vital requirements that have not been addressed to reveal hidden security vulnerabilities, facilitate mutual trust, and enable developers to build more secure MCC applications. Beneficial review results that can contribute to future research are presented.

/pdf/systematic-literature-review-on-penetration-testing-for-2c8lny6ia3.pdf

Systematic Literature Review on Penetration Testing for Mobile Cloud Computing Applications

https://onlinelibrary.wiley.com/doi/pdf/10.1002/spy2.145

Fog computing security and privacy for the Internet of Thing applications: State-of-the-art

As mobile cloud computing applications are becoming widely used to mitigate the limitations of mobile devices and exploit cloud services, testing these applications becomes critical Testing mobile cloud application is complicated further due to dynamic assign of cloud resource to applications that run on mobile devices, due to the increased multiple factors in test case generations and execution This paper will critically review the three related areas of mobile, cloud and mobile cloud applications testing, in terms of features and models Furthermore, it will show the necessity of a mobile cloud computing applications testing model due to its uniqueness from both native mobile application testing models and cloud applications testing models Based on this review, although testing mobile cloud computing applications is critical, there is currently no model to test mobile cloud computing applications

Ahmad Salah Al-Ahmad

Papers

Combining Deep and Handcrafted Image Features for MRI Brain Scan Classification

Mobile cloud computing models security issues: A systematic review

Systematic Literature Review on Penetration Testing for Mobile Cloud Computing Applications

Fog computing security and privacy for the Internet of Thing applications: State-of-the-art

Mobile Cloud Computing Testing Review