Towards Secure Data Exchange in Peer-to-Peer Data Management Systems

In a peer-to-peer data management system (P2PDMS) peers exchange data in a pair-wise fashion on-the-fly in response to user queries without any centralized control. When peers exchange highly confidential data over an insecure communication channel, the data might be intercepted and read by intruders. As there is no centralized control for data exchange among peers in a P2PDMS, we cannot assume any central third party security infrastructure (e.g. PKI) to protect confidential data. This paper proposes a security protocol for data exchange in P2PDMSs based on pairing-based cryptography and data exchange policy. The protocol allows the peers to compute their secret session keys dynamically during data exchange session by computing a pairing on an elliptic curve, that is based on the policies between them.We show using a formal verification tool that the proposed protocol is safe, and is robust against different attacks including man-in-the middle, the masquerade, and the reply. Furthermore, the computational and communication overhead of the protocol are analyzed

Towards Secure Data Exchange in Peer-to-Peer Data Management Systems

In a peer-to-peer data management system (P2PDMS) peers exchange data in a pair-wise fashion on-the-fly in response to user queries without any centralized control. When peers exchange highly confidential data over an insecure communication channel, the data might be intercepted and read by intruders. As there is no centralized control for data exchange among peers in a P2PDMS, we cannot assume any central third party security infrastructure (e.g. PKI) to protect confidential data. This paper proposes a security protocol for data exchange in P2PDMSs based on pairing-based cryptography and data exchange policy. The protocol allows the peers to compute their secret session keys dynamically during data exchange session by computing a pairing on an elliptic curve, that is based on the policies between them.We show using a formal verification tool that the proposed protocol is safe, and is robust against different attacks including man-in-the middle, the masquerade, and the reply. Furthermore, the computational and communication overhead of the protocol are analyzed

Synchronizing Data through Update Queries in Interoperable E-Health and Technology Enhanced Learning Data Sharing Systems

Data interoperability and synchronization among heterogeneous data providers in collaborative e-health systems are challenging research issues. Efficient data management techniques promote an efficient way of sharing data. This paper describes existing approaches to data interoperability (platform independency) for exchanging and synchronizing data between heterogeneous data sources or various platforms. We also illustrate an update query execution protocol, which can reduce query execution cost and query response time. We further perform different experiments to validate the effectiveness of the proposed approaches

Light Deep Model for Pulmonary Nodule Detection from CT Scan Images for Mobile Devices

The emergence of cognitive computing and big data analytics revolutionize the healthcare domain, more specifically in detecting cancer. Lung cancer is one of the major reasons for death worldwide. The pulmonary nodules in the lung can be cancerous after development. Early detection of the pulmonary nodules can lead to early treatment and a significant reduction of death. In this paper, we proposed an end-to-end convolutional neural network- (CNN-) based automatic pulmonary nodule detection and classification system. The proposed CNN architecture has only four convolutional layers and is, therefore, light in nature. Each convolutional layer consists of two consecutive convolutional blocks, a connector convolutional block, nonlinear activation functions after each block, and a pooling block. The experiments are carried out using the Lung Image Database Consortium (LIDC) database. From the LIDC database, 1279 sample images are selected of which 569 are noncancerous, 278 are benign, and the rest are malignant. The proposed system achieved 97.9% accuracy. Compared to other famous CNN architecture, the proposed architecture has much lesser flops and parameters and is thereby suitable for real-time medical image analysis

Fully integrated high gain s-band triangular slot antenna for cubesat communications

Among other CubeSat subsystems, Antenna is one of the most important CubeSat components as its design determines all the telecommunication subsystems’ performances. This paper presents a coplanar wave-guide (CPW)-fed equilateral triangular slot antenna constructed and analyzed for CubeSat communications at S-band. The proposed antenna alone presents high gain and ultra-wide band while its radiation pattern is bidirectional at an unlicensed frequency of 2450 MHz. The objective is to use the CubeSat chassis as a reflector for reducing the back-lobe radiation and hence minimizing interferences with electronic devices inside the CubeSat. This leads to a high gain of 8.20 dBi and a unidirectional radiation pattern at an industrial, scientific and mdical (ISM) band operating frequency of 2450 MHz. In addition to that, the presented antenna is low-profile and exhibits high return loss, ultra-wide impedance bandwidth, and good impedance matching at 2450 MHz

Leveraging Deep Learning Techniques for Malaria Parasite Detection Using Mobile Application

Malaria is a contagious disease that affects millions of lives every year. Traditional diagnosis of malaria in laboratory requires an experienced person and careful inspection to discriminate healthy and infected red blood cells (RBCs). It is also very time-consuming and may produce inaccurate reports due to human errors. Cognitive computing and deep learning algorithms simulate human intelligence to make better human decisions in applications like sentiment analysis, speech recognition, face detection, disease detection, and prediction. Due to the advancement of cognitive computing and machine learning techniques, they are now widely used to detect and predict early disease symptoms in healthcare field. With the early prediction results, healthcare professionals can provide better decisions for patient diagnosis and treatment. Machine learning algorithms also aid the humans to process huge and complex medical datasets and then analyze them into clinical insights. This paper looks for leveraging deep learning algorithms for detecting a deadly disease, malaria, for mobile healthcare solution of patients building an effective mobile system. The objective of this paper is to show how deep learning architecture such as convolutional neural network (CNN) which can be useful in real-time malaria detection effectively and accurately from input images and to reduce manual labor with a mobile application. To this end, we evaluate the performance of a custom CNN model using a cyclical stochastic gradient descent (SGD) optimizer with an automatic learning rate finder and obtain an accuracy of 97.30% in classifying healthy and infected cell images with a high degree of precision and sensitivity. This outcome of the paper will facilitate microscopy diagnosis of malaria to a mobile application so that reliability of the treatment and lack of medical expertise can be solved

A Novel Bayesian Optimization-Based Machine Learning Framework for COVID-19 Detection From Inpatient Facility Data

The whole world faces a pandemic situation due to the deadly virus, namely COVID-19. It takes considerable time to get the virus well-matured to be traced, and during this time, it may be transmitted among other people. To get rid of this unexpected situation, quick identification of COVID-19 patients is required. We have designed and optimized a machine learning-based framework using inpatient's facility data that will give a user-friendly, cost-effective, and time-efficient solution to this pandemic. The proposed framework uses Bayesian optimization to optimize the hyperparameters of the classifier and ADAptive SYNthetic (ADASYN) algorithm to balance the COVID and non-COVID classes of the dataset. Although the proposed technique has been applied to nine state-of-the-art classifiers to show the efficacy, it can be used to many classifiers and classification problems. It is evident from this study that eXtreme Gradient Boosting (XGB) provides the highest Kappa index of 97.00%. Compared to without ADASYN, our proposed approach yields an improvement in the kappa index of 96.94%. Besides, Bayesian optimization has been compared to grid search, random search to show efficiency. Furthermore, the most dominating features have been identified using SHapely Adaptive exPlanations (SHAP) analysis. A comparison has also been made among other related works. The proposed method is capable enough of tracing COVID patients spending less time than that of the conventional techniques. Finally, two potential applications, namely, clinically operable decision tree and decision support system, have been demonstrated to support clinical staff and build a recommender system

Deep Learning Approaches for Detecting Pneumonia in COVID-19 Patients by Analyzing Chest X-Ray Images

The COVID-19 pandemic has wreaked havoc in the daily life of human beings and devastated many economies worldwide, claiming millions of lives so far. Studies on COVID-19 have shown that older adults and people with a history of various medical issues, specifically prior cases of pneumonia, are at a higher risk of developing severe complications from COVID-19. As pneumonia is a common type of infection that spreads in the lungs, doctors usually perform chest X-ray to identify the infected regions of the lungs. In this study, machine learning tools such as LabelBinarizer are used to perform one-hot encoding on the labeled chest X-ray images and transform them into categorical form using Python’s to_categorical tool. Subsequently, various deep learning features such as convolutional neural network (CNN), VGG16, AveragePooling2D, dropout, flatten, dense, and input are used to build a detection model. Adam is used as an optimizer, which can be further applied to predict pneumonia in COVID-19 patients. The model predicted pneumonia with an average accuracy of 91.69%, sensitivity of 95.92%, and specificity of 100%. The model also efficiently reduces training loss and increases accuracy