Rapid spread of Coronavirus disease COVID-19 leads to severe pneumonia and it is estimated to create a high impact on the healthcare system. An urgent need for early diagnosis is required for precise treatment, which in turn reduces the pressure in the health care system. Some of the standard image diagnosis available is Computed Tomography (CT) scan and Chest X-Ray (CXR). Even though a CT scan is considered a gold standard in diagnosis, CXR is most widely used due to widespread, faster, and cheaper. This study aims to provide a solution for identifying pneumonia due to COVID-19 and healthy lungs (normal person) using CXR images. One of the remarkable methods used for extracting a high dimensional feature from medical images is the Deep learning method. In this research, the state-of-the-art techniques used is Genetic Deep Learning Convolutional Neural Network (GDCNN). It is trained from the scratch for extracting features for classifying them between COVID-19 and normal images. A dataset consisting of more than 5000 CXR image samples is used for classifying pneumonia, normal and other pneumonia diseases. Training a GDCNN from scratch proves that, the proposed method performs better compared to other transfer learning techniques. Classification accuracy of 98.84%, the precision of 93%, the sensitivity of 100%, and specificity of 97.0% in COVID-19 prediction is achieved. Top classification accuracy obtained in this research reveals the best nominal rate in the identification of COVID-19 disease prediction in an unbalanced environment. The novel model proposed for classification proves to be better than the existing models such as ReseNet18, ReseNet50, Squeezenet, DenseNet-121, and Visual Geometry Group (VGG16).
In the current scenario most of the business enterprises are running through web applications. But the major drawback is that they fail to provide a secure environment. To overcome this security issue in web applications, there are many vulnerability detection tools are available at present. But these tools are not proactive and consistent as it does not adapt to all kinds of recent updates and is unable to track new emerging vulnerabilities. For the longterm functioning of a business enterprise, statistical data with efficient analytics on vulnerabilities is required to enhance its security impacts. Predictive Analytics is a powerful solution to effectively arm the recent incident response to modern-day threats. Predictive Analytics provides a proactive and decision-making approach and insights into how well security programs are working. It can also help to identify problem areas and can warn about imminent or active attacks in heterogeneous web applications to enhance the former features and analyze the origin and pattern of the attack in a more effective manner. The pattern analyzed through research is given as an input to the Machine Learning techniques such as Deterministic Arithmetic Automata (DAA), Probabilistic Arithmetic Automata (PAA) to predict the probabilistic value as an output. From the obtained probabilistic values, we can detect the cause of an attack, prevent the heterogeneous web application of business enterprises from further impacts and find the penetration level of an attack from web application to web service.INDEX TERMS Security Analytics, Deterministic Arithmetic Automata (DAA), Probabilistic Arithmetic Automata (PAA), heterogeneous web application.
Vitamin D Deficiency (VDD) is one of the most significant global health problem and there is a strong demand for the prediction of its severity using non-invasive methods. The primary data containing serum Vitamin D levels were collected from a total of 3044 college students between 18-21 years of age. The independent parameters like age, sex, weight, height, body mass index (BMI), waist circumference, body fat, bone mass, exercise, sunlight exposure, and milk consumption were used for prediction of VDD. The study aims to compare and evaluate different machine learning models in the prediction of severity in VDD. The objectives of our approach are to apply various powerful machine learning algorithms in prediction and evaluate the results with different performance measures like Precision, Recall, F1-measure, Accuracy, and Area under the curve of receiver operating characteristic (ROC). The McNemar's test was conducted to validate the empirical results which is a statistical test. The final objective is to identify the best machine learning classifier in the prediction of the severity of VDD. The most popular and powerful machine learning classifiers like K-Nearest Neighbor (KNN), Decision Tree (DT), Random Forest (RF), AdaBoost (AB), Bagging Classifier (BC), ExtraTrees (ET), Stochastic Gradient Descent (SGD), Gradient Boosting (GB), Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP) were implemented to predict the severity of VDD. The final experimentation results showed that the Random Forest Classifier achieves better accuracy of 96 % and outperforms well on training and testing Vitamin D dataset. The McNemar's statistical test results support that the RF classifier outperforms than the other classifiers.
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