Detecting COVID-19 in X-ray Images using Transfer Learning

Accurate and speedy detection of COVID-19 is essential to curb the spread of the disease and avoid overwhelming the health care system. COVID-19 detection using X-ray images is commonly practiced at medical centers; however, it requires the intervention of medical professionals trained in diagnosing and interpreting medical imagining. In this paper, we employ deep transfer learning models to detect COVID-19 on a dataset of over 20,000 X-ray images. Our results on 5 pretrained models (VGG19, InceptionV3, MobileNetV2, DenseNet121, and ResNet101V2) show high performance of 99% without image augmentation, and 93\% when image augmentation is used

Node-Replication Attack Detection in Vehicular Ad-hoc Networks based on Automatic Approach

Recent advances in smart cities applications enforce security threads such as node replication attacks. Such attack is take place when the attacker plants a replicated network node within the network. Vehicular Ad hoc networks are connecting sensors that have limited resources and required the response time to be as low as possible. In this type networks, traditional detection algorithms of node replication attacks are not efficient. In this paper, we propose an initial idea to apply a newly adapted statistical methodology that can detect node replication attacks with high performance as compared to state-of-the-art techniques. We provide a sufficient description of this methodology and a road-map for testing and experiment its performance

Negative Correlation Learning for Customer Churn Prediction: A Comparison Study

Recently, telecommunication companies have been paying more attention toward the problem of identification of customer churn behavior. In business, it is well known for service providers that attracting new customers is much more expensive than retaining existing ones. Therefore, adopting accurate models that are able to predict customer churn can effectively help in customer retention campaigns and maximizing the profit. In this paper we will utilize an ensemble of Multilayer perceptrons (MLP) whose training is obtained using negative correlation learning (NCL) for predicting customer churn in a telecommunication company. Experiments results confirm that NCL based MLP ensemble can achieve better generalization performance (high churn rate) compared with ensemble of MLP without NCL (flat ensemble) and other common data mining techniques used for churn analysis

Supporting effective common ground construction in asynchronous collaborative visual analytics

Asynchronous Collaborative Visual Analytics (ACVA) leverages group sensemaking by releasing the constraints on when, where, and who works collaboratively. A significant task to be addressed before ACVA can reach its full potential is effective common ground construction, namely the process in which users evaluate insights from individual work to develop a shared understanding of insights and collectively pool them. This is challenging due to the lack of instant communication and scale of collaboration in ACVA. We propose a novel visual analytics approach that automatically gathers, organizes, and summarizes insights to form common ground with reduced human effort. The rich set of visualization and interaction techniques provided in our approach allows users to effectively and flexibly control the common ground construction and review, explore, and compare insights in detail. A working prototype of the approach has been implemented. We have conducted a case study and a user study to demonstrate its effectiveness

Churn Prediction: A Comparative Study Using KNN and Decision Trees

Churn prediction represents one of the most important components of Customer Relationship Management (CRM). In the purpose of retaining customers and maintaining their satisfaction, researchers of many fields including business intelligence, marketing and information technology were motivated to investigate the best methods that deliver the best services for customers. Many machine learning algorithms had been implemented in the purpose of optimally predicting the possible churning customers and making the right decisions at the right moments. Researchers had conducted several studies on various types of algorithms and results were found very promising. In this paper, we are conducting a comparison study of the performance towards churn prediction between two of the most powerful machine learning algorithms which are Decision Tree and K-Nearest Neighbor algorithms. Results were quite interesting showing a quite large dissimilarity in many areas between the two algorithms

Realtime visualization of streaming text with a force-based dynamic system

An interactive visualization system, STREAMIT, enables users to explore text streams on-the-fly without prior knowledge of the data. It incorporates incoming documents from a continuous source into existing visualization context with automatic grouping and separation based on document similarities. STREAMIT supports interactive exploration with good scalability: First, keyword importance is adjustable on-the-fly for preferred clustering effects from varying interests. Second, topic modeling is used to represent the documents with higher level semantic meanings. Third, document clusters are generated to promote better understanding. The system performance is optimized to achieve instantaneous animated visualization even for a very large data collection. STREAMIT provides a powerful user interface for in-depth data analysis. Case studies are presented to demonstrate the effectiveness of STREAMIT.