COVID-19 is a growing problem worldwide with high mortality rate. As result, the World Health Organization (WHO) declared it pandemic. In order to limit spread of disease, fast and accurate diagnosis required. A reverse transcript polymerase chain reaction (RT-PCR) test often used detect disease. However, since this time-consuming, chest computed tomography (CT) or plain X-ray (CXR) sometimes i...

Material properties are very important in most mechanical engineering computations. Numerous approaches have been proposed to estimate these material properties such as State of Equations, Statistical Regression, and Neural Networks modeling schemes. Unfortunately, accuracy of some of these earlier approaches is often limited. Recently, extreme learning machine has been proposed as a new comput...

WiFi network usage is increased rapidly these days while the number of attacks in WiFi network are growing as well. Intrusion Detection System (IDS) is one of the popular defense mechanisms that often uses e.g., machine learning algorithms in order to detect both known and unknown attacks in a particular network. We leverage an unsupervised deep learning approach, so called Stacked Auto Encoder...

In the Internet-age, malware poses a serious and evolving threat to security, making the detection of malware of utmost concern. Many research efforts have been conducted on intelligent malware detection by applying data mining and machine learning techniques. Though great results have been obtained with these methods, most of them are built on shallow learning architectures, which are still so...

Maximum margin clustering (MMC) is a newly proposed clustering method, which extends large margin computation of support vector machine (SVM) to unsupervised learning. But in nonlinear cases, time complexity is still high. Since extreme learning machine (ELM) has achieved similar generalization performance at much faster learning speed than traditional SVM and LS-SVM, we propose an extreme maxi...

Because of the impact extreme heat waves and domes on society biodiversity, their study is a key challenge. We specifically long-lasting waves, which are among most important for climate impacts. Physics driven weather forecast systems or models can be used to occurrence predict probability. The present work explores use deep learning architectures, trained using outputs model, as an alternativ...

In modern manufacturing systems and industries, more and more research efforts have been made in developing effective machine health monitoring systems. Among various machine health monitoring approaches, data-driven methods are gaining in popularity due to the development of advanced sensing and data analytic techniques. However, considering the noise, varying length and irregular sampling beh...

We present a novel formalism for introducing deep belief features to Hierarchical Machine Translation Model. The deep features are generated by unsupervised training of a deep belief network built with stacked sets of Restricted Boltzmann Machines. We show that our new deep feature based hierarchical model is better than the baseline hierarchical model with gains for two different languages pai...