Street vendors are quite common in countries across the world. By the prevalence of mobile surveillance systems, increasing demand for automatic detection of street vendors for further decisions and planning by the city ...

Increasing population density in cities, and the increasing demand for efficiency in resource usage call for architectures enabling smart cities, such as the Internet of Things (IoT). In most such scenarios, the data ...

Industry 4.0 (I4.0) defines a new paradigm to produce high-quality products at the low cost by reacting quickly and effectively to changing demands in the highly volatile global markets. In Industry 4.0, the adoption of ...

Network proximity computations are among the most common operations in various data mining applications, including link prediction and collaborative filtering. A common measure of network proximity is Katz index, which has ...

Almost all real-world datasets contain missing values. Classification of data with missing values can adversely affect the performance of a classifier if not handled correctly. A common approach used for classification ...

In the last two decades, there have been massive advancements in high throughput technologies, which resulted in the exponential growth of public repositories of gene expression datasets for various phenotypes. It is ...

Background: Link prediction is an important and well-studied problem in network biology. Recently, graph representation learning methods, including Graph Convolutional Network (GCN)-based node embedding have drawn increasing ...

Deep-learning based computer vision models have proved themselves to be ground-breaking approaches to human activity recognition (HAR). However, most existing works are dedicated to improve the prediction accuracy through ...

There is a close relationship between the tertiary structure and the function of a protein. One of the important steps to determine the tertiary structure is protein secondary structure prediction (PSSP). For this reason, ...

Gene expression data classification provides a challenge in classification due to it having high dimensionality and a relatively small sample size. Different feature selection approaches have been used to overcome this ...