Both Big Data and Software-Defined Network have a significant impact in both academic and practical aspects. These two areas have been addressed separately, but both did not contribute to the same subset area of contribution. However, Big Data can greatly facilitate, improve, and have a great impact on Software Defined Network, and vice versa. In this paper, we show how SDN helps Big Data solve

The Internet of Vehicles (IoV) aims to connect vehicles with their surroundings and share data. In IoV, various wireless technologies like 5G, WIFI, DSRC, WiMAX, and ZigBee are used. To share data within wireless surroundings in a secure way, some security aspects need to be fulfilled. Blockchain technology is a good fit to cover these countermeasures. IoV uses a lot of technologies and interacts

An electrocardiograph (ECG) is employed as a primary tool for diagnosing cardiovascular diseases (CVDs). ECG signals provide a framework to probe the underlying properties and enhance the initial diagnosis obtained via traditional tools and patient-doctor dialogs. Notwithstanding its proven utility, deciphering large data sets to determine appropriate information remains a challenge in ECG-based

With the daily increase of data production and collection, Hadoop is a platform for processing big data on a distributed system. A master node globally manages running jobs, whereas worker nodes process partitions of the data locally. Hadoop uses MapReduce as an effective computing model. However, Hadoop experiences a high level of security vulnerability over hybrid and public clouds. Specially

The real-world data analysis and processing using data mining techniques often are facing observations that contain missing values. The main challenge of mining datasets is the existence of missing values. The missing values in a dataset should be imputed using the imputation method to improve the data mining methods’accuracy and performance. There are existing techniques that use k-nearest

Deep learning has emerged as a powerful machine learning technique to employ in multimodal sentiment analysis tasks. In the recent years, many deep learning models and various algorithms have been proposed in the field of multimodal sentiment analysis which urges the need to have survey papers that summarize the recent research trends and directions. This survey paper tackles a comprehensive

In this research, an approximation symbolic algorithm is suggested to obtain an approximate solution of multipantograph system of type delay differential equations (DDEs) using a combination of Laplace transform and variational iteration algorithm (VIA). The corresponding convergence results are acquired, and an efficient algorithm for choosing a feasible Lagrange multiplier is designed in the

As optimization algorithms have a great power to solve nonlinear, complex, and hard optimization problems, nature-inspired algorithms have been applied extensively in distinct fields in order to solve real life optimization cases. In this paper, modifications for the recently proposed Gaining-Sharing-Knowledge based algorithm (GSK) are presented for enhancing its performance. Gaining-Sharing

This paper proposes a technique based on a MAT-LAB code capable of getting an optimized preliminary design of an efficient low-speed compressor qualified for laboratory experiments with relatively low cost. The code was made to design five repeated compressor stages on two steps conducted iteratively, namely 'mean line and radial design' to determine the optimum compressor geometry and then the

A comprehensive theoretical and experimental investigation is presented of the behavior of polyurea composites subjected to high strain-rate impact loading. The composites under consideration consist of an assembly of steel sections and inserts manufactured from layers of polyurea or polyurea augmented with aluminum layers (AL). A finite element model (FEM) is developed to predict the dynamics of