: The Aluminium (Al) material emerged as a plasmonic material in the wavelength ranges from the ultraviolet to the visible bands in different on-chip plasmonic applications. In this paper, we demonstrate the effect of using Al on the electromagnetic (EM) field distribution of a compact hybrid plasmonic waveguide (HPW) acting as a polarization rotator. We compare the performance of Al with other familiar metals that are widely used as plasmonic materials, which are Silver (Ag) and Gold (Au). Furthermore, we study the effect of reducing the geometrical dimensions of the used materials on the EM field distributions inside the HPW and, consequently, on the efficiency of the polarization rotation. We perform the study based o

This research aims to solve the nonlinear model formulated in a system of differential equations with an initial value problem (IVP) represented in COVID-19 mathematical epidemiology model as an application using new approach: Approximate Shrunken are proposed to solve such model under investigation, which combines classic numerical method and numerical simulation techniques in an effective statistical form which is shrunken estimation formula. Two numerical simulation methods are used firstly to solve this model: Mean Monte Carlo Runge-Kutta and Mean Latin Hypercube Runge-Kutta Methods. Then two approximate simulation methods are proposed to solve the current study. The results of the proposed approximate shrunken methods and the numerical

This investigation proposed an identification system of offline signature by utilizing rotation compensation depending on the features that were saved in the database. The proposed system contains five principle stages, they are: (1) data acquisition, (2) signature data file loading, (3) signature preprocessing, (4) feature extraction, and (5) feature matching. The feature extraction includes determination of the center point coordinates, and the angle for rotation compensation (θ), implementation of rotation compensation, determination of discriminating features and statistical condition. During this work seven essential collections of features are utilized to acquire the characteristics: (i) density (D), (ii) average (A), (iii) s

Early detection of brain tumors is critical for enhancing treatment options and extending patient survival. Magnetic resonance imaging (MRI) scanning gives more detailed information, such as greater contrast and clarity than any other scanning method. Manually dividing brain tumors from many MRI images collected in clinical practice for cancer diagnosis is a tough and time-consuming task. Tumors and MRI scans of the brain can be discovered using algorithms and machine learning technologies, making the process easier for doctors because MRI images can appear healthy when the person may have a tumor or be malignant. Recently, deep learning techniques based on deep convolutional neural networks have been used to analyze med

Recently, there has been an increasing advancement in the communications technology, and due to the increment in using the cellphone applications in the diverse aspects of life, it became possible to automate home appliances, which is the desired goal from residences worldwide, since that provides lots of comfort by knowing that their appliances are working in their highest effi ciency whenever it is required without their knowledge, and it also allows them to control the devices when they are away from home, including turning them on or off whenever required. The design and implementation of this system is carried out by using the Global System of Mobile communications (GSM) technique to control the home appliances – In this work, an ele

Analyzing sentiment and emotions in Arabic texts on social networking sites has gained wide interest from researchers. It has been an active research topic in recent years due to its importance in analyzing reviewers' opinions. The Iraqi dialect is one of the Arabic dialects used in social networking sites, characterized by its complexity and, therefore, the difficulty of analyzing sentiment. This work presents a hybrid deep learning model consisting of a Convolution Neural Network (CNN) and the Gated Recurrent Units (GRU) to analyze sentiment and emotions in Iraqi texts. Three Iraqi datasets (Iraqi Arab Emotions Data Set (IAEDS), Annotated Corpus of Mesopotamian-Iraqi Dialect (ACMID), and Iraqi Arabic Dataset (IAD)) col

In this paper, a national grid-connected photovoltaic (PV) system is proposed. It extracts the maximum power point (MPP) using three-incremental-steps perturb and observe (TISP&O) maximum power point tracking (MPPT) method. It improves the classic P&O by using three incremental duty ratio (ΔD) instead of a single one in the conventional P and O MPPT method. Therefore, the system's performance is improved to a higher speed and less power fluctuation around the MPP. The Boost converter controls the MPPT and then is connected to a three-phase voltage source inverter (VSI). This type of inverter needs a high and constant input voltage. A second-order low pass (LC) filter is connected to the output of VSI to reduce t

With the development of computer architecture and its technologies in recent years, applications like e-commerce, e-government, e-governance and e-finance are widely used, and they act as active research areas. In addition, in order to increase the quality and quantity of the ordinary everyday transactions, it is desired to migrate from the paper-based environment to a digital-based computerized environment. Such migration increases efficiency, saves time, eliminates paperwork, increases safety and reduces the cost in an organization. Digital signatures are playing an essential role in many electronic and automatic based systems and facilitate this migration. The digital signatures are used to provide many services and s