Purpose: The research aims to estimate models representing phenomena that follow the logic of circular (angular) data, accounting for the 24-hour periodicity in measurement. Theoretical framework: The regression model is developed to account for the periodic nature of the circular scale, considering the periodicity in the dependent variable y, the explanatory variables x, or both. Design/methodology/approach: Two estimation methods were applied: a parametric model, represented by the Simple Circular Regression (SCR) model, and a nonparametric model, represented by the Nadaraya-Watson Circular Regression (NW) model. The analysis used real data from 50 patients at Al-Kindi Teaching Hospital in Baghdad. Findings: The Mean Circular Erro

The last few years witnessed great and increasing use in the field of medical image analysis. These tools helped the Radiologists and Doctors to consult while making a particular diagnosis. In this study, we used the relationship between statistical measurements, computer vision, and medical images, along with a logistic regression model to extract breast cancer imaging features. These features were used to tell the difference between the shape of a mass (Fibroid vs. Fatty) by looking at the regions of interest (ROI) of the mass. The final fit of the logistic regression model showed that the most important variables that clearly affect breast cancer shape images are Skewness, Kurtosis, Center of mass, and Angle, with an AUCROC of

This paper describes a new proposed structure of the Proportional Integral Derivative (PID) controller based on modified Elman neural network for the DC-DC buck converter system which is used in battery operation of the portable devices. The Dolphin Echolocation Optimization (DEO) algorithm is considered as a perfect on-line tuning technique therefore, it was used for tuning and obtaining the parameters of the modified Elman neural-PID controller to avoid the local minimum problem during learning the proposed controller. Simulation results show that the best weight parameters of the proposed controller, which are taken from the DEO, lead to find the best action and unsaturated state that will stabilize the Buck converter system performan

The therapeutic value of the phenolic component and pure thymol was well known; this study comprised the extraction of crude phenol from two plants (Thymus vulgaris and Artemisia annua) which contain thymol with pure thymol and evaluate their effect on hematological and histological by using three different concentrations of each plant extract and pure thymol to tested them on lab mice. All the mice were allowed free access to water and feed for 21 days in laboratory conditions; orally, pure water was administered to the control mice (group I), while groups II, III, and IV were given orally with T. vulgaris, A. annua, combination of last two crude phenol plant extract 50:50 and pure thymol respectively. The levels of CHO, TRI, and HDL were

This paper presents a comparative study of two learning algorithms for the nonlinear PID neural trajectory tracking controller for mobile robot in order to follow a pre-defined path. As simple and fast tuning technique, genetic and particle swarm optimization algorithms are used to tune the nonlinear PID neural controller's parameters to find the best velocities control actions of the right wheel and left wheel for the real mobile robot. Polywog wavelet activation function is used in the structure of the nonlinear PID neural controller. Simulation results (Matlab) and experimental work (LabVIEW) show that the proposed nonlinear PID controller with PSO
learning algorithm is more effective and robust than genetic learning algorithm; thi

The investigation of signature validation is crucial to the field of personal authenticity. The biometrics-based system has been developed to support some information security features.Aperson’s signature, an essential biometric trait of a human being, can be used to verify their identification. In this study, a mechanism for automatically verifying signatures has been suggested. The offline properties of handwritten signatures are highlighted in this study which aims to verify the authenticity of handwritten signatures whether they are real or forged using computer-based machine learning techniques. The main goal of developing such systems is to verify people through the validity of their signatures. In this research, images of a group o

Image Fusion Using A Convolutional Neural Network

The aim of this study was ‎the‎ discrimination of Salmonella‎‎ isolated from chicken and their feed ‎and drinking water for the epidemiological control of salmonellosis. Totally, 289 samples, ‎including 217 chicken cloaca swabs, 46 water, and 26 feed samples were collected from five ‎different farms in Karbala governorate, Iraq. Conventional bacteriology tests, API 20E, Vitek 2, ‎and serology were used for bacterial identification. Random amplified polymorphic ‎DNA (RAPD)-polymerase chain reaction (PCR) was applied to analyze the genetic relationships ‎among Salmonella‎‎ isolates. The isolation rate of Salmonella‎‎ spp. was 21.1% (61/289). While the ‎water samples constituted the highest rate (30.4%), a rate of