Biometrics represent the most practical method for swiftly and reliably verifying and identifying individuals based on their unique biological traits. This study addresses the increasing demand for dependable biometric identification systems by introducing an efficient approach to automatically recognize ear patterns using Convolutional Neural Networks (CNNs). Despite the widespread adoption of facial recognition technologies, the distinct features and consistency inherent in ear patterns provide a compelling alternative for biometric applications. Employing CNNs in our research automates the identification process, enhancing accuracy and adaptability across various ear shapes and orientations. The ear, being visible and easily captured in

The objective of this study was tointroduce a recursive least squares (RLS) parameter estimatorenhanced by using a neural network (NN) to facilitate the computing of a bit error rate (BER) (error reduction) during channels estimation of a multiple input-multiple output orthogonal frequency division multiplexing (MIMO-OFDM) system over a Rayleigh multipath fading channel.Recursive least square is an efficient approach to neural network training:first, the neural network estimator learns to adapt to the channel variations then it estimates the channel frequency response. Simulation results show that the proposed method has better performance compared to the conventional methods least square (LS) and the original RLS and it is more robust a

      The Khor Mor gas-condensate processing plant in Iraq is currently facing operational challenges due to foaming issues in the sweetening tower caused by high-soluble hydrocarbon liquids entering the tower. The root cause of the problem could be liquid carry-over as the separation vessels within the plant fail to remove liquid droplets from the gas phase. This study employs Aspen HYSYS v.11 software to investigate the performance of the industrial three-phase horizontal separator, Bravo #2, located upstream of the Khor Mor sweetening tower, under both current and future operational conditions. The simulation results, regarding the size distribution of liquid droplets in the gas product and the efficiency gas/liquid separation, r

Experimental investigation for small horizontal portable wind turbine (SHPWT) of NACA-44, BP-44, and NACA-63, BP-63 profiles under laboratory conditions at different wind velocity range of (3.7-5.8 m/s) achieved in present work. Experimental data tabulated for 2, 3, 4, and 6- bladed rotor of both profiles within range of blade pitch angles . A mathematical model formulated and computer Code for MATLAB software developed. The least-squares regression is used to fit experimental data. As the majority of previous works have been presented for large scale wind turbines, the aims were to present the performance of (SHPWT) and also to make a comparisons between both profiles to conclude which is the best performance. The overall efficiency and el

Survival analysis is widely applied in data describing for the life time of item until the occurrence of an event of interest such as death or another event of understudy . The purpose of this paper is to use the dynamic approach in the deep learning neural network method, where in this method a dynamic neural network that suits the nature of discrete survival data and time varying effect. This neural network is based on the Levenberg-Marquardt (L-M) algorithm in training, and the method is called Proposed Dynamic Artificial Neural Network (PDANN). Then a comparison was made with another method that depends entirely on the Bayes methodology is called Maximum A Posterior (MAP) method. This method was carried out using numerical algorithms re

Direct measurements of drag force on two interacting particles  arranged in the longitudinal direction for particle Reynolds numbers varying from J O to 103 are conducted using a micro-force measurement system. The effect of the interparticle distance and Reynolds number on the drag forces  is examined. An empirical equation is obtained to describe the effect of the interparticle distance (l/d) on the dimensionless drag.