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

This study examined the effect of essential oils extracted from peel of Citrus paradisi and Citrus sinensis on two species of fungi: Penicillium oxalicum and Fusarium oxysporum as well as effect of two fungicides: Carbendazim and Thiophanatemethyl against above fungi. Results showed that the essential oil of Citrus paradisi inhibited the radial growth of Penicillium oxalicum and Fusarium oxysporum at concentration 4%. Nevertheless, the essential oil of Citrus sinensis inhibited the radial growth at concentration 5 and 4%, respectively. Furthermore, the two studied fungicides inhibited radial growth of these fungi too. Therefore, there are a positive relationship between the evaluating of concentration and the percentage of inhibiting of rad

   The petroleum industry, which is one of the pillars of the national economy, has the potential to generate vast wealth and employment possibilities. The transportation of petroleum products is complicated and changeable because of the hazards caused by the corrosion consequences. Hazardous chemical leaks caused by natural disasters may harm the environment, resulting in significant economic losses. It significantly threatens the aim for sustainable development. When a result, determining the likelihood of leakage and the potential for environmental harm, it becomes a top priority for decision-makers as they develop maintenance plans. This study aims to provide an in-depth understanding of the risks associated with oil and gas pipeli

A series of overbased magnesium fatty acids such as caprylate, caprate, laurate, myristate, palmitate, stearate and oleate) were synthesized by the reaction of the fatty acids with active – 60 magnesium oxide and carbon dioxide (CO2) gas at 60 oC in the presence of ammonia solution as catalyst, toluene / ethanol solvent mixture (9:1vol/vol) was added.
The prepared detergent additives were characterized by FTIR, 1HNMR and evaluated by blending each additive in various concentrations with medium lubricant oil fraction (60 stock) supplied by Iraqi Midland Refineries Company. The total base number (TBN, mg of KOH/g) was determined, and the results of TBN were treated by using two-way analysis of variance (ANOVA) test. It was found that

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

Software Defined Networking (SDN) with centralized control provides a global view and achieves efficient network resources management. However, using centralized controllers has several limitations related to scalability and performance, especially with the exponential growth of 5G communication. This paper proposes a novel traffic scheduling algorithm to avoid congestion in the control plane. The Packet-In messages received from different 5G devices are classified into two classes: critical and non-critical 5G communication by adopting Dual-Spike Neural Networks (DSNN) classifier and implementing it on a Virtualized Network Function (VNF). Dual spikes identify each class to increase the reliability of the classification

Abstract\

In this research, estimated the reliability of water system network in Baghdad was done. to assess its performance during a specific period. a fault tree through static and dynamic gates was belt and these gates represent logical relationships between the main events in the network and analyzed using dynamic Bayesian networks . As it has been applied Dynamic Bayesian networks estimate reliability by translating dynamic fault tree to Dynamic Bayesian networks and reliability of the system appreciated. As was the potential for the expense of each phase of the network for each gate . Because there are two parts to the Dynamic Bayesian networks and two part of gate (AND), which includes the three basic units of the

This study aimed to analyze and measure the relationship between oil revenues and financial sustainability in Iraq, the study used the stylistic approach inductive and deductive approach. Accompanied by the use of quantitative and analytical style, which was based on two variables oil revenues and net general budget on annual data covered the period (1990-2013). Among the most important findings of the study contain the time-series variables study on the root of the unit and is not stable in the general level, and become stable after the use of mathematical processors to gain access to a stable by taking the first difference of natural Ogartm of the series. The way (Johnson) to a long-term relationship between oil revenues and ne