Artificial intelligence (AI) is entering many fields of life nowadays. One of these fields is biometric authentication. Palm print recognition is considered a fundamental aspect of biometric identification systems due to the inherent stability, reliability, and uniqueness of palm print features, coupled with their non-invasive nature. In this paper, we develop an approach to identify individuals from palm print image recognition using Orange software in which a hybrid of AI methods: Deep Learning (DL) and traditional Machine Learning (ML) methods are used to enhance the overall performance metrics. The system comprises of three stages: pre-processing, feature extraction, and feature classification or matching. The SqueezeNet deep le

This paper presents a hybrid approach for solving null values problem; it hybridizes rough set theory with intelligent swarm algorithm. The proposed approach is a supervised learning model. A large set of complete data called learning data is used to find the decision rule sets that then have been used in solving the incomplete data problem. The intelligent swarm algorithm is used for feature selection which represents bees algorithm as heuristic search algorithm combined with rough set theory as evaluation function. Also another feature selection algorithm called ID3 is presented, it works as statistical algorithm instead of intelligent algorithm. A comparison between those two approaches is made in their performance for null values estima

Artificial intelligence (AI) is entering many fields of life nowadays. One of these fields is biometric authentication. Palm print recognition is considered a fundamental aspect of biometric identification systems due to the inherent stability, reliability, and uniqueness of palm print features, coupled with their non-invasive nature. In this paper, we develop an approach to identify individuals from palm print image recognition using Orange software in which a hybrid of AI methods: Deep Learning (DL) and traditional Machine Learning (ML) methods are used to enhance the overall performance metrics. The system comprises of three stages: pre-processing, feature extraction, and feature classification or matching. The SqueezeNet deep le

This study aims to demonstrate the role of artificial intelligence and metaverse techniques, mainly logistical Regression, in reducing earnings management in Iraqi private banks. Synthetic intelligence approaches have shown the capability to detect irregularities in financial statements and mitigate the practice of earnings management. In contrast, many privately owned banks in Iraq historically relied on manual processes involving pen and paper for recording and posting financial information in their accounting records. However, the banking sector in Iraq has undergone technological advancements, leading to the Automation of most banking operations. Conventional audit techniques have become outdated due to factors such as the accuracy of d

Insurance Companies perform a prominent role in providing services and bolstering national economy, and they contribute in achieving the aims of the economic development plans in addition to many other social and economic benefits. So it is necessary to evaluate the activity of these companies continuously to define performance level and its nature. Financial analysis is one of the important instruments that helps in analyzing and explanation of data in such a way that enable administration to define performance level and diagnosing strength and weakness points. This study tackles the state of affairs of the Iraqi Insurance Company which is considered as one of the active national institutions, due to its distinguished role in collecting

In this paper, a compact genetic algorithm (CGA) is enhanced by integrating its selection strategy with a steepest descent algorithm (SDA) as a local search method to give I-CGA-SDA. This system is an attempt to avoid the large CPU time and computational complexity of the standard genetic algorithm. Here, CGA dramatically reduces the number of bits required to store the population and has a faster convergence. Consequently, this integrated system is used to optimize the maximum likelihood function lnL(φ1, θ1) of the mixed model. Simulation results based on MSE were compared with those obtained from the SDA and showed that the hybrid genetic algorithm (HGA) and I-CGA-SDA can give a good estimator of (φ1, θ1) for the ARMA(1,1) model. Anot

Flexible molecular docking is a computational method of structure-based drug design to evaluate binding interactions between receptor and ligand and identify the ligand conformation within the receptor pocket. Currently, various molecular docking programs are extensively applied; therefore, realizing accuracy and performance of the various docking programs could have a significant value. In this comparative study, the performance and accuracy of three widely used non-commercial docking software (AutoDock Vina, 1-Click Docking, and UCSF DOCK) was evaluated through investigations of the predicted binding affinity and binding conformation of the same set of small molecules (HIV-1 protease inhibitors) and a protein target HIV-1 protease enzy

In this work, the optical properties of Cu₂S with different thickness
(1400, 2400, 4400) Ǻ have been prepared by chemical spray pyrolys
is method onto clean glass substrate heated at 283 ^oC ±2. The effect
of thickness on the optical properties of Cu2S has been studied. It
was found that the optical properties of the electronic transitions on
fundamental absorption edge were direct allowed and the value of the
optical energy gap of Cu₂S (Eg) for direct transition decreased from
(2.4-2.1) eV with increasing of the thickness from (1400 - 4400)Ǻ
respectively. Also it was found that the absorption coefficient is
increased with increasing of thicknesses. The optical constants such<

Study was made on the optical properties of Ge2oSe8othinfilms prepared by vac-uum evaporation as radiated by (0,34,69) Gy of 13 ray.The optical band gab Eg and tailing band A.Et were studied in the photon energy range ( 1 to 3)eV. The a-Ge20Se8o film was found to be indirect gap with energy gap of (1.965,1.9 , 1.82) eV at radiated by B ray with absorption doses of (0,34,69)Gy respectively.The Ea and AEt of Ge20Se80 films showed adecrease in E8 and an increase in AEt with radiation. This be-havior may be related to structural defects and dangling bonds.