In this paper, an enhanced artificial potential field (EAPF) planner is introduced. This planner is proposed to rapidly find online solutions for the mobile robot path planning problems, when the underlying environment contains obstacles with unknown locations and sizes. The classical artificial potential field represents both the repulsive force due to the detected obstacle and the attractive force due to the target. These forces can be considered as the primary directional indicator for the mobile robot. However, the classical artificial potential field has many drawbacks. So, we suggest two secondary forces which are called the midpoint

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 Conventional audit techniques have become outdated due to factors such as the accuracy of d

In this paper, first and second order sliding mode controllers are designed for a single link robotic arm actuated by two Pneumatic Artificial Muscles (PAMs). A new mathematical model for the arm has been developed based on the model of large scale pneumatic muscle actuator model. Uncertainty in parameters has been presented and tested for the two controllers. The simulation results of the second-order sliding mode controller proves to have a low tracking error and chattering effect as compared to the first order one. The verification has been done by using MATLAB and Simulink software.

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

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

Abstract

Due to the lack of previous statistical study of the behavior of payments, specifically health insurance, which represents the largest proportion of payments in the general insurance companies in Iraq, this study was selected and applied in the Iraqi insurance company.

In order to find the convenient model representing the health insurance payments, we initially detected two probability models by using (Easy Fit) software:

First, a single Lognormal for the whole sample and the other is a Compound Weibull  for the two Sub samples (small payments and large payments), and we focused on the compoun

In recent years, the field of research around the congestion problem of 4G and 5G networks has grown, especially those based on artificial intelligence (AI). Although 4G with LTE is seen as a mature technology, there is a continuous improvement in the infrastructure that led to the emergence of 5G networks. As a result of the large services provided in industries, Internet of Things (IoT) applications and smart cities, which have a large amount of exchanged data, a large number of connected devices per area, and high data rates, have brought their own problems and challenges, especially the problem of congestion. In this context, artificial intelligence (AI) models can be considered as one of the main techniques that can be used to solve ne

Experimental and numerical studies have been conducted on the effects of bed roughness elements such as cubic and T-section elements that are regularly half-channel arrayed on one side of the river on turbulent flow characteristics and bed erosion downstream of the roughness elements. The experimental study has been done for two types of bed roughness elements (cubic and T-section shape) to study the effect of these elements on the velocity profile downstream the elements with respect to different water flow discharges and water depths. A comparison between the cubic and T-section artificial bed roughness showed that the velocity profile downstream the T-section increased in smooth side from the river and decrease in the rough side

Background: The purpose of this study was to evaluate the effect of in vitro long-term simulation of oral conditions on the bond strength of PEEK CAD/CAM lingual retainers.

Material and methods: The sample consisted of 12 PEEK CAD/CAM retainers each composed of 2 centrally perforated 3x4mm pads joined by a connector. They were treated by 98% sulfuric acid for 1 minute and then conditioned with Single Bond Universal and bonded to the lingual surface of premolar teeth by 3M Transbond TM System. Half of the retainers were artificially aged using a 30-day water storage and 5000 thermocycling protocol before bond strength testing to compare with the non-aged specimens.

Results: The artificially aged retainers showed a marginally