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

The study focuses on assessment of the quality of some image enhancement methods which were implemented on renal X-ray images. The enhancement methods included Imadjust, Histogram Equalization (HE) and Contrast Limited Adaptive Histogram Equalization (CLAHE). The images qualities were calculated to compare input images with output images from these three enhancement techniques. An eight renal x-ray images are collected to perform these methods. Generally, the x-ray images are lack of contrast and low in radiation dosage. This lack of image quality can be amended by enhancement process. Three quality image factors were done to assess the resulted images involved (Naturalness Image Quality Evaluator (NIQE), Perception based Image Qual

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

     Oil is considered a commodity and is still an important and prominent role in drawing and shaping the Iraqi economic scene. The revenues generated from the export of oil are considered the main source of the general budget in cash flows.  

     Since the revenues consist of quantity and price and the latter is an external factor which is difficult to predict, The effect of any commodity on its price, which is proven in the theory of micro-economic, but it is observed through the research that the response is slow, which means not to take advantage of the rise in prices, by increasing the quantity exported, the result of several facto

The researcher has studied in his research (International Public Relations methods in building the state's image through Cyberspace)
, analytical study of the Facebook and twitter pages for British foreign office , the role was played by the International Public Relations in building the mental image of British , especially after the new media and internet have became influential role in political life . and became an important tools used by political institutions as ministries of foreign affairs in the twenty: one century .
The researcher identified the problem of this study with the following question:
(what is the role of the International Public Relations in building the mental image of state through Cyberspace)
To answer

      In this work, a new formula of intensity distribution in image plane of elliptical object was founded (Elliptical spread function), by using optical system including circular aperture. The Gauss quadrature method of numerical integral was used for calculating equation's integrals. Curves are shown for system having focal error and intensity distribution in focal axis.  

Semantic segmentation is an exciting research topic in medical image analysis because it aims to detect objects in medical images. In recent years, approaches based on deep learning have shown a more reliable performance than traditional approaches in medical image segmentation. The U-Net network is one of the most successful end-to-end convolutional neural networks (CNNs) presented for medical image segmentation. This paper proposes a multiscale Residual Dilated convolution neural network (MSRD-UNet) based on U-Net. MSRD-UNet replaced the traditional convolution block with a novel deeper block that fuses multi-layer features using dilated and residual convolution. In addition, the squeeze and execution attention mechanism (SE) and the s