Image compression plays an important role in reducing the size and storage of data while increasing the speed of its transmission through the Internet significantly. Image compression is an important research topic for several decades and recently, with the great successes achieved by deep learning in many areas of image processing, especially image compression, and its use is increasing Gradually in the field of image compression. The deep learning neural network has also achieved great success in the field of processing and compressing various images of different sizes. In this paper, we present a structure for image compression based on the use of a Convolutional AutoEncoder (CAE) for deep learning, inspired by the diversity of human eyes' observation of the different colors and features of images. We propose a multi-layer hybrid system for deep learning using the unsupervised CAE architecture and using the color clustering of the K-mean algorithm to compress images and determine their size and color intensity. The system is implemented using Kodak and Challenge on Learned Image Compression (CLIC) dataset for deep learning. Experimental results show that our proposed method is superior to the traditional compression methods of the autoencoder, and the proposed work has better performance in terms of performance speed and quality measures Peak Signal To Noise Ratio (PSNR) and Structural Similarity Index (SSIM) where the results achieved better performance and high efficiency With high compression bit rates and low Mean Squared Error (MSE) rate the results recorded the highest compression ratios that ranged between (0.7117 to 0.8707) for the Kodak dataset and (0.7191 to 0.9930) for CLIC dataset. The system achieved high accuracy and quality in comparison to the error coefficient, which was recorded (0.0126 to reach 0.0003) below, and this system is onsidered the most quality and accurate compared to the methods of deep learning compared to the deep learning methods of the autoencoder
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The imperative of achieving financial stability has transcended national boundaries, necessitating heightened attention from both researchers and policymakers. Consequently, this article delves into an examination of the impact of government debt and public debt on financial development within the context of Iraq. The study employs monetary policy, interest rate, inflation, and population growth as control variables to prognosticate financial development. Utilizing data extracted from the World Development Indicators (WDI) spanning the period from 1995 to 2022, the study employs the dynamic autoregressive distributed lag (DARDL) approach to scrutinize the associations under investigation. The findings underscore a negative association betwe
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The treasury bills one internal government debt instruments in iraq that were used to finance temporary deficits in the government budget, There fore the government resorted to issuing for the purpose of providing liquidity, As well as to invest in productive progects, After that it was financing the budget deficit by the monetary authovity of the central baalpennek [the new cash velease] which led to negative effects on the Iraqi econome, Thus we find that the treasury transfers have formed alarge proportion of the gross domestic government debt to finance the deficit ayear ago 2003. &nbs
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This work represents implementation and investigation of optical coherent communication system between two computers. A single mode optical fiber is selected as transmission medium. The data are sent via the RS-232 standard interface with a bit rate of 9.6 kbps from personal computer (PC1) by line receive to convert the data from electrical levels (-12/+12 V) into TTL level (0/5 V). The modulation of this data was accomplished by internal modulation using laser diode type (HFCT-5208M) 1310 nm wavelength. The optical D-coupler was used to combine the optical signal that come from laser source with optical signal of laser local oscillator (OTS-304XI) at 1310/1550 nm wavelength to obtain coherent (homodyne and heterodyne) detection respective
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Previously, many empirical models have been used to predict corrosion rates under different CO2 corrosion parameters conditions. Most of these models did not predict the corrosion rate exactly, besides it determined effects of variables by holding some variables constant and changing the values of other variables to obtain the regression model. As a result the experiments will be large and cost too much. In this paper response surface methodology (RSM) was proposed to optimize the experiments and reduce the experimental running. The experiments studied effects of temperature (40 – 60 °C), pH (3-5), acetic acid (HAc) concentration (1000-3000 ppm) and rotation speed (1000-1500 rpm) on CO2 corrosion performance of t
... Show MoreCox regression model have been used to estimate proportion hazard model for patients with hepatitis disease recorded in Gastrointestinal and Hepatic diseases Hospital in Iraq for (2002 -2005). Data consists of (age, gender, survival time terminal stat). A Kaplan-Meier method has been applied to estimate survival function and hazerd function.
The work in this paper focuses on the system quality of direct and coherent communication system for two computers. A system quality is represented by Signal to Noise ratio (SNR) and Bit Error Rate (BER). First part of the work includes implementation of direct optical fiber communication system and measure the system quality .The second part of the work include implementation both the( homodyne and heterodyne)coherent optical fiber communication system and measure the system quality . Laser diode 1310 nm wavelength with its drive circuit used in the transmitter circuit . A single mode of 62.11 km optical fiber is selected as transmission medium . A PIN photo detector is used in the receiver circuit. The optical D-coupler was u
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This paper discusses the method for determining the permeability values of Tertiary Reservoir in Ajeel field (Jeribe, dhiban, Euphrates) units and this study is very important to determine the permeability values that it is needed to detect the economic value of oil in Tertiary Formation. This study based on core data from nine wells and log data from twelve wells. The wells are AJ-1, AJ-4, AJ-6, AJ-7, AJ-10, AJ-12, AJ-13, AJ-14, AJ-15, AJ-22, AJ-25, and AJ-54, but we have chosen three wells (AJ4, AJ6, and AJ10) to study in this paper. Three methods are used for this work and this study indicates that one of the best way of obtaining permeability is the Neural network method because the values of permeability obtained be
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This paper describes the application of consensus optimization for Wireless Sensor Network (WSN) system. Consensus algorithm is usually conducted within a certain number of iterations for a given graph topology. Nevertheless, the best Number of Iterations (NOI) to reach consensus is varied in accordance with any change in number of nodes or other parameters of . graph topology. As a result, a time consuming trial and error procedure will necessary be applied
to obtain best NOI. The implementation of an intellig ent optimization can effectively help to get the optimal NOI. The performance of the consensus algorithm has considerably been improved by the inclusion of Particle Swarm Optimization (PSO). As a case s
Data-driven models perform poorly on part-of-speech tagging problems with the square Hmong language, a low-resource corpus. This paper designs a weight evaluation function to reduce the influence of unknown words. It proposes an improved harmony search algorithm utilizing the roulette and local evaluation strategies for handling the square Hmong part-of-speech tagging problem. The experiment shows that the average accuracy of the proposed model is 6%, 8% more than HMM and BiLSTM-CRF models, respectively. Meanwhile, the average F1 of the proposed model is also 6%, 3% more than HMM and BiLSTM-CRF models, respectively.
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