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Hybrid Deep Learning Model for Singing Voice Separation
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Monaural source separation is a challenging issue due to the fact that there is only a single channel available; however, there is an unlimited range of possible solutions. In this paper, a monaural source separation model based hybrid deep learning model, which consists of convolution neural network (CNN), dense neural network (DNN) and recurrent neural network (RNN), will be presented. A trial and error method will be used to optimize the number of layers in the proposed model. Moreover, the effects of the learning rate, optimization algorithms, and the number of epochs on the separation performance will be explored. Our model was evaluated using the MIR-1K dataset for singing voice separation. Moreover, the proposed approach achieves (4.81) dB GNSDR gain, (7.28) dB GSIR gain, and (3.39) dB GSAR gain in comparison to current approaches

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Publication Date
Mon Apr 26 2021
Journal Name
Journal Of Electrical Engineering & Technology
ANFIS Based Reinforcement Learning Strategy for Control A Nonlinear Coupled Tanks System
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Publication Date
Tue Dec 01 2009
Journal Name
Journal Of Al-nahrain University
SEPARATION AND IDENTIFICATION OF NAPHTHALENE, ACENAPHTHYLENE, PYRENE, BENZ{A} ANTHRACENE AND 1,3,2,4-DIBENZANTHRACENE
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Reverse Phase High Performance Liquid Chromatography (RP-HPLC) was coupled with ultraviolet absorption sepectoscopy (UV) for separation and identification of Naphthalene, Acenaphthylene, Pyrene, Benz{a} anthracene and 1,3,2,4-Dibenzanthracene. RP-HPLC was performed on an ODS-C18 column (150×4.6 mm I.D) using acetonitrile–buffer phosphate as mobile phase. UV absorption spectra of the elutes was detected in 254 nm, and studying the chromatographic variables include organic modifier ratio, PH, column temperature and concentration of buffer to maximize resolution and minimize separation time. the results showed that using mobile phase( 80:20) v/v acetonitrile:0.01M phosphate buffer solution at PH 6 with flow rate 1ml/min and column te

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Publication Date
Thu Jun 30 2016
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Radiological Assessment and Mechanical Separation of NORM Contaminated Soil from Iraqi Oil Fields
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Naturally occurring radioactive materials (NORM) contaminated sites at Al-Rumaila Iraqi oil fields have been characterized as a part of soil remediation project. Activity of radium isotopes in contaminated soil have been determined using gamma spectrometer High Purity Germanium detector (HPGe) and found to be very high for Al-Markezia, Al-Qurainat degassing stations and storage area at Khadhir Almay region. The activity concentration of samples ranges from 6474.11±563.8 Bq/kg to 1232.5±60.9 Bq/kg with mean value of 3853.3 Bq/kg for 226Ra, 843.59±8.39 Bq/kg to 302.2±9.2 Bq/kg  with mean value of 572.9 Bq/kg for 232Th and 294.31±18.56 Bq/kg to 156.64±18.1 Bq/kg with mean value of 225.5 for 40K. S

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Publication Date
Tue Dec 29 2020
Journal Name
Iraqi Journal Of Pharmaceutical Sciences ( P-issn 1683 - 3597 E-issn 2521 - 3512)
Separation and Identification of Phenolic Acid from Borago officinalis (F:Boraginaceae) Cultivated in Iraq
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    The plant Borago officinalis, which belongs to the Boraginaceae family and Celebrated as borage, is one of the useful medicinal plants cultivated in Iraq. It was used in olde medicine in Iraq, Irane, Syria and Europe for management of various diseases. It is commonly used as an atonic, tranquilliser, management of cough, sore throat, pneumonia, swelling, inflammatory diseases, antioxidant, and anticancer. This project provides the first comprehensive research done in Iraq to study the phytochemicals and the methods of extraction and isolation of active constituents from Borago officinalis cultivated in Iraq. The plant was harvested in spring from AL-Rifai, Nassiriyah city, IRAQ in February 2019.were w

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Publication Date
Thu Jan 01 2009
Journal Name
مجلة العلوم الاحصائية
Robust Estimator for Semiparametric Generalized Additive Model
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Generalized Additive Model has been considered as a multivariate smoother that appeared recently in Nonparametric Regression Analysis. Thus, this research is devoted to study the mixed situation, i.e. for the phenomena that changes its behaviour from linear (with known functional form) represented in parametric part, to nonlinear (with unknown functional form: here, smoothing spline) represented in nonparametric part of the model. Furthermore, we propose robust semiparametric GAM estimator, which compared with two other existed techniques.

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Publication Date
Tue Oct 22 2024
Journal Name
Iraqi Statisticians Journal
Inferential Methods for the Dagum Regression Model
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The Dagum Regression Model, introduced to address limitations in traditional econometric models, provides enhanced flexibility for analyzing data characterized by heavy tails and asymmetry, which is common in income and wealth distributions. This paper develops and applies the Dagum model, demonstrating its advantages over other distributions such as the Log-Normal and Gamma distributions. The model's parameters are estimated using Maximum Likelihood Estimation (MLE) and the Method of Moments (MoM). A simulation study evaluates both methods' performance across various sample sizes, showing that MoM tends to offer more robust and precise estimates, particularly in small samples. These findings provide valuable insights into the ana

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Publication Date
Thu Feb 09 2023
Journal Name
Artificial Intelligence Review
Community detection model for dynamic networks based on hidden Markov model and evolutionary algorithm
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Finding communities of connected individuals in complex networks is challenging, yet crucial for understanding different real-world societies and their interactions. Recently attention has turned to discover the dynamics of such communities. However, detecting accurate community structures that evolve over time adds additional challenges. Almost all the state-of-the-art algorithms are designed based on seemingly the same principle while treating the problem as a coupled optimization model to simultaneously identify community structures and their evolution over time. Unlike all these studies, the current work aims to individually consider this three measures, i.e. intra-community score, inter-community score, and evolution of community over

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Publication Date
Tue Dec 01 2020
Journal Name
Iraqi Journal Of Physics
Modifications to Accelerate the Iterative Algorithm for the Single Diode Model of PV Model
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This paper discussed the solution of an equivalent circuit of solar cell, where a single diode model is presented. The nonlinear equation of this model has suggested and analyzed an iterative algorithm, which work well for this equation with a suitable initial value for the iterative. The convergence of the proposed method is discussed. It is established that the algorithm has convergence of order six. The proposed algorithm is achieved with a various values of load resistance. Equation by means of equivalent circuit of a solar cell so all the determinations is achieved using Matlab in ambient temperature. The obtained results of this new method are given and the absolute errors is demonstrated.

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Publication Date
Sun Sep 07 2008
Journal Name
Baghdad Science Journal
Hybrid Cipher System using Neural Network
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The objective of this work is to design and implement a cryptography system that enables the sender to send message through any channel (even if this channel is insecure) and the receiver to decrypt the received message without allowing any intruder to break the system and extracting the secret information. In this work, we implement an interaction between the feedforward neural network and the stream cipher, so the secret message will be encrypted by unsupervised neural network method in addition to the first encryption process which is performed by the stream cipher method. The security of any cipher system depends on the security of the related keys (that are used by the encryption and the decryption processes) and their corresponding le

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Publication Date
Thu Dec 01 2011
Journal Name
Journal Of Engineering
Image Reconstruction Using Modified Hybrid Transform
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In this paper, an algorithm for reconstruction of a completely lost blocks using Modified
Hybrid Transform. The algorithms examined in this paper do not require a DC estimation
method or interpolation. The reconstruction achieved using matrix manipulation based on
Modified Hybrid transform. Also adopted in this paper smart matrix (Detection Matrix) to detect
the missing blocks for the purpose of rebuilding it. We further asses the performance of the
Modified Hybrid Transform in lost block reconstruction application. Also this paper discusses
the effect of using multiwavelet and 3D Radon in lost block reconstruction.

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