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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 Mar 01 2021
Journal Name
Iop Conference Series: Materials Science And Engineering
Speech Enhancement Algorithm Based on a Hybrid Estimator
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Abstract<p>Speech is the essential way to interact between humans or between human and machine. However, it is always contaminated with different types of environment noise. Therefore, speech enhancement algorithms (SEA) have appeared as a significant approach in speech processing filed to suppress background noise and return back the original speech signal. In this paper, a new efficient two-stage SEA with low distortion is proposed based on minimum mean square error sense. The estimation of clean signal is performed by taking the advantages of Laplacian speech and noise modeling based on orthogonal transform (Discrete Krawtchouk-Tchebichef transform) coefficients distribution. The Discrete Kra</p> ... Show More
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Publication Date
Thu Apr 25 2019
Journal Name
Iraqi Journal Of Market Research And Consumer Protection
SEPARATION OF PROTEIN AND LACTOSE FROM WHEY DISPOSED FROM ABO-GHRAAB DAIRY FACTORY BY USING MEMBRANE TECHNOLOGY: SEPARATION OF PROTEIN AND LACTOSE FROM WHEY DISPOSED FROM ABO-GHRAAB DAIRY FACTORY BY USING MEMBRANE TECHNOLOGY
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In this research, the performance of a two kind of membrane was examined to recovering the nutrients (protein and lactose) from the whey produced by the soft cheese industry in the General Company for Food Products inAbo-ghraab.Wheyare treated in two stages, the first including press whey into micron filter made of poly vinylidene difluoride (PVDF) standard plate type 800 kilo dalton, The membrane separates the whey to permeate which represent is the main nutrients and to remove the fat and microorganisms.The second stage is to isolate the protein by using ultra filter made of polyethylsulphone(PES)type plate with a measurement of 10,60 kilo dalton and the recovery of lactose in the form of permeate.
The results showed that the percen

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Publication Date
Thu Jun 30 2011
Journal Name
Al-khwarizmi Engineering Journal
Nahrain Mobile Learning System (NMLS)
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The work in this paper involves the planning, design and implementation of a mobile learning system called Nahrain Mobile Learning System (NMLS). This system provides complete teaching resources, which can be accessed by the students, instructors and administrators through the mobile phones. It presents a viable alternative to Electronic learning. It focuses on the mobility and flexibility of the learning practice, and emphasizes the interaction between the learner and learning content. System users are categorized into three categories: administrators, instructors and students. Different learning activities can be carried out throughout the system, offering necessary communication tools to allow the users to communicate with each other

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Publication Date
Tue Jan 01 2019
Journal Name
Opcion, Año
Active Learning And Creative Thinking
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Active Learning And Creative Thinking

Publication Date
Tue Dec 27 2022
Journal Name
2022 3rd Information Technology To Enhance E-learning And Other Application (it-ela)
Diabetes Prediction Using Machine Learning
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Diabetes is one of the increasing chronic diseases, affecting millions of people around the earth. Diabetes diagnosis, its prediction, proper cure, and management are compulsory. Machine learning-based prediction techniques for diabetes data analysis can help in the early detection and prediction of the disease and its consequences such as hypo/hyperglycemia. In this paper, we explored the diabetes dataset collected from the medical records of one thousand Iraqi patients. We applied three classifiers, the multilayer perceptron, the KNN and the Random Forest. We involved two experiments: the first experiment used all 12 features of the dataset. The Random Forest outperforms others with 98.8% accuracy. The second experiment used only five att

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Publication Date
Mon Jan 02 2023
Journal Name
Pakistan Heart Jornal
The Effect of the Strategy of Differentiated Education According to the Auditory Learning Style by Using Assistance in Learning the Back Kick (T-Chagi) for the Young Players of …
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Publication Date
Sat Oct 01 2022
Journal Name
Colloids And Surfaces A: Physicochemical And Engineering Aspects
Application of natural deep eutectic solvents in bulk liquid membrane system for removal of free glycerol from crude fatty acid methyl ester
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Fatty Acid Methyl Ester (FAME) produced from biomass offers several advantages such as renewability and sustainability. The typical production process of FAME is accompanied by various impurities such as alcohol, soap, glycerol, and the spent catalyst. Therefore, the most challenging part of the FAME production is the purification process. In this work, a novel application of bulk liquid membrane (BLM) developed from conventional solvent extraction methods was investigated for the removal of glycerol from FAME. The extraction and stripping processes are combined into a single system, allowing for simultaneous solvent recovery whereby low-cost quaternary ammonium salt-glycerol-based deep eutectic solvent (DES) is used as the membrane phase.

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Publication Date
Sun Jun 07 2015
Journal Name
Baghdad Science Journal
Separation and Determination of Some Organic Acids in Dry Calyces of Iraqi Hibiscus Sabdariffa Linn
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A new reversed phase- high performance liquid chromatographic (RP-HPLC) method with Ultraviolet-Visible spectrophotometry has been optimized and validated for the simultaneous extraction and determination of organic acids present in Iraqi calyces of Hibiscus Sabdraffia Linn. The method is based on using ultrasonic bath for extracting organic acids. Limit of detection in µg/ml of Formic acid, Acetic acid, Oxalic acid, Citric acid, Succinic acid, Tartaric acid, and Malic acid 126.8498×10-6, 113.6005×10-6, 97.0513×10-6, 49.7925×10-6, 84.0753×10-6, 92.6551×10-6, and 106.1633×10-6 ,respectively. The concentration of organic acids found in dry spacemen of calyces of Iraqi Hibiscus Sabdraffia Linn. under study: Formic acid, Acetic acid,

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Publication Date
Wed May 24 2017
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
An Experimental Study of Collection ,Separation , Enumeration and Cryopreservation of Umbilical Cord Blood Stem Cells
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   Umbilical cord blood (UCB) contains hematopoietic and mesenchymal stem cells(HSCs,MSCs) that have proven useful clinically to reconstitute the hematopoietic system in children and some adults  .   Fifteen cord blood samples were collected from placenta of  newly delivered women in AlKadhemia hospital in Baghdad for normal vaginal delivery.      Mono nucleated cells (MNCs) were isolated by using density gradient centrifugation and the MNCs  count and viability  were  determinated by trypan blue.MNCs were cryopreserved using the cryoprotectant solution of 10% concentration of dimethyl sulfoxid (DMSO)using slow cooling and rapid thaw.  The aim of the present study

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Publication Date
Tue Jul 17 2018
Journal Name
International Journal Of Adaptive Control And Signal Processing
Single channel informed signal separation using artificial-stereophonic mixtures and exemplar-guided matrix factor deconvolution
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