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Reduced hardware requirements of deep neural network for breast cancer diagnosis
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Identifying breast cancer utilizing artificial intelligence technologies is valuable and has a great influence on the early detection of diseases. It also can save humanity by giving them a better chance to be treated in the earlier stages of cancer. During the last decade, deep neural networks (DNN) and machine learning (ML) systems have been widely used by almost every segment in medical centers due to their accurate identification and recognition of diseases, especially when trained using many datasets/samples. in this paper, a proposed two hidden layers DNN with a reduction in the number of additions and multiplications in each neuron. The number of bits and binary points of inputs and weights can be changed using the mask configuration on each subsystem to futher reduce the hardware requirements. The DNN was designed using a system generator and implemented using very hardware description language (VHDL). The system achievments outcomes the superior’s accuracy rate of approximately 99.6 percent in distinguishing bengin from malignant tissue. Also, the hardware resources were reduced by 30 percent from works of literature with an error rate of 7e-4 when using the Kintex-7 xc7k325t-3fbg676 board.

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Publication Date
Thu Jun 01 2023
Journal Name
Baghdad Science Journal
In vitro isolation and expansion of neural stem cells NSCs
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   Neural stem cells (NSCs) are progenitor cells which have the ability to self‑renewal and potential for differentiating into neurons, oligodendrocytes, and astrocytes. The in vitro isolation, culturing, identification, cryopreservation were investigated to produce neural stem cells in culture as successful sources for further studies before using it for clinical trials. In this study, mouse bone marrow was the source of neural stem cells. The results of morphological study and immunocytochemistry of isolated cells showed that NSCs can be produced successfully and maintaining their self‑renewal and successfully forming neurosphere for multiple passages. The spheres preserved their morphology in culture and cryopreserved t

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Publication Date
Tue Jul 09 2024
Journal Name
Diagnostics
A Novel Hybrid Machine Learning-Based System Using Deep Learning Techniques and Meta-Heuristic Algorithms for Various Medical Datatypes Classification
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Medicine is one of the fields where the advancement of computer science is making significant progress. Some diseases require an immediate diagnosis in order to improve patient outcomes. The usage of computers in medicine improves precision and accelerates data processing and diagnosis. In order to categorize biological images, hybrid machine learning, a combination of various deep learning approaches, was utilized, and a meta-heuristic algorithm was provided in this research. In addition, two different medical datasets were introduced, one covering the magnetic resonance imaging (MRI) of brain tumors and the other dealing with chest X-rays (CXRs) of COVID-19. These datasets were introduced to the combination network that contained deep lea

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Publication Date
Sun Jan 01 2017
Journal Name
Pertanika Journal Of Science & Technology
Modified Kohonen network algorithm for selection of the initial centres of Gustafson-Kessel algorithm in credit scoring
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Credit risk assessment has become an important topic in financial risk administration. Fuzzy clustering analysis has been applied in credit scoring. Gustafson-Kessel (GK) algorithm has been utilised to cluster creditworthy customers as against non-creditworthy ones. A good clustering analysis implemented by good Initial Centres of clusters should be selected. To overcome this problem of Gustafson-Kessel (GK) algorithm, we proposed a modified version of Kohonen Network (KN) algorithm to select the initial centres. Utilising similar degree between points to get similarity density, and then by means of maximum density points selecting; the modified Kohonen Network method generate clustering initial centres to get more reasonable clustering res

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Publication Date
Sat Dec 02 2017
Journal Name
Al-khwarizmi Engineering Journal
Direction Finding Using GHA Neural Networks
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 This paper adapted the neural network for the estimating of the direction of arrival (DOA). It uses an unsupervised adaptive neural network with GHA algorithm to extract the principal components that in turn, are used by Capon method to estimate the DOA, where by the PCA neural network we take signal subspace only and use it in Capon (i.e. we will ignore the noise subspace, and take the signal subspace only).

 

 

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Publication Date
Tue Oct 01 2019
Journal Name
Journal Of Economics And Administrative Sciences
the role of time management in facilitate the work requirements for employees of the administrative department at the ministry of higher education and scientific research
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The aim of the present study is to provide the adequate knowledge about the role of time management in facilitate the work requirements for employees of the administrative department at the Ministry of Higher Education and Scientific Research. The research depend on studying four important dimensions which are (time planning, time organization, time direction and time observation). In addition to study other five dimensions which are (new procedures, clear procedures, short procedures, the available information and the simplicity of the methods

used).Questionnaire sheets consist of (38 questions) distributed to (170) employees and (146)  sheets only were considered in the study. SPSS program was used

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Publication Date
Sun Aug 25 2024
Journal Name
University Of Samarra
Preparation, diagnosis and testing of the biological ‎activity of new complexes of Schiff bases
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‎ Sixteen new complexes with the general formula [M(L)2(H2O)2] were ‎prepared resulting from the reaction of the two new Schiff base ligands, which ‎are‏: -‏ L1= (E)-5-((2-hydroxybenzylidene)amino)-2-phenyl-2,4-dihydro-3H-pyrazol-3-one)‎ L2 = (E)-5-((2-hydroxy-3-methoxybenzylidene)amino)-2-phenylpyrazolidin-3-one) ‎ ‎ With divalent metal ions (manganese, cobalt, nickel, copper, zinc, cadmium, ‎mercury) and (tetravalent platinum).‎‏ ‏ ‎ Ligands was derived from the reaction of the amine (5-amino-2-phenyl-2,4-‎dihydro-3H-pyrazol-3-one) with Salicylaldehyde and ortho-vanillin, which is ‎linked to the metal ions via the nitrogen atoms are the isomethene group and ‎the oxygen is the hydroxide group of t

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Publication Date
Fri Mar 31 2017
Journal Name
Iraqi Journal Of Biotechnology
Reliable Reference Gene for Normalization of RT- qPCR Data in Human Cancer Cell Lines
Subjected to Gene Knockdown
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Quantitative real-time Polymerase Chain Reaction (RT-qPCR) has become a valuable molecular technique in biomedical research. The selection of suitable endogenous reference genes is necessary for normalization of target gene expression in RT-qPCR experiments. The aim of this study was to determine the suitability of each 18S rRNA and ACTB as internal control genes for normalization of RT-qPCR data in some human cell lines transfected with small interfering RNA (siRNA). Four cancer cell lines including MCF-7, T47D, MDA-MB-231 and Hela cells along with HEK293 representing an embryonic cell line were depleted of E2F6 using siRNA specific for E2F6 compared to negative control cells, which were transfected with siRNA not specific for any gene. Us

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Publication Date
Wed Dec 01 2021
Journal Name
Baghdad Science Journal
Advanced Intelligent Data Hiding Using Video Stego and Convolutional Neural Networks
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Steganography is a technique of concealing secret data within other quotidian files of the same or different types. Hiding data has been essential to digital information security. This work aims to design a stego method that can effectively hide a message inside the images of the video file.  In this work, a video steganography model has been proposed through training a model to hiding video (or images) within another video using convolutional neural networks (CNN). By using a CNN in this approach, two main goals can be achieved for any steganographic methods which are, increasing security (hardness to observed and broken by used steganalysis program), this was achieved in this work as the weights and architecture are randomized. Thus,

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Publication Date
Tue Dec 30 2008
Journal Name
Al-kindy College Medical Journal
The Sensitivity and Specificity of Oral Tumors and Premalignant Lesions Diagnosis by Otolaryngologists
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Background: Tumors of the oral cavity are under
estimated in general dental and medical practice,
some authors describe it as the forgetting disease,
others wondering if the attention paid to this disease
compared to its fatality (The 5-year survival rate is
about 50%) is enough for disease control? However;
this disease deserves a comprehensive assessment by
all dental and medical fields assumed to examine the
oral cavity regularly, especially otolaryngologist.
Objectives: To find out the sensitivity and specificity
of clinical examination in diagnosing oral tumors and
premalignant conditions by otolaryngologist.
Methods: Across sectional retrospective study was
conducted in the:
-study design:

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Publication Date
Sat Mar 07 2020
Journal Name
Systematic Review Pharmacy
Preparation, Diagnosis, Thermodynamic and Biological Studies of New Complexes Derived from Heterocyclic Ligand
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The ligand Schiff base [(E)-3-(2-hydroxy-5-methylbenzylideneamino)- 1- phenyl-1H-pyrazol-5(4H) –one] with some metals ion as Mn(II); Co(II); Ni(II); Cu(II); Cd(II) and Hg(II) complexes have been preparation and characterized on the basic of mass spectrum for L, elemental analyses, FTIR, electronic spectral, magnetic susceptibility, molar conductivity measurement and functions thermodynamic data study (∆H°, ∆S° and ∆G°). Results of conductivity indicated that all complexes were non electrolytes. Spectroscopy and other analytical studies reveal distorted octahedral geometry for all complexes. The antibacterial activity of the ligand and preparers metal complexes was also studied against gram and negative bacteria.