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Deep Learning-Based Segmentation and Classification Techniques for Brain Tumor MRI: A Review
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Early detection of brain tumors is critical for enhancing treatment options and extending patient survival. Magnetic resonance imaging (MRI) scanning gives more detailed information, such as greater contrast and clarity than any other scanning method. Manually dividing brain tumors from many MRI images collected in clinical practice for cancer diagnosis is a tough and time-consuming task. Tumors and MRI scans of the brain can be discovered using algorithms and machine learning technologies, making the process easier for doctors because MRI images can appear healthy when the person may have a tumor or be malignant. Recently, deep learning techniques based on deep convolutional neural networks have been used to analyze medical images with favorable results. It can help save lives faster and rectify some medical errors. In this study, we look at the most up-to-date methodologies for medical image analytics that use convolutional neural networks on MRI images. There are several approaches to diagnosing and classifying brain cancers. Inside the brain, irregular cells grow so that a brain tumor appears. The size of the tumor and the part of the brain affected impact the symptoms.

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Publication Date
Fri Jul 15 2022
Journal Name
International Journal Of Research In Social Sciences & Humanities
SUBJECT REVIEW: APPLIED LINGUISTICS AND LANGUAGE TEACHING
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Language mirrors understanding of the same or other culture. Meaning is hardly encoded in the world community. Some linguists cannot solve language problems because of its abstractedness. Language communicators learn the second or foreign language at schools for three reasons. First, English is not their first language. Second, they have little or no educational background in experiencing what they know in another language. Third, they have not improved their communication skills in English. Analyzing the language as a mean of communication would not be learned enough without its practice. This paper gives an exclusive preview of applied linguistics. It defines the conversion of different areas of the study of language with second o

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Publication Date
Wed Apr 03 2019
Journal Name
Al-kindy College Medical Journal
Low Back Pain and Obesity (Review Article)
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Low back pain a major causes of morbidity throughout the world and it is a most debilitating condition ,and can lead to decreased physical function ,compromised quality of life, and psychological distress.

Obesity is nowadays a pandemic condition. Obese subjects are commonly characterized by musculoskeletal disorders and particularly by non-specific LBP. However, the relationship between obesity and LBP remain to date unsupported by objective measurements of mechanical behavior of spine and it is morphology in obese subjects.                                   &nb

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Publication Date
Sun Nov 01 2020
Journal Name
Solid State Technology
Access Control Security Review: Concepts and Models
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HS Saeed, SS Abdul-Jabbar, SG Mohammed, EA Abed, HS Ibrahem, Solid State Technology, 2020

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Publication Date
Thu May 20 2021
Journal Name
International Journal Of Research In Social Sciences And Humanities
SUBJECT REVIEW: ACCULTURATION AND SECOND LANGUAGE ACQUISITION
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This paper aims to give an overview of acculturation in the literature by means of its explicit definitions, main characteristics, and categorical process. It also foreshadows the significance of acquiring novice culture with particular connection to second language acquisition. Schumann and John Berry underscored acculturation each with its own model of acculturation. In the case of this review, you will realize that Schumann's two main aspects are firmly rooted in berry's model of acculturation as an exclusive description in relation to second language acquisition. Steven Krashen outlined acquiring language from the intellectual and linguistic view of learning, whereas Micheal Long demanded for communication in social perspective. It has

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Publication Date
Mon Jan 01 2024
Journal Name
Aip Conference Proceedings
Non-linear support vector machine classification models using kernel tricks with applications
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The support vector machine, also known as SVM, is a type of supervised learning model that can be used for classification or regression depending on the datasets. SVM is used to classify data points by determining the best hyperplane between two or more groups. Working with enormous datasets, on the other hand, might result in a variety of issues, including inefficient accuracy and time-consuming. SVM was updated in this research by applying some non-linear kernel transformations, which are: linear, polynomial, radial basis, and multi-layer kernels. The non-linear SVM classification model was illustrated and summarized in an algorithm using kernel tricks. The proposed method was examined using three simulation datasets with different sample

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Publication Date
Mon Apr 03 2023
Journal Name
Journal Of Educational And Psychological Researches
The Effectiveness of a Training Program for Children with Autism and their Mothers Based on the Picture Exchange Commination System to Confront Some of Basic Disorders for these Children
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Abstract

The current research aims to examine the effectiveness of a training program for children with autism and their mothers based on the Picture Exchange Communication System to confront some basic disorders in a sample of children with autism. The study sample was (16) children with autism and their mothers in the different centers in Taif city and Tabuk city. The researcher used the quasi-experimental approach, in which two groups were employed: an experimental group and a control group. Children aged ranged from (6-9) years old. In addition, it was used the following tools: a list of estimation of basic disorders for a child with autism between (6-9) years, and a training program for children with autism

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Publication Date
Tue Jan 01 2019
Journal Name
Advances On Computational Intelligence In Energy
A Theoretical Framework for Big Data Analytics Based on Computational Intelligent Algorithms with the Potential to Reduce Energy Consumption
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Within the framework of big data, energy issues are highly significant. Despite the significance of energy, theoretical studies focusing primarily on the issue of energy within big data analytics in relation to computational intelligent algorithms are scarce. The purpose of this study is to explore the theoretical aspects of energy issues in big data analytics in relation to computational intelligent algorithms since this is critical in exploring the emperica aspects of big data. In this chapter, we present a theoretical study of energy issues related to applications of computational intelligent algorithms in big data analytics. This work highlights that big data analytics using computational intelligent algorithms generates a very high amo

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Publication Date
Wed Oct 25 2023
Journal Name
Journal Of Optics
Integrated gallium phosphide-waveguide polarization rotator based on rotating a horizontal slot by 45 degree for 700 nm wavelength
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Abstract<p>Polarization manipulation elements operating at visible wavelengths represent a critical component of quantum communication sub-systems, equivalent to their telecom wavelength counterparts. The method proposed involves rotating the optic axis of the polarized input light by an angle of 45 degree, thereby converting the fundamental transverse electric (TE<sub>0</sub>) mode to the fundamental transverse magnetic (TM<sub>0</sub>) mode. This paper outlines an integrated gallium phosphide-waveguide polarization rotator, which relies on the rotation of a horizontal slot by 45 degree at a wavelength of 700 nm. This will ultimately lead to the conception of a mode hybridization phenomeno</p> ... Show More
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Publication Date
Fri Feb 08 2019
Journal Name
Iraqi Journal Of Laser
Random Number Generation for Quantum Key Distribution Systems Based on Shot-Noise Fluctuations in a P-I-N Photodiode
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A simple setup of random number generator is proposed. The random number generation is based on the shot-noise fluctuations in a p-i-n photodiode. These fluctuations that are defined as shot noise are based on a stationary random process whose statistical properties reflect Poisson statistics associated with photon streams. It has its origin in the quantum nature of light and it is related to vacuum fluctuations. Two photodiodes were used and their shot noise fluctuations were subtracted. The difference was applied to a comparator to obtain the random sequence.

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Publication Date
Wed Oct 25 2023
Journal Name
Journal Of Optics
Integrated gallium phosphide-waveguide polarization rotator based on rotating a horizontal slot by 45 degree for 700 nm wavelength
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Polarization manipulation elements operating at visible wavelengths represent a critical component of quantum communication sub-systems, equivalent to their telecom wavelength counterparts. The method proposed involves rotating the optic axis of the polarized input light by an angle of 45 degree, thereby converting the fundamental transverse electric (TE0) mode to the fundamental transverse magnetic (TM0) mode. This paper outlines an integrated gallium phosphide-waveguide polarization rotator, which relies on the rotation of a horizontal slot by 45 degree at a wavelength of 700 nm. This will ultimately lead to the conception of a mode hybridization phenomenon in the waveguide. The simulation results demonstrate a polarization co

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