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Comparison study of classification methods of intramuscular electromyography data for non-human primate model of traumatic spinal cord injury
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Traumatic spinal cord injury is a serious neurological disorder. Patients experience a plethora of symptoms that can be attributed to the nerve fiber tracts that are compromised. This includes limb weakness, sensory impairment, and truncal instability, as well as a variety of autonomic abnormalities. This article will discuss how machine learning classification can be used to characterize the initial impairment and subsequent recovery of electromyography signals in an non-human primate model of traumatic spinal cord injury. The ultimate objective is to identify potential treatments for traumatic spinal cord injury. This work focuses specifically on finding a suitable classifier that differentiates between two distinct experimental stages (pre-and post-lesion) using electromyography signals. Eight time-domain features were extracted from the collected electromyography data. To overcome the imbalanced dataset issue, synthetic minority oversampling technique was applied. Different ML classification techniques were applied including multilayer perceptron, support vector machine, K-nearest neighbors, and radial basis function network; then their performances were compared. A confusion matrix and five other statistical metrics (sensitivity, specificity, precision, accuracy, and F-measure) were used to evaluate the performance of the generated classifiers. The results showed that the best classifier for the left- and right-side data is the multilayer perceptron with a total F-measure of 79.5% and 86.0% for the left and right sides, respectively. This work will help to build a reliable classifier that can differentiate between these two phases by utilizing some extracted time-domain electromyography features.

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Publication Date
Sun Jan 03 2016
Journal Name
Journal Of Educational And Psychological Researches
"Social affiliation and its relation with compliance for Iraqi expatriates students"
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Individual cannot live alone due to to his need for others to facilitate His supplies for living, thus social affiliation is considered one of the most important psychological, social needs in individuals life through his willing to affiliate to others whether they were friends ,family, colleague , or even home to reach some degree of  psychological stability.

       Affiliation is a tool to search for Satiate through living with group from the same type or comply for group or to be compatible with them or even to be adherent and accept what the group agreed about of criterion.

     &nbsp

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Publication Date
Sun Dec 01 2013
Journal Name
Journal Of Accounting And Financial Studies ( Jafs )
Lean accounting and its rolls for calculated costs on value stream
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At the last two decades , The environment has witnessed tremendous changes in many fields with the huge competition , various technological development and customer satisfaction , that are reflected in economic units a doption for lean production system .                 

      Lean Accounting that has appeared as aresponse for changes occurred of economic units adoption for lean accounting system instead of wide production system : through it management of economic units has been changed from management by top departments into management by value flows : has provide new method for accounting costs according to value flow

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Publication Date
Mon Jan 28 2019
Journal Name
Journal Of The College Of Education For Women
Alexithymia and its relation with emotional intelleigence for 6th grade students
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This current study aims to:
1st: The recognizing of Alexithymia level for 6th grade students (Study Specimen) through the next Zero Hypothesis:1. There are no statistically significant differences at (0.05) level between the arithmetic mean of the specimen degrees as a whole and the central assumption for the scale of the lack in emotions expression
2. There are no statistically significant differences at (0.05) level between the arithmetic mean of the male students specimen and the arithmetic meanc of the female students specimen for the scale of Alexithymia.
2nd: ldentification the level of the emotional intelligence among 6th grade students (Study Specimen) through the next Zero Hypothesis:
1) There are no statistically si

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Publication Date
Thu May 15 2014
Journal Name
Monthly Notices Of The Royal Astronomical Society
Gravitational lensing in WDM cosmologies: the cross-section for giant arcs
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The nature of the dark sector of the Universe remains one of the outstanding problems in modern cosmology, with the search for new observational probes guiding the development of the next generation of observational facilities. Clues come from tension between the predictions from Λ cold dark matter (ΛCDM) and observations of gravitationally lensed galaxies. Previous studies showed that galaxy clusters in the ΛCDM are not strong enough to reproduce the observed number of lensed arcs. This work aims to constrain the warm dark matter (WDM) cosmologies by means of the lensing efficiency of galaxy clusters drawn from these alternative models. The lensing characteristics of two samples of simulated clusters in the Λ warm dark matter and ΛCDM

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Publication Date
Sat Jan 01 2022
Journal Name
1st Samarra International Conference For Pure And Applied Sciences (sicps2021): Sicps2021
Consequences for the groups 𝒮𝓛(2,𝒰), 𝒰 = 23 and 29
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Publication Date
Mon Jan 01 2018
Journal Name
Matec Web Of Conferences
Brain Tumour Detection using Fine-Tuning Mechanism for Magnetic Resonance Imaging
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In this paper, new brain tumour detection method is discovered whereby the normal slices are disassembled from the abnormal ones. Three main phases are deployed including the extraction of the cerebral tissue, the detection of abnormal block and the mechanism of fine-tuning and finally the detection of abnormal slice according to the detected abnormal blocks. Through experimental tests, progress made by the suggested means is assessed and verified. As a result, in terms of qualitative assessment, it is found that the performance of proposed method is satisfactory and may contribute to the development of reliable MRI brain tumour diagnosis and treatments.

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Publication Date
Sat Mar 15 2025
Journal Name
Journal Of Physical Education
Comparing Angiotensin Gene polymorphisms According to Mechanical Ability for Weightlifter Selection
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Publication Date
Thu Dec 28 2017
Journal Name
Al-khwarizmi Engineering Journal
Tuning PID Controller by Neural Network for Robot Manipulator Trajectory Tracking
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Ziegler and Nichols proposed the well-known Ziegler-Nichols method to tune the coefficients of PID controller. This tuning method is simple and gives fixed values for the coefficients which make PID controller have weak adaptabilities for the model parameters variation and changing in operating conditions. In order to achieve adaptive controller, the Neural Network (NN) self-tuning PID control is proposed in this paper which combines conventional PID controller and Neural Network learning capabilities. The proportional, integral and derivative (KP, KI, KD) gains are self tuned on-line by the NN output which is obtained due to the error value on the desired output of the system under control. The conventio

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Publication Date
Sun Jun 05 2011
Journal Name
Baghdad Science Journal
Correlation between Paris function parameters to crack velocity for Alumina ceramics
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The question about the existence of correlation between the parameters A and m of the Paris function is re-examined theoretically for brittle material such as alumina ceramic (Al2O3) with different grain size. Investigation about existence of the exponential function which fit a good approximation to the majority of experimental data of crack velocity versus stress intensity factor diagram. The rate theory of crack growth was applied for data of alumina ceramics samples in region I and making use of the values of the exponential function parameters the crack growth rate theory parameters were estimated.

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Publication Date
Sun Mar 03 2024
Journal Name
Mesopotamian Journal Of Cybersecurity
Using Information Technology for Comprehensive Analysis and Prediction in Forensic Evidence
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With the escalation of cybercriminal activities, the demand for forensic investigations into these crimeshas grown significantly. However, the concept of systematic pre-preparation for potential forensicexaminations during the software design phase, known as forensic readiness, has only recently gainedattention. Against the backdrop of surging urban crime rates, this study aims to conduct a rigorous andprecise analysis and forecast of crime rates in Los Angeles, employing advanced Artificial Intelligence(AI) technologies. This research amalgamates diverse datasets encompassing crime history, varioussocio-economic indicators, and geographical locations to attain a comprehensive understanding of howcrimes manifest within the city. Lev

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