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Skull Stripping Based on the Segmentation Models
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Skull image separation is one of the initial procedures used to detect brain abnormalities. In an MRI image of the brain, this process involves distinguishing the tissue that makes up the brain from the tissue that does not make up the brain. Even for experienced radiologists, separating the brain from the skull is a difficult task, and the accuracy of the results can vary quite a little from one individual to the next. Therefore, skull stripping in brain magnetic resonance volume has become increasingly popular due to the requirement for a dependable, accurate, and thorough method for processing brain datasets. Furthermore, skull stripping must be performed accurately for neuroimaging diagnostic systems since neither non-brain tissues nor the removal of brain sections can be addressed in the subsequent steps, resulting in an unfixed mistake during further analysis. Therefore, accurate skull stripping is necessary for neuroimaging diagnostic systems. This paper proposes a system based on deep learning and Image processing, an innovative method for converting a pre-trained model into another type of pre-trainer using pre-processing operations and the CLAHE filter as a critical phase. The global IBSR data set was used as a test and training set. For the system's efficacy, work was performed based on the principle of three dimensions and three sections of MR images and two-dimensional images, and the results were 99.9% accurate.

Publication Date
Tue Jun 23 2020
Journal Name
Baghdad Science Journal
Anomaly Detection Approach Based on Deep Neural Network and Dropout
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   Regarding to the computer system security, the intrusion detection systems are fundamental components for discriminating attacks at the early stage. They monitor and analyze network traffics, looking for abnormal behaviors or attack signatures to detect intrusions in early time. However, many challenges arise while developing flexible and efficient network intrusion detection system (NIDS) for unforeseen attacks with high detection rate. In this paper, deep neural network (DNN) approach was proposed for anomaly detection NIDS. Dropout is the regularized technique used with DNN model to reduce the overfitting. The experimental results applied on NSL_KDD dataset. SoftMax output layer has been used with cross entropy loss funct

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Publication Date
Sat Oct 29 2022
Journal Name
Computers
Intelligent Robotic Welding Based on a Computer Vision Technology Approach
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Robots have become an essential part of modern industries in welding departments to increase the accuracy and rate of production. The intelligent detection of welding line edges to start the weld in a proper position is very important. This work introduces a new approach using image processing to detect welding lines by tracking the edges of plates according to the required speed by three degrees of a freedom robotic arm. The two different algorithms achieved in the developed approach are the edge detection and top-hat transformation. An adaptive neuro-fuzzy inference system ANFIS was used to choose the best forward and inverse kinematics of the robot. MIG welding at the end-effector was applied as a tool in this system, and the wel

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Publication Date
Fri Feb 20 2026
Journal Name
Journal Mustansiriyah Of Sports Science
The influence of excellent offensive basketball skill-learning activities based on sensory perceptions for female pupils.
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The aim of this research is to develop qualitative workouts based on certain sensory perceptions for the development of offensive basketball abilities and to determine their impact on female pupils. Several findings, based on the au-thor's extensive expertise instructing basketball materials and our closeness to the sample, revealed deficits in some sensory perceptions “in the game of basketball”, which impair the accuracy of passing the ball to the best team-mate. It also affects the pace of dribbling and the difficulty of selecting the op-timal moment and distance to fire. Therefore, the researcher designs qualita-tive activities based on many sensory experiences, including distance, speed, force, and direction shift. In addition, the

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Publication Date
Tue Dec 01 2009
Journal Name
2009 7th International Conference On Information, Communications And Signal Processing (icics)
The effect of traffic load on using XCAST based routing protocol in wireless ad hoc networks
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Publication Date
Fri Mar 29 2024
Journal Name
Iraqi Journal Of Science
Finding the Best Route for Connecting Citizens with Service Centers in Baghdad Based on NN Technology
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     A geographic information system (GIS) is a very effective management and analysis tool. Geographic locations rely on data. The use of artificial neural networks (ANNs) for the interpretation of natural resource data has been shown to be beneficial. Back-propagation neural networks are one of the most widespread and prevalent designs. The combination of geographic information systems with artificial neural networks provides a method for decreasing the cost of landscape change studies by shortening the time required to evaluate data. Numerous designs and kinds of ANNs have been created; the majority of them are PC-based service domains. Using the ArcGIS Network Analyst add-on, you can locate service regions around any network

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Publication Date
Sun Oct 20 2024
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Synthesis and Investigation The Biological Activity of Some New Alkenes Based on Thiazoldin-4-one Compounds
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This work has been done to prepare a series of new alkene compounds derived from 4-thiozolidinones by substituting different aldehydes, P-acetamido-phenol, and 2-mercapto-benzoimidazole, which were used as starting materials to form ester [I]a,b and then make hydrazides [II]a,b, which were used to prepare 1, 3, and 4-oxadiazoles [III]a,b, which were then used for prepared Schiff bases [IV]a-f, The next step was the synthesis of 4-thiazoldinone derivatives [V]a-f  from Schiff bases. The final step was the synthesis of alkenes [VII]a-f, the prepared derivatives were identified with spectral methods (FT-IR, 1H-NMR, mass, and CHNS). The antibacterial activity of the prepared derivatives was evaluated against four types of bacteria, pos

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Publication Date
Mon May 27 2024
Journal Name
Eureka: Physics And Engineering
Systematic development of an autonomous robotic car for fire-fighting based on the interactive design approach
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Fire incidences are classed as catastrophic events, which mean that persons may experience mental distress and trauma. The development of a robotic vehicle specifically designed for fire extinguishing purposes has significant implications, as it not only addresses the issue of fire but also aims to safeguard human lives and minimize the extent of damage caused by indoor fire occurrences. The primary goal of the AFRC is to undergo a metamorphosis, allowing it to operate autonomously as a specialized support vehicle designed exclusively for the task of identifying and extinguishing fires. Researchers have undertaken the tasks of constructing an autonomous vehicle with robotic capabilities, devising a universal algorithm to be employed

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Publication Date
Tue Nov 01 2016
Journal Name
International Journal Of Pharmaceutical Sciences Review And Research
A study on some variables affecting the preparation of ethyl cellulose based floating microspheres of lafutidine
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Lafutidine (LAF) a newly developed histamine H2-receptor antagonist with absorption window makes it a good candidate to be prepared as floating drug delivery system. The current study involves formulation and in- Vitro evaluation of lafutidine as floating microspheres. Different formulation variables that affect the physicochemical properties of the prepared microspheres besides to the drug release behavior were investigated. Fourteen formulas were prepared by emulsion (o/w) solvent evaporation method using Ethyl cellulose (EC) as the polymeric matrix and tween 80 (TW80) as an emulsifying agent. The prepared formulas were evaluated for their percentage buoyancy (%), Percentage yield (%) and Entrapment efficiency (EE %). The results obt

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Publication Date
Sun Dec 01 2019
Journal Name
Baghdad Science Journal
Comparison of Some Suggested Estimators Based on Differencing Technique in the Partial Linear Model Using Simulation
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In this paper new methods were presented based on technique of differences which is the difference- based modified jackknifed generalized ridge regression estimator(DMJGR) and difference-based generalized  jackknifed ridge regression estimator(DGJR), in estimating the parameters of linear part of the partially linear model. As for the nonlinear part represented by the nonparametric function, it was estimated using Nadaraya Watson smoother. The partially linear model was compared using these proposed methods with other estimators based on differencing technique through the MSE comparison criterion in simulation study.

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Publication Date
Tue Sep 21 2021
Journal Name
Earth Resources And Environmental Remote Sensing/gis Applications Xii
Investigating the old city of Babylon: tracing buried structural history based on photogrammetry and integrated approaches
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