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PFDINN: Comparison between Three Back-propagation Algorithms for Pear Fruit Disease Identification
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     The diseases presence in various species of fruits are the crucial parameter of economic composition and degradation of the cultivation industry around the world. The proposed pear fruit disease identification neural network (PFDINN) frame-work to identify three types of pear diseases was presented in this work. The major phases of the presented frame-work were as the following: (1) the infected area in the pear fruit was detected by using the algorithm of K-means clustering. (2) hybrid statistical features were computed over the segmented pear image and combined to form one descriptor. (3) Feed forward neural network (FFNN), which depends on three learning algorithms of back propagation (BP) training, namely Scaled conjugate gradient (SCG-BP), Resilient (R-BP) and Bayesian regularization (BR-BP), was used in the identification process. Pear fruit was taken as the experiment case during this work with three classifications of diseases, namely fire blight, pear scab, and sooty blotch, as compared to healthy pears. PFDINN framework was trained and tested using 2D pear fruit images collected from the Fruit Crops Diseases Database (FCDD). The presented framework achieved 94.6%, 97.3%, and 96.3% efficiency for SCG-BP, R-BP, and BR-BP, respectively. An accuracy value of 100% was achieved when the R-BP learning algorithm was trained for identification.

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Publication Date
Sun Mar 03 2013
Journal Name
Baghdad Science Journal
Synthesis and Identification of Some New Derivative of Trimethoprim and Paracetemol Drugs
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In this research two series of the new derivatives of Trimethoprim and paracetamol drugs have been prepared which known as a high medicinal effectiveness. Series (A) is including the interaction of diazonium salt of trimethoprim and coupling with some substituted phenol compounds (2-amino phenol, 3-ethyl phenol, 1-naphthol, 2-nitro phenol, Salbutamol). Series (B) is including the interaction coupling alkali solution of paracetamol with diazonium salt of some substituted aniline compounds (Benzedine, 2, 3-di chloro aniline, Trimethoprim, Anilinium chloride, 2-nitro- 4-chloro aniline).Chemical structures of all synthesized compounds were confirmed by UV-visible and FTIR spectroscopy.

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Publication Date
Tue Sep 21 2021
Journal Name
Journal Of Healthcare Engineering
Complexity and Entropy Analysis to Improve Gender Identification from Emotional-Based EEGs
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Investigating gender differences based on emotional changes becomes essential to understand various human behaviors in our daily life. Ten students from the University of Vienna have been recruited by recording the electroencephalogram (EEG) dataset while watching four short emotional video clips (anger, happiness, sadness, and neutral) of audiovisual stimuli. In this study, conventional filter and wavelet (WT) denoising techniques were applied as a preprocessing stage and Hurst exponent

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Publication Date
Tue Apr 30 2013
Journal Name
International Journal Of Microbiology Research
IDENTIFICATION AND TYPING OF Haemophilus influenzae IN IRAQI CHILDREN DIAGNOSED WITH MENINGITIS
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Publication Date
Fri Jan 01 2021
Journal Name
International Journal Of Agricultural And Statistical Sciences
DYNAMIC MODELING FOR DISCRETE SURVIVAL DATA BY USING ARTIFICIAL NEURAL NETWORKS AND ITERATIVELY WEIGHTED KALMAN FILTER SMOOTHING WITH COMPARISON
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Survival analysis is widely applied in data describing for the life time of item until the occurrence of an event of interest such as death or another event of understudy . The purpose of this paper is to use the dynamic approach in the deep learning neural network method, where in this method a dynamic neural network that suits the nature of discrete survival data and time varying effect. This neural network is based on the Levenberg-Marquardt (L-M) algorithm in training, and the method is called Proposed Dynamic Artificial Neural Network (PDANN). Then a comparison was made with another method that depends entirely on the Bayes methodology is called Maximum A Posterior (MAP) method. This method was carried out using numerical algorithms re

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Publication Date
Mon Aug 26 2019
Journal Name
Iraqi Journal Of Science
Depth dose measurement in water phantom for two X-ray energies (6MeV and 10MeV) in comparison with actual planning
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The purpose of this study is to measure doses delivered at different depths in water phantom at vertical position in comparison with the actual planning in order to verify the dose delivered to the tumor in addition to the measurement of the effect penumbra dose to assess the dose leaking to the healthy soft tissue.

     Percentage depth dose (PDD) values was measured at field sizes (5×5,10×10,15×15, and 20×20) cm2, and the depth dose was measured between (0-16) cm deep at 4cm intervals, for both energies 6 MeV and 10 MeV X-ray beam. Other readings were taken at different distances 1cm and 2cm outside of the actual beam in orthogonal directions at depth of 4 cm. These measurements we

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Publication Date
Tue Mar 08 2022
Journal Name
Multimedia Tools And Applications
Comparison study on the performance of the multi classifiers with hybrid optimal features selection method for medical data diagnosis
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Publication Date
Wed Jul 01 2020
Journal Name
Proceedings Of The Institution Of Mechanical Engineers, Part H: Journal Of Engineering In Medicine
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

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Publication Date
Sat Jan 20 2024
Journal Name
Ibn Al-haitham Journal For Pure And Applied Sciences
Comparison of Complex Sadik and KAJ Transforms for Ordinary Differential Equations to the Response of an Uncompressed Forced Oscillator
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In this paper we have presented a comparison between two novel integral transformations that are of great importance in the solution of differential equations. These two transformations are the complex Sadik transform and the KAJ transform. An uncompressed forced oscillator, which is an important application, served as the basis for comparison. The application was solved and exact solutions were obtained. Therefore, in this paper, the exact solution was found based on two different integral transforms: the first integral transform complex Sadik and the second integral transform KAJ. And these exact solutions obtained from these two integral transforms were new methods with simple algebraic calculations and applied to different problems.

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Publication Date
Thu Jan 20 2022
Journal Name
Journal Of Clinical Medicine
Comparison of the Safety and Efficacy of Laser Versus Pneumatic Intracorporeal Lithotripsy for Treatment of Bladder Stones in Children
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This study aimed to compare the safety and efficacy of laser lithotripsy and pneumatic lithotripsy, the two most commonly used transurethral lithotripsy methods for treating bladder stones in children in Iraq. Between January 2013 and December 2016, 64 children with bladder stones were included in this prospective randomized study, after ethical committee approval and written consent from the children’s parents or caregivers were obtained. Patients were assigned randomly by computer software to two groups treated with either pneumatic cystolithotripsy or laser lithotripsy. A 9 Fr. semirigid ureteroscope was used to pass the lithotripter through and fragment the stone. A catheter of 8–12 Fr. was then introduced and kept in place

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Publication Date
Sat Apr 30 2022
Journal Name
Revue D'intelligence Artificielle
Performance Evaluation of SDN DDoS Attack Detection and Mitigation Based Random Forest and K-Nearest Neighbors Machine Learning Algorithms
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Software-defined networks (SDN) have a centralized control architecture that makes them a tempting target for cyber attackers. One of the major threats is distributed denial of service (DDoS) attacks. It aims to exhaust network resources to make its services unavailable to legitimate users. DDoS attack detection based on machine learning algorithms is considered one of the most used techniques in SDN security. In this paper, four machine learning techniques (Random Forest, K-nearest neighbors, Naive Bayes, and Logistic Regression) have been tested to detect DDoS attacks. Also, a mitigation technique has been used to eliminate the attack effect on SDN. RF and KNN were selected because of their high accuracy results. Three types of ne

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