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Enhancing the Delta Training Rule for a Single Layer Feedforward Heteroassociative Memory Neural Network
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In this paper, an algorithm is suggested to train a single layer feedforward neural network to function as a heteroassociative memory. This algorithm enhances the ability of the memory to recall the stored patterns when partially described noisy inputs patterns are presented. The algorithm relies on adapting the standard delta rule by introducing new terms, first order term and second order term to it. Results show that the heteroassociative neural network trained with this algorithm perfectly recalls the desired stored pattern when 1.6% and 3.2% special partially described noisy inputs patterns are presented.

Publication Date
Tue Jul 17 2018
Journal Name
International Journal Of Adaptive Control And Signal Processing
Single channel informed signal separation using artificial-stereophonic mixtures and exemplar-guided matrix factor deconvolution
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Publication Date
Thu Jan 01 2015
Journal Name
Aip Conference Proceedings
Performance measurements of single server fuzzy queues with unreliable server using left and right method
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The fuzzy assignment models (FAMs) have been explored by various literature to access classical values, which are more precise in our real-life accomplishment. The novelty of this paper contributed positively to a unique application of pentagonal fuzzy numbers for the evaluation of FAMs. The new method namely Pascal's triangle graded mean (PT-GM) has presented a new algorithm in accessing the critical path to solve the assignment problems (AP) based on the fuzzy objective function of minimising total cost. The results obtained have been compared to the existing methods such as, the centroid formula (CF) and centroid formula integration (CFI). It has been demonstrated that operational efficiency of this conducted method is exquisitely develo

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Publication Date
Tue Aug 08 2023
Journal Name
مجلة حمورابي للدراسات
adolescents exposure to the specialized satellite channel cartoon network and its relationship to their level of technological culture
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Publication Date
Thu Oct 29 2020
Journal Name
Complexity
Training and Testing Data Division Influence on Hybrid Machine Learning Model Process: Application of River Flow Forecasting
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The hydrological process has a dynamic nature characterised by randomness and complex phenomena. The application of machine learning (ML) models in forecasting river flow has grown rapidly. This is owing to their capacity to simulate the complex phenomena associated with hydrological and environmental processes. Four different ML models were developed for river flow forecasting located in semiarid region, Iraq. The effectiveness of data division influence on the ML models process was investigated. Three data division modeling scenarios were inspected including 70%–30%, 80%–20, and 90%–10%. Several statistical indicators are computed to verify the performance of the models. The results revealed the potential of the hybridized s

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Publication Date
Sat Oct 01 2022
Journal Name
Baghdad Science Journal
Using Pomegranate Peel Extract to Change the Adverse Effect of Ethephon by Enhancing its Antioxidant, Anti-inflammatory, and Anti-apoptotic Effects in Rats
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Organophosphorus insecticide and growth regulator namely Ethephon (2-chloroethylphosphonic acid) are widely used as a ripening process accelerator and a cultivation duration inhibitor. Pomegranate extract (PPE) has recently been taken into consideration due to its pharmacological effects especially those associated with renal diseases. Thus, this study aims to investigate the possible protective effect of PPE against ethephon-induced nephrotoxicity in rats. In this study four groups of adult male rats were divided into control group, PPE 400 mg/kg group, Ethephon 250 mg/kg group, and finally, PPE + Ethephon group (treated with the same dose of PPE group and Ethephon group). In the current study, kidney function parameters (KIM-1, creatin

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Publication Date
Tue Jan 01 2019
Journal Name
Mustansiriyah Journal Of Sports Science
The use of training methods in accordance with special exercises to develop flexibility and perform the skills of shooting from above the chest (abduction) to wrestlers ages (14-15)"
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Evolution has become a feature of this era because of the speed that makes it open multiple horizons and many to identify everything that is new in different areas and also characterized by the competitive position of emotional attitudes changing depending on the positions of winning and defeat, and the use of training methods are the most important pillars of the game of wrestling, The methods contribute to raising the level of the wrestler and refining his physical and skill potential. The problem of the research is that the shooting exercises from above the chest are very important in Roman wrestling and can be terminated by the player. Through very personal interviews for coaches and concluded that there is a weakness in the level of fl

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Publication Date
Sun Mar 31 2013
Journal Name
Inventi Impact: Artificial Intelligence
SIMULATION OF IDENTIFICATION AND CONTROL OF SCARA ROBOT USING MODIFIED RECURRENT NEURAL NETWORKS
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This paper presents a modified training method for Recurrent Neural Networks. This method depends on the Non linear Auto Regressive (NARX) model with Modified Wavelet Function as activation function (MSLOG) in the hidden layer. The modified model is known as Modified Recurrent Neural (MRN). It is used for identification Forward dynamics of four Degrees of Freedom (4-DOF) Selective Compliance Assembly Robot Arm (SCARA) manipulator robot. This model is also used in the design of Direct Inverse Control (DIC). This method is compared with Recurrent Neural Networks that used Sigmoid activation function (RS) in the hidden layer and Recurrent Neural Networks with Wavelet activation function (RW). Simulation results shows that the MRN model is bett

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Publication Date
Tue Dec 05 2023
Journal Name
Baghdad Science Journal
Recognizing Different Foot Deformities Using FSR Sensors by Static Classification of Neural Networks
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Sensing insole systems are a promising technology for various applications in healthcare and sports. They can provide valuable information about the foot pressure distribution and gait patterns of different individuals. However, designing and implementing such systems poses several challenges, such as sensor selection, calibration, data processing, and interpretation. This paper proposes a sensing insole system that uses force-sensitive resistors (FSRs) to measure the pressure exerted by the foot on different regions of the insole. This system classifies four types of foot deformities: normal, flat, over-pronation, and excessive supination. The classification stage uses the differential values of pressure points as input for a feedforwar

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Publication Date
Wed Dec 27 2017
Journal Name
Al-khwarizmi Engineering Journal
Human Face Recognition Using GABOR Filter And Different Self Organizing Maps Neural Networks
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This work implements the face recognition system based on two stages, the first stage is feature extraction stage and the second stage is the classification stage. The feature extraction stage consists of Self-Organizing Maps (SOM) in a hierarchical format in conjunction with Gabor Filters and local image sampling. Different types of SOM’s were used and a comparison between the results from these SOM’s was given.

The next stage is the classification stage, and consists of self-organizing map neural network; the goal of this stage is to find the similar image to the input image. The proposal method algorithm implemented by using C++ packages, this work is successful classifier for a face database consist of 20

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Publication Date
Mon Aug 27 2018
Journal Name
International Research Journal Of Pharmacy
PRELIMINARY PHYTOCHEMICAL SCREENIG AND HIGH PERFORMANCE THIN LAYER CHROMATOGRAPHY[HPTLC] DETECTION OF PHENOLIC ACIDS IN LANATA CAMARA LEAVES CULTIVATED IN IRAQ
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