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MODIFIED TRAINING METHOD FOR FEEDFORWARD NEURAL NETWORKS AND ITS APPLICATION in 4-LINK SCARA ROBOT IDENTIFICATION
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In this research the results of applying Artificial Neural Networks with modified activation function to perform the online and offline identification of four Degrees of Freedom (4-DOF) Selective Compliance Assembly Robot Arm (SCARA) manipulator robot will be described. The proposed model of identification strategy consists of a feed-forward neural network with a modified activation function that operates in parallel with the SCARA robot model. Feed-Forward Neural Networks (FFNN) which have been trained online and offline have been used, without requiring any previous knowledge about the system to be identified. The activation function that is used in the hidden layer in FFNN is a modified version of the wavelet function. This approach has been performed very successfully, with better results obtained with the FFNN with modified wavelet activation function (FFMW) when compared with classic FFNN with Sigmoid activation function (FFS) .One can notice from the simulation that the FFMW can be capable of identifying the 4-Links of SCARA robot more efficiently than the classic FFS

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Publication Date
Sat Jan 01 2022
Journal Name
Journal Of Intelligent Systems
Trip generation modeling for a selected sector in Baghdad city using the artificial neural network
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Abstract<p>This study is planned with the aim of constructing models that can be used to forecast trip production in the Al-Karada region in Baghdad city incorporating the socioeconomic features, through the use of various statistical approaches to the modeling of trip generation, such as artificial neural network (ANN) and multiple linear regression (MLR). The research region was split into 11 zones to accomplish the study aim. Forms were issued based on the needed sample size of 1,170. Only 1,050 forms with responses were received, giving a response rate of 89.74% for the research region. The collected data were processed using the ANN technique in MATLAB v20. The same database was utilized to</p> ... Show More
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Publication Date
Thu Oct 13 2022
Journal Name
Computation
A Pattern-Recognizer Artificial Neural Network for the Prediction of New Crescent Visibility in Iraq
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Various theories have been proposed since in last century to predict the first sighting of a new crescent moon. None of them uses the concept of machine and deep learning to process, interpret and simulate patterns hidden in databases. Many of these theories use interpolation and extrapolation techniques to identify sighting regions through such data. In this study, a pattern recognizer artificial neural network was trained to distinguish between visibility regions. Essential parameters of crescent moon sighting were collected from moon sight datasets and used to build an intelligent system of pattern recognition to predict the crescent sight conditions. The proposed ANN learned the datasets with an accuracy of more than 72% in comp

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Publication Date
Mon Jul 18 2022
Journal Name
Ieee Access
Moderately Multispike Return Neural Network for SDN Accurate Traffic Awareness in Effective 5G Network Slicing
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Due to the huge variety of 5G services, Network slicing is promising mechanism for dividing the physical network resources in to multiple logical network slices according to the requirements of each user. Highly accurate and fast traffic classification algorithm is required to ensure better Quality of Service (QoS) and effective network slicing. Fine-grained resource allocation can be realized by Software Defined Networking (SDN) with centralized controlling of network resources. However, the relevant research activities have concentrated on the deep learning systems which consume enormous computation and storage requirements of SDN controller that results in limitations of speed and accuracy of traffic classification mechanism. To fill thi

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Publication Date
Tue Jun 30 2020
Journal Name
Journal Of Economics And Administrative Sciences
Comparison of weighted estimated method and proposed method (BEMW) for estimation of semi-parametric model under incomplete data
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Generally, statistical methods are used in various fields of science, especially in the research field, in which Statistical analysis is carried out by adopting several techniques, according to the nature of the study and its objectives. One of these techniques is building statistical models, which is done through regression models. This technique is considered one of the most important statistical methods for studying the relationship between a dependent variable, also called (the response variable) and the other variables, called covariate variables. This research describes the estimation of the partial linear regression model, as well as the estimation of the “missing at random” values (MAR). Regarding the

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Publication Date
Fri Jun 20 1930
Journal Name
College Of Islamic Sciences
Public leave for modernists Its benefits and the judgment of its operation
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2 The benefit of the leave of our time is the leakage and inclusion in the series of narrators of modern.
3 The leave is therefore permissible to narrate the hadeeth or other sciences from a sheik or sheiks, and not
the talk.
4 It is not necessary that the grantee of the leave has studied the student who is the holder of the leave.
5 - in which a great meaning is the sense of the recipient seeking blessing through the link
With attribution to our master Muhammad peace be upon him.

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Publication Date
Sat Feb 16 2013
Journal Name
European Chemical Bulletin Scopus
SYNTHESIS AND CHARACTERISATION OF 4-(4-NITROBENZENEAZO)- 3-AMINOBENZOIC ACID COMPLEXES WITH Y(III) AND La(III) IONS
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Coupling reaction of 4-nitroaniline with 3-aminobenzoic acid provided the corresponding bidentate azo ligand. The prepared ligand was identified by Microelemental Analysis, 1H-NMR, FT-IR, and UV-Vis spectroscopic techniques. Treatment of the prepared ligand with Y(III) and La(III) metal ions in 1:3 M:L ratio in aqueous ethanol at optimum pH yielded a series of neutral complexes with the general formula of [M(L)3]. The prepared complexes were characterized by flame atomic absorption, Elemental Analysis (C, H, N), FT-IR, and UV-Vis spectroscopic methods, as well as conductivity measurements. The nature of the complexes formed were studied following the mole ratio and continuous variation methods; Beer's law obeyed over a concentration range o

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Publication Date
Tue Sep 16 2025
Journal Name
Revista Iberoamericana De Psicología Del Ejercicio Y El Deporte, Issn 1886-8576, Vol. 18, Nº. 4, 2023, Págs. 361-363
The Effect of a Training Curriculum in Developing the Speed Endurance and Strength Characterized By Speed for the Muscles of the Legs for Female Basketball Students
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Autorías: Mariam Liwa Abdel Fattah, Liqaa Abdullah Ali. Localización: Revista iberoamericana de psicología del ejercicio y el deporte. Nº. 4, 2023. Artículo de Revista en Dialnet.

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Publication Date
Sat Sep 01 2012
Journal Name
2012 8th International Conference On Wireless Communications, Networking And Mobile Computing
Performance Evaluation of Location Management in GSM Networks
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Publication Date
Wed Sep 01 2021
Journal Name
Acta Pharmaceutica Sinica B
Discovery of a potent and dual-selective bisubstrate inhibitor for protein arginine methyltransferase 4/5
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Publication Date
Fri Mar 01 2024
Journal Name
Construction And Building Materials
Uni- and tri-axial tests and property characterization for thermomechanical effect on hydrated lime modified asphalt concrete
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Permanent deformation, fatigue and thermal cracking are the three typical distresses of flexible pavement. Using hydrated lime (HL) into the conventional limestone mineral additive has been widely practiced, including in Europe, to improve the mechanical properties of hot mix asphalt (HMA) concrete and as the result the durability of the constructed pavement. Large number of experimental studies have been reported to find the optimum addition of HL for the improvement on HMA concrete mechanical properties, moisture susceptibility and fatigue resistance. Pavement in service is under complex thermomechanical stress-strain conditions due to coupled atmospheric and surrounding environment temperature variation and the traffic loading. To predic

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