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Multi-Layer Feedforward Neural Network Modelling of a Kinematics Solution of A 3-DoF Manipulator Robot
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Modeling forward kinematics with neural networks allows for efficient handling of nonlinear relationships and realistic error correction in time-critical applications by relying on accurate training data. This paper presents a Multi-Layer Feed-Forward Neural Network (MLFFNN) to solve the forward kinematics of a 3-DOF robot. The proposed MLFFNN consists of 50 hidden neurons and was trained using 628319 samples to find only the position (x, y, z) of the end-effector. Data were generated by MATLAB, assuming an incremental motion of joints. The joint variables ( , , and ) are the inputs of the NN, which outputs the positions of the end effector (x, y, z) calculated using the Denavit-Hartenberg (DH) method. The results demonstrate that the proposed MLFFNN has high performance and is efficient for solving the forward kinematics, with a Mean Squared Error (MSE) between the desired and estimated position of 4.3881×10-11. This performance clearly demonstrates that, despite the large size of the dataset, it can be effectively mastered with only a small number of neurons. The simplicity of the network allows it to learn a compact and efficient representation of the data. This improves the reliability of using the proposed network for similar applications in other robotic systems.

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Publication Date
Wed Jan 01 2025
Journal Name
Journal Of Intelligent Systems And Internet Of Things
Enhancing Convolutional Neural Network for Image Retrieval
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With the continuous progress of image retrieval technology, the speed of searching for the required image from a large amount of image data has become an important issue. Convolutional neural networks (CNNs) have been used in image retrieval. However, many image retrieval systems based on CNNs have poor ability to express image features. Content-based Image Retrieval (CBIR) is a method of finding desired images from image databases. However, CBIR suffers from lower accuracy in retrieving images from large-scale image databases. In this paper, the proposed system is an improvement of the convolutional neural network for greater accuracy and a machine learning tool that can be used for automatic image retrieval. It includes two phases

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Publication Date
Sat Aug 03 2024
Journal Name
Proceedings Of Ninth International Congress On Information And Communication Technology
Offline Signature Verification Based on Neural Network
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The investigation of signature validation is crucial to the field of personal authenticity. The biometrics-based system has been developed to support some information security features.Aperson’s signature, an essential biometric trait of a human being, can be used to verify their identification. In this study, a mechanism for automatically verifying signatures has been suggested. The offline properties of handwritten signatures are highlighted in this study which aims to verify the authenticity of handwritten signatures whether they are real or forged using computer-based machine learning techniques. The main goal of developing such systems is to verify people through the validity of their signatures. In this research, images of a group o

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Publication Date
Fri Mar 01 2019
Journal Name
Al-khwarizmi Engineering Journal
Comparison Study of Electromyography Using Wavelet and Neural Network
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In this paper we present a method to analyze five types with fifteen wavelet families for eighteen different EMG signals. A comparison study is also given to show performance of various families after modifying the results with back propagation Neural Network. This is actually will help the researchers with the first step of EMG analysis. Huge sets of results (more than 100 sets) are proposed and then classified to be discussed and reach the final.

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Publication Date
Tue Jan 17 2017
Journal Name
International Journal Of Science And Research (ijsr)
Detection System of Varicose Disease using Probabilistic Neural Network
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Publication Date
Fri Jun 01 2007
Journal Name
Al-khwarizmi Engineering Journal
Reduction of the error in the hardware neural network
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Specialized hardware implementations of Artificial Neural Networks (ANNs) can offer faster execution than general-purpose microprocessors by taking advantage of reusable modules, parallel processes and specialized computational components. Modern high-density Field Programmable Gate Arrays (FPGAs) offer the required flexibility and fast design-to-implementation time with the possibility of exploiting highly parallel computations like those required by ANNs in hardware. The bounded width of the data in FPGA ANNs will add an additional error to the result of the output. This paper derives the equations of the additional error value that generate from bounded width of the data and proposed a method to reduce the effect of the error to give

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Publication Date
Fri Aug 28 2015
Journal Name
Al-khwarizmi Engineering Journal
wavelength division multiplexing passive optical network modelling using optical system simulator
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Due to the continuing demand for larger bandwidth, the optical transport becoming general in the access network. Using optical fiber technologies, the communications infrastructure becomes powerful, providing very high speeds to transfer a high capacity of data. Existing telecommunications infrastructures is currently widely used Passive Optical Network that apply Wavelength Division Multiplexing (WDM) and is awaited to play an important role in the future Internet supporting a large diversity of services and next generation networks. This paper presents a design of WDM-PON network, the simulation and analysis of transmission parameters in the Optisystem 7.0 environment for bidirectional traffic. The simulation shows the behavior of optical

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Publication Date
Sun Sep 01 2019
Journal Name
Al-khwarizmi Engineering Journal
Electrodepositing of Multi-Layer Ni-Ag Coated by Copper Nanoparticles for Solar Absorber
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In this work, the effect of the addition of bright nickel plating and silver carried out by the electroplating method has been studied, on the coating of copper nanoparticles on the copper base metal via the process of thermal evaporation. The improvement of the solar absorber using CuNP in combination with the bright nickel and silver was obtained to be better than copper nanoparticles individually. A bright nickel enhanced the absorbed thermal stability. Also, other optical properties, absorptions, and emissivity slightly decreased from (93% to 87%), while the existence of silver had a slight impact on absorption of about (86.50%). On the other hand, thermal conductivity was evaluated using hot disk analyzer. The results showed a good

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Publication Date
Sun Mar 01 2020
Journal Name
Sustainable Chemistry And Pharmacy
A sustainable approach to utilize olive pips for the sorption of lead ions: Numerical modeling with aid of artificial neural network
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Publication Date
Tue Jun 25 2024
Journal Name
Journal Européen Des Systèmes Automatisés
Whole-Body Anti-Input Saturation Control of a Bipedal Robot
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Publication Date
Mon Feb 04 2019
Journal Name
Journal Of The College Of Education For Women
THE PORTRAIT OF THE ARTIST AS A ROBOT: A LITERARY LOOK AT E-POETRY
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This study examines the validity of e-poetry as an acceptable literary genre. The
thematic, stylistic and esthetic features of a selected number of e-poems produced by
poetry generators are analyzed for this purpose. The e-poems are then compared with a
number of works written by Dada poets in order to establish the literary merit of the
former.

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