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EEG Signal Classification Based on Orthogonal Polynomials, Sparse Filter and SVM Classifier
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This work implements an Electroencephalogram (EEG) signal classifier. The implemented method uses Orthogonal Polynomials (OP) to convert the EEG signal samples to moments. A Sparse Filter (SF) reduces the number of converted moments to increase the classification accuracy. A Support Vector Machine (SVM) is used to classify the reduced moments between two classes. The proposed method’s performance is tested and compared with two methods by using two datasets. The datasets are divided into 80% for training and 20% for testing, with 5 -fold used for cross-validation. The results show that this method overcomes the accuracy of other methods. The proposed method’s best accuracy is 95.6% and 99.5%, respectively. Finally, from the results, it is obvious that the number of moments selected by the SP should exceed 30% of the overall EEG samples for accuracy to be over 90%.

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Publication Date
Sat Sep 30 2017
Journal Name
Al-khwarizmi Engineering Journal
Robot Arm Path Planning Using Modified Particle Swarm Optimization based on D* algorithm
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Abstract

Much attention has been paid for the use of robot arm in various applications. Therefore, the optimal path finding has a significant role to upgrade and guide the arm movement. The essential function of path planning is to create a path that satisfies the aims of motion including, averting obstacles collision, reducing time interval, decreasing the path traveling cost and satisfying the kinematics constraints. In this paper, the free Cartesian space map of 2-DOF arm is constructed to attain the joints variable at each point without collision. The D*algorithm and Euclidean distance are applied to obtain the exact and estimated distances to the goal respectively. The modified Particle Swarm Optimization al

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Publication Date
Sat Apr 01 2023
Journal Name
Baghdad Science Journal
Interior Visual Intruders Detection Module Based on Multi-Connect Architecture MCA Associative Memory
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Most recent studies have focused on using modern intelligent techniques spatially, such as those
developed in the Intruder Detection Module (IDS). Such techniques have been built based on modern
artificial intelligence-based modules. Those modules act like a human brain. Thus, they should have had the
ability to learn and recognize what they had learned. The importance of developing such systems came after
the requests of customers and establishments to preserve their properties and avoid intruders’ damage. This
would be provided by an intelligent module that ensures the correct alarm. Thus, an interior visual intruder
detection module depending on Multi-Connect Architecture Associative Memory (MCA)

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Publication Date
Tue Sep 01 2020
Journal Name
International Journal Of Hydrogen Energy
Hydrogen gas sensing based on nanocrystalline SnO2 thin films operating at low temperatures
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Publication Date
Tue Feb 01 2022
Journal Name
Civil Engineering Journal
Calibration of a New Concrete Damage Plasticity Theoretical Model Based on Experimental Parameters
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The introduction of concrete damage plasticity material models has significantly improved the accuracy with which the concrete structural elements can be predicted in terms of their structural response. Research into this method's accuracy in analyzing complex concrete forms has been limited. A damage model combined with a plasticity model, based on continuum damage mechanics, is recommended for effectively predicting and simulating concrete behaviour. The damage parameters, such as compressive and tensile damages, can be defined to simulate concrete behavior in a damaged-plasticity model accurately. This research aims to propose an analytical model for assessing concrete compressive damage based on stiffness deterioration. The prop

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Publication Date
Thu Jun 04 2020
Journal Name
Journal Of Discrete Mathematical Sciences And Cryptography
User authentication system based specified brain waves
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A security system can be defined as a method of providing a form of protection to any type of data. A sequential process must be performed in most of the security systems in order to achieve good protection. Authentication can be defined as a part of such sequential processes, which is utilized in order to verify the user permission to entree and utilize the system. There are several kinds of methods utilized, including knowledge, and biometric features. The electroencephalograph (EEG) signal is one of the most widely signal used in the bioinformatics field. EEG has five major wave patterns, which are Delta, Theta, Alpha, Beta and Gamma. Every wave has five features which are amplitude, wavelength, period, speed and frequency. The linear

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Publication Date
Thu May 23 2019
Journal Name
The International Journal Of Artificial Organs
Real-time classification of shoulder girdle motions for multifunctional prosthetic hand control: A preliminary study
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In every country in the world, there are a number of amputees who have been exposed to some accidents that led to the loss of their upper limbs. The aim of this study is to suggest a system for real-time classification of five classes of shoulder girdle motions for high-level upper limb amputees using a pattern recognition system. In the suggested system, the wavelet transform was utilized for feature extraction, and the extreme learning machine was used as a classifier. The system was tested on four intact-limbed subjects and one amputee, with eight channels involving five electromyography channels and three-axis accelerometer sensor. The study shows that the suggested pattern recognition system has the ability to classify the sho

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Publication Date
Wed Dec 01 2021
Journal Name
Computers & Electrical Engineering
Utilizing different types of deep learning models for classification of series arc in photovoltaics systems
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Publication Date
Wed Jan 01 2020
Journal Name
Ieee Access
A Novel Approach to Improving Brain Image Classification Using Mutual Information-Accelerated Singular Value Decomposition
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Publication Date
Wed Jan 01 2020
Journal Name
Periodicals Of Engineering And Natural Sciences
Bayesian and non-Bayesian estimation of the lomax model based on upper record values under weighted LINEX loss function
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In this article, we developed a new loss function, as the simplification of linear exponential loss function (LINEX) by weighting LINEX function. We derive a scale parameter, reliability and the hazard functions in accordance with upper record values of the Lomax distribution (LD). To study a small sample behavior performance of the proposed loss function using a Monte Carlo simulation, we make a comparison among maximum likelihood estimator, Bayesian estimator by means of LINEX loss function and Bayesian estimator using square error loss (SE) function. The consequences have shown that a modified method is the finest for valuing a scale parameter, reliability and hazard functions.

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Publication Date
Thu Jun 03 2021
Journal Name
021 8th International Conference On Computing For Sustainable Global Development (indiacom)
Tilting-rotors Quadcopters: A New Dynamics Modelling and Simulation based on the Newton-Euler Method with Lead Compensator Control
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