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Facial Emotion Images Recognition Based On Binarized Genetic Algorithm-Random Forest
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Most recognition system of human facial emotions are assessed solely on accuracy, even if other performance criteria are also thought to be important in the evaluation process such as sensitivity, precision, F-measure, and G-mean. Moreover, the most common problem that must be resolved in face emotion recognition systems is the feature extraction methods, which is comparable to traditional manual feature extraction methods. This traditional method is not able to extract features efficiently. In other words, there are redundant amount of features which are considered not significant, which affect the classification performance. In this work, a new system to recognize human facial emotions from images is proposed. The HOG (Histograms of Oriented Gradients) is utilized to extract from the images. In addition, the Binarized Genetic Algorithm (BGA) is utilized as a features selection in order to select the most effective features of HOG. Random Forest (RF) functions as a classifier to categories facial emotions in people according to the image samples. The facial human examples of photos that have been extracted from the Yale Face dataset, where it contains the eleven human facial expressions are as follows; normal, left light, no glasses, joyful, centre light, sad, sleepy, wink and surprised. The proposed system performance is evaluated relates to accuracy, sensitivity (i.e., recall), precision, F-measure (i.e., F1-score), and G-mean. The highest accuracy for the proposed BGA-RF method is up to 96.03%. Besides, the proposed BGA-RF has performed more accurately than its counterparts. In light of the experimental findings, the suggested BGA-RF technique has proved its effectiveness in the human facial emotions identification utilizing images.

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Publication Date
Sun Jun 07 2015
Journal Name
Baghdad Science Journal
On The Nearby-Tip Strain Investigation and Failure-Propability Evaluation for Impacted Thin Plates Using the 2-Random-Variables Multi-Canonical-Based Joint Propability Distributions
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The study of the validity and probability of failure in solids and structures is highly considered as one of the most incredibly-highlighted study fields in many science and engineering applications, the design analysts must therefore seek to investigate the points where the failing strains may be occurred, the probabilities of which these strains can cause the existing cracks to propagate through the fractured medium considered, and thereafter the solutions by which the analysts can adopt the approachable techniques to reduce/arrest these propagating cracks.In the present study a theoretical investigation upon simply-supported thin plates having surface cracks within their structure is to be accomplished, and the applied impact load to the

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Publication Date
Sat Sep 30 2017
Journal Name
College Of Islamic Sciences
The dualism in the poetry of Ahmed Bakheet
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The study of the (dualist antagonism) of a poet, will be determined by the poet's potential, and according to the system of concepts and creativity, and this means that there is a difference between the rooting of the meaning and its roots, and poetic sources by tracking the poets and their attitudes.
Therefore, poetry is a variable function according to the values ​​of the movement, which is the freedom and continuous fuel. With these units of measurement, can measure the dose (Ahmed Bakheet) of freedom and mechanisms of investment, Bakheet (moment) of the time of hair dimensions of the limits of his experience. This is what the researchers will address in their research.

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Publication Date
Sat Mar 01 2008
Journal Name
Al-khwarizmi Engineering Journal
Minimizing error in robot arm based on design optimization for high stiffness to weight ratio
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In this work the effect of choosing tri-circular tube section had been addressed to minimize the end effector’s error, a comparison had been made between the tri-tube section and the traditional square cross section for a robot arm, the study shows that for the same weight of square section and tri-tube section the error may be reduced by about 33%.

A program had been built up by the use of MathCAD software to calculate the minimum weight of a square section robot arm that could with stand a given pay load and gives a minimum deflection. The second part of the program makes an optimization process for the dimension of the cross section and gives the dimensions of the tri-circular tube cross section that have the same weight of

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Publication Date
Wed Jan 01 2014
Journal Name
Scienceasia
A combined compact genetic algorithm and local search method for optimizing the ARMA(1,1) model of a likelihood estimator
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In this paper, a compact genetic algorithm (CGA) is enhanced by integrating its selection strategy with a steepest descent algorithm (SDA) as a local search method to give I-CGA-SDA. This system is an attempt to avoid the large CPU time and computational complexity of the standard genetic algorithm. Here, CGA dramatically reduces the number of bits required to store the population and has a faster convergence. Consequently, this integrated system is used to optimize the maximum likelihood function lnL(φ1, θ1) of the mixed model. Simulation results based on MSE were compared with those obtained from the SDA and showed that the hybrid genetic algorithm (HGA) and I-CGA-SDA can give a good estimator of (φ1, θ1) for the ARMA(1,1) model. Anot

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Publication Date
Sat Dec 17 2022
Journal Name
Iraqi Journal Of Laser
PDF S and U shape offset studying of the refractive index sensor based on coreless fiber: Aya R. Mejble* Hanan J.Taher
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Abstract:  Two different shapes of offset optical fiber was studied based on coreless fiber for refractive index (RI)/concentration (con.) measurement, and compare them. These shapes are U and S-shapes, both shapes structures were formed by one segment of coreless fiber (CF) was joined between two single mode (SMF) lead in /lead out with the same displacement (12.268µm) at both sides, the results shows the high sensitive was achieved in a novel S-shape equal 98.768nm/RIU, to our knowledge, no one has ever mentioned or experienced it, it’s the best shape rather than the U-shape which equal 85.628nm/RIU. In this research, it was proved that the offset form has a significant effect on the sensitivity of the sensor. Addi

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Publication Date
Sat Sep 30 2017
Journal Name
Al-khwarizmi Engineering Journal
Influence of Nanoreinforced Particles (Al2O3) on Fatigue Life and Strength of Aluminium Based Metal Matrix Composite
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Abstract

     In this investigation, Al2O3 nano material of 50nm particles size were added to the 6061 Al aluminium alloy by using the stir casting technique to fabricate the nanocomposite of 10wt% Al2O3. The experimental results observed that the addition of 10wt% Al2O3 improved the fatigue life and strength of constant and cumulative fatigue. Comparison between the S-N curves behaviour of metal matrix (AA6061) and the nanocomposite 10wt% Al2O3 has been made. The comparison revealed that 12.8% enhancement in fatigue strength at 107cycles due to 10wt% nano reinforcement. Also cumulative fatigue l

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Publication Date
Sun Dec 01 2019
Journal Name
Baghdad Science Journal
Comparison of Some Suggested Estimators Based on Differencing Technique in the Partial Linear Model Using Simulation
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In this paper new methods were presented based on technique of differences which is the difference- based modified jackknifed generalized ridge regression estimator(DMJGR) and difference-based generalized  jackknifed ridge regression estimator(DGJR), in estimating the parameters of linear part of the partially linear model. As for the nonlinear part represented by the nonparametric function, it was estimated using Nadaraya Watson smoother. The partially linear model was compared using these proposed methods with other estimators based on differencing technique through the MSE comparison criterion in simulation study.

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Publication Date
Tue Apr 02 2019
Journal Name
Artificial Intelligence Research
A three-stage learning algorithm for deep multilayer perceptron with effective weight initialisation based on sparse auto-encoder
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A three-stage learning algorithm for deep multilayer perceptron (DMLP) with effective weight initialisation based on sparse auto-encoder is proposed in this paper, which aims to overcome difficulties in training deep neural networks with limited training data in high-dimensional feature space. At the first stage, unsupervised learning is adopted using sparse auto-encoder to obtain the initial weights of the feature extraction layers of the DMLP. At the second stage, error back-propagation is used to train the DMLP by fixing the weights obtained at the first stage for its feature extraction layers. At the third stage, all the weights of the DMLP obtained at the second stage are refined by error back-propagation. Network structures an

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Publication Date
Sat Jul 08 2017
Journal Name
Neural Computing And Applications
A new algorithm of modified binary particle swarm optimization based on the Gustafson-Kessel for credit risk assessment
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Publication Date
Sun Oct 15 2017
Journal Name
Research Journal Of Applied Sciences, Engineering And Technology
Optimization of IPv6 Protocol Independent Multicast-Sparse Mode Multicast Routing Protocol based on Greedy Rendezvous Point Selection Algorithm
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