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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
Mon Oct 02 2023
Journal Name
Journal Of Engineering
Skull Stripping Based on the Segmentation Models
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Skull image separation is one of the initial procedures used to detect brain abnormalities. In an MRI image of the brain, this process involves distinguishing the tissue that makes up the brain from the tissue that does not make up the brain. Even for experienced radiologists, separating the brain from the skull is a difficult task, and the accuracy of the results can vary quite a little from one individual to the next. Therefore, skull stripping in brain magnetic resonance volume has become increasingly popular due to the requirement for a dependable, accurate, and thorough method for processing brain datasets. Furthermore, skull stripping must be performed accurately for neuroimaging diagnostic systems since neither no

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Publication Date
Tue Jun 01 2021
Journal Name
Telkomnika (telecommunication Computing Electronics And Control)
An efficient method for stamps recognition using Haar wavelet sub-bands
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Publication Date
Sat Sep 09 2017
Journal Name
International Journal Of Science And Research (ijsr)
Fingerprints Recognition Using the Local Energy Distribution over Haar Wavelet Subbands
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Fingerprints are commonly utilized as a key technique and for personal recognition and in identification systems for personal security affairs. The most widely used fingerprint systems utilizing the distribution of minutiae points for fingerprint matching and representation. These techniques become unsuccessful when partial fingerprint images are capture, or the finger ridges suffer from lot of cuts or injuries or skin sickness. This paper suggests a fingerprint recognition technique which utilizes the local features for fingerprint representation and matching. The adopted local features have determined using Haar wavelet subbands. The system was tested experimentally using FVC2004 databases, which consists of four datasets, each set holds

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Publication Date
Sat Feb 01 2025
Journal Name
Algorithms
Three-Dimensional Object Recognition Using Orthogonal Polynomials: An Embedded Kernel Approach
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Computer vision seeks to mimic the human visual system and plays an essential role in artificial intelligence. It is based on different signal reprocessing techniques; therefore, developing efficient techniques becomes essential to achieving fast and reliable processing. Various signal preprocessing operations have been used for computer vision, including smoothing techniques, signal analyzing, resizing, sharpening, and enhancement, to reduce reluctant falsifications, segmentation, and image feature improvement. For example, to reduce the noise in a disturbed signal, smoothing kernels can be effectively used. This is achievedby convolving the distributed signal with smoothing kernels. In addition, orthogonal moments (OMs) are a cruc

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Publication Date
Fri Feb 08 2019
Journal Name
Journal Of The College Of Education For Women
Assessing EFL Learners Ability in the Recognition And Production of Homophones
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This study deals with the orthographic processing ability of homophones
which can account for variance in word recognition and production skills due to
phonological processing. The study aims at: A)Investigating whether the students
can recognize correct usage and spelling comprehension of different homophones
by using appropriate word that overlapped in both phonology and orthography.
B)Assessing spelling production word association to the written form of the
homophone in the sentence comprehension task. To achieve these aims, two tests
have been conducted and distributed on 50 students at first stage at the College of
Education(Ibn-Rushd) for the academic year 2010-2011. The two tests are exposed
to a jury of

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Publication Date
Fri Mar 12 2021
Journal Name
Sensors
A Robust Handwritten Numeral Recognition Using Hybrid Orthogonal Polynomials and Moments
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Numeral recognition is considered an essential preliminary step for optical character recognition, document understanding, and others. Although several handwritten numeral recognition algorithms have been proposed so far, achieving adequate recognition accuracy and execution time remain challenging to date. In particular, recognition accuracy depends on the features extraction mechanism. As such, a fast and robust numeral recognition method is essential, which meets the desired accuracy by extracting the features efficiently while maintaining fast implementation time. Furthermore, to date most of the existing studies are focused on evaluating their methods based on clean environments, thus limiting understanding of their potential a

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Publication Date
Tue Aug 01 2023
Journal Name
Biomedical Signal Processing And Control
Decoding transient sEMG data for intent motion recognition in transhumeral amputees
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Publication Date
Wed Feb 08 2012
Journal Name
Journal Of The College Of Education For Women
Assessing EFL Learners Ability in the Recognition and Production of Homophones
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This study deals with the orthographic processing ability of homophones which can account for variance in word recognition and production skills due to phonological processing. The study aims at: A )Investigating whether the students can recognize correct usage and spelling comprehension of different homophones by using appropriate word that overlapped in both phonology and orthography. B )Assessing spelling production word association to the written form of the homophone in the sentence comprehension task. To achieve these aims, two tests have been conducted and distributed on 50 students at first stage at the College of Education(Ibn-Rushd) for the academic year 2010-2011. The two tests are exposed to a jury of experts for the purpose of

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Publication Date
Sun May 02 2021
Journal Name
Journal Of Accounting And Financial Studies ( Jafs )
Application of banking institutions' governance variables and their impact on financial performance as measured by (ROA & ROE): an applied study on a sample of local Islamic banks listed on the Iraq Stock Exchange
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The research seeks to achieve its goal of demonstrating the impact of applying banking governance variables on the financial performance of Islamic banks, and the independent research variables are represented by (X) by (the number of independent members in the board (X1), the number of directors in the board (X2), the number of committees emanating from the board ( X3), the percentage of shares owned by major shareholders in the board (X4), the number of members of the Sharia supervisory board (X5)), and the dependent variable (Y) is represented by (rate of return on assets (Y1), rate of return on equity (Y2)).

The research sample included (4) Islamic banks, namely (Iraqi Islamic Bank, National Islamic Bank, Jihan Islamic Bank,

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Publication Date
Fri Apr 01 2022
Journal Name
Baghdad Science Journal
Improved Firefly Algorithm with Variable Neighborhood Search for Data Clustering
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Among the metaheuristic algorithms, population-based algorithms are an explorative search algorithm superior to the local search algorithm in terms of exploring the search space to find globally optimal solutions. However, the primary downside of such algorithms is their low exploitative capability, which prevents the expansion of the search space neighborhood for more optimal solutions. The firefly algorithm (FA) is a population-based algorithm that has been widely used in clustering problems. However, FA is limited in terms of its premature convergence when no neighborhood search strategies are employed to improve the quality of clustering solutions in the neighborhood region and exploring the global regions in the search space. On the

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