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Two Proposed Models for Face Recognition: Achieving High Accuracy and Speed with Artificial Intelligence
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In light of the development in computer science and modern technologies, the impersonation crime rate has increased. Consequently, face recognition technology and biometric systems have been employed for security purposes in a variety of applications including human-computer interaction, surveillance systems, etc. Building an advanced sophisticated model to tackle impersonation-related crimes is essential. This study proposes classification Machine Learning (ML) and Deep Learning (DL) models, utilizing Viola-Jones, Linear Discriminant Analysis (LDA), Mutual Information (MI), and Analysis of Variance (ANOVA) techniques. The two proposed facial classification systems are J48 with LDA feature extraction method as input, and a one-dimensional Convolutional Neural Network Hybrid Model (1D-CNNHM). The MUCT database was considered for training and evaluation. The performance, in terms of classification, of the J48 model reached 96.01% accuracy whereas the DL model that merged LDA with MI and ANOVA reached 100% accuracy. Comparing the proposed models with other works reflects that they are performing very well, with high accuracy and low processing time.

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Publication Date
Sun Jan 01 2023
Journal Name
Lecture Notes In Networks And Systems
Using Artificial Intelligence and Metaverse Techniques to Reduce Earning Management
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This study aims to demonstrate the role of artificial intelligence and metaverse techniques, mainly logistical Regression, in reducing earnings management in Iraqi private banks. Synthetic intelligence approaches have shown the capability to detect irregularities in financial statements and mitigate the practice of earnings management. In contrast, many privately owned banks in Iraq historically relied on manual processes involving pen and paper for recording and posting financial information in their accounting records. However, the banking sector in Iraq has undergone technological advancements, leading to the Automation of most banking operations. Conventional audit techniques have become outdated due to factors such as the accuracy of d

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Publication Date
Wed Jul 20 2022
Journal Name
Journal Of Materials Engineering And Performance
High Speed Shock Peening by Fiber Laser for Al Alloy 6061-T6 Thin Sheets
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Under cyclic loading, aluminum alloys exhibit less fatigue life than steel alloys of similar strength and this is considered as Achilles's heel of such alloys. A nanosecond fiber laser was used to apply high speed laser shock peening process on thin aluminum plates in order to enhance the fatigue life by introducing compressive residual stresses. The effect of three working parameters namely the pulse repetition rate (PRR), spot size (ω) and scanning speed (v) on limiting the fatigue failure was investigated. The optimum results, represented by the longer fatigue life, were at PRR of 22.5 kHz, ω of 0.04 mm and at both v's of 200 and 500 mm/sec. The research yielded significant results represented by a maximum percentage increase in the fa

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Publication Date
Thu Jul 01 2021
Journal Name
Computers & Electrical Engineering
A new proposed statistical feature extraction method in speech emotion recognition
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Publication Date
Thu Oct 22 2020
Journal Name
2020 4th International Symposium On Multidisciplinary Studies And Innovative Technologies (ismsit)
Artificial Intelligence in Smart Agriculture: Modified Evolutionary Optimization Approach for Plant Disease Identification
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Publication Date
Sat Jan 01 2022
Journal Name
Revista Iberoamericana De PsicologÍa Del Ejercicio Y El Deporte
THE EFFECT OF TRAINING NETWORK TRAINING IN TWO WAYS, HIGH INTERVAL TRAINING AND REPETITION TO DEVELOP SPEED ENDURANCE ADAPT HEART RATE AND ACHIEVE 5000 METERS YOUTH
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Publication Date
Wed May 10 2023
Journal Name
International Journal Of Emerging Technologies In Learning
The Effect of Cognitive Modeling in Mathematics Achievement and Creative Intelligence for High School Students
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Publication Date
Mon Aug 01 2022
Journal Name
Baghdad Science Journal
Optimized Artificial Neural network models to time series
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        Artificial Neural networks (ANN) are powerful and effective tools in time-series applications. The first aim of this paper is to diagnose better and more efficient ANN models (Back Propagation, Radial Basis Function Neural networks (RBF), and Recurrent neural networks) in solving the linear and nonlinear time-series behavior. The second aim is dealing with finding accurate estimators as the convergence sometimes is stack in the local minima. It is one of the problems that can bias the test of the robustness of the ANN in time series forecasting. To determine the best or the optimal ANN models, forecast Skill (SS) employed to measure the efficiency of the performance of ANN models. The mean square error and

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Publication Date
Mon Jan 01 2024
Journal Name
Explainable Artificial Intelligence In The Digital Sustainability Administration
Artificial Intelligence and Trends Using in Sustainability Audit: A Bibliometric Analysis
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Publication Date
Fri Jan 01 2021
Journal Name
Special Exercises For Offensive Tactical Skills And Their Impact On Developing The Speed And Accuracy Of Some Basic Skills In Youth Football
SPECIAL EXERCISES FOR OFFENSIVE TACTICAL SKILLS AND THEIR IMPACT ON DEVELOPING THE SPEED AND ACCURACY OF SOME BASIC SKILLS IN YOUTH FOOTBALL
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Publication Date
Sun Dec 01 2019
Journal Name
Computers And Electronics In Agriculture
Meteorological data mining and hybrid data-intelligence models for reference evaporation simulation: A case study in Iraq
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