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Constructing a Software Tool for Detecting Face Mask-wearing by Machine Learning
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       In the pandemic era of COVID19, software engineering and artificial intelligence tools played a major role in monitoring, managing, and predicting the spread of the virus. According to reports released by the World Health Organization, all attempts to prevent any form of infection are highly recommended among people. One side of avoiding infection is requiring people to wear face masks. The problem is that some people do not incline to wear a face mask, and guiding them manually by police is not easy especially in a large or public area to avoid this infection. The purpose of this paper is to construct a software tool called Face Mask Detection (FMD) to detect any face that does not wear a mask in a specific public area by using CCTV (closed-circuit television). The problem also occurs in case the software tool is inaccurate. The technique of this notion is to use large data of face images, some faces are wearing masks, and others are not wearing masks. The methodology is by using machine learning, which is characterized by a HOG (histogram orientation gradient) for extraction of features, then an SVM(support vector machine) for classification, as it can contribute to the literature and enhance mask detection accuracy. Several public datasets for masked and unmasked face images have been used in the experiments. The findings for accuracy are as follows: 97.00%, 100.0%, 97.50%, 95.0% for RWMFD (Real-world Masked Face Dataset)& GENK14k, SMFDB (Simulated Masked Face Recognition Dataset), MFRD (Masked Face Recognition Dataset), and MAFA (MAsked FAces)& GENK14k for databases, respectively. The results are promising as a comparison of this work has been made with the state-of-the-art. The workstation of this research used a webcam programmed by Matlab for real-time testing.

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Publication Date
Thu Feb 01 2024
Journal Name
Baghdad Science Journal
Building a Sustainable GARCH Model to Forecast Rubber Price: Modified Huber Weighting Function Approach
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The unstable and uncertain nature of natural rubber prices makes them highly volatile and prone to outliers, which can have a significant impact on both modeling and forecasting. To tackle this issue, the author recommends a hybrid model that combines the autoregressive (AR) and Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models. The model utilizes the Huber weighting function to ensure the forecast value of rubber prices remains sustainable even in the presence of outliers. The study aims to develop a sustainable model and forecast daily prices for a 12-day period by analyzing 2683 daily price data from Standard Malaysian Rubber Grade 20 (SMR 20) in Malaysia. The analysis incorporates two dispersion measurements (I

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Publication Date
Sat Feb 01 2014
Journal Name
Journal Of Economics And Administrative Sciences
Principles of Learning Organizations and It`s Role to achieve Group Working: A Viewed Study in Public Company for Webbing Industries in Hilla
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     Organizations must interact with the environment around them, so the environment must be suitable for that interaction. These companies are now trying to become Learning Organizations because it try to face that challenges may rise from its environments. The Learning Organization is a concept that is becoming an increasingly widespread philosophy in modern companies, from the largest multinationals to the smallest ventures. What is achieved by this philosophy depends considerably on one's interpretation of it and commitment to it. This study gives a definition that we felt was the true ideology behind the Learning Organization and Group Working. A Learning Organization is one in which people at all levels

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Publication Date
Mon Jun 01 2020
Journal Name
Al-khwarizmi Engineering Journal
Prediction of Cutting Force in Turning Process by Using Artificial Neural Network
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Cutting forces are important factors for determining machine serviceability and product quality. Factors such as speed feed, depth of cut and tool noise radius affect on surface roughness and cutting forces in turning operation. The artificial neural network model was used to predict cutting forces with related to inputs including cutting speed (m/min), feed rate (mm/rev), depth of cut (mm) and work piece hardness (Map). The outputs of the ANN model are the machined cutting force parameters, the neural network showed that all (outputs) of all components of the processing force cutting force FT (N), feed force FA (N) and radial force FR (N) perfect accordance with the experimental data. Twenty-five samp

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Publication Date
Sat Jun 28 2025
Journal Name
Journal Of Physical Education
The effect of using the strategy Learning by playing for some skills handball patting According to the curriculum for female students Second stage, College of Physical Education and Sports Sciences, University of Baghdad
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The aim of the research was to identify  On the effect of the learning-by-play strategy on learning some handball skills among second-stage female students,Comparing the results of students' performance before and after implementing the learning by playing strategy,Providing development proposals for teaching handball skills in light of the research results.,I dependresearcherCurriculumexperimentalIn research procedures as an appropriate approach to achieving research objectives,For two groups, one experimental and the other control,Community of female studentsSecondFor the academic year 2024-2025, consisting of (4Female people (numbering)151) StudentChosen.Research sample30 female students from Section (B) were selected and (4) fe

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Publication Date
Fri Dec 02 2022
Journal Name
Journal Of Physical Education
The Effect of Jigsaw Strategy on Learning Spiking in Volleyball for Sophomore Students
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The research aimed at designing teaching program using jigsaw in learning spiking in volleyball as well as identifying the effect of these exercises on learning spring in volleyball. The researchers used the experimental method on (25) students as experimental group and (27) students as controlling group and (15) students as pilot study group. The researchers conducted spiking tests then the data was collected and treated using proper statistical operations to conclude that the strategy have a positive effect in experimental group. Finally, the researchers recommended using the strategy in making similar studies on other subjects and skills.

Publication Date
Mon Jul 13 2026
Journal Name
International Journal Of Robotics And Control Systems
Integrating Multimodal Emotion Recognition with Deep Q-Learning for Adaptive Social Robot Interaction
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Publication Date
Wed Sep 03 2025
Journal Name
Plos One
Effective SMOTE boost with deep learning for IDC identification in whole-slide images
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Breast cancer is highlighted in recent research as one of the most prevalent types of cancer. Timely identification is essential for enhancing patient results and decreasing fatality rates. Utilizing computer-assisted detection and diagnosis early on may greatly improve the chances of recovery by accurately predicting outcomes and developing suitable treatment plans. Grading breast cancer properly, especially evaluating nuclear atypia, is difficult owing to faults and inconsistencies in slide preparation and the intricate nature of tissue patterns. This work explores the capability of deep learning to extract characteristics from histopathology photos of breast cancer. The research introduces a new method called SMOTE-based Convolut

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Publication Date
Thu Jan 01 2026
Journal Name
Ieee Transactions On Cognitive And Developmental Systems
M2RU: Memristive Minion Recurrent Unit for On-Chip Continual Learning at the Edge
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Continual learning on edge platforms remains challenging because recurrent networks depend on energy-intensive training procedures and frequent data movement that are impractical for embedded deployments. This work introduces M2RU, a mixed-signal architecture that implements the minion recurrent unit for efficient temporal processing with on-chip continual learning. The architecture integrates weighted-bit streaming, which enables multi-bit digital inputs to be processed in crossbars without high-resolution conversion, and an experience replay mechanism that stabilizes learning under domain shifts. M2RU achieves ∼13 GOPS at 16.76 mW, corresponding to 776 GOPS per watt, and maintains accuracy within 5 percent of software baselines on seque

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Publication Date
Sun Aug 30 2020
Journal Name
Journal Of Economics And Administrative Sciences
The Effect of Brainstorming on audit Quality and its Reflection on Detecting the Risk of Fraud
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Brainstorming is one of the fundamental and necessary concepts for practicing the auditing profession, as auditing standards encouraged the implementation of brainstorming sessions to reach reasonable assurance about the validity of the evidence and information obtained by the auditor to detect fraud, as the implementation of brainstorming sessions and the practice of professional suspicion during the audit process lead to increase the quality of auditing and thus raise the financial community's confidence in the auditing profession again after it was exposed to several crises that led to the financial community losing confidence in the auditing profession.

The research aims to explain the effect of brain

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Publication Date
Fri Oct 30 2020
Journal Name
Journal Of Economics And Administrative Sciences
The Effect of Brainstorming on audit Quality and its Reflection on Detecting the Risk of Fraud
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Brainstorming is one of the fundamental and necessary concepts for practising the auditing profession, as auditing standards encouraged the implementation of brainstorming sessions to reach reasonable assurance about the validity of the evidence and information obtained by the auditor to detect fraud, as the implementation of brainstorming sessions and the practice of professional suspicion during the audit process lead To increase the quality of auditing and thus raise the financial community's confidence in the auditing profession again after it was exposed to several crises that led to the financial community losing confidence in the auditing profession.

The research aims to explain the effect of brain

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