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An Overview of Audio-Visual Source Separation Using Deep Learning
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    In this article, the research presents a general overview of deep learning-based AVSS (audio-visual source separation) systems. AVSS has achieved exceptional results in a number of areas, including decreasing noise levels, boosting speech recognition, and improving audio quality. The advantages and disadvantages of each deep learning model are discussed throughout the research as it reviews various current experiments on AVSS. The TCD TIMIT dataset (which contains top-notch audio and video recordings created especially for speech recognition tasks) and the Voxceleb dataset (a sizable collection of brief audio-visual clips with human speech) are just a couple of the useful datasets summarized in the paper that can be used to test AVSS systems. In its basic form, this review aims to highlight the growing importance of AVSS in improving the quality of audio signals.

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Publication Date
Mon Jan 07 2019
Journal Name
Arab Science Heritage Journal
هجرة هجرة العقول العربية عامة والعراقية خاصة وأثرها على الواقع الاجتماعي
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         استأثرت ظاهرة هجرة العقول او (الكفاءات العلمية) خلال العقدين الاخيرين باهتمام البلدان المتقدمة والنامية على حد سواء ، وذلك نسبة لاثارها السلبية والايجابية ، على الواقع الاجتماعي والتنموي في الدول المرسلة والمستقبلة ، وزاد الاهتمام بهذه الظاهرة في البلدان النامية لما ينجم عن هجرة العقول من خسارة البلدان المرسلة للموارد البشرية العالية التاهيل ، واللازمة لتحقيق النمو الاقتصادي ذ

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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 02 2019
Journal Name
Journal Of Educational And Psychological Researches
Training Program based on Some Strategies of Self-Regulated Learning for the Teachers of Mathematics and its Effect on their Relational Mathematics
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This research aims at building a proposed training program according to the self-regulated strategies for the mathematics teachers and to identify the effect of this program on relational Mathematics of teachers. The sample of the research was (60) Math teachers; (30) teachers as experimental group and (30) teachers as control group. The results of the current research reacheded that the proposed training program according to some self-managed learning strategies, meets the needs of trainees with remarkable effectiveness to improve the level of their teaching performance to achieve the desired goals. Training teacher according to self-managed learning strategies is effective in bringing about the transition of training to their students

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Publication Date
Sun Jul 03 2011
Journal Name
Journal Of Educational And Psychological Researches
Effect of using the active learning in the achievement of third grade intermediate students in mathematics and them tendency towards the study of its
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Current research aims to find out:

  1. Effect of using the active learning in the achievement of third grade intermediate students in mathematics.
  2. Effect of using of active learning in the tendency towards the study of mathematics for students of third grade intermediate.

In order to achieve the goals of the research, the researcher formulated the following two hypotheses null:

  1. There is no difference statistically significant at the level of significance (0.05) between two average of degrees to achievement

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Publication Date
Thu Dec 01 2011
Journal Name
Journal Of Economics And Administrative Sciences
The effect of organizational learning dimensions on availability of learning organization dimensions in Iraqi planning ministry
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The aim of this research to study.

The dimensions of organizational learning have been defined(learning dynamics, individuals empowerment, knowledge management and technology application) as well as the dimensions of learning organization have been defined (culture values, knowledge transfer, communication and employee characteristics), Asset completion questionnaire was used to collect data of this research from a purposely sample represent forty employees who works in Iraqi Planning Ministry at different positions. The research divided to four parts :

The first to the research methodology, the second to the theoretical review o

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Publication Date
Tue Feb 21 2023
Journal Name
مجلة علوم الرياضة
The Effect of using Linear programming and Branching programming by computer in Learning and Retention of movement concatenation (Linkwork) in Parallel bars in Artistic Gymnastics
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The aim of this study was to Identifying The Effect of using Linear programming and Branching programming by computer in Learning and Retention of movement concatenation(Linkwork) in parallel bars in Artistic Gymnastics. The searchers have used the experimental method. The search subject of this article has been taken (30) male - students in the second class from the College of Physical Education/University of Baghdad divided into three groups; the first group applied linear programming by computer, and the second group has been applicated branching programming by computer, while precision group used traditional method in the college. The researchers concluded the results by using the statistical bag for social sciences (spss) such as both

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Publication Date
Sun Feb 10 2019
Journal Name
Journal Of The College Of Education For Women
Brain Dominance Learning Styles for Secondary School Students Distinguished and Ordinary
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The current research aims at detecting Brain Dominance Learning Styles distinguished
and ordinary secondary school students (males and females).The researcher adopted Torrance
measure, known as ‘the style of your learning and thinking to measure Brain Dominance
Learning Styles’, the codified version of Joseph Qitami (1986); picture (a). The researcher
verified the standard properties of tool. The final application sample was 352 distinguished
and ordinary students; 176 distinguished male and female students and 176 ordinary male and
female students at the scientific fifth level of secondary school from schools in the province of
Baghdad, AL- KarKh Education Directorates in the First and Second . and who have been

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Publication Date
Tue Dec 05 2023
Journal Name
Baghdad Science Journal
AlexNet-Based Feature Extraction for Cassava Classification: A Machine Learning Approach
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Cassava, a significant crop in Africa, Asia, and South America, is a staple food for millions. However, classifying cassava species using conventional color, texture, and shape features is inefficient, as cassava leaves exhibit similarities across different types, including toxic and non-toxic varieties. This research aims to overcome the limitations of traditional classification methods by employing deep learning techniques with pre-trained AlexNet as the feature extractor to accurately classify four types of cassava: Gajah, Manggu, Kapok, and Beracun. The dataset was collected from local farms in Lamongan Indonesia. To collect images with agricultural research experts, the dataset consists of 1,400 images, and each type of cassava has

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Publication Date
Sat Jan 01 2022
Journal Name
Indonesian Journal Of Electrical Engineering And Computer Science (ijeecs)
Increasing validation accuracy of a face mask detection by new deep learning model-based classification
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During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve

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Publication Date
Sat Jan 01 2022
Journal Name
Indonesian Journal Of Electrical Engineering And Computer Science
Increasing validation accuracy of a face mask detection by new deep learning model-based classification
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During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve

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