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A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications
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Abstract<p>Data scarcity is a major challenge when training deep learning (DL) models. DL demands a large amount of data to achieve exceptional performance. Unfortunately, many applications have small or inadequate data to train DL frameworks. Usually, manual labeling is needed to provide labeled data, which typically involves human annotators with a vast background of knowledge. This annotation process is costly, time-consuming, and error-prone. Usually, every DL framework is fed by a significant amount of labeled data to automatically learn representations. Ultimately, a larger amount of data would generate a better DL model and its performance is also application dependent. This issue is the main barrier for many applications dismissing the use of DL. Having sufficient data is the first step toward any successful and trustworthy DL application. This paper presents a holistic survey on state-of-the-art techniques to deal with training DL models to overcome three challenges including small, imbalanced datasets, and lack of generalization. This survey starts by listing the learning techniques. Next, the types of DL architectures are introduced. After that, state-of-the-art solutions to address the issue of lack of training data are listed, such as Transfer Learning (TL), Self-Supervised Learning (SSL), Generative Adversarial Networks (GANs), Model Architecture (MA), Physics-Informed Neural Network (PINN), and Deep Synthetic Minority Oversampling Technique (DeepSMOTE). Then, these solutions were followed by some related tips about data acquisition needed prior to training purposes, as well as recommendations for ensuring the trustworthiness of the training dataset. The survey ends with a list of applications that suffer from data scarcity, several alternatives are proposed in order to generate more data in each application including Electromagnetic Imaging (EMI), Civil Structural Health Monitoring, Medical imaging, Meteorology, Wireless Communications, Fluid Mechanics, Microelectromechanical system, and Cybersecurity. To the best of the authors’ knowledge, this is the first review that offers a comprehensive overview on strategies to tackle data scarcity in DL.</p>
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Publication Date
Sat Jan 01 2011
Journal Name
Iraqi Journal Of Physics
Natural radioactivity survey in Al-Jabal Al-Gharbi Mountain Region Libya
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The measurement of natural radioactivity in a given region or country is essential to provide a reference base-line map to follow up a possible variation in future. In order to perform such measurement, the natural radioactivity was measured in different locations. The locations (50 sites) were distributed over Al-Jabal Al-Gharbi Mountain, starting from the city Al-Azeeziah in the eastern part to Wazen on the Tunisian border in the west. The measurements showed obvious variation from one site to another. The levels were fluctuating from (12.8 counts/minute) in Bir-Ayad to (45.7 counts/minute) in Gherian.
In order to investigate the cause for such variation, samples were collected from (27) sites for detailed study. The levels of natur

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Publication Date
Thu Aug 22 2024
Journal Name
Indian Journal Of Ecology
Survey of Insects Fauna of Al-Tar Caves in Karbala, Iraq
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Publication Date
Tue Jul 01 2014
Journal Name
Bulletin Of The Iraq Natural History Museum (p-issn: 1017-8678 , E-issn: 2311-9799)
SURVEY OF THE GENUS PHYTOMYZA FALLEN,1810 (DIPTERA: AGROMYZIDAE) OF IRAQ
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    The aim of this study to survey the leaf miner Phytomyza Fallen of Iraq, many leaf plants which infested by leaf miners  were collected from several regions of Iraq.  The paper showed there are  four species of this genus during the work: Phytomyza horticola Gourear,1840; Ph. atricornis Meigen, 1838; Ph. rufipes Meigen,1830; Ph. ranunculi (Schrank,1803)

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Publication Date
Tue Sep 01 2015
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Kick tolerance control during well drilling in southern Iraqi deep wells
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The importance of kick tolerance in well operations has recently increased due to its implications in well design, in drilling and well control. To study a simple method for the application of kick tolerance concept in an effective way on the basis of field data, this research purpose is to improve knowledge about Kick Tolerance and represents a technical basis for the discussion on revision of standard procedure. The objective of this work is to review and to present a methodology of determination the kick tolerance parameters using the circulation kicks tolerance concepts. The proposed method allows to know, to evaluate and to analyze the kick tolerance problem in order to make the drilling execution safer and more economical by reducin

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Publication Date
Wed Sep 30 2015
Journal Name
Iraqi Journal Of Chemical And Petroleum Engineering
Kick tolerance control during well drilling in southern Iraqi deep wells
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The importance of kick tolerance in well operations has recently increased due to its implications in well design, in drilling and well control. To study a simple method for the application of kick tolerance concept in an effective way on the basis of field data, this research purpose is to improve knowledge about Kick Tolerance and represents a technical basis for the discussion on revision of standard procedure.

   The objective of this work is to review and to present a methodology of determination the kick tolerance parameters using the circulation kicks tolerance concepts.

   The proposed method allows to know, to evaluate and to analyze the kick tolerance problem in order to make the drilling exe

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Publication Date
Thu Dec 01 2022
Journal Name
Iaes International Journal Of Artificial Intelligence
Reduced hardware requirements of deep neural network for breast cancer diagnosis
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Identifying breast cancer utilizing artificial intelligence technologies is valuable and has a great influence on the early detection of diseases. It also can save humanity by giving them a better chance to be treated in the earlier stages of cancer. During the last decade, deep neural networks (DNN) and machine learning (ML) systems have been widely used by almost every segment in medical centers due to their accurate identification and recognition of diseases, especially when trained using many datasets/samples. in this paper, a proposed two hidden layers DNN with a reduction in the number of additions and multiplications in each neuron. The number of bits and binary points of inputs and weights can be changed using the mask configuration

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Publication Date
Tue Aug 01 2023
Journal Name
Baghdad Science Journal
An Effective Hybrid Deep Neural Network for Arabic Fake News Detection
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Recently, the phenomenon of the spread of fake news or misinformation in most fields has taken on a wide resonance in societies. Combating this phenomenon and detecting misleading information manually is rather boring, takes a long time, and impractical. It is therefore necessary to rely on the fields of artificial intelligence to solve this problem. As such, this study aims to use deep learning techniques to detect Arabic fake news based on Arabic dataset called the AraNews dataset. This dataset contains news articles covering multiple fields such as politics, economy, culture, sports and others. A Hybrid Deep Neural Network has been proposed to improve accuracy. This network focuses on the properties of both the Text-Convolution Neural

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Publication Date
Tue Apr 01 2025
Journal Name
Natural And Engineering Sciences
Comparison of Some Soil Mineral’s Ability to Adsorb and Release Lead and Rates of its Removal From its Aqueous Solutions
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To study the comparative use of some soil minerals (zeolite, bentonite, phosphate rock, and limestone) in the adsorption and release of lead and its removal rates from its aqueous solutions using adsorption equations. Two laboratory experiments were carried out for the adsorption and release of lead. The adsorption experiment took 0.5 g of some of the above soil minerals. Lead was added as Pb (NO3)2 at levels of 3.0, 2.0, 1.5, 1.0, 0.5, and 0.0 mmol L-1 containing a concentration of 0.01M of calcium chloride. The experimental unit’s number was 72, the concentration of dissolved lead in the equilibrium solution was estimated and the amount of lead adsorbed was calculated. As for the lead release experiment, samples fo

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Publication Date
Tue Dec 05 2023
Journal Name
Baghdad Science Journal
Enhancing Smart Cities with IoT and Cloud Computing: A Study on Integrating Wireless Ad Hoc Networks for Efficient Communication
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Smart cities have recently undergone a fundamental evolution that has greatly increased their potentials. In reality, recent advances in the Internet of Things (IoT) have created new opportunities by solving a number of critical issues that are allowing innovations for smart cities as well as the creation and computerization of cutting-edge services and applications for the many city partners. In order to further the development of smart cities toward compelling sharing and connection, this study will explore the information innovation in smart cities in light of the Internet of Things (IoT) and cloud computing (CC). IoT data is first collected in the context of smart cities. The data that is gathered is uniform. The Internet of Things,

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Publication Date
Thu Jun 03 2021
Journal Name
021 8th International Conference On Computing For Sustainable Global Development (indiacom)
Tilting-rotors Quadcopters: A New Dynamics Modelling and Simulation based on the Newton-Euler Method with Lead Compensator Control
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