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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
Sun Jan 04 2026
Journal Name
International Journal Of Environmental Science And Technology
Novel EDTA-modified Santa Barbara Amorphous-15 and Fe3O4@SiO2 adsorbents for highly efficient Ni (II) removal from aqueous solutions
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Publication Date
Fri Jan 01 2016
Journal Name
Journal Of The College Of Languages (jcl)
Investigating English Composition Writing Problems Encountered by Preparatory School Students and Finding Solutions to These Problems through Schema- Based Approach
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Writing in English is one of the essential factors for successful                      EFL learning .Iraqi students at the preparatory schools encounter problems when using their background knowledge in handling subskills                                  of writing(Burhan,2013:164).Therefore, this study aims to investigate the 4thyear preparatory school students’ problems in English composition writing, and find solutions to these pro

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Publication Date
Thu Apr 01 2021
Journal Name
Complexity
Bayesian Regularized Neural Network Model Development for Predicting Daily Rainfall from Sea Level Pressure Data: Investigation on Solving Complex Hydrology Problem
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Prediction of daily rainfall is important for flood forecasting, reservoir operation, and many other hydrological applications. The artificial intelligence (AI) algorithm is generally used for stochastic forecasting rainfall which is not capable to simulate unseen extreme rainfall events which become common due to climate change. A new model is developed in this study for prediction of daily rainfall for different lead times based on sea level pressure (SLP) which is physically related to rainfall on land and thus able to predict unseen rainfall events. Daily rainfall of east coast of Peninsular Malaysia (PM) was predicted using SLP data over the climate domain. Five advanced AI algorithms such as extreme learning machine (ELM), Bay

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Publication Date
Sun Oct 11 2026
Journal Name
Journal Of Baghdad College Of Dentistry
Measurement of salivary Immunoglobulin A of participants with a healthy, gingivitis and chronic periodontitis conditions
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Background: Secretory Immunoglobulin A (SIgA) is a subclass of Immunoglobulin A (IgA), It is an antibody that plays an important role in mucosal immunity. It is the main immunoglobulin found in mucous secretions from mammary glands, tear glands and salivary glands, every pathologic process in the body involves the immune system, and periodontal inflammation is one of them and is not an exception. Material and methods: this study was consisted of 60 healthy male participants of an age ranged between (35-50) years old ; 25 of them with generalized moderate chronic periodontists(Clinical Attachment Loss equal to 3-4mm at ≥ 30% of the sites; 20 participants with plaque induced gingivitis and 15 participants had clinically healthy pe

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Publication Date
Sat Mar 21 2026
Journal Name
Iraqi Journal Of Hematology
Assessment of the immunomodulatory cytokines interleukin-35, interleukin-10, and transforming growth factor-β in patients with idiopathic immune thrombocytopenia purpura on treatment with romiplostim: A cross-sectional analysis
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BACKGROUND: Idiopathic immune thrombocytopenia purpura is an autoimmune bleeding disorder that accounts for approximately one-third of clinical hemorrhagic diseases. Pathophysiologically, it involves a complicated imbalance in the immune system. OBJECTIVES: This study focuses on evaluating the levels of the immunomodulatory cytokines interleukin (IL)-35, interleukin-10, and transforming growth factor-β (TGF)-β in patients with refractory idiopathic immune thrombocytopenia undergoing treatment with romiplostim. MATERIALS AND METHODS: A cross-sectional study encompassed 78 individuals with idiopathic immune thrombocytopenia and conducted measurements of different immunomodulatory cytokines in response to different responses of idiopath

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Publication Date
Sun Jul 09 2023
Journal Name
Journal Of Engineering
Deep Desulfurization of Diesel Fuel by Guard Bed Adsorption of Activated Carbon and Locally Prepared Cu-Y Zeolite
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Desulfurization of a simulated diesel fuel by different adsorbents was studied in a fixed-bed adsorption process operated at ambient temperature and pressure.  Three different adsorption beds were used, commercial activated carbon, Cu-Y zeolite, and layered bed of 15wt% activated carbon followed by Cu-Y zeolite.Initially Y-zeolite was prepared from Iraqi rice husk and then impregnated with copper. In general, the adsorbents tested for total sulfur adsorption capacity at break through followed the order Ac/Cu-Y zeolite>Cu-Y zeolite>Ac. The best adsorbent, Ac/Cu-Y zeolite is capable of producing more than 30 cm3 of simulated diesel fuel per gram of adsorbent with a weighted average content of 5 ppm-S, while Cu-Y zeolite producing of

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Publication Date
Sun Sep 01 2024
Journal Name
Journal Of Cleaner Production
Emulsion liquid membrane pertraction of soap from crude biodiesel using activated carbon and glycol based deep eutectic solvents
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Publication Date
Tue Dec 09 2025
Journal Name
Petroleum And Coal
Extraction and Analysis of Compressional, Shear, and Stoneley Slowness, Rock Mechanical Properties, and Shear Anisotropy Using Sonic Scanner Data
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The Sonic Scanner is a multifunctional instrument designed to log wells, assess elastic characteristics, and support reservoir characterisation. Furthermore, it facilitates comprehension of rock mechanics, gas detection, and well positioning, while also furnishing data for geomechanical computations and sand management. The present work involved the application of the Sonic Scanner for both basic and advanced processing of oil-well-penetrating carbonate media. The study aimed to characterize the compressional, shear, Stoneley slowness, rock mechanical properties, and Shear anisotropy analysis of the formation. Except for intervals where significant washouts are encountered, the data quality of the Monopole, Dipole, and Stoneley modes is gen

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Publication Date
Wed Aug 01 2018
Journal Name
Journal Of Economics And Administrative Sciences
A Comparative Study of Some Methods of Estimating Robust Variance Covariance Matrix of the Parameters Estimated by (OLS) in Cross-Sectional Data
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Abstract

The Classical Normal Linear Regression Model Based on Several hypotheses, one of them is Heteroscedasticity as it is known that the wing of least squares method (OLS), under the existence of these two problems make the estimators, lose their desirable properties, in addition the statistical inference becomes unaccepted table. According that we put tow alternative,  the first one is  (Generalized Least Square) Which is denoted by (GLS), and the second alternative is to (Robust covariance matrix estimation) the estimated parameters method(OLS), and that the way (GLS) method neat and certified, if the capabilities (Efficient) and the statistical inference Thread on the basis of an acceptable

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Publication Date
Wed Mar 02 2022
Journal Name
Journal Of Educational And Psychological Researches
The Effectiveness of a Training Program to Improve some Aspects of Sensory Integration Disorder and Its Effect on Self-Direction Among a Sample of Children with Intellectual Disabilities
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The present research was conducted to investigate the effectiveness of a training program to improve some aspects of sensory integration disorder and its effect on self-direction among a sample of children with intellectual disabilities. The study sample consists of (10 subjects as an experimental group) were exposed to the training program، and the control group consists of (10 subjects as a control group) were not exposed to the training program. The study included the following tools: A scale of self-direction for intellectual disability (prepared by the researcher). Training program (prepared by the researcher). The Results of the study showed the following: There are no statistically significant differences between the means ranks

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