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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
Tue Dec 16 2025
Journal Name
Journal Of Kufa For Chemical Sciences
Review Article Miscellaneous Azo Dyes: A Review on Recent Advancements and Applications
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The dyes Azo have a lengthy history and are a vital part of our daily lives. There are numerous potentials uses for these substances and their derivatives in various industries and environmental and biological research. In this study conversion of various azo compounds into other derivatives, complexes, and polymers was accomplished. This review included examining the chemistry reactions, synthesis, and applications of azo dye ligands and their complexes, mentioned spectral, analytical, thermal, and morphology methods of investigation, and confirmed by mass fragment mechanisms for some azo dyes and metal complexes. One of the aims of this review is to explain the role of these azo dye derivatives and the effect of metal complexes on leather

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Publication Date
Tue Dec 02 2025
Journal Name
Journal Of Kufa For Chemical Sciences
Review Article: Miscellaneous Azo Dyes: A Review on Recent Advancements and Applications
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The dyes Azo have a lengthy history and are a vital part of our daily lives. There are numerous potentials uses for these substances and their derivatives in various industries and environmental and biological research. In this study conversion of various azo compounds into other derivatives, complexes, and polymers was accomplished. This review included examining the chemistry reactions, synthesis, and applications of azo dye ligands and their complexes, mentioned spectral, analytical, thermal, and morphology methods of investigation, and confirmed by mass fragment mechanisms for some azo dyes and metal complexes. One of the aims of this review is to explain the role of these azo dye derivatives and the effect of metal complexes on leather

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Publication Date
Tue Oct 01 2013
Journal Name
Journal Of Economics And Administrative Sciences
Damage Currency Swap the Resulting From Government Departments Dealing With Trade Bank of Iraq From
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Interested current Research measuring damage currency Swap by converting The ministry of higher Education and scientific Research money The Iraqi dinar To U.S dollar by Trade Bank Of Iraq , And that The damage Generated resulting from Deferent Between the Exchange Rate adopted From Central Bank of Iraq and Market Exchange Rate adopted by The Trade Bank Of Iraq , and Which led to the greet damage ( losses ) in Bearing by the ministry, which led to the reduction of the financial allocations for licensed curriculum outside of Iraq , and this in turn leads  to reduction in the number of students Sender ( scholarships )  outside Iraq.

Where the estimated loss (damage) that suffer by the Ministry of H

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Publication Date
Fri Nov 18 2022
Journal Name
International Journal Of Nanoscience
Plasma Production and Applications: A Review
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Large amounts of plasma, the universe’s fourth most common kind of stuff, may be found across our galaxy and other galaxies. There are four types of matter in the cosmos, and plasma is the most common. By heating the compressed air or inert gases to create negatively and positively charged particles known as ions, electrically neutral particles in their natural state are formed. Many scientists are currently focusing their efforts on the development of artificial plasma and the possible advantages it may have for humankind in the near future. In the literature, there is a scarcity of information regarding plasma applications. It’s the goal of this page to describe particular methods for creating and using plasma, which may be us

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Publication Date
Mon Jan 01 2024
Journal Name
Ieee Access
Transfer Learning and Hybrid Deep Convolutional Neural Networks Models for Autism Spectrum Disorder Classification From EEG Signals
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Publication Date
Sun Jan 01 2023
Journal Name
Lecture Notes On Data Engineering And Communications Technologies
Deep Learning-Based Approach for Classifying the Severity of Metal Corrosion Using Sem Images
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Publication Date
Thu Aug 31 2023
Journal Name
Journal Européen Des Systèmes Automatisés​
Deep Learning Approach for Oil Pipeline Leakage Detection Using Image-Based Edge Detection Techniques
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Natural gas and oil are one of the mainstays of the global economy. However, many issues surround the pipelines that transport these resources, including aging infrastructure, environmental impacts, and vulnerability to sabotage operations. Such issues can result in leakages in these pipelines, requiring significant effort to detect and pinpoint their locations. The objective of this project is to develop and implement a method for detecting oil spills caused by leaking oil pipelines using aerial images captured by a drone equipped with a Raspberry Pi 4. Using the message queuing telemetry transport Internet of Things (MQTT IoT) protocol, the acquired images and the global positioning system (GPS) coordinates of the images' acquisition are

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Publication Date
Sun Jun 15 2025
Journal Name
Iraqi Journal Of Laser
Performance Enhancement of Metasurface Grating Polarizer Using Deep Learning for Quantum Key Distribution Systems
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Metasurface polarizers are essential optical components in modern integrated optics and play a vital role in many optical applications including Quantum Key Distribution systems in quantum cryptography. However, inverse design of metasurface polarizers with high efficiency depends on the proper prediction of structural dimensions based on required optical response. Deep learning neural networks can efficiently help in the inverse design process, minimizing both time and simulation resources requirements, while better results can be achieved compared to traditional optimization methods. Hereby, utilizing the COMSOL Multiphysics Surrogate model and deep neural networks to design a metasurface grating structure with high extinction rat

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Publication Date
Mon Jun 01 2026
Journal Name
Iraqi Journal For Computers And Informatics
Explainable Federated Learning for Brain Tumor Classification Using Multi-Source MRI Data
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Early diagnosis and clinical decision-making depend on accurate brain tumor classification using magnetic resonance imaging (MRI). However, traditional deep learning methods usually rely on centralized medical data, which raises privacy concerns and limits the use of distributed clinical data. This research proposes a privacy-preserving federated learning framework for MRI image-based binary brain tumor classification using a decentralized ResNet-18 architecture that enables collaborative training without sharing raw patient data. To reflect realistic clinical conditions, the framework integrates heterogeneous multi-source datasets in different image formats (PNG and JPG) and evaluates performance under both IID and non-IID settings

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Publication Date
Wed Aug 30 2023
Journal Name
Iraqi Journal Of Science
Epidemiological Complex Networks: A Survey
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     In this review paper, several research studies were surveyed to assist future researchers to identify available techniques in the field of infectious disease modeling across complex networks. Infectious disease modelling is becoming increasingly important because of the microbes and viruses that threaten people’s lives and societies in all respects. It has long been a focus of research in many domains, including mathematical biology, physics, computer science, engineering, economics, and the social sciences, to properly represent and analyze spreading processes. This survey first presents a brief overview of previous literature and some graphs and equations to clarify the modeling in complex networks, the detection of soc

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