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.
After the outbreak of COVID-19, immediately it converted from epidemic to pandemic. Radiologic images of CT and X-ray have been widely used to detect COVID-19 disease through observing infrahilar opacity in the lungs. Deep learning has gained popularity in diagnosing many health diseases including COVID-19 and its rapid spreading necessitates the adoption of deep learning in identifying COVID-19 cases. In this study, a deep learning model, based on some principles has been proposed for automatic detection of COVID-19 from X-ray images. The SimpNet architecture has been adopted in our study and trained with X-ray images. The model was evaluated on both binary (COVID-19 and No-findings) classification and multi-class (COVID-19, No-findings
... Show MoreThe rapid rise in the use of artificially generated faces has significantly increased the risk of identity theft in biometric authentication systems. Modern facial recognition technologies are now vulnerable to sophisticated attacks using printed images, replayed videos, and highly realistic 3D masks. This creates an urgent need for advanced, reliable, and mobile-compatible fake face detection systems. Research indicates that while deep learning models have demonstrated strong performance in detecting artificially generated faces, deploying these models on consumer mobile devices remains challenging due to limitations in computing power, memory, privacy, and processing speed. This paper highlights several key challenges: (1) optimiz
... Show MoreThe rapid rise in the use of artificially generated faces has significantly increased the risk of identity theft in biometric authentication systems. Modern facial recognition technologies are now vulnerable to sophisticated attacks using printed images, replayed videos, and highly realistic 3D masks. This creates an urgent need for advanced, reliable, and mobile-compatible fake face detection systems. Research indicates that while deep learning models have demonstrated strong performance in detecting artificially generated faces, deploying these models on consumer mobile devices remains challenging due to limitations in computing power, memory, privacy, and processing speed. This paper highlights several key challenges: (1) optimiz
... Show MoreIn this investigation, deep eutectic solvent based on propionic acid and choline chloride was used to prepare polyaniline, polypyrrole, and poly(aniline-co-pyrrole) samples using chemical oxidation and polymerization. Quantum mechanical calculations were conducted using time-dependent density functional theory to determine the electronic, spectroscopic properties and structure of homopolymers and the copolymer which were compared with the respective experimental data. Furthermore, the synthesized materials were characterized using Fourier transform IR spectroscopy, scanning electron microscopy (SEM), UV-visible spectroscopy, and thermogravimetric analysis. IR and UV-visible spectra confirmed copolymerization, and SEM revealed that the copol
... Show MoreBlends of polyvinyl pyrrolidone (PVP) and polyvinyl alcohol (PVA) in equal weight ratios (50 wt.% PVP + 50 wt.% PVA) were doped with 15% of various lithium salts (Li2CO3, LiNO3, Li2SO4H2O, and LiCl) and prepared using the solution-casting method, with dimethylformamide (DMF) as the solvent. The impact of these salts on the blends was analyzed and the results showed that the energy gap was decreased by adding lithium salts Li2CO3, LiNO3 and Li2SO4.H₂O. The minimum energy gap value was 3.5 eV obtained from PVP/PVA:15% Li2CO3. The optical constants were determined in the range of 300-1100 nm. The results showed that all the optical constants for doped blends were grown with the addition of lithium salts. The FTIR study confirms the c
... Show Moremethodology six sigma Help to reduce defects by solving problems effectively, and works Lean to reduce losses through the flow of the manufacturing process and when integrating these two methodologies (Lean and six sigma), the methodology of Lean six sigma will form the entrance to the organizers of the optimization process and increase the quality and reduce lead times and costs . by focusing on the needs of the customer. this process uses statistical tools and techniques to analyze and improve processes.
We have conducted this research in the General Company for Electrical Industries and adopted its product (machine cooling water three taps) as a sample for research. In order to determine t
... Show MoreThe scholars differed very much in determining the grammatical doctrine of Ibn Qaysan. It was said that he confused between the two sects and took the two groups(1) and that he took from Abu Abbas the fox and Abu al-Abbas almabrd(2).He was descended from the two sheikhs almbrad and fox(3), This research to find out his grammatical opinions in the tools and grammatical words that appeared in the Book of Resonance of Multiplication to the many reported by Ibn Hayyan in it, he has seen the books of Ibn Kisan did not reach us.
The collection of these opinions and analysis is important for the scholars. Because the books of Ibn Kaysan specialized in grammar have lost their most important, the books that are similar to the book of Ibn al-Ha
The objective of the study is to demonstrate the predictive ability is better between the logistic regression model and Linear Discriminant function using the original data first and then the Home vehicles to reduce the dimensions of the variables for data and socio-economic survey of the family to the province of Baghdad in 2012 and included a sample of 615 observation with 13 variable, 12 of them is an explanatory variable and the depended variable is number of workers and the unemployed.
Was conducted to compare the two methods above and it became clear by comparing the logistic regression model best of a Linear Discriminant function written
... Show MoreSchiff base ligand (H2CANPT) was prepared by two steps: first, by the condensation of curcumin with 4-amino antipyrin produces4,4'-(((1E,3Z,5Z,6E)-1,7-bis(4-hydroxy-3- methoxyphenyl)hepta-1,6-diene-3,5-diylidene)bis(azanylylidene))bis(1,5-dimethyl-2-phenyl- 1,2-dihydro-3H-pyrazol-3-one) (CANP). Second, by the condensation of (CANP) with L-tyrosine produces2,2'-(((3Z,3'Z)-(((1E,3Z,5Z,6E)-1,7-bis(4-hydroxy-3-methoxyphenyl)hepta 1,6-diene-3,5-diylidene)bis(azanylylidene))bis(1,5-dimethyl-2-phenyl-1,2-dihydro-3-H-pyrazole- 4-yl-3-ylidene))bis(azanylylidene))bis(3-(4-hydroxyphenyl)propanoic acid) (H2CANPT). The resulted Schiff comported as hexadentate coordinated with (N4O2) atoms, then it was treated with some transition and non-transaction met
... Show MoreThe banking industry, as a result of the great challenges it faced, required continuous development of the principles of management, control and mechanisms used. The Basel Committee on Banking Supervision has played a leading role in legalizing many of these developments and has been able to contribute significantly to establishing a common framework for banking supervision, The central role in the various countries of the world is based on coordination between the various regulatory authorities and thinking about finding mechanisms to confront the risks faced by banks, recognizing the importance of the banking sector in the stability of domestic and international banking systems or the danger of this sector in the emergence of F
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