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.
Atmospheric transmission is disturbed by scintillation, where scintillation caused more beam divergence. In this work target image spot radius was calculated in presence of atmospheric scintillation. The calculation depend on few relevant equation based on atmospheric parameter (for Middle East), tracking range, expansion ratio of applied beam expander's, receiving unit lens F-number, and the laser wavelength besides photodetector parameter. At maximum target range Rmax =20 km, target image radius is at its maximum Rs=0.4 mm. As the range decreases spot radius decreases too, until the range reaches limit (4 km) at which target image spot radius at its minimum value (0.22 mm). Then as the range decreases, spot radius increases due to geom
... Show MoreThe research aims to determine the impact of Human Resources Accounting (HRA) on employee’s performance. The research’s problem was embodied in the lack of interest in HRA, which was reflected on the performance of employees in the Ministry of Education; the research adopted the descriptive-analytical approach, and the research community included the directors of departments and people at the headquarters of the Ministry of Education. The sample size was (224) individuals from the total community of 533. The questionnaire was adopted as the main tool for collecting data and information, as well as the interviews that were conducted by the researcher. In order to analyze t
... Show MoreIntroduction: Diabetic foot infections are one of the most severe complications of diabetes. This study was aimed to determine the common bacterial isolates of diabetic foot infections and the in vitro antibiotic susceptibility then treatment.
Methods: A swab was taken from the foot ulcer, and the aerobic bacteria were isolated and identified by cultural, microscopic and biochemical test, then by api-20E system. After that their antibiotic susceptibility pattern was determined. Then local and systemic treatment was used to treat the diabetic foot patients.
Results: Bacterial isolates belonging to twelve species were obtained from diabetic foot patients. Gram (-) bacteria were the predominant pathogens in the diabetic foot infection
The meniscus has a crucial function in human anatomy, and Magnetic Resonance Imaging (M.R.I.) plays an essential role in meniscus assessment. It is difficult to identify cartilage lesions using typical image processing approaches because the M.R.I. data is so diverse. An M.R.I. data sequence comprises numerous images, and the attributes area we are searching for may differ from each image in the series. Therefore, feature extraction gets more complicated, hence specifically, traditional image processing becomes very complex. In traditional image processing, a human tells a computer what should be there, but a deep learning (D.L.) algorithm extracts the features of what is already there automatically. The surface changes become valuable when
... Show MoreThis study investigates the Linguistic and Conceptual equivalence of Conner’s Revised Scales when applied on a Sudanese sample. Sudanese parents and teachers completed behavior-rating scales on a stratified sample of 200 children. These instruments were based on Conner’s parent -48 and teacher-28 questionnaires. Following a reliable translation into Sudanese Arabic the test-retest reliability of the items and the internal consistency of the original Conner’s' revised scales were explored. The associations between scale scores and between parents and teachers scores were also examined. Both instruments displayed good reliability and the original Conners scales had satisfactory internal consistency. The inter-correlation sugg
... Show MoreThe determiner phrase is a syntactic category that appears inside the noun phrase and makes it definite or indefinite or quantifies it. The present study has found wide parametric differences between the English and Arabic determiner phrases in terms of the inflectional features, the syntactic distribution of determiners and the word order of the determiner phrase itself. In English, the determiner phrase generally precedes the head noun or its premodifying adjectival phrase, with very few exceptions where some determiners may appear after the head noun. In Arabic, parts of the determiner phrase precede the head noun and parts of it must appear after the head noun or after its postmodifying adjectival phrase creating a discontinu
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