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.
Background : Xanthomatosis is a disease in which large tendon tumors can occur, especially in the Achilles tendon. This disease is a rare interesting orthopaedic condition. Case Report:A case of a twenty eight year old girl patient with giant bilateral Achilles tendon xanthomas in which both tumors were resected. There was no ulceration on the both sides. The patient was treated by total resection of the lesion and reconstruction using tendon transfer of the Peroneus brevis and Flexor hallusis longus. Postoperative treatment consisted of six weeks lower leg cast immobilization followed by partial weight bearing. After 4 months the patient was able to walk pain free without any difficulties. It has been suggested that total resection with au
... Show MoreBackground : Xanthomatosis is a disease in which large tendon tumors can occur, especially in the Achilles tendon. This disease is a rare interesting orthopaedic condition.
Case Report:A case of a twenty eight year old girl patient with giant bilateral Achilles tendon xanthomas in which both tumors were resected.
There was no ulceration on the both sides. The patient was treated by total resection of the lesion and reconstruction using tendon transfer of the Peroneus brevis and Flexor hallusis longus. Postoperative treatment consisted of six weeks lower leg cast immobilization followed by partial weight bearing. After 4 months the patient was able to walk pain free without any difficultie
... Show MorePolycystic ovary syndrome (PCOS) is a significant cause of infertility due to ovulation dysfunction in women of childbearing age. Although the pathogenesis of PCOS is still not clear, many studies have shown that many factors within the ovary promote infection. With this syndrome, the disruption of the natural monthly ovulation process causes an imbalance in the body's hormones, and the high level of insulin in the body and the blood sugar imbalance leads to the occurrence of hyperandrogenism, which is the main factor for the occurrence of pathogens, in addition to genetic factors, if any. This study aims to identify this disease and its most important causes, symptoms, and modern treatments to prevent and get rid of it. Polycystic
... Show MoreMaximizing the net present value (NPV) of oil field development is heavily dependent on optimizing well placement. The traditional approach entails the use of expert intuition to design well configurations and locations, followed by economic analysis and reservoir simulation to determine the most effective plan. However, this approach often proves inadequate due to the complexity and nonlinearity of reservoirs. In recent years, computational techniques have been developed to optimize well placement by defining decision variables (such as well coordinates), objective functions (such as NPV or cumulative oil production), and constraints. This paper presents a study on the use of genetic algorithms for well placement optimization, a ty
... Show MoreContemporary life is racing against time in its temptations and variables, and it has become shaped and changed in an amazing way in its various aspects and fields. This was facilitated by intellectual and scientific communication between civilizations, and the rapid progression in successive inventions and discoveries in the fields of science and arts of knowledge. This contributed to a great economic and commercial renaissance. Then, these economic developments entered the world into a very strong competition, which forced producers to calculate all production costs, to reach the highest profits by reducing the price of the produced commodity on the one hand, and achieving quality in appearance (especially) on the other hand. Since the ma
... Show MoreThis study investigates asset returns within the Iraq Stock Exchange by employing both the Fama-MacBeth regression model and the Fama-French three-factor model. The research involves the estimation of cross-sectional regressions wherein model parameters are subject to temporal variation, and the independent variables function as proxies. The dataset comprises information from the first quarter of 2010 to the first quarter of 2024, encompassing 22 publicly listed companies across six industrial sectors. The study explores methodological advancements through the application of the Single Index Model (SIM) and Kernel Weighted Regression (KWR) in both time series and cross-sectional analyses. The SIM outperformed the K
... Show MoreThis study examines the structural performance of concrete-encased pultruded Glass Fiber Reinforced Polymer (GFRP) I-sections with shear connections. It specifically focuses on how different parameters affect the latter’s ductility, flexural strength, and load-carrying capacity. The key variables studied include various shear connector types, spacing, and geometries, as well as the compressive strength of concrete and the properties of GFRP. The finite element modeling and experimental validation show that the shear connectors significantly improve the ductility, ultimate capacity, and load transmission efficiency. The present review emphasizes that the shear connectors greatly enhance the structural performance when they are prop
... Show MoreBackground: The aim of this study was to evaluate the shear bond strength (SBS) and adhesive remnant index (ARI) of different orthodontic adhesive systems after exposure to aging media (water storage and acid challenge). Materials and methods: Eighty human upper premolar teeth were extracted for orthodontic purposes and randomly divided into two groups (40 teeth each): the first group in which the bonded teeth were stored in distilled water for 30 days at 37°C, and the second group in which the bonded teeth were subjected to acid challenge. Each group was further subdivided into four subgroups (10 teeth each) according to the type of adhesive system that would be bonded to metal brackets: either non-fluoride releasing adhesive (NFRA),
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