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.
Carbazone Derivatives (CD) (semicarbazone, semithiocarbasone) are produced by the condensation reaction between a aldehyde (or ketone) with a carbazide derivatives (semicarbazide, semithiocarbazide). CD and their metal complexes existent a wide range of implementation that stretch from their ply in the medicinal and pharmaceutical area because of their major significant pharmacological characteristic such as anti-fungal,anti-bacterial, anti-cancer, anti-human immunodeficiency virus, anti-inflammation, anti-neoplastic,inhibition corrosion, antioxidation, antiradical. This paper reviews the definition, importance and various applications of carbazone derivatives with transitional meta
The term "nano gold," also known as "gold nanoparticles," is commonly used. These particles are extremely small, with a diameter of less than 100 nm, which is only a fraction of the width of a human hair. Due to their tiny size, nano gold particles are often found in a colloidal solution, where they are suspended in a liquid stabilizer. This colloidal gold is essentially another name for nano gold. The main method for producing gold nanoparticles in a colloidal solution is the citrate synthesis technique, which involves combining different solutions to precipitate the gold nanoparticles. In biological systems, copper complexes play a significant role at the active sites of many metalloproteins. These complexes have potential applications in
... Show MoreThe study aimed to prepare a measure of metacognitive thinking commensurate with learning the skill, preparing educational units using the Claus Meyer model for metacognitive thinking, and learning the skill of defending the court in volleyball. To identify the effect of educational units using the model (and Claus Meyer) for metacognitive thinking and learning the skill of defending the court in volleyball. The two researchers used the experimental approach with the design of the experimental and control groups.The research community consisted of students of the second stage / College of Physical Education and Sports Sciences / University of Baghdad for the academic year 2021-2022, whose number is (385) st
... Show MoreMH Hamzah, AF Abbas, International Journal of Early Childhood Special Education, 2022
The artistic mediation in theater connects the human elements with the material elements in the theatrical production process. The human artistic mediator plays a crucial role in achieving the artistic and expressive goals of the theatrical work, requiring high skills and expertise. Their role involves coordinating and executing the technical aspects of production, such as lighting, sound, set design, costumes, and acting, using their skills and techniques to transform the theatrical text into a successful stage performance. The artistic mediator significantly impacts the quality of the theatrical work and creates a distinct viewing experience for the audience. Technological advancements and continuous updates in theater contribute to th
... Show MoreINTRODUCTION: A range of tools and technologies are at disposal for the purpose of defect detection. These include but are not limited to sensors, Statistical Process Control (SPC) software, Artificial Intelligence (AI) and machine learning (ML) algorithms, X-ray systems, ultrasound systems, and eddy current systems. OBJECTIVES: The determination of the suitable instrument or combination of instruments is contingent upon the precise production procedure and the category of flaw being identified. In certain cases, defects may necessitate real-time monitoring and analysis through the use of sensors and SPC software, whereas more comprehensive analysis may be required for other defects through the utilization of X-ray or ultrasound sy
... Show MorePurpose: The research aims to estimate models representing phenomena that follow the logic of circular (angular) data, accounting for the 24-hour periodicity in measurement. Theoretical framework: The regression model is developed to account for the periodic nature of the circular scale, considering the periodicity in the dependent variable y, the explanatory variables x, or both. Design/methodology/approach: Two estimation methods were applied: a parametric model, represented by the Simple Circular Regression (SCR) model, and a nonparametric model, represented by the Nadaraya-Watson Circular Regression (NW) model. The analysis used real data from 50 patients at Al-Kindi Teaching Hospital in Baghdad. Findings: The Mean Circular Erro
... Show MoreIn 1939, the Japanese scientist Michio Takaoka first mentioned resveratrol from Veratrum grandiflorum O. Loes. Majority of plants, such as grapes, berries, and peanuts, are significant sources of resveratrol, a well-known polyphenolic. resveratrol (RV) is noted for its links to several health care benefits, including glucose metabolism, anti-aging, cardioprotective, neuroprotective, antitumor, antidiabetic, and antioxidant effects. Importantly, there have been reports of promising therapeutic qualities in atherosclerosis, dementia, and various malignancies. These properties are controlled through a number of cooperative techniques, which control inflammation besides the effects of oxidative stress and cell death. However, circulating resver
... Show MoreIn this paper, a new class of nonconvex sets and functions called strongly -convex sets and strongly -convex functions are introduced. This class is considered as a natural extension of strongly -convex sets and functions introduced in the literature. Some basic and differentiability properties related to strongly -convex functions are discussed. As an application to optimization problems, some optimality properties of constrained optimization problems are proved. In these optimization problems, either the objective function or the inequality constraints functions are strongly -convex.