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.
This research seeks to shed light on what you add intangible assets of benefit to the company and this antagonize pause for consideration because it makes the company in a good competitive position stimulates the rest of the companies to acquire those assets.
That many companies have achieved competitive advantages in the market do not even achieved monopolies increased the value and reaped extraordinary profits as a result of those assets which requires the need to be measured to determine the extent to which contribution in the emergence of the value added to the value of the company on the one hand and to make the presentatio
... Show MoreThe Compressional-wave (Vp) data are useful for reservoir exploration, drilling operations, stimulation, hydraulic fracturing employment, and development plans for a specific reservoir. Due to the different nature and behavior of the influencing parameters, more complex nonlinearity exists for Vp modeling purposes. In this study, a statistical relationship between compressional wave velocity and petrophysical parameters was developed from wireline log data for Jeribe formation in Fauqi oil field south Est Iraq, which is studied using single and multiple linear regressions. The model concentrated on predicting compressional wave velocity from petrophysical parameters and any pair of shear waves velocity, porosity, density, a
... Show MoreThe Compressional-wave (Vp) data are useful for reservoir exploration, drilling operations, stimulation, hydraulic fracturing employment, and development plans for a specific reservoir. Due to the different nature and behavior of the influencing parameters, more complex nonlinearity exists for Vp modeling purposes. In this study, a statistical relationship between compressional wave velocity and petrophysical parameters was developed from wireline log data for Jeribe formation in Fauqi oil field south Est Iraq, which is studied using single and multiple linear regressions. The model concentrated on predicting compressional wave velocity from petrophysical parameters and any pair of shear waves velocity, porosity, density, and
... Show MoreWith the increasing demands to use remote sensing approaches, such as aerial photography, satellite imagery, and LiDAR in archaeological applications, there is still a limited number of studies assessing the differences between remote sensing methods in extracting new archaeological finds. Therefore, this work aims to critically compare two types of fine-scale remotely sensed data: LiDAR and an Unmanned Aerial Vehicle (UAV) derived Structure from Motion (SfM) photogrammetry. To achieve this, aerial imagery and airborne LiDAR datasets of Chun Castle were acquired, processed, analyzed, and interpreted. Chun Castle is one of the most remarkable ancient sites in Cornwall County (Southwest England) that had not been surveyed and explored
... Show MoreThe objective of the research is to identify the effect of an instructional design according to the active learning modelsالباحثين in the achievement of the students of the fifth grade, the instructional design was constructed according to the active learning models for the design of education. The research experience was applied for a full academic year (the first & the second term of 2017-2018). The sample consisted of 58 students, 28 students for the experimental group and 30 students for the control group. The experimental design was adopted with partial and post-test, the final achievement test consisted of (50) objectives and essays items on two terms, the validity of the test was verified by the adoption of the Kudoric
... Show MoreObjective: To identify the effect of the cube model on visual-spatial intelligence and learning the skill of spikinging in volleyball for female students, The researchers used the experimental method by designing two equivalent groups with pre- and post-measurements. Research methodology: The main research sample of (30) female students was selected from the research community represented by second-stage students in the College of Physical Education and Sports Sciences - University of Baghdad for the academic year (2024-2025). The sample was divided equally into two control and experimental groups. The researchers conducted the sample homogenization process and the equivalence process between the two groups in the variables of visua
... Show More