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.
The aim of this study is to utilize the behavior of a mathematical model consisting of three-species with Lotka Volterra functional response with incorporating of fear and hunting cooperation factors with both juvenile and adult predators. The existence of equilibrium points of the system was discussed the conditions with variables. The behavior of model referred by local stability in nearness of any an equilibrium point and the conditions for the method of approximating the solution has been studied locally. We define a suitable Lyapunov function that covers every element of the nonlinear system and illustrate that it works. The effect of the death factor was observed in some periods, leading to non-stability. To confirm the theore
... Show MoreAn experiment was conducted to study how SAE 50 engine oil contaminated with diesel fuel affects engine performance. The engine oil was contaminated with diesel fuel at concentrations of 0%, 1%, and 3%. The following performance characteristics were studied: brake-specific fuel consumption, brake thermal efficiency, friction power, and exhaust gas temperature. Each treatment was tested three times. The three treatments (0%, 1%, and 3%) were analyzed statistically with a one-way ANOVA model at the 5% probability level to determine if the three treatments produced significant differences in engine performance. The statistical results showed that there were significant differences in engine performance metrics among the three treatments. The 3
... Show MoreThe study aims to provide a Suggested model for the application of Virtual Private Network is a tool that used to protect the transmitted data through the Web-based information system, and the research included using case study methodology in order to collect the data about the research area ( Al-Rasheed Bank) by using Visio to design and draw the diagrams of the suggested models and adopting the data that have been collected by the interviews with the bank's employees, and the research used the modulation of data in order to find solutions for the research's problem.
The importance of the study Lies in dealing with one of the vital topics at the moment, namely, how to make the information transmitted via
... Show Morehe current research aims at showing the correlation and effect of the internal environment analysis in the quality of health service in AL_Elwiya Maternity Teaching Hospital, researcher depended on the integral method in the "analytical descriptive method" for the study of the relation between the main variables and the sub-variables throughout data collection required by the theme of this study, a case study" in tackling the data and information related to the financial aspects of the internal environment.
The problem of the study was manifested in the essential question, that is (is there internal struc
... Show MoreBackground: Economic Globalization affects work condition by increasing work stress. Chronic work stress ended with burnout syndrome.
Objectives: To estimate the prevalence of burnout syndrome and the association of job title, and violence with it among physicians in Baghdad, and to assess the burnout syndrome at patient and work levels by structured interviews.
Subjects and Methods: A cross section study was conducted on Physicians in Baghdad. Sampling was a multistage, stratified sampling to control the confounders in the design phase. A mixed qualitative and quantitative
... Show MoreBackground: Economic Globalization affects work condition by increasing work stress. Chronic work stress ended with burnout syndrome. Objectives: To estimate the prevalence of burnout syndrome and the association of job title, and violence with it among physicians in Baghdad, and to assess the burnout syndrome at patient and work levels by structured interviews. Subjects and Methods: A cross section study was conducted on Physicians in Baghdad. Sampling was a multistage, stratified sampling to control the confounders in the design phase. A mixed qualitative and quantitative approach (triangulation) was used. Quantitative method used self-administered questionnaires of Maslach Burn out Inventory. Qualitative approach used an open-end
... Show MoreThe paper presents a highly accurate power flow solution, reducing the possibility of ending at local minima, by using Real-Coded Genetic Algorithm (RCGA) with system reduction and restoration. The proposed method (RCGA) is modified to reduce the total computing time by reducing the system in size to that of the generator buses, which, for any realistic system, will be smaller in number, and the load buses are eliminated. Then solving the power flow problem for the generator buses only by real-coded GA to calculate the voltage phase angles, whereas the voltage magnitudes are specified resulted in reduced computation time for the solution. Then the system is restored by calculating the voltages of the load buses in terms
... Show MoreIn this paper a system is designed and implemented using a Field Programmable Gate Array (FPGA) to move objects from a pick up location to a delivery location. This transportation of objects is done via a vehicle equipped with a robot arm and an FPGA. The path between the two locations is followed by recognizing a black line between them. The black line is sensed by Infrared sensors (IR) located on the front and on the back of the vehicle. The Robot was successfully implemented by programming the Field Programmable Gate Array with the designed system that was described as a state diagram and the robot operated properly.