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 survey and checklist of invasive species of the insects in some different localities of Iraq are revised; 24 invasive species were documented until December 2018 during the current investigations. The species distributions, common names and synonyms are given.
The current investigation included all of exotic species in Iraq, which are not collected during this study.
Objective This study aimed to evaluate the effects of disinfectant solutions, namely, the alcoholic extract of Salvadora persica L. (A1 = 10% and A2 = 15%) and chlorhexidine digluconate (A3 = 2%), on the tear strength and hardness of room temperature vulcanizing (RTV) VST50F and heat temperature vulcanizing (HTV) Cosmesil M511 silicone elastomers before and after reinforcement with nanofillers (TiO2) and intrinsic pigment. Materials and Methods: A total of 320 specimens were prepared, with 160 specimens each for RTV and HTV silicone. Forty specimens were evaluated before disinfection and divided into two equal groups, namely, control (without additive) and experimental (with ad
Vol. 6, Issue 1 (2025)
One of the most serious health disasters in recent memory is the COVID-19 epidemic. Several restriction rules have been forced to reduce the virus spreading. Masks that are properly fitted can help prevent the virus from spreading from the person wearing the mask to others. Masks alone will not protect against COVID-19; they must be used in conjunction with physical separation and avoidance of direct contact. The fast spread of this disease, as well as the growing usage of prevention methods, underscore the critical need for a shift in biometrics-based authentication schemes. Biometrics systems are affected differently depending on whether are used as one of the preventive techniques based on COVID-19 pandemic rules. This study provides an
... Show MoreThis study investigates the complex challenges of managing heritage sites in Iraq, focusing on the Prophet Tho Al-Kifl Shrine in Babylon due to its religious, historical, and architectural significance. The site exemplifies critical management issues, including institutional fragmentation, limited technical and financial resources, and insufficient legislative frameworks. Left unaddressed, these challenges threaten the site's material integrity and symbolic identity through uncoordinated interventions and neglect. The research aims to propose a context-sensitive framework for sustainable heritage management by combining theoretical perspectives with practical analysis. Using a case study methodology, the study draws on field observations, h
... Show MoreThe aim the research that definition on the impact a lot of Analysis and evaluation jobs impact in support the employees performance the property that are Analysis and evaluation jobs is one of the jobs however of the human resource management on organization and the impact footpace big on the chractericties and performance of the people and the impact that success of the organization , And here problem stool of the research in the omission the role for the Analysis and evaluation jobs impact in support the employees performance from the upward management in the organization , Polls were adopted as tools for obtaining data and the Depart
... Show MoreImage classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class
... Show MoreImage classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class
... Show MoreThe study aimed to identify the treatment of the press image of the Great Return Marches in the French international news agency AFP by knowing the most important issues, their direction and the degree of interest in them. The study belongs to the descriptive research, and used the survey method, within the context of the content analysis method, and the researcher relied on the content analysis form tool and the interview tool to collect data. The study population is represented in the photos published by the French News Agency about the Great Return Marches during the period (end of March / 2018 until the end of November / 2019. The researcher chose an intentional sample using the Complete Census method. The study material represented
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