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.
abstract
The grammatical tools (the letters of meanings) are of great importance in understanding the meanings of the Arabic sentences,
This research is a simple attempt to show how our venerable scholars employed the meanings of these tools when they interpreted the linguistic evidence, that is, the grammatical structure largely depends on the tool in forming the meaning within the sentences and employing the meanings of these grammatical tools in explaining the linguistic evidence by clarifying their significance in the contexts of their use and effectiveness. Synthesis of the meanings of grammatical tools is an important tool in understanding the linguistic structure in order to reveal its meaning.
... Show MoreTraumatic Brain Injury (TBI) is still considered a worldwide leading cause of mortality and morbidity. Within the last decades, different modalities were used to assess severity and outcome including Glasgow Coma Scale (GCS), imaging modalities, and even genetic polymorphism, however, determining the prognosis of TBI victims is still challenging requiring the emerging of more accurate and more applicable tools to surrogate other old modalities
Sports commentary improves the audience’s engagement while delivering a real-time description and analysis of sporting events. However, sometimes the fast-paced nature leads to occasional linguistic errors which includes grammatical inconsistencies, lexical inaccuracies, and discourse level ambiguities. This study will categorize these errors and evaluate various NLP models for detection and processing. The data set of 100 h of transcribed football basketball and tennis commentary was preprocessed and annotated. Several NLP rule-based models such as languageTool and Hunspell, machine learning models such as SpaCy and Stanford NLP, and deep learning models such as AraBERT and GPT-4 Fine-Tuned we’re all assessed based on their precision,
... Show MoreTourism plays an important role in Malaysia’s economic development as it can boost business opportunity in its surrounding economic. By apply data mining on tourism data for predicting the area of business opportunity is a good choice. Data mining is the process that takes data as input and produces outputs knowledge. Due to the population of travelling in Asia country has increased in these few years. Many entrepreneurs start their owns business but there are some problems such as wrongly invest in the business fields and bad services quality which affected their business income. The objective of this paper is to use data mining technology to meet the business needs and customer needs of tourism enterprises and find the most effective
... Show MoreCloud computing represents the most important shift in computing and information technology (IT). However, security and privacy remain the main obstacles to its widespread adoption. In this research we will review the security and privacy challenges that affect critical data in cloud computing and identify solutions that are used to address these challenges. Some questions that need answers are: (a) User access management, (b) Protect privacy of sensitive data, (c) Identity anonymity to protect the Identity of user and data file. To answer these questions, a systematic literature review was conducted and structured interview with several security experts working on cloud computing security to investigate the main objectives of propo
... Show MoreThis work discusses the beginning of fractional calculus and how the Sumudu and Elzaki transforms are applied to fractional derivatives. This approach combines a double Sumudu-Elzaki transform strategy to discover analytic solutions to space-time fractional partial differential equations in Mittag-Leffler functions subject to initial and boundary conditions. Where this method gets closer and closer to the correct answer, and the technique's efficacy is demonstrated using numerical examples performed with Matlab R2015a.
This paper investigates an effective computational method (ECM) based on the standard polynomials used to solve some nonlinear initial and boundary value problems appeared in engineering and applied sciences. Moreover, the effective computational methods in this paper were improved by suitable orthogonal base functions, especially the Chebyshev, Bernoulli, and Laguerre polynomials, to obtain novel approximate solutions for some nonlinear problems. These base functions enable the nonlinear problem to be effectively converted into a nonlinear algebraic system of equations, which are then solved using Mathematica®12. The improved effective computational methods (I-ECMs) have been implemented to solve three applications involving nonli
... Show MoreThe research aims to shed light on the concept of Visibal management and its reflection on the organizational culture of the organization. The visual administration is a modern administrative method that contributes to the renewal and development of the organization's reality through surveying the opinions of a sample of 61 employees in the R & D / Ministry of Higher Education and Scientific Research. (130) individuals. The questionnaire was used as a main tool for collecting data and information, and their answers were analyzed using the SPSS program in data entry and analysis. The most important tools are computational circles, standard deviations, method of analysis and regression equation. There is a possibility to apply
... Show MoreThe administrative leadership relies on a variety of behavioral paths in the functional areas in which it operates, and thus indicates its ability and thus has the upper hand in the organizational events, and in such a way that it can draw lessons and evaluate the results so as to test the expectations within the framework of the changes.
Does the study sample leadership have the characteristics that enable it to contain both crises and environmental stresses?
The aim of the study was to determine the location of the administrative leaders in the system of the study sample from the issue of crises and environmental pressures. The study concluded with a number of conclusions, the m
... Show MoreA Survey Study Of A Sample Of The Public Of Baghdad Governorate
The current study aimed to identify the most prominent psychological and behavioral repercussions of the exposure of the elderly to the news of the Corona pandemic and to determine the mechanisms of their exposure. On an intended sample on both sides of (Al-Karkh and Al-Rasafa) and the simple random sample was adopted to choose the place of distribution of the questionnaire and the intentional sample.
The research concluded several results, most TV news is still a primary source of information and that most of the sample move between stations to see more information about the pandemic and that the presentation of views confuses the elderly ،There
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