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.
Pharmaceutical-instigated pollution is a major concern, especially in relation to aquatic environments and drugs such as meropenem antibiotics. Adsorbents, such as multi-walled carbon nanotubes, offer potential as means of removing polluting meropenem antibiotics and other similar compounds from water. In order to evaluate the effectiveness of multi-walled carbon nanotubes in this capacity, various experimental parameters, including contact time, initial concentration, pH, temperature and the dose of adsorbent have been investigated. The Langmuir and the Freundlich isotherm models have been used. The data obtained using a modified Langmuir model have been consistent with the experimental ones; the best pH value has been obtained to have the
... Show MoreRemoval of heavy metals from waste water has received a great deal of attention. The compare Cr
(VI) adsorption characteristics removing from wastewater by using thermally modified and non-modified
eggshells were examined
It is out of question that USA foreign policy has a great superiority and
influence all over the world.
This study deals with all dimensions; aims; and challenges of the American
foreign policy. It aims to answer the following question: within the current changes in
the world, how can the aims of the American foreign policy be realized?
The objective of this study is to highlight the skills of office managers and it's impact on the effectiveness of time management in the institutes and faculties of middle technical university and a group of cognitive and practical aims. The managers skills forms mthe modern trend and the main source to provide organizations with highly skilled managers with distinctive performance and because of the sharp changes in the environment which today's organizations works in it , business organizations generally and managers especially realise the importance of time management and it's role in achieving competitive advantage . The problem of this study raised from this point which reflect the extent of departments managers realisation
... Show MoreOpenStreetMap (OSM), recognised for its current and readily accessible spatial database, frequently serves regions lacking precise data at the necessary granularity. Global collaboration among OSM contributors presents challenges to data quality and uniformity, exacerbated by the sheer volume of input and indistinct data annotation protocols. This study presents a methodological improvement in the spatial accuracy of OSM datasets centred over Baghdad, Iraq, utilising data derived from OSM services and satellite imagery. An analytical focus was placed on two geometric correction methods: a two-dimensional polynomial affine transformation and a two-dimensional polynomial conformal transformation. The former involves twelve coefficients for ad
... Show MoreThe successive international changes in the economic, political, cultural and other fields resulted in many phenomena that occupied different levels of interest, and have been followed up, studied and analysed by specialists and researchers especially under the development of the media in the global communication.
Even though these phenomena had reflections in the communication domain like development and changes in the mechanism of behaviours between the international communities, they created in the meantime a phenomenon that caused an imbalance in the production, spread and the use of the informations that were supposed to be for fewer than 5 countries whereas it actually it is for more than 180 countries.