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.
In this paper, integrated quantum neural network (QNN), which is a class of feedforward
neural networks (FFNN’s), is performed through emerging quantum computing (QC) with artificial neural network(ANN) classifier. It is used in data classification technique, and here iris flower data is used as a classification signals. For this purpose independent component analysis (ICA) is used as a feature extraction technique after normalization of these signals, the architecture of (QNN’s) has inherently built in fuzzy, hidden units of these networks (QNN’s) to develop quantized representations of sample information provided by the training data set in various graded levels of certainty. Experimental results presented here show that
... Show MoreData compression offers an attractive approach to reducing communication costs using available bandwidth effectively. It makes sense to pursue research on developing algorithms that can most effectively use available network. It is also important to consider the security aspect of the data being transmitted is vulnerable to attacks. The basic aim of this work is to develop a module for combining the operation of compression and encryption on the same set of data to perform these two operations simultaneously. This is achieved through embedding encryption into compression algorithms since both cryptographic ciphers and entropy coders bear certain resemblance in the sense of secrecy. First in the secure compression module, the given text is p
... Show MoreDifferent ANN architectures of MLP have been trained by BP and used to analyze Landsat TM images. Two different approaches have been applied for training: an ordinary approach (for one hidden layer M-H1-L & two hidden layers M-H1-H2-L) and one-against-all strategy (for one hidden layer (M-H1-1)xL, & two hidden layers (M-H1-H2-1)xL). Classification accuracy up to 90% has been achieved using one-against-all strategy with two hidden layers architecture. The performance of one-against-all approach is slightly better than the ordinary approach
Green synthesis of bimetallic nanoparticles of Fe/Ni (G-Fe/Ni-NPs) and zeolite-5A supported (G-Z-Fe/Ni-NPs) as heterogeneous Fenton-like oxidation for the decolourisation of reactive red 120-dye (RR120) from the aqueous medium using green tea extract as a reducing agent. Zeolite-5A from local kaolin is prepared and characterised using the hydrothermal method and is used as a supporting material for Fe/Ni-NPs. (SEM), (EDX), (AFM), (XRD), (FT-IR), (BET). Its zeta potential were used to characterise G-Fe/Ni-NPs and G-Z-Fe/Ni-NPs. The decolourisation efficiency (Ed) of the RR120-dye using a heterogeneous Fenton-like for G-Fe/Ni-NPs and G-Z-Fe/Ni-NPs is 99.8% and 99.9%, respectively, under the optimum conditions: [H2O2] = 20 and 1 mmol/L
... Show MoreThe current research aims at: - Identifying the role played by the leadership in empowerment and organizational learning abilities and their reflection on the knowledge capital, and the extent to which these concepts can be applied effectively at Wasit University. The problem of research .... In a series of questions: The most important is that the dimensions leadership empowerment and distance learning organizational capacity correlation relationship and impact and significant statistical significance with the capital knowledge.
To understand the nature of the relationship and the impact between the variables, leadership was adopted by empowerment as the fir
... Show MoreChronic kidney disease is one of the leading public health problems that affect millions of women and men worldwide.
This study aims to examine the effect of deep breathing to reduce discomfort amongst patient undergoing haemodialysis (HD).
This randomised controlled experimental study was conducted consisted of 108 patients (54 in each group) who undergoing HD in hospitalised adults’ patients between November 2024 an
Interested in this research shed light on the reality of foreign trade to Iraq Who suffers from a marked deterioration due to poor economic diversification of the country And increase the degree of economic exposure , Which creates a state of extreme caution towards the question of accession to the (WTO) , As controls Iraq's foreign trade commodity , a president of one oil As well as the contribution of this item , and by a large formation in GDP , And that such a large and dangerous decline in the degree of economic diversification will create negative effects On overall economic activity components&
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