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A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications
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Abstract<p>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.</p>
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Publication Date
Mon Aug 01 2022
Journal Name
Baghdad Science Journal
A Novel Technique for Secure Data Cryptosystem Based on Chaotic Key Image Generation
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The advancements in Information and Communication Technology (ICT), within the previous decades, has significantly changed people’s transmit or store their information over the Internet or networks. So, one of the main challenges is to keep these information safe against attacks. Many researchers and institutions realized the importance and benefits of cryptography in achieving the efficiency and effectiveness of various aspects of secure communication.This work adopts a novel technique for secure data cryptosystem based on chaos theory. The proposed algorithm generate 2-Dimensional key matrix having the same dimensions of the original image that includes random numbers obtained from the 1-Dimensional logistic chaotic map for given con

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Publication Date
Fri Jan 01 2016
Journal Name
Journal Of Sensors
WDARS: A Weighted Data Aggregation Routing Strategy with Minimum Link Cost in Event-Driven WSNs
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Realizing the full potential of wireless sensor networks (WSNs) highlights many design issues, particularly the trade-offs concerning multiple conflicting improvements such as maximizing the route overlapping for efficient data aggregation and minimizing the total link cost. While the issues of data aggregation routing protocols and link cost function in a WSNs have been comprehensively considered in the literature, a trade-off improvement between these two has not yet been addressed. In this paper, a comprehensive weight for trade-off between different objectives has been employed, the so-called weighted data aggregation routing strategy (WDARS) which aims to maximize the overlap routes for efficient data aggregation and link cost

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Publication Date
Tue Jul 01 2025
Journal Name
Ain Shams Engineering Journal
Deep neural networks for speech enhancement and speech recognition: A systematic review
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Publication Date
Tue Jul 01 2025
Journal Name
Ain Shams Engineering Journal
Deep neural networks for speech enhancement and speech recognition: A systematic review
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Publication Date
Wed Jun 01 2022
Journal Name
مجلة كلية العلوم الاسلامية
The most prominent challenges facing Muslim youth and addressing them from a Quranic perspective
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The research talks about the most important challenges facing Muslim youth of their ideological, social and economic types, and the youth is facing several problems, the most important of which are the intellectual and social invasion to which the Islamic nation has been exposed and ways to address them from a Quranic perspective and find solutions to these problems and these challenges in accordance with Islamic Sharia and the texts of the Holy Quran. From three topics and several demands, during which the researcher tried to find solutions to each challenge through the verses of the Noble Qur’an.

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Publication Date
Wed Jun 03 2020
Journal Name
Political Sciences Journal
The presidential system in Turkey between reality and challenges: a vision of the future
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The nature of the political system of any country in the international system must have an impact on the policy of the State, whether in the interior or exterior, both regional and international, Turkey is one of the most important countries in the Middle East, which have a place and effectiveness on the surrounding environment, hence the importance of research lies in its political system and the transfer of power in it and its transformation from the form of the parliamentary system to the presidential system of the repercussions of the task and mutual effects between Turkey and the countries of the surrounding region in terms of the extent of achieving political stability in the Turkish interior or not and how this can be refl

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Publication Date
Wed Jun 29 2022
Journal Name
College Of Islamic Sciences
The most prominent challenges facing Muslim youth and addressing them from a Quranic perspective
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The research talks about the most important challenges facing Muslim youth of their ideological, social and economic types, and the youth is facing several problems, the most important of which are the intellectual and social invasion to which the Islamic nation has been exposed and ways to address them from a Quranic perspective and find solutions to these problems and these challenges in accordance with Islamic Sharia and the texts of the Holy Quran. From three topics and several demands, during which the researcher tried to find solutions to each challenge through the verses of the Noble Qur’an.

 

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Publication Date
Thu Aug 21 2025
Journal Name
Computers
A Comprehensive Review of Sensor Technologies in IoT: Technical Aspects, Challenges, and Future Directions
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The rapid advancements in wireless technology and digital electronics have led to the widespread adoption of compact, intelligent devices in various aspects of daily life. These advanced systems possess the capability to sense environmental changes, process data, and communicate seamlessly within interconnected networks. Typically, such devices integrate low-power radio transmitters and multiple smart sensors, hence enabling efficient functionality across wide ranges of applications. Alongside these technological developments, the concept of the IoT has emerged as a transformative paradigm, facilitating the interconnection of uniquely identifiable devices through internet-based networks. This paper aims to provide a comprehensive ex

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Publication Date
Mon Jan 01 2018
Journal Name
Proceedings Of The 10th International Joint Conference On Computational Intelligence
Deep Classifier Structures with Autoencoder for Higher-level Feature Extraction
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Publication Date
Tue Jun 01 2021
Journal Name
2021 Ieee/cvf Conference On Computer Vision And Pattern Recognition Workshops (cvprw)
Alps: Adaptive Quantization of Deep Neural Networks with GeneraLized PositS
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