A comprehensive survey on deep neural networks for stock market: The need, challenges, and future directions

Graph Neural Networks (GNNs), a generalization of deep neural networks on graph data have been widely used in various domains, ranging from drug discovery to recommender systems. However, GNNs on such applications are limited when there are few available samples. Meta-learning has been an important framework to address the lack of samples in machine learning, and in recent years, researchers have started to apply meta-learning to GNNs. In this work, we provide a comprehensive survey of different metalearning approaches involving GNNs on various graph problems showing the power of using these two approaches together. We categorize the literature based on proposed architectures, shared representations, and applications. Finally, we discuss several exciting future research directions and open problems.

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A Review of Recent Deep Learning Models in COVID-19 Diagnosis

European Journal of Engineering and Technology Research ◽

10.24018/ejers.2021.6.5.2485 ◽

2021 ◽

Vol 6 (5) ◽

pp. 10-15

Author(s):

Ela Bhattacharya ◽

D. Bhattacharya

Keyword(s):

Artificial Intelligence ◽

Neural Networks ◽

Deep Learning ◽

Deep Neural Networks ◽

Test Results ◽

Learning Models ◽

Future Directions ◽

Human Contact ◽

The World ◽

Short Span

COVID-19 has emerged as the latest worrisome pandemic, which is reported to have its outbreak in Wuhan, China. The infection spreads by means of human contact, as a result, it has caused massive infections across 200 countries around the world. Artificial intelligence has likewise contributed to managing the COVID-19 pandemic in various aspects within a short span of time. Deep Neural Networks that are explored in this paper have contributed to the detection of COVID-19 from imaging sources. The datasets, pre-processing, segmentation, feature extraction, classification and test results which can be useful for discovering future directions in the domain of automatic diagnosis of the disease, utilizing artificial intelligence-based frameworks, have been investigated in this paper.

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Transfer Learning and Deep Domain Adaptation

Advances and Applications in Deep Learning ◽

10.5772/intechopen.94072 ◽

2020 ◽

Author(s):

Wen Xu ◽

Jing He ◽

Yanfeng Shu

Keyword(s):

Machine Learning ◽

Neural Networks ◽

Transfer Learning ◽

Real World ◽

Deep Neural Networks ◽

Domain Adaptation ◽

Fine Tuning ◽

Real World Applications ◽

Comprehensive Survey ◽

Sample Reconstruction

Transfer learning is an emerging technique in machine learning, by which we can solve a new task with the knowledge obtained from an old task in order to address the lack of labeled data. In particular deep domain adaptation (a branch of transfer learning) gets the most attention in recently published articles. The intuition behind this is that deep neural networks usually have a large capacity to learn representation from one dataset and part of the information can be further used for a new task. In this research, we firstly present the complete scenarios of transfer learning according to the domains and tasks. Secondly, we conduct a comprehensive survey related to deep domain adaptation and categorize the recent advances into three types based on implementing approaches: fine-tuning networks, adversarial domain adaptation, and sample-reconstruction approaches. Thirdly, we discuss the details of these methods and introduce some typical real-world applications. Finally, we conclude our work and explore some potential issues to be further addressed.

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Forecasting Stock Market Price Using Deep Neural Networks

2019 7th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS) ◽

10.1109/cfis.2019.8692169 ◽

2019 ◽

Author(s):

Nima Gozalpour ◽

Mohammad Teshnehlab

Keyword(s):

Neural Networks ◽

Stock Market ◽

Deep Neural Networks ◽

Market Price

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Adversarial Perturbation Defense on Deep Neural Networks

ACM Computing Surveys ◽

10.1145/3465397 ◽

2022 ◽

Vol 54 (8) ◽

pp. 1-36

Author(s):

Xingwei Zhang ◽

Xiaolong Zheng ◽

Wenji Mao

Keyword(s):

Machine Learning ◽

Neural Networks ◽

High Probability ◽

Deep Neural Networks ◽

State Of The Art ◽

Learning Systems ◽

Small Perturbations ◽

Image Objects ◽

Comprehensive Survey ◽

Human Eyes

Deep neural networks (DNNs) have been verified to be easily attacked by well-designed adversarial perturbations. Image objects with small perturbations that are imperceptible to human eyes can induce DNN-based image class classifiers towards making erroneous predictions with high probability. Adversarial perturbations can also fool real-world machine learning systems and transfer between different architectures and datasets. Recently, defense methods against adversarial perturbations have become a hot topic and attracted much attention. A large number of works have been put forward to defend against adversarial perturbations, enhancing DNN robustness against potential attacks, or interpreting the origin of adversarial perturbations. In this article, we provide a comprehensive survey on classical and state-of-the-art defense methods by illuminating their main concepts, in-depth algorithms, and fundamental hypotheses regarding the origin of adversarial perturbations. In addition, we further discuss potential directions of this domain for future researchers.

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