Deep learning modelling techniques: current progress, applications, advantages, and challenges
Deep learning (DL) is revolutionizing evidence-based decision-making techniques that can be applied across various sectors. Specifically, it possesses the ability to utilize two or more levels of non-linear feature transformation of the given data via representation learning in order to overcome lim...
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| Veröffentlicht in: | The Artificial intelligence review Jg. 56; H. 11; S. 13521 - 13617 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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01.11.2023
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| ISSN: | 0269-2821, 1573-7462 |
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| Abstract | Deep learning (DL) is revolutionizing evidence-based decision-making techniques that can be applied across various sectors. Specifically, it possesses the ability to utilize two or more levels of non-linear feature transformation of the given data via representation learning in order to overcome limitations posed by large datasets. As a multidisciplinary field that is still in its nascent phase, articles that survey DL architectures encompassing the full scope of the field are rather limited. Thus, this paper comprehensively reviews the state-of-art DL modelling techniques and provides insights into their advantages and challenges. It was found that many of the models exhibit a highly domain-specific efficiency and could be trained by two or more methods. However, training DL models can be very time-consuming, expensive, and requires huge samples for better accuracy. Since DL is also susceptible to deception and misclassification and tends to get stuck on local minima, improved optimization of parameters is required to create more robust models. Regardless, DL has already been leading to groundbreaking results in the healthcare, education, security, commercial, industrial, as well as government sectors. Some models, like the convolutional neural network (CNN), generative adversarial networks (GAN), recurrent neural network (RNN), recursive neural networks, and autoencoders, are frequently used, while the potential of other models remains widely unexplored. Pertinently, hybrid conventional DL architectures have the capacity to overcome the challenges experienced by conventional models. Considering that capsule architectures may dominate future DL models, this work aimed to compile information for stakeholders involved in the development and use of DL models in the contemporary world. |
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| AbstractList | Deep learning (DL) is revolutionizing evidence-based decision-making techniques that can be applied across various sectors. Specifically, it possesses the ability to utilize two or more levels of non-linear feature transformation of the given data via representation learning in order to overcome limitations posed by large datasets. As a multidisciplinary field that is still in its nascent phase, articles that survey DL architectures encompassing the full scope of the field are rather limited. Thus, this paper comprehensively reviews the state-of-art DL modelling techniques and provides insights into their advantages and challenges. It was found that many of the models exhibit a highly domain-specific efficiency and could be trained by two or more methods. However, training DL models can be very time-consuming, expensive, and requires huge samples for better accuracy. Since DL is also susceptible to deception and misclassification and tends to get stuck on local minima, improved optimization of parameters is required to create more robust models. Regardless, DL has already been leading to groundbreaking results in the healthcare, education, security, commercial, industrial, as well as government sectors. Some models, like the convolutional neural network (CNN), generative adversarial networks (GAN), recurrent neural network (RNN), recursive neural networks, and autoencoders, are frequently used, while the potential of other models remains widely unexplored. Pertinently, hybrid conventional DL architectures have the capacity to overcome the challenges experienced by conventional models. Considering that capsule architectures may dominate future DL models, this work aimed to compile information for stakeholders involved in the development and use of DL models in the contemporary world. |
| Audience | Academic |
| Author | Ishtiak, Taoseef Mofijur, M. Alam, Md. Sakib Bin Gandomi, Amir H. Shawkat Ali, A. B. M. Rozbu, Mahtabin Rodela Hassan, Maruf Ahmed, Shams Forruque Rafa, Nazifa |
| Author_xml | – sequence: 1 givenname: Shams Forruque surname: Ahmed fullname: Ahmed, Shams Forruque email: shams.ahmed@auw.edu.bd, shams.f.ahmed@gmail.com organization: Science and Math Program, Asian University for Women – sequence: 2 givenname: Md. Sakib Bin surname: Alam fullname: Alam, Md. Sakib Bin organization: Data Science and Artificial Intelligence, Asian Institute of Technology – sequence: 3 givenname: Maruf surname: Hassan fullname: Hassan, Maruf organization: Science and Math Program, Asian University for Women – sequence: 4 givenname: Mahtabin Rodela surname: Rozbu fullname: Rozbu, Mahtabin Rodela organization: Department of Computational Biology, Carnegie Mellon University – sequence: 5 givenname: Taoseef surname: Ishtiak fullname: Ishtiak, Taoseef organization: School of Computer Science, Carleton University – sequence: 6 givenname: Nazifa surname: Rafa fullname: Rafa, Nazifa organization: Department of Geography, University of Cambridge – sequence: 7 givenname: M. surname: Mofijur fullname: Mofijur, M. organization: Centre for Technology in Water and Wastewater, School of Civil and Environmental Engineering, University of Technology Sydney, Mechanical Engineering Department, Prince Mohammad Bin Fahd University – sequence: 8 givenname: A. B. M. surname: Shawkat Ali fullname: Shawkat Ali, A. B. M. organization: School of Engineering and Technology, Central Queensland University, School of Science and Technology, The University of Fiji – sequence: 9 givenname: Amir H. orcidid: 0000-0002-2798-0104 surname: Gandomi fullname: Gandomi, Amir H. email: gandomi@uts.edu.au organization: Faculty of Engineering and Information Technology, University of Technology Sydney, University Research and Innovation Center (EKIK), Óbuda University |
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| CitedBy_id | crossref_primary_10_1108_AGJSR_06_2023_0252 |
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