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Recent advances in deep learning models: a systematic literature review

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Abstract

In recent years, deep learning has evolved as a rapidly growing and stimulating field of machine learning and has redefined state-of-the-art performances in a variety of applications. There are multiple deep learning models that have distinct architectures and capabilities. Up to the present, a large number of novel variants of these baseline deep learning models is proposed to address the shortcomings of the existing baseline models. This paper provides a comprehensive review of one hundred seven novel variants of six baseline deep learning models viz. Convolutional Neural Network, Recurrent Neural Network, Long Short Term Memory, Generative Adversarial Network, Autoencoder and Transformer Neural Network. The current review thoroughly examines the novel variants of each of the six baseline models to identify the advancements adopted by them to address one or more limitations of the respective baseline model. It is achieved by critically reviewing the novel variants based on their improved approach. It further provides the merits and demerits of incorporating the advancements in novel variants compared to the baseline deep learning model. Additionally, it reports the domain, datasets and performance measures exploited by the novel variants to make an overall judgment in terms of the improvements. This is because the performance of the deep learning models are subject to the application domain, type of datasets and may also vary on different performance measures. The critical findings of the review would facilitate the researchers and practitioners with the most recent progressions and advancements in the baseline deep learning models and guide them in selecting an appropriate novel variant of the baseline to solve deep learning based tasks in a similar setting.

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Data availability

Data sharing is not applicable to this article as this is a review article. The detail of the selected primary studies is presented in Table 3.

Abbreviations

DL:

Deep Leering

AE:

Autoencoder

CNN:

Convolutional Neural Network

RNN:

Recurrent Neural Network

GAN:

Generative Adversarial Network

LSTM:

Long Short-Term Memory

TNN:

Transformer Neural Network

DLM:

Deep Learning Models

SLR:

Systematic Literature Review

NV:

Novel Variant

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  1. Department of Software Engineering, Delhi Technological University, Delhi, India

    Ruchika Malhotra & Priya Singh

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For the Systematic Literature Review-

1. Ruchika Malhotra proposed the idea for the article.

2. Primary Study selection was done by Priya Singh followed by a review by Dr. Ruchika Malhotra.

3. Data Extraction was done by Ruchika Malhotra and Priya Singh both separately, resolving differences where applicable at the time of merging.

4. Result Reporting was done by Priya Singh and reviewed by Ruchika Malhotra.

5. Proofreading and final review were done by Ruchika Malhotra and Priya Singh.

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Appendix

Appendix

1.1 Quality assessment results

We provide the quality scores to 166 studies selected after Inclusion–Exclusion criteria according to 16 quality assessment questions stated in Table 2. Table 10 reports the percentage of candidate studies that answered a given quality question as “Yes”, “Partly” or “No”.

Table 10 Result of Quality Assessment
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Malhotra, R., Singh, P. Recent advances in deep learning models: a systematic literature review. Multimed Tools Appl 82, 44977–45060 (2023). https://doi.org/10.1007/s11042-023-15295-z

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