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Deep Learning Based on Neural Network Design
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Deep Learning Based on Neural Network Design

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 11714

Special Issue Editor

Dr. Nianyin Zeng

E-Mail Website
Guest Editor
Department of Instrumental and Electrical Engineering, Xiamen University, Xiamen 61102, China
Interests: intelligent data analysis; machine learning and computer vision; computational intelligent; artificial intelligence in biomedical and industry
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are inviting submissions to the Special Issue on deep learning-based neural network design.

Neural networks are powerful and practical deep learning algorithms which have been applied into various areas and witnessed a great many of successes. So far, diverse kinds of neural networks have emerged, such as the convolutional neural network (CNN), the recurrent neural network (RNN), the generative adversarial network (GAN), etc. However, how to configure the structure of networks remains a challenging work, which is of vital significance for the overall performance. In most application scenarios, such structural designing is achieved manually after repeated experiments, which is of low efficiency. Recently, methods of automatically designing neural networks have attracted increasing academic interest, with the aim to apply deep learning techniques to realize optimization of the structural configuration as well as corresponding parameter settings. The inherent motivation is both reasonable and feasible, and the task can be modeled as a specific network-oriented optimization problem, where other techniques such as evolutionary computation is also accessible.

In this Special Issue, we invite submissions exploring state-of-the-art researches in the fields of deep learning-based neural network design. Both theoretical and experimental studies are welcome, as well as comprehensive reviews and survey papers.

Prof. Dr. Zeng Nianyin
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • intelligent data analysis
  • artificial intelligence
  • computational intelligence

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Published Papers (3 papers)

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Research

10 pages, 626 KiB  
Article
Knowing Knowledge: Epistemological Study of Knowledge in Transformers
by Leonardo Ranaldi and Giulia Pucci
Appl. Sci. 2023, 13(2), 677; https://doi.org/10.3390/app13020677 - 4 Jan 2023
Cited by 46 | Viewed by 3380
Abstract
Statistical learners are leading towards auto-epistemic logic, but is it the right way to progress in artificial intelligence (AI)? Ways to discover AI fit the senses and the intellect. The structure of symbols–the operations by which the intellectual solution is realized–and the search [...] Read more.
Statistical learners are leading towards auto-epistemic logic, but is it the right way to progress in artificial intelligence (AI)? Ways to discover AI fit the senses and the intellect. The structure of symbols–the operations by which the intellectual solution is realized–and the search for strategic reference points evoke essential issues in the analysis of AI. Studying how knowledge can be represented through methods of theoretical generalization and empirical observation is only the latest step in a long process of evolution. In this paper, we try to outline the origin of knowledge and how modern artificial minds have inherited it. Full article
(This article belongs to the Special Issue Deep Learning Based on Neural Network Design)
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14 pages, 3840 KiB  
Article
Gender Recognition of Bangla Names Using Deep Learning Approaches
by Md. Humaun Kabir, Faruk Ahmad, Md. Al Mehedi Hasan and Jungpil Shin
Appl. Sci. 2023, 13(1), 522; https://doi.org/10.3390/app13010522 - 30 Dec 2022
Cited by 1 | Viewed by 2781
Abstract
The name of individuals has a specific meaning and great significance. Individuals’ names generally have substantial gender differences, and explicitly, Bengali names usually have a solid sexual identity. We can determine if a stranger is a man or a woman based on their [...] Read more.
The name of individuals has a specific meaning and great significance. Individuals’ names generally have substantial gender differences, and explicitly, Bengali names usually have a solid sexual identity. We can determine if a stranger is a man or a woman based on their name with remarkably suitable precision. In this research, we primarily conducted a thorough investigation into gender prediction based on a person’s name using DL-based methods. While various techniques have been explored for the English language, there has been little progress in the Bengali language. We address this gap by presenting a large-scale experiment with 2030 Bangladeshi unique names. We used both convolutional neural network (CNN)- and recurrent neural network (RNN)-based deep learning methods to infer gender from the Bangladeshi names in the Bengali language. We presented the one-dimensional CNN (Conv1D), simple long short-term memory (LSTM), bidirectional LSTM, stacked LSTM, and combined Conv1D and stacked bidirectional LSTM-based models and evaluated the performance of each scheme using our own dataset. Experimental results are analyzed on the basis of accuracy, precision, recall, F1-score, ROC AUC score, and loss performance metrics. The performance evaluative results show that Conv1D outperforms with 91.18% accuracy, which is likely to improve as the size of the training data grows. Full article
(This article belongs to the Special Issue Deep Learning Based on Neural Network Design)
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23 pages, 4765 KiB  
Article
CASVM: An Efficient Deep Learning Image Classification Method Combined with SVM
by Shuqiu Tan, Jiahao Pan, Jianxun Zhang and Yahui Liu
Appl. Sci. 2022, 12(22), 11690; https://doi.org/10.3390/app122211690 - 17 Nov 2022
Cited by 5 | Viewed by 4238
Abstract
Recent advances in convolutional neural networks (CNNs) for image feature extraction have achieved extraordinary performance, but back-propagation algorithms tend to fall into local minima. To alleviate this problem, this paper proposes a coordinate attention-support vector machine-convolutional neural network (CASVM). This proposed to enhance [...] Read more.
Recent advances in convolutional neural networks (CNNs) for image feature extraction have achieved extraordinary performance, but back-propagation algorithms tend to fall into local minima. To alleviate this problem, this paper proposes a coordinate attention-support vector machine-convolutional neural network (CASVM). This proposed to enhance the model’s ability by introducing coordinate attention while obtaining enhanced image features. Training is carried out by back-propagating the loss function of support vector machines (SVMs) to improve the generalization capability, which can effectively avoid falling into local optima. The image datasets used in this study for benchmark experiments are Fashion-MNIST, Cifar10, Cifar100, and Animal10. Experimental results show that compared with softmax, CASVM can improve the image classification accuracy of the original model under different image resolution datasets. Under the same structure, CASVM shows better performance and robustness and has higher accuracy. Under the same network parameters, the loss function of CASVM enables the model to realize a lower loss value. Among the standard CNN models, the highest accuracy rate can reach 99%, and the optimal number of accuracy indicators is 5.5 times that of softmax, whose accuracy rate can be improved by up to 56%. Full article
(This article belongs to the Special Issue Deep Learning Based on Neural Network Design)
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