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Advances in Deep Learning and Intelligent Computing
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Advances in Deep Learning and Intelligent Computing

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 (20 January 2026) | Viewed by 6826

Special Issue Editors

Dr. Jinyang Guo

E-Mail Website
Guest Editor
State Key Laboratory of Complex & Critical Software Environment, Institute of Artificial Intelligence, Beihang University, Beijing 100191, China
Interests: AI; computer vision; deep learning; efficient methods
Dr. Giacomo Fiumara

E-Mail Website
Guest Editor
Department of Mathematics and Computer Science, Physical Sciences and Earth Sciences, University of Messina, 98166 Messina, Italy
Interests: network science; criminal networks; machine learning; data science; social network analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recently, deep learning has achieved many successes in various fields, such as computer vision and natural language processing. These successful applications make deep learning an important technique in artificial intelligence. To better discuss the recent developments of artificial intelligence, our Special Issue on "Advances in Deep Learning and Intelligent Computing" aims to explore the latest breakthroughs and innovations in the fields of deep learning and intelligent computing. This Special Issue seeks to provide a platform where researchers, scientists, and practitioners can present their latest research findings, methodologies, and applications in deep learning and intelligent computing. Topics of interest include, but are not limited to, the following:

  • Novel deep learning architectures and algorithms;
  • Reinforcement learning and its applications;
  • Transfer learning and domain adaptation techniques;
  • General deep learning technologies;
  • Techniques on generative large models;
  • Intelligent computing systems;
  • Efficient artificial intelligence techniques.

We invite submissions of original research papers, review articles, and case studies that contribute to advancing the state of the art in deep learning and intelligent computing. Manuscripts submitted to this Special Issue will undergo a rigorous peer-review process to ensure they are high-quality and relevant to the theme.

By providing a platform for researchers to share their insights, experiences, and challenges, this Special Issue aims to foster collaboration and accelerate the development and adoption of deep learning and intelligent computing technologies across various domains.

We look forward to receiving your contributions and participation in this exciting journey towards unlocking the full potential of deep learning and intelligent computing.

Dr. Jinyang Guo
Prof. Dr. Giacomo Fiumara
Guest Editors

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 250 words) can be sent to the Editorial Office for assessment.

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

  • artificial intelligence
  • deep learning
  • intelligent computing
  • efficient methods
  • generative large model

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Published Papers (3 papers)

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Research

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21 pages, 1315 KB  
Article
Ensemble Deep Learning Models for Multi-Class DNA Sequence Classification: A Comparative Study of CNN, BiLSTM, and GRU Architectures
by Elias Tabane, Ernest Mnkandla and Zenghui Wang
Appl. Sci. 2026, 16(3), 1545; https://doi.org/10.3390/app16031545 - 3 Feb 2026
Viewed by 428
Abstract
DNA sequence classification is a fundamental problem in bioinformatics, playing an indispensable role in gene annotation and disease prediction. Whereas most deep learning models, such as CNNs, BiLSTM networks, and GRUs, have been found individually optimal, each of these methods excels in modeling [...] Read more.
DNA sequence classification is a fundamental problem in bioinformatics, playing an indispensable role in gene annotation and disease prediction. Whereas most deep learning models, such as CNNs, BiLSTM networks, and GRUs, have been found individually optimal, each of these methods excels in modeling a specific aspect of sequence data: local motifs, long-range dependencies, and efficient temporal modeling of the sequences. Here, we present and evaluate an ensemble model that integrates CNN, BiLSTM, and GRU architectures via a majority voting combination scheme so that their complementary strengths can be harnessed. We trained and evaluated each standalone and the integrated model on a DNA dataset comprising 4380 sequences falling under five functional categories. The ensemble model achieved a classification accuracy of 90.6% with precision, recall, and F1 score equal to 0.91, thereby outperforming the state-of-the-art techniques by large margins. Although previous studies have tried analyzing each Deep Learning method individually for DNA classification tasks, none have attempted a systematic combination of CNN, BiLSTM, and GRU based on their ability to extract features simultaneously. The current research aims at presenting a novel method that combines these architectures based on a Majority Voting strategy and proves how their combination is better at extracting local patterns and long dependency information when compared individually. In particular, the proposed ensemble model smoothed the high recall of BiLSTM with the high precision of CNN, leading to more robust and reliable classification. The experiments involved a publicly available DNA sequence data set of 4380 sequences distributed over 5 classes. Our results emphasized the prospect of hybrid ensemble deep learning as a strong approach for complex genomic data analysis, opening ways toward more accurate and interpretable bioinformatics research. Full article
(This article belongs to the Special Issue Advances in Deep Learning and Intelligent Computing)
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21 pages, 3582 KB  
Article
A Cascade of Encoder–Decoder with Atrous Convolution and Ensemble Deep Convolutional Neural Networks for Tuberculosis Detection
by Noppadol Maneerat, Athasart Narkthewan and Kazuhiko Hamamoto
Appl. Sci. 2025, 15(13), 7300; https://doi.org/10.3390/app15137300 - 28 Jun 2025
Cited by 1 | Viewed by 927
Abstract
Tuberculosis (TB) is the most serious worldwide infectious disease and the leading cause of death among people with HIV. Early diagnosis and prompt treatment can cut off the rising number of TB deaths, and analysis of chest X-rays is a cost-effective method. We [...] Read more.
Tuberculosis (TB) is the most serious worldwide infectious disease and the leading cause of death among people with HIV. Early diagnosis and prompt treatment can cut off the rising number of TB deaths, and analysis of chest X-rays is a cost-effective method. We describe a deep learning-based cascade algorithm for detecting TB in chest X-rays. Firstly, the lung regions were segregated from other anatomical structures by an encoder–decoder with an atrous separable convolution network—DeepLabv3+ with an XceptionNet backbone, DLabv3+X, and then cropped by a bounding box. Using the cropped lung images, we trained several pre-trained Deep Convolutional Neural Networks (DCNNs) on the images with hyperparameters optimized by a Bayesian algorithm. Different combinations of trained DCNNs were compared, and the combination with the maximum accuracy was retained as the winning combination. The ensemble classifier was designed to predict the presence of TB by fusing DCNNs from the winning combination via weighted averaging. Our lung segmentation was evaluated on three publicly available datasets: it provided better Intercept over Union (IoU) values: 95.1% for Montgomery County (MC), 92.8% for Shenzhen (SZ), and 96.1% for JSRT datasets. For TB prediction, our ensemble classifier produced a better accuracy of 92.7% for the MC dataset and obtained a comparable accuracy of 95.5% for the SZ dataset. Finally, occlusion sensitivity and gradient-weighted class activation maps (Grad-CAM) were generated to indicate the most influential regions for the prediction of TB and to localize TB manifestations. Full article
(This article belongs to the Special Issue Advances in Deep Learning and Intelligent Computing)
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Review

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38 pages, 2502 KB  
Review
A Modular Perspective on the Evolution of Deep Learning: Paradigm Shifts and Contributions to AI
by Yicheng Wei, Yifu Wang and Junzo Watada
Appl. Sci. 2025, 15(19), 10539; https://doi.org/10.3390/app151910539 - 29 Sep 2025
Viewed by 4473
Abstract
The rapid development of deep learning (DL) has demonstrated its modular contributions to artificial intelligence (AI) techniques, such as large language models (LLMs). DL variants have proliferated across domains such as feature extraction, normalization, lightweight architecture design, and module integration, yielding substantial advancements [...] Read more.
The rapid development of deep learning (DL) has demonstrated its modular contributions to artificial intelligence (AI) techniques, such as large language models (LLMs). DL variants have proliferated across domains such as feature extraction, normalization, lightweight architecture design, and module integration, yielding substantial advancements in these subfields. However, the absence of a unified review framework to contextualize DL’s modular evolutions within AI development complicates efforts to pinpoint future research directions. Existing review papers often focus on narrow technical aspects or lack systemic analysis of modular relationships, leaving gaps in our understanding how these innovations collectively drive AI progress. This work bridges this gap by providing a roadmap for researchers to navigate DL’s modular innovations, with a focus on balancing scalability and sustainability amid evolving AI paradigms. To address this, we systematically analyze extensive literature from databases including Web of Science, Scopus, arXiv, ACM Digital Library, IEEE Xplore, SpringerLink, Elsevier, etc., with the aim of (1) summarizing and updating recent developments in DL algorithms, with performance benchmarks on standard dataset; (2) identifying innovation trends in DL from a modular viewpoint; and (3) evaluating how these modular innovations contribute to broader advances in artificial intelligence, with particular attention to scalability and sustainability amid shifting AI paradigms. Full article
(This article belongs to the Special Issue Advances in Deep Learning and Intelligent Computing)
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