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Advances in Deep Learning for Complex Combinatorial Optimization: Applications in Cybersecurity, Healthcare, and Intelligent Systems

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

Deadline for manuscript submissions: 31 January 2026 | Viewed by 3747

Special Issue Editors

Prof. Dr. Ernest Mnkandla

E-Mail Website
Guest Editor
Centre for Augmented Intelligence and Data Science, School of Computing, University of South Africa, Johannesburg 1709, South Africa
Interests: artificial intelligence; software engineering; artificial intelligence ethics
Prof. Dr. Zenghui Wang

E-Mail Website
Guest Editor
Department of Electrical and Smart Systems Engineering, Science Campus, University of South Africa, Tshwane, South Africa
Interests: artificial intelligence (AI) and its applications; automatic control; renewable energy system design/optimization/simulation; Industry 4.0/5.0
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Traditional combinatorial optimization methods, while robust, often encounter limitations in dynamic and high-dimensional environments where deep learning can introduce significant advantages. This Special Issue focuses on innovative applications of deep learning in combinatorial optimization, exploring how deep learning techniques can address complex optimization challenges across various domains.

This Special Issue invites contributions from researchers and practitioners on topics such as intelligent routing, predictive scheduling, network optimization, and adaptive resource allocation. Emphasis will be placed on applications in critical areas including healthcare, cybersecurity, intelligent transportation, and education technology. By uniting theory and practice, this Special Issue aims to showcase methodologies and frameworks that push the boundaries of current optimization techniques, drive AI-augmented decision-making, and address real-world, multi-dimensional problems that demand efficient and scalable solutions. Contributions focused on interdisciplinary and emerging applications are highly encouraged.

Prof. Dr. Ernest Mnkandla
Prof. Dr. Zenghui Wang
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

  • deep learning
  • combinatorial optimization
  • healthcare systems optimization
  • cybersecurity applications
  • intelligent transportation systems
  • adaptive resource allocation
  • AI-driven decision-making
  • predictive scheduling
  • network optimization
  • data-driven optimization
  • optimization techniques
  • terrestrial laser scanning

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Published Papers (2 papers)

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21 pages, 1194 KB  
Article
Retentive-HAR: Human Activity Recognition from Wearable Sensors with Enhanced Temporal and Inter-Feature Dependency Retention
by Ayokunle Olalekan Ige, Daniel Ayo Oladele and Malusi Sibiya
Appl. Sci. 2025, 15(23), 12661; https://doi.org/10.3390/app152312661 - 29 Nov 2025
Viewed by 176
Abstract
Human Activity Recognition (HAR) using wearable sensor data plays a vital role in health monitoring, context-aware computing, and smart environments. Many existing deep learning models for HAR incorporate MaxPooling layers after convolutional operations to reduce dimensionality and computational load. While this approach is [...] Read more.
Human Activity Recognition (HAR) using wearable sensor data plays a vital role in health monitoring, context-aware computing, and smart environments. Many existing deep learning models for HAR incorporate MaxPooling layers after convolutional operations to reduce dimensionality and computational load. While this approach is effective in image-based tasks, it is less suitable for the sensor signals used in HAR. MaxPooling introduces a form of temporal downsampling that can discard subtle yet crucial temporal information. Also, traditional CNNs often struggle to capture long-range dependencies within each window due to their limited receptive fields, and they lack effective mechanisms to aggregate information across multiple windows without stacking multiple layers, which increases computational cost. In this study, we introduce Retentive-HAR, a model designed to enhance feature learning by capturing dependencies both within and across sliding windows. The proposed model intentionally omits the MaxPooling layer, thereby preserving the full temporal resolution throughout the network. The model begins with parallel dilated convolutions, which capture long-range dependencies within each window. Feature outputs from these convolutional layers are then concatenated along the feature dimension and transposed, allowing the Retentive Module to analyze dependencies across both window and feature dimensions. Additional 1D-CNN layers are then applied to the transposed feature maps to capture complex interactions across concatenated window representations before including Bi-LSTM layers. Experiments on PAMAP2, HAPT, and WISDM datasets achieve a performance of 96.40%, 94.70%, and 96.16%, respectively, which outperforms the existing methods with minimal computational cost. Full article
(This article belongs to the Special Issue Advances in Deep Learning for Complex Combinatorial Optimization: Applications in Cybersecurity, Healthcare, and Intelligent Systems)
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24 pages, 1572 KB  
Article
Optimizing DNA Sequence Classification via a Deep Learning Hybrid of LSTM and CNN Architecture
by Elias Tabane, Ernest Mnkandla and Zenghui Wang
Appl. Sci. 2025, 15(15), 8225; https://doi.org/10.3390/app15158225 - 24 Jul 2025
Cited by 1 | Viewed by 2458
Abstract
This study addresses the performance of deep learning models for predicting human DNA sequence classification through an exploration of ideal feature representation, model architecture, and hyperparameter tuning. It contrasts traditional machine learning with advanced deep learning approaches to ascertain performance with respect to [...] Read more.
This study addresses the performance of deep learning models for predicting human DNA sequence classification through an exploration of ideal feature representation, model architecture, and hyperparameter tuning. It contrasts traditional machine learning with advanced deep learning approaches to ascertain performance with respect to genomic data complexity. A hybrid network combining long short-term memory (LSTM) and convolutional neural networks (CNN) was developed to extract long-distance dependencies as well as local patterns from DNA sequences. The hybrid LSTM + CNN model achieved a classification accuracy of 100%, which is significantly higher than traditional approaches such as logistic regression (45.31%), naïve Bayes (17.80%), and random forest (69.89%), as well as other machine learning models such as XGBoost (81.50%) and k-nearest neighbor (70.77%). Among deep learning techniques, the DeepSea model also accounted for good performance (76.59%), while others like DeepVariant (67.00%) and graph neural networks (30.71%) were relatively lower. Preprocessing techniques, one-hot encoding, and DNA embeddings were mainly at the forefront of transforming sequence data to a compatible form for deep learning. The findings underscore the robustness of hybrid structures in genomic classification tasks and warrant future research on encoding strategy, model and parameter tuning, and hyperparameter tuning to further improve accuracy and generalization in DNA sequence analysis. Full article
(This article belongs to the Special Issue Advances in Deep Learning for Complex Combinatorial Optimization: Applications in Cybersecurity, Healthcare, and Intelligent Systems)
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