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A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: 30 November 2026 | Viewed by 4859

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Special Issue Editors

Dr. Lingyu Yan

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Guest Editor

School of Computer Science, Hubei University of Technology, Wuhan 430068, China
Interests: deep learning; data mining; computer vision; image processing

Dr. Xing Tang

E-Mail Website
Guest Editor

School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan 430070, China
Interests: artificial intelligence; digital communications; mobile communication system; wireless communications
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Special Issue Information

Dear Colleagues,

With the continuous evolution of deep neural network architectures, convolutional neural networks (CNN) have achieved sub-pixel accuracy in medical image segmentation and remote sensing image interpretation. The Transformer model has reshaped temporal data analysis paradigms through self-attention mechanism, demonstrating strong generalization capabilities in areas such as financial forecasting and energy management. Meanwhile, the fusion of generative adversarial networks (GANs) and variational autoencoders (VAEs) has driven significant advances in tasks such as synthetic data generation and anomaly detection.

This Special Issue highlights the latest progress in applying learning to data analysis. It aims to bring together cutting-edge research results from leading scholars and practitioners worldwide, exploring innovative applications, theoretical breakthroughs, challenges, and solutions related to deep learning technologies across diverse data types, such as structured, unstructured, and temporal data. Topics of interest include, but are not limited to, the following:

Design of new neural network architecture;
Multimodal data fusion;
Self supervised and unsupervised learning algorithms;
Edge computing and lightweight;
Distributed and parallel computing framework;
The application of deep learning in data analysis in different fields.

Dr. Lingyu Yan
Dr. Xing Tang
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. Algorithms is an international peer-reviewed open access monthly 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 1800 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

computer vision
data generation
anomaly detection
temporal data analysis

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Published Papers (8 papers)

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Research

27 pages, 18591 KB

Open AccessArticle

Managing Cost–Stability Trade-Offs in Industrial Object Detection: A Unified Decision Support Framework

by Kuhyun Lee, Jihoon Hong, Beom-Seok Kim, Yuna Song and Dong-Hee Lee

Algorithms 2026, 19(5), 409; https://doi.org/10.3390/a19050409 - 19 May 2026

Viewed by 179

Abstract

Object detection is a core component of industrial vision systems in manufacturing, infrastructure monitoring, and safety-critical sensing. While the mean average precision (mAP) averages the performance over all confidence thresholds, real-world deployment demands committing to a single operating threshold under score imprecision, distribution shifts, and asymmetric—often only approximately known—error costs. From a soft-computing perspective, deployment should explicitly manage this uncertainty rather than rely on a static validation optimum. We propose domain-specific and robust localization recall precision (DSR-LRP), a three-phase decision-support framework. The framework elicits soft domain preferences—such as asymmetric error costs, tolerable localization imprecision, and expected perturbations—from practitioner knowledge and encodes them as three quantitative parameters (k,

α_{IoU}

β

). A cost-sensitive, threshold-local objective aggregates the performance within a robustness band around each candidate threshold, jointly capturing the accuracy and local stability. Finally, it yields an interpretable recommendation package comprising the operating threshold, its DSR-LRP score, and visual evidence. Experiments on four practical datasets (blood cell screening, wildfire smoke monitoring, pothole detection, and semiconductor sensor inspection) showed that DSR-LRP consistently selected operating thresholds that were robust and cost-aligned. For example, in pothole detection, an LRP-optimal threshold degraded by 15.6% under simulated shifts, while the DSR-LRP recommendation changed by only 1.8%. DSR-LRP complements global metrics such as the mAP and provides a soft-computing-oriented tool for reliable, evidence-driven deployment of industrial object detectors. Full article

(This article belongs to the Special Issue Advances in Deep Learning-Based Data Analysis)

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17 pages, 11141 KB

Open AccessArticle

Dynamic Fine-Tuning Rotation Network for Semantic Segmentation of Rock Paintings

by Chuanping Bai, Donglin Jing, Zhixue Wang and Fangqin Zhang

Algorithms 2026, 19(5), 349; https://doi.org/10.3390/a19050349 - 1 May 2026

Viewed by 210

Abstract

The scale features of rock art exhibit significant diversity and graduality. Among the existing semantic segmentation methods for rock art, although some models have taken note of the scale differences in rock art patterns and the complexity of directional features, and proposed targeted improvement strategies, most of these methods view scale adaptation and directional representation as unconnected problems. They fail to model the intrinsic correlation between the scale adaptation and directional representation, and particularly overlook the restrictive effect of scale accuracy on the extraction of directional features. This ultimately leads to the problem of “spatial representation misalignment” in the semantic segmentation of rock art. To address the above problems, this paper proposes a Dynamic Fine-tuning Rotation Network (DFTR-Net), which aims to solve the problems of imprecise scale feature extraction and directional misalignment for rock art patterns with arbitrary orientations. The network consists of a dynamic selective convolution structure and a shapeaware spatial feature extraction module. Specifically, the dynamic selective convolution dynamically adjusts the coverage range of the receptive field through inter-layer feature aggregation. It uses stacked small dilated convolution kernels to replace large convolution kernels with the same receptive field for extracting the neighborhood details of patterns. Then, by combining with feature aggregation, it constructs spatial feature differences and realizes intra-layer dynamic weighted fusion, thereby achieving accurate scale feature extraction. After obtaining fine-grained scale features, the shape-aware module first corrects the initial segmentation candidate regions of the patterns to generate directional guide boxes. Subsequently, it drives the rotational sampling of convolution kernels based on the angles of the guide boxes, forming region-constrained deformable convolutions that adapt to the shape of the patterns. These convolution kernels obtain strong supervision based on pixel-level annotations, which enhances the sensitivity to the directional features of the patterns and effectively alleviates the problem of directional misalignment. Extensive experiments show that DFTR-Net can achieve higher performance on the 3D-pitoti and Petroglyph Annotation datasets compared with the existing methods. Full article

(This article belongs to the Special Issue Advances in Deep Learning-Based Data Analysis)

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22 pages, 2255 KB

Open AccessArticle

Distributed Stochastic Multi-GPU Hyperparameter Optimization for Transfer Learning-Based Vehicle Detection Under Degraded Visual Conditions

by Zhi-Ren Tsai and Jeffrey J. P. Tsai

Algorithms 2026, 19(4), 296; https://doi.org/10.3390/a19040296 - 10 Apr 2026

Viewed by 331

Abstract

Robust vehicle detection in real-world traffic surveillance remains challenging due to degraded imagery caused by motion blur, adverse weather, and low illumination, which significantly increases detector sensitivity to hyperparameter configurations. This study proposes a “Frugal AI” distributed multi-GPU framework that optimizes hyperparameters via a stochastic simplex-based search coupled with five-fold cross-validation. Utilizing three low-cost NVIDIA GTX 1050 Ti GPUs, the framework performs parallel candidate exploration with an asynchronous model-level exchange mechanism to escape local optima without the overhead of gradient synchronization. Seven CNN backbones—VGG16, VGG19, GoogLeNet, MobileNetV2, ResNet18, ResNet50, and ResNet101—were evaluated within YOLOv2 and Faster R-CNN detectors. To address memory constraints (4 GB VRAM), YOLOv2 was selected for extensive benchmarking. Performance was measured using a harmonic precision–recall-based cost metric to strictly penalize imbalanced outcomes. Experimental results demonstrate that under identical wall-clock time budgets, the proposed framework achieves an average 1.38% reduction in aggregated cost across all models, with the highly sensitive VGG19 backbone showing a 4.00% improvement. Benchmarking against Bayesian optimization, genetic algorithms, and random search confirms that our method achieves superior optimization quality with statistical significance (

p < 0.05

). Under a rigorous IoU = 0.75 threshold, the optimized models consistently yielded F1-scores 0.8444

\pm

0.0346. Ablation studies further validate that the collaborative model exchange is essential for accelerating convergence in rugged loss landscapes. This research offers a practical, scalable, and cost-efficient solution for deploying robust AI surveillance in resource-constrained smart city infrastructure. Full article

(This article belongs to the Special Issue Advances in Deep Learning-Based Data Analysis)

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14 pages, 621 KB

Open AccessArticle

Accelerating Realization of Effective Capacity in Lightweight Vision Models via Self-Competitive Distillation

by Weidong Zhang, Baoxin Li, Huan Liu, Pak Lun Kevin Ding and Ahmet Arda Dalyanci

Algorithms 2026, 19(4), 262; https://doi.org/10.3390/a19040262 - 1 Apr 2026

Viewed by 894

Abstract

We introduce Self-Competitive Distillation (SCD), a parameter-neutral training strategy aimed at influencing optimization dynamics without increasing model size or relying on external teachers. Two identical instances of the same architecture, initialized with different random seeds, are trained jointly and dynamically exchange asymmetric teacher–student roles based on instantaneous performance, enabling knowledge transfer between diverging optimization trajectories. Under fixed parameter and training budgets, SCD is observed to improve the realized effective capacity of lightweight architectures, yielding a higher test accuracy at matched epochs. Across multiple lightweight vision models and datasets, SCD demonstrates gains in both in-domain performance and cross-domain generalization, as measured by xScore. These results suggest that, within the evaluated experimental conditions, SCD can help mobile models make more effective use of training dynamics, while the underlying architecture remains the primary determinant of effective capacity in resource-constrained settings. Full article

(This article belongs to the Special Issue Advances in Deep Learning-Based Data Analysis)

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22 pages, 1876 KB

Open AccessArticle

Extended LSTM to Enhance Learner Performance Prediction

by Adel Ihichr, Soukaina Hakkal, Omar Oustous, Younès El Bouzekri El Idrissi and Ayoub Ait Lahcen

Algorithms 2026, 19(4), 251; https://doi.org/10.3390/a19040251 - 25 Mar 2026

Viewed by 602

Abstract

Knowledge Tracing (KT) is a fundamental task in intelligent education systems, designed to track students’ evolving knowledge states and predict their future performance. While Deep Learning-based Knowledge Tracing (DLKT) models have advanced the field, they often face significant limitations in jointly capturing short-term performance fluctuations and long-term knowledge retention, which restricts their predictive precision in complex learning trajectories. This paper proposes the Extended Deep Knowledge Tracing (xDKT) model, which integrates the Extended Long Short-Term Memory (xLSTM) architecture to enhance multi-scale temporal learning representations. Specifically, through rigorous ablation studies over extended learning sequences (up to 1000 steps), our analysis indicates that the exponential gating and advanced scalar memory of sLSTM units are the primary drivers of performance. This architecture effectively captures both short-term performance shifts and long-term knowledge retention without the vanishing gradient degradation inherent to standard LSTMs. We evaluate xDKT across six diverse benchmark datasets, including Synthetic, Algebra2005–2006, Statics2011, and the ASSISTments series, covering over 22,000 learners. Experimental results show that xDKT yields improved Area Under the ROC Curve (AUC) scores on Statics2011 (0.8562) and ASSISTments2009 (0.8318) compared to baseline models such as DKT, DKVMN, and AKT. Finally, through extensive validation, these findings suggest that xDKT architecture provides a robust and promising framework for accurate and adaptive learning environments. Full article

(This article belongs to the Special Issue Advances in Deep Learning-Based Data Analysis)

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22 pages, 2762 KB

Open AccessArticle

Automated Classification of Medical Image Modality and Anatomy

by Jean de Smidt, Kian Anderson and Andries Engelbrecht

Algorithms 2026, 19(3), 222; https://doi.org/10.3390/a19030222 - 16 Mar 2026

Viewed by 555

Abstract

Radiological departments face challenges in efficiency and diagnostic consistency. The interpretation of radiographs remains highly variable between practitioners, which creates potential disparities in patient care. This study explores how artificial intelligence (AI), specifically transfer learning techniques, can automate parts of the radiological workflow to improve service quality and efficiency. Transfer learning methods were applied to various convolutional neural network (CNN) architectures and compared to classify medical images across different modalities, i.e., X-rays, ultrasound, magnetic resonance imaging (MRI), and angiography, through a two-component model: medical image modality prediction and anatomical region prediction. Several publicly available datasets were combined to create a representative dataset to evaluate residual networks (ResNet), dense networks (DenseNet), efficient networks (EfficientNet), and the Swin Transformer (Swin-T). The models were evaluated through accuracy, precision, recall, and F1-score metrics with macro-averaging to account for class imbalance. The results demonstrate that lightweight transfer learning methods effectively classify medical imagery, with an accuracy of 97.21% on test data for the combined transfer learning pipeline. EfficientNet-B4 demonstrated the best performance on both components of the proposed pipeline and achieved a 99.6% accuracy for modality prediction and 99.21% accuracy for anatomical region prediction on unseen test data. This approach offers the potential for streamlined radiological workflows while maintaining diagnostic quality. The strong model performance across diverse modalities and anatomical regions indicates robust generalisability for practical implementation in clinical settings. Full article

(This article belongs to the Special Issue Advances in Deep Learning-Based Data Analysis)

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20 pages, 21647 KB

Open AccessArticle

Spatial Orthogonal and Boundary-Aware Network for Rotated and Elongated-Target Detection

by Yong Liu, Zhengbiao Jing, Yinghong Chang and Donglin Jing

Algorithms 2026, 19(3), 206; https://doi.org/10.3390/a19030206 - 9 Mar 2026

Viewed by 321

Abstract

In recent years, the refinement of bounding box representations has emerged as a major research focus in remote sensing. Nevertheless, mainstream detection algorithms typically ignore the disruptive impacts induced by the diverse morphologies and arbitrary orientations of high-aspect-ratio aerial objects throughout model training, thereby giving rise to several critical technical challenges: (1) Anisotropic information distribution: Target features are highly concentrated in one spatial dimension but sparse in the other, with significant feature differences across bounding box parameters, breaking the symmetry of feature distribution. (2) Missing high-quality positive samples: IoU-based assignment strategies fail to adequately capture the symmetric structural characteristics of elongated targets, resulting in incomplete coverage of critical features. (3) Loss function gradient instability: Small deviations in large-aspect-ratio bounding boxes cause drastic loss value fluctuations, as the asymmetric gradient changes hinder stable optimization directions during training. To address the challenges, we propose a Spatial Orthogonal and Boundary-Aware Network (SOBA-Net) for rotated and elongated target detection, leveraging symmetry-aware designs to enhance feature representation. Specifically, spatial staggered convolutions are constructed to fuse local and directional contextual features, effectively modeling long-range symmetric information across multiple spatial scales and reducing background noise interference. Secondly, the designed Symmetric-Constrained Label Assignment (SC-LA) introduces an IoU-weighted function, ensuring high-quality samples with symmetric structural features are classified as positive samples. Ultimately, the designed Gradient Dynamic Equilibrium Loss Function mitigates the problem of unstable gradients associated with high-aspect-ratio objects by enforcing symmetrical gradient regulation across samples with negligible localization deviations. Comprehensive evaluations across three representative remote sensing benchmarks—DOTA, UCAS-AOD, and HRSC2016—sufficiently corroborate the superiority of symmetry-aware enhancement schemes, which boast straightforward implementation and efficient inference deployment. Full article

(This article belongs to the Special Issue Advances in Deep Learning-Based Data Analysis)

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16 pages, 341 KB

Open AccessArticle

xScore: A Simple Metric for Cross-Domain Robustness in Lightweight Vision Models

by Weidong Zhang, Pak Lun Kevin Ding, Baoxin Li and Huan Liu

Algorithms 2026, 19(1), 14; https://doi.org/10.3390/a19010014 - 23 Dec 2025

Cited by 1 | Viewed by 1047

Abstract

Lightweight vision models are widely deployed in mobile and embedded systems, where strict computational and memory budgets demand compact architectures. However, their evaluation remains dominated by ImageNet—a single, large natural-image dataset that requires substantial training resources. This creates a dilemma: lightweight models trained on ImageNet often reach capacity limits due to their constrained size, while scaling them to billions of parameters with specialized training tricks to achieve top-tier ImageNet accuracy does not guarantee proportional performance once the architectures are scaled back down to meet mobile constraints, particularly when re-evaluated on diverse data domains. These challenges raise two key questions: How should cross-dataset robustness be quantified in a simple and lightweight way, and which architectural elements consistently support generalization under tight resource constraints? To answer them, we introduce the Cross-Dataset Score (xScore), a simple metric that captures both average accuracy across domains and the stability of model rankings. Evaluating 11 representative lightweight models (2.5 M parameters) across seven datasets, we find that (1) ImageNet accuracy is a weak proxy for cross-domain performance, (2) xScore provides a simple and interpretable robustness metric, and (3) high-xScore models reveal architectural patterns linked to stronger generalization. Finally, the architectural insights and evaluation framework presented here provide practical guidance for measuring the xScore of future lightweight models. Full article

(This article belongs to the Special Issue Advances in Deep Learning-Based Data Analysis)

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