Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.4
2.6
Journals
Algorithms
Editor’s Choice
share announcement

Editor’s Choice Articles

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Order results
Result details
Results per page
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
26 pages, 1305 KB  
Article
Continuous-Variable Quantum Fourier Layer: Applications to Filtering and PDE Solving
by Paolo Marcandelli, Stefano Mariani, Martina Siena and Stefano Markidis
Algorithms 2026, 19(5), 370; https://doi.org/10.3390/a19050370 - 8 May 2026
Cited by 1 | Viewed by 900
Abstract
Fourier representations play a central role in operator learning for partial differential equations and are increasingly being explored in quantum machine learning architectures. The classical fast Fourier transform (FFT), particularly in its Cooley–Tukey decomposition, exhibits a structure that naturally matches continuous-variable quantum circuits. [...] Read more.
Fourier representations play a central role in operator learning for partial differential equations and are increasingly being explored in quantum machine learning architectures. The classical fast Fourier transform (FFT), particularly in its Cooley–Tukey decomposition, exhibits a structure that naturally matches continuous-variable quantum circuits. This correspondence establishes a direct structural isomorphism between the Cooley–Tukey butterfly network and Gaussian photonic gates, enabling the FFT to be realized as a native optical computation in continuous-variable quantum computing. Building on this observation, we introduce a continuous-variable Quantum Fourier Layer (CV–QFL) based on a bipartite Gaussian encoding and a Cooley–Tukey quantum Fourier transform, enabling exact two-dimensional spectral processing within a Gaussian photonic circuit. We test the CV–QFL on two representative tasks: spectral low-pass filtering and Fourier-domain integration of the heat equation. In both cases, the results match the classical reference to machine precision. More broadly, this work lays the foundation for continuous-variable approaches to quantum scientific computing and for the development of native spectral architectures in quantum machine learning. Full article
(This article belongs to the Section Analysis of Algorithms and Complexity Theory)
►▼ Show Figures

Figure 1

23 pages, 1815 KB  
Article
Scalable Bayesian–XAI Framework for Multi-Objective Decision-Making in Uncertain Dynamic Systems
by Mostafa Aboulnour Salem and Zeyad Aly Khalil
Algorithms 2026, 19(5), 340; https://doi.org/10.3390/a19050340 - 28 Apr 2026
Cited by 4 | Viewed by 781
Abstract
This study proposes a scalable Explainable Artificial Intelligence (XAI)–driven Bayesian–AI decision–control framework for multi-objective optimisation in uncertain and dynamic systems. The framework integrates Bayesian networks, stochastic control, and expected utility theory within a unified probabilistic architecture. Unlike traditional black-box models, the proposed framework [...] Read more.
This study proposes a scalable Explainable Artificial Intelligence (XAI)–driven Bayesian–AI decision–control framework for multi-objective optimisation in uncertain and dynamic systems. The framework integrates Bayesian networks, stochastic control, and expected utility theory within a unified probabilistic architecture. Unlike traditional black-box models, the proposed framework provides intrinsic interpretability through probabilistic reasoning and dependency-aware modelling. This allows users to understand how decisions are formed and how variables influence outcomes. To further strengthen explainability, the framework incorporates post hoc XAI techniques, including SHAP-based feature attribution and sensitivity-based local explanations. These methods quantify the contribution of each variable and provide clear explanations at both global and local levels. The system is formulated as a stochastic state-space model and implemented as a closed-loop adaptive architecture. It updates decisions continuously as new data becomes available. Scalable inference is achieved using variational inference, Markov Chain Monte Carlo, and Sequential Monte Carlo methods. This ensures efficient performance in complex and high-dimensional environments. A simulation study based on 370 observations shows that the proposed framework improves decision quality, robustness under uncertainty, and transparency compared to conventional methods. Explainability is evaluated using Fidelity, Stability, and Transparency metrics. The results confirm that the model produces consistent and reliable explanations. The framework supports human-centred decision-making by providing visual analytics and clear probabilistic explanations. This makes it suitable for high-stakes applications such as cyber–physical systems, intelligent platforms, and real-time AI systems. The main contribution of this study is the integration of intrinsic probabilistic interpretability with post hoc XAI techniques into a single, scalable framework. This approach bridges a key gap in XAI research and offers a practical and transparent solution for decision-making under uncertainty. Full article
(This article belongs to the Special Issue Explainable AI: Advances in Interpretability Algorithms and Applications)
►▼ Show Figures

Graphical abstract

24 pages, 7178 KB  
Article
Complex-Time Neural Networks: Geometric Temporal Access for Long-Range Reasoning
by Gerardo Iovane, Giovanni Iovane and Antonio De Rosa
Algorithms 2026, 19(5), 334; https://doi.org/10.3390/a19050334 - 25 Apr 2026
Cited by 2 | Viewed by 612
Abstract
Most neural architectures model time as a one-dimensional real-valued variable, constraining temporal reasoning to sequential propagation along a single axis. We introduce Complex-Time Neural Networks (CTNN), a new class of architectures in which temporal coordinates are elements of the complex plane [...] Read more.
Most neural architectures model time as a one-dimensional real-valued variable, constraining temporal reasoning to sequential propagation along a single axis. We introduce Complex-Time Neural Networks (CTNN), a new class of architectures in which temporal coordinates are elements of the complex plane T=t+iτ∈C, where ReT preserves chronological ordering and ImT encodes an orthogonal experiential dimension. Within this geometry, ImT<0 defines a memory domain enabling retrospective retrieval, ImT=0 corresponds to present-moment computation, and ImT>0 defines an imagination domain for prospective projection. We prove the Expressive Separation Theorem (Theorem 1), establishing that, within the temporally coupled function class GTCP and under explicit Assumptions A1–A4 (in particular the bounded projection Assumption A3), CTNN accesses temporally coupled functions at O(1) cost with respect to temporal distance Δ1, Δ2, while real-time architectures incur Ω(Δ1 + Δ2) sequential steps. For layered compositions, this yields an exponential composition gap within GTCP under A1–A4. These advantages hold under the stated assumptions and may not directly generalize to broader function classes or large-scale settings where A3 cannot be maintained. Therefore, Theorem 1 provides a formal separation result for GTCP, while CTNN more broadly defines a geometric framework for temporal computation. As the first concrete instantiation of this framework, we develop Complex-Time Convolutional Neural Networks (CTCNN). CTCNN achieves state-of-the-art performance on Something-Something V2 (70.2±0.4%, +1.1% over VideoMAE v2, p<0.01), strong performance on Kinetics-400 (78.4±0.3%), and substantial gains on Long Range Arena Path-X (87.3% vs. 79.6%, +7.7%), using 3.4× fewer parameters than VideoMAE v2. Learnable angular parameters α and β provide computationally interpretable parameters related to memory-access span and prospection breadth, with values varying systematically across task families. Full article
(This article belongs to the Special Issue Deep Neural Networks and Optimization Algorithms (2nd Edition))
►▼ Show Figures

Figure 1

34 pages, 1552 KB  
Review
On-Orbit Space AI: Federated, Multi-Agent, and Collaborative Algorithms for Satellite Constellations
by Ziyang Wang
Algorithms 2026, 19(4), 318; https://doi.org/10.3390/a19040318 - 17 Apr 2026
Cited by 1 | Viewed by 2814
Abstract
Satellite constellations are transforming space systems from isolated spacecraft into networked, software-defined platforms capable of on-orbit perception, decision making, and adaptation. Yet many of the existing AI studies remain centered on single-satellite inference, while constellation-scale autonomy introduces fundamentally new algorithmic requirements: learning and [...] Read more.
Satellite constellations are transforming space systems from isolated spacecraft into networked, software-defined platforms capable of on-orbit perception, decision making, and adaptation. Yet many of the existing AI studies remain centered on single-satellite inference, while constellation-scale autonomy introduces fundamentally new algorithmic requirements: learning and coordination under dynamic inter-satellite connectivity, strict SWaP-C limits, radiation-induced faults, non-IID data, concept drift, and safety-critical operational constraints. This survey consolidates the emerging field of on-orbit space AI through three complementary paradigms: (i) federated learning for cross-satellite training, personalization, and secure aggregation; (ii) multi-agent algorithms for cooperative planning, resource allocation, scheduling, formation control, and collision avoidance; and (iii) collaborative sensing and distributed inference for multi-satellite fusion, tracking, split/early-exit inference, and cross-layer co-design with constellation networking. We provide a system-level view and a taxonomy that unifies collaboration architectures, temporal mechanisms, and trust models. Full article
(This article belongs to the Collection Feature Papers on Artificial Intelligence Algorithms and Their Applications)
►▼ Show Figures

Figure 1

25 pages, 4742 KB  
Article
An Edge-Enabled Predictive Maintenance Approach Based on Anomaly-Driven Health Indicators for Industrial Production Systems
by Bouzidi Lamdjad and Adem Chaiter
Algorithms 2026, 19(4), 286; https://doi.org/10.3390/a19040286 - 8 Apr 2026
Cited by 2 | Viewed by 1085
Abstract
This study develops a data-driven framework for predictive maintenance and prognostic health management in industrial systems using edge-enabled predictive algorithms. The objective is to support early identification of abnormal operating conditions and improve maintenance decision making under real production environments. The proposed approach [...] Read more.
This study develops a data-driven framework for predictive maintenance and prognostic health management in industrial systems using edge-enabled predictive algorithms. The objective is to support early identification of abnormal operating conditions and improve maintenance decision making under real production environments. The proposed approach combines edge-level monitoring, anomaly detection, and predictive modeling to analyze operational signals and estimate system health conditions from high-frequency industrial data. Empirical validation was conducted using operational datasets collected from two industrial production facilities between 2024 and 2025. The model evaluates patterns associated with operational instability and degradation-related anomalies and translates them into interpretable health indicators that can support proactive intervention. The empirical results show strong predictive performance, with R2 reaching 0.989, a mean absolute percentage error of 3.67%, and a root mean square error of 0.79. In addition, the mitigation of early anomaly signals was associated with an observed improvement of approximately 3.99% in system stability. Unlike many existing studies that treat anomaly detection, predictive modeling, and prognostic analysis as separate tasks, the proposed framework connects these stages within a unified analytical structure designed for deployment in industrial environments. The findings indicate that edge-generated anomaly signals can provide meaningful early information about potential system deterioration and can assist in planning timely maintenance actions even when explicit failure labels are limited. The study contributes to the development of scalable predictive maintenance solutions that integrate artificial intelligence with edge-based industrial monitoring systems. Full article
(This article belongs to the Special Issue AI-Powered Predictive Maintenance: Transforming Industrial Operations Through Intelligent Fault Diagnosis (2nd Edition))
►▼ Show Figures

Figure 1

30 pages, 2535 KB  
Article
Optimizing the Permutation Flowshop Scheduling Problem with an Improved Sparrow Search Algorithm
by Maria Tsiftsoglou, Yannis Marinakis and Magdalene Marinaki
Algorithms 2026, 19(4), 283; https://doi.org/10.3390/a19040283 - 6 Apr 2026
Cited by 1 | Viewed by 1008
Abstract
The Sparrow Search Algorithm (SSA) is a novel optimization method inspired by sparrows’ foraging and anti-predator behavior. It mimics their exploration and exploitation strategies to find near-optimal solutions for various optimization problems. This paper presents the first application of SSA to the widely [...] Read more.
The Sparrow Search Algorithm (SSA) is a novel optimization method inspired by sparrows’ foraging and anti-predator behavior. It mimics their exploration and exploitation strategies to find near-optimal solutions for various optimization problems. This paper presents the first application of SSA to the widely recognized Permutation Flowshop Scheduling Problem (PFSP) with the makespan criterion as the optimization target. Our study aims to assess the effectiveness and robustness of this cutting-edge metaheuristic through computational experiments and statistical analysis. The proposed SSA is a hybrid variant that incorporates the Variable Neighborhood Search (VNS) algorithm along with a Path Relinking Strategy. The effectiveness of the proposed method is evaluated through computational experiments on PFSP benchmark instances. The performance of the hybrid SSA is compared against several well-established swarm-intelligence metaheuristics, namely Grey Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Tuna Swarm Optimization Algorithm (TSO), Particle Swarm Optimization Algorithm (PSO), Firefly Algorithm (FA), Bat Algorithm (BA), and the Artificial Bee Colony (ABC). To ensure fair comparison, all methods are implemented within the same computational framework as the hybrid SSA. The experimental results show that the proposed hybrid SSA achieves the lowest average mean error compared with the competing methods in solving the PFSP. The results were further validated through a comprehensive non-parametric statistical analysis using Friedman, Aligned Friedman, and Quade tests, followed by post-hoc analysis with p-adjusted values, as well as Kruskal–Wallis and Wilcoxon post-hoc tests. Full article
(This article belongs to the Special Issue Swarm Intelligence and Evolutionary Algorithms for Real World Applications (3rd Edition))
►▼ Show Figures

Figure 1

18 pages, 10428 KB  
Article
T2C-DETR: A Transformer + Convolution Dual-Channel Backbone Network for Underwater Sonar Image Object Detection
by Xiaobing Wu, Panlong Tan, Xiaoyu Zhang and Hao Sun
Algorithms 2026, 19(4), 281; https://doi.org/10.3390/a19040281 - 3 Apr 2026
Cited by 1 | Viewed by 968
Abstract
Underwater sonar object detection is challenging because targets are often small, boundaries are blurred, background clutter is strong, and labeled sonar data are limited. To address these issues, we propose T2C-DETR, a detector built on RT-DETR with three task-oriented improvements: (i) a Transformer–Convolution [...] Read more.
Underwater sonar object detection is challenging because targets are often small, boundaries are blurred, background clutter is strong, and labeled sonar data are limited. To address these issues, we propose T2C-DETR, a detector built on RT-DETR with three task-oriented improvements: (i) a Transformer–Convolution dual-channel backbone (TCDCNet) for complementary global-context and local-detail modeling, (ii) a Noise Filtering Module (NFM) inserted before neck fusion to suppress noise-dominated activations, and (iii) a stage-wise transfer-learning strategy tailored to small sonar datasets. We evaluate the method under three pre-training sources (COCO 2017, DOTA, and an infrared dataset) and then fine-tune on a self-built sonar dataset. Experimental results show that T2C-DETR achieves AP50 of 97.8%, 98.2%, and 98.5% at 72–73 FPS, consistently outperforming the RT-DETR baseline, YOLOv5-Imp, and MLFFNet in the accuracy–speed trade-off. These results indicate that combining global–local representation learning with targeted noise suppression is effective for practical real-time sonar detection. Full article
►▼ Show Figures

Figure 1

19 pages, 3480 KB  
Article
Adapting Vision–Language Models for Few-Shot Industrial Defect Detection
by Chayanon Sub-r-pa and Rung-Ching Chen
Algorithms 2026, 19(4), 259; https://doi.org/10.3390/a19040259 - 27 Mar 2026
Cited by 5 | Viewed by 2755
Abstract
Automated surface defect detection often faces a “cold-start” problem due to limited annotated data for new anomalies. Traditional object detectors struggle to converge in such few-shot settings. To address this, we adapt Vision–Language Models (VLMs), specifically YOLO-World. We use semantic pre-training to mitigate [...] Read more.
Automated surface defect detection often faces a “cold-start” problem due to limited annotated data for new anomalies. Traditional object detectors struggle to converge in such few-shot settings. To address this, we adapt Vision–Language Models (VLMs), specifically YOLO-World. We use semantic pre-training to mitigate data scarcity. We evaluate this approach on the MVTec AD dataset in bounding-box format. We use a strict 1:9 train-validation split, resulting in an average of 11.8 defect instances per category. YOLO-World surpasses traditional baselines, like YOLOv11s and YOLOv26s, in 12 of 15 categories. The optimized VLM pipeline achieves up to 64.9% mAP@50 on texture-heavy categories, such as Tile, with only nine training instances. Ablation studies show standard optimization techniques are limited under 10-shot constraints. We find a critical augmentation divide. Disabling spatial distortions (Mosaic) is vital to preserving rigid-object geometry. The Normalized Wasserstein Distance (NWD) improves the localization of microscopic anomalies. Varifocal Loss (VFL) often causes model collapse. Ultimately, VLMs offer a superior foundation for cold-start inspection but require carefully tailored pipelines for robustness. Full article
(This article belongs to the Special Issue Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes: 3rd Edition)
►▼ Show Figures

Figure 1

31 pages, 13534 KB  
Article
CSFADet: Dual-Modal Anti-UAV Detection via Cross-Spectral Feature Alignment and Adaptive Multi-Scale Refinement
by Heqin Yuan and Yuheng Li
Algorithms 2026, 19(4), 254; https://doi.org/10.3390/a19040254 - 26 Mar 2026
Cited by 3 | Viewed by 1217
Abstract
Anti-unmanned aerial vehicle (Anti-UAV) detection is critical for airspace security, yet existing single-modality approaches suffer from severe performance degradation under adverse illumination, thermal crossover, and extreme scale variation. In this paper, we propose CSFADet, a dual-modal detection framework that jointly exploits visible and [...] Read more.
Anti-unmanned aerial vehicle (Anti-UAV) detection is critical for airspace security, yet existing single-modality approaches suffer from severe performance degradation under adverse illumination, thermal crossover, and extreme scale variation. In this paper, we propose CSFADet, a dual-modal detection framework that jointly exploits visible and infrared imagery through four tightly integrated modules. First, a Cross-Spectral Feature Alignment (CSFA) module performs early-stage spectral calibration by computing cross-modal query–value attention maps, generating modality-aware channel descriptors that re-weight and concatenate the two spectral streams. Second, a Dual-path Texture Enhancement Module (DTEM) enriches fine-grained spatial details via cascaded convolutions with residual connections. Third, a Dual-path Cross-Attention Module (DCAM) introduces a feature-shrinking token generation strategy followed by symmetric cross-attention branches with learnable scaling factors, Squeeze-and-Excitation recalibration, and a 1×1 convolution fusion head, enabling deep bidirectional interaction between modalities. Fourth, a Dual-path Information Refinement Module (DIRM) embeds Adaptive Residual Groups (ARGs) that cascade Multi-modal Spatial Attention Blocks (MSABs) with channel and dynamic spatial attention, culminating in a Multi-scale Scale-aware Fusion Refinement (MSFR) unit that employs three parallel multi-head attention branches with a Scale Reasoning Gate and Channel Fusion Layer to produce scale-discriminative enhanced features. Experiments on the public Anti-UAV300 benchmark show that CSFADet achieves 91.4% mAP@0.5 and 58.7% mAP@0.5:0.95, surpassing fifteen representative detectors spanning single-stage, two-stage, YOLO-family, and Transformer-based categories. Ablation studies confirm the complementary contributions of each module, and heatmap visualizations verify the model’s capacity to focus on small, distant UAV targets under challenging conditions. Full article
(This article belongs to the Topic Theoretical Foundations and Applications of Deep Learning Techniques)
►▼ Show Figures

Figure 1

21 pages, 7573 KB  
Article
A Real-Time Detection Approach for Bridge Crack
by Tingjuan Wang, Jiuyuan Huo and Xinping Wu
Algorithms 2026, 19(4), 247; https://doi.org/10.3390/a19040247 - 25 Mar 2026
Cited by 1 | Viewed by 848
Abstract
To meet the requirement of real-time bridge crack detection, this paper proposes a lightweight detection model based on YOLOv7-tiny. First, an edge-preserved image enhancement method is proposed. It effectively enhances the image contrast and preserves the structural features of crack edges. This provides [...] Read more.
To meet the requirement of real-time bridge crack detection, this paper proposes a lightweight detection model based on YOLOv7-tiny. First, an edge-preserved image enhancement method is proposed. It effectively enhances the image contrast and preserves the structural features of crack edges. This provides a high-quality data foundation for the detection network. Second, a LWCSP module is introduced. This module integrates hybrid convolution and shuffle operations. It reduces the model’s parameter count and computation. Simultaneously, it maintains strong feature representation capability. A good balance between detection performance and efficiency is achieved. Finally, an improved SWise-IoU is proposed to optimize the bounding box regression in YOLOv7-tiny. This method dynamically evaluates sample quality. It enables differentiated gradient adjustment for samples of different qualities. This promotes sufficient learning of sample features by the model, thereby improving detection accuracy. Experimental results show that the proposed model delivers strong performance on a public bridge crack dataset. Compared to the baseline, the mAP@0.5 is 12.1 higher, and model size, parameter count, and FLOPs are reduced by 7.3%, 8.03%, and 10%, respectively. The final model size is only 11.4 MB, and mAP@0.5 is 86.1%, suitable for a real-time crack detection task. Full article
►▼ Show Figures

Figure 1

37 pages, 5953 KB  
Article
Fire Detection Using Sound Analysis Based on a Hybrid Artificial Intelligence Algorithm
by Robert-Nicolae Boştinaru, Sebastian-Alexandru Drǎguşin, Nicu Bizon, Dumitru Cazacu and Gabriel-Vasile Iana
Algorithms 2026, 19(3), 240; https://doi.org/10.3390/a19030240 - 23 Mar 2026
Cited by 4 | Viewed by 1113
Abstract
Fire detection is a critical task for early warning systems, particularly in environments where visual sensing is unreliable. While most existing approaches rely on image-based or smoke-based detection, acoustic signals provide complementary information capable of capturing early combustion-related events. This study investigates deep [...] Read more.
Fire detection is a critical task for early warning systems, particularly in environments where visual sensing is unreliable. While most existing approaches rely on image-based or smoke-based detection, acoustic signals provide complementary information capable of capturing early combustion-related events. This study investigates deep learning models for sound-based fire detection, focusing on convolutional and Transformer-based architectures. VGG16 and VGG19 convolutional neural networks are adapted to process time-frequency audio representations for binary classification into Fire and No-Fire classes. An Audio Spectrogram Transformer (AST) is further employed to model long-range temporal dependencies in acoustic data. Finally, a hybrid VGG19-AST architecture is proposed, in which convolutional layers extract local spectral–temporal features, and Transformer-based self-attention performs global sequence modeling. The models are evaluated on a curated dataset containing fire sounds and diverse environmental background noises under multiple noise conditions. Experimental results demonstrate competitive performance across convolutional and Transformer-based models, while the proposed hybrid VGG19-AST architecture achieves the most consistent overall results. The findings suggest that integrating convolutional feature extraction with self-attention-based global modeling enhances robustness under complex acoustic variability. The proposed hybrid framework provides a scalable and cost-effective solution for sound-based fire detection, particularly in scenarios where visual monitoring may be obstructed or ineffective. Full article
►▼ Show Figures

Figure 1

21 pages, 6949 KB  
Article
Cross-Domain Bearing Fault Diagnosis Under Class Imbalance: A Dynamic Maximum Triple-View Classifier Discrepancy Network
by Rui Luo, Huiyang Xie, Haitian Wen, Hongying He, Yitong Li and Kai Wang
Algorithms 2026, 19(3), 228; https://doi.org/10.3390/a19030228 - 18 Mar 2026
Cited by 3 | Viewed by 631
Abstract
Traditional domain adaptation methods often assume balanced data distributions. However, this assumption is frequently violated in real-world industrial scenarios, where normal samples predominate while fault samples are inherently scarce. Under severe class imbalance, conventional decision boundaries tend to shift toward minority fault regions. [...] Read more.
Traditional domain adaptation methods often assume balanced data distributions. However, this assumption is frequently violated in real-world industrial scenarios, where normal samples predominate while fault samples are inherently scarce. Under severe class imbalance, conventional decision boundaries tend to shift toward minority fault regions. This shift leads to persistently high misclassification rates for rare fault samples. To overcome this limitation, we propose the Dynamic Maximum Triple-View Classifier Discrepancy (DMTVCD) network, which integrates a Triple-View Classifier (TVC) Architecture and a Primary–Auxiliary Fused Cooperative Loss (PAFL). Specifically, the TVC employs auxiliary binary classifiers to aggregate fine-grained fault sub-classes into a unified “Fault Super-class.” This constructs a robust “normal-fault” binary boundary that effectively counteracts class imbalance. Driven by the PAFL, this boundary acts as a hierarchical geometric constraint to suppress the primary classifier’s tendency to misclassify faults as normal samples, thereby enhancing feature discriminability. Furthermore, a dynamic weighting strategy is introduced to assign large initial weights. This forces the model to bypass simple decision logic dominated by the majority class, ensuring a smooth transition from global exploration to fine-grained alignment. Extensive evaluations on the CWRU and JNU datasets demonstrate that DMTVCD consistently outperforms state-of-the-art approaches under high imbalance ratios (e.g., 20:1). Full article
(This article belongs to the Special Issue Intelligent Algorithms and Signal Processing Techniques for Fault Diagnosis in Mechanical and Electrical Systems)
►▼ Show Figures

Figure 1

25 pages, 2297 KB  
Article
A Multi-Agent Advisory Board Reinforcement Learning Framework for Adaptive Cooperative Control
by Onur Osman, Tolga Kudret Karaca, Bahar Yalcin Kavus, Gokalp Tulum and Sajjad Nematzadeh
Algorithms 2026, 19(3), 230; https://doi.org/10.3390/a19030230 - 18 Mar 2026
Cited by 1 | Viewed by 820
Abstract
This study proposes Advisory Board Reinforcement Learning (AdvB-RL), a cooperative reinforcement-learning framework that integrates multiple advisory neural networks to guide policy optimization. Unlike conventional single-agent architectures, AdvB-RL maintains a set of independently trained advisory networks that contribute to action selection through a dynamic [...] Read more.
This study proposes Advisory Board Reinforcement Learning (AdvB-RL), a cooperative reinforcement-learning framework that integrates multiple advisory neural networks to guide policy optimization. Unlike conventional single-agent architectures, AdvB-RL maintains a set of independently trained advisory networks that contribute to action selection through a dynamic aggregation mechanism. This design preserves diverse experiential knowledge while improving learning stability and the exploration–exploitation balance. The framework is evaluated on three benchmark control tasks, namely LunarLander-v2, CartPole-v1, and MountainCar-v0, using advisory board sizes of 1, 5, and 10 members against a Double Deep Q-Network (DDQN) baseline. The best-performing configuration, 10 AdvB, achieved 270.02 ± 24.74 on LunarLander-v2 versus 227.92 ± 86.02 for DDQN, 497.79 ± 5.18 on CartPole-v1 versus 304.37 ± 144.04, and −103.16 ± 15.46 on MountainCar-v0 versus −130.71 ± 31.64, indicating higher returns together with markedly lower variability. Across the three environments, these results show that increasing the number of advisory members improves both reward consistency and overall robustness, with the 10-member setting providing the strongest performance. Within the tested configurations, the advisory board mechanism remains computationally feasible, while preliminary experiments beyond 10 advisors show diminishing returns relative to added complexity. Overall, AdvB-RL provides a robust and modular alternative to single-policy reinforcement learning for adaptive cooperative control. Full article
►▼ Show Figures

Graphical abstract

21 pages, 526 KB  
Article
Understanding Tradeoffs in Clinical Text Extraction: Prompting, Retrieval-Augmented Generation, and Supervised Learning on Electronic Health Records
by Tanya Yadav, Aditya Tekale, Jeff Chong and Mohammad Masum
Algorithms 2026, 19(3), 215; https://doi.org/10.3390/a19030215 - 13 Mar 2026
Cited by 1 | Viewed by 1380
Abstract
Clinical discharge summaries contain rich patient information but remain difficult to convert into structured representations for downstream analysis. Recent advances in large language models (LLMs) have introduced new approaches for clinical text extraction, yet their relative strengths compared with supervised methods remain unclear. [...] Read more.
Clinical discharge summaries contain rich patient information but remain difficult to convert into structured representations for downstream analysis. Recent advances in large language models (LLMs) have introduced new approaches for clinical text extraction, yet their relative strengths compared with supervised methods remain unclear. This study presents a controlled evaluation of three dominant strategies for structured clinical information extraction from electronic health records: prompting-based extraction using LLMs, retrieval-augmented generation for terminology canonicalization, and supervised fine-tuning of domain-specific transformer models. Using discharge summaries from the MIMIC-IV dataset, we compare zero-shot, few-shot, and verification-based prompting across closed-source and open-source LLMs, evaluate retrieval-augmented canonicalization as a post-processing mechanism, and benchmark these methods against a fine-tuned BioClinicalBERT model. Performance is assessed using a multi-level evaluation framework that combines exact matching, fuzzy lexical matching, and semantic assessment via an LLM-based judge. The results reveal clear tradeoffs across approaches: prompting achieves strong semantic correctness with minimal supervision, retrieval augmentation improves terminology consistency without expanding extraction coverage, and supervised fine-tuning yields the highest overall accuracy when labeled data are available. Across all methods, we observe a consistent 40−50% gap between exact-match and semantic correctness, highlighting the limitations of string-based metrics for clinical Natural Language Processing (NLP). These findings provide practical guidance for selecting extraction strategies under varying resource constraints and emphasize the importance of evaluation methodologies that reflect clinical equivalence rather than surface-form similarity. Full article
(This article belongs to the Special Issue Advanced Algorithms for Biomedical Data Analysis)
►▼ Show Figures

Graphical abstract

14 pages, 392 KB  
Review
Distributed Trust in the Age of Malware Blockchain Applications
by Paul A. Gagniuc, Maria-Iuliana Dascălu and Ionel-Bujorel Păvăloiu
Algorithms 2026, 19(3), 185; https://doi.org/10.3390/a19030185 - 2 Mar 2026
Cited by 1 | Viewed by 984
Abstract
Blockchain technology is redefining the foundations of cybersecurity by introducing decentralized, tamper-resistant mechanisms for data integrity, trust management, and malware intelligence sharing. Traditional detection systems, which are dependent on centralized control and opaque validation, remain vulnerable to data manipulation and systemic compromise. The [...] Read more.
Blockchain technology is redefining the foundations of cybersecurity by introducing decentralized, tamper-resistant mechanisms for data integrity, trust management, and malware intelligence sharing. Traditional detection systems, which are dependent on centralized control and opaque validation, remain vulnerable to data manipulation and systemic compromise. The integration of blockchain transforms these paradigms because it provides verifiable provenance, distributed consensus, and autonomous enforcement through smart contracts. This review synthesizes fifteen years of progress (2010–2025) at the intersection of blockchain and malware detection and discusses core architectures, consensus protocols, and cryptographic properties that underpin decentralized defenses. The review follows a structured literature review methodology, which focuses on blockchain architectures, consensus protocols, and malware-detection pipelines reported in the cybersecurity literature. It also analyzes blockchain detection pipelines, performance tradeoffs, and data protection mechanisms in distributed learning systems and artificial intelligence models. Special attention is given to scalability constraints, regulatory compliance, and interoperability challenges that shape adoption. The review identifies three dominant design patterns: (i) decentralized threat-intelligence sharing with provenance guarantees, (ii) consensus-driven validation of malware artifacts, and (iii) on-chain trust and reputation mechanisms for detector accountability. Through the union of blockchain, artificial intelligence, edge computation, and federated learning, cybersecurity attains an auditable and adaptive architecture resilient to adversarial threats. The study concludes that blockchain provides a verifiable trust infrastructure for malware detection, but its practical deployment requires faster transaction validation and stronger protection of sensitive data; future research should address performance optimization and regulatory compliance. Full article
(This article belongs to the Special Issue Algorithms for Cyber Defense: From Cryptography to Behavioral Analysis)
►▼ Show Figures

Figure 1

25 pages, 920 KB  
Systematic Review
A Systematic Literature Review on the Pedagogical Implications and Impact of GenAI on Students’ Critical Thinking
by Trini Balart, Brayan Díaz and Kristi Shryock
Algorithms 2026, 19(3), 179; https://doi.org/10.3390/a19030179 - 27 Feb 2026
Cited by 1 | Viewed by 6187
Abstract
Critical Thinking (CT) is recognized as a foundational competency for professional readiness, innovation, and ethical reasoning in higher education, enabling students to analyze information, evaluate evidence, and make reasoned decisions in complex environments. The rapid integration of Generative Artificial Intelligence (GenAI) tools, such [...] Read more.
Critical Thinking (CT) is recognized as a foundational competency for professional readiness, innovation, and ethical reasoning in higher education, enabling students to analyze information, evaluate evidence, and make reasoned decisions in complex environments. The rapid integration of Generative Artificial Intelligence (GenAI) tools, such as large language models, presents new opportunities and risks for CT development. This study conducts a systematic literature review to synthesize empirical evidence on the pedagogical implications and cognitive impact of GenAI on students’ CT. Following PRISMA guidelines, and search terms around GenAI Tools, Critical Thinking And Higher Education, on five major education research databases—Web of Science; Scopus; EBSCOhost (Education Source, ERIC, and APA PsycInfo); and Compendex and Inspec (Elsevier)—63 empirical studies published between January 2023 and April 2025 were analyzed across higher education contexts, disciplines, and intervention designs. Results indicate that GenAI offers notable cognitive affordances, including scaffolding reflective reasoning, promoting self-regulation, and facilitating iterative dialogue and argument evaluation. Pedagogical strategies clustered into four primary integration typologies: AI-based feedback prompts, dialogue simulation and reflection, AI-supported peer review, and critical engagement with AI-generated content. Nearly half of the studies reported statistically significant CT improvements, particularly when GenAI use was guided by structured prompts, reflective activities, and performance-based assessment. However, multiple risks persist, including cognitive offloading, uncritical acceptance of AI outputs, and diminished intellectual autonomy, especially in unguided or surface-level usage. This review highlights the need for intentional pedagogical design, validated CT assessment tools, and longitudinal studies to ensure GenAI acts as a catalyst rather than a substitute for human reasoning. By identifying effective integration strategies and outlining potential pitfalls, this study provides evidence-informed guidance for educators and institutions aiming to responsibly leverage GenAI to strengthen students’ CT skills. Full article
(This article belongs to the Special Issue Artificial Intelligence in Education: Innovations and Implications)
►▼ Show Figures

Figure 1

19 pages, 2229 KB  
Article
Optimizing Data Migration and Classification Accuracy in Parallel Hybrid Storage Systems Using Efficient Data Mining Techniques
by Mais Haj Qasem, Ayman Ghaben, Tariq Bishtawi, Dyala Ibrahim, Omar Isam AL Mrayat, Ashraf Alyanbaawi, Fawzy Habeeb and El-Sayed Atlam
Algorithms 2026, 19(3), 174; https://doi.org/10.3390/a19030174 - 26 Feb 2026
Cited by 1 | Viewed by 1036
Abstract
A hybrid parallel storage system is a system which has a hierarchy of storage; each is a primary or secondary storage device, which has significantly different rates of reading the data. The more one goes up the storage hierarchy, greater will be the [...] Read more.
A hybrid parallel storage system is a system which has a hierarchy of storage; each is a primary or secondary storage device, which has significantly different rates of reading the data. The more one goes up the storage hierarchy, greater will be the speed of data access. Moving critical application data to higher levels can greatly lessen application I/O wait time. In general, it assists in cutting down the total time consumption of the app to finish a process. This study presents a prototype of a two-level parallel hybrid storage system with SSD and HDD. The proposed system uses data mining methods to systematically evaluate and classify the application data. While the application runs and requests data, the solution at the same time works to reactively predict what data will be useful next. Data blocks that are classified as high priority are automatically elevated to SSD within the same hybrid cloud storage disk group. With the help of simulation, the data migration strategy backed up by data mining will improve user productivity. The application execution time decreases significantly if any of the application’s data access trace is particular and over 57.21% is used. New hybrid storage architectures paired with predictive data management help applications run faster than ever before. Full article
►▼ Show Figures

Figure 1

33 pages, 3164 KB  
Article
Co-Creation by Human–AI Sophimatics Framework and Applications
by Gerardo Iovane and Giovanni Iovane
Algorithms 2026, 19(3), 175; https://doi.org/10.3390/a19030175 - 26 Feb 2026
Cited by 1 | Viewed by 1303
Abstract
Phase 6 of the Sophimatics framework represents the culmination of a comprehensive research program integrating philosophical wisdom with computational sophistication to address fundamental challenges in artificial intelligence systems. Building upon the Complex-Time Recursive Model established in Phase 5, this phase introduces a human-in-the-loop [...] Read more.
Phase 6 of the Sophimatics framework represents the culmination of a comprehensive research program integrating philosophical wisdom with computational sophistication to address fundamental challenges in artificial intelligence systems. Building upon the Complex-Time Recursive Model established in Phase 5, this phase introduces a human-in-the-loop iterative refinement methodology specifically designed for security-critical applications. Through systematic validation across real-world cybersecurity datasets, including NSL-KDD and CICIDS2017, alongside healthcare privacy scenarios using MIMIC-III derived data, we demonstrate that collaborative human–AI co-creation significantly enhances system performance across multiple dimensions, including interpretive accuracy, contextual fidelity, and ethical consistency. The proposed architecture implements three complementary feedback mechanisms: symbolic knowledge base refinement through expert-provided ontological corrections, neural parameter optimization guided by human evaluation of ethical alignment, and dynamic weight adjustment for value-system integration. Experimental results show substantial improvements over baseline approaches, with intrusion detection accuracy reaching 98.7% on NSL-KDD while maintaining 94.3% privacy preservation scores as measured by differential privacy guarantees. The healthcare privacy experiments demonstrate 97.2% sensitive attribute protection with only 2.1% utility loss compared to non-private baselines. Critical analysis reveals that human oversight mechanisms reduce false positive rates in ethical constraint violations by 67% compared to purely automated systems, while convergence analysis indicates stable performance after approximately 12–15 iterations across diverse application domains. These findings establish Phase 6 as an essential bridge between theoretical Sophimatics foundations and practical deployment in privacy-sensitive contexts, demonstrating that philosophically grounded AI architectures can achieve superior performance when augmented with structured human feedback loops. The work contributes both methodological innovations in human–AI collaboration and empirical validation, demonstrating the viability of Sophimatics principles for addressing contemporary challenges in data protection and cybersecurity. Full article
(This article belongs to the Special Issue Crowdsourcing, Human-AI Interaction, and the Future of Digital Platforms)
►▼ Show Figures

Figure 1

25 pages, 639 KB  
Article
A Sparse L∞-Norm Regularized Least Squares Support Vector Regression
by Xiaoyong Liu, Dong Li and Chengbin Zeng
Algorithms 2026, 19(2), 160; https://doi.org/10.3390/a19020160 - 18 Feb 2026
Cited by 1 | Viewed by 765
Abstract
Although Least Squares Support Vector Regression (LSSVR) reduces the hyperparameter space to two, it sacrifices sparsity, causing all training samples to become support vectors and increasing storage costs. In contrast, standard Support Vector Regression (SVR) preserves sparsity but requires tuning three highly coupled [...] Read more.
Although Least Squares Support Vector Regression (LSSVR) reduces the hyperparameter space to two, it sacrifices sparsity, causing all training samples to become support vectors and increasing storage costs. In contrast, standard Support Vector Regression (SVR) preserves sparsity but requires tuning three highly coupled hyperparameters, leading to higher computational burden. To address these limitations, this paper proposes a sparse L∞-norm regularized least squares SVR framework that incorporates the infinity norm of approximation errors into both the objective function and inequality constraints. The resulting optimization problem minimizes model complexity while controlling the maximum prediction deviation through a single slack variable, thereby transforming the conventional three-hyperparameter SVR tuning task into a two-parameter problem involving only the regularization coefficient and kernel width. This formulation restores sparsity by enabling a compact support vector set, while preserving the stability and convexity advantages of LSSVR. Experiments on both static and dynamic datasets demonstrate that the proposed method consistently achieves higher predictive accuracy and improved robustness compared with standard SVR and LSSVR. These results indicate that the proposed L∞-norm regularized framework offers a mathematically principled and computationally efficient alternative for sparse, robust, and scalable regression modeling. Full article
(This article belongs to the Topic Machine Learning and Data Mining: Theory and Applications)
►▼ Show Figures

Figure 1

29 pages, 2553 KB  
Article
Adaptive Path Planning for Autonomous Underwater Vehicle (AUV) Based on Spatio-Temporal Graph Neural Networks and Conditional Normalizing Flow Probabilistic Reconstruction
by Guoshuai Li, Jinghua Wang, Jichuan Dai, Tian Zhao, Danqiang Chen and Cui Chen
Algorithms 2026, 19(2), 147; https://doi.org/10.3390/a19020147 - 11 Feb 2026
Cited by 1 | Viewed by 1404
Abstract
In underwater reconnaissance and patrol, AUV has to sense and judge traversability in cluttered areas that include reefs, cliffs, and seabed infrastructure. A narrow sonar field of view, occlusion, and current-driven disturbances leave the vehicle with local, time-varying information, so decisions are made [...] Read more.
In underwater reconnaissance and patrol, AUV has to sense and judge traversability in cluttered areas that include reefs, cliffs, and seabed infrastructure. A narrow sonar field of view, occlusion, and current-driven disturbances leave the vehicle with local, time-varying information, so decisions are made with incomplete and uncertain observations. A path-planning framework is built around two coupled components: spatiotemporal graph neural network prediction and conditional normalizing flow (CNF)-based probabilistic environment reconstruction. Forward-looking sonar and inertial navigation system (INS) measurements are fused online to form a local environment graph with temporal encoding. Cross-temporal message passing captures how occupancy and maneuver patterns evolve, which supports path prediction under dynamic reachability and collision-avoidance constraints. For regions that remain unobserved, CNF performs conditional generation from the available local observations, producing probabilistic completion and an explicit uncertainty output. Conformal calibration then maps model confidence to credible intervals with controlled miscoverage, giving a consistent probabilistic interface for risk budgeting. To keep pace with ocean currents and moving targets, edge weights and graph connectivity are updated online as new observations arrive. Compared with Informed Random Tree star (RRT*), D* Lite, Soft Actor-Critic (SAC), and Graph Neural Network-Probabilistic Roadmap (GNN-PRM), the proposed method achieves a near 100% success rate at 20% occlusion and maintains about an 80% success rate even under 70% occlusion. In dynamic obstacle scenarios, it yields about a 4% collision rate at low speeds and keeps the collision rate below 20% when obstacle speed increases to 3 m/s. Ablation studies further demonstrate that temporal modeling improves success rate by about 7.1%, CNF-based probabilistic completion boosts success rate by about 13.2% and reduces collisions by about 17%, while conformal calibration reduces coverage error by about 6.6%, confirming robust planning under heavy occlusion and time-varying uncertainty. Full article
►▼ Show Figures

Figure 1

26 pages, 15341 KB  
Article
A Multimodal Three-Channel Bearing Fault Diagnosis Method Based on CNN Fusion Attention Mechanism Under Strong Noise Conditions
by Yingyong Zou, Chunfang Li, Yu Zhang, Zhiqiang Si and Long Li
Algorithms 2026, 19(2), 144; https://doi.org/10.3390/a19020144 - 10 Feb 2026
Cited by 3 | Viewed by 826
Abstract
Bearings, as core components of mechanical equipment, play a critical role in ensuring equipment safety and reliability. Early fault detection holds significant importance. Addressing the challenges of insufficient robustness in bearing fault diagnosis under industrial high-noise conditions and the difficulty of extracting fault [...] Read more.
Bearings, as core components of mechanical equipment, play a critical role in ensuring equipment safety and reliability. Early fault detection holds significant importance. Addressing the challenges of insufficient robustness in bearing fault diagnosis under industrial high-noise conditions and the difficulty of extracting fault features from a single modality, this study proposes a three-channel multimodal fault diagnosis method that integrates a Convolutional Auto-Encoder (CAE) with a dual attention mechanism (M-CNNBiAM). This approach provides an effective technical solution for the precise diagnosis of bearing faults in high-noise environments. To suppress substantial noise interference, a CAE denoising module was designed to filter out intense noise, providing high-quality input for subsequent diagnostic networks. To address the limitations of single-modal feature extraction and restricted generalization capabilities, a three-channel time–frequency signal joint diagnosis model combining the Continuous Wavelet Transform (CWT) with an attention mechanism was proposed. This approach enables deep mining and efficient fusion of multi-domain features, thereby enhancing fault diagnosis accuracy and generalization capabilities. Experimental results demonstrate that the designed CAE module maintains excellent noise reduction performance even under −10 dB strong noise conditions. When combined with the proposed diagnostic model, it achieves an average diagnostic accuracy of 98% across both the CWRU and self-test datasets, demonstrating outstanding diagnostic precision. Furthermore, under −4 dB noise conditions, it achieves a 94% diagnostic accuracy even without relying on the CAE denoising module. With a single training cycle taking only 6.8 s, it balances training efficiency and diagnostic performance, making it well-suited for real-time, reliable bearing fault diagnosis in industrial environments with high noise levels. Full article
►▼ Show Figures

Figure 1

19 pages, 311 KB  
Article
Investing in AI Interpretability, Control, and Robustness
by Maikel Leon
Algorithms 2026, 19(2), 136; https://doi.org/10.3390/a19020136 - 9 Feb 2026
Cited by 2 | Viewed by 1871
Abstract
Artificial intelligence (AI) powers breakthroughs in language processing, computer vision, and scientific discovery; yet, the increasing complexity of frontier models makes their reasoning opaque. This opacity undermines public trust, complicates deployment in safety-critical settings, and frustrates compliance with emerging regulations. In response to [...] Read more.
Artificial intelligence (AI) powers breakthroughs in language processing, computer vision, and scientific discovery; yet, the increasing complexity of frontier models makes their reasoning opaque. This opacity undermines public trust, complicates deployment in safety-critical settings, and frustrates compliance with emerging regulations. In response to initiatives such as the White House AI Action Plan, we synthesize the scientific foundations and policy landscape for interpretability, control, and robustness. We clarify key concepts and survey intrinsically interpretable and post-hoc explanation techniques, discuss human-centered evaluation and governance, and analyze how adversarial threats and distributional shifts motivate robustness research. An empirical case study compares logistic regression, random forests, and gradient boosting on a synthetic dataset with a binary-sensitive attribute using accuracy, F1 score, and group-fairness metrics, and illustrates trade-offs between performance and fairness. We integrate ethical and policy perspectives, including recommendations from America’s AI Action Plan and recent civil rights frameworks, and conclude with guidance for researchers, practitioners, and policymakers on advancing trustworthy AI. Full article
(This article belongs to the Special Issue AI-Driven Business Analytics Revolution)
►▼ Show Figures

Graphical abstract

39 pages, 5230 KB  
Review
An In-Depth Review of Speech Enhancement Algorithms: Classifications, Underlying Principles, Challenges, and Emerging Trends
by Nisreen Talib Abdulhusein and Basheera M. Mahmmod
Algorithms 2026, 19(2), 134; https://doi.org/10.3390/a19020134 - 7 Feb 2026
Cited by 2 | Viewed by 2427
Abstract
Speech enhancement aims to improve speech quality and intelligibility in noisy environments and is important in applications such as hearing aids, mobile communications and automatic speech recognition (ASR). This paper shows a structured review of speech enhancement techniques, classified depending on the channel [...] Read more.
Speech enhancement aims to improve speech quality and intelligibility in noisy environments and is important in applications such as hearing aids, mobile communications and automatic speech recognition (ASR). This paper shows a structured review of speech enhancement techniques, classified depending on the channel configuration and signal processing framework. Both traditional and modern approaches are discussed, including classical signal processing methods, machine learning techniques, and recent deep learning-based models. Furthermore, common noise types, widely used speech datasets, and standard evaluation metrics for evaluating speech quality and intelligibility are reviewed. Key challenges such as non-stationary noise, data limitations, reverberation, and generalization to unseen noise conditions are highlighted. This review presents the advancements in speech enhancement and discusses the challenges and trends of this field. Valuable insights are provided for researchers, engineers, and practitioners in the area. The findings aid in the selection of suitable techniques for improved speech quality and intelligibility, and we concluded that the trend in speech enhancement has shifted from standard algorithms to deep learning methods that can efficiently learn information regarding speech signals. Full article
►▼ Show Figures

Figure 1

18 pages, 421 KB  
Article
Symbolic Algorithm for Solving Nonlocal Boundary Value Problems for Systems of Ordinary Integrodifferential Equations
by Efthimios Providas, Ioannis N. Parasidis and Jeyhun E. Musayev
Algorithms 2026, 19(2), 126; https://doi.org/10.3390/a19020126 - 4 Feb 2026
Cited by 1 | Viewed by 910
Abstract
Boundary value problems for systems of integrodifferential equations appear in many branches of science and engineering. Accuracy in modeling complex processes requires the specification of nonlocal boundary conditions, including multipoint and integral conditions. These kinds of problems are even harder to solve. In [...] Read more.
Boundary value problems for systems of integrodifferential equations appear in many branches of science and engineering. Accuracy in modeling complex processes requires the specification of nonlocal boundary conditions, including multipoint and integral conditions. These kinds of problems are even harder to solve. In this paper, we present solvability criteria and a direct operator method for constructing the exact solution to systems of linear ordinary integrodifferential equations with general nonlocal boundary conditions. A symbolic algorithm is also proposed. Several examples are solved to demonstrate the effectiveness of the method. The results obtained are equally valid for nonlocal boundary value problems for systems of ordinary differential, loaded differential, and loaded integrodifferential equations. Full article
(This article belongs to the Special Issue Algorithms for Dynamical Systems and Differential Equations: Theory, Computation Innovations and Applications)
►▼ Show Figures

Graphical abstract

23 pages, 12136 KB  
Article
DOA Estimation for Underwater Coprime Arrays with Sensor Failure Based on Segmented Array Validation and Multipath Matching Pursuit
by Xiao Chen and Ying Zhang
Algorithms 2026, 19(2), 125; https://doi.org/10.3390/a19020125 - 4 Feb 2026
Cited by 1 | Viewed by 631
Abstract
Coprime arrays enable enhanced degrees of freedom through the construction of virtual array equivalent signals. However, the presence of large “holes” leads to discontinuous co-arrays, which severely hampers direction-of-arrival (DOA) estimation techniques that rely on uniform array structures. This paper explores the practical [...] Read more.
Coprime arrays enable enhanced degrees of freedom through the construction of virtual array equivalent signals. However, the presence of large “holes” leads to discontinuous co-arrays, which severely hampers direction-of-arrival (DOA) estimation techniques that rely on uniform array structures. This paper explores the practical application of co-array domain signal processing for underwater acoustic coprime arrays. We propose a novel array configuration based on coprime minimum disordered pairs, enabling the formation of continuously connected co-arrays without interpolating. To address the challenge of limited snapshots in underwater environments, DOA estimation can be achieved by utilizing traditional multipath matching pursuit (MMP) algorithms under the proposed continuous co-array implementation scheme. In practical applications, physical array element failures are inevitable, and faulty elements can create holes in the originally continuous co-array. While interpolation techniques can mitigate small gaps, their performance deteriorates significantly in the presence of large holes or uneven data distribution. To overcome these limitations, we introduce a sparse signal recovery (SSR) method using a fragment array data validation technique for sparse DOA estimation with an underwater acoustic coprime array. Based on the designed continuous array expansion scheme, the resulting continuous co-array is used to map the positions of element failures, revealing the gaps in the co-array. A validation model is established for partially continuous sub-arrays within the discontinuous co-array, enabling signal direction estimation based on the fragmented array validation. Both simulation and sea trial results confirm that the proposed approach maximizes the utilization of co-array elements without relying on interpolation or prediction, offering a robust solution for scenarios involving sensor failures. Full article
(This article belongs to the Special Issue Signal Processing, Intelligent Analysis, and Optimization for Communication and Electronic Systems)
►▼ Show Figures

Figure 1

33 pages, 5039 KB  
Article
Sub-Hourly Multi-Horizon Quantile Forecasting of Photovoltaic Power Using Meteorological Data and a HybridCNN–STTransformer
by Guldana Taganova, Alma Zakirova, Assel Abdildayeva, Bakhyt Nurbekov, Zhanar Akhayeva and Talgat Azykanov
Algorithms 2026, 19(2), 123; https://doi.org/10.3390/a19020123 - 3 Feb 2026
Cited by 3 | Viewed by 952
Abstract
The rapid deployment of photovoltaic generation increases uncertainty in power-system operation and strengthens the need for ultra-short-term forecasts with reliable uncertainty estimates. Point-forecasting approaches alone are often insufficient for dispatch and reserve decisions because they do not quantify risk. This study investigates probabilistic [...] Read more.
The rapid deployment of photovoltaic generation increases uncertainty in power-system operation and strengthens the need for ultra-short-term forecasts with reliable uncertainty estimates. Point-forecasting approaches alone are often insufficient for dispatch and reserve decisions because they do not quantify risk. This study investigates probabilistic forecasting of short-horizon solar generation using quantile regression on a public dataset of solar output and meteorological variables. This study proposes a hybrid attention–convolution model that combines an attention-based encoder to capture long-range temporal dependencies with a causal temporal convolution module that extracts fast local fluctuations using only past information, preventing information leakage. The two representations are fused and decoded jointly across multiple future horizons to produce consistent quantile trajectories. Experiments against representative machine-learning and deep-learning baselines show improved probabilistic accuracy and competitive central forecasts, while illustrating an important sharpness–calibration trade-off relevant to risk-aware grid operation. Key novelties include a multi-horizon quantile formulation at 15 min resolution for one-hour-ahead PV increments, a HybridCNN–STTransformer that fuses causal temporal convolutions with Transformer attention, and a horizon-token decoder that models inter-horizon dependencies to produce consistent multi-step quantile trajectories; reliability/sharpness diagnostics and post hoc calibration are discussed for operational risk-aware use. Full article
(This article belongs to the Section Algorithms for Multidisciplinary Applications)
►▼ Show Figures

Graphical abstract

22 pages, 967 KB  
Article
GRU-Based Short-Term Forecasting for Microgrid Operation: Modeling and Simulation Using Simulink
by Yu-Kuei Liu, Goran Rafajlovski and Saiful Islam
Algorithms 2026, 19(2), 116; https://doi.org/10.3390/a19020116 - 2 Feb 2026
Cited by 1 | Viewed by 1366
Abstract
This paper examines how hour-ahead forecasting uncertainty propagates to microgrid operation under intermittent renewable generation. Using hourly public data for Ontario and focusing on the FSA K0K in 2018, we evaluate four representative months (January, April, July, and December) to capture seasonal dynamics. [...] Read more.
This paper examines how hour-ahead forecasting uncertainty propagates to microgrid operation under intermittent renewable generation. Using hourly public data for Ontario and focusing on the FSA K0K in 2018, we evaluate four representative months (January, April, July, and December) to capture seasonal dynamics. We benchmark three univariate forecasting approaches for load demand, photovoltaic (PV) generation, and wind generation under a consistent 24-to-1 input setup, including GRU, LSTM, and a persistence baseline. We report point-forecast metrics (RMSE, MAE, and R2) and also provide 90% prediction intervals (PI90) using conformal calibration to quantify uncertainty. To assess downstream impact, forecasts are coupled with a dual-branch MATLAB/Simulink microgrid model. One branch uses True profiles and the other uses forecast-driven Pred inputs, while both branches share the same rule-based EMS and BESS constraints. System performance is evaluated using time-series comparisons and monthly key performance indicators (KPIs) covering grid import and export, grid peak power, battery throughput, and state-of-charge (SoC) statistics. We further report an illustrative cost sensitivity under a flat tariff and a throughput-based degradation proxy. Results show that forecasting performance is target dependent. GRU achieves the best overall point accuracy for load and PV, whereas wind is strongly driven by short term persistence at the one hour horizon, and in this measurement only setup without meteorological covariates the persistence baseline can match or outperform the deep learning models. In the microgrid simulations, Pred and True trajectories remain qualitatively consistent, and SoC-related indicators and peak power remain comparatively consistent across months. In contrast, energy-flow indicators, especially grid export and battery throughput, show larger deviations and dominate the observed cost sensitivity. Overall, the findings suggest that compact hour-ahead forecasts can be adequate to preserve operational reliability under a constraint-driven EMS, while forecast improvements mainly translate into economic efficiency gains rather than reliability-critical benefits. Full article
►▼ Show Figures

Figure 1

15 pages, 1018 KB  
Article
Evolutionary Optimization for Job Shop Scheduling with Blocking: A Genetic Algorithm Approach
by John Valencia and Elkin Rodríguez-Velásquez
Algorithms 2026, 19(2), 115; https://doi.org/10.3390/a19020115 - 1 Feb 2026
Cited by 1 | Viewed by 1413
Abstract
The Blocking Job Shop Scheduling Problem (BJSSP) is a variant of the classical Job Shop Scheduling Problem in which a job completed on one machine cannot be transferred to the next machine until the latter becomes available, causing the current machine to remain [...] Read more.
The Blocking Job Shop Scheduling Problem (BJSSP) is a variant of the classical Job Shop Scheduling Problem in which a job completed on one machine cannot be transferred to the next machine until the latter becomes available, causing the current machine to remain blocked. Numerous real-world applications have been modeled as the BJSSP, which is classified as a strongly NP-hard problem. Previous studies indicate that several proposed approaches fail to guarantee the generation of feasible solutions during the search process, thereby requiring a solution reconstruction. In this study, we propose a Genetic Algorithm (GA) designed to operate strictly within the feasible solution space of the BJSSP, where the objective function is the minimization of the makespan. Experimental results show that no specific factor levels significantly influenced the solution quality obtained by the GA across all problem sets. On the other hand, incorporating an assignment operator into the solution representation enhanced the diversity of the population. The proposed GA yields solutions that outperform some of the best-known makespan values for the Lawrence benchmark problems. The runtime of the GA ranged from 20 s for instances with 10 jobs and five machines to 600 s for instances with 30 jobs and 10 machines. Full article
►▼ Show Figures

Figure 1

19 pages, 2072 KB  
Article
A Reconfigurable CNN-2D Hardware Architecture for Real-Time Brain Cancer Multi-Classification on FPGA
by Ayoub Mhaouch, Wafa Gtifa, Ibtihel Nouira, Abdessalem Ben Abdelali and Mohsen Machhout
Algorithms 2026, 19(2), 107; https://doi.org/10.3390/a19020107 - 1 Feb 2026
Cited by 1 | Viewed by 1321
Abstract
Brain cancer classification using deep learning has gained significant attention due to its potential to improve early diagnosis and treatment planning. In this work, we propose a reconfigurable and hardware-optimized CNN-2D architecture implemented on FPGA for multiclass classification of brain tumors from MRI [...] Read more.
Brain cancer classification using deep learning has gained significant attention due to its potential to improve early diagnosis and treatment planning. In this work, we propose a reconfigurable and hardware-optimized CNN-2D architecture implemented on FPGA for multiclass classification of brain tumors from MRI images. The contribution of this study lies in the development of a lightweight CNN model and a modular hardware design, where three key IP coresConv2D, MaxPooling, and ReLUare architected with parameterizable kernels, efficient dataflow, and optimized memory reuse to support real-time processing on resource-constrained platforms. These IPs are iteratively reconfigured to process each CNN layer, enabling flexibility while maintaining low latency. To evaluate the proposed architecture, we first implement the model in software on a Dual-Core Cortex-A9 processor and then deploy the hardware-accelerated version on an XC7Z020 FPGA. Performance is assessed in terms of execution time, power consumption, and classification accuracy. The FPGA implementation achieves a 93.21% reduction in latency and a 67.5% reduction in power consumption, while maintaining a competitive accuracy of 96.09% compared with 98.43% for the software version. These results demonstrate that the proposed reconfigurable FPGA-based architecture offers a strong balance between accuracy, real-time performance, and energy efficiency, making it highly suitable for embedded brain tumor classification systems. Full article
►▼ Show Figures

Figure 1

22 pages, 5743 KB  
Article
SvelteNeck by EHConv: A Cross-Generational Lightweight Neck for Real-Time Object Detection
by Tianyi Wang, Haifeng Wang, Wenbin Wang, Kun Zhang, Baojiang Ye and Huilin Dong
Algorithms 2026, 19(2), 113; https://doi.org/10.3390/a19020113 - 1 Feb 2026
Cited by 1 | Viewed by 969
Abstract
Efficient object detection is vital for Remotely Operated Vehicles (ROVs) performing marine debris cleanup, yet existing lightweight designs frequently encounter efficiency bottlenecks when adapted to deeper neural networks. This research identifies a critical “Inverted Bottleneck” anomaly in the Slim-Neck architecture on the YOLO11 [...] Read more.
Efficient object detection is vital for Remotely Operated Vehicles (ROVs) performing marine debris cleanup, yet existing lightweight designs frequently encounter efficiency bottlenecks when adapted to deeper neural networks. This research identifies a critical “Inverted Bottleneck” anomaly in the Slim-Neck architecture on the YOLO11 backbone, where deep-layer Memory Access Cost (MAC) abnormally spikes. To address this, we propose SvelteNeck-YOLO. By incorporating the proposed EHSCSP module and EHConv operator, the model systematically eliminates computational redundancies. Empirical validation on the TrashCan and URPC2019 datasets demonstrates that the model resolves the memory wall issue, achieving a state-of-the-art trade-off with only 5.8 GFLOPs. Specifically, it delivers a 34% relative reduction in computational load compared to specialized underwater models while maintaining a superior Recall of 0.859. Consequently, SvelteNeck-YOLO establishes a robust, cross-generational solution, optimizing the Pareto frontier between inference speed and detection sensitivity for resource-constrained underwater edge computing. Full article
(This article belongs to the Section Algorithms for Multidisciplinary Applications)
►▼ Show Figures

Figure 1

22 pages, 561 KB  
Review
A Systematic Review of Anomaly and Fault Detection Using Machine Learning for Industrial Machinery
by Syed Haseeb Haider Zaidi, Alex Shenfield, Hongwei Zhang and Augustine Ikpehai
Algorithms 2026, 19(2), 108; https://doi.org/10.3390/a19020108 - 1 Feb 2026
Cited by 9 | Viewed by 3471
Abstract
Unplanned downtime in industrial machinery remains a major challenge, causing substantial economic losses and safety risks across sectors such as manufacturing, food processing, oil and gas, and transportation. This systematic review investigates the application of machine learning (ML) techniques for anomaly and fault [...] Read more.
Unplanned downtime in industrial machinery remains a major challenge, causing substantial economic losses and safety risks across sectors such as manufacturing, food processing, oil and gas, and transportation. This systematic review investigates the application of machine learning (ML) techniques for anomaly and fault detection within the broader context of predictive maintenance. Following a hybrid review methodology, relevant studies published between 2010 and 2025 were collected from major databases including IEEE Xplore, ScienceDirect, SpringerLink, Scopus, Web of Science, and arXiv. The review categorizes approaches into supervised, unsupervised, and hybrid paradigms, analyzing their pipelines from data collection and preprocessing to model deployment. Findings highlight the effectiveness of deep learning architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), autoencoders, and hybrid frameworks in detecting faults from time series and multimodal sensor data. At the same time, key limitations persist, including data scarcity, class imbalance, limited generalizability across equipment types, and a lack of interpretability in deep models. This review concludes that while ML-based predictive maintenance systems are enabling a transition from reactive to proactive strategies, future progress requires improved hybrid architectures, Explainable AI, and scalable real-time deployment. Full article
(This article belongs to the Special Issue Machine Learning for Pattern Recognition (3rd Edition))
►▼ Show Figures

Figure 1

18 pages, 758 KB  
Article
An Adaptive Task Difficulty Model for Personalized Reading Comprehension in AI-Based Learning Systems
by Aray M. Kassenkhan, Mateus Mendes and Akbayan Bekarystankyzy
Algorithms 2026, 19(2), 100; https://doi.org/10.3390/a19020100 - 27 Jan 2026
Cited by 2 | Viewed by 1809
Abstract
This article proposes an interpretable adaptive control model for dynamically regulating task difficulty in Artificial intelligence (AI)-augmented reading-comprehension learning systems. The model adjusts, on the fly, the level of task complexity associated with reading comprehension and post-text analytical tasks based on learner performance, [...] Read more.
This article proposes an interpretable adaptive control model for dynamically regulating task difficulty in Artificial intelligence (AI)-augmented reading-comprehension learning systems. The model adjusts, on the fly, the level of task complexity associated with reading comprehension and post-text analytical tasks based on learner performance, with the objective of maintaining an optimal difficulty level. Grounded in adaptive control theory and learning theory, the proposed algorithm updates task difficulty according to the deviation between observed learner performance and a predefined target mastery rate, modulated by an adaptivity coefficient. A simulation study involving heterogeneous learner profiles demonstrates stable convergence behavior and a strong positive correlation between task difficulty and learning performance (r = 0.78). The results indicate that the model achieves a balanced trade-off between learner engagement and cognitive load while maintaining low computational complexity, making it suitable for real-time integration into intelligent learning environments. The proposed approach contributes to AI-supported education by offering a transparent, control-theoretic alternative to heuristic difficulty adjustment mechanisms commonly used in e-learning systems. Full article
(This article belongs to the Section Algorithms for Multidisciplinary Applications)
►▼ Show Figures

Graphical abstract

32 pages, 3217 KB  
Review
Architecting the Orthopedical Clinical AI Pipeline: A Review of Integrating Foundation Models and FHIR for Agentic Clinical Assistants and Digital Twins
by Assiya Boltaboyeva, Zhanel Baigarayeva, Baglan Imanbek, Bibars Amangeldy, Nurdaulet Tasmurzayev, Kassymbek Ozhikenov, Zhadyra Alimbayeva, Chingiz Alimbayev and Nurgul Karymsakova
Algorithms 2026, 19(2), 99; https://doi.org/10.3390/a19020099 - 27 Jan 2026
Cited by 1 | Viewed by 1559
Abstract
The exponential growth of multimodal orthopedic data, ranging from longitudinal Electronic Health Records to high-resolution musculoskeletal imaging, has rendered manual analysis insufficient. This has established Large Language Models (LLMs) as algorithmically necessary for managing healthcare complexity. However, their deployment in high-stakes surgical environments [...] Read more.
The exponential growth of multimodal orthopedic data, ranging from longitudinal Electronic Health Records to high-resolution musculoskeletal imaging, has rendered manual analysis insufficient. This has established Large Language Models (LLMs) as algorithmically necessary for managing healthcare complexity. However, their deployment in high-stakes surgical environments presents a fundamental algorithmic paradox: while generic foundation models possess vast reasoning capabilities, they often lack the precise, protocol-driven domain knowledge required for safe orthopedic decision support. This review provides a structured synthesis of the emerging algorithmic frameworks required to build modern clinical AI assistants. We deconstruct current methodologies into their core components: large-language-model adaptation, multimodal data fusion, and standardized data interoperability pipelines. Rather than proposing a single proprietary architecture, we analyze how recent literature connects specific algorithmic choices such as the trade-offs between full fine-tuning and Low-Rank Adaptation to their computational costs and factual reliability. Furthermore, we examine the theoretical architectures required for ‘agentic’ capabilities, where AI systems integrate outputs from deep convolutional neural networks and biosensors. The review concludes by outlining the unresolved challenges in algorithmic bias, security, and interoperability that must be addressed to transition these technologies from research prototypes to scalable clinical solutions. Full article
(This article belongs to the Special Issue Artificial Intelligence Algorithms for Healthcare: 2nd Edition)
►▼ Show Figures

Figure 1

26 pages, 2205 KB  
Article
A Wind Field–Perception Hybrid Algorithm for UAV Path Planning in Strong Wind Conditions
by Hongping Pu, Xinshuai Liu, Shiyong Yang, Chunlan Luo, Yuanyuan He, Mingju Chen and Xiaoxia Zheng
Algorithms 2026, 19(2), 97; https://doi.org/10.3390/a19020097 - 26 Jan 2026
Cited by 1 | Viewed by 755
Abstract
As unmanned aerial vehicles (UAVs) are increasingly utilized in urban inspection and emergency rescue missions, path planning under strong wind conditions persists as a critical challenge. Traditional algorithms frequently exhibit deficiencies in environmental adaptability or encounter difficulties in balancing exploration and exploitation. This [...] Read more.
As unmanned aerial vehicles (UAVs) are increasingly utilized in urban inspection and emergency rescue missions, path planning under strong wind conditions persists as a critical challenge. Traditional algorithms frequently exhibit deficiencies in environmental adaptability or encounter difficulties in balancing exploration and exploitation. This paper presents a dynamic-proportion Bat–Cuckoo Search (BA-CS) Hybrid Algorithm enhanced with wind field perception to tackle the challenges of UAV path planning in urban environments with strong winds, specifically addressing the issues of insufficient environmental adaptation and the exploration–exploitation imbalance. The algorithm integrates a dual-feedback mechanism that dynamically modifies the ratio of the BA/CS subpopulations in accordance with real-time iteration progress and population diversity. By incorporating wind field perception into population initialization, interpopulation information exchange, and wind resistance perturbation strategies, it attains efficient path optimization under multiple constraints. Experimental results under strong winds with speeds ranging from 10.8 to 13.8 m/s indicate that the proposed algorithm generates paths that are smooth, continuous, and entirely collision-free. It achieves a superior average wind resistance cost of 0.92, which is 9.8%, 17.1%, and 52.6% lower than those of the A*, RRT, and PSO algorithms, respectively. With a planning time of 3.95 s, it satisfies the path wind resistance stability requirements stipulated in the GB/T 38930-2020 standard, providing an effective solution for UAV inspection and emergency rescue operations in urban wind scenarios. Full article
(This article belongs to the Special Issue Signal Processing, Intelligent Analysis, and Optimization for Communication and Electronic Systems)
►▼ Show Figures

Figure 1

30 pages, 3115 KB  
Article
HST–MB–CREH: A Hybrid Spatio-Temporal Transformer with Multi-Branch CNN/RNN for Rare-Event-Aware PV Power Forecasting
by Guldana Taganova, Jamalbek Tussupov, Assel Abdildayeva, Mira Kaldarova, Alfiya Kazi, Ronald Cowie Simpson, Alma Zakirova and Bakhyt Nurbekov
Algorithms 2026, 19(2), 94; https://doi.org/10.3390/a19020094 - 23 Jan 2026
Cited by 2 | Viewed by 704
Abstract
We propose the Hybrid Spatio-Temporal Transformer with Multi-Branch CNN/RNN and Extreme-Event Head (HST–MB–CREH), a hybrid spatio-temporal deep learning architecture for joint short-term photovoltaic (PV) power forecasting and the detection of rare extreme events, to support the reliable operation of renewable-rich power systems. The [...] Read more.
We propose the Hybrid Spatio-Temporal Transformer with Multi-Branch CNN/RNN and Extreme-Event Head (HST–MB–CREH), a hybrid spatio-temporal deep learning architecture for joint short-term photovoltaic (PV) power forecasting and the detection of rare extreme events, to support the reliable operation of renewable-rich power systems. The model combines a spatio-temporal transformer encoder with three convolutional neural network (CNN)/recurrent neural network (RNN) branches (CNN → long short-term memory (LSTM), LSTM → gated recurrent unit (GRU), CNN → GRU) and a dense pathway for tabular meteorological and calendar features. A multitask output head simultaneously performs the regression of PV power and binary classification of extremes defined above the 95th percentile. We evaluate HST–MB–CREH on the publicly available Renewable Power Generation and Weather Conditions dataset with hourly resolutions from 2017 to 2022, using a 5-fold TimeSeriesSplit protocol to avoid temporal leakage and to cover multiple seasons. Compared with tree ensembles (RandomForest, XGBoost), recurrent baselines (Stacked GRU, LSTM), and advanced hybrid/transformer models (Hybrid Multi-Branch CNN–LSTM/GRU with Dense Path and Extreme-Event Head (HMB–CLED) and Spatio-Temporal Multitask Transformer with Extreme-Event Head (STM–EEH)), the proposed architecture achieves the best overall trade-off between accuracy and rare-event sensitivity, with normalized performance of RMSE_z = 0.2159 ± 0.0167, MAE_z = 0.1100 ± 0.0085, mean absolute percentage error (MAPE) = 9.17 ± 0.45%, R2 = 0.9534 ± 0.0072, and AUC_ext = 0.9851 ± 0.0051 across folds. Knowledge extraction is supported via attention-based analysis and permutation feature importance, which highlight the dominant role of global horizontal irradiance, diurnal harmonics, and solar geometry features. The results indicate that hybrid spatio-temporal multitask architectures can substantially improve both the forecast accuracy and robustness to extremes, making HST–MB–CREH a promising building block for intelligent decision-support tools in smart grids with a high share of PV generation. Full article
(This article belongs to the Section Evolutionary Algorithms and Machine Learning)
►▼ Show Figures

Graphical abstract

46 pages, 4076 KB  
Review
A Review of AI-Driven Engineering Modelling and Optimization: Methodologies, Applications and Future Directions
by Jian-Ping Li, Nereida Polovina and Savas Konur
Algorithms 2026, 19(2), 93; https://doi.org/10.3390/a19020093 - 23 Jan 2026
Cited by 4 | Viewed by 2967
Abstract
Engineering is suffering a significant change driven by the integration of artificial intelligence (AI) into engineering optimization in design, analysis, and operational efficiency across numerous disciplines. This review synthesizes the current landscape of AI-driven optimization methodologies and their impacts on engineering applications. In [...] Read more.
Engineering is suffering a significant change driven by the integration of artificial intelligence (AI) into engineering optimization in design, analysis, and operational efficiency across numerous disciplines. This review synthesizes the current landscape of AI-driven optimization methodologies and their impacts on engineering applications. In the literature, several frameworks for AI-based engineering optimization have been identified: (1) machine learning models are trained as objective and constraint functions for optimization problems; (2) machine learning techniques are used to improve the efficiency of optimization algorithms; (3) neural networks approximate complex simulation models such as finite element analysis (FEA) and computational fluid dynamics (CFD) and this makes it possible to optimize complex engineering systems; and (4) machine learning predicts design parameters/initial solutions that are subsequently optimized. Fundamental AI technologies, such as artificial neural networks and deep learning, are examined in this paper, along with commonly used AI-assisted optimization strategies. Representative applications of AI-driven engineering optimization have been surveyed in this paper across multiple fields, including mechanical and aerospace engineering, civil engineering, electrical and computer engineering, chemical and materials engineering, energy and management. These studies demonstrate how AI enables significant improvements in computational modelling, predictive analytics, and generative design while effectively handling complex multi-objective constraints. Despite these advancements, challenges remain in areas such as data quality, model interpretability, and computational cost, particularly in real-time environments. Through a systematic analysis of recent case studies and emerging trends, this paper provides a critical assessment of the state of the art and identifies promising research directions, including physics-informed neural networks, digital twins, and human–AI collaborative optimization frameworks. The findings highlight AI’s potential to redefine engineering optimization paradigms, while emphasizing the need for robust, scalable, and ethically aligned implementations. Full article
(This article belongs to the Special Issue AI-Driven Engineering Optimization)
►▼ Show Figures

Figure 1

21 pages, 1401 KB  
Article
Embedding-Based Detection of Indirect Prompt Injection Attacks in Large Language Models Using Semantic Context Analysis
by Mohammed Alamsabi, Michael Tchuindjang and Sarfraz Brohi
Algorithms 2026, 19(1), 92; https://doi.org/10.3390/a19010092 - 22 Jan 2026
Cited by 2 | Viewed by 4083
Abstract
Large Language Models (LLMs) are vulnerable to Indirect Prompt Injection Attacks (IPIAs), where malicious instructions are embedded within external content rather than direct user input. This study presents an embedding-based detection approach that analyses the semantic relationship between user intent and external content, [...] Read more.
Large Language Models (LLMs) are vulnerable to Indirect Prompt Injection Attacks (IPIAs), where malicious instructions are embedded within external content rather than direct user input. This study presents an embedding-based detection approach that analyses the semantic relationship between user intent and external content, enabling the early identification of IPIAs that conventional defences overlook. We also provide a dataset of 70,000 samples, constructed using 35,000 malicious instances from the Benchmark for Indirect Prompt Injection Attacks (BIPIA) and 35,000 benign instances generated using ChatGPT-4o-mini. Furthermore, we performed a comparative analysis of three embedding models, namely OpenAI text-embedding-3-small, GTE-large, and MiniLM-L6-v2, evaluated in combination with XGBoost, LightGBM, and Random Forest classifiers. The best-performing configuration using OpenAI embeddings with XGBoost achieved an accuracy of 97.7% and an F1-score of 0.977, matching or exceeding the performance of existing IPIA detection methods while offering practical deployment advantages. Unlike prevention-focused approaches that require modifications to the underlying LLM architecture, the proposed method operates as a model-agnostic external detection layer with an average inference time of 0.001 ms per sample. This detection-based approach complements existing prevention mechanisms by providing a lightweight, scalable solution that can be integrated into LLM pipelines without requiring architectural changes. Full article
(This article belongs to the Special Issue Artificial Intelligence in Modern Cybersecurity: Changes, Applications and Challenges)
►▼ Show Figures

Figure 1

23 pages, 1109 KB  
Review
A Review of End-to-End Decision Optimization Research: An Architectural Perspective
by Wenya Zhang and Gendao Li
Algorithms 2026, 19(1), 86; https://doi.org/10.3390/a19010086 - 20 Jan 2026
Cited by 1 | Viewed by 1620
Abstract
Traditional decision optimization methods primarily focus on model construction and solution, leaving parameter estimation and inter-variable relationships to statistical research. The traditional approach divides problem-solving into two independent stages: predict first and then optimize. This decoupling leads to the propagation of prediction errors-even [...] Read more.
Traditional decision optimization methods primarily focus on model construction and solution, leaving parameter estimation and inter-variable relationships to statistical research. The traditional approach divides problem-solving into two independent stages: predict first and then optimize. This decoupling leads to the propagation of prediction errors-even minor inaccuracies in predictions can be amplified into significant decision biases during the optimization phase. To tackle this issue, scholars have proposed end-to-end decision optimization methods, which integrate the prediction and decision-making stages into a unified framework. By doing so, these approaches effectively mitigate error propagation and enhance overall decision performance. From an architectural design perspective, this review focuses on categorizing end-to-end decision optimization methods based on how the prediction and decision modules are integrated. It classifies mainstream approaches into three typical paradigms: constructing closed-loop loss functions, building differentiable optimization layers, and parameterizing the representation of optimization problems. It also examines their implementation pathways leveraging deep learning technologies. The strengths and limitations of these paradigms essentially stem from the inherent trade-offs in their architectural designs. Through a systematic analysis of existing research, this paper identifies key challenges in three core areas: data, variable relationships, and gradient propagation. Among these, handling non-convexity and complex constraints is critical for model generalization, while quantifying decision-dependent endogenous uncertainty remains an indispensable challenge for practical deployment. Full article
►▼ Show Figures

Figure 1

26 pages, 544 KB  
Article
Physics-Aware Deep Learning Framework for Solar Irradiance Forecasting Using Fourier-Based Signal Decomposition
by Murad A. Yaghi and Huthaifa Al-Omari
Algorithms 2026, 19(1), 81; https://doi.org/10.3390/a19010081 - 17 Jan 2026
Cited by 3 | Viewed by 1365
Abstract
Photovoltaic Systems have been a long-standing challenge to integrate with electrical Power Grids due to the randomness of solar irradiance. Deep Learning (DL) has potential to forecast solar irradiance; however, black-box DL models typically do not offer interpretation, nor can they easily distinguish [...] Read more.
Photovoltaic Systems have been a long-standing challenge to integrate with electrical Power Grids due to the randomness of solar irradiance. Deep Learning (DL) has potential to forecast solar irradiance; however, black-box DL models typically do not offer interpretation, nor can they easily distinguish between deterministic astronomical cycles, and random meteorological variability. The objective of this study was to develop and apply a new Physics-Aware Deep Learning Framework that identifies and utilizes physical attributes of solar irradiance via Fourier-based signal decomposition. The proposed method decomposes the time-series into polynomial trend, Fourier-based seasonal component and stochastic residual, each of which are processed within different neural network paths. A wide variety of architectures were tested (Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN)), at multiple historical window sizes and forecast horizons on a diverse dataset from a three-year span. All of the architectures tested demonstrated improved accuracy and robustness when using the physics aware decomposition as opposed to all other methods. Of the architectures tested, the GRU architecture was the most accurate and performed well in terms of overall evaluation. The GRU model had an RMSE of 78.63 W/m2 and an R2 value of 0.9281 for 15 min ahead forecasting. Additionally, the Fourier-based methodology was able to reduce the maximum absolute error by approximately 15% to 20%, depending upon the architecture used, and therefore it provided a way to reduce the impact of the larger errors in forecasting during periods of unstable weather. Overall, this framework represents a viable option for both physically interpretive and computationally efficient real-time solar forecasting that provides a bridge between Physical Modeling and Data-Driven Intelligence. Full article
(This article belongs to the Special Issue Artificial Intelligence in Sustainable Development)
►▼ Show Figures

Figure 1

37 pages, 4259 KB  
Article
Image-Based Segmentation of Hydrogen Bubbles in Alkaline Electrolysis: A Comparison Between Ilastik and U-Net
by José Pereira, Reinaldo Souza, Arthur Normand and Ana Moita
Algorithms 2026, 19(1), 77; https://doi.org/10.3390/a19010077 - 16 Jan 2026
Cited by 1 | Viewed by 1334
Abstract
This study aims to enhance the efficiency of hydrogen production through alkaline water electrolysis by analyzing hydrogen bubble dynamics using high-speed image processing and machine learning algorithms. The experiments were conducted to evaluate the effects of electrical current and ultrasound oscillations on the [...] Read more.
This study aims to enhance the efficiency of hydrogen production through alkaline water electrolysis by analyzing hydrogen bubble dynamics using high-speed image processing and machine learning algorithms. The experiments were conducted to evaluate the effects of electrical current and ultrasound oscillations on the system performance. The bubble formation and detachment process were recorded and analyzed using two segmentation models: Ilastik, a GUI-based tool, and U-Net, a deep learning convolutional network implemented in PyTorch. v. 2.9.0. Both models were trained on a dataset of 24 images under varying experimental conditions. The evaluation metrics included Intersection over Union (IoU), Root Mean Square Error (RMSE), and bubble diameter distribution. Ilastik achieved better accuracy and lower RMSE, while U-Net. U-Net offered higher scalability and integration flexibility within Python environments. Both models faced challenges when detecting small bubbles and under complex lighting conditions. Improvements such as expanding the training dataset, increasing image resolution, and adopting patch-based processing were proposed. Overall, the result demonstrates the automated image segmentation can provide reliable bubble characterization, contributing to the optimization of electrolysis-based hydrogen production. Full article
(This article belongs to the Special Issue Algorithms for Electrical and Electronic Engineering with a Focus on Renewable Energy Sources (2nd Edition))
►▼ Show Figures

Graphical abstract

26 pages, 3391 KB  
Article
An Intelligent Browser History Forensics Method for Automated Analysis of Web Activity Logs, Credentials, and User Behavioral Profiles
by Leila Rzayeva, Aliya Zhetpisbayeva, Alisher Batkuldin, Nursultan Nyssanov, Alissa Ryzhova and Faisal Saeed
Algorithms 2026, 19(1), 75; https://doi.org/10.3390/a19010075 - 16 Jan 2026
Cited by 2 | Viewed by 2694
Abstract
In digital forensics, one of the complicated tasks is analyzing web browser data due to different types of devices, browsers, and the absence of modern analytical approaches. Browsers store a large amount of information about user activity because users most often access the [...] Read more.
In digital forensics, one of the complicated tasks is analyzing web browser data due to different types of devices, browsers, and the absence of modern analytical approaches. Browsers store a large amount of information about user activity because users most often access the internet through them. However, existing approaches to analyzing this browser data still have gaps. Existing approaches fail to provide a comprehensive and precise representation of user activity. This article examines the internal architecture of web browsers as stored in the memory and storage subsystems of various devices, including desktop and mobile platforms. A novel method is proposed that integrates machine learning algorithms, such as k-nearest neighbors and Naive Bayes, to automatically analyze browser data, identify suspicious login activities, and construct user behavior profiles. The results indicate that the proposed method and the developed platform can effectively construct individual user behavior profiles. Moreover, this approach not only productively observes top visited domains and main user’s favorite website categories, but also highlights suspicious websites and user’s login attempts. Compared to existing browser forensic tools which have less capabilities, the proposed technique provides increased accuracy (more than 90%) in automated user profiling and detection of suspicious user activity. Full article
(This article belongs to the Special Issue Artificial Intelligence in Modern Cybersecurity: Changes, Applications and Challenges)
►▼ Show Figures

Figure 1

18 pages, 3360 KB  
Article
ZechariahNet: A Novel Method of MS Lesion Diagnosis Through MRI Images by the Combination of C-LSTM and 3D CNN Algorithms
by Mahshid Dehghanpour, Mansoor Fateh, Zeynab Mohammadpoory and Saideh Ferdowsi
Algorithms 2026, 19(1), 72; https://doi.org/10.3390/a19010072 - 15 Jan 2026
Cited by 1 | Viewed by 848
Abstract
In light of the growing prevalence of the autoimmune disease multiple sclerosis (MS), accurate detection of MS lesions in brain magnetic resonance imaging (MRI) images plays a critical role in assisting neurologists with timely diagnosis. The high similarity between MS lesions and normal [...] Read more.
In light of the growing prevalence of the autoimmune disease multiple sclerosis (MS), accurate detection of MS lesions in brain magnetic resonance imaging (MRI) images plays a critical role in assisting neurologists with timely diagnosis. The high similarity between MS lesions and normal brain tissues, however, makes this task particularly challenging. Although numerous deep-learning-based approaches have been proposed for the automatic segmentation of MS lesions, the method presented in this study has achieved superior results. ZechariahNet is a U-Net-based architecture that integrates transition down blocks, squeeze-attention (SA) blocks, dense blocks, and Convolutional LSTM (C-LSTM) blocks within a 3D CNN framework. By jointly exploiting spatial–temporal information from three consecutive MRI slices (previous, current, and subsequent) and strategically applying C-LSTM modules across the encoder and decoder paths, the proposed model effectively captures the neighborhood dependencies for enhanced feature extraction and reconstruction. These architectural innovations significantly improve segmentation accuracy, enabling ZechariahNet to achieve a dice similarity coefficient (DSC) of 84.72%, outperforming existing state-of-the-art methods. Full article
(This article belongs to the Special Issue Machine Learning and Signal Processing for EEG, ECG, EDA, and Other Biosignals)
►▼ Show Figures

Figure 1

36 pages, 9776 KB  
Article
Signal Timing Optimization Method for Intersections Under Mixed Traffic Conditions
by Hongwu Li, Yangsheng Jiang and Bin Zhao
Algorithms 2026, 19(1), 71; https://doi.org/10.3390/a19010071 - 14 Jan 2026
Cited by 1 | Viewed by 804
Abstract
The increasing proliferation of new energy vehicles and autonomous vehicles has led to the formation of mixed traffic flows characterized by diverse driving behaviors, posing new challenges for intersection signal control. To address this issue, this study proposes a multi-class customer feedback queuing [...] Read more.
The increasing proliferation of new energy vehicles and autonomous vehicles has led to the formation of mixed traffic flows characterized by diverse driving behaviors, posing new challenges for intersection signal control. To address this issue, this study proposes a multi-class customer feedback queuing network (MCFFQN) model that incorporates state-dependent road capacity and congestion propagation mechanisms to accurately capture the stochastic and dynamic nature of mixed traffic flows. An evaluation framework for intersection performance is established based on key indicators such as vehicle delay, the energy consumption of new energy vehicles, and the fuel consumption and emissions of conventional vehicles. A recursive solution algorithm is developed and validated through simulations under various traffic demand scenarios. Building on this model, a signal timing optimization model aimed at minimizing total costs—including delay and environmental impacts—is formulated and solved using the Mesh Adaptive Direct Search (MADS) algorithm. A case study demonstrates that the optimized signal timing scheme significantly enhances intersection performance, reducing vehicle delay, energy consumption, fuel consumption, and emissions by over 20%. The proposed methodology provides a theoretical foundation for sustainable traffic management under mixed traffic conditions. Full article
►▼ Show Figures

Figure 1

34 pages, 5835 KB  
Review
RIS-UAV Cooperative ISAC Technology for 6G: Architecture, Optimization, and Challenges
by Yuanfei Zhang, Zhongqiang Luo, Wenjie Wu and Wencheng Tian
Algorithms 2026, 19(1), 65; https://doi.org/10.3390/a19010065 - 12 Jan 2026
Cited by 3 | Viewed by 2630
Abstract
With the development of 6G technology, conventional wireless communication systems are increasingly unable to meet stringent performance requirements in complex and dynamic environments. Therefore, integrated sensing and communication (ISAC), which enables efficient spectrum sharing, has attracted growing attention as a promising solution. This [...] Read more.
With the development of 6G technology, conventional wireless communication systems are increasingly unable to meet stringent performance requirements in complex and dynamic environments. Therefore, integrated sensing and communication (ISAC), which enables efficient spectrum sharing, has attracted growing attention as a promising solution. This paper provides a comprehensive survey of reconfigurable intelligent surface (RIS)-unmanned aerial vehicle (UAV)-assisted ISAC systems. It first introduces a four-dimensional quantitative evaluation framework grounded in information theory. Then, we provide a structured overview of coordination mechanisms between different types of RIS and UAV platforms within ISAC architectures. Furthermore, we analyze the application characteristics of various multiple access schemes in these systems. Finally, the main technical challenges and potential future research directions are discussed and analyzed. Full article
(This article belongs to the Special Issue Signal Processing, Intelligent Analysis, and Optimization for Communication and Electronic Systems)
►▼ Show Figures

Graphical abstract

20 pages, 11896 KB  
Article
Improved Secretary Bird Optimization Algorithm for UAV Path Planning
by Huanlong Zhang, Hang Cheng, Xin Wang, Liao Zhu, Dian Jiao and Zhoujingzi Qiu
Algorithms 2026, 19(1), 64; https://doi.org/10.3390/a19010064 - 12 Jan 2026
Cited by 4 | Viewed by 759
Abstract
In view of the complex flight scenarios existing in UAV path planning, it is necessary to model the UAV flight trajectory. When constructing the model, cost factors such as the minimum flight path of the UAV, obstacle avoidance, flight altitude, and trajectory smoothness [...] Read more.
In view of the complex flight scenarios existing in UAV path planning, it is necessary to model the UAV flight trajectory. When constructing the model, cost factors such as the minimum flight path of the UAV, obstacle avoidance, flight altitude, and trajectory smoothness are fully taken into account. To reduce the overall flight cost, a novel secretary bird optimization algorithm (NSBOA) is proposed in this paper, which effectively addresses the limitations of traditional algorithms in handling UAV path planning tasks. First of all, the Singer chaotic map is adopted to initialize the population instead of the conventional random initialization method. This improvement increases population diversity, enables the initial population to be more evenly distributed in the search space, and further accelerates the algorithm’s convergence speed in the subsequent optimization process. Second, an adaptive adjustment mechanism is integrated with the Levy flight mechanism to optimize the core logic of the algorithm, with a specific focus on improving the exploitation stage. By introducing appropriate perturbations near the current optimal solution, the algorithm is guided to jump out of local optimal traps, thereby enhancing its global optimization capability and avoiding premature convergence caused by insufficient population diversity. By comparing and analyzing NSBOA with SBOA, WOA, PSO, POA, NGO, and HHO algorithms in 12 common evaluation functions and CEC 2017 test functions, and applying NSBOA to the UAV path optimization problem, the simulation results show the effectiveness and superiority of the proposed scheme. Full article
►▼ Show Figures

Figure 1

32 pages, 539 KB  
Article
Empirical Study on Automation, AI Trust, and Framework Readiness in Cybersecurity Incident Response
by Olufunsho I. Falowo and Jacques Bou Abdo
Algorithms 2026, 19(1), 62; https://doi.org/10.3390/a19010062 - 11 Jan 2026
Cited by 5 | Viewed by 2274
Abstract
The accelerating integration of artificial intelligence (AI) into cybersecurity operations has introduced new challenges and opportunities for modernizing incident response (IR) practices. This study explores how cybersecurity practitioners perceive the adoption of intelligent automation and the readiness of legacy frameworks to address AI-driven [...] Read more.
The accelerating integration of artificial intelligence (AI) into cybersecurity operations has introduced new challenges and opportunities for modernizing incident response (IR) practices. This study explores how cybersecurity practitioners perceive the adoption of intelligent automation and the readiness of legacy frameworks to address AI-driven threats. A structured, two-part quantitative survey was conducted among 194 U.S.-based professionals, capturing perceptions on operational effectiveness, trust in autonomous systems, and the adequacy of frameworks such as NIST and SANS. Using binary response formats and psychometric validation items, the study quantified views on AI’s role in reducing mean time to detect and respond, willingness to delegate actions to autonomous agents, and the perceived obsolescence of static playbooks. Findings indicate broad support for the modernization of incident response frameworks to better align with emerging AI capabilities and evolving operational demands. The results reveal a clear demand for modular, adaptive frameworks that integrate AI-specific risk models and decision auditability. These insights provide empirical grounding for the design of next-generation IR models and contribute to the strategic discourse on aligning automation capabilities with ethical, scalable, and operationally effective cybersecurity response. Full article
(This article belongs to the Special Issue Artificial Intelligence in Modern Cybersecurity: Changes, Applications and Challenges)
►▼ Show Figures

Figure 1

16 pages, 834 KB  
Article
A Game-Theoretic Analysis of COVID-19 Dynamics with Self-Isolation and Vaccination Behavior
by Folashade B. Agusto, Igor V. Erovenko and Gleb Gribovskii
Algorithms 2026, 19(1), 58; https://doi.org/10.3390/a19010058 - 9 Jan 2026
Cited by 1 | Viewed by 700
Abstract
Standard epidemiological models often treat human behavior as static, failing to capture the dynamic feedback loops that shape epidemic waves. To address this, we developed a compartmental model of COVID-19 that couples the disease dynamics with two co-evolving behavioral games governed by imitation [...] Read more.
Standard epidemiological models often treat human behavior as static, failing to capture the dynamic feedback loops that shape epidemic waves. To address this, we developed a compartmental model of COVID-19 that couples the disease dynamics with two co-evolving behavioral games governed by imitation dynamics: an altruistic self-isolation game for infected individuals and a self-interested vaccination game for susceptible individuals. Our simulations reveal a fundamental behavioral paradox: strong adherence to self-isolation, while effective at reducing peak infections, diminishes the perceived risk of disease, thereby undermining the incentive to vaccinate. This dynamic highlights a critical trade-off between managing acute crises through non-pharmaceutical interventions and achieving long-term population immunity. We conclude that vaccination has a powerful stabilizing effect that can prevent the recurrent waves often driven by behavioral responses to non-pharmaceutical interventions. Public health policy must therefore navigate the tension between encouraging short-term mitigation behaviors and communicating the long-term benefits of vaccination to ensure lasting population resilience. Full article
►▼ Show Figures

Figure 1

24 pages, 4180 KB  
Article
CSSA: An Enhanced Sparrow Search Algorithm with Hybrid Strategies for Engineering Optimization
by Yancang Li and Jiawei Li
Algorithms 2026, 19(1), 51; https://doi.org/10.3390/a19010051 - 6 Jan 2026
Cited by 1 | Viewed by 965
Abstract
To address the limitations of the standard Sparrow Search Algorithm (SSA) in complex optimization problems—such as insufficient convergence accuracy and susceptibility to local optima—this paper proposes a Composite Strategy Sparrow Search Algorithm (CSSA) for multidimensional optimization. The algorithm first employs chaotic mapping during [...] Read more.
To address the limitations of the standard Sparrow Search Algorithm (SSA) in complex optimization problems—such as insufficient convergence accuracy and susceptibility to local optima—this paper proposes a Composite Strategy Sparrow Search Algorithm (CSSA) for multidimensional optimization. The algorithm first employs chaotic mapping during initialization to enhance population diversity; second, it integrates coordinate axis pattern search to strengthen local exploitation capabilities; third, it applies intelligent crossover operations to promote effective information exchange among individuals; and finally, it introduces an adaptive vigilance mechanism to dynamically balance exploration and exploitation throughout the optimization process. Compared with seven state-of-the-art algorithms, CSSA demonstrates superior performance in both 30-dimensional low-dimensional and 100-dimensional high-dimensional test scenarios. It achieves optimal solutions in three real-world engineering applications: thermal management of electric vehicle battery packs, photovoltaic power system configuration, and data center cooling systems. Wilcoxon rank-sum tests further confirm the statistical significance of these improvements. Experimental results show that CSSA significantly outperforms mainstream optimization methods in terms of convergence accuracy and speed, demonstrating substantial theoretical value and practical engineering significance. Full article
►▼ Show Figures

Figure 1

26 pages, 1266 KB  
Article
Hybrid Evolutionary Multi-Objective Method for Automatic Design of a Lightweight CNN Architecture Applied to Coronary Stenosis Classification
by Miguel-Angel Gil-Rios, Ivan Cruz-Aceves, Arturo Hernandez-Aguirre, Erick-G. G.-de-Paz and Juan-Manuel Lopez-Hernandez
Algorithms 2026, 19(1), 47; https://doi.org/10.3390/a19010047 - 5 Jan 2026
Cited by 1 | Viewed by 877
Abstract
This paper presents a novel method based on a Hybrid Multi-Objective Evolutionary strategy for the automatic design of a lightweight convolutional neural network used for coronary stenosis classification. The hybrid methodology consists of two search stages, starting with the Multi-Objective Evolutionary Algorithm based [...] Read more.
This paper presents a novel method based on a Hybrid Multi-Objective Evolutionary strategy for the automatic design of a lightweight convolutional neural network used for coronary stenosis classification. The hybrid methodology consists of two search stages, starting with the Multi-Objective Evolutionary Algorithm based on Decomposition, to generate a set of optimal solutions focused on the minimization of two objectives: the accuracy classification error and the number of learning parameters in the convolutional neural network. Subsequently, the Simulated Annealing algorithm is applied to improve a subset of the solutions produced in the previous step. After the method was complete, a convolutional neural network model consisting of 3498 learning parameters was found by the proposed hybrid strategy, which is a considerably low number compared with the other architectures reported in the literature. Consequently, the found model achieved the highest classification performance rate in terms of the Accuracy and Jaccard Similarity Coefficient metrics with values of 0.94 and 0.89, respectively, using a database consisting of 608 images of regions with positive and negative coronary stenosis cases. On a second test, the model was tested using a database consisting of 2788 instances of natural and synthetic images of coronary stenosis cases. Corresponding maximum classification rates of 0.97 and 0.93 for the Accuracy and Jaccard Similarity Coefficient metrics, respectively, were achieved. In addition, the average required time to classify a single instance was 0.009 seconds. The obtained results showed that the proposed method is feasible for the automatic design of lightweight convolutional neural networks that can be used as a part of decision-making systems in clinical practice. Full article
(This article belongs to the Special Issue Algorithmic Innovations: Bridging Theoretical Foundations and Practical Applications)
►▼ Show Figures

Figure 1

43 pages, 554 KB  
Review
A Survey of Six Classical Classifiers, Including Algorithms, Methodological Characteristics, Foundational Variants, and Recent Advances
by Ali Hussein Alshammari, Gergely Bencsik and Almashhadani Hasnain Ali
Algorithms 2026, 19(1), 37; https://doi.org/10.3390/a19010037 - 1 Jan 2026
Cited by 7 | Viewed by 3320
Abstract
Classification is a core supervised learning task in data analysis, and six classical classifier families (k-Nearest Neighbors, Support Vector Machine, Decision Tree, Random Forest, Logistic Regression, and Naïve Bayes) remain widely used in practice and underpin many subsequent variants. Although both single-family and [...] Read more.
Classification is a core supervised learning task in data analysis, and six classical classifier families (k-Nearest Neighbors, Support Vector Machine, Decision Tree, Random Forest, Logistic Regression, and Naïve Bayes) remain widely used in practice and underpin many subsequent variants. Although both single-family and multi-classifier surveys exist, there is still a gap for a method-centered study that, within a coherent framework, combines algorithmic representations for training and prediction, methodological characteristics, an explicit methodological comparison of the foundational variants within each family, and method-oriented advances published between 2020 and 2025. The survey is organized around a fixed set of performance-related perspectives, including accuracy, hyperparameter tuning, scalability, class imbalance, behavior in high-dimensional settings, decision-boundary complexity, interpretability, computational efficiency, and multiclass handling. It highlights strengths, weaknesses, and trade-offs across the six families and their variants, helping researchers and practitioners select or extend classification approaches. It also outlines future research directions arising from the limitations across the examined methods. Full article
(This article belongs to the Special Issue Machine Learning for Pattern Recognition (3rd Edition))
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying article 1-50 on page 1 of 4.
Go to page     1 2 3 4 chevron_right last_page
Back to TopTop