Algorithms

28 pages, 2027 KB

Open AccessArticle

Dynamic Resource Games in the Wood Flooring Industry: A Bayesian Learning and Lyapunov Control Framework

by Yuli Wang and Athanasios V. Vasilakos

Algorithms 2026, 19(1), 78; https://doi.org/10.3390/a19010078 - 16 Jan 2026

Wood flooring manufacturers face complex challenges in dynamically allocating resources across multi-channel markets, characterized by channel conflicts, demand uncertainty, and long-term cumulative effects of decisions. Traditional static optimization or myopic approaches struggle to address these intertwined factors, particularly when critical market states like [...] Read more.

Wood flooring manufacturers face complex challenges in dynamically allocating resources across multi-channel markets, characterized by channel conflicts, demand uncertainty, and long-term cumulative effects of decisions. Traditional static optimization or myopic approaches struggle to address these intertwined factors, particularly when critical market states like brand reputation and customer base cannot be precisely observed. This paper establishes a systematic and theoretically grounded online decision framework to tackle this problem. We first model the problem as a Partially Observable Stochastic Dynamic Game. The core innovation lies in introducing an unobservable market position vector as the central system state, whose evolution is jointly influenced by firm investments, inter-channel competition, and macroeconomic randomness. The model further captures production lead times, physical inventory dynamics, and saturation/cross-channel effects of marketing investments, constructing a high-fidelity dynamic system. To solve this complex model, we propose a hierarchical online learning and control algorithm named L-BAP (Lyapunov-based Bayesian Approximate Planning), which innovatively integrates three core modules. It employs particle filters for Bayesian inference to nonparametrically estimate latent market states online. Simultaneously, the algorithm constructs a Lyapunov optimization framework that transforms long-term discounted reward objectives into tractable single-period optimization problems through virtual debt queues, while ensuring stability of physical systems like inventory. Finally, the algorithm embeds a game-theoretic module to predict and respond to rational strategic reactions from each channel. We provide theoretical performance analysis, rigorously proving the mean-square boundedness of system queues and deriving the performance gap between long-term rewards and optimal policies under complete information. This bound clearly quantifies the trade-off between estimation accuracy (determined by particle count) and optimization parameters. Extensive simulations demonstrate that our L-BAP algorithm significantly outperforms several strong baselines—including myopic learning and decentralized reinforcement learning methods—across multiple dimensions: long-term profitability, inventory risk control, and customer service levels. Full article

(This article belongs to the Section Analysis of Algorithms and Complexity Theory)

37 pages, 4259 KB

Open AccessArticle

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

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))

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29 pages, 7379 KB

Open AccessArticle

Boundary-Aware Multi-Point Preview Control: An Algorithm for Autonomous Articulated Mining Vehicles Operating in Highly Constrained Underground Spaces

by Shuo Huang, Yiting Kang, Jue Yang, Xiao Lv and Ming Zhu

Algorithms 2026, 19(1), 76; https://doi.org/10.3390/a19010076 - 16 Jan 2026

Abstract

To achieve the automation and intelligence of mining equipment, it is essential to address the challenge of autonomous driving, with the core task being how to navigate safely from the starting point to the mining area endpoint. This paper proposes a boundary-aware multi-point [...] Read more.

To achieve the automation and intelligence of mining equipment, it is essential to address the challenge of autonomous driving, with the core task being how to navigate safely from the starting point to the mining area endpoint. This paper proposes a boundary-aware multi-point preview control algorithm to tackle the strong dependency on predefined paths and the lack of foresight in the autonomous driving of underground articulated mining vehicles in highly confined underground spaces. The algorithm determines the driving direction by calculating the vehicle’s real-time state and LiDAR data, previewing road conditions without relying on preset path planning. Experiments conducted in a ROS Noetic/GAZEBO 11 simulation environment compared the proposed method with single-point and two-point preview algorithms, validating the effectiveness of the boundary-aware multi-point preview control. The results show that the proposed control strategy yields the lowest lateral deviation and the highest steering smoothness compared to single-point and two-point preview algorithms; it also outperforms the standard multi-point preview algorithm. This demonstrates its superior performance. Specifically, the proposed boundary-aware multi-point preview algorithm outperformed other methods in terms of steering smoothness and stability, significantly enhancing the vehicle system’s adaptability, robustness, and safety. Full article

(This article belongs to the Section Algorithms for Multidisciplinary Applications)

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26 pages, 3391 KB

Open AccessArticle

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

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)

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23 pages, 2992 KB

Open AccessArticle

Key-Value Mapping-Based Text-to-Image Diffusion Model Backdoor Attacks

by Lujia Chai, Yang Hou, Guozhao Liao and Qiuling Yue

Algorithms 2026, 19(1), 74; https://doi.org/10.3390/a19010074 - 15 Jan 2026

Abstract

Text-to-image (T2I) generation, a core component of generative artificial intelligence(AI), is increasingly important for creative industries and human–computer interaction. Despite impressive progress in realism and diversity, diffusion models still exhibit critical security blind spots particularly in the Transformer key-value mapping mechanism that underpins [...] Read more.

Text-to-image (T2I) generation, a core component of generative artificial intelligence(AI), is increasingly important for creative industries and human–computer interaction. Despite impressive progress in realism and diversity, diffusion models still exhibit critical security blind spots particularly in the Transformer key-value mapping mechanism that underpins cross-modal alignment. Existing backdoor attacks often rely on large-scale data poisoning or extensive fine-tuning, leading to low efficiency and limited stealth. To address these challenges, we propose two efficient backdoor attack methods AttnBackdoor and SemBackdoor grounded in the Transformer’s key-value storage principle. AttnBackdoor injects precise mappings between trigger prompts and target instances by fine-tuning the key-value projection matrices in U-Net cross-attention layers (≈5% of parameters). SemBackdoor establishes semantic-level mappings by editing the text encoder’s MLP projection matrix (≈0.3% of parameters). Both approaches achieve high attack success rates (>90%), with SemBackdoor reaching 98.6% and AttnBackdoor 97.2%. They also reduce parameter updates and training time by 1–2 orders of magnitude compared to prior work while preserving benign generation quality. Our findings reveal dual vulnerabilities at visual and semantic levels and provide a foundation for developing next generation defenses for secure generative AI. Full article

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25 pages, 91838 KB

Open AccessArticle

ICCA: Independent Multi-Agent Algorithm for Distributed Jamming Scheduling

by Wenpeng Wu, Zhenhua Wei, Haiyang You, Zhaoguang Zhang, Chenxi Li, Jianwei Zhan and Shan Zhao

Algorithms 2026, 19(1), 73; https://doi.org/10.3390/a19010073 - 15 Jan 2026

Abstract

In extreme scenarios, to prevent the leakage of jamming coordination information, the jammers must proactively terminate their communication functions and implement jamming resource scheduling via Non-Networked Cooperation. However, current research on this non-networked jamming approach is relatively limited. Furthermore, existing algorithms either rely [...] Read more.

In extreme scenarios, to prevent the leakage of jamming coordination information, the jammers must proactively terminate their communication functions and implement jamming resource scheduling via Non-Networked Cooperation. However, current research on this non-networked jamming approach is relatively limited. Furthermore, existing algorithms either rely on networked interactions or lack cognitive strategies for the surrounding communication countermeasure situation. For example, they fail to adapt to dynamic changes in electromagnetic noise and struggle to determine jamming effectiveness, leading to low jamming efficiency and severe energy waste in non-networked scenarios. To address this issue, this paper establishes a game process and corresponding algorithm for non-networked communication countermeasures and designs cognitive, cooperative, and scheduling strategies for individual jammers. Meanwhile, a novel performance metric called the “Overall Communication Suppression Ratio (OCSR)” is proposed. This metric quantifies the relationship between “sustained full-suppression duration” and “ operating duration of the jamming system,” overcoming the defect that traditional metrics cannot evaluate the dynamic jamming effectiveness in non-networked scenarios. Experimental results indicate that although the OCSR of the proposed Intelligent Concentric Circle Algorithm (ICCA) is significantly lower than that of the Full-Power Jamming Algorithm (FPJA), ICCA extends the operating duration of the jamming system by 4.8%. This achieves non-uniform power setting of jammers, enabling flexible and dynamic jamming in non-networked scenarios and retaining more battery capacity for jammers after overall jamming failure. Full article

(This article belongs to the Special Issue Advances in Algorithms Through Heuristics: Theory, Applications, and Innovations)

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18 pages, 3360 KB

Open AccessArticle

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

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)

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36 pages, 9776 KB

Open AccessArticle

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

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

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32 pages, 999 KB

Open AccessArticle

A Robust Hybrid Metaheuristic Framework for Training Support Vector Machines

by Khalid Nejjar, Khalid Jebari and Siham Rekiek

Algorithms 2026, 19(1), 70; https://doi.org/10.3390/a19010070 - 13 Jan 2026

Abstract

Support Vector Machines (SVMs) are widely used in critical decision-making applications, such as precision agriculture, due to their strong theoretical foundations and their ability to construct an optimal separating hyperplane in high-dimensional spaces. However, the effectiveness of SVMs is highly dependent on the [...] Read more.

Support Vector Machines (SVMs) are widely used in critical decision-making applications, such as precision agriculture, due to their strong theoretical foundations and their ability to construct an optimal separating hyperplane in high-dimensional spaces. However, the effectiveness of SVMs is highly dependent on the efficiency of the optimization algorithm used to solve their underlying dual problem, which is often complex and constrained. Classical solvers, such as Sequential Minimal Optimization (SMO) and Stochastic Gradient Descent (SGD), present inherent limitations: SMO ensures numerical stability but lacks scalability and is sensitive to heuristics, while SGD scales well but suffers from unstable convergence and limited suitability for nonlinear kernels. To address these challenges, this study proposes a novel hybrid optimization framework based on Open Competency Optimization and Particle Swarm Optimization (OCO–PSO) to enhance the training of SVMs. The proposed approach combines the global exploration capability of PSO with the adaptive competency-based learning mechanism of OCO, enabling efficient exploration of the solution space, avoidance of local minima, and strict enforcement of dual constraints on the Lagrange multipliers. Across multiple datasets spanning medical (diabetes), agricultural yield, signal processing (sonar and ionosphere), and imbalanced synthetic data, the proposed OCO-PSO–SVM consistently outperforms classical SVM solvers (SMO and SGD) as well as widely used classifiers, including decision trees and random forests, in terms of accuracy, macro-F1-score, Matthews correlation coefficient (MCC), and ROC-AUC. On the Ionosphere dataset, OCO-PSO achieves an accuracy of

95.71 %

, an F1-score of

0.954

, and an MCC of

0.908

, matching the accuracy of random forest while offering superior interpretability through its kernel-based structure. In addition, the proposed method yields a sparser model with only 66 support vectors compared to 71 for standard SVC (a reduction of approximately

7 %

), while strictly satisfying the dual constraints with a near-zero violation of

1.3 \times 10^{- 3}

. Notably, the optimal hyperparameters identified by OCO-PSO (

C = 2

,

γ \approx 0.062

) differ substantially from those obtained via Bayesian optimization for SVC (

C = 10

,

γ \approx 0.012

), indicating that the proposed approach explores alternative yet equally effective regions of the hypothesis space. The statistical significance and robustness of these improvements are confirmed through extensive validation using 1000 bootstrap replications, paired Student’s t-tests, Wilcoxon signed-rank tests, and Holm–Bonferroni correction. These results demonstrate that the proposed metaheuristic hybrid optimization framework constitutes a reliable, interpretable, and scalable alternative for training SVMs in complex and high-dimensional classification tasks. Full article

(This article belongs to the Special Issue Data Sensing Techniques and Processing Algorithms for Smart and Sustainable Agriculture)

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22 pages, 884 KB

Open AccessArticle

Sentiment-Augmented RNN Models for Mini-TAIEX Futures Prediction

by Yu-Heng Hsieh, Keng-Pei Lin, Ching-Hsi Tseng, Xiaolong Liu and Shyan-Ming Yuan

Algorithms 2026, 19(1), 69; https://doi.org/10.3390/a19010069 - 13 Jan 2026

Abstract

Accurate forecasting in low-liquidity futures markets is essential for effective trading. This study introduces a hybrid decision-support framework that combines Mini-TAIEX (MTX) futures data with sentiment signals extracted from 13 financial news sources and PTT forum discussions. Sentiment features are generated using three [...] Read more.

Accurate forecasting in low-liquidity futures markets is essential for effective trading. This study introduces a hybrid decision-support framework that combines Mini-TAIEX (MTX) futures data with sentiment signals extracted from 13 financial news sources and PTT forum discussions. Sentiment features are generated using three domain-adapted large language models—FinGPT-internLM, FinGPT-llama, and FinMA—trained on more than 360,000 finance-related texts. These features are integrated with technical indicators in four deep learning models: LSTM, GRU, Informer, and PatchTST. Experiments from June 2024 to June 2025 show that sentiment-augmented models consistently outperform baselines. Backtesting further demonstrates that the sentiment-enhanced PatchTST achieves a 526% cumulative return with a Sharpe ratio of 0.407, highlighting the value of incorporating sentiment into AI-driven futures trading systems. Full article

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18 pages, 2883 KB

Open AccessArticle

A Multi-Objective Giant Trevally Optimizer with Feasibility-Aware Archiving for Constrained Optimization

by Nashwan Hussein and Adnan Abdulazeez

Algorithms 2026, 19(1), 68; https://doi.org/10.3390/a19010068 - 13 Jan 2026

Abstract

Multi-objective optimization (MOO) plays a critical role in mechanical and industrial engineering, where conflicting design goals must be balanced under complex constraints. In this study, we introduce the Multi-Objective Giant Trevally Optimizer (MOGTO), a novel extension of the Giant Trevally Optimizer inspired by [...] Read more.

Multi-objective optimization (MOO) plays a critical role in mechanical and industrial engineering, where conflicting design goals must be balanced under complex constraints. In this study, we introduce the Multi-Objective Giant Trevally Optimizer (MOGTO), a novel extension of the Giant Trevally Optimizer inspired by predatory foraging dynamics. MOGTO integrates predation-regime switching into a Pareto-based framework, enhanced with feasibility-aware archiving, knee-biased selection, and adaptive constraint handling. We benchmark MOGTO against established algorithms—NSGA-II, SPEA2, MOEA/D, and ParetoSearch—using synthetic test suites (ZDT1–3, DTLZ2) and classical engineering problems (welded beam, spring, and pressure vessel). Performance was assessed with Hypervolume (HV), Inverted Generational Distance (IGD), Spacing, and coverage metrics across 30 independent runs. The results demonstrate that MOGTO consistently achieves competitive or superior HV and IGD, maintains more uniform spacing, and generates larger feasible archives than the baselines. Particularly on constrained engineering problems, MOGTO yields more feasible non-dominated solutions, confirming its robustness and industrial applicability. These findings establish MOGTO as a reliable and general-purpose metaheuristic for multi-objective optimization in engineering design. Full article

(This article belongs to the Section Evolutionary Algorithms and Machine Learning)

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Graphical abstract

18 pages, 998 KB

Open AccessArticle

A Stock Price Prediction Network That Integrates Multi-Scale Channel Attention Mechanism and Sparse Perturbation Greedy Optimization

by Jiarun He, Fangying Wan and Mingfang He

Algorithms 2026, 19(1), 67; https://doi.org/10.3390/a19010067 - 12 Jan 2026

Abstract

The stock market is of paramount importance to economic development. Investors who accurately predict stock price fluctuations based on its high volatility can effectively mitigate investment risks and achieve higher returns. Traditional time series models face limitations when dealing with long sequences and [...] Read more.

The stock market is of paramount importance to economic development. Investors who accurately predict stock price fluctuations based on its high volatility can effectively mitigate investment risks and achieve higher returns. Traditional time series models face limitations when dealing with long sequences and short-term volatility issues, often yielding unsatisfactory predictive outcomes. This paper proposes a novel algorithm, MSNet, which integrates a Multi-scale Channel Attention mechanism (MSCA) and Sparse Perturbation Greedy Optimization (SPGO) onto an xLSTM framework. The MSCA enhances the model’s spatio-temporal information modeling capabilities, effectively preserving key price features within stock data. Meanwhile, SPGO improves the exploration of optimal solutions during training, thereby strengthening the model’s generalization stability against short-term market fluctuations. Experimental results demonstrate that MSNet achieves an MSE of 0.0093 and an MAE of 0.0152 on our proprietary dataset. This approach effectively extracts temporal features from complex stock market data, providing empirical insights and guidance for time series forecasting. Full article

(This article belongs to the Section Algorithms for Multidisciplinary Applications)

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24 pages, 4886 KB

Open AccessArticle

YOLOv8-ECCα: Enhancing Object Detection for Power Line Asset Inspection Under Real-World Visual Constraints

by Rita Ait el haj, Badr-Eddine Benelmostafa and Hicham Medromi

Algorithms 2026, 19(1), 66; https://doi.org/10.3390/a19010066 - 12 Jan 2026

Abstract

Unmanned Aerial Vehicles (UAVs) have revolutionized power-line inspection by enhancing efficiency, safety, and enabling predictive maintenance through frequent remote monitoring. Central to automated UAV-based inspection workflows is the object detection stage, which transforms raw imagery into actionable data by identifying key components such [...] Read more.

Unmanned Aerial Vehicles (UAVs) have revolutionized power-line inspection by enhancing efficiency, safety, and enabling predictive maintenance through frequent remote monitoring. Central to automated UAV-based inspection workflows is the object detection stage, which transforms raw imagery into actionable data by identifying key components such as insulators, dampers, and shackles. However, the real-world complexity of inspection scenes poses significant challenges to detection accuracy. For example, the InsPLAD-det dataset—characterized by over 30,000 annotations across diverse tower structures and viewpoints, with more than 40% of components partially occluded—illustrates the visual and structural variability typical of UAV inspection imagery. In this study, we introduce YOLOv8-ECCα, a novel object detector tailored for these demanding inspection conditions. Our contributions include: (1) integrating CoordConv, selected over deformable convolution for its efficiency in preserving fine spatial cues without heavy computation; (2) adding Efficient Channel Attention (ECA), preferred to SE or CBAM for its ability to enhance feature relevance using only a single 1D convolution and no dimensionality reduction; and (3) adopting Alpha-IoU, chosen instead of CIoU or GIoU to produce smoother gradients and more stable convergence, particularly under partial overlap or occlusion. Evaluated on the InsPLAD-det dataset, YOLOv8-ECCα achieves an mAP@50 of 82.75%, outperforming YOLOv8s (81.89%) and YOLOv9-E (82.61%) by +0.86% and +0.14%, respectively, while maintaining real-time inference at 86.7 FPS—exceeding the baseline by +2.3 FPS. Despite these improvements, the model retains a compact footprint (28.5 GFLOPs, 11.1 M parameters), confirming its suitability for embedded UAV deployment in real inspection environments. Full article

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34 pages, 5835 KB

Open AccessReview

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

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)

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20 pages, 11896 KB

Open AccessArticle

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

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

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32 pages, 3198 KB

Open AccessReview

Explainability in Deep Learning in Healthcare and Medicine: Panacea or Pandora’s Box? A Systemic View

by Wullianallur Raghupathi

Algorithms 2026, 19(1), 63; https://doi.org/10.3390/a19010063 - 12 Jan 2026

Abstract

Explainability in deep learning (XDL) for healthcare is increasingly portrayed as essential for addressing the “black box” problem in clinical artificial intelligence. However, this universal transparency mandate may create unintended consequences, including cognitive overload, spurious confidence, and workflow disruption. This paper examines a [...] Read more.

Explainability in deep learning (XDL) for healthcare is increasingly portrayed as essential for addressing the “black box” problem in clinical artificial intelligence. However, this universal transparency mandate may create unintended consequences, including cognitive overload, spurious confidence, and workflow disruption. This paper examines a fundamental question: Is explainability a panacea that resolves AI’s trust deficit, or a Pandora’s box that introduces new risks? Drawing on general systems theory we demonstrate that the answer is profoundly context dependent. Through systemic analysis of current XDL methods, Saliency Maps, LIME, SHAP, and attention mechanisms, we reveal systematic disconnects between technical transparency and clinical utility. This paper argues that XDL is a context-dependent systemic property rather than a universal requirement. It functions as a panacea when proportionately applied to high-stakes reasoning tasks (cancer treatment planning, complex diagnosis) within integrated socio-technical architectures. Conversely, it becomes a Pandora’s box when superficially imposed on routine operational functions (scheduling, preprocessing) or time-critical emergencies (e.g., cardiac arrest) where comprehensive explanation delays lifesaving intervention. The paper proposes a risk-stratified framework recognizing that a specific subset of healthcare AI applications—those involving high-stakes clinical reasoning—require comprehensive explainability, while other applications benefit from calibrated transparency appropriate to their clinical context. We conclude that explainability is neither a cure-all nor an inevitable harm, but rather a dynamic equilibrium requiring continuous rebalancing across technical, cognitive, and organizational dimensions. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence in Healthcare, Biomedicine and Medical Informatics)

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32 pages, 539 KB

Open AccessArticle

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

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)

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19 pages, 8626 KB

Open AccessArticle

GAD-YOLO: A Sight-Distance Adaptive Detection Algorithm for General Aviation Aircraft Skin Damage

by Tao Wu, Jifei Zhong, Zhanhai Wang, Chen Chen and Zhenghong Xia

Algorithms 2026, 19(1), 61; https://doi.org/10.3390/a19010061 - 10 Jan 2026

Abstract

To address the challenges in detecting surface damage on general aviation aircraft skin—such as feature degradation under varying imaging distances, significant target scale variations, and low recognition accuracy—this paper proposes GAD-YOLO, a sight-distance adaptive detection algorithm. First, a P2 small-target detection layer is [...] Read more.

To address the challenges in detecting surface damage on general aviation aircraft skin—such as feature degradation under varying imaging distances, significant target scale variations, and low recognition accuracy—this paper proposes GAD-YOLO, a sight-distance adaptive detection algorithm. First, a P2 small-target detection layer is integrated into the shallow network to enhance the capture of fine damage details. Second, an HMFHead detection head is introduced to mitigate scale variation effects through progressive semantic fusion and edge-aware constraints. Third, an LDown downsampling module is designed to construct a multi-scale feature fusion architecture. This module reduces redundancy via cross-level interaction and a lightweight kernel design, thereby decreasing the number of parameters and computational cost. Additionally, a DySample-based dynamic sampling operator is proposed to preserve local details through proximity-aware sampling while enriching the contextual semantics of distant damage features, effectively improving recognition performance. Experiments on a self-constructed general aviation aircraft skin damage dataset show that GAD-YOLO achieves 87.4% precision, 80.4% recall, 86.6% mAP@0.5, and 59.7% mAP@0.5:0.95. These results outperform the YOLOv11n baseline by 2.0%, 9.4%, 6.7%, and 7.6%, respectively. The proposed method significantly improves detection performance and provides a valuable reference for intelligent inspection and maintenance in general aviation. Full article

(This article belongs to the Special Issue Applications of Intelligent Optimization Algorithms in Integrated Transportation Systems)

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28 pages, 2058 KB

Open AccessArticle

Tiny Language Model Guided Flow Q Learning for Optimal Task Scheduling in Fog Computing

by Bhargavi K and Sajjan G. Shiva

Algorithms 2026, 19(1), 60; https://doi.org/10.3390/a19010060 - 10 Jan 2026

Abstract

Fog computing is one of the rapidly growing platforms with an exponentially increasing demand for real-time data processing. The fog computing market is expected to reach USD 8358 million by the year 2030 with a compound annual growth of 50%. The wide adaptation [...] Read more.

Fog computing is one of the rapidly growing platforms with an exponentially increasing demand for real-time data processing. The fog computing market is expected to reach USD 8358 million by the year 2030 with a compound annual growth of 50%. The wide adaptation of fog computing by the industries worldwide is due to the advantages like reduced latency, high operational efficiency, and high-level data privacy. The highly distributed and heterogeneous nature of fog computing leads to significant challenges related to resource management, data security, task scheduling, data privacy, and interoperability. The task typically represents a job generated by the IoT device. The action indicates the way of executing the tasks whose decision is taken by the scheduler. Task scheduling is one of the prominent issues in fog computing which includes the process of effectively scheduling the tasks among fog devices to effectively utilize the resources and meet the Quality of Service (QoS) requirements of the applications. Improper task scheduling leads to increased execution time, overutilization of resources, data loss, and poor scalability. Hence there is a need to do proper task scheduling to make optimal task distribution decisions in a highly dynamic resource-constrained heterogeneous fog computing environment. Flow Q learning (FQL) is a potential form of reinforcement learning algorithm which uses the flow matching policy for action distribution. It can handle complex forms of data and multimodal action distribution which make it suitable for the highly volatile fog computing environment. However, flow Q learning struggles to achieve a proper trade-off between the expressive flow model and a reduction in the Q function, as it relies on a one-step optimization policy that introduces bias into the estimated Q function value. The Tiny Language Model (TLM) is a significantly smaller form of a Large Language Model (LLM) which is designed to operate over the device-constrained environment. It can provide fair and systematic guidance to disproportionally biased deep learning models. In this paper a novel TLM guided flow Q learning framework is designed to address the task scheduling problem in fog computing. The neutrality and fine-tuning capability of the TLM is combined with the quick generable ability of the FQL algorithm. The framework is simulated using the Simcan2Fog simulator considering the dynamic nature of fog environment under finite and infinite resources. The performance is found to be good with respect to parameters like execution time, accuracy, response time, and latency. Further the results obtained are validated using the expected value analysis method which is found to be satisfactory. Full article

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