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Algorithms
Volume 19
Issue 5
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Algorithms, Volume 19, Issue 5 (May 2026) – 99 articles

Cover Story (view full-size image): Selecting the right hardware for machine learning training involves balancing execution speed, energy consumption, and cost, yet systematic cross-platform evidence remains scarce. This paper introduces a unified, reproducible benchmarking framework evaluated across CUDA GPUs, Apple Silicon, x86 CPUs, ARM embedded systems, and cloud environments, using five supervised learning models: CNN, Simple RNN, LSTM, BiLSTM, and XGBoost. Results show hardware suitability is strongly model-dependent: CUDA GPUs deliver the fastest and most stable training, while Apple Silicon achieves superior energy efficiency for recurrent architectures. XGBoost remains competitive on CPUs regardless of platform. The framework also incorporates batch size sensitivity, epoch-based stability validation, and performance-per-dollar analysis, offering a practical, model-aware foundation for hardware decision-making. View this paper
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17 pages, 3232 KB  
Article
An Improved YOLOv11 for Tiny Surface Defect Detection on Electrical Commutators
by Jichen Yuan, Zepeng Su and Zhulin Liu
Algorithms 2026, 19(5), 422; https://doi.org/10.3390/a19050422 - 21 May 2026
Viewed by 423
Abstract
Aiming at the challenges of class imbalance, tiny defect scales, and complex brushed background interference in the surface defect detection of electrical commutators, this paper proposes a high-precision and lightweight improved instance segmentation algorithm named WG-YOLOv11. Firstly, to overcome the barrier of highly [...] Read more.
Aiming at the challenges of class imbalance, tiny defect scales, and complex brushed background interference in the surface defect detection of electrical commutators, this paper proposes a high-precision and lightweight improved instance segmentation algorithm named WG-YOLOv11. Firstly, to overcome the barrier of highly imbalanced positive and negative samples in actual industrial data collection, a Balanced Defect Synthesis (BDS) data augmentation strategy is introduced to effectively enrich the morphological diversity of tiny defects. Secondly, a Wavelet Transform Convolution (WTConv) module is collaboratively integrated into the feature extraction network to expand the receptive field while preserving the high-frequency edge details of hairline cracks. Thirdly, a Group CBAM Enhancer (GCE) module is introduced to filter out high-reflection and brushed background noise through grouped attention and weight re-calibration mechanisms. Finally, addressing the difficulty of pixel-level alignment for tiny defects, an α-IoU loss function is utilized to improve the high-precision segmentation and localization capabilities by dynamically adjusting the gradient distribution. Comprehensive evaluations are conducted on two real-world electrical commutator surface defect datasets: KolektorSDD2 and KolektorSDD. Experimental results show that on the KolektorSDD2 dataset, compared to the YOLOv11 baseline, the Mask mAP@50 of WG-YOLOv11 increases from 85.2% to 89.2%, and the stringent metric Mask mAP@50:95 improves from 52.7% to 56.9%. Additional computational analysis on the same dataset validates that the proposed method maintains high efficiency, matching the baseline computational cost without compromising real-time inference speed. Furthermore, evaluations on the public MSD dataset confirm the model’s cross-domain generalization capabilities. The proposed framework effectively achieves a balance between detection accuracy, anti-interference robustness, and a lightweight architecture. Full article
(This article belongs to the Collection Feature Papers on Artificial Intelligence Algorithms and Their Applications)
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26 pages, 2568 KB  
Article
Simulation of a Four-Stroke Diesel Engine for Propulsion in Wave
by Zhe Chen, Fan Shi, Jiawang Li and Guangnian Li
Algorithms 2026, 19(5), 421; https://doi.org/10.3390/a19050421 - 21 May 2026
Viewed by 381
Abstract
With the development of shipping to harsh marine environment, it is very important to understand the transient behavior of a marine diesel engine in high sea conditions. Wave-induced hull motion will lead to severe load fluctuations and air-fuel ratio imbalance. In this study, [...] Read more.
With the development of shipping to harsh marine environment, it is very important to understand the transient behavior of a marine diesel engine in high sea conditions. Wave-induced hull motion will lead to severe load fluctuations and air-fuel ratio imbalance. In this study, an integrated simulation platform coupled with environmental loads, hull dynamics, propeller characteristics and a high-fidelity thermodynamic engine model was constructed to explore the response characteristics of the propulsion system. The model integrates a zero-dimensional multi-zone combustion method, turbocharger dynamic characteristics and an incremental PID governor, and has been verified based on the bench test data of TBD234V12 diesel engine and the 20 m Wigley standard ship. The simulation results under the sea conditions from level 7 to 9 show that the transient load has a nonlinear amplification effect. Specifically, from sea state 7 to sea state 9, the engine load fluctuation range expands by 2.0 times, while the main peak amplitude of speed fluctuation increases by 3.7 times. Furthermore, the peak exhaust pressure rises by 1.8 times, and the exhaust temperature fluctuation amplitude broadens by 35%. Frequency domain analysis further identified the low-frequency energy concentration phenomenon in the exhaust pressure spectrum and the precursor characteristics of compressor surge. The research results quantify the deterioration law of thermodynamic stability and mechanical stress under wave disturbance, and provide an important reference for the formulation of an engine robust control strategy and fatigue life assessment under high sea conditions. Full article
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20 pages, 410 KB  
Article
When Learned Action Rules Matter: A Matched-Seed Ablation in an Agent-Based Spatial Ecology
by Vladimir Ternovski
Algorithms 2026, 19(5), 420; https://doi.org/10.3390/a19050420 - 21 May 2026
Viewed by 284
Abstract
Whether learned cognition can affect evolutionary outcomes remains a long-standing question. This study addresses a narrower mechanism: whether a model-based planner benefits from learned rules that explicitly condition on the action just taken. The testbed is a spatial artificial ecology with plants, shelters, [...] Read more.
Whether learned cognition can affect evolutionary outcomes remains a long-standing question. This study addresses a narrower mechanism: whether a model-based planner benefits from learned rules that explicitly condition on the action just taken. The testbed is a spatial artificial ecology with plants, shelters, a predator, reproduction, and a day/night cycle. Five rule-use arms are evaluated on matched simulation seeds. At age 200, agents switch to a weaker learned-lite planner that relies more strongly on learned rule predictions. The pre-specified hypothesis is that access to filtered action-conditioned rules improves outcomes relative to an otherwise identical no-rule-policy baseline, in which rules are still induced and stored but are not used for action selection. In thirty paired replicates under the default reproductive gates, the action-conditioned arm outperforms the no-rule baseline on all four pre-specified primary endpoints. The strongest effect is behavioural: the action arm produces 91.4 additional successful post-switch eating events per run (dz=1.56, 93.3% paired win rate, p<10−4). It also produces 10 additional crystallized clean-causal rules per replicate (dz=0.58, pt=0.0034). All four primary paired-t p-values remain significant after Bonferroni correction across the four-endpoint family. A diagnostic check shows that omitting reproductive cooldown from the planner’s rollout reverses the arm ordering on the same paired seeds; reinstating cooldown recovers the reported result. Two exploratory checks delimit the claim: broad unfiltered rule access can impair foraging, and a means–ends extension shifts behaviour toward reproduction without producing a robust whole-life fitness gain. Within this simulation, access to action-conditioned rules has a measurable effect on post-switch behaviour that is distinct from passive environmental prediction and from clean-crystallized rules alone. Full article
(This article belongs to the Section Evolutionary Algorithms and Machine Learning)
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26 pages, 7601 KB  
Article
State-Separated SARSA: A Practical Sequential Decision-Making Algorithm with Recovering Rewards
by Yuto Tanimoto and Kenji Fukumizu
Algorithms 2026, 19(5), 419; https://doi.org/10.3390/a19050419 - 21 May 2026
Viewed by 401
Abstract
While many multi-armed bandit algorithms assume that rewards for all arms are constant across rounds, this assumption does not hold in many real-world scenarios. This paper considers the setting of recovering bandits, where the reward depends on the number of rounds elapsed since [...] Read more.
While many multi-armed bandit algorithms assume that rewards for all arms are constant across rounds, this assumption does not hold in many real-world scenarios. This paper considers the setting of recovering bandits, where the reward depends on the number of rounds elapsed since the last time an arm was pulled. We propose a new reinforcement learning (RL) algorithm tailored to this setting, named the State-Separated SARSA (SS-SARSA) algorithm, which treats the elapsed rounds as states. The SS-SARSA algorithm achieves efficient learning by reducing the number of state combinations required for Q-learning/SARSA, which often suffers from combinatorial explosion for large-scale RL problems. Additionally, it makes minimal assumptions about the reward structure and has lower computational complexity. Furthermore, we prove asymptotic convergence to an optimal policy under mild assumptions. Simulation studies demonstrate the superior performance of our algorithm across various settings. Full article
(This article belongs to the Section Evolutionary Algorithms and Machine Learning)
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37 pages, 4241 KB  
Article
Boosting Energy Quality in Hybrid Power Systems Through Fractional-Order Adaptive Fuzzy Logic–Based Direct Power Control of SAPF
by Khaoula Nermine Khallouf, Habib Benbouhenni and Nicu Bizon
Algorithms 2026, 19(5), 418; https://doi.org/10.3390/a19050418 - 21 May 2026
Viewed by 776
Abstract
The intermittent nature of renewable power sources, nonlinear load effects, and harmonic distortions induced by power electronic converters complicate the maintenance of high energy quality in microgrid-connected hybrid renewable power systems. In a range of operating conditions, conventional strategies-including fractional-order proportional-integral (FOPI) controllers-frequently [...] Read more.
The intermittent nature of renewable power sources, nonlinear load effects, and harmonic distortions induced by power electronic converters complicate the maintenance of high energy quality in microgrid-connected hybrid renewable power systems. In a range of operating conditions, conventional strategies-including fractional-order proportional-integral (FOPI) controllers-frequently prove ineffective in delivering both robust harmonic mitigation and expeditious dynamic response. To surmount these constraints, the present paper puts forth an intelligent control solution that is predicated on a fractional-order fuzzy logic (FOFL). The FOFL is integrated into a multi-converter HRPS, comprising a photovoltaic generator, a lithium-ion battery power storage system, and a wind turbine equipped with a permanent magnet synchronous generator. A multifunctional voltage source inverter has been developed to control these parts, which are interfaced via a common DC bus. Through the implementation of MATLAB 2021 simulation studies, the efficacy of the suggested algorithm is verified and evaluated in comparison to the FOPI. The findings indicate that the FOFL enhances system efficacy by minimizing harmonic distortion, improving energy quality, and achieving a faster dynamic response under various circumstances. In the context of grid-connected microgrid environments, the FOFL has been demonstrated to offer superior overall energy management, robustness, and adaptability when compared to other evaluated strategies. Full article
(This article belongs to the Special Issue Advanced Artificial Intelligence/Machine Learning Techniques for Safe Operation and Control in Power and Sustainable Energy Systems: 2nd Edition)
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31 pages, 7581 KB  
Article
Adapting the IDS-ML Framework for Automated Attack Detection on Edge Devices
by Ryan V. Cooper and Arslan Munir
Algorithms 2026, 19(5), 417; https://doi.org/10.3390/a19050417 - 21 May 2026
Viewed by 823
Abstract
As modern networks expand, the volume and destructiveness of cyberattacks continue to escalate, necessitating effective defense mechanisms. Intrusion Detection Systems (IDSs) are critical for maintaining network security; however, traditional signature-based systems often fail to detect zero-day attacks. This study explores recent advancements in [...] Read more.
As modern networks expand, the volume and destructiveness of cyberattacks continue to escalate, necessitating effective defense mechanisms. Intrusion Detection Systems (IDSs) are critical for maintaining network security; however, traditional signature-based systems often fail to detect zero-day attacks. This study explores recent advancements in Deep Learning (DL) for cybersecurity by analyzing and replicating the “IDS-ML” framework, an open-source repository for IDS development. We evaluate the performance of five deep learning Convolutional Neural Network (CNN) architectures adapted for intrusion detection via transfer learning on the CICIDS2017 dataset, and propose an enhancement by integrating Automated Machine Learning (AutoML) techniques that achieves a 94.7% reduction in model parameters while maintaining comparable accuracy, thus making our enhanced models suitable for deployment on edge devices. We further validate deployment feasibility by benchmarking both the baseline InceptionV3 and AutoML models on a Raspberry Pi 4, demonstrating an 18.7× inference speedup and 3.5× CPU reduction, with no change in predicted classes from model conversion. Our results confirm that lightweight AutoML architectures enable practical “zero-touch” edge-based intrusion detection on resource-constrained hardware. Full article
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17 pages, 4283 KB  
Article
A Hybrid Semantic-Acoustic Transformer for Vocal Burst Emotion Recognition Using Wav2Vec 2.0 and Whisper ASR
by Suryakant Tyagi and Sándor Szénási
Algorithms 2026, 19(5), 416; https://doi.org/10.3390/a19050416 - 21 May 2026
Viewed by 347
Abstract
Finding emotions in human speech is a difficult task. It is even harder for sounds without words, like laughs, gasps, and sighs. Normal audio models fail at this task because these sounds are very short and the audio patterns are complex. To fix [...] Read more.
Finding emotions in human speech is a difficult task. It is even harder for sounds without words, like laughs, gasps, and sighs. Normal audio models fail at this task because these sounds are very short and the audio patterns are complex. To fix this problem, we created a new model called the Hybrid Semantic-Acoustic Transformer. Our system uses a Wav2Vec 2.0 model to get acoustic features. At the same time, it uses a Whisper ASR model to get phonetic features. We mix these two types of data together using a Cross-Attention layer. We tested our model on the EmoGator dataset. This dataset has 32,130 audio files across 30 different emotion classes. We split the data strictly into 80% for training, 10% for validation, and 10% for testing. Our new model achieved an overall accuracy of 74.8%. We also did an ablation study. This study proves that using cross-attention is much better than simply adding the features together. Our final result is a 6.4% increase in the F1-score compared to the original EmoGator baseline model. This sets a new high score for classifying non-speech sounds in different noisy environments. Our model also reached over 90% precision when telling the difference between a ‘Sigh’ and a ‘Gasp’. Standard speech models usually fail at this specific task. Full article
(This article belongs to the Special Issue Bio-Inspired Algorithms: 2nd Edition)
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27 pages, 8237 KB  
Article
Metaheuristic-Based Model Selection Framework for EOQ and Inventory Policies Using Machine Learning and Multi-Objective Optimization
by Ádám Francuz and Tamás Bányai
Algorithms 2026, 19(5), 415; https://doi.org/10.3390/a19050415 - 21 May 2026
Viewed by 427
Abstract
The challenge of inventory optimization is extremely important for all manufacturing companies, as inventory costs significantly impact operational efficiency. The Economic Order Quantity (EOQ) model was developed to address this issue, and it is widely used to formulate it, as it generally considers [...] Read more.
The challenge of inventory optimization is extremely important for all manufacturing companies, as inventory costs significantly impact operational efficiency. The Economic Order Quantity (EOQ) model was developed to address this issue, and it is widely used to formulate it, as it generally considers only a few parameters and a single objective. This research develops a simulation-based framework that integrates multiple EOQ-based inventory policies and performs multi-objective optimization using the NSGA-II algorithm. The framework optimizes total cost, fill rate, and average inventory level and finally generates a Pareto front as a result. To reduce computational costs, we use a machine learning-based random forest model, which replaces a significant amount of the simulations with predictions. This reduces the simulation cost to approximately one-sixth of the original, while the quality of the simulation changes only minimally, as the hypervolume value decreases by only 4%. The proposed framework can be used as an effective decision-support tool for inventory optimization under stochastic demand conditions. Full article
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23 pages, 2699 KB  
Article
Improving Classification of Hand Osteoarthritis Using Deep Learning with Synthesized Data and Focal Loss Optimization
by Hetali Tank, Zhen Cao, Juan Shan and Ming Zhang
Algorithms 2026, 19(5), 414; https://doi.org/10.3390/a19050414 - 20 May 2026
Viewed by 546
Abstract
Osteoarthritis (OA) severity grading from hand distal interphalangeal (DIP) joint radiographs using the Kellgren–Lawrence (KL) scale is challenged by severe class imbalance, with higher grades (KL3 and KL4) markedly underrepresented in clinical datasets. To address this limitation, we propose a VGG19-based classification framework [...] Read more.
Osteoarthritis (OA) severity grading from hand distal interphalangeal (DIP) joint radiographs using the Kellgren–Lawrence (KL) scale is challenged by severe class imbalance, with higher grades (KL3 and KL4) markedly underrepresented in clinical datasets. To address this limitation, we propose a VGG19-based classification framework that systematically evaluates six training strategies targeting imbalance at the data level, algorithmic level, or in combination. Synthetic images for minority classes were generated using CycleGAN and subsequently filtered through rheumatologist validation. The evaluated strategies include baseline training, rheumatologist-validated synthetic augmentation (SD), oversampling (OS), focal loss (FL) optimization, and multiple combinations of these approaches. The results show that strategies incorporating oversampling demonstrated the most consistent and statistically robust improvements in minority-class performance. Specifically, the combination of synthetic data and oversampling (SD + OS) achieved the highest binary OA sensitivity (96.12%) and significantly improved OA F1 score compared to baseline (0.613 vs. 0.416, p = 0.029). The full combined strategy (SD + OS + FL) yielded the highest KL3 F1 score (0.527 vs. 0.280 baseline, p = 0.048) and significantly improved KL4 F1 score (0.730 vs. 0.570 baseline, p = 0.150). Importantly, all strategies maintained higher or similar overall performance with no significant change in majority-class performance (p > 0.10), indicating that improvements in minority classes were not achieved at the expense of sacrificing majority classes or overall model reliability. These findings suggest that the proposed imbalance-mitigation strategies may improve minority class OA detection, particularly when oversampling and validated synthetic augmentation are combined. It is worth noting that the above results are derived from a held-out test set comprising 1626 samples, among which only 43 are OA-positive due to data imbalance. The results should be treated as preliminary findings subject to change upon validation in larger cohorts of OA patients. Full article
(This article belongs to the Special Issue Machine Learning for Advanced Healthcare: Bridging Innovation and Clinical Implementation)
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21 pages, 269 KB  
Article
Exploring Data Augmentation in a Low-Resource Language Context: A Case Study on Text Generation for Reading Comprehension in Turkish
by Seyma N. Yildirim-Erbasli and Okan Bulut
Algorithms 2026, 19(5), 413; https://doi.org/10.3390/a19050413 - 20 May 2026
Viewed by 449
Abstract
This study presents a controlled empirical and comparative analysis of existing data augmentation techniques for text generation in Turkish, a morphologically rich, low-resource language. A collection of 265 Turkish reading passages for Grades 4 and 5 was augmented using four techniques: paraphrasing with [...] Read more.
This study presents a controlled empirical and comparative analysis of existing data augmentation techniques for text generation in Turkish, a morphologically rich, low-resource language. A collection of 265 Turkish reading passages for Grades 4 and 5 was augmented using four techniques: paraphrasing with GPT-3.5-turbo (Generative Pre-trained Transformer 3.5 Turbo), back translation (Turkish–English–Turkish and Turkish–French–Turkish) via Google Translate, synonym replacement via GPT-3.5-turbo, and random insertion via GPT-3.5-turbo. Human evaluators assessed the fluency, coherence, grammaticality, logical flow, and naturalness of the augmented datasets. Each augmented dataset, along with the original, was then used to fine-tune a Turkish GPT-2-medium model, which was evaluated using automatic metrics such as BLEU (Bilingual Evaluation Understudy), ROUGE (Recall-Oriented Understudy for Gisting Evaluation), METEOR (Metric for Evaluation of Translation with Explicit ORdering), chrF (CHaRacter-level F-score), BERTScore (Bidirectional Encoder Representations from Transformers Score), and cosine similarity. According to the human evaluation of the original and augmented datasets, the original texts received the highest ratings, followed by those generated through random insertion, paraphrasing, synonym replacement, and back translation variants, with cosine similarity results between original and augmented texts showing a comparable trend; however, the differences between methods were generally small. The results from text generation indicate that models trained on the original dataset generally achieved slightly higher performance across evaluation metrics compared to those trained on augmented datasets. Among the augmented methods, synonym replacement showed marginally better performance, followed by back translation, random insertion, and paraphrasing; however, the differences between methods were small and not statistically significant. Full article
25 pages, 585 KB  
Article
Code Smells Thresholds Optimization: Defect Prediction as a Case Study
by Tom Mashiach, Gilad Katz and Meir Kalech
Algorithms 2026, 19(5), 412; https://doi.org/10.3390/a19050412 - 20 May 2026
Viewed by 635
Abstract
In software engineering, detecting and managing code smells are pivotal for maintaining software quality and reducing the risk of defects. Code smells signify potential issues in code that, while not problematic in themselves, may indicate deeper design flaws or future complications. Traditional code [...] Read more.
In software engineering, detecting and managing code smells are pivotal for maintaining software quality and reducing the risk of defects. Code smells signify potential issues in code that, while not problematic in themselves, may indicate deeper design flaws or future complications. Traditional code smells detection methods, which compare code metrics against fixed or statistically derived thresholds, may not always yield the most accurate code smells relevant to specific software practices. Addressing this gap, this research introduces an innovative methodology that utilizes a neural threshold generator, trained via a cooperative critic, to dynamically generate threshold values for detecting code smells in software components. Although the critic is conceptually related to the discriminator in a Generative Adversarial Network (GAN), its training objective is aligned with rather than adversarial to that of the generator. By integrating relevant code metrics, the proposed model generates customized thresholds for each software component. Our current evaluation focuses on a set of 11 class-level code smells defined by single or AND-connected conditions. It then uses these thresholds to identify code smells, which serve as input features to train a defect prediction model. A key feature of our approach is a cooperative-critic feedback mechanism that continuously refines the thresholds based on the defect prediction outcomes, ensuring the model’s effectiveness in identifying potential software issues is consistently improved. This advanced approach has demonstrated superior defect prediction performance, as evidenced by improved metrics such as the F1-score, AUC-ROC, and AUC-PRC, compared with the results of a defect prediction model that uses the traditional thresholds. Our study underscores the effectiveness of generating context-specific thresholds through neural networks, suggesting a promising avenue for exploring related software practices. Full article
(This article belongs to the Special Issue Algorithms and Machine Learning in Software Engineering)
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27 pages, 1116 KB  
Article
A Method for Detecting Data Tampering Attacks Based on Prior Information
by Zimeng Zhou, Qingxiang Zhang, Yanpeng Hu, Fengwei Jing and Jin Guo
Algorithms 2026, 19(5), 411; https://doi.org/10.3390/a19050411 - 19 May 2026
Viewed by 288
Abstract
Addressing the challenge of effectively detecting data tampering attacks in cyber-physical systems, this paper proposes an attack detection method based on prior information for the identification of a class of Hammerstein nonlinear systems measured by binary sensors. This method leverages the periodic structure [...] Read more.
Addressing the challenge of effectively detecting data tampering attacks in cyber-physical systems, this paper proposes an attack detection method based on prior information for the identification of a class of Hammerstein nonlinear systems measured by binary sensors. This method leverages the periodic structure of the system inputs and the statistical properties of the binary observation data to characterize the asymptotic properties of the parameter estimators; furthermore, by incorporating prior information regarding the system parameters, it constructs a detection criterion that enables the effective identification of attack behaviors. To enhance the computational efficiency of the algorithm in practical applications, a Multilayer Perceptron (MLP) is employed to approximate the implicit nonlinear inverse mapping, thereby circumventing the numerical difficulties associated with directly solving systems of nonlinear equations. On a theoretical level, the asymptotic distributions of the detection algorithm’s false alarm rate and missed detection rate are derived, and a systematic analysis is conducted on how detection performance is affected by factors such as system input period, prior information scope, and data length. Numerical simulations validate the efficacy of the proposed method; the results demonstrate that as the data length increases, both the false alarm rate and the missed detection rate of the algorithm decrease. Moreover, a broader scope of prior information leads to a lower false alarm rate but a higher missed detection rate, thereby illustrating the “double-edged sword” effect of prior information in the context of attack detection. This study provides a theoretical foundation and technical support for attack detection in nonlinear systems operating under conditions of data constraints and security threats. Full article
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29 pages, 1911 KB  
Article
A Leakage-Resistant Digital Inheritance Distribution Scheme Based on Sparse-Matrix Secret Sharing
by Yucong Ma, Huiying Hou, Xuerui Gan and Zisu Zhao
Algorithms 2026, 19(5), 410; https://doi.org/10.3390/a19050410 - 19 May 2026
Viewed by 295
Abstract
With digital assets increasingly comprising a significant portion of personal wealth, the secure management and transfer of digital legacies have emerged as a pressing concern. Secret sharing offers a solution to this problem. However, distributing shares containing the unique private key for digital [...] Read more.
With digital assets increasingly comprising a significant portion of personal wealth, the secure management and transfer of digital legacies have emerged as a pressing concern. Secret sharing offers a solution to this problem. However, distributing shares containing the unique private key for digital assets poses significant risks of theft or tampering, potentially leading to the illegal appropriation of user assets. This paper presents a leakage-resistant digital inheritance distribution scheme based on sparse-matrix secret sharing. It employs an efficient thresholding scheme that uses sparse matrices, achieving near-linear complexity for share reconstruction via a random striped matrix. Reconstruction time is significantly reduced compared to traditional polynomial interpolation methods. To address the realistic scenario where an asset owner holds multiple independent digital accounts, we propose a multi-account blinding and aggregation mechanism. This mechanism allows the dealer to establish isolated group keys for each account in a single round of communication, while preventing adversaries from linking different accounts to the same owner. A key-derivation and encrypted-transmission mechanism is then designed based on the aggregated group keys. Group keys are established by consensus among heirs, from which each heir derives a unique session key. Authenticated encryption ensures the confidentiality, integrity, and identity-bound transmission of shares. Through security proofs and experimental performance evaluation, it is demonstrated that the proposed scheme satisfies adaptive security requirements with the hash function H modeled as a random oracle, while all other cryptographic primitives (PRF, AES-GCM, HMAC) are assumed to be secure under standard computational assumptions. Full article
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27 pages, 18591 KB  
Article
Managing Cost–Stability Trade-Offs in Industrial Object Detection: A Unified Decision Support Framework
by Kuhyun Lee, Jihoon Hong, Beom-Seok Kim, Yuna Song and Dong-Hee Lee
Algorithms 2026, 19(5), 409; https://doi.org/10.3390/a19050409 - 19 May 2026
Viewed by 444
Abstract
Object detection is a core component of industrial vision systems in manufacturing, infrastructure monitoring, and safety-critical sensing. While the mean average precision (mAP) averages the performance over all confidence thresholds, real-world deployment demands committing to a single operating threshold under score imprecision, distribution [...] Read more.
Object detection is a core component of industrial vision systems in manufacturing, infrastructure monitoring, and safety-critical sensing. While the mean average precision (mAP) averages the performance over all confidence thresholds, real-world deployment demands committing to a single operating threshold under score imprecision, distribution shifts, and asymmetric—often only approximately known—error costs. From a soft-computing perspective, deployment should explicitly manage this uncertainty rather than rely on a static validation optimum. We propose domain-specific and robust localization recall precision (DSR-LRP), a three-phase decision-support framework. The framework elicits soft domain preferences—such as asymmetric error costs, tolerable localization imprecision, and expected perturbations—from practitioner knowledge and encodes them as three quantitative parameters (k, αIoU, β). A cost-sensitive, threshold-local objective aggregates the performance within a robustness band around each candidate threshold, jointly capturing the accuracy and local stability. Finally, it yields an interpretable recommendation package comprising the operating threshold, its DSR-LRP score, and visual evidence. Experiments on four practical datasets (blood cell screening, wildfire smoke monitoring, pothole detection, and semiconductor sensor inspection) showed that DSR-LRP consistently selected operating thresholds that were robust and cost-aligned. For example, in pothole detection, an LRP-optimal threshold degraded by 15.6% under simulated shifts, while the DSR-LRP recommendation changed by only 1.8%. DSR-LRP complements global metrics such as the mAP and provides a soft-computing-oriented tool for reliable, evidence-driven deployment of industrial object detectors. Full article
(This article belongs to the Special Issue Advances in Deep Learning-Based Data Analysis)
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19 pages, 3132 KB  
Article
Interpretable Non-Separable Spatio-Temporal Interaction Cox Model for Diffusion Prediction in Invasive Species Management
by Yantao Zhang, Yangyang Li, Shuxin Wang, Jingxuan Wang, Robail Yasrab and Xinli Wu
Algorithms 2026, 19(5), 408; https://doi.org/10.3390/a19050408 - 19 May 2026
Viewed by 307
Abstract
Accurate prediction of invasive species diffusion is essential for effective management and ecological conservation. Existing spatio-temporal Cox process models face limitations due to the separability assumption, which fails to capture spatio-temporal coupling dynamics inherent in biological diffusion processes. This study proposes a Spatio-Temporal [...] Read more.
Accurate prediction of invasive species diffusion is essential for effective management and ecological conservation. Existing spatio-temporal Cox process models face limitations due to the separability assumption, which fails to capture spatio-temporal coupling dynamics inherent in biological diffusion processes. This study proposes a Spatio-Temporal Interaction Kernel Cox (STIK-Cox) model that constructs a non-separable conditional intensity function integrating baseline intensity, spatial and temporal proximity kernels, seasonal fluctuation, and a spatio-temporal interaction term. The model employs maximum likelihood estimation with Limited-memory Broyden–Fletcher–Goldfarb–Shanno with Bounds (L-BFGS-B) optimisation and incorporates SHapley Additive exPlanations (SHAP) for interpretability analysis. Using the Vespa mandarinia (Hymenoptera, Vespidae) monitoring dataset from Washington State, the model achieves a comprehensive accuracy score of 0.957, a capture rate of 98.74% at a 0.5° threshold, and a mean prediction error of 0.0802°. K-function analysis confirms effective capture of spatial clustering patterns, while SHAP analysis reveals longitude as the primary predictive driver. The non-separable design outperforms conventional methods including inverse distance weighting and Poisson point processes. This framework demonstrates the potential of non-separable spatio-temporal point processes for invasive species early warning, providing a scientific basis for targeted monitoring and resource allocation in ecological management. Full article
(This article belongs to the Topic Applications of NLP, AI, and ML in Software Engineering)
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21 pages, 6434 KB  
Article
Few-Shot Class-Incremental Learning with Prompt Alignment and Subspace Prototype Aggregation
by Qiang Huang
Algorithms 2026, 19(5), 407; https://doi.org/10.3390/a19050407 - 19 May 2026
Cited by 1 | Viewed by 344
Abstract
Few-Shot Class-Incremental Learning (FSCIL) aims to learn new classes with only a few samples, making it more challenging than traditional Class-Incremental Learning (CIL) due to the scarcity of available samples. The imbalance in sample distribution further complicates balancing the abundant base data with [...] Read more.
Few-Shot Class-Incremental Learning (FSCIL) aims to learn new classes with only a few samples, making it more challenging than traditional Class-Incremental Learning (CIL) due to the scarcity of available samples. The imbalance in sample distribution further complicates balancing the abundant base data with the scarce incremental data. While the model must fully leverage the extensive base data to guide the learning of subsequent tasks, it must also avoid over-relying on these data, as doing so could degrade its generalization capability and impede the learning of new incremental tasks. To address these challenges, we propose a novel framework for few-shot incremental learning, incorporating tailored prompt alignment strategies for both the base and incremental session. In the base session, we strike a balance between task-specific and task-agnostic knowledge to preserve the model’s generalization ability. In the incremental session, we mitigate the overfitting issue typically associated with few-shot learning. Furthermore, to tackle the prototype network bias caused by the imbalance in sample distribution, we propose a subspace prototype aggregation module, which effectively alleviates prediction bias in the incremental phase. Extensive experiments conducted on three benchmark datasets—CIFAR-100, miniImageNet, and CUB-200—demonstrate that our approach achieves state-of-the-art (SOTA) performance in FSCIL. Full article
(This article belongs to the Special Issue Advances in Deep Learning and Next-Generation Internet Technologies)
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37 pages, 1964 KB  
Article
Newly Improved Intuitionistic Fuzzy EDAS with Interdependent Criteria Weights for Comparing Large Language Models in Text Summarization Tasks
by Anesito Cutillas, Fritz Bacalso, Christine Joy Tomol, Melanie Albarracin, Rose Ann Campita, Eingilbert Benolirao, Kafferine Yamagishi and Lanndon Ocampo
Algorithms 2026, 19(5), 406; https://doi.org/10.3390/a19050406 - 18 May 2026
Viewed by 517
Abstract
Despite advances in using multi-criteria decision-making (MCDM) methods and their fuzzy set extensions for human evaluations of large language models (LLMs), several gaps remain in the literature, particularly in task-specific evaluations that offer a more tractable and interpretable approach. Thus, this work develops [...] Read more.
Despite advances in using multi-criteria decision-making (MCDM) methods and their fuzzy set extensions for human evaluations of large language models (LLMs), several gaps remain in the literature, particularly in task-specific evaluations that offer a more tractable and interpretable approach. Thus, this work develops a generalized intuitionistic fuzzy MCDM approach that bridges methodological gaps by outlining two contributions. First, the integration of SWARA (Stepwise Weight Assessment Ratio Analysis) and WINGS (Weighted Influence Non-linear Gauge System) is demonstrated to compute the priority weights of the evaluation criteria, thereby augmenting the independence limitation in prior relevant studies. Second, we introduce a newly improved IF-EDAS (intuitionistic fuzzy Evaluation based on Distance from Average Solution) that preserves more uncertain information and provides a more natural extension of the canonical EDAS framework, starting with the adoption of the IFWAM (intuitionistic fuzzy weighted arithmetic mean) operator for a more intuitive approach in generating the intuitionistic fuzzy average solution vector. Also, the proposed IF-EDAS variant employs three decision rules and the Hamming distance metric in its novel computational approach. The proposed hybrid approach was deployed in two case studies evaluating five popular LLMs for text summarization across seven interdependent criteria. Results show that SWARA initially prioritizes accuracy, coherence, and consistency, but these were revised when accounting for criteria interdependence, with coherence and language quality emerging as the most preferred criteria. Both case studies suggest that Gemini may perform favorably, while Copilot may consistently rank last. The findings of the case studies share similar insights with those of three other similar IF-EDAS variants, although our claims may have limited external validity, which requires more case studies and experts in future task-specific human evaluations. The proposed approach, along with its deployment in two case studies, demonstrates human evaluations of LLMs with greater computational interpretability, which contribute to the general MCDM literature. Full article
(This article belongs to the Special Issue Innovative Applications of Multi-Criteria Decision Analysis in Industry 4.0 and Digital Transformation)
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43 pages, 4101 KB  
Review
Machine Learning-Based Optimization for Renewable Energy Systems: A Comprehensive Review
by Mohammad Shehab, Afaf Edinat, Mariam Al Ghamri, Mamdouh Gomaa, Fatima Alhaj, Israa Wahbi Kamal and Ahmed E. Fakhry
Algorithms 2026, 19(5), 405; https://doi.org/10.3390/a19050405 - 18 May 2026
Cited by 1 | Viewed by 399
Abstract
Machine learning (ML) has become a key enabling technology for optimizing renewable energy systems and supporting global sustainability objectives. This paper presents a comprehensive review of recent advances in ML-based optimization techniques applied to clean and renewable energy systems, with particular emphasis on [...] Read more.
Machine learning (ML) has become a key enabling technology for optimizing renewable energy systems and supporting global sustainability objectives. This paper presents a comprehensive review of recent advances in ML-based optimization techniques applied to clean and renewable energy systems, with particular emphasis on wind energy, hybrid energy systems, energy storage, and intelligent energy management. A systematic literature review covering peer-reviewed publications from 2021 to 2025 was conducted, resulting in the analysis of 138 high-quality journal and conference studies. The reviewed studies were categorized according to evolutionary algorithm-based hybrid models, classical neural networks, and deep learning architectures, including Convolutional Neural Network (CNN), LSTMs, GRUs, and attention-based models. The analysis demonstrates that hybrid ML–metaheuristic frameworks significantly enhance forecasting accuracy, system reliability, fault diagnosis, and multi-objective optimization compared to traditional methods. These intelligent approaches directly contribute to Sustainable Development Goals SDG-7 (Affordable and Clean Energy), SDG-9 (Industry, Innovation, and Infrastructure), and SDG-13 (Climate Action). Key challenges and future research directions are discussed, highlighting the need for scalable, explainable, and real-time ML solutions to enable resilient, low-carbon, and sustainable energy systems. Full article
(This article belongs to the Section Evolutionary Algorithms and Machine Learning)
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35 pages, 962 KB  
Article
Ensemble Approach for Financial Time Series Modeling
by Aveer Nannoolal and Andries P. Engelbrecht
Algorithms 2026, 19(5), 404; https://doi.org/10.3390/a19050404 - 18 May 2026
Viewed by 660
Abstract
This study provides a comprehensive evaluation of bagging ensemble models for financial time series (FTS) classification and addresses a gap in the literature regarding how bootstrap methods, ensemble sizes, voting mechanisms, and loss functions jointly influence model performance. The analysis evaluates decision tree [...] Read more.
This study provides a comprehensive evaluation of bagging ensemble models for financial time series (FTS) classification and addresses a gap in the literature regarding how bootstrap methods, ensemble sizes, voting mechanisms, and loss functions jointly influence model performance. The analysis evaluates decision tree (DT), logistic regression (LR), and multi-layer perceptron (MLP) ensemble models modified by six time series bootstrap methods, five ensemble sizes, and three voting mechanisms across six FTS data sets. The study also examines the influence of entropy- and profit-based loss functions within particle swarm (PSO) and quantum-inspired particle swarm (QPSO) optimization for weighted voting. The results show that LR-based ensembles provide the strongest overall performance and outperform ARIMA, DT, LR, MLP, and LSTM baseline models on both accuracy and profit metrics. Bootstrap effects are model specific. DT and MLP ensembles perform best under the Tukey bootstrap, while LR ensembles achieve strong results under the block bootstrap, the sub-sample bootstrap method, and the Tukey method, and remain the strongest performers across all bootstrap configurations. Optimized voting mechanisms yield clear improvements over equal-weight majority voting, with the profit loss function producing the most consistent gains. The findings also indicate that FTS classification problems exhibit an optimal range of ensemble sizes, as larger ensembles do not always improve performance. The study contributes a systematic assessment of ensemble design choices for FTS classification and highlights the importance of jointly considering bootstrap diversity, ensemble size, and voting strategy when developing ensemble models for financial applications. Full article
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33 pages, 8195 KB  
Article
A Guided Collaborative Optimization Framework for the Stability-Constrained UAV Routing and Three-Dimensional Loading Problem
by Changhui Han, Mengmeng Zhang, Jie Zhang and Xiaolong Ma
Algorithms 2026, 19(5), 403; https://doi.org/10.3390/a19050403 - 18 May 2026
Viewed by 386
Abstract
The joint optimization of routing and three-dimensional loading is a highly complex NP-hard combinatorial problem, particularly when stringent center-of-gravity (CoG) stability constraints are required for unmanned aerial vehicle (UAV) operations. Existing algorithms typically adopt a route-first, load-second evaluation strategy for these interconnected components, [...] Read more.
The joint optimization of routing and three-dimensional loading is a highly complex NP-hard combinatorial problem, particularly when stringent center-of-gravity (CoG) stability constraints are required for unmanned aerial vehicle (UAV) operations. Existing algorithms typically adopt a route-first, load-second evaluation strategy for these interconnected components, often yielding distance-optimal yet physically infeasible solutions. To address this bottleneck, this paper formulates the Three-Dimensional Loading-Constrained UAV Routing Problem (3DLC-UAVRP), integrating unloading sequence consistency, spatial packing feasibility, and CoG deviation control into the routing decision process. A guided collaborative optimization framework, GLS-WSCPA, is proposed, coupling an Improved White Shark Optimization (IWSO) algorithm for global route exploration with a Human-like Divide-and-Conquer Packing Strategy (HLDCPS) for spatial arrangement. Unlike conventional decoupled approaches that treat loading feasibility as a post hoc filter, a Center-of-Gravity-Guided Path Adjustment (CGPA) and Local Loading Repair (LLR) mechanism is introduced to establish a dynamic feedback loop between routing search and loading evaluation, so that CoG violations are actively translated into guided routing perturbations rather than simply triggering solution rejection. Experimental results demonstrate that GLS-WSCPA generally achieves better solutions than the compared algorithms across the tested problem scales, with the performance gap tending to widen as the instance size increases within the tested range. Ablation studies verify the complementary roles of CGPA and LLR, and sensitivity analysis confirms that moderately relaxing payload and CoG constraints reduces routing distance within safety boundaries. Case analysis shows that the proposed method reduces fleet size by 20% and total delivery distance by 6.85% compared to traditional decoupled strategies. Full article
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20 pages, 7789 KB  
Article
Simulation and Analysis of the Second-Order Memristive System in the CUDAynamics Suite
by Alexander Khanov, Maksim Gozhan, Denis Butusov, Yulia Bobrova and Valerii Ostrovskii
Algorithms 2026, 19(5), 402; https://doi.org/10.3390/a19050402 - 17 May 2026
Viewed by 496
Abstract
Cycle-to-cycle variability of switching parameters inherent to memristive devices introduces significant problems in the design of neuromorphic systems and non-volatile memory. This study investigates the dynamics of a second-order memristive system incorporating capacitive effects that model parasitic charge within individual memristors, addressing both [...] Read more.
Cycle-to-cycle variability of switching parameters inherent to memristive devices introduces significant problems in the design of neuromorphic systems and non-volatile memory. This study investigates the dynamics of a second-order memristive system incorporating capacitive effects that model parasitic charge within individual memristors, addressing both the technical need for accurate analysis of complex regimes and the demand for exploratory environments. Simulations were performed using CUDAynamics, an interactive software suite developed by the authors, which utilizes parallel computing, primarily via NVIDIA Compute Unified Device Architecture (CUDA). It integrates multiple analysis tools for dynamical systems, including bifurcation diagrams, the largest Lyapunov exponent and periodicity mapping, and interactive navigation in multidimensional parameter spaces. The memristive system was discretized applying multiple integration methods with a fixed time step and various waveforms of the input signal. Analysis tools revealed well-defined regions of chaotic dynamics in the memristor resistance parameter space as functions of input signal properties. Sinusoidal and triangular waveforms produced topologically similar distributions of dynamical regimes, whereas the square waveform, mimicking digital inputs, generated distinct dynamical patterns while still preserving chaotic trajectories under specific conditions. Interactive visualization capabilities of CUDAynamics effectively demonstrate attractor evolution and hysteresis deformation, providing immediate visual feedback that significantly enhances conceptual comprehension of nonlinear feedback mechanisms. Beyond its practical implications for the design of analog and digital memristive devices, CUDAynamics offers a scalable, open-source toolkit to aid researchers and engineers in exploring complex dynamical phenomena. Full article
(This article belongs to the Special Issue Recent Advances in Numerical Algorithms and Their Applications)
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18 pages, 1340 KB  
Article
Spatiotemporal Optimization of Oilfield Electricity Consumption: A Multi-Objective Modeling Approach with Machine Learning
by Wenrong Song, Yuan Xu, Bin Lyu, Wenbin Liu, Yuxuan Zhang and Jin Wang
Algorithms 2026, 19(5), 401; https://doi.org/10.3390/a19050401 - 17 May 2026
Viewed by 355
Abstract
Oil enterprises face the challenge of reconciling escalating energy conservation targets with persistent production requirements, necessitating sophisticated electricity management solutions. The conventional ton-per-kWh allocation approach, often manually adjusted based on historical production and planning data, lacks a scientific basis and fails to accurately [...] Read more.
Oil enterprises face the challenge of reconciling escalating energy conservation targets with persistent production requirements, necessitating sophisticated electricity management solutions. The conventional ton-per-kWh allocation approach, often manually adjusted based on historical production and planning data, lacks a scientific basis and fails to accurately identify efficiency differences or assess energy-saving potential, making it difficult to convince participating units. To address this, we propose a dynamic spatiotemporal allocation scheme and develop a multi-objective optimization model that integrates electricity efficiency, operational stability, and production priority. The model incorporates nonlinear efficiency terms, stability components, and priority-weighted items, with constraints including total balance, monthly adjustment limits, and key area protection. Central to the efficiency term is the accurate prediction of liquid production from electricity consumption. We decompose electricity use into three components—core production electricity, auxiliary production electricity, and product transportation electricity—and derive their proportional coefficients through regression of historical data, enabling high-precision liquid production prediction via machine learning using the Light Gradient Boosting Machine (LGBM). The resulting constrained optimization problem is solved using the Sequential Least Squares Programming (SLSQP) algorithm. Validation using both simulated data and Daqing Oilfield field data demonstrates that the scheme effectively achieves electricity reduction targets while significantly mitigating associated liquid production loss, reducing it by 18.0% in simulated experiments and 32.5% in field validation compared to the conventional ton-per-kWh method. This offers a scientific and adaptive electricity management framework that supports refined energy control and facilitates the petroleum industry’s green and low-carbon transformation. Full article
(This article belongs to the Special Issue Machine Learning for Planning and Logistics)
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10 pages, 3376 KB  
Brief Report
Fuzzy PID Speed Control System for Sprayer Vehicles Based on Canopy Density
by Yanxin Wang, Nwabueze Emekwuru, Chengqian Jin and Fernando Auat Cheein
Algorithms 2026, 19(5), 400; https://doi.org/10.3390/a19050400 - 16 May 2026
Viewed by 582
Abstract
This study proposes an intelligent spraying vehicle speed control system integrating real-time canopy density detection with a fuzzy PID control algorithm. Utilizing LiDAR-acquired 3D point cloud data for canopy density calculation, the system dynamically adjusts PID parameters through fuzzy logic to achieve coordinated [...] Read more.
This study proposes an intelligent spraying vehicle speed control system integrating real-time canopy density detection with a fuzzy PID control algorithm. Utilizing LiDAR-acquired 3D point cloud data for canopy density calculation, the system dynamically adjusts PID parameters through fuzzy logic to achieve coordinated optimization of vehicle speed and spray volume. Based on the designed canopy density prediction model, a MATLAB/Simulink co-simulation framework integrating canopy perception with vehicle dynamics was established. Simulation results based on the MATLAB/Simulink platform demonstrate that the fuzzy PID controller achieves superior performance compared to conventional PID control. While maintaining a tracking accuracy of ±0.15 m/s, the proposed controller reduces speed overshoot by 5.8 percentage points. The developed control system ensures optimal speed tracking under varying canopy conditions, providing an extensible technical framework for intelligent sprayer vehicles. Full article
(This article belongs to the Special Issue Algorithmic Approaches to Control Theory and System Modeling)
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21 pages, 3289 KB  
Article
Efficient Temporal Modeling for Real-World Sign Language Recognition: A Comparative Study Under Data-Constrained Scenarios
by Meryem Cherrate, Imane El Manaa, My Abdelouahed Sabri, Yassine Abouch, Ali Yahyaouy and Abdellah Aarab
Algorithms 2026, 19(5), 399; https://doi.org/10.3390/a19050399 - 16 May 2026
Viewed by 612
Abstract
Designing effective temporal modeling strategies for video-based sign language recognition (SLR) remains challenging, particularly in low-resource settings where the behavior of modern architectures is not fully understood. In this study, we present a controlled comparative evaluation of temporal models, including recurrent architectures (RNN, [...] Read more.
Designing effective temporal modeling strategies for video-based sign language recognition (SLR) remains challenging, particularly in low-resource settings where the behavior of modern architectures is not fully understood. In this study, we present a controlled comparative evaluation of temporal models, including recurrent architectures (RNN, LSTM, GRU) and a Transformer encoder, within a unified spatio-temporal framework based on a shared MobileNetV2 feature extractor. All models are trained and evaluated under identical conditions on a curated subset of the WLASL dataset (37 classes), ensuring a fair and reproducible comparison. The results show that recurrent models consistently achieve higher performance than the Transformer-based approach in data-constrained scenarios, with the CNN–LSTM model reaching an accuracy of 90.02%. In contrast, the Transformer model exhibits lower generalization capability, which may be attributed to its higher data requirements. Additionally, increasing architectural complexity through hybrid temporal designs does not result in performance improvements. These findings suggest that simpler recurrent architectures remain effective for temporal modeling in limited data settings and highlight the importance of aligning model complexity with data availability for practical SLR applications. Full article
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46 pages, 16095 KB  
Article
A Comparative Simulation Study of the Fairness and Accuracy of Predictive Policing Systems in Baltimore City
by Samin Semsar, Kiran Laxmikant Prabhu, Gabriella Waters and James Foulds
Algorithms 2026, 19(5), 398; https://doi.org/10.3390/a19050398 - 16 May 2026
Viewed by 373
Abstract
There are ongoing discussions about predictive policing systems being unfair, for example, by exhibiting racial bias. Law enforcement in some cities, such as Los Angeles, California, and Baltimore, Maryland, have initiated the integration of these systems into their decision-making processes, and some of [...] Read more.
There are ongoing discussions about predictive policing systems being unfair, for example, by exhibiting racial bias. Law enforcement in some cities, such as Los Angeles, California, and Baltimore, Maryland, have initiated the integration of these systems into their decision-making processes, and some of these systems were advertised as being unbiased. However, later studies discovered that these methods could also be unfair due to feedback loops and being trained on historically biased recorded data. Comparative studies on predictive policing systems are few and insufficiently comprehensive. Crucially, the relative fairness of predictive policing methods with regard to traditional hot spot-based policing has not been established. Moreover, the relationship between fairness and accuracy is complex and requires further study. Furthermore, the case of Baltimore City, Maryland, USA, has not yet been systematically analyzed despite its relevance as an early adopter of predictive policing technologies with a fraught history of social justice concerns around policing. An improved understanding of these questions could better inform policy decisions around predictive policing technologies both in Baltimore and beyond. Therefore, in this work we perform a comprehensive comparative simulation study on the fairness and accuracy of predictive policing technologies in Baltimore. Our results suggest that the situation around bias in predictive policing is more complex than previously assumed. While we find that predictive policing exhibits bias due to feedback loops, as previously reported, we also find traditional hot spot-based policing to have similar issues. Although predictive policing is found to be more fair and accurate than hot spot policing in the short term, it also amplifies bias more quickly, suggesting the potential for worse long-run behavior. In Baltimore, the bias in these systems tended toward over-policing White neighborhoods in some cases, unlike in previous studies. However, when the analysis was restricted to some specific crime types, this tendency differed. Overall, this work demonstrates a methodology for city-specific evaluation and compares behavioral tendencies of predictive policing systems, showing how such simulations can reveal inequities and long-term tendencies. We recommend that authorities and community stakeholders use simulation methodologies to assist in collaboratively navigating the complexities around fairness in predictive policing. Full article
(This article belongs to the Special Issue Algorithms for Smart Cities (3rd Edition))
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16 pages, 280 KB  
Article
Indexed Subset Construction: A Structured Algorithmic Framework
by Bakhtgerey Sinchev, Askar Sinchev, Aksulu Mukhanova, Tolkynai Sadykova, Anel Auyezova and Kuanysh Baimirov
Algorithms 2026, 19(5), 397; https://doi.org/10.3390/a19050397 - 15 May 2026
Viewed by 315
Abstract
This paper studies subset construction in NP-complete problems from the perspective of structured exploration of combinatorial search spaces. Classical approaches rely on exhaustive enumeration of subsets, which leads to exponential growth in time and memory requirements. To address this limitation, we introduce an [...] Read more.
This paper studies subset construction in NP-complete problems from the perspective of structured exploration of combinatorial search spaces. Classical approaches rely on exhaustive enumeration of subsets, which leads to exponential growth in time and memory requirements. To address this limitation, we introduce an indexed framework based on the correspondence between a finite set and its associated index set. Within this framework, subsets are represented as ordered index sequences, allowing subset construction to be reformulated as a constraint-guided search process over index space. Candidate subsets are characterized by numerical descriptors derived from their indices (referred to as index certificates), which guide and filter the construction process. Subset generation is further organized through admissible index intervals that restrict feasible transitions and reduce the effective search space. The framework is based on an index-based representation and structured traversal of pairwise index combinations. Computational experiments on representative instances illustrate the behavior of the indexed construction procedure and indicate its efficiency relative to classical enumeration-based methods for small and medium-sized instances. The proposed approach provides a structured perspective on combinatorial search and offers a basis for further development of algorithms based on constrained exploration of subset structures. Full article
(This article belongs to the Collection Feature Papers in Combinatorial Optimization, Graph, and Network Algorithms)
21 pages, 1192 KB  
Article
A Bayesian Inference Algorithm for Equipment Software Price Estimation Based on Nonlinear Contribution Models
by Tian Meng and Guoping Jiang
Algorithms 2026, 19(5), 396; https://doi.org/10.3390/a19050396 - 15 May 2026
Viewed by 281
Abstract
To address the challenges of difficult value quantification, lack of market benchmarks, and scarcity of historical data for embedded software amidst the intelligent transformation of equipment systems, this study develops a scientific price estimation method based on functional capability contribution. A nonlinear pricing [...] Read more.
To address the challenges of difficult value quantification, lack of market benchmarks, and scarcity of historical data for embedded software amidst the intelligent transformation of equipment systems, this study develops a scientific price estimation method based on functional capability contribution. A nonlinear pricing model is constructed to accurately characterize the two-stage evolution of software price: diminishing marginal utility during the mature technology accumulation stage and exponential growth during the technical bottleneck breakthrough stage. To ensure the consistency of pricing logic between hardware and software, a penalty function is innovatively designed to modify the standard likelihood function, effectively transforming practical business logic into a model regularization term. Parameter estimation is achieved by employing a Bayesian inference framework integrated with operational constraints, utilizing Markov Chain Monte Carlo (MCMC) sampling to realize robust posterior inference under small-sample constraints. Empirical analysis demonstrates that the proposed method achieves superior cross-domain data transfer performance compared to traditional baseline models, with a Leave-One-Out Cross-Validation (LOOCV) Mean Absolute Percentage Error (MAPE) of 21.2%. This research provides a practical value-oriented price estimation method for embedded equipment software pricing. Full article
(This article belongs to the Section Algorithms for Multidisciplinary Applications)
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25 pages, 1519 KB  
Article
IoT-Based Air Quality Monitoring with Low-Cost Sensors: Adaptive Filtering and RPA-Based Decision Automation
by Aiman Moldagulova, Zhuldyz Kalpeyeva, Raissa Uskenbayeva, Nurdaulet Tasmurzayev, Bibars Amangeldy and Yeldos Altay
Algorithms 2026, 19(5), 395; https://doi.org/10.3390/a19050395 - 15 May 2026
Cited by 1 | Viewed by 689
Abstract
Low-cost IoT-based air quality sensors enable dense monitoring networks but suffer from significant measurement noise and instability particularly in dynamic environments. Conventional fixed-window smoothing reduces noise but introduces a trade-off between signal stability and temporal responsiveness, often attenuating short-term pollution events. This paper [...] Read more.
Low-cost IoT-based air quality sensors enable dense monitoring networks but suffer from significant measurement noise and instability particularly in dynamic environments. Conventional fixed-window smoothing reduces noise but introduces a trade-off between signal stability and temporal responsiveness, often attenuating short-term pollution events. This paper proposes an adaptive filtering algorithm that dynamically adjusts the averaging window size based on short-term signal variability. The method relies on real-time variance estimation to balance noise suppression and sensitivity to rapid changes without increasing computational complexity. The approach is implemented within an IoT-based monitoring framework and evaluated using parallel measurements with a certified reference device. Comparative analysis against a certified reference device demonstrates strong agreement, with Pearson correlation coefficients reaching r = 0.88 for PM2.5 and r = 0.86 for PM10, and low error levels (RMSE ≈ 2.1–2.2 µg/m3). The proposed adaptive filtering approach preserves temporal dynamics while improving signal stability and robustness compared to raw and fixed-window filtering. In addition, this method improves event detection stability, achieving low false alarm rates and near real-time response (latency < 1 sampling interval), supporting RPA-based workflow triggering. The results show that the proposed adaptive filtering provides an efficient and lightweight solution for real-time signal processing on resource-constrained devices, making it suitable for large-scale deployment in environmental monitoring systems. Full article
(This article belongs to the Section Algorithms for Multidisciplinary Applications)
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30 pages, 1071 KB  
Article
An Enhanced Hybrid CNN–LSTM Model for Improved Precipitation Forecasting
by Huthaifa Al-Omari, Murad A. Yaghi and Layan Alrifai
Algorithms 2026, 19(5), 394; https://doi.org/10.3390/a19050394 - 15 May 2026
Viewed by 405
Abstract
Accurate precipitation forecasting is essential for water resource management, flood early-warning systems, and agriculture, but remains difficult because of the nonlinear and highly variable spatiotemporal nature of rainfall. This paper compares four deep learning architectures—a standalone LSTM, a standalone CNN, a hybrid CNN–LSTM, [...] Read more.
Accurate precipitation forecasting is essential for water resource management, flood early-warning systems, and agriculture, but remains difficult because of the nonlinear and highly variable spatiotemporal nature of rainfall. This paper compares four deep learning architectures—a standalone LSTM, a standalone CNN, a hybrid CNN–LSTM, and a Transformer encoder—against three classical baselines (persistence, day-of-year climatology, and per-grid-point ARIMA) for daily precipitation forecasting over Washington State at lead times of one to four days. A 40-year ERA5 dataset (1985–2024) of near-surface air temperature, mean sea-level pressure, and total precipitation is split into training (1985–2012), validation (2013–2015), and test (2016–2024) periods, with the test years held out completely. Each (model, horizon) is trained with three random seeds and evaluated in physical units (mm/day). On the held-out test period, the hybrid CNN–LSTM achieves the lowest RMSE at every horizon h≥2, with R2=0.576±0.007 and RMSE =15.08±0.07 mm/day at h=4. Diebold–Mariano tests, paired t-tests, and bootstrap 95% confidence intervals confirm that the CNN–LSTM advantage over the LSTM is statistically significant at horizons 2–4 (but not at h=1), while CNN–LSTM is significantly better than every classical baseline and the Transformer at every horizon. The headline result is reproduced under a rolling-origin temporal cross-validation across three non-overlapping splits (R2∈[0.576,0.590]). Practically, the sub-millisecond inference cost of the CNN–LSTM makes it directly deployable in operational forecasting pipelines used for flood early-warning, irrigation scheduling, and reservoir management, where even modest improvements in 3–4-day-ahead RMSE translate into measurable risk reduction and improved decision lead time for water managers and emergency planners. Full article
(This article belongs to the Special Issue Artificial Intelligence in Sustainable Development)
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20 pages, 3490 KB  
Article
Three-Dimensional UAV Omnidirectional Path Planning Algorithm Based on Urban Obstacle Environment
by Yijie Zhang and Jizhou Chen
Algorithms 2026, 19(5), 393; https://doi.org/10.3390/a19050393 - 14 May 2026
Cited by 1 | Viewed by 370
Abstract
To address the challenges of high computational complexity, inferior path performance, and the balance between path quality and efficiency in traditional 3D omnidirectional path planning algorithms for UAVs, this study proposes an innovative precision algorithm for solving 3D omnidirectional shortest paths. The algorithm [...] Read more.
To address the challenges of high computational complexity, inferior path performance, and the balance between path quality and efficiency in traditional 3D omnidirectional path planning algorithms for UAVs, this study proposes an innovative precision algorithm for solving 3D omnidirectional shortest paths. The algorithm innovatively introduces the concepts of circling path and overpass path, reducing three-dimensional omnidirectional path computation to two-dimensional processing. It designs a three-view obstacle detection algorithm to achieve efficient obstacle avoidance judgment, formulates separate path-solving strategies for discrete and continuous obstacles, respectively, and obtains optimal solutions through recursive adjustments and path optimization. Experimental results demonstrate that compared to A* and Theta* algorithms, our approach achieves shorter path lengths with superior stability; the proposed algorithm achieves a 21.86% reduction compared to RRT*, 10.48% compared to A*, and 0.89% compared to Lazy_Theta*. In addition, the proposed algorithm exhibits enhanced adaptability in high-obstacle environments (particularly irregular obstacles). These findings provide an effective solution for complex spatial path planning in UAV applications. Full article
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