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A Review on Inverse Kinematics, Control and Planning for Robotic Manipulators With and Without Obstacles via Deep Neural Networks
Unsupervised Temporal Adaptation in Skeleton-Based Human Action Recognition
Applying Recommender Systems to Predict Personalized Film Age Ratings for Parents
Stability Analysis and Experimental Validation of Standard Proportional-Integral-Derivative Control in Bilateral Teleoperators with Time-Varying Delays

Journal Description

Algorithms

Algorithms is a peer-reviewed, open access journal which provides an advanced forum for studies related to algorithms and their applications. Algorithms is published monthly online by MDPI. The European Society for Fuzzy Logic and Technology (EUSFLAT) is affiliated with Algorithms and their members receive discounts on the article processing charges.

Open Access — free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, ESCI (Web of Science), Ei Compendex, and other databases.
Journal Rank: JCR - Q2 (Computer Science, Theory and Methods) / CiteScore - Q1 (Numerical Analysis)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 18.9 days after submission; acceptance to publication is undertaken in 3.4 days (median values for papers published in this journal in the second half of 2024).
Testimonials: See what our editors and authors say about Algorithms.
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.

Impact Factor: 1.8 (2023); 5-Year Impact Factor: 1.9 (2023)

Imprint Information Journal Flyer Open Access ISSN: 1999-4893

Latest Articles

16 pages, 2075 KiB

Open AccessArticle

Improved Trimming Ant Colony Optimization Algorithm for Mobile Robot Path Planning

by Junxia Ma, Qilin Liu, Zixu Yang and Bo Wang

Algorithms 2025, 18(5), 240; https://doi.org/10.3390/a18050240 - 23 Apr 2025

Traditional ant colony algorithms for mobile robot path planning often suffer from slow convergence, susceptibility to local optima, and low search efficiency, limiting their applicability in dynamic and complex environments. To address these challenges, this paper proposes an improved trimming ant colony optimization [...] Read more.

Traditional ant colony algorithms for mobile robot path planning often suffer from slow convergence, susceptibility to local optima, and low search efficiency, limiting their applicability in dynamic and complex environments. To address these challenges, this paper proposes an improved trimming ant colony optimization (ITACO) algorithm. The method introduces a dynamic weighting factor into the state transition probability formula to balance global exploration and local exploitation, effectively avoiding local optima. Additionally, the traditional heuristic function is replaced with an artificial potential field attraction function, dynamically adjusting the potential field strength to enhance search efficiency. A path-length-dependent pheromone increment mechanism is also proposed to accelerate convergence, while a triangular pruning strategy is employed to remove redundant path nodes and shorten the optimal path length. Simulation experiments show that the ITACO algorithm improves the path length by up to 62.86% compared to the classical ACO algorithm. The ITACO algorithm improves the path length by 6.68% compared to the latest related research results. These improvements highlight the ITACO algorithm as an efficient and reliable solution for mobile robot path planning in challenging scenarios. Full article

(This article belongs to the Special Issue Evolutionary and Swarm Computing for Emerging Applications)

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21 pages, 1150 KiB

Open AccessArticle

Laplacian Controllability and Observability of Multi-Agent Systems: Recent Advances in Tree Graphs

by Gianfranco Parlangeli

Algorithms 2025, 18(5), 239; https://doi.org/10.3390/a18050239 - 23 Apr 2025

Laplacian controllability and observability of a consensus network is a widely considered topic in the area of multi-agent systems, complex networks, and large-scale systems. In this paper, this problem is addressed when the communication among nodes is described through a starlike tree topology. [...] Read more.

Laplacian controllability and observability of a consensus network is a widely considered topic in the area of multi-agent systems, complex networks, and large-scale systems. In this paper, this problem is addressed when the communication among nodes is described through a starlike tree topology. After a brief description of the mathematical setting of the problem adopted in a wide number of multi-agent systems engineering applications, some novel results are drawn based on node positions within the network only. The resulting methods are graphical and thus effective and exempt from numerical errors, and the final algorithm is provided to perform the analysis by machine computation. Several examples are provided to show the effectiveness of the algorithm proposed. Full article

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

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22 pages, 4631 KiB

Open AccessArticle

ChurnKB: A Generative AI-Enriched Knowledge Base for Customer Churn Feature Engineering

by Maryam Shahabikargar, Amin Beheshti, Wathiq Mansoor, Xuyun Zhang, Eu Jin Foo, Alireza Jolfaei, Ambreen Hanif and Nasrin Shabani

Algorithms 2025, 18(4), 238; https://doi.org/10.3390/a18040238 - 21 Apr 2025

Customers are the cornerstone of business success across industries. Companies invest significant resources in acquiring new customers and, more importantly, retaining existing ones. However, customer churn remains a major challenge, leading to substantial financial losses. Addressing this issue requires a deep understanding of [...] Read more.

Customers are the cornerstone of business success across industries. Companies invest significant resources in acquiring new customers and, more importantly, retaining existing ones. However, customer churn remains a major challenge, leading to substantial financial losses. Addressing this issue requires a deep understanding of customers’ cognitive status and behaviours, as well as early signs of churn. Predictive and Machine Learning (ML)-based analysis, when trained with appropriate features indicative of customer behaviour and cognitive status, can be highly effective in mitigating churn. A robust ML-driven churn analysis depends on a well-developed feature engineering process. Traditional churn analysis studies have primarily relied on demographic, product usage, and revenue-based features, overlooking the valuable insights embedded in customer–company interactions. Recognizing the importance of domain knowledge and human expertise in feature engineering and building on our previous work, we propose the Customer Churn-related Knowledge Base (ChurnKB) to enhance feature engineering for churn prediction. ChurnKB utilizes textual data mining techniques such as Term Frequency-Inverse Document Frequency (TF-IDF), cosine similarity, regular expressions, word tokenization, and stemming to identify churn-related features within customer-generated content, including emails. To further enrich the structure of ChurnKB, we integrate Generative AI, specifically large language models, which offer flexibility in handling unstructured text and uncovering latent features, to identify and refine features related to customer cognitive status, emotions, and behaviours. Additionally, feedback loops are incorporated to validate and enhance the effectiveness of ChurnKB.Integrating knowledge-based features into machine learning models (e.g., Random Forest, Logistic Regression, Multilayer Perceptron, and XGBoost) improves predictive performance of ML models compared to the baseline, with XGBoost’s F1 score increasing from 0.5752 to 0.7891. Beyond churn prediction, this approach potentially supports applications like personalized marketing, cyberbullying detection, hate speech identification, and mental health monitoring, demonstrating its broader impact on business intelligence and online safety. Full article

(This article belongs to the Special Issue Machine Learning Algorithms and Optimization in the Digital Transition (2nd Edition))

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31 pages, 9472 KiB

Open AccessArticle

Mathematics-Driven Analysis of Offshore Green Hydrogen Stations

by Álvaro García-Ruiz, Pablo Fernández-Arias and Diego Vergara

Algorithms 2025, 18(4), 237; https://doi.org/10.3390/a18040237 - 21 Apr 2025

Renewable energy technologies have become an increasingly important component of the global energy supply. In recent years, photovoltaic and wind energy have been the fastest-growing renewable sources. Although oceans present harsh environments, their estimated energy generation potential is among the highest. Ocean-based solutions [...] Read more.

Renewable energy technologies have become an increasingly important component of the global energy supply. In recent years, photovoltaic and wind energy have been the fastest-growing renewable sources. Although oceans present harsh environments, their estimated energy generation potential is among the highest. Ocean-based solutions are gaining significant momentum, driven by the advancement of offshore wind, floating solar, tidal, and wave energy, among others. The integration of various marine energy sources with green hydrogen production can facilitate the exploitation and transportation of renewable energy. This paper presents a mathematics-driven analysis for the simulation of a technical model designed as a generic framework applicable to any location worldwide and developed to analyze the integration of solar energy generation and green hydrogen production. It evaluates the impact of key factors such as solar irradiance, atmospheric conditions, water surface flatness, as well as the parameters of photovoltaic panels, electrolyzers, and adiabatic compressors, on both energy generation and hydrogen production capacity. The proposed mathematics-based framework serves as an innovative tool for conducting multivariable parametric analyses, selecting optimal design configurations based on specific solar energy and/or hydrogen production requirements, and performing a range of additional assessments including, but not limited to, risk evaluations, cause–effect analyses, and/or degradation studies. Enhancing the efficiency of solar energy generation and hydrogen production processes can reduce the required photovoltaic surface area, thereby simplifying structural and anchoring requirements and lowering associated costs. Simpler, more reliable, and cost-effective designs will foster the expansion of floating solar energy and green hydrogen production in marine environments. Full article

(This article belongs to the Special Issue Mathematical Modelling in Engineering and Human Behaviour (3rd Edition))

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20 pages, 5586 KiB

Open AccessArticle

iCOR: End-to-End Electrocardiography Morphology Classification Combining Multi-Layer Filter and BiLSTM

by Siti Nurmaini, Wisnu Jatmiko, Satria Mandala, Bambang Tutuko, Erwin Erwin, Alexander Edo Tondas, Annisa Darmawahyuni, Firdaus Firdaus, Muhammad Naufal Rachmatullah, Ade Iriani Sapitri, Anggun Islami, Akhiar Wista Arum and Muhammad Ikhwan Perwira

Algorithms 2025, 18(4), 236; https://doi.org/10.3390/a18040236 - 18 Apr 2025

Accurate delineation of ECG signals is critical for effective cardiovascular diagnosis and treatment. However, previous studies indicate that models developed for specific datasets and environments perform poorly when used with varying ECG signal morphology characteristics. This paper presents a novel approach to ECG [...] Read more.

Accurate delineation of ECG signals is critical for effective cardiovascular diagnosis and treatment. However, previous studies indicate that models developed for specific datasets and environments perform poorly when used with varying ECG signal morphology characteristics. This paper presents a novel approach to ECG signal delineation using a multi-layer filter (MLF) combined with a bidirectional long short-term memory (BiLSTM) model, namely iCOR. The proposed iCOR architecture enhances noise removal and feature extraction, resulting in improved classification of the P-QRS-T-wave morphology with a simpler model. Our method is evaluated on a combination of two standard ECG databases, the Lobachevsky University Electrocardiography Database (LUDB) and QT Database (QTDB). It can be observed that the classification performance for unseen sets of LUDB datasets yields above 90.4% and 98% accuracy, for record-based and beat-based approaches, respectively. Beat-based approaches outperformed the record-based approach in overall performance metric results. Similar results were shown in an unseen set of the QTDB, in which beat-based approaches performed with accuracy above 97%. These results highlight the robustness and efficacy of the iCOR model across diverse ECG signal datasets. The proposed approach offers a significant advancement in ECG signal analysis, paving the way for more reliable and precise cardiac health monitoring. Full article

(This article belongs to the Special Issue Machine Learning in Medical Signal and Image Processing (3rd Edition))

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32 pages, 8687 KiB

Open AccessArticle

Hybrid Deep Learning Methods for Human Activity Recognition and Localization in Outdoor Environments

by Yirga Yayeh Munaye, Metadel Addis, Yenework Belayneh, Atinkut Molla and Wasyihun Admass

Algorithms 2025, 18(4), 235; https://doi.org/10.3390/a18040235 - 18 Apr 2025

Activity recognition and localization in outdoor environments involve identifying and tracking human movements using sensor data, computer vision, or deep learning techniques. This process is crucial for applications such as smart surveillance, autonomous systems, healthcare monitoring, and human–computer interaction. However, several challenges arise [...] Read more.

Activity recognition and localization in outdoor environments involve identifying and tracking human movements using sensor data, computer vision, or deep learning techniques. This process is crucial for applications such as smart surveillance, autonomous systems, healthcare monitoring, and human–computer interaction. However, several challenges arise in outdoor settings, including varying lighting conditions, occlusions caused by obstacles, environmental noise, and the complexity of differentiating between similar activities. This study presents a hybrid deep learning approach that integrates human activity recognition and localization in outdoor environments using Wi-Fi signal data. The study focuses on applying the hybrid long short-term memory–bi-gated recurrent unit (LSTM-BIGRU) architecture, designed to enhance the accuracy of activity recognition and location estimation. Moreover, experiments were conducted using a real-world dataset collected with the PicoScene Wi-Fi sensing device, which captures both magnitude and phase information. The results demonstrated a significant improvement in accuracy for both activity recognition and localization tasks. To mitigate data scarcity, this study utilized the conditional tabular generative adversarial network (CTGAN) to generate synthetic channel state information (CSI) data. Additionally, carrier frequency offset (CFO) and cyclic shift delay (CSD) preprocessing techniques were implemented to mitigate phase fluctuations. The experiments were conducted in a line-of-sight (LoS) outdoor environment, where CSI data were collected using the PicoScene Wi-Fi sensor platform across four different activities at outdoor locations. Finally, a comparative analysis of the experimental results highlights the superior performance of the proposed hybrid LSTM-BIGRU model, achieving 99.81% and 98.93% accuracy for activity recognition and location prediction, respectively. Full article

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

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30 pages, 683 KiB

Open AccessArticle

Utilizing a Bounding Procedure Based on Simulated Annealing to Effectively Locate the Bounds for the Parameters of Radial Basis Function Networks

by Ioannis G. Tsoulos, Vasileios Charilogis and Dimitrios Tsalikakis

Algorithms 2025, 18(4), 234; https://doi.org/10.3390/a18040234 - 18 Apr 2025

Radial basis function (RBF) networks are an established parametric machine learning tool which has been extensively utilized in data classification and data fitting problems. These specific machine learning tools have been applied in various scientific areas, such as problems in physics, chemistry, and [...] Read more.

Radial basis function (RBF) networks are an established parametric machine learning tool which has been extensively utilized in data classification and data fitting problems. These specific machine learning tools have been applied in various scientific areas, such as problems in physics, chemistry, and medicine, with excellent results. A two-step technique is usually used to adjust the parameters of these models, which is in most cases extremely effective. However, it does not effectively explore the value space of the network parameters and often results in parameter stability problems. In this paper, the use of a bounding technique that explores the value space of the parameters of these networks using intervals generated by a procedure based on the Simulated Annealing method is recommended. After finding a promising range of values for the network parameters, a genetic algorithm is applied within this range of values to more effectively adjust its parameters. The new method was applied on a wide range of classification and regression datasets from the relevant literature and the results are reported in the current manuscript. Full article

(This article belongs to the Special Issue Recent Advances in Algorithms for Swarm Systems)

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26 pages, 8000 KiB

Open AccessArticle

Patient-Specific Hyperparameter Optimization of a Deep Learning-Based Tumor Autocontouring Algorithm on 2D Liver, Prostate, and Lung Cine MR Images: A Pilot Study

by Gawon Han, Keith Wachowicz, Nawaid Usmani, Don Yee, Jordan Wong, Arun Elangovan, Jihyun Yun and B. Gino Fallone

Algorithms 2025, 18(4), 233; https://doi.org/10.3390/a18040233 - 18 Apr 2025

Linear accelerator–magnetic resonance (linac-MR) hybrid systems allow for real-time magnetic resonance imaging (MRI)-guided radiotherapy for more accurate dose delivery to the tumor and improved sparing of the adjacent healthy tissues. However, for real-time tumor detection, it is unfeasible for a human expert to [...] Read more.

Linear accelerator–magnetic resonance (linac-MR) hybrid systems allow for real-time magnetic resonance imaging (MRI)-guided radiotherapy for more accurate dose delivery to the tumor and improved sparing of the adjacent healthy tissues. However, for real-time tumor detection, it is unfeasible for a human expert to manually contour (gold standard) the tumor at the fast imaging rate of a linac-MR. This study aims to develop a neural network-based tumor autocontouring algorithm with patient-specific hyperparameter optimization (HPO) and to validate its contouring accuracy using in vivo MR images of cancer patients. Two-dimensional (2D) intrafractional MR images were acquired at 4 frames/s using 3 tesla (T) MRI from 11 liver, 24 prostate, and 12 lung cancer patients. A U-Net architecture was applied for tumor autocontouring and was further enhanced by implementing HPO using the Covariance Matrix Adaptation Evolution Strategy. Six hyperparameters were optimized for each patient, for which intrafractional images and experts’ manual contours were input into the algorithm to find the optimal set of hyperparameters. For evaluation, Dice’s coefficient (DC), centroid displacement (CD), and Hausdorff distance (HD) were computed between the manual contours and autocontours. The performance of the algorithm was benchmarked against two standardized autosegmentation methods: non-optimized U-Net and nnU-Net. For the proposed algorithm, the mean (standard deviation) DC, CD, and HD of the 47 patients were 0.92 (0.04), 1.35 (1.03), and 3.63 (2.17) mm, respectively. Compared to the two benchmarking autosegmentation methods, the proposed algorithm achieved the best overall performance in terms of contouring accuracy and speed. This work presents the first tumor autocontouring algorithm applicable to the intrafractional MR images of liver and prostate cancer patients for real-time tumor-tracked radiotherapy. The proposed algorithm performs patient-specific HPO, enabling accurate tumor delineation comparable to that of experts. Full article

(This article belongs to the Special Issue Machine Learning in Medical Signal and Image Processing (3rd Edition))

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25 pages, 10513 KiB

Open AccessArticle

A Comparative Study of Machine Learning Techniques for Cell Annotation of scRNA-Seq Data

by Shahid Ahmad Wani, SMK Quadri, Mohammad Shuaib Mir and Yonis Gulzar

Algorithms 2025, 18(4), 232; https://doi.org/10.3390/a18040232 - 18 Apr 2025

Accurate cell type annotation is a critical step in single-cell RNA sequencing (scRNA-seq) analysis, enabling deeper insights into cellular heterogeneity and biological processes. In this study, we conducted a comprehensive comparative evaluation of various machine learning techniques, including support vector machine (SVM), decision [...] Read more.

Accurate cell type annotation is a critical step in single-cell RNA sequencing (scRNA-seq) analysis, enabling deeper insights into cellular heterogeneity and biological processes. In this study, we conducted a comprehensive comparative evaluation of various machine learning techniques, including support vector machine (SVM), decision tree, random forest, logistic regression, gradient boosting, k-nearest neighbour, transformer, and naive Bayes, to determine their effectiveness for single-cell annotation. These methods were evaluated using four diverse datasets comprising hundreds of cell types across several tissues. Our results revealed that SVM consistently outperformed other techniques, emerging as the top performer in three out of the four datasets, followed closely by logistic regression. Most methods demonstrated robust capabilities in annotating major cell types and identifying rare cell populations, though naive Bayes was the least effective due to its inherent limitations in handling high-dimensional and interdependent data. This study provides valuable insights into the relative strengths and weaknesses of machine learning methods for single-cell annotation, offering guidance for selecting appropriate techniques in scRNA-seq analyses. Full article

(This article belongs to the Special Issue Advanced Research on Machine Learning Algorithms in Bioinformatics)

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37 pages, 980 KiB

Open AccessReview

Digital Transformation in Aftersales and Warranty Management: A Review of Advanced Technologies in I4.0

by Vicente González-Prida, Carlos Parra Márquez, Pablo Viveros Gunckel, Fredy Kristjanpoller Rodríguez and Adolfo Crespo Márquez

Algorithms 2025, 18(4), 231; https://doi.org/10.3390/a18040231 - 17 Apr 2025

This research examines how Industry 4.0 technologies such as artificial intelligence (AI), the Internet of Things (IoT), and digital twins (DT) are used in the digital transformation process of warranty management. This research focuses on converting traditional warranty management practices from reactive systems [...] Read more.

This research examines how Industry 4.0 technologies such as artificial intelligence (AI), the Internet of Things (IoT), and digital twins (DT) are used in the digital transformation process of warranty management. This research focuses on converting traditional warranty management practices from reactive systems to predictive and proactive ones, improving operational performance and customer experiences. Based on an already established eight-phase framework for warranty management, this paper reviews machine learning (ML), natural language processing (NLP), and predictive analytics, among other advanced technologies, to enhance warranty optimization processes. Best practices in the automotive sector, as well as in the railway and aeronautics industries, have experienced substantial achievements, including optimized resource utilization and savings, together with tailored services. This study describes the limitations of capital investments, labor training requirements, and data protection issues. Therefore, it suggests implementation sequencing and staff education approaches as solutions. In addition to the current evolution of Industry 4.0, this research’s conclusion highlights how digital warranty management advancements optimize resources and reduce costs while adhering to international standards and ethical data practices. Full article

(This article belongs to the Special Issue Machine Learning Algorithms and Optimization in the Digital Transition (2nd Edition))

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22 pages, 2988 KiB

Open AccessArticle

Scalable Resource Provisioning Framework for Fog Computing Using LLM-Guided Q-Learning Approach

by Bhargavi Krishnamurthy and Sajjan G. Shiva

Algorithms 2025, 18(4), 230; https://doi.org/10.3390/a18040230 - 17 Apr 2025

Fog computing is one of the growing distributed computing platforms incorporated by Industries today as it performs real-time data analysis closer to the edge of the IoT network. It offers cloud capabilities at the edge of the fog networks through improved efficiency and [...] Read more.

Fog computing is one of the growing distributed computing platforms incorporated by Industries today as it performs real-time data analysis closer to the edge of the IoT network. It offers cloud capabilities at the edge of the fog networks through improved efficiency and flexibility. As the demands of Internet of Things (IoT) devices keep varying, it is important to rapidly modify the resource allocation policies to satisfy them. Constant fluctuation of the demands leads to over or under provisioning of resources. The computing capability of the fog nodes is small, and hence there is a necessity to develop resource provisioning policies that reduce the delay and bandwidth consumption. In this paper, a novel large language model (LLM)-guided Q-learning framework is designed and developed. The uncertainty in the fog environment in terms of delay incurred, bandwidth usage, and heterogeneity of fog nodes is represented using the LLM model. The reward shaping of a Q-learning agent is enriched by considering the heuristic value of the LLM model. The experimental results ensure that the proposed framework is good with respect to processing delay, energy consumption, load balancing, and service level agreement violation under a finite and infinite fog computing environment. The results are further validated through the expected value analysis statistical methodology. Full article

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

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12 pages, 551 KiB

Open AccessArticle

Deep-Learning-Based Optimization of the Signal/Background Ratio for Λ Particles in the CBM Experiment at FAIR

by Ivan Kisel, Robin Lakos and Gianna Zischka

Algorithms 2025, 18(4), 229; https://doi.org/10.3390/a18040229 - 16 Apr 2025

Machine learning algorithms have become essential tools in modern physics experiments, enabling the precise and efficient analysis of large-scale experimental data. The Compressed Baryonic Matter (CBM) experiment at the Facility for Antiproton and Ion Research (FAIR) demands innovative methods for processing the vast [...] Read more.

Machine learning algorithms have become essential tools in modern physics experiments, enabling the precise and efficient analysis of large-scale experimental data. The Compressed Baryonic Matter (CBM) experiment at the Facility for Antiproton and Ion Research (FAIR) demands innovative methods for processing the vast data volumes generated at high collision rates of up to 10 MHz. This study presents a deep-learning-based approach to enhance the signal/background (S/B) ratio for

Λ

particles within the Kalman Filter (KF) Particle Finder framework. Using the Artificial Neural Networks for First Level Event Selection (ANN4FLES) package of CBM, a multi-layer perceptron model was designed and trained on simulated data to classify

Λ

particle candidates as signal or background. The model achieved over 98% classification accuracy, enabling significant reductions in background—in particular, a strong suppression of the combinatorial background that lacks physical meaning—while preserving almost the whole

Λ

particle signal. This approach improved the S/B ratio by a factor of 10.97, demonstrating the potential of deep learning to complement existing particle reconstruction techniques and contribute to the advancement of data analysis methods in heavy-ion physics. Full article

(This article belongs to the Special Issue 2024 and 2025 Selected Papers from Algorithms Editorial Board Members)

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20 pages, 3991 KiB

Open AccessArticle

A New GIS-Based Detection Technique for Urban Heat Islands Using the Fuzzy C-Means Clustering Algorithm: A Case Study of Naples, (Italy)

by Rosa Cafaro, Barbara Cardone, Valeria D’Ambrosio, Ferdinando Di Martino and Vittorio Miraglia

Algorithms 2025, 18(4), 228; https://doi.org/10.3390/a18040228 - 15 Apr 2025

This study proposes a novel urban heat island detection method implemented in a GIS-based framework, designed to identify the most critical urban areas during heatwave events. The framework employs the fuzzy C-means clustering algorithm with remotely sensed land surface temperature and normalized difference [...] Read more.

This study proposes a novel urban heat island detection method implemented in a GIS-based framework, designed to identify the most critical urban areas during heatwave events. The framework employs the fuzzy C-means clustering algorithm with remotely sensed land surface temperature and normalized difference vegetation index data to delineate and visualize hotspots. The proposed approach is compared with other established methods for urban heat island detection to evaluate their relative accuracy and effectiveness. This methodology integrates advanced spatial analysis with environmental indicators such as vegetation cover and permeable open spaces to assess urban vulnerability. The city of Naples, Italy, serves as a case study for testing the framework. The results from the case study indicate that the proposed method outperforms alternative methods in identifying heat hotspots, providing higher accuracy and suggesting potential adaptability to other urban contexts. This GIS-based approach not only provides a robust tool for urban climate assessment but also serves as a decision support framework that enables urban planners and policymakers to identify critical areas and prioritize interventions for climate adaptation and mitigation. Full article

(This article belongs to the Special Issue Machine Learning Algorithms and Optimization in the Digital Transition (2nd Edition))

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30 pages, 10466 KiB

Open AccessArticle

Prompt Once, Segment Everything: Leveraging SAM 2 Potential for Infinite Medical Image Segmentation with a Single Prompt

by Juan D. Gutiérrez, Emilio Delgado, Carlos Breuer, José M. Conejero and Roberto Rodriguez-Echeverria

Algorithms 2025, 18(4), 227; https://doi.org/10.3390/a18040227 - 14 Apr 2025

Semantic segmentation of medical images holds significant potential for enhancing diagnostic and surgical procedures. Radiology specialists can benefit from automated segmentation tools that facilitate identifying and isolating regions of interest in medical scans. Nevertheless, to obtain precise results, sophisticated deep learning models tailored [...] Read more.

Semantic segmentation of medical images holds significant potential for enhancing diagnostic and surgical procedures. Radiology specialists can benefit from automated segmentation tools that facilitate identifying and isolating regions of interest in medical scans. Nevertheless, to obtain precise results, sophisticated deep learning models tailored to this specific task must be developed and trained, a capability not universally accessible. Segment Anything Model (SAM) 2 is a foundational model designed for image and video segmentation tasks, built on its predecessor, SAM. This paper introduces a novel approach leveraging SAM 2’s video segmentation capabilities to reduce the prompts required to segment an entire volume of medical images. The study first compares SAM and SAM 2’s performance in medical image segmentation. Evaluation metrics such as the Jaccard index and Dice score are used to measure precision and segmentation quality. Then, our novel approach is introduced. Statistical tests include comparing precision gains and computational efficiency, focusing on the trade-off between resource use and segmentation time. The results show that SAM 2 achieves an average improvement of 1.76% in the Jaccard index and 1.49% in the Dice score compared to SAM, albeit with a ten-fold increase in segmentation time. Our novel approach to segmentation reduces the number of prompts needed to segment a volume of medical images by 99.95%. We demonstrate that it is possible to segment all the slices of a volume and, even more, of a whole dataset, with a single prompt, achieving results comparable to those obtained by state-of-the-art models explicitly trained for this task. Our approach simplifies the segmentation process, allowing specialists to devote more time to other tasks. The hardware and personnel requirements to obtain these results are much lower than those needed to train a deep learning model from scratch or to modify the behavior of an existing one using model modification techniques. Full article

(This article belongs to the Special Issue Algorithms and Applications of Machine Learning Techniques for Healthcare)

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19 pages, 2677 KiB

Open AccessArticle

Proving Properties of Dekker’s Algorithm for Mutual Exclusion of N Processes

by Libero Nigro and Franco Cicirelli

Algorithms 2025, 18(4), 226; https://doi.org/10.3390/a18040226 - 13 Apr 2025

Dekker’s algorithm for mutual exclusion of two processes is the well-known first developed correct solution based only on software mechanisms. The algorithm served as the starting point for researchers to create subsequent safe solutions both for two and N > 2 processes. In [...] Read more.

Dekker’s algorithm for mutual exclusion of two processes is the well-known first developed correct solution based only on software mechanisms. The algorithm served as the starting point for researchers to create subsequent safe solutions both for two and N > 2 processes. In recent years, Dekker proposed a generalization of his mutual exclusion algorithm for N > 2 processes (here referred to as Dekker-N). To the best of our knowledge, Dekker-N correctness was only proven by the author using informal arguments. This paper’s original contribution consists of formal modeling and verification of Dekker-N using an approach based on timed automata (TA) and the Uppaal model checker. The Dekker-N model is checked under atomic registers and also in cases where non-atomic registers are used. This paper first demonstrates that Dekker-N is correct and fair with atomic registers and effectively ensures bounded waiting for competing processes through a linear overtaking. Unfortunately, the algorithm becomes incorrect when non-atomic registers are used. The adopted formal approach, though, allowed us to prove that by making just one single common variable safe, Dekker-N turns out to be fully correct and fair with non-atomic registers as well. The paper presents the TA-based formal approach and goes on by presenting models of Dekker-N and by verifying all its mutual-exclusion properties. Full article

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

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22 pages, 763 KiB

Open AccessArticle

The Impact of Environmental Risk on Business Failure: A Fuzzy-Set Qualitative Comparative Analysis Approach with Extreme Gradient Boosting Feature Selection

by Mariano Romero Martínez, Pedro Carmona Ibáñez and José Pozuelo Campillo

Algorithms 2025, 18(4), 225; https://doi.org/10.3390/a18040225 - 13 Apr 2025

Corporate performance is increasingly impacted by environmental issues, but their specific role in business failure remains underexplored, which leads to a gap in research that is often focused exclusively on financial metrics. By investigating the relationship between environmental financial exposure and business failure, [...] Read more.

Corporate performance is increasingly impacted by environmental issues, but their specific role in business failure remains underexplored, which leads to a gap in research that is often focused exclusively on financial metrics. By investigating the relationship between environmental financial exposure and business failure, this study addresses this gap, integrating financial ratios and environmental variables to understand how environmental performance affects financial viability. A novel dual-stage methodology was employed, first using Extreme Gradient Boosting (XGBoost) for feature selection to identify the most significant predictors of failure from a dataset of Spanish companies (N = 38,456) using 2022 ORBIS data. Next, a fuzzy-set qualitative comparative analysis (fsQCA) was applied to analyze the sufficient causal configurations leading to a high propensity for business failure. The analysis identified three distinct causal configurations associated with failure. All highlighted poor financial performance indicators, such as low results per employee and low profit per employee. Notably, one configuration identified high environmental risk (measured by TRUCAM) as a core condition contributing significantly to financial distress. These findings highlight the critical link between environmental responsibility and financial health, demonstrating the benefits of combining fsQCA with machine learning to identify intricate causal configurations and providing information to companies and governments who want to support long-term financial stability and corporate sustainability. Full article

(This article belongs to the Special Issue Artificial Intelligence Algorithms in Sustainability)

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17 pages, 8061 KiB

Open AccessArticle

Optimal View Estimation Algorithm and Evaluation with Deviation Angle Analysis

by Meng Yuan and Hongjun Li

Algorithms 2025, 18(4), 224; https://doi.org/10.3390/a18040224 - 12 Apr 2025

Image-based viewpoint estimation is one of the tasks in image analysis, and another is the inverse problem of selecting the best viewpoint for displaying a three-dimensional object. Currently, two issues need further exploration in image-based viewpoint estimation research: insufficient labeled data and a [...] Read more.

Image-based viewpoint estimation is one of the tasks in image analysis, and another is the inverse problem of selecting the best viewpoint for displaying a three-dimensional object. Currently, two issues need further exploration in image-based viewpoint estimation research: insufficient labeled data and a limited number of evaluation methods for estimation results. To address the first issue, this paper proposes a spherical viewpoint sampling method based on a combination of analytical methods and motion adjustment, and designs a viewpoint-based projection image acquisition algorithm. Considering the difference between viewpoint inference and image classification, we propose an accuracy evaluation method with deviation angle tolerance for viewpoint estimation. Based on constructing a new dataset with viewpoint labels, the new accuracy evaluation method has been validated through experiments. The experimental results show that its estimation accuracy can reach 89% according to the new estimation evaluation indicators. Additionally, we applied our method to estimate the viewpoints of images from a furniture website and analyzed the viewpoint preferences in its furniture displays. Full article

(This article belongs to the Special Issue Machine Learning Algorithms for Image Understanding and Analysis)

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19 pages, 311 KiB

Open AccessArticle

Improved Cryptanalysis of Some RSA Variants

by Mohammed Rahmani, Abderrahmane Nitaj and Mhammed Ziane

Algorithms 2025, 18(4), 223; https://doi.org/10.3390/a18040223 - 12 Apr 2025

Several RSA variants enforce a constraint between their public and private keys through the relation

e d \equiv 1 (mod (p^{2} - 1) (q^{2} - 1))

, where p and q are the prime factors [...] Read more.

Several RSA variants enforce a constraint between their public and private keys through the relation

e d \equiv 1 (mod (p^{2} - 1) (q^{2} - 1))

, where p and q are the prime factors of their RSA modulus

N = p q

. In this paper, we introduce a novel attack on RSA variant schemes where the public exponent satisfies an equation of the form

e u \equiv z (mod (p^{2} - 1) (q^{2} - 1))

, with sufficiently small

| z |

,

| u |

, in a scenario where the attacker has access to an approximation of one of the prime factors. Our new attack utilizes Coppersmith’s method, combined with lattice basis reduction techniques, to efficiently recover the prime factors of the RSA modulus in these scenarios. This method offers a significant improvement over prior attacks on RSA variants with small private exponents or partial prime information. Full article

(This article belongs to the Special Issue Algorithmic Innovations in Cryptanalysis of Public Key Cryptography)

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26 pages, 510 KiB

Open AccessArticle

Integrating Feature Selection and Deep Learning: A Hybrid Approach for Smart Agriculture Applications

by Ali Roman, Md Mostafizer Rahman, Sajjad Ali Haider, Tallha Akram and Syed Rameez Naqvi

Algorithms 2025, 18(4), 222; https://doi.org/10.3390/a18040222 - 12 Apr 2025

This research tackles the critical challenge of achieving precise and efficient feature selection in machine learning-based classification, particularly for smart agriculture, where existing methods often fail to balance exploration and exploitation in complex, high-dimensional datasets. While current approaches, such as standalone nature-inspired optimization [...] Read more.

This research tackles the critical challenge of achieving precise and efficient feature selection in machine learning-based classification, particularly for smart agriculture, where existing methods often fail to balance exploration and exploitation in complex, high-dimensional datasets. While current approaches, such as standalone nature-inspired optimization algorithms, leverage biological behaviors for feature selection, they are limited by their inability to synergize diverse strategies, resulting in suboptimal performance and scalability. To address this, we introduce the Hybrid Predator Algorithm for Classification (HPA-C), a novel hybrid feature selection algorithm that uniquely integrates the framework of a nature-inspired feature selection technique with position update equations from other algorithms, harnessing diverse biological behaviors like echolocation, foraging, and collaborative hunting. Coupled with a custom convolutional neural network (CNN), HPA-C achieves superior classification accuracy (98.6–99.8%) on agricultural datasets (Plant Leaf Diseases, Weed Detection, Fruits-360, and Fresh n Rotten) and demonstrates exceptional adaptability across diverse imagery applications. Full article

(This article belongs to the Special Issue Development of Machine Learning and Artificial Intelligence Algorithms in Environmental Retrieval Tasks)

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26 pages, 14078 KiB

Open AccessArticle

Proposal of a Methodology Based on Using a Wavelet Transform as a Convolution Operation in a Convolutional Neural Network for Feature Extraction Purposes

by Nora Isabel Pérez-Quezadas, Héctor Benítez-Pérez and Adrián Durán-Chavesti

Algorithms 2025, 18(4), 221; https://doi.org/10.3390/a18040221 - 11 Apr 2025

Using methodological tools to construct feature extraction from multidimensional data is challenging. Different treatments are required to build a coherent representation with those features that can be attenuated by various phenomena inherent to the observed process. It is interesting to note that in [...] Read more.

Using methodological tools to construct feature extraction from multidimensional data is challenging. Different treatments are required to build a coherent representation with those features that can be attenuated by various phenomena inherent to the observed process. It is interesting to note that in this methodological generation, several methods converge, such as Wavelet transform, focusing on convolution processing, windowed data shifting, and classification via Self-Organizing Maps. Likewise, a case study is presented in this work, allowing us to understand the scope of this methodological tool using an information cube to detect common features, as discussed previously. Full article

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

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