Algorithms

23 pages, 843 KB

Open AccessArticle

SAIN: Search-And-INfer, a Mathematical and Computational Framework for Personalised Multimodal Data Modelling with Applications in Healthcare

by Cristian S. Calude, Patrick Gladding, Alec Henderson and Nikola Kasabov

Algorithms 2025, 18(10), 605; https://doi.org/10.3390/a18100605 - 26 Sep 2025

Abstract

Personalised modelling has become dominant in personalised medicine and precision health. It creates a computational model for an individual based on large data repositories of existing personalised data, aiming to achieve the best possible personal diagnosis or prognosis and derive an informative explanation [...] Read more.

Personalised modelling has become dominant in personalised medicine and precision health. It creates a computational model for an individual based on large data repositories of existing personalised data, aiming to achieve the best possible personal diagnosis or prognosis and derive an informative explanation for it. Current methods are still working on a single data modality or treating all modalities with the same method. The proposed method, SAIN (Search-And-INfer), offers better results and an informative explanation for classification and prediction tasks on a new multimodal object (sample) using a database of similar multimodal objects. The method is based on different distance measures suitable for each data modality and introduces a new formula to aggregate all modalities into a single vector distance measure to find the closest objects to a new one, and then use them for a probabilistic inference. This paper describes SAIN and applies it to two types of multimodal data, cardiovascular diagnosis and EEG time series, modelled by integrating modalities, such as numbers, categories, images, and time series, and using a software implementation of SAIN. Full article

(This article belongs to the Special Issue Algorithms for Computer Aided Diagnosis: 2nd Edition)

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21 pages, 2419 KB

Open AccessArticle

Application Features of a VOF Method for Simulating Boiling and Condensation Processes

by Andrey Kozelkov, Andrey Kurkin, Andrey Puzan, Vadim Kurulin, Natalya Tarasova and Vitaliy Gerasimov

Algorithms 2025, 18(10), 604; https://doi.org/10.3390/a18100604 - 26 Sep 2025

Abstract

This article presents the results of a study on the possibility of using a single-speed multiphase model with free surface allowance for simulating boiling and condensation processes. The simulation is based on the VOF method, which allows the position of the interphase boundary [...] Read more.

This article presents the results of a study on the possibility of using a single-speed multiphase model with free surface allowance for simulating boiling and condensation processes. The simulation is based on the VOF method, which allows the position of the interphase boundary to be tracked. To increase the stability of the iterative procedure for numerically solving volume fraction transfer equations using a finite volume discretization method on arbitrary unstructured grids, the basic VOF method is been modified by writing these equations in a semi-divergent form. The models of Tanasawa, Lee, and Rohsenow are considered models of interphase mass transfer, in which the evaporated or condensed mass linearly depends on the difference between the local temperature and the saturation temperature with accuracy in empirical parameters. This paper calibrates these empirical parameters for each mass transfer model. The results of our study of the influence of the values of the empirical parameters of models on the intensity of boiling and evaporation, as well as on the dynamics of the interphase boundary, are presented. This research is based on Stefan’s problem of the movement of the interphase boundary due to the evaporation of a liquid and the problem of condensation of vapor bubbles water columns. As a result of a series of numerical experiments, it is shown that the average error in the position of the interfacial boundary for the Tanasawa and Lee models does not exceed 3–6%. For the Rohsenow model, the result is somewhat worse, since the interfacial boundary moves faster than it should move according to calculations based on analytical formulas. To investigate the possibility of condensation modeling, the results of a numerical solution of the problem of an emerging condensing vapor bubble are considered. A numerical assessment of its position in space and the shape and dynamics of changes in its diameter over time is carried out using the VOF method, taking into account the free surface. It is shown herein that the Tanasawa model has the highest accuracy for modeling the condensation process using a VOF method taking into account the free surface, while the Rohsenow model is most unstable and prone to deformation of the bubble shape. At the same time, the dynamics of bubble ascent are modeled by all three models. The results obtained confirm the fundamental possibility of using a VOF method to simulate the processes of boiling and condensation and taking into account the dynamics of the free surface. At the same time, the problem of the studied models of phase transitions is revealed, which consists of the need for individual selection of optimal values of empirical parameters for each specific task. Full article

(This article belongs to the Special Issue Dynamic System Modelling from Data: Emerging Algorithms and Applications: 2nd Edition)

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12 pages, 259 KB

Open AccessArticle

Two-Derivative Runge–Kutta Methods with Frequency Dependent Coefficients for Solving Orbital or Oscillatory Problems

by Theodoros Monovasilis and Zacharoula Kalogiratou

Algorithms 2025, 18(10), 603; https://doi.org/10.3390/a18100603 - 26 Sep 2025

Abstract

In this work, explicit Two-Derivative Runge–Kutta methods of the general case (that use several evaluations of the right-hand side function and its derivative per step) are considered. In order to address problems of orbital or oscillatory character, we develop methods with frequency-dependent coefficients. [...] Read more.

In this work, explicit Two-Derivative Runge–Kutta methods of the general case (that use several evaluations of the right-hand side function and its derivative per step) are considered. In order to address problems of orbital or oscillatory character, we develop methods with frequency-dependent coefficients. We construct three exponentially/trigonometrically fitted methods following two approaches suggested by Vanden Berghe and Simos. Also, we construct a phase-fitted and amplification-fitted method. The efficiency of the new modified methods is demonstrated by numerical examples. Full article

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

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30 pages, 4648 KB

Open AccessArticle

Bootstrap-Based Stabilization of Sparse Solutions in Tensor Models: Theory, Assessment, and Application

by Gresky Gutiérrez-Sánchez, María Purificación Vicente-Galindo and Purificación Galindo-Villardón

Algorithms 2025, 18(10), 602; https://doi.org/10.3390/a18100602 - 26 Sep 2025

Abstract

This paper introduces BCenetTucker, a novel bootstrap-enhanced extension of the CenetTucker model designed to address the instability of sparse support recovery in high-dimensional tensor settings. By integrating mode-specific resampling directly into the penalized tensor decomposition process, BCenetTucker improves the reliability and reproducibility [...] Read more.

This paper introduces BCenetTucker, a novel bootstrap-enhanced extension of the CenetTucker model designed to address the instability of sparse support recovery in high-dimensional tensor settings. By integrating mode-specific resampling directly into the penalized tensor decomposition process, BCenetTucker improves the reliability and reproducibility of latent structure estimation without compromising the model′s interpretability. The proposed method is systematically benchmarked against classical CenetTucker, Stability Selection, and Bolasso, using real-world gene expression data from the GSE13159 leukemia dataset. Across multiple stability metrics—including support-size deviation, average Jaccard index, inclusion frequency, proportion of stable support, and Stable Selection Index (SSI)—BCenetTucker consistently demonstrates superior robustness and structural coherence relative to competing approaches. In the real data application, BCenetTucker preserved all essential signals originally identified by CenetTucker while uncovering additional marginal yet reproducible features. The method achieved high reproducibility (Jaccard index = 0.975; support-size deviation = 1.7 genes), confirming its sensitivity to weak but stable signals. The protocol was implemented in the GSparseBoot R library, enabling reproducibility, transparency, and applicability to diverse domains involving structured high-dimensional data. Altogether, these results establish BCenetTucker as a powerful and extensible framework for achieving stable sparse decompositions in modern tensor analytics. Full article

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

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17 pages, 32387 KB

Open AccessArticle

Neural Network Architectures for Secure and Sustainable Data Processing in E-Government Systems

by Shadi AlZu’bi, Fatima Quiam, Ala’ M. Al-Zoubi and Muder Almiani

Algorithms 2025, 18(10), 601; https://doi.org/10.3390/a18100601 - 25 Sep 2025

Abstract

In the digital transformation of public services, reliable and secure data handling has become central to effective E-government operations. This study introduces a symmetry-driven neural network architecture tailored for secure, scalable, and energy-efficient data processing. The model integrates weight-sharing and symmetrical configurations to [...] Read more.

In the digital transformation of public services, reliable and secure data handling has become central to effective E-government operations. This study introduces a symmetry-driven neural network architecture tailored for secure, scalable, and energy-efficient data processing. The model integrates weight-sharing and symmetrical configurations to enhance efficiency and resilience. Experimental validation on three E-government datasets (95,000–230,000 records) demonstrates that the proposed model improves processing speed by up to 40% and enhances adversarial robustness by maintaining accuracy reductions below 2.5% under attack scenarios. Compared with baseline neural networks, the architecture achieves higher accuracy (up to 95.1%), security (up to 98% attack prevention), and efficiency (processing up to 1600 records/sec). These results confirm the model’s applicability for large-scale, real-time E-government systems, providing a practical path for sustainable and secure digital public administration. Full article

(This article belongs to the Special Issue Artificial Intelligence in Sustainable Development)

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15 pages, 2748 KB

Open AccessArticle

A Physics-Enhanced CNN–LSTM Predictive Condition Monitoring Method for Underground Power Cable Infrastructure

by Zaki Moutassem, Doha Bounaim and Gang Li

Algorithms 2025, 18(10), 600; https://doi.org/10.3390/a18100600 - 25 Sep 2025

Abstract

Underground high-voltage transmission cables, especially high-pressure fluid-filled (HPFF) pipe-type cable systems, are critical components of urban power networks. These systems consist of insulated conductor cables housed within steel pipes filled with pressurized fluids that provide essential insulation and cooling. Despite their reliability, HPFF [...] Read more.

Underground high-voltage transmission cables, especially high-pressure fluid-filled (HPFF) pipe-type cable systems, are critical components of urban power networks. These systems consist of insulated conductor cables housed within steel pipes filled with pressurized fluids that provide essential insulation and cooling. Despite their reliability, HPFF cables experience faults caused by insulation degradation, thermal expansion, and environmental stressors, which, due to their subtle and gradual nature, complicate incipient fault detection and subsequent fault localization. This study presents a novel, proactive, and retrofit-friendly predictive condition monitoring method. It leverages distributed accelerometer sensors non-intrusively mounted on the HPFF steel pipe within existing manholes to continuously monitor vibration signals in real time. A physics-enhanced convolutional neural network–long short-term memory (CNN–LSTM) deep learning architecture analyzes these signals to detect incipient faults before they evolve into critical failures. The CNN–LSTM model captures temporal dependencies in acoustic data streams, applying time-series analysis techniques tailored for the predictive condition monitoring of HPFF cables. Experimental validation uses vibration data from a scaled-down HPFF laboratory test setup, comparing normal operation to incipient fault events. The model reliably identifies subtle changes in sequential acoustic patterns indicative of incipient faults. Laboratory experimental results demonstrate a high accuracy of the physics-enhanced CNN–LSTM architecture for incipient fault detection with effective data feature extraction. This approach aims to support enhanced operational resilience and faster response times without intrusive infrastructure modifications, facilitating early intervention to mitigate service disruptions. Full article

(This article belongs to the Section Algorithms and Mathematical Models for Computer-Assisted Diagnostic Systems)

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25 pages, 3707 KB

Open AccessArticle

Evaluating the Effectiveness of Large Language Models (LLMs) Versus Machine Learning (ML) in Identifying and Detecting Phishing Email Attempts

by Saed Tarapiah, Linda Abbas, Oula Mardawi, Shadi Atalla, Yassine Himeur and Wathiq Mansoor

Algorithms 2025, 18(10), 599; https://doi.org/10.3390/a18100599 - 25 Sep 2025

Abstract

Phishing emails remain a significant concern and a growing cybersecurity threat in online communication. They often bypass traditional filters due to their increasing sophistication. This study presents a comparative evaluation of machine learning (ML) models and transformer-based large language models (LLMs) for phishing [...] Read more.

Phishing emails remain a significant concern and a growing cybersecurity threat in online communication. They often bypass traditional filters due to their increasing sophistication. This study presents a comparative evaluation of machine learning (ML) models and transformer-based large language models (LLMs) for phishing email detection, with embedded URL analysis. This study assessed ML training and LLM fine-tuning on both balanced and imbalanced datasets. We evaluated multiple ML models, including Random Forest, Logistic Regression, Support Vector Machine, Naïve Bayes, Gradient Boosting, Decision Tree, and K-Nearest Neighbors, alongside transformer-based LLMs DistilBERT, ALBERT, BERT-Tiny, ELECTRA, MiniLM, and RoBERTa. To further enhance realism, phishing emails generated by LLMs were included in the evaluation. Across all configurations, both the ML models and the fine-tuned LLMs demonstrated robust performance. Random Forest achieved over 98% accuracy in both email detection and URL classification. DistilBERT obtained almost as high scores on emails and URLs. Balancing the dataset led to slight accuracy gains in ML models but minor decreases in LLMs, likely due to their sensitivity to majority class reductions during training. Overall, LLMs are highly effective at capturing complex language patterns, while traditional ML models remain efficient and require low computational resources. Combining both approaches through a hybrid or ensemble method could enhance phishing detection effectiveness. Full article

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

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22 pages, 2268 KB

Open AccessArticle

Optimal Parameter Estimation for Solar PV Panel Based on ANN and Adaptive Particle Swarm Optimization

by Wai Lun Lo, Henry Shu Hung Chung, Richard Tai Chiu Hsung, Hong Fu, Tony Yulin Zhu, Tak Wai Shen and Harris Sik Ho Tsang

Algorithms 2025, 18(10), 598; https://doi.org/10.3390/a18100598 - 24 Sep 2025

Abstract

Parameter estimation for solar photovoltaic panels is a popular research topic in green energy. Model parameters can be used for fault diagnosis in solar panels. Artificial neural network (ANN) approaches have been developed to estimate the model parameters of solar panels. In this [...] Read more.

Parameter estimation for solar photovoltaic panels is a popular research topic in green energy. Model parameters can be used for fault diagnosis in solar panels. Artificial neural network (ANN) approaches have been developed to estimate the model parameters of solar panels. In this study, an ANN and Adaptive Particle Swarm Optimization (APSO) approach for model parameter estimation of solar panel is proposed. Load perturbation is injected at the output of the solar PV panel, and the load voltage and current time series are measured. The current and voltage vectors are used as inputs for an ANN, which is used as a classifier for the ranges of the model parameters. The population of the APSO is initialized according to the results of the ANN classifier, and the APSO algorithm is then used to estimate the model parameters of the PV panel. Simulations and experimental studies show that the proposed method has better performance than conventional PSO, and it requires a smaller number of generations to achieve an average parameter estimation error of less than 5%. Full article

(This article belongs to the Section Combinatorial Optimization, Graph, and Network Algorithms)

14 pages, 442 KB

Open AccessArticle

Evaluating the Promethee ii Ranking Quality

by Boris Coquelet, Gilles Dejaegere and Yves De Smet

Algorithms 2025, 18(10), 597; https://doi.org/10.3390/a18100597 - 24 Sep 2025

Abstract

Multicriteria decision aid consists in helping decision makers to compare (rank, choose, sort, etc.) different alternatives which are evaluated on conflicting criteria. One well-known family of multicriteria decision aid methods is Promethee. It is based on pairwise comparisons of the alternatives to [...] Read more.

Multicriteria decision aid consists in helping decision makers to compare (rank, choose, sort, etc.) different alternatives which are evaluated on conflicting criteria. One well-known family of multicriteria decision aid methods is Promethee. It is based on pairwise comparisons of the alternatives to produce a complete or a partial ranking. Promethee is widely used in practical applications, and different aspects of its theoretical properties have already been studied in the literature. However, no work has been proposed to assess the quality of the rankings it produces with regard to the pairwise preference matrix it exploits. This work addresses this problem by proposing a new indicator of the quality of the rankings produced by Promethee ii. This indicator is derived from a new property of the net flow score procedure. The indicator is illustrated on both artificial and real datasets. Full article

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

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21 pages, 2310 KB

Open AccessArticle

Development of a Model for Detecting Spectrum Sensing Data Falsification Attack in Mobile Cognitive Radio Networks Integrating Artificial Intelligence Techniques

by Lina María Yara Cifuentes, Ernesto Cadena Muñoz and Rafael Cubillos Sánchez

Algorithms 2025, 18(10), 596; https://doi.org/10.3390/a18100596 - 24 Sep 2025

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Abstract

Mobile Cognitive Radio Networks (MCRNs) have emerged as a promising solution to address spectrum scarcity by enabling dynamic access to underutilized frequency bands assigned to Primary or Licensed Users (PUs). These networks rely on Cooperative Spectrum Sensing (CSS) to identify available spectrum, but [...] Read more.

Mobile Cognitive Radio Networks (MCRNs) have emerged as a promising solution to address spectrum scarcity by enabling dynamic access to underutilized frequency bands assigned to Primary or Licensed Users (PUs). These networks rely on Cooperative Spectrum Sensing (CSS) to identify available spectrum, but this collaborative approach also introduces vulnerabilities to security threats—most notably, Spectrum Sensing Data Falsification (SSDF) attacks. In such attacks, malicious nodes deliberately report false sensing information, undermining the reliability and performance of the network. This paper investigates the application of machine learning techniques to detect and mitigate SSDF attacks in MCRNs, particularly considering the additional challenges introduced by node mobility. We propose a hybrid detection framework that integrates a reputation-based weighting mechanism with Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) classifiers to improve detection accuracy and reduce the influence of falsified data. Experimental results on software defined radio (SDR) demonstrate that the proposed method significantly enhances the system’s ability to identify malicious behavior, achieving high detection accuracy, reduces the rate of data falsification by approximately 5–20%, increases the probability of attack detection, and supports the dynamic creation of a blacklist to isolate malicious nodes. These results underscore the potential of combining machine learning with trust-based mechanisms to strengthen the security and reliability of mobile cognitive radio networks. Full article

(This article belongs to the Special Issue Artificial Intelligence in Modern Cybersecurity: Changes, Applications and Challenges)

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20 pages, 2181 KB

Open AccessArticle

Optimal Unit Scheduling Considering Multi-Scenario Source–Load Uncertainty and Frequency Security

by Xiaodong Yang, Yue Chang, Lun Cheng, Yujing Su and Tao Wang

Algorithms 2025, 18(10), 595; https://doi.org/10.3390/a18100595 - 23 Sep 2025

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Abstract

In response to the significant challenges posed by the volatility and low-inertia characteristics of new energy outputs to the safe operation of power systems, a day-ahead scheduling model for generating units that takes into account source–load uncertainty and frequency security is proposed. To [...] Read more.

In response to the significant challenges posed by the volatility and low-inertia characteristics of new energy outputs to the safe operation of power systems, a day-ahead scheduling model for generating units that takes into account source–load uncertainty and frequency security is proposed. To address source–load uncertainty, kernel density estimation and copula theory are employed to model the correlation between wind and solar energy and generate a set of typical daily scenarios. Considering the low-inertia characteristic, frequency security constraints are incorporated into the scheduling model, and an optimization model for generating units that takes into account multi-scenario uncertainty is constructed. By solving the expected value of the objective function, economic and safe scheduling of the system under uncertain environments is achieved. An experimental analysis and case studies verify the advantages and feasibility of the proposed model through a comparison of scheduling costs and frequency security. Full article

(This article belongs to the Special Issue Multi-Objective and Multi-Level Optimization: Algorithms and Applications (2nd Edition))

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20 pages, 1860 KB

Open AccessArticle

Backward Signal Propagation: A Symmetry-Based Training Method for Neural Networks

by Kun Jiang and Zhihong Fu

Algorithms 2025, 18(10), 594; https://doi.org/10.3390/a18100594 - 23 Sep 2025

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Abstract

While backpropagation (BP) has long served as the cornerstone of training deep neural networks, it relies heavily on strict differentiation logic and global gradient information, lacking biological plausibility. In this paper, we systematically present a novel neural network training paradigm that depends solely [...] Read more.

While backpropagation (BP) has long served as the cornerstone of training deep neural networks, it relies heavily on strict differentiation logic and global gradient information, lacking biological plausibility. In this paper, we systematically present a novel neural network training paradigm that depends solely on signal propagation, which we term Backward Signal Propagation (BSP). The core idea of this framework is to reinterpret network training as a symmetry-driven process of discovering inverse causal relationships. Starting from symmetry principles, we define symmetric differential equations and leverage their inherent properties to implement a learning mechanism analogous to differentiation. Furthermore, we introduce the concept of causal distance, a core invariant that bridges the forward propagation and inverse learning processes. It quantifies the influence strength between any two elements in the network, leading to a generalized form of the chain rule. With these innovations, we achieve precise, pointwise adjustment of model parameters. Unlike traditional BP, the BSP method enables parameter updates based solely on local signal features. This work offers a new direction toward efficient and biologically plausible learning algorithms. Full article

(This article belongs to the Special Issue Algorithmic Innovations: Bridging Theoretical Foundations and Practical Applications)

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Algorithms, Volume 18, Issue 10 (October 2025) – 12 articles

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