Algorithms

9 pages, 880 KiB

Open AccessArticle

Machine Learning Models to Predict Google Stock Prices

by Cosmina Elena Bucura and Paolo Giudici

Algorithms 2025, 18(2), 81; https://doi.org/10.3390/a18020081 (registering DOI) - 3 Feb 2025

The aim of this paper is to predict Google stock price using different datasets and machine learning models, and understand which models perform better. The novelty of our approach is that we compare models not only by predictive accuracy but also by explainability [...] Read more.

The aim of this paper is to predict Google stock price using different datasets and machine learning models, and understand which models perform better. The novelty of our approach is that we compare models not only by predictive accuracy but also by explainability and robustness. Our findings show that the choice of the best model to employ to predict Google stock prices depends on the desired objective. If the goal is accuracy, the recurrent neural network is the best model, while, for robustness, the Ridge regression model is the most resilient to changes and, for explainability, the Gradient Boosting model is the best choice. Full article

(This article belongs to the Special Issue Machine Learning for Edge Computing)

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20 pages, 3878 KiB

Open AccessArticle

Energy Scheduling of Hydrogen Hybrid UAV Based on Model Predictive Control and Deep Deterministic Policy Gradient Algorithm

by Haitao Li, Chenyu Wang, Shufu Yuan, Hui Zhu, Bo Li, Yuexin Liu and Li Sun

Algorithms 2025, 18(2), 80; https://doi.org/10.3390/a18020080 (registering DOI) - 2 Feb 2025

Abstract

Energy scheduling for hybrid unmanned aerial vehicles (UAVs) is of critical importance to their safe and stable operation. However, traditional approaches, predominantly rule-based, often lack the dynamic adaptability and stability necessary to address the complexities of changing operational environments. To overcome these limitations, [...] Read more.

Energy scheduling for hybrid unmanned aerial vehicles (UAVs) is of critical importance to their safe and stable operation. However, traditional approaches, predominantly rule-based, often lack the dynamic adaptability and stability necessary to address the complexities of changing operational environments. To overcome these limitations, this paper proposes a novel energy scheduling framework that integrates the Model Predictive Control (MPC) with a Deep Reinforcement Learning algorithm, specifically the Deep Deterministic Policy Gradient (DDPG). The proposed method is designed to optimize energy management in hydrogen-powered UAVs across diverse flight missions. The energy system comprises a proton exchange membrane fuel cell (PEMFC), a lithium-ion battery, and a hydrogen storage tank, enabling robust optimization through the synergistic application of MPC and DDPG. The simulation results demonstrate that the MPC effectively minimizes electric power consumption under various flight conditions, while the DDPG achieves convergence and facilitates efficient scheduling. By leveraging advanced mechanisms, including continuous action space representation, efficient policy learning, experience replay, and target networks, the proposed approach significantly enhances optimization performance and system stability in complex, continuous decision-making scenarios. Full article

(This article belongs to the Special Issue Algorithms for Electrical and Electronic Engineering with Renewable Energy Sources)

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19 pages, 746 KiB

Open AccessArticle

Analytical MPC Algorithm Using Steady-State Process Model

by Piotr M. Marusak

Algorithms 2025, 18(2), 79; https://doi.org/10.3390/a18020079 (registering DOI) - 2 Feb 2025

Abstract

For some classes of control plants (e.g., large time delay or inverse response), the PID controllers may offer unsatisfactory results; on the other hand, a Model Predictive Control (MPC) algorithm based on a linear model may offer insufficient control quality when applied to [...] Read more.

For some classes of control plants (e.g., large time delay or inverse response), the PID controllers may offer unsatisfactory results; on the other hand, a Model Predictive Control (MPC) algorithm based on a linear model may offer insufficient control quality when applied to nonlinear control plants. To improve the MPC algorithm operation, one can use a steady-state process model; this paper describes how to do this skillfully. The obtained algorithm, based on the popular Dynamic Matrix Control (DMC) algorithm, is detailed. The proposed approach consists in modifying the analytical version of the DMC algorithm in such a way that it can still be expressed as the control law. Thus, the algorithm can still be applied to fast control plants, requiring short sampling times. Though the proposed approach does not modify the DMC algorithm much, it offers improvement in the control quality when the algorithm is employed in a nonlinear control plant. Experiments illustrating the efficiency of the proposed approach were conducted in the control system of a nonlinear chemical reactor. The results show improvement in the control quality compared to a case when the classical MPC algorithm is used. Full article

(This article belongs to the Special Issue Algorithms for PID Controller 2024)

21 pages, 5270 KiB

Open AccessArticle

Three-Dimensional Object Recognition Using Orthogonal Polynomials: An Embedded Kernel Approach

by Aqeel Abdulazeez Mohammed, Ahlam Hanoon Al-sudani, Alaa M. Abdul-Hadi, Almuntadher Alwhelat, Basheera M. Mahmmod, Sadiq H. Abdulhussain, Muntadher Alsabah and Abir Hussain

Algorithms 2025, 18(2), 78; https://doi.org/10.3390/a18020078 (registering DOI) - 1 Feb 2025

Abstract

Computer vision seeks to mimic the human visual system and plays an essential role in artificial intelligence. It is based on different signal reprocessing techniques; therefore, developing efficient techniques becomes essential to achieving fast and reliable processing. Various signal preprocessing operations have been [...] Read more.

Computer vision seeks to mimic the human visual system and plays an essential role in artificial intelligence. It is based on different signal reprocessing techniques; therefore, developing efficient techniques becomes essential to achieving fast and reliable processing. Various signal preprocessing operations have been used for computer vision, including smoothing techniques, signal analyzing, resizing, sharpening, and enhancement, to reduce reluctant falsifications, segmentation, and image feature improvement. For example, to reduce the noise in a disturbed signal, smoothing kernels can be effectively used. This is achievedby convolving the distributed signal with smoothing kernels. In addition, orthogonal moments (OMs) are a crucial technique in signal preprocessing, serving as key descriptors for signal analysis and recognition. OMs are obtained by the projection of orthogonal polynomials (OPs) onto the signal domain. However, when dealing with 3D signals, the traditional approach of convolving kernels with the signal and computing OMs beforehand significantly increases the computational cost of computer vision algorithms. To address this issue, this paper develops a novel mathematical model to embed the kernel directly into the OPs functions, seamlessly integrating these two processes into a more efficient and accurate approach. The proposed model allows the computation of OMs for smoothed versions of 3D signals directly, thereby reducing computational overhead. Extensive experiments conducted on 3D objects demonstrate that the proposed method outperforms traditional approaches across various metrics. The average recognition accuracy improves to 83.85% when the polynomial order is increased to 10. Experimental results show that the proposed method exhibits higher accuracy and lower computational costs compared to the benchmark methods in various conditions for a wide range of parameter values. Full article

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

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18 pages, 4019 KiB

Open AccessArticle

Seizure Detection in Medical IoT: Hybrid CNN-LSTM-GRU Model with Data Balancing and XAI Integration

by Hanaa Torkey, Sonia Hashish, Samia Souissi, Ezz El-Din Hemdan and Amged Sayed

Algorithms 2025, 18(2), 77; https://doi.org/10.3390/a18020077 (registering DOI) - 1 Feb 2025

Abstract

The brain acts as the body’s central command, overseeing diverse functions including thought, memory, speech, movement, and the regulation of various organs. When healthy, the brain functions seamlessly and automatically; however, disruptions can lead to serious conditions such as Alzheimer’s Disease, Brain Cancer, [...] Read more.

The brain acts as the body’s central command, overseeing diverse functions including thought, memory, speech, movement, and the regulation of various organs. When healthy, the brain functions seamlessly and automatically; however, disruptions can lead to serious conditions such as Alzheimer’s Disease, Brain Cancer, Stroke, and Epilepsy. Epilepsy, a neurological disorder marked by recurrent seizures, results from irregular electrical activity in the brain. These seizures, which can strain both patients and neurologists, are characterized by symptoms like the loss of awareness, unusual behavior, and confusion. This study presents an efficient EEG-based epileptic seizure detection framework utilizing a hybrid Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models approach to support automated and accurate diagnosis. Handling imbalanced EEG data, which can otherwise bias model outcomes and reduce predictive accuracy, is a key focus. Experimental results indicate that the proposed framework generally outperforms other Deep Learning and Machine Learning techniques with the highest accuracy at 99.13%. Likewise, an Explainable Artificial Intelligence (XAI) called SHAP (SHapley Additive exPlanations) is utilized to analyze the results and to improve the interpretability of the models from medical decision-making. This framework aligns with the objectives of the Medical Internet of Things (MIoT), advancing smart medical applications and services for effective epileptic seizure detection. Full article

(This article belongs to the Special Issue Advancing Smart Health and Biomedical Research with Artificial Intelligence Algorithms)

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13 pages, 258 KiB

Open AccessArticle

Broadcasting in Stars of Cliques and Path-Connected Cliques

by Akash Ambashankar and Hovhannes A. Harutyunyan

Algorithms 2025, 18(2), 76; https://doi.org/10.3390/a18020076 (registering DOI) - 1 Feb 2025

Abstract

Broadcasting is a fundamental information dissemination problem in a connected network where one node, referred to as the originator, must distribute a message to all other nodes through a series of calls along the network’s links. Once informed, nodes assist the originator by [...] Read more.

Broadcasting is a fundamental information dissemination problem in a connected network where one node, referred to as the originator, must distribute a message to all other nodes through a series of calls along the network’s links. Once informed, nodes assist the originator by forwarding the message to their neighbors. Determining the broadcast time for a node in an arbitrary network is NP-complete. While polynomial-time algorithms exist for specific network topologies, the problem remains open for many others. In this paper, we focus on addressing the broadcasting problem in network topologies represented by specialized clique-based structures. Specifically, we investigate the windmill graph

W d_{k, l}

, which consists of k cliques of size l connected to a universal node, and extend our study to the star of cliques, a generalization of the windmill graph with cliques of arbitrary sizes. Our primary objective is to propose an efficient algorithm for determining the broadcast time of any node in an arbitrary star of cliques and to rigorously prove its optimality. Additionally, we broaden the scope by examining the broadcasting problem in path-connected cliques, a topology featuring k cliques of varying sizes sequentially connected along a path. For this structure, we develop a computationally efficient algorithm that leverages clique sizes and adjacency to optimize broadcast strategies, with broader implications for understanding communication in block graphs. Full article

(This article belongs to the Special Issue Selected Algorithmic Papers from IWOCA 2024)

22 pages, 18158 KiB

Open AccessArticle

A Novel Model for Noninvasive Haemoglobin Detection Based on Visibility Network and Clustering Network for Multi-Wavelength PPG Signals

by Lei Liu, Ziyi Wang, Xiaohan Zhang, Yan Zhuang and Yongbo Liang

Algorithms 2025, 18(2), 75; https://doi.org/10.3390/a18020075 (registering DOI) - 1 Feb 2025

Abstract

Non-invasive haemoglobin (Hb) testing devices enable large-scale haemoglobin screening, but their accuracy is not comparable to traditional blood tests. To this end, this paper aims to design a non-invasive haemoglobin testing device and propose a classification-regression prediction framework for non-invasive testing of haemoglobin [...] Read more.

Non-invasive haemoglobin (Hb) testing devices enable large-scale haemoglobin screening, but their accuracy is not comparable to traditional blood tests. To this end, this paper aims to design a non-invasive haemoglobin testing device and propose a classification-regression prediction framework for non-invasive testing of haemoglobin using visibility graphs (VG) with network clustering of multi-sample pulse-wave-weighted undirected graphs as the features to optimize the detection accuracy of non-invasive haemoglobin measurements. Different prediction methods were compared by analyzing 608 segments of multiwavelength fingertip PPG signal data from 152 volunteers and analyzing and comparing the data and methods. The results showed that the classification using NVG with complex network clustering as features in the interval classification model was the best, with its classification accuracy (acc) of 93.35% and model accuracy of 88.28%. Among the regression models, the classification regression stack: SVM-Light Gradient Boosting Machine (LGBM) was the most effective, with a Mean Absolute Error (MAE) of 6.67 g/L, a Root Mean Square Error (RMSE) of 8.21 g/L, and an R-Square (R2) of 0.64. The results of this study indicate that the use of complex network technology in non-invasive haemoglobin detection can effectively improve its accuracy, and the detector designed in this study is promising to carry out a more accurate large-scale haemoglobin screening. Full article

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

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27 pages, 1314 KiB

Open AccessArticle

Optimizing Apache Spark MLlib: Predictive Performance of Large-Scale Models for Big Data Analytics

by Leonidas Theodorakopoulos, Aristeidis Karras and George A. Krimpas

Algorithms 2025, 18(2), 74; https://doi.org/10.3390/a18020074 (registering DOI) - 1 Feb 2025

Abstract

In this study, we analyze the performance of the machine learning operators in Apache Spark MLlib for K-Means, Random Forest Regression, and Word2Vec. We used a multi-node Spark cluster along with collected detailed execution metrics computed from the data of diverse datasets and [...] Read more.

In this study, we analyze the performance of the machine learning operators in Apache Spark MLlib for K-Means, Random Forest Regression, and Word2Vec. We used a multi-node Spark cluster along with collected detailed execution metrics computed from the data of diverse datasets and parameter settings. The data were used to train predictive models that had up to 98% accuracy in forecasting performance. By building actionable predictive models, our research provides a unique treatment for key hyperparameter tuning, scalability, and real-time resource allocation challenges. Specifically, the practical value of traditional models in optimizing Apache Spark MLlib workflows was shown, achieving up to 30% resource savings and a 25% reduction in processing time. These models enable system optimization, reduce the amount of computational overheads, and boost the overall performance of big data applications. Ultimately, this work not only closes significant gaps in predictive performance modeling, but also paves the way for real-time analytics over a distributed environment. Full article

(This article belongs to the Special Issue Algorithms in Data Classification (2nd Edition))

25 pages, 6410 KiB

Open AccessArticle

Intelligent Multi-Fault Diagnosis for a Simplified Aircraft Fuel System

by Jiajin Li, Steve King and Ian Jennions

Algorithms 2025, 18(2), 73; https://doi.org/10.3390/a18020073 (registering DOI) - 1 Feb 2025

Abstract

Machine learning (ML) techniques are increasingly used to diagnose faults in aerospace applications, but diagnosing multiple faults in aircraft fuel systems (AFSs) remains challenging due to complex component interactions. This paper evaluates the accuracy and introduces an innovative approach to quantify and compare [...] Read more.

Machine learning (ML) techniques are increasingly used to diagnose faults in aerospace applications, but diagnosing multiple faults in aircraft fuel systems (AFSs) remains challenging due to complex component interactions. This paper evaluates the accuracy and introduces an innovative approach to quantify and compare the interpretability of four ML classification methods—artificial neural networks (ANNs), support vector machines (SVMs), decision trees (DTs), and logistic regressions (LRs)—for diagnosing fault combinations present in AFSs. While the ANN achieved the highest diagnostic accuracy at 90%, surpassing other methods, its interpretability was limited. By contrast, the decision tree model showed an 82% consistency between global explanations and engineering insights, highlighting its advantage in interpretability despite the lower accuracy. Interpretability was assessed using two widely accepted tools, LIME and SHAP, alongside engineering understanding. These findings underscore a trade-off between prediction accuracy and interpretability, which is critical for trust in ML applications in aerospace. Although an ANN can deliver high diagnostic accuracy, a decision tree offers more transparent results, facilitating better alignment with engineering expectations even at a slight cost to accuracy. Full article

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

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26 pages, 4970 KiB

Open AccessArticle

Do What You Say—Computing Personal Values Associated with Professions Based on the Words They Use

by Aditya Jha and Peter A. Gloor

Algorithms 2025, 18(2), 72; https://doi.org/10.3390/a18020072 (registering DOI) - 1 Feb 2025

Abstract

Members of a profession frequently show similar personality characteristics. In this research, we leverage recent advances in NLP to compute personal values using a moral values framework, distinguishing between four different personas that assist in categorizing different professions by personal values: “fatherlanders”—valuing tradition [...] Read more.

Members of a profession frequently show similar personality characteristics. In this research, we leverage recent advances in NLP to compute personal values using a moral values framework, distinguishing between four different personas that assist in categorizing different professions by personal values: “fatherlanders”—valuing tradition and authority, “nerds”—valuing scientific achievements, “spiritualists”—valuing compassion and non-monetary achievements, and “treehuggers”—valuing sustainability and the environment. We collected 200 YouTube videos and podcasts for each professional category of lawyers, academics, athletes, engineers, creatives, managers, and accountants, converting their audio to text. We also categorize these professions by team player personas into “bees”—collaborative creative team players, “ants”—competitive hard workers, and “leeches”—selfish egoists using pre-trained models. We find distinctive personal value profiles for each of our seven professions computed from the words that members of each profession use. Full article

(This article belongs to the Special Issue Algorithms in Data Classification (2nd Edition))

16 pages, 2204 KiB

Open AccessArticle

EGSDK-Net: Edge-Guided Stepwise Dual Kernel Update Network for Panoptic Segmentation

by Pengyu Mu, Hongwei Zhao and Ke Ma

Algorithms 2025, 18(2), 71; https://doi.org/10.3390/a18020071 (registering DOI) - 1 Feb 2025

Abstract

In recent years, panoptic segmentation has garnered increasing attention from researchers aiming to better understand scenes in images. Although many excellent studies have been proposed, they share some common unresolved issues. Firstly, panoptic segmentation, as a novel task, is still confined within inherent [...] Read more.

In recent years, panoptic segmentation has garnered increasing attention from researchers aiming to better understand scenes in images. Although many excellent studies have been proposed, they share some common unresolved issues. Firstly, panoptic segmentation, as a novel task, is still confined within inherent frameworks. Secondly, the prevalent kernel update strategies do not adequately utilize the information from each stage. To address these two issues, redwe propose an edge-guided stepwise dual kernel update network (EGSDK-Net) for panoptic segmentation; the core components are the real-time edge guidance module and the stepwise dual kernel update module. The first component, after extracting and positioning edge features through an extra branch, applies these features to the normally transmitted feature maps within the network to highlight the edges. The input image is initially processed with the Canny edge detector to generate and store the predicted edge map, which acts as the ground truth for supervising the extracted edge feature map. The stepwise dual kernel update module enhances the utilization of information by allowing each stage to update both its own kernel and that of the subsequent stage, thereby improving the judgment capabilities of the kernels. redEGSDK-Net achieves a PQ of 60.6, representing a 2.19% improvement over RT-K-Net. Full article

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

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17 pages, 4690 KiB

Open AccessArticle

Advantages of Density in Tensor Network Geometries for Gradient-Based Training

by Sergi Masot-Llima and Artur Garcia-Saez

Algorithms 2025, 18(2), 70; https://doi.org/10.3390/a18020070 - 31 Jan 2025

Abstract

Tensor networks are a very powerful data structure tool originating from simulations of quantum systems. In recent years, they have seen increased use in machine learning, mostly in trainings with gradient-based techniques, due to their flexibility and performance achieved by exploiting hardware acceleration. [...] Read more.

Tensor networks are a very powerful data structure tool originating from simulations of quantum systems. In recent years, they have seen increased use in machine learning, mostly in trainings with gradient-based techniques, due to their flexibility and performance achieved by exploiting hardware acceleration. As ansatzes, tensor networks can be used with flexible geometries, and it is known that for highly regular ones, their dimensionality has a large impact on performance and representation power. For heterogeneous structures, however, these effects are not completely characterized. In this article, we train tensor networks with different geometries to encode a random quantum state, and see that densely connected structures achieve better infidelities than more sparse structures, with higher success rates and less time. Additionally, we give some general insight on how to improve the memory requirements of these sparse structures and the impact of such improvement on the trainings. Finally, as we use HPC resources for the calculations, we discuss the requirements for this approach and showcase performance improvements with GPU acceleration on a last-generation supercomputer. Full article

(This article belongs to the Special Issue Algorithms for Quantum Computing and Quantum-Centric High-Performance Computing)

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59 pages, 3383 KiB

Open AccessArticle

Enhanced Hybrid Deep Learning Models-Based Anomaly Detection Method for Two-Stage Binary and Multi-Class Classification of Attacks in Intrusion Detection Systems

by Hesham Kamal and Maggie Mashaly

Algorithms 2025, 18(2), 69; https://doi.org/10.3390/a18020069 - 28 Jan 2025

Abstract

As security threats become more complex, the need for effective intrusion detection systems (IDSs) has grown. Traditional machine learning methods are limited by the need for extensive feature engineering and data preprocessing. To overcome this, we propose two enhanced hybrid deep learning models, [...] Read more.

As security threats become more complex, the need for effective intrusion detection systems (IDSs) has grown. Traditional machine learning methods are limited by the need for extensive feature engineering and data preprocessing. To overcome this, we propose two enhanced hybrid deep learning models, an autoencoder–convolutional neural network (Autoencoder–CNN) and a transformer–deep neural network (Transformer–DNN). The Autoencoder reshapes network traffic data, addressing class imbalance, and the CNN performs precise classification. The transformer component extracts contextual features, which the DNN uses for accurate classification. Our approach utilizes an enhanced hybrid adaptive synthetic sampling–synthetic minority oversampling technique (ADASYN-SMOTE) for binary classification and enhanced SMOTE for multi-class classification, along with edited nearest neighbors (ENN) for further class imbalance handling. The models were designed to minimize false positives and negatives, improve real-time detection, and identify zero-day attacks. Evaluations based on the CICIDS2017 dataset showed 99.90% accuracy for Autoencoder–CNN and 99.92% for Transformer–DNN in binary classification, and 99.95% and 99.96% in multi-class classification, respectively. On the NF-BoT-IoT-v2 dataset, the Autoencoder–CNN achieved 99.98% in binary classification and 97.95% in multi-class classification, while the Transformer–DNN reached 99.98% and 97.90%, respectively. These results demonstrate the superior performance of the proposed models compared with traditional methods for handling diverse network attacks. Full article

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

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25 pages, 11877 KiB

Open AccessArticle

Characterization of Rock Pore Geometry and Mineralization Process via a Random Walk-Based Clogging Scheme

by Linh Thi Hoai Nguyen, Tomoyuki Shirai and Takeshi Tsuji

Algorithms 2025, 18(2), 68; https://doi.org/10.3390/a18020068 - 26 Jan 2025

Abstract

Among the technologies for carbon neutral, CO

_{2}

geological sequestration is one of the most promising. The mechanisms of CO

_{2}

behavior within pore space are complex and influenced by multiple factors, with the geometric structure of porous formations being particularly critical to [...] Read more.

Among the technologies for carbon neutral, CO

_{2}

geological sequestration is one of the most promising. The mechanisms of CO

_{2}

behavior within pore space are complex and influenced by multiple factors, with the geometric structure of porous formations being particularly critical to the technology’s efficiency. Among several important and challenging problems in geological sequestration, this work addresses the issue of selecting lithologies based on their geometrical structure. This study proposes a mathematical approach to characterize the geometric structure of porous rock and fluid flow using a random walk (RW) method. Our approach simulates the time evolution of particle flow through highly disordered and heterogeneous digital rock models under a pressure gradient imposed between inlet and outlet surfaces. Through RW simulations, a probabilistic model for mineralization via a clogging (pore-filling) model is introduced, to examine the accumulation of particles within porous structures over time: single-phase clogging and multiple-phase clogging. In single-phase clogging, the porosity decrease can be described as a monotonically non-increasing function of the deposition probability. However, this is no longer true in the multiple-phase strategy because large deposition probability blocks the capillaries near the inlet surface, preventing the fluid from easily invading easily the outlet. In this study, numerical studies conducted on four types of natural rocks—Bentheimer, Doddington, Estaillades, and Ketton—revealed that Ketton exhibits the highest permeability. Our results suggest that Bentheimer, Doddington, and Ketton formations are suitable candidates for CO

_{2}

sequestration, while Estaillades is less favorable from a geometric standpoint. The methods presented in this work contribute to effectively identifying natural rocks with geometric structures advantageous for CO

_{2}

storage. Full article

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

15 pages, 1605 KiB

Open AccessArticle

CSpredR: A Multi-Site mRNA Subcellular Localization Prediction Method Based on Fusion Encoding and Hybrid Neural Networks

by Xiao Wang, Wenshuai Suo and Rong Wang

Algorithms 2025, 18(2), 67; https://doi.org/10.3390/a18020067 - 26 Jan 2025

Abstract

Current research widely acknowledges that the subcellular localization of mRNA is crucial for understanding its biological functions. However, current methods for mRNA subcellular localization based on k-mer frequency features may overlook the sequential information of the sequence, and a single encoding method may [...] Read more.

Current research widely acknowledges that the subcellular localization of mRNA is crucial for understanding its biological functions. However, current methods for mRNA subcellular localization based on k-mer frequency features may overlook the sequential information of the sequence, and a single encoding method may not adequately extract the sequence’s features. This paper proposes a novel deep learning prediction method, CSpredR, specifically designed for predicting the subcellular localization of multi-site mRNAs. Unlike previous methods, CSpredR first employs k-mer to tokenize the mRNA sequences, then converts the tokenized sequences into de Bruijn graphs, thereby enabling a more precise capture of the structural information within the sequences. To mitigate the impact of lost sequential information and better capture sequence features, we combine word2vec and fasttext models to extract the features of each node in the graph and retain the sequence order. They can encode the k-mer units in the sequence into word vectors, thus serving as the node feature vectors of the graph. In this way, each node in the graph is assigned a feature vector containing rich semantic information. Subsequently, we utilize multi-scale convolutional neural networks and bidirectional long short-term memory networks to capture sequence features, respectively, and fuse the results as input for a multi-head attention mechanism model. The information from these heads is integrated into the node representations, and finally, the attention-processed data are fed into an MLP (Multi-Layer Perceptron) for prediction tasks. Extensive experiments reveal that CSpredR achieves a 2% improvement over the best existing predictors, offering a more effective tool for mRNA subcellular localization prediction. Full article

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

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28 pages, 9307 KiB

Open AccessArticle

Application Framework and Optimal Features for UAV-Based Earthquake-Induced Structural Displacement Monitoring

by Ruipu Ji, Shokrullah Sorosh, Eric Lo, Tanner J. Norton, John W. Driscoll, Falko Kuester, Andre R. Barbosa, Barbara G. Simpson and Tara C. Hutchinson

Algorithms 2025, 18(2), 66; https://doi.org/10.3390/a18020066 - 26 Jan 2025

Abstract

Unmanned aerial vehicle (UAV) vision-based sensing has become an emerging technology for structural health monitoring (SHM) and post-disaster damage assessment of civil infrastructure. This article proposes a framework for monitoring structural displacement under earthquakes by reprojecting image points obtained courtesy of UAV-captured videos [...] Read more.

Unmanned aerial vehicle (UAV) vision-based sensing has become an emerging technology for structural health monitoring (SHM) and post-disaster damage assessment of civil infrastructure. This article proposes a framework for monitoring structural displacement under earthquakes by reprojecting image points obtained courtesy of UAV-captured videos to the 3-D world space based on the world-to-image point correspondences. To identify optimal features in the UAV imagery, geo-reference targets with various patterns were installed on a test building specimen, which was then subjected to earthquake shaking. A feature point tracking-based algorithm for square checkerboard patterns and a Hough Transform-based algorithm for concentric circular patterns are developed to ensure reliable detection and tracking of image features. Photogrammetry techniques are applied to reconstruct the 3-D world points and extract structural displacements. The proposed methodology is validated by monitoring the displacements of a full-scale 6-story mass timber building during a series of shake table tests. Reasonable accuracy is achieved in that the overall root-mean-square errors of the tracking results are at the millimeter level compared to ground truth measurements from analog sensors. Insights on optimal features for monitoring structural dynamic response are discussed based on statistical analysis of the error characteristics for the various reference target patterns used to track the structural displacements. Full article

(This article belongs to the Special Issue Algorithms for Image Processing and Machine Vision)

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15 pages, 6487 KiB

Open AccessArticle

SA-Net: Leveraging Spatial Correlations Spatial-Aware Net for Multi-Perspective Robust Estimation Algorithm

by Yuxiang Shao, Longyang Zhou, Xiang Li, Chunsheng Feng and Xinyu Jin

Algorithms 2025, 18(2), 65; https://doi.org/10.3390/a18020065 - 26 Jan 2025

Abstract

Robust estimation aims to provide accurate and reliable parameter estimations, particularly when data are affected by noise or outliers. Traditional methods like random sample consensus (RANSAC) struggle with handling outliers because they treat all observations as equally important. A series of advanced deep [...] Read more.

Robust estimation aims to provide accurate and reliable parameter estimations, particularly when data are affected by noise or outliers. Traditional methods like random sample consensus (RANSAC) struggle with handling outliers because they treat all observations as equally important. A series of advanced deep learning methods have recently emerged, which use deep learning techniques to estimate the probability of each sample being selected, prioritizing higher confidence for observations that are closer to the ground truth model. However, optimizing solely based on proximity to the ground truth model does not guarantee higher-quality estimations. Meanwhile spatial relationships between the data points in the minimum sampled set also influence the accuracy of the final estimated model. To address these issues, we propose Spatial-Aware Net (SA-Net), a dual-branch neural network that integrates both confidence and spatial encodings. SA-Net employs a confidence distribution encoder to learn the confidence distribution and a spatial distribution encoder to capture spatial correlations between point features. By incorporating multi-perspective sampling, the minimum sample set can be selected based on different spatial distributions in the output of the neural network, and applying Chamfer Loss constraints, our approach improves model optimization and effectively mitigates suboptimal solutions. Extensive experiments demonstrate that SA-Net outperforms the state of the art across various real-world robust estimation tasks. Full article

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

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30 pages, 588 KiB

Open AccessArticle

A Comparative Study of Differential Quadrature Methods for METE Nanobeam Vibrations

by Waleed Mohammed Abdelfattah

Algorithms 2025, 18(2), 64; https://doi.org/10.3390/a18020064 - 25 Jan 2025

Abstract

This study investigates the use of three different quadrature schemes, as well as an iterative quadrature methodology, to analyze vibrations in magneto-electro-thermo-elastic nanobeams. Individual MATLAB programs for each method are developed with the goal of minimizing errors in comparison to accurate findings, as [...] Read more.

This study investigates the use of three different quadrature schemes, as well as an iterative quadrature methodology, to analyze vibrations in magneto-electro-thermo-elastic nanobeams. Individual MATLAB programs for each method are developed with the goal of minimizing errors in comparison to accurate findings, as well as determining the execution time for each strategy. This study shows that the Discrete Singular-Convolution Differential Quadrature Method with a Regularized Shannon Kernel (DSCDQM-RSK) and specified parameters produces the best accurate and efficient results for this particular situation. A subsequent parametric study is carried out to determine the effect of various factors on the vibrated nanobeam, including boundary conditions, material types, linear and nonlinear elastic foundation properties, nonlocal parameters, length-to-thickness ratios, external electric and magnetic potentials, axial forces, and temperature variations. Important discoveries include insights into the relationship between fundamental frequency, linear elastic foundation features, axial loads, external magnetic fields, temperature fluctuations, and material types. According to this study, these findings could be critical in the development of sophisticated nanostructures made from magneto-electro-thermo-elastic materials for use in a variety of electromechanical applications. This would entail utilizing nanobeams’ unique properties in applications such as sensors, resonators, and transducers for nanoelectronics and biology. Full article

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

31 pages, 11725 KiB

Open AccessArticle

Evaluation of PID-Based Algorithms for UGVs

by Tiago Gameiro, Tiago Pereira, Hamid Moghadaspoura, Francesco Di Giorgio, Carlos Viegas, Nuno Ferreira, João Ferreira, Salviano Soares and António Valente

Algorithms 2025, 18(2), 63; https://doi.org/10.3390/a18020063 - 24 Jan 2025

Abstract

The autonomous navigation of unmanned ground vehicles (UGVs) in unstructured environments, such as agricultural or forestry settings, has been the subject of extensive research by various investigators. The navigation capability of a UGV in unstructured environments requires considering numerous factors, including the quality [...] Read more.

The autonomous navigation of unmanned ground vehicles (UGVs) in unstructured environments, such as agricultural or forestry settings, has been the subject of extensive research by various investigators. The navigation capability of a UGV in unstructured environments requires considering numerous factors, including the quality of data reception that allows reliable interpretation of what the UGV perceives in a given environment, as well as the use these data to control the UGV’s navigation. This article aims to study different PID control algorithms to enable autonomous navigation on a robotic platform. The robotic platform consists of a forestry tractor, used for forest cleaning tasks, which was converted into a UGV through the integration of sensors. Using sensor data, the UGV’s position and orientation are obtained and utilized for navigation by inputting these data into a PID control algorithm. The correct choice of PID control algorithm involved the study, analysis, and implementation of different controllers, leading to the conclusion that the Vector Field control algorithm demonstrated better performance compared to the others studied and implemented in this paper. Full article

(This article belongs to the Special Issue Algorithms for PID Controller 2024)

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