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, MathSciNet and other databases.
- Journal Rank: CiteScore - Q2 (Numerical Analysis)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 17.6 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 2022).
- 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.
Latest Articles
Improving Accuracy of Face Recognition in the Era of Mask-Wearing: An Evaluation of a Pareto-Optimized FaceNet Model with Data Preprocessing Techniques
Algorithms 2023, 16(6), 292; https://doi.org/10.3390/a16060292 - 05 Jun 2023
Abstract
The paper presents an evaluation of a Pareto-optimized FaceNet model with data preprocessing techniques to improve the accuracy of face recognition in the era of mask-wearing. The COVID-19 pandemic has led to an increase in mask-wearing, which poses a challenge for face recognition
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The paper presents an evaluation of a Pareto-optimized FaceNet model with data preprocessing techniques to improve the accuracy of face recognition in the era of mask-wearing. The COVID-19 pandemic has led to an increase in mask-wearing, which poses a challenge for face recognition systems. The proposed model uses Pareto optimization to balance accuracy and computation time, and data preprocessing techniques to address the issue of masked faces. The evaluation results demonstrate that the model achieves high accuracy on both masked and unmasked faces, outperforming existing models in the literature. The findings of this study have implications for improving the performance of face recognition systems in real-world scenarios where mask-wearing is prevalent. The results of this study show that the Pareto optimization allowed improving the overall accuracy over the 94% achieved by the original FaceNet variant, which also performed similarly to the ArcFace model during testing. Furthermore, a Pareto-optimized model no longer has a limitation of the model size and is much smaller and more efficient version than the original FaceNet and derivatives, helping to reduce its inference time and making it more practical for use in real-life applications.
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(This article belongs to the Special Issue Machine Learning and Deep Learning in Pattern Recognition)
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An Effective Local Particle Swarm Optimization-Based Algorithm for Solving the School Timetabling Problem
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, , and
Algorithms 2023, 16(6), 291; https://doi.org/10.3390/a16060291 - 04 Jun 2023
Abstract
This paper deals with the school timetabling problem. The problem was formulated as encountered in a typical Greek high school. A local version of the particle swarm optimization algorithm was developed and applied to the problem at hand. Results on well-established benchmark instances
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This paper deals with the school timetabling problem. The problem was formulated as encountered in a typical Greek high school. A local version of the particle swarm optimization algorithm was developed and applied to the problem at hand. Results on well-established benchmark instances showed that the proposed algorithm achieved the proven optima provided from an integer programming method presented in earlier research. In almost all cases, the current algorithm beat the integer programming method, either concerning the lower bound yielded or the execution time needed.
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(This article belongs to the Collection Feature Paper in Metaheuristic Algorithms and Applications)
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Integration of Polynomials Times Double Step Function in Quadrilateral Domains for XFEM Analysis
Algorithms 2023, 16(6), 290; https://doi.org/10.3390/a16060290 - 04 Jun 2023
Abstract
The numerical integration of discontinuous functions is an abiding problem addressed by various authors. This subject gained even more attention in the context of the extended finite element method (XFEM), in which the exact integration of discontinuous functions is crucial to obtaining reliable
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The numerical integration of discontinuous functions is an abiding problem addressed by various authors. This subject gained even more attention in the context of the extended finite element method (XFEM), in which the exact integration of discontinuous functions is crucial to obtaining reliable results. In this scope, equivalent polynomials represent an effective method to circumvent the problem while exploiting the standard Gauss quadrature rule to exactly integrate polynomials times step function. Certain scenarios, however, might require the integration of polynomials times two step functions (i.e., problems in which branching cracks, kinking cracks or crack junctions within a single finite element occur). In this context, the use of equivalent polynomials has been investigated by the authors, and an algorithm to exactly integrate arbitrary polynomials times two Heaviside step functions in quadrilateral domains has been developed and is presented in this paper. Moreover, the algorithm has also been implemented into a software library (DD_EQP) to prove its precision and effectiveness and also the proposed method’s ease of implementation into any existing computational software or framework. The presented algorithm is the first step towards the numerical integration of an arbitrary number of discontinuities in quadrilateral domains. Both the algorithm and the library have a wide application range, in addition to fracture mechanics, from mathematical computing of complex geometric regions, to computer graphics and computational mechanics.
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(This article belongs to the Special Issue Computational Methods and Optimization for Numerical Analysis)
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Adding a Tail in Classes of Perfect Graphs
Algorithms 2023, 16(6), 289; https://doi.org/10.3390/a16060289 - 03 Jun 2023
Abstract
Consider a graph G which belongs to a graph class . We are interested in connecting a node to G by a single edge where ; we call
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Consider a graph G which belongs to a graph class . We are interested in connecting a node to G by a single edge where ; we call such an edge a tail. As the graph resulting from G after the addition of the tail, denoted , need not belong to the class , we want to compute the number of non-edges of G in a minimum -completion of , i.e., the minimum number of non-edges (excluding the tail ) to be added to so that the resulting graph belongs to . In this paper, we study this problem for the classes of split, quasi-threshold, threshold and -sparse graphs and we present linear-time algorithms by exploiting the structure of split graphs and the tree representation of quasi-threshold, threshold and -sparse graphs.
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(This article belongs to the Special Issue Graph Theoretic Methods in Scientific Computing & Industrial Applications)
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An Adaptive Deep Learning Neural Network Model to Enhance Machine-Learning-Based Classifiers for Intrusion Detection in Smart Grids
Algorithms 2023, 16(6), 288; https://doi.org/10.3390/a16060288 - 02 Jun 2023
Abstract
Modern smart grids are built based on top of advanced computing and networking technologies, where condition monitoring relies on secure cyberphysical connectivity. Over the network infrastructure, transported data containing confidential information, must be protected as smart grids are vulnerable and subject to various
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Modern smart grids are built based on top of advanced computing and networking technologies, where condition monitoring relies on secure cyberphysical connectivity. Over the network infrastructure, transported data containing confidential information, must be protected as smart grids are vulnerable and subject to various cyberattacks. Various machine learning based classifiers were proposed for intrusion detection in smart grids. However, each of them has respective advantage and disadvantages. Aiming to improve the performance of existing machine learning based classifiers, this paper proposes an adaptive deep learning algorithm with a data pre-processing module, a neural network pre-training module and a classifier module, which work together classify intrusion data types using their high-dimensional data features. The proposed Adaptive Deep Learning (ADL) algorithm obtains the number of layers and the number of neurons per layer by determining the characteristic dimension of the network traffic. With transfer learning, the proposed ADL algorithm can extract the original data dimensions and obtain new abstract features. By combining deep learning models with traditional machine learning-based classification models, the performance of classification of network traffic data is significantly improved. By using the Network Security Laboratory-Knowledge Discovery in Databases (NSL-KDD) dataset, experimental results show that the proposed ADL algorithm improves the effectiveness of existing intrusion detection methods and reduces the training time, indicating a promising candidate to enhance network security in smart grids.
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(This article belongs to the Special Issue AI for Cybersecurity: Robust models for Authentication, Threat and Anomaly Detection)
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A Fast Hybrid Pressure-Correction Algorithm for Simulating Incompressible Flows by Projection Methods
Algorithms 2023, 16(6), 287; https://doi.org/10.3390/a16060287 - 02 Jun 2023
Abstract
To enforce the conservation of mass principle, a pressure Poisson equation arises in the numerical solution of incompressible fluid flow using the pressure-based segregated algorithms such as projection methods. For unsteady flows, the pressure Poisson equation is solved at each time step usually
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To enforce the conservation of mass principle, a pressure Poisson equation arises in the numerical solution of incompressible fluid flow using the pressure-based segregated algorithms such as projection methods. For unsteady flows, the pressure Poisson equation is solved at each time step usually in physical space using iterative solvers, and the resulting pressure gradient is then applied to make the velocity field divergence-free. It is generally accepted that this pressure-correction stage is the most time-consuming part of the flow solver and any meaningful acceleration would contribute significantly to the overall computational efficiency. The objective of the present work was to develop a fast hybrid pressure-correction algorithm for numerical simulation of incompressible flows around obstacles in the context of projection methods. The key idea is to adopt different numerical methods/discretisations in the sub-steps of projection methods. Here, a classical second-order time-marching projection method, which consists of two sub-steps, was chosen for the purposes of demonstration. In the first sub-step, the momentum equations were discretised on unstructured grids and solved by conventional numerical methods, here a meshless method. In the second sub-step (pressure-correction), the proposed algorithm adopts a double-discretisation system and combines the weighted least-squares approximation with the essence of immersed boundary methods. Such a design allowed us to develop an FFT-based solver to speed up the solution of the pressure Poisson equation for flow cases with obstacles, while keeping the implementation of the boundary conditions for the momentum equations as easy as conventional numerical methods do with unstructured grids. The numerical experiments of five test cases were performed to verify and validate the proposed hybrid algorithm and evaluate its computational performance. The results showed that the new FFT-based hybrid algorithm works and is robust, and it was significantly faster than the multigrid-based reference method. The hybrid algorithm opens an avenue for the development of next-generation high-performance parallel computational fluid dynamics solvers for incompressible flows.
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(This article belongs to the Collection Feature Papers in Algorithms)
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Developing Prediction Model of Travel Times of the Logistics Fleets of Large Convenience Store Chains Using Machine Learning
Algorithms 2023, 16(6), 286; https://doi.org/10.3390/a16060286 - 01 Jun 2023
Abstract
Convenience store chains are many people’s top choice for dining and leisure and have logistics procedures that involve each store receiving multiple deliveries because of the varying delivery periods and suitable temperatures for different goods. The estimated arrival time for each delivery has
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Convenience store chains are many people’s top choice for dining and leisure and have logistics procedures that involve each store receiving multiple deliveries because of the varying delivery periods and suitable temperatures for different goods. The estimated arrival time for each delivery has a huge impact on the route arrangement and convenience store preparation for dispatchers to schedule future deliveries. This study collected global positioning system travel data from a fleet of one of the top convenience store chains in Taiwan between April 2021 and March 2022 and proposed machine learning to establish a model to predict travel times. For unavailable data, we proposed the nonlinear regression equation to fill in the missing GPS data. Moreover, the study used the data between April 2022 and September 2022 with mean absolute percentage error to validate the prediction effects exceeding 97%. Therefore, the proposed model based on historical data and the machine learning algorithm in this study can help logistics fleets estimate accurate travel times for their scheduling of future delivery tasks and arranging routes.
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(This article belongs to the Special Issue Optimization Algorithms in Logistics, Transportation, and SCM)
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Evolving Dispatching Rules for Dynamic Vehicle Routing with Genetic Programming
Algorithms 2023, 16(6), 285; https://doi.org/10.3390/a16060285 - 01 Jun 2023
Abstract
Many real-world applications of the vehicle routing problem (VRP) are arising today, which range from physical resource planning to virtual resource management in the cloud computing domain. A common trait of these applications is usually the large scale size of problem instances, which
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Many real-world applications of the vehicle routing problem (VRP) are arising today, which range from physical resource planning to virtual resource management in the cloud computing domain. A common trait of these applications is usually the large scale size of problem instances, which require fast algorithms to generate solutions of acceptable quality. The basis for many VRP approaches is a heuristic which builds a candidate solution that may subsequently be improved by a local search procedure. Since there are many variants of the basic VRP model, specialised algorithms must be devised that take into account specific constraints and user-defined objective measures. Another factor is that the scheduling process may be carried out in dynamic conditions, where future information may be uncertain or unavailable or may be subject to change. When all of this is considered, there is a need for customised heuristics, devised for a specific problem variant, that could be used in highly dynamic environments. In this paper, we use genetic programming (GP) to evolve a suitable dispatching rule to build solutions for different objectives and classes of VRP problems, applicable in both dynamic and stochastic conditions. The results show great potential, since this method may be used for different problem classes and user-defined performance objectives.
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(This article belongs to the Special Issue Algorithms for Natural Computing Models)
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Fully Parallel Homological Region Adjacency Graph via Frontier Recognition
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, , , , and
Algorithms 2023, 16(6), 284; https://doi.org/10.3390/a16060284 - 31 May 2023
Abstract
Relating image contours and regions and their attributes according to connectivity based on incidence or adjacency is a crucial task in numerous applications in the fields of image processing, computer vision and pattern recognition. In this paper, the crucial incidence topological information of
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Relating image contours and regions and their attributes according to connectivity based on incidence or adjacency is a crucial task in numerous applications in the fields of image processing, computer vision and pattern recognition. In this paper, the crucial incidence topological information of 2-dimensional images is extracted in an efficient manner through the computation of a new structure called the HomDuRAG of an image; that is, the dual graph of the HomRAG (a topologically consistent extended version of the classical RAG). These representations are derived from the two traditional self-dual square grids (in which physical pixels play the role of 2-dimensional cells) and encapsulate the whole set of topological features and relations between the three types of objects embedded in a digital image: 2-dimensional (regions), 1-dimensional (contours) and 0-dimensional objects (crosses). Here, a first version of a fully parallel algorithm to compute this new representation is presented, whose timing complexity order (in the worst case and supposing one processing element per 0-cell) is , M and N being the height and width of the image. Efficient implementations of this parallel algorithm would allow images to be processed in real time, as well as permit us to uncover fast algorithms for contour detection and segmentation, opening new perspectives within the image processing field.
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(This article belongs to the Collection Parallel and Distributed Computing: Algorithms and Applications)
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The Porcupine Measure for Comparing the Performance of Multi-Objective Optimization Algorithms
Algorithms 2023, 16(6), 283; https://doi.org/10.3390/a16060283 - 31 May 2023
Abstract
In spite of being introduced over twenty-five years ago, Fonseca and Fleming’s attainment surfaces have not been widely used. This article investigates some of the shortcomings that may have led to the lack of adoption of this performance measure. The quantitative measure based
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In spite of being introduced over twenty-five years ago, Fonseca and Fleming’s attainment surfaces have not been widely used. This article investigates some of the shortcomings that may have led to the lack of adoption of this performance measure. The quantitative measure based on attainment surfaces, introduced by Knowles and Corne, is analyzed. The analysis shows that the results obtained by the Knowles and Corne approach are influenced (biased) by the shape of the attainment surface. Improvements to the Knowles and Corne approach for bi-objective pof comparisons are proposed. Furthermore, assuming M objective functions, an M-dimensional attainment-surface-based quantitative measure, named the porcupine measure, is proposed for comparing the performance of multi-objective optimization algorithms. A computationally optimized version of the porcupine measure is presented and empirically analyzed.
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(This article belongs to the Special Issue Multi-Objective and Multi-Level Optimization: Algorithms and Applications)
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Iterative Oblique Decision Trees Deliver Explainable RL Models
Algorithms 2023, 16(6), 282; https://doi.org/10.3390/a16060282 - 31 May 2023
Abstract
The demand for explainable and transparent models increases with the continued success of reinforcement learning. In this article, we explore the potential of generating shallow decision trees (DTs) as simple and transparent surrogate models for opaque deep reinforcement learning (DRL) agents. We investigate
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The demand for explainable and transparent models increases with the continued success of reinforcement learning. In this article, we explore the potential of generating shallow decision trees (DTs) as simple and transparent surrogate models for opaque deep reinforcement learning (DRL) agents. We investigate three algorithms for generating training data for axis-parallel and oblique DTs with the help of DRL agents (“oracles”) and evaluate these methods on classic control problems from OpenAI Gym. The results show that one of our newly developed algorithms, the iterative training, outperforms traditional sampling algorithms, resulting in well-performing DTs that often even surpass the oracle from which they were trained. Even higher dimensional problems can be solved with surprisingly shallow DTs. We discuss the advantages and disadvantages of different sampling methods and insights into the decision-making process made possible by the transparent nature of DTs. Our work contributes to the development of not only powerful but also explainable RL agents and highlights the potential of DTs as a simple and effective alternative to complex DRL models.
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(This article belongs to the Special Issue Advancements in Reinforcement Learning Algorithms)
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Folding Every Point on a Polygon Boundary to a Point
Algorithms 2023, 16(6), 281; https://doi.org/10.3390/a16060281 - 31 May 2023
Abstract
We consider a problem in computational origami. Given a piece of paper as a convex polygon P and a point f located within, we fold every point on a boundary of P to f and compute a region that is safe from folding,
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We consider a problem in computational origami. Given a piece of paper as a convex polygon P and a point f located within, we fold every point on a boundary of P to f and compute a region that is safe from folding, i.e., the region with no creases. This problem is an extended version of a problem by Akitaya, Ballinger, Demaine, Hull, and Schmidt that only folds corners of the polygon. To find the region, we prove structural properties of intersections of parabola-bounded regions and use them to devise a linear-time algorithm. We also prove a structural result regarding the complexity of the safe region as a variable of the location of point f, i.e., the number of arcs of the safe region can be determined using the straight skeleton of the polygon P.
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(This article belongs to the Special Issue Machine Learning in Computational Geometry)
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Reinforcement Learning in a New Keynesian Model
Algorithms 2023, 16(6), 280; https://doi.org/10.3390/a16060280 - 31 May 2023
Abstract
We construct a New Keynesian (NK) behavioural macroeconomic model with bounded-rationality (BR) and heterogeneous agents. We solve and simulate the model using a third-order approximation for a given policy and evaluate its properties using this solution. The model is inhabited by fully rational
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We construct a New Keynesian (NK) behavioural macroeconomic model with bounded-rationality (BR) and heterogeneous agents. We solve and simulate the model using a third-order approximation for a given policy and evaluate its properties using this solution. The model is inhabited by fully rational (RE) and BR agents. The latter are anticipated utility learners, given their beliefs of aggregate states, and they use simple heuristic rules to forecast aggregate variables exogenous to their micro-environment. In the most general form of the model, RE and BR agents learn from their forecasting errors by observing and comparing them with each other, making the composition of the two types endogenous. This reinforcement learning is then at the core of the heterogeneous expectations model and leads to the striking result that increasing the volatility of exogenous shocks, by assisting the learning process, increases the proportion of RE agents and is welfare-increasing.
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(This article belongs to the Special Issue Advancements in Reinforcement Learning Algorithms)
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A Shadowed Type-2 Fuzzy Approach for Crossover Parameter Adaptation in Differential Evolution
Algorithms 2023, 16(6), 279; https://doi.org/10.3390/a16060279 - 31 May 2023
Abstract
The shadowed type-2 fuzzy systems are used more frequently today as they provide an alternative to classical fuzzy logic. The primary purpose of fuzzy logic is to simulate reasoning in a computer. This work aims to use shadowed type-2 fuzzy systems (ST2-FS) to
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The shadowed type-2 fuzzy systems are used more frequently today as they provide an alternative to classical fuzzy logic. The primary purpose of fuzzy logic is to simulate reasoning in a computer. This work aims to use shadowed type-2 fuzzy systems (ST2-FS) to dynamically adapt the crossing parameter of differential evolution (DE). To test the performance of the dynamic crossing parameter, the motor position control problem was used, which contains an interval type-2 fuzzy system (IT2-FS) for controlling the motor. A comparison is made between the original DE and the algorithm using shadowed type-2 fuzzy systems (DE-ST2-FS), as well as a comparison with the results of other state-of-the-art metaheuristics.
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(This article belongs to the Special Issue Algorithms for PID Controller 2023)
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Forgetful Forests: Data Structures for Machine Learning on Streaming Data under Concept Drift
Algorithms 2023, 16(6), 278; https://doi.org/10.3390/a16060278 - 31 May 2023
Abstract
Database and data structure research can improve machine learning performance in many ways. One way is to design better algorithms on data structures. This paper combines the use of incremental computation as well as sequential and probabilistic filtering to enable “forgetful” tree-based learning
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Database and data structure research can improve machine learning performance in many ways. One way is to design better algorithms on data structures. This paper combines the use of incremental computation as well as sequential and probabilistic filtering to enable “forgetful” tree-based learning algorithms to cope with streaming data that suffers from concept drift. (Concept drift occurs when the functional mapping from input to classification changes over time). The forgetful algorithms described in this paper achieve high performance while maintaining high quality predictions on streaming data. Specifically, the algorithms are up to 24 times faster than state-of-the-art incremental algorithms with, at most, a 2% loss of accuracy, or are at least twice faster without any loss of accuracy. This makes such structures suitable for high volume streaming applications.
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(This article belongs to the Special Issue Machine Learning for Time Series Analysis)
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Nakagami-m Fading Channel Identification Using Adaptive Continuous Wavelet Transform and Convolutional Neural Networks
Algorithms 2023, 16(6), 277; https://doi.org/10.3390/a16060277 - 30 May 2023
Abstract
Channel identification is a useful function to support wireless telecommunication operations because the knowledge of the radio frequency propagation channel characteristics can improve communication efficiency and robustness. In recent times, the application of machine learning (ML) algorithms to the problem of channel identification
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Channel identification is a useful function to support wireless telecommunication operations because the knowledge of the radio frequency propagation channel characteristics can improve communication efficiency and robustness. In recent times, the application of machine learning (ML) algorithms to the problem of channel identification has been proposed in the literature. In particular, Deep Learning (DL) has demonstrated superior performance to ’shallow’ machine learning algorithms for many wireless communication functions. Inspired by the success of DL in literature, the authors in this paper apply Convolutional Neural Networks (CNN) to the problem of channel identification, which is still an emerging research area. CNN is a deep learning algorithm that has demonstrated superior performance to ML algorithms, in particular for image processing tasks. Because the digitized RF signal is a one-dimensional time series, different algorithms are applied to convert the time series to images using various Time Frequency Transform (TFT) including the CWTs, spectrogram, and Wigner Ville distribution. The images are then provided as input to the CNN. The approach is applied to a data set based on weather radar pulse signals generated in the laboratory of the author’s facilities on which different fading models are applied. These models are inspired by the tap-delay-line 3GPP configurations defined in the standards, but they have been customized with Nakagami-m fading distribution (3GPP-like fading models). The results show the superior performance of time–frequency CNN in comparison to 1D CNN for different values of Signal to Noise Ratio (SNR) in dB. In particular, the study shows that the Continuous Wavelet Transform (CWT) has the optimal performance in this data set, but the choice of the mother wavelet remains a problem to be solved (this is a well-known problem in the research literature). Then, this study also proposes an adaptive technique for the choice of the optimal mother wavelet, which is evaluated on the mentioned data set. The results show that the adaptive proposed approach is able to obtain the optimal performance for most of the SNR conditions.
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(This article belongs to the Special Issue Algorithms for Communication Networks)
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Clinical Validation of a New Enhanced Stent Imaging Method
by
, , , , , and
Algorithms 2023, 16(6), 276; https://doi.org/10.3390/a16060276 - 30 May 2023
Abstract
(1) Background: Stent underexpansion is the main cause of stent thrombosis and restenosis. Coronary angiography has limitations in the assessment of stent expansion. Enhanced stent imaging (ESI) methods allow a detailed visualization of stent deployment. We qualitatively compare image results from two ESI
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(1) Background: Stent underexpansion is the main cause of stent thrombosis and restenosis. Coronary angiography has limitations in the assessment of stent expansion. Enhanced stent imaging (ESI) methods allow a detailed visualization of stent deployment. We qualitatively compare image results from two ESI system vendors (StentBoost™ (SB) and CAAS StentEnhancer™ (SE)) and report quantitative results of deployed stents diameters by quantitative coronary angiography (QCA) and by SE. (2) Methods: The ESI systems from SB and SE were compared and graded by two blinded observers for different characteristics: 1 visualization of the proximal and distal edges of the stents; 2 visualization of the stent struts; 3 presence of underexpansion and 4 calcifications. Stent diameters were quantitatively measured using dedicated QCA and SE software and compared to chart diameters according to the pressure of implantation. (3) Results: A total of 249 ESI sequences were qualitatively compared. Inter-observer variability was noted for strut visibility and total scores. Inter-observer agreement was found for the assessment of proximal stent edge and stent underexpansion. The predicted chart diameters were 0.31 ± 0.30 mm larger than SE diameters (p < 0.05). Stent diameters by SE after post-dilatation were 0.47 ± 0.31 mm smaller than the post-dilation balloon diameter (p < 0.05). SE-derived diameters significantly differed from QCA; by Bland–Altman analysis the bias was −0.37 ± 0.42 mm (p < 0.001). (4) Conclusions: SE provides an enhanced visualization and allows precise quantitative assessment of stent expansion without the limitations of QCA when overlapping coronary side branches are present.
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(This article belongs to the Special Issue Algorithms for Biomedical Image Analysis and Processing)
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An Interactive Differential Evolution Algorithm Based on Backtracking Strategy Applied in Interior Layout Design
Algorithms 2023, 16(6), 275; https://doi.org/10.3390/a16060275 - 30 May 2023
Abstract
The Interior layout model is to optimize the arrangement position of each room to maximize the comfort and quality of life of residents. Due to the complexity of the Interior layout problem, the computation of fitness function costs lots of time. To reduce
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The Interior layout model is to optimize the arrangement position of each room to maximize the comfort and quality of life of residents. Due to the complexity of the Interior layout problem, the computation of fitness function costs lots of time. To reduce the high computational cost while maintaining the solution performance. An interactive differential evolution algorithm based on Backtracking operator (IDE-BO) is proposed as the solver of the Interior layout model. The human-computer interaction mechanism of IDE benefits the automatic adjustment of fitness parameters that best meet the user’s subjective preferences to achieve the optimal solution. At the same time, the backtracking strategy can also help jump out when the algorithm falls into local optimization. The IDE is compared to other two conventional optimization methods based on two different layout scenarios. The experimental results show that in interior layout model IDE-BO is better than conventional interactive genetic algorithm (IGA) and IDE which do not use BO strategy, the super-performance of IDE-BO in complex situations in terms of execution time and convergence rate.
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(This article belongs to the Section Evolutionary Algorithms and Machine Learning)
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VLSD—An Efficient Subgroup Discovery Algorithm Based on Equivalence Classes and Optimistic Estimate
Algorithms 2023, 16(6), 274; https://doi.org/10.3390/a16060274 - 29 May 2023
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Subgroup Discovery (SD) is a supervised data mining technique for identifying a set of relations (subgroups) among attributes from a dataset with respect to a target attribute. Two key components of this technique are (i) the metric used to quantify a subgroup extracted,
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Subgroup Discovery (SD) is a supervised data mining technique for identifying a set of relations (subgroups) among attributes from a dataset with respect to a target attribute. Two key components of this technique are (i) the metric used to quantify a subgroup extracted, called quality measure, and (ii) the search strategy used, which determines how the search space is explored and how the subgroups are obtained. The proposal made in this work consists of two parts, (1) a new and efficient SD algorithm which is based on the equivalence class exploration strategy, and which uses a pruning based on optimistic estimate, and (2) a data structure used when implementing the algorithm in order to compute subgroup refinements easily and efficiently. One of the most important advantages of this algorithm is its easy parallelization. We have tested the performance of our SD algorithm with respect to some other well-known state-of-the-art SD algorithms in terms of runtime, max memory usage, subgroups selected, and nodes visited. This was completed using a collection of standard, well-known, and popular datasets obtained from the relevant literature. The results confirmed that our algorithm is more efficient than the other algorithms considered.
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Real-Time Interval Type-2 Fuzzy Control of an Unmanned Aerial Vehicle with Flexible Cable-Connected Payload
Algorithms 2023, 16(6), 273; https://doi.org/10.3390/a16060273 - 29 May 2023
Abstract
This study presents the design and real-time applications of an Interval Type-2 Fuzzy PID (IT2-FPID) control system on an unmanned aerial vehicle (UAV) with a flexible cable-connected payload in comparison to the PID and Type-1 Fuzzy PID (T1-FPID) counterparts. The IT2-FPID control has
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This study presents the design and real-time applications of an Interval Type-2 Fuzzy PID (IT2-FPID) control system on an unmanned aerial vehicle (UAV) with a flexible cable-connected payload in comparison to the PID and Type-1 Fuzzy PID (T1-FPID) counterparts. The IT2-FPID control has significant stability, disturbance rejection, and response time advantages. To prove and show these advantages, the DJI Tello, a commercial UAV, is used with a flexible cable-connected payload to test the robustness of PID, T1-FPID, and IT2-FPID controllers. First, the optimal coefficients of the compared controllers are found using the Big Bang–Big Crunch algorithm via the nonlinear UAV model without the payload. Second, once optimised, the controllers are tested using several scenarios, including disturbing the payload and the coverage path planning area to examine their robustness. Third, the controller performance results are evaluated according to reference achievement and point-based tracking under disturbances. Finally, the superiority of the IT2-FPID controller is shown via simulations and real-time experiments with a better overshoot, a faster settling time, and good properties of disturbance rejection compared with the PID and the T1-FPID controllers.
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(This article belongs to the Special Issue Algorithms for PID Controller 2023)
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Topics
Topic in
Algorithms, Automation, Axioms, Entropy, Fractal Fract, MCA
Advances in Optimization and Nonlinear Analysis Volume II
Topic Editor: Savin TreanţăDeadline: 10 June 2023
Topic in
Energies, Machines, Robotics, Sensors, Systems, Algorithms, Mathematics, Automation
Intelligent Systems and Robotics
Topic Editors: Shuai Li, Dechao Chen, Mohammed Aquil Mirza, Vasilios N. Katsikis, Dunhui Xiao, Predrag S. StanimirovicDeadline: 30 June 2023
Topic in
Algorithms, Behavioral Sciences, Societies, Technologies
AI in the Everyday Life of Older Adults: Panacea or Pandora's Box?
Topic Editors: Eugène Loos, Loredana Ivan, Kim Sawchuk, Mireia Fernández-ArdèvolDeadline: 10 July 2023
Topic in
Algorithms, Applied Sciences, Mathematics, Symmetry, AI
Applied Metaheuristic Computing: 2nd Volume
Topic Editors: Peng-Yeng Yin, Ray-I Chang, Jen-Chun LeeDeadline: 31 August 2023

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Special Issues
Special Issue in
Algorithms
Algorithms for Biomedical Image Analysis and Processing
Guest Editors: Lucia Maddalena, Laura AntonelliDeadline: 15 June 2023
Special Issue in
Algorithms
Self-Learning and Self-Adapting Algorithms in Machine Learning
Guest Editors: Patrizia Ribino, Giovanni ParagliolaDeadline: 30 June 2023
Special Issue in
Algorithms
Blockchain Consensus Algorithms
Guest Editor: Manki MinDeadline: 15 July 2023
Special Issue in
Algorithms
Graph Algorithms for Social Network Analysis
Guest Editor: Adele Anna RescignoDeadline: 29 July 2023
Topical Collections
Topical Collection in
Algorithms
Feature Papers in Algorithms for Multidisciplinary Applications
Collection Editor: Francesc Pozo
Topical Collection in
Algorithms
Feature Papers in Randomized, Online and Approximation Algorithms
Collection Editor: Frank Werner
Topical Collection in
Algorithms
Featured Reviews of Algorithms
Collection Editors: Arun Kumar Sangaiah, Xingjuan Cai