Algorithms

Open AccessArticle

Random forest Algorithm for the Classification of Spectral Data of Astronomical Objects

by

José-Luis Solorio-Ramírez

,

Raúl Jiménez-Cruz

,

Yenny Villuendas-Rey

and

Cornelio Yáñez-Márquez

Algorithms 2023, 16(6), 293; https://doi.org/10.3390/a16060293 - 08 Jun 2023

Abstract

Over time, human beings have built increasingly large astronomical observatories to increase the number of discoveries related to celestial objects. However, the amount of collected elements far exceeds the human capacity to analyze findings without help. For this reason, researchers must now turn [...] Read more.

Over time, human beings have built increasingly large astronomical observatories to increase the number of discoveries related to celestial objects. However, the amount of collected elements far exceeds the human capacity to analyze findings without help. For this reason, researchers must now turn to machine learning to analyze such data, identifying and classifying transient objects or events within extensive observations of the firmament. Algorithms from the family of random forests (an ensemble of decision trees) have become a powerful tool that can be used to classify astronomical events and objects. This work aims to illustrate the versatility of machine learning algorithms, such as decision trees, to facilitate the identification and classification of celestial bodies by manipulating hyperparameters and studying the attributes of celestial body datasets. By applying a random forest algorithm to a well-known dataset that includes three types of celestial bodies, its effectiveness was compared against some supervised classifiers of the most important approaches (Bayes, nearest neighbors, support vector machines, and neural networks). The results show that random forests are a good alternative for data analysis and classification in astronomical observations. Full article

(This article belongs to the Collection Feature Papers in Algorithms for Multidisciplinary Applications)

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Open AccessArticle

Improving Accuracy of Face Recognition in the Era of Mask-Wearing: An Evaluation of a Pareto-Optimized FaceNet Model with Data Preprocessing Techniques

by

Damilola Akingbesote

,

Ying Zhan

,

Rytis Maskeliūnas

and

Robertas Damaševičius

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Machine Learning and Deep Learning in Pattern Recognition)

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Open AccessArticle

An Effective Local Particle Swarm Optimization-Based Algorithm for Solving the School Timetabling Problem

by

Ioannis X. Tassopoulos

,

Christina A. Iliopoulou

,

Iosif V. Katsaragakis

and

Grigorios N. Beligiannis

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 [...] Read more.

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. Full article

(This article belongs to the Collection Feature Paper in Metaheuristic Algorithms and Applications)

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Open AccessArticle

Integration of Polynomials Times Double Step Function in Quadrilateral Domains for XFEM Analysis

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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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Computational Methods and Optimization for Numerical Analysis)

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Open AccessArticle

Adding a Tail in Classes of Perfect Graphs

by

Anna Mpanti

,

Stavros D. Nikolopoulos

and

Leonidas Palios

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

C

. We are interested in connecting a node

w \notin V (G)

to G by a single edge

u w

where

u \in V (G)

; we call [...] Read more.

Consider a graph G which belongs to a graph class

C

. We are interested in connecting a node

w \notin V (G)

to G by a single edge

u w

where

u \in V (G)

; we call such an edge a tail. As the graph resulting from G after the addition of the tail, denoted

G + u w

, need not belong to the class

C

, we want to compute the number of non-edges of G in a minimum

C

-completion of

G + u w

, i.e., the minimum number of non-edges (excluding the tail

u w

) to be added to

G + u w

so that the resulting graph belongs to

C

. In this paper, we study this problem for the classes of split, quasi-threshold, threshold and

P_{4}

-sparse graphs and we present linear-time algorithms by exploiting the structure of split graphs and the tree representation of quasi-threshold, threshold and

P_{4}

-sparse graphs. Full article

(This article belongs to the Special Issue Graph Theoretic Methods in Scientific Computing & Industrial Applications)

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Open AccessArticle

An Adaptive Deep Learning Neural Network Model to Enhance Machine-Learning-Based Classifiers for Intrusion Detection in Smart Grids

by

Xue Jun Li

,

Maode Ma

and

Yihan Sun

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue AI for Cybersecurity: Robust models for Authentication, Threat and Anomaly Detection)

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Open AccessArticle

A Fast Hybrid Pressure-Correction Algorithm for Simulating Incompressible Flows by Projection Methods

by

Jiannong Fang

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 [...] Read more.

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. Full article

(This article belongs to the Collection Feature Papers in Algorithms)

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Open AccessArticle

Developing Prediction Model of Travel Times of the Logistics Fleets of Large Convenience Store Chains Using Machine Learning

by

Yang-Kuei Lin

,

Chien-Fu Chen

and

Tien-Yin Chou

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Optimization Algorithms in Logistics, Transportation, and SCM)

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Open AccessArticle

Evolving Dispatching Rules for Dynamic Vehicle Routing with Genetic Programming

by

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and

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Algorithms for Natural Computing Models)

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Open AccessArticle

Fully Parallel Homological Region Adjacency Graph via Frontier Recognition

by

Fernando Díaz-del-Río

,

Pablo Sanchez-Cuevas

,

María José Moron-Fernández

,

Daniel Cascado-Caballero

,

Helena Molina-Abril

and

Pedro Real

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 [...] Read more.

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

O (l o g (M \times N))

, 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. Full article

(This article belongs to the Collection Parallel and Distributed Computing: Algorithms and Applications)

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Open AccessArticle

The Porcupine Measure for Comparing the Performance of Multi-Objective Optimization Algorithms

by

Christiaan Scheepers

and

Andries Engelbrecht

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 [...] Read more.

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 Pareto-optimal front (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. Full article

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

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Open AccessArticle

Iterative Oblique Decision Trees Deliver Explainable RL Models

by

Raphael C. Engelhardt

,

,

,

and

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Advancements in Reinforcement Learning Algorithms)

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Open AccessArticle

Folding Every Point on a Polygon Boundary to a Point

by

Nattawut Phetmak

and

Jittat Fakcharoenphol

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, [...] Read more.

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. Full article

(This article belongs to the Special Issue Machine Learning in Computational Geometry)

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Open AccessArticle

Reinforcement Learning in a New Keynesian Model

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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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Advancements in Reinforcement Learning Algorithms)

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Open AccessArticle

A Shadowed Type-2 Fuzzy Approach for Crossover Parameter Adaptation in Differential Evolution

by

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and

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Algorithms for PID Controller 2023)

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Open AccessArticle

Forgetful Forests: Data Structures for Machine Learning on Streaming Data under Concept Drift

by

Zhehu Yuan

,

Yinqi Sun

and

Dennis Shasha

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Machine Learning for Time Series Analysis)

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Open AccessArticle

Nakagami-m Fading Channel Identification Using Adaptive Continuous Wavelet Transform and Convolutional Neural Networks

by

Gianmarco Baldini

and

Fausto Bonavitacola

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Algorithms for Communication Networks)

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Open AccessArticle

Clinical Validation of a New Enhanced Stent Imaging Method

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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 [...] Read more.

(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. Full article

(This article belongs to the Special Issue Algorithms for Biomedical Image Analysis and Processing)

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Open AccessArticle

An Interactive Differential Evolution Algorithm Based on Backtracking Strategy Applied in Interior Layout Design

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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 [...] Read more.

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. Full article

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

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