Algorithms

15 pages, 647 KiB

Open AccessArticle

Anchor-Based Method for Inter-Domain Mobility Management in Software-Defined Networking

by Akichy Adon Jean Rodrigue Kanda, Amanvon Ferdinand Atta, Zacrada Françoise Odile Trey, Michel Babri and Ahmed Dooguy Kora

Algorithms 2024, 17(12), 566; https://doi.org/10.3390/a17120566 - 11 Dec 2024

Abstract

Recently, there has been an explosive growth in wireless devices capable of connecting to the Internet and utilizing various services anytime, anywhere, often while on the move. In the realm of the Internet, such devices are called mobile nodes. When these devices are [...] Read more.

Recently, there has been an explosive growth in wireless devices capable of connecting to the Internet and utilizing various services anytime, anywhere, often while on the move. In the realm of the Internet, such devices are called mobile nodes. When these devices are in motion or traverse different domains while communicating, effective mobility management becomes essential to ensure the continuity of their services. Software-defined networking (SDN), a new paradigm in networking, offers numerous possibilities for addressing the challenges of mobility management. By decoupling the control and data planes, SDN enables greater flexibility and adaptability, making them a powerful framework for solving mobility-related issues. However, communication can still be momentarily disrupted due to frequent changes in IP addresses, a drop in radio signals, or configuration issues associated with gateways. Therefore, this paper introduces Routage Inter-domains in SDN (RI-SDN), a novel anchor-based routing method designed for inter-domain mobility in SDN architectures. The method identifies a suitable anchor domain, a critical intermediary domain that contributes to reducing delays during data transfer because it is the closest domain (i.e., node) to the destination. Once the anchor domain is identified, the best routing path is determined as the route with the smallest metric, incorporating elements such as bandwidth, flow operations, and the number of domain hops. Simulation results demonstrate significant improvements in data transfer delay and handover latency compared to existing methods. By leveraging SDN’s potential, RI-SDN presents a robust and innovative solution for real-world scenarios requiring reliable mobility management. Full article

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

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21 pages, 2687 KiB

Open AccessArticle

A Random PRIM Based Algorithm for Interpretable Classification and Advanced Subgroup Discovery

by Rym Nassih and Abdelaziz Berrado

Algorithms 2024, 17(12), 565; https://doi.org/10.3390/a17120565 - 10 Dec 2024

Abstract

Machine-learning algorithms have made significant strides, achieving high accuracy in many applications. However, traditional models often need large datasets, as they typically peel substantial portions of the data in each iteration, complicating the development of a classifier without sufficient data. In critical fields [...] Read more.

Machine-learning algorithms have made significant strides, achieving high accuracy in many applications. However, traditional models often need large datasets, as they typically peel substantial portions of the data in each iteration, complicating the development of a classifier without sufficient data. In critical fields like healthcare, there is a growing need to identify and analyze small yet significant subgroups within data. To address these challenges, we introduce a novel classifier based on the patient rule-induction method (PRIM), a subgroup-discovery algorithm. PRIM finds rules by peeling minimal data at each iteration, enabling the discovery of highly relevant regions. Unlike traditional classifiers, PRIM requires experts to select input spaces manually. Our innovation transforms PRIM into an interpretable classifier by starting with random input space selections for each class, then pruning rules using metarules, and finally selecting definitive rules for the classifier. Tested against popular algorithms such as random forest, logistic regression, and XG-Boost, our random PRIM-based classifier (R-PRIM-Cl) demonstrates comparable robustness, superior interpretability, and the ability to handle categorical and numeric variables. It discovers more rules in certain datasets, making it especially valuable in fields where understanding the model’s decision-making process is as important as its predictive accuracy. Full article

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

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39 pages, 25059 KiB

Open AccessArticle

Exploratory Study of a Green Function Based Solver for Nonlinear Partial Differential Equations

by Pablo Solano-López, Jorge Saavedra and Raúl Molina

Algorithms 2024, 17(12), 564; https://doi.org/10.3390/a17120564 - 10 Dec 2024

Abstract

This work explores the numerical translation of the weak or integral solution of nonlinear partial differential equations into a numerically efficient, time-evolving scheme. Specifically, we focus on partial differential equations separable into a quasilinear term and a nonlinear one, with the former defining [...] Read more.

This work explores the numerical translation of the weak or integral solution of nonlinear partial differential equations into a numerically efficient, time-evolving scheme. Specifically, we focus on partial differential equations separable into a quasilinear term and a nonlinear one, with the former defining the Green function of the problem. Utilizing the Green function under a short-time approximation, it becomes possible to derive the integral solution of the problem by breaking it into three integral terms: the propagation of initial conditions and the contributions of the nonlinear and boundary terms. Accordingly, we follow this division to describe and separately analyze the resulting algorithm. To ensure low interpolation error and accurate numerical Green functions, we adapt a piecewise interpolation collocation method to the integral scheme, optimizing the positioning of grid points near the boundary region. At the same time, we employ a second-order quadrature method in time to efficiently implement the nonlinear terms. Validation of both adapted methodologies is conducted by applying them to problems with known analytical solution, as well as to more challenging, norm-preserving problems such as the Burgers equation and the soliton solution of the nonlinear Schrödinger equation. Finally, the boundary term is derived and validated using a series of test cases that cover the range of possible scenarios for boundary problems within the introduced methodology. Full article

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

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21 pages, 391 KiB

Open AccessArticle

Issues on a 2–Dimensional Quadratic Sub–Problem and Its Applications in Nonlinear Programming: Trust–Region Methods (TRMs) and Linesearch Based Methods (LBMs)

by Giovanni Fasano, Christian Piermarini and Massimo Roma

Algorithms 2024, 17(12), 563; https://doi.org/10.3390/a17120563 - 9 Dec 2024

Abstract

This paper analyses the solution of a specific quadratic sub-problem, along with its possible applications, within both constrained and unconstrained Nonlinear Programming frameworks. We give evidence that this sub–problem may appear in a number of Linesearch Based Methods (LBM) schemes, and to some [...] Read more.

This paper analyses the solution of a specific quadratic sub-problem, along with its possible applications, within both constrained and unconstrained Nonlinear Programming frameworks. We give evidence that this sub–problem may appear in a number of Linesearch Based Methods (LBM) schemes, and to some extent it reveals a close analogy with the solution of trust–region sub–problems. Namely, we refer to a two-dimensional structured quadratic problem, where five linear inequality constraints are included. Finally, we detail how to compute an exact global solution of our two-dimensional quadratic sub-problem, exploiting first order Karush-Khun-Tucker (KKT) conditions. Full article

(This article belongs to the Special Issue Numerical Optimization and Algorithms: 2nd Edition)

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26 pages, 5833 KiB

Open AccessReview

Unlocking the Potential of Remanufacturing Through Machine Learning and Data-Driven Models—A Survey

by Yong Han Kim, Wei Ye, Ritbik Kumar, Finn Bail, Julia Dvorak, Yanchao Tan, Marvin Carl May, Qing Chang, Ragu Athinarayanan, Gisela Lanza, John W. Sutherland, Xingyu Li and Chandra Nath

Algorithms 2024, 17(12), 562; https://doi.org/10.3390/a17120562 - 8 Dec 2024

Abstract

As a key strategy for achieving a circular economy, remanufacturing involves bringing end-of-use (EoU) products or cores back to a ‘like new’ condition, providing more affordable and sustainable alternatives to new products. Despite the potential for substantial resources and energy savings, the industry [...] Read more.

As a key strategy for achieving a circular economy, remanufacturing involves bringing end-of-use (EoU) products or cores back to a ‘like new’ condition, providing more affordable and sustainable alternatives to new products. Despite the potential for substantial resources and energy savings, the industry faces operational challenges. These challenges arise from uncertainties surrounding core quality and functionality, return times, process variation required to meet product specifications, and the end-of-use (EoU) product values, as well as their new life expectancy after extended use as a ‘market product’. While remanufacturing holds immense promise, its full potential can only be realized through concerted efforts towards resolving the inherent complexities and obstacles that impede its operations. Machine learning (ML) and data-driven models emerge as transformative tools to mitigate numerous challenges encountered by manufacturing industry. Recently, the integration of cutting-edge technologies, such as sensor-based product data acquisition and storage, data analytics, machine health management, artificial intelligence (AI)-driven scheduling, and human–robot collaboration (HRC), in remanufacturing procedures has received significant attention from remanufacturers and the circular economy community. These advanced computational technologies help remanufacturers to implement flexible operation scheduling, enhance quality control, and streamline workflows for EoU products. This study embarks on a comprehensive review and in-depth analysis of state-of-the-art algorithms across various facets of remanufacturing processes and operations. Additionally, it identifies key challenges to advancing remanufacturing practices through data-driven and ML methods and uncovers research opportunities in synergy with smart manufacturing techniques. The study aims to offer guidelines for stakeholders and to reinforce the industry’s pivotal role in circular economy initiatives. Full article

(This article belongs to the Special Issue Scheduling Theory and Algorithms for Sustainable Manufacturing)

15 pages, 418 KiB

Open AccessArticle

Variational Autoencoders-Based Algorithm for Multi-Criteria Recommendation Systems

by Salam Fraihat, Qusai Shambour, Mohammed Azmi Al-Betar and Sharif Naser Makhadmeh

Algorithms 2024, 17(12), 561; https://doi.org/10.3390/a17120561 - 8 Dec 2024

Abstract

In recent years, recommender systems have become a crucial tool, assisting users in discovering and engaging with valuable information and services. Multi-criteria recommender systems have demonstrated significant value in assisting users to identify the most relevant items by considering various aspects of user [...] Read more.

In recent years, recommender systems have become a crucial tool, assisting users in discovering and engaging with valuable information and services. Multi-criteria recommender systems have demonstrated significant value in assisting users to identify the most relevant items by considering various aspects of user experiences. Deep learning (DL) models demonstrated outstanding performance across different domains: computer vision, natural language processing, image analysis, pattern recognition, and recommender systems. In this study, we introduce a deep learning model using VAE to improve multi-criteria recommendation systems. Specifically, we propose a variational autoencoder-based model for multi-criteria recommendation systems (VAE-MCRS). The VAE-MCRS model is sequentially trained across multiple criteria to uncover patterns that allow for better representation of user–item interactions. The VAE-MCRS model utilizes the latent features generated by the VAE in conjunction with user–item interactions to enhance recommendation accuracy and predict ratings for unrated items. Experiments carried out using the Yahoo! Movies multi-criteria dataset demonstrate that the proposed model surpasses other state-of-the-art recommendation algorithms, achieving a Mean Absolute Error (MAE) of 0.6038 and a Root Mean Squared Error (RMSE) of 0.7085, demonstrating its superior performance in providing more precise recommendations for multi-criteria recommendation tasks. Full article

(This article belongs to the Special Issue Algorithms for Complex Problems)

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29 pages, 4333 KiB

Open AccessReview

Sensors, Techniques, and Future Trends of Human-Engagement-Enabled Applications: A Review

by Zhuangzhuang Dai, Vincent Gbouna Zakka, Luis J. Manso, Martin Rudorfer, Ulysses Bernardet, Johanna Zumer and Manolya Kavakli-Thorne

Algorithms 2024, 17(12), 560; https://doi.org/10.3390/a17120560 - 6 Dec 2024

Abstract

Human engagement is a vital test research area actively explored in cognitive science and user experience studies. The rise of big data and digital technologies brings new opportunities into this field, especially in autonomous systems and smart applications. This article reviews the latest [...] Read more.

Human engagement is a vital test research area actively explored in cognitive science and user experience studies. The rise of big data and digital technologies brings new opportunities into this field, especially in autonomous systems and smart applications. This article reviews the latest sensors, current advances of estimation methods, and existing domains of application to guide researchers and practitioners to deploy engagement estimators in various use cases from driver drowsiness detection to human–robot interaction (HRI). Over one hundred references were selected, examined, and contrasted in this review. Specifically, this review focuses on accuracy and practicality of use in different scenarios regarding each sensor modality, as well as current opportunities that greater automatic human engagement estimation could unlock. It is highlighted that multimodal sensor fusion and data-driven methods have shown significant promise in enhancing the accuracy and reliability of engagement estimation. Upon compiling the existing literature, this article addresses future research directions, including the need for developing more efficient algorithms for real-time processing, generalization of data-driven approaches, creating adaptive and responsive systems that better cater to individual needs, and promoting user acceptance. Full article

(This article belongs to the Special Issue AI Algorithms for Positive Change in Digital Futures)

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22 pages, 1599 KiB

Open AccessArticle

Single-Stage Entity–Relation Joint Extraction of Pesticide Registration Information Based on HT-BES Multi-Dimensional Labeling Strategy

by Chenyang Dong, Shiyu Xi, Yinchao Che, Shufeng Xiong, Xinming Ma, Lei Xi and Shuping Xiong

Algorithms 2024, 17(12), 559; https://doi.org/10.3390/a17120559 - 6 Dec 2024

Abstract

Pesticide registration information is an essential part of the pesticide knowledge base. However, the large amount of unstructured text data that it contains pose significant challenges for knowledge storage, retrieval, and utilization. To address the characteristics of pesticide registration text such as high [...] Read more.

Pesticide registration information is an essential part of the pesticide knowledge base. However, the large amount of unstructured text data that it contains pose significant challenges for knowledge storage, retrieval, and utilization. To address the characteristics of pesticide registration text such as high information density, complex logical structures, large spans between entities, and heterogeneous entity lengths, as well as to overcome the challenges faced when using traditional joint extraction methods, including triplet overlap, exposure bias, and redundant computation, we propose a single-stage entity–relation joint extraction model based on HT-BES multi-dimensional labeling (MD-SERel). First, in the encoding layer, to address the complex structural characteristics of pesticide registration texts, we employ RoBERTa combined with a multi-head self-attention mechanism to capture the deep semantic features of the text. Simultaneously, syntactic features are extracted using a syntactic dependency tree and graph neural networks to enhance the model’s understanding of text structure. Subsequently, we integrate semantic and syntactic features, enriching the character vector representations and thus improving the model’s ability to represent complex textual data. Secondly, in the multi-dimensional labeling framework layer, we use HT-BES multi-dimensional labeling, where the model assigns multiple labels to each character. These labels include entity boundaries, positions, and head–tail entity association information, which naturally resolves overlapping triplets. Through utilizing a parallel scoring function and fine-grained classification components, the joint extraction of entities and relations is transformed into a multi-label sequence labeling task based on relation dimensions. This process does not involve interdependent steps, thus enabling single-stage parallel labeling, preventing exposure bias and reducing computational redundancy. Finally, in the decoding layer, entity–relation triplets are decoded based on the predicted labels from the fine-grained classification. The experimental results demonstrate that the MD-SERel model performs well on both the Pesticide Registration Dataset (PRD) and the general DuIE dataset. On the PRD, compared to the optimal baseline model, the training time is 1.2 times faster, the inference time is 1.2 times faster, and the F1 score is improved by 1.5%, demonstrating its knowledge extraction capabilities in pesticide registration documents. On the DuIE dataset, the MD-SERel model also achieved better results compared to the baseline, demonstrating its strong generalization ability. These findings will provide technical support for the construction of pesticide knowledge bases. Full article

(This article belongs to the Special Issue Algorithms for Feature Selection (3rd Edition))

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13 pages, 7696 KiB

Open AccessArticle

From Stationary to Nonstationary UAVs: Deep-Learning-Based Method for Vehicle Speed Estimation

by Muhammad Waqas Ahmed, Muhammad Adnan, Muhammad Ahmed, Davy Janssens, Geert Wets, Afzal Ahmed and Wim Ectors

Algorithms 2024, 17(12), 558; https://doi.org/10.3390/a17120558 - 6 Dec 2024

Abstract

The development of smart cities relies on the implementation of cutting-edge technologies. Unmanned aerial vehicles (UAVs) and deep learning (DL) models are examples of such disruptive technologies with diverse industrial applications that are gaining traction. When it comes to road traffic monitoring systems [...] Read more.

The development of smart cities relies on the implementation of cutting-edge technologies. Unmanned aerial vehicles (UAVs) and deep learning (DL) models are examples of such disruptive technologies with diverse industrial applications that are gaining traction. When it comes to road traffic monitoring systems (RTMs), the combination of UAVs and vision-based methods has shown great potential. Currently, most solutions focus on analyzing traffic footage captured by hovering UAVs due to the inherent georeferencing challenges in video footage from nonstationary drones. We propose an innovative method capable of estimating traffic speed using footage from both stationary and nonstationary UAVs. The process involves matching each pixel of the input frame with a georeferenced orthomosaic using a feature-matching algorithm. Subsequently, a tracking-enabled YOLOv8 object detection model is applied to the frame to detect vehicles and their trajectories. The geographic positions of these moving vehicles over time are logged in JSON format. The accuracy of this method was validated with reference measurements recorded from a laser speed gun. The results indicate that the proposed method can estimate vehicle speeds with an absolute error as low as 0.53 km/h. The study also discusses the associated problems and constraints with nonstationary drone footage as input and proposes strategies for minimizing noise and inaccuracies. Despite these challenges, the proposed framework demonstrates considerable potential and signifies another step towards automated road traffic monitoring systems. This system enables transportation modelers to realistically capture traffic behavior over a wider area, unlike existing roadside camera systems prone to blind spots and limited spatial coverage. Full article

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

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17 pages, 5357 KiB

Open AccessArticle

Integrating Explanations into CNNs by Adopting Spiking Attention Block for Skin Cancer Detection

by Inzamam Mashood Nasir, Sara Tehsin, Robertas Damaševičius and Rytis Maskeliūnas

Algorithms 2024, 17(12), 557; https://doi.org/10.3390/a17120557 - 5 Dec 2024

Abstract

Lately, there has been a substantial rise in the number of identified individuals with skin cancer, making it the most widespread form of cancer worldwide. Until now, several machine learning methods that utilize skin scans have been directly employed for skin cancer classification, [...] Read more.

Lately, there has been a substantial rise in the number of identified individuals with skin cancer, making it the most widespread form of cancer worldwide. Until now, several machine learning methods that utilize skin scans have been directly employed for skin cancer classification, showing encouraging outcomes in terms of enhancing diagnostic precision. In this paper, multimodal Explainable Artificial Intelligence (XAI) is presented that offers explanations that (1) address a gap regarding interpretation by identifying specific dermoscopic features, thereby enabling (2) dermatologists to comprehend them during melanoma diagnosis and allowing for an (3) evaluation of the interaction between clinicians and XAI. The specific goal of this article is to create an XAI system that closely aligns with the perspective of dermatologists when it comes to diagnosing melanoma. By building upon previous research on explainability in dermatology, this work introduces a novel soft attention mechanism, called Convolutional Spiking Attention Module (CSAM), to deep neural architectures, which focuses on enhancing critical elements and reducing noise-inducing features. Two instances of the proposed CSAM were placed inside the proposed Spiking Attention Block (SAB). The InceptionResNetV2, DenseNet201, and Xception architectures with and without the proposed SAB mechanism were compared for skin lesion classification. Pretrained networks with SAB outperform state-of-the-art methods on the HAM10000 dataset. The proposed method used the ISIC-2019 dataset for the crossdataset validation process. The proposed model provides attention regarding cancer pixels without using an external explainer, which proves the importance and significance of the SAB module. Full article

(This article belongs to the Special Issue Supervised and Unsupervised Classification Algorithms (2nd Edition))

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14 pages, 2052 KiB

Open AccessArticle

Human Activity Recognition: A Comparative Study of Validation Methods and Impact of Feature Extraction in Wearable Sensors

by Saeed Ur Rehman, Anwar Ali, Adil Mehmood Khan and Cynthia Okpala

Algorithms 2024, 17(12), 556; https://doi.org/10.3390/a17120556 - 5 Dec 2024

Abstract

With the increasing availability of wearable devices for data collection, studies in human activity recognition have gained significant popularity. These studies report high accuracies on k-fold cross validation, which is not reflective of their generalization performance but is a result of the inappropriate [...] Read more.

With the increasing availability of wearable devices for data collection, studies in human activity recognition have gained significant popularity. These studies report high accuracies on k-fold cross validation, which is not reflective of their generalization performance but is a result of the inappropriate split of testing and training datasets, causing these models to evaluate the same subjects that they were trained on, making them subject-dependent. This study comparatively discusses this validation approach with a universal approach, Leave-One-Subject-Out (LOSO) cross-validation which is not subject-dependent and ensures that an entirely new subject is used for evaluation in each fold, validated on four different machine learning models trained on windowed data and select hand-crafted features. The random forest model, with the highest accuracy of 76% when evaluated on LOSO, achieved an accuracy of 89% on k-fold cross-validation, demonstrating data leakage. Additionally, this experiment underscores the significance of hand-crafted features by contrasting their accuracy with that of raw sensor models. The feature models demonstrate a remarkable 30% higher accuracy, underscoring the importance of feature engineering in enhancing the robustness and precision of HAR systems. Full article

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

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19 pages, 6896 KiB

Open AccessArticle

Optimization of Distribution Network Current Protection for Inverter-Based Distributed Power Access

by Chun Xiao, Qiong Cao, Yulu Ren and Xiaoqing Han

Algorithms 2024, 17(12), 555; https://doi.org/10.3390/a17120555 - 4 Dec 2024

Abstract

In light of the accelerated development of renewable energy, inverter-based distributed power supply (IIDG) is assuming an increasingly pivotal role in contemporary power systems. This paper investigates the impact of inverter-based distributed power access on current protection in distribution networks and proposes an [...] Read more.

In light of the accelerated development of renewable energy, inverter-based distributed power supply (IIDG) is assuming an increasingly pivotal role in contemporary power systems. This paper investigates the impact of inverter-based distributed power access on current protection in distribution networks and proposes an optimization method. Firstly, the IIDG power model is introduced for the purpose of analyzing the impact of distributed power supply access to an urban distribution network on the current magnitude of the distribution network. Subsequently, the issue of protection sensitivity following IIDG access is examined in the context of time-limited current flow protection and fixed-time overcurrent protection, respectively. To address the issue of inadequate sensitivity, a refined enhancement strategy for the real-time monitoring of the line current and modifying the action setting values is put forth. Finally, the proposed protection methodology is validated through PSCAD simulation, thereby verifying the effectiveness of the proposed optimization and improvement solutions in the real-time monitoring of the line current and adjustment of the action setting values. Full article

(This article belongs to the Section Parallel and Distributed Algorithms)

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27 pages, 6325 KiB

Open AccessArticle

Handling Exponentially Growing Strategies in Spatial Cooperative Games: The Case of the European Union

by Mehmet Küçükmehmetoğlu, Yasin Fahjan and Muhammed Ziya Paköz

Algorithms 2024, 17(12), 554; https://doi.org/10.3390/a17120554 - 4 Dec 2024

Abstract

This paper introduces a comprehensive cooperative game theory framework to measure the significance of location and neighborhood relations in conjunction with the magnitude of players/parties. The significances of these relations are measured over the EU geography. In this case, there are (i) the [...] Read more.

This paper introduces a comprehensive cooperative game theory framework to measure the significance of location and neighborhood relations in conjunction with the magnitude of players/parties. The significances of these relations are measured over the EU geography. In this case, there are (i) the test of availability of a core solution that satisfies all associated parties/players; (ii) the measurement of players’/parties’ rational minimal and maximal return expectations from the grand coalition regarding their all individual and sub-group strategies and associated return rationalities; (iii) the determination of the critical players/parties in the grand coalition. The study’s main contributions are the provision of a methodology that identifies spatially/geographically critical players/parties and the design of an algorithm for handling exponentially growing strategies alongside increasing numbers of players/parties. In sum, a comprehensive cooperative game theory framework is introduced to measure the significance of location and neighborhood relations in conjunction with the magnitude of the players/parties. The case of the EU has revealed the union’s geographically critical countries, with Germany being found to be the most influential. Full article

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

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11 pages, 2651 KiB

Open AccessArticle

m-QAM Receiver Based on Data Stream Spectral Clustering for Optical Channels Dominated by Nonlinear Phase Noise

by Miguel Solarte-Sanchez, David Marquez-Viloria, Andrés E. Castro-Ospina, Erick Reyes-Vera, Neil Guerrero-Gonzalez and Juan Botero-Valencia

Algorithms 2024, 17(12), 553; https://doi.org/10.3390/a17120553 - 3 Dec 2024

Abstract

Optical communication systems face challenges like nonlinear noises, particularly Kerr-induced phase noise, which worsens with higher-order m-QAM formats due to their dense data-symbol sets. Advanced signal processing, including machine learning, is increasingly used to enhance signal integrity during demodulation. This paper explores the [...] Read more.

Optical communication systems face challenges like nonlinear noises, particularly Kerr-induced phase noise, which worsens with higher-order m-QAM formats due to their dense data-symbol sets. Advanced signal processing, including machine learning, is increasingly used to enhance signal integrity during demodulation. This paper explores the application of a spectral clustering algorithm adapted to deal with data streaming to mitigate nonlinear noise in long-haul optical channels dominated by nonlinear phase noise, offering a promising solution to a pressing issue. The spectral clustering algorithm was adapted to handle data streams, enabling potential real-time applications. Additionally, it was combined with a demapping process for m-QAM to resolve labeling inconsistencies when processing windowed data. We demonstrate that the spectral clustering algorithm outperforms the k-means algorithm in the face of nonlinear phase noise in −90, −100, and −110 dBc/Hz scenarios at 1 MHz in a simulated 10 GHz symbol rate channel. Full article

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

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19 pages, 556 KiB

Open AccessArticle

A Temporal Graph Network Algorithm for Detecting Fraudulent Transactions on Online Payment Platforms

by Diego Saldaña-Ulloa, Guillermo De Ita Luna and J. Raymundo Marcial-Romero

Algorithms 2024, 17(12), 552; https://doi.org/10.3390/a17120552 - 3 Dec 2024

Abstract

A temporal graph network (TGN) algorithm is introduced to identify fraudulent activities within a digital platform. The central premise is that digital transactions can be modeled via a graph network where various entities interact. The data used to build an event-based temporal graph [...] Read more.

A temporal graph network (TGN) algorithm is introduced to identify fraudulent activities within a digital platform. The central premise is that digital transactions can be modeled via a graph network where various entities interact. The data used to build an event-based temporal graph (ETG) were sourced from an online payment platform and include details such as users, cards, devices, bank accounts, and features related to all these entities. Based on these data, seven distinct graphs were created; the first three represent individual interaction events (card registration, device registration, and bank account registration), while the remaining four are combinations of these graphs (card–device, card–bank account, device–bank account, and card–device–bank account registration). This approach was adopted to determine if the graph’s structure influenced the detection of fraudulent transactions. The results demonstrate that integrating more interaction events into the graph enhances the metrics, meaning graphs containing more interaction events yield superior fraud detection results than those based on individual events. In addition, the data used in this work correspond to Latin American payment transactions, which is relevant in the context of fraud detection since this region has the highest fraud rate in the world, yet few studies have focused on this issue. Full article

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

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23 pages, 1530 KiB

Open AccessArticle

Ellipsoidal K-Means: An Automatic Clustering Approach for Non-Uniform Data Distributions

by Alaa E. Abdel-Hakim, Abdel-Monem M. Ibrahim, Kheir Eddine Bouazza, Wael Deabes and Abdel-Rahman Hedar

Algorithms 2024, 17(12), 551; https://doi.org/10.3390/a17120551 - 3 Dec 2024

Abstract

Traditional K-means clustering assumes, to some extent, a uniform distribution of data around predefined centroids, which limits its effectiveness for many realistic datasets. In this paper, a new clustering technique, simulated-annealing-based ellipsoidal clustering (SAELLC), is proposed to automatically partition data into an [...] Read more.

Traditional K-means clustering assumes, to some extent, a uniform distribution of data around predefined centroids, which limits its effectiveness for many realistic datasets. In this paper, a new clustering technique, simulated-annealing-based ellipsoidal clustering (SAELLC), is proposed to automatically partition data into an optimal number of ellipsoidal clusters, a capability absent in traditional methods. SAELLC transforms each identified cluster into a hyperspherical cluster, where the diameter of the hypersphere equals the minor axis of the original ellipsoid, and the center is encoded to represent the entire cluster. During the assignment of points to clusters, local ellipsoidal properties are independently considered. For objective function evaluation, the method adaptively transforms these ellipsoidal clusters into a variable number of global clusters. Two objective functions are simultaneously optimized: one reflecting partition compactness using the silhouette function (SF) and Euclidean distance, and another addressing cluster connectedness through a nearest-neighbor algorithm. This optimization is achieved using a newly-developed multiobjective simulated annealing approach. SAELLC is designed to automatically determine the optimal number of clusters, achieve precise partitioning, and accommodate a wide range of cluster shapes, including spherical, ellipsoidal, and non-symmetric forms. Extensive experiments conducted on UCI datasets demonstrated SAELLC’s superior performance compared to six well-known clustering algorithms. The results highlight its remarkable ability to handle diverse data distributions and automatically identify the optimal number of clusters, making it a robust choice for advanced clustering analysis. Full article

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

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24 pages, 2247 KiB

Open AccessArticle

Enhancing Intrusion Detection Systems with Dimensionality Reduction and Multi-Stacking Ensemble Techniques

by Ali Mohammed Alsaffar, Mostafa Nouri-Baygi and Hamed Zolbanin

Algorithms 2024, 17(12), 550; https://doi.org/10.3390/a17120550 - 3 Dec 2024

Abstract

The deployment of intrusion detection systems (IDSs) is essential for protecting network resources and infrastructure against malicious threats. Despite the wide use of various machine learning methods in IDSs, such systems often struggle to achieve optimal performance. The key challenges include the curse [...] Read more.

The deployment of intrusion detection systems (IDSs) is essential for protecting network resources and infrastructure against malicious threats. Despite the wide use of various machine learning methods in IDSs, such systems often struggle to achieve optimal performance. The key challenges include the curse of dimensionality, which significantly impacts IDS efficacy, and the limited effectiveness of singular learning classifiers in handling complex, imbalanced, and multi-categorical traffic datasets. To overcome these limitations, this paper presents an innovative approach that integrates dimensionality reduction and stacking ensemble techniques. We employ the LogitBoost algorithm with XGBRegressor for feature selection, complemented by a Residual Network (ResNet) deep learning model for feature extraction. Furthermore, we introduce multi-stacking ensemble (MSE), a novel ensemble method, to enhance attack prediction capabilities. The evaluation on benchmark datasets such as CICIDS2017 and UNSW-NB15 demonstrates that our IDS surpasses current models across various performance metrics. Full article

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

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17 pages, 1893 KiB

Open AccessArticle

New Insights into Fuzzy Genetic Algorithms for Optimization Problems

by Oleksandr Syzonov, Stefania Tomasiello and Nicola Capuano

Algorithms 2024, 17(12), 549; https://doi.org/10.3390/a17120549 - 2 Dec 2024

Abstract

In this paper, we shed light on the use of two types of fuzzy genetic algorithms, which stand out from the literature due to the innovative ideas behind them. One is the Gendered Fuzzy Genetic Algorithm, where the crossover mechanism is regulated by [...] Read more.

In this paper, we shed light on the use of two types of fuzzy genetic algorithms, which stand out from the literature due to the innovative ideas behind them. One is the Gendered Fuzzy Genetic Algorithm, where the crossover mechanism is regulated by the gender and the age of the population to generate offspring through proper fuzzy rules. The other one is the Elegant Fuzzy Genetic Algorithm, where the priority of the parent genome is updated based on the child’s fitness. Both algorithms present a significant computational burden. To speed up the computation, we propose to adopt a nearest-neighbor caching strategy. We first performed several experiments, using some well-known benchmark functions, and tried different types of membership functions and logical connectives. Afterward, some additional benchmarks were retrieved from the literature for a fair comparison against published results, which were obtained by means of former variants of fuzzy genetic algorithms. A real-world application problem, which was retrieved from the literature and dealt with rice production, was also tackled. All the numerical results show the potential of the proposed strategy. Full article

(This article belongs to the Special Issue Numerical Optimization and Algorithms: 2nd Edition)

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22 pages, 2453 KiB

Open AccessReview

A Tertiary Study for Process Mining

by Elia Kouzari and Ioannis Stamelos

Algorithms 2024, 17(12), 548; https://doi.org/10.3390/a17120548 - 2 Dec 2024

Abstract

Background: This tertiary study lists the secondary studies published in the process mining domain and provides an analysis related to a set of research questions. It is the first tertiary study in this area. The objective is to provide information about the available [...] Read more.

Background: This tertiary study lists the secondary studies published in the process mining domain and provides an analysis related to a set of research questions. It is the first tertiary study in this area. The objective is to provide information about the available secondary studies in process mining, respond to research questions relating to the thematic areas covered in the studies, as well as trends regarding their quality, and report on findings for publication venues, citations, guidelines used, and demographics. Method: A tertiary study based on systematic secondary studies published up to March 2023. A total of 25 secondary studies related to process mining have been identified following the application of inclusion/exclusion criteria and quality assessment. Results: The most popular thematic areas addressed are technologies and applications for process mining and healthcare. The secondary studies in process mining have a medium quality score of 3.5. The guidelines introduced by Kitchenham over the years are preferred in secondary studies in this field. There is no trend related to the number of primary studies included in secondary studies in process mining. Conclusion: Although numerous secondary studies exist for process mining, there is still room for more research, specifically in the areas highlighted in this study. Future researchers can use this study for reference, and they can also use the listed research topics to dive deep into the issues identified. Full article

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

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