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A multi-objective social-based algorithm for community detection

Multimedia Tools and Applications 2025年 1-30页

作者： Farrokhpour Dizaj, Yousef Lotfi, Shahriar Hajipour, Javad Department of Computer Science Faculty of Mathematics Statistics and Computer Science University of Tabriz Tabriz Iran

The detection of community structures in complex networks has garnered significant attention in recent years. Given its NP-hardness, numerous evolutionary optimization-based approaches have been proposed. However, there is still a need for improvement, since no previous method has been able to find the true community structure on all benchmark networks. In this paper, we propose a multi-objective approach based on the social-based algorithm (SBA). SBA is a hybrid of the imperialist competitive algorithm and the standard evolutionary algorithm. SBA has demonstrated remarkable performance in finding optimal solutions and can effectively identify correct community structures. In our method, instead of a single solution, a set of non-dominated solutions is produced, each representing a different compromise between the objective functions. To evaluate the performance of the proposed algorithm, various tests were conducted on synthetic and real-world datasets. The results of experiments on several social network benchmarks show up to a 57% improvement on benchmark networks compared to previous works. This indicates that the proposed algorithm performs well in community detection. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Consensus algorithm

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Quantum spin systems: Toroidal classification and geometric duality

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Physical Review B 2024年第14期110卷 L140403页

作者： Vahid Azimi-Mousolou Anders Bergman Anna Delin Olle Eriksson Manuel Pereiro Danny Thonig Erik Sjöqvist Department of Applied Mathematics and Computer Science Faculty of Mathematics and Statistics Department of Physics and Astronomy Department of Applied Physics School of Engineering Sciences (SeRC) Wallenberg Initiative Materials Science for Sustainability (WISE) Wallenberg Initiative Materials Science WISE School of Science and Technology

We demonstrate a toroidal classification for quantum spin systems, revealing an intrinsic geometric duality within this structure. Through our classification and duality, we reveal that various bipartite quantum features in magnon systems can manifest equivalently in both bipartite ferromagnetic and antiferromagnetic materials, based upon the availability of relevant Hamiltonian parameters. Additionally, the results highlight the antiferromagnetic regime as an ultrafast dual counterpart to the ferromagnetic regime, both exhibiting identical capabilities for quantum spintronics and technological applications. Concrete illustrations are provided, demonstrating how splitting and squeezing types of two-mode magnon quantum correlations can be realized across ferro- and antiferromagnetic regimes.

关键词： Magnons Quantum correlations in quantum information Quantum information processing Quantum information theory Spintronics

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Modified DS np Chart Using Generalized Multiple Dependent State Sampling under Time Truncated Life Test

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computer Modeling in Engineering & sciences 2024年第3期138卷 2471-2495页

作者： Wimonmas Bamrungsetthapong Pramote Charongrattanasakul Division of Applied Statistics Department of Mathematics and Computer ScienceFaculty of Science and TechnologyRajamangala University of Technology ThanyaburiPathum Thani12110Thailand Division of Mathematics Department of Mathematics and Computer ScienceFaculty of Science and TechnologyRajamangala University of Technology KrungthepBangkok10120Thailand

This study presents the design of a modified attributed control chart based on a double sampling(DS)np chart applied in combination with generalized multiple dependent state(GMDS)sampling to monitor the mean life of the product based on the time truncated life test employing theWeibull *** control chart developed supports the examination of the mean lifespan variation for a particular product in the process of *** control limit levels are used:the warning control limit,inner control limit,and outer control ***,they enhance the capability for variation detection.A genetic algorithm can be used for optimization during the in-control process,whereby the optimal parameters can be established for the proposed control *** control chart performance is assessed using the average run length,while the influence of the model parameters upon the control chart solution is assessed via sensitivity analysis based on an orthogonal experimental design withmultiple linear regression.A comparative study was conducted based on the out-of-control average run length,in which the developed control chart offered greater sensitivity in the detection of process shifts while making use of smaller samples on average than is the case for existing control ***,to exhibit the utility of the developed control chart,this paper presents its application using simulated data with parameters drawn from the real set of data.

关键词： Modified DS np chart generalizedmultiple dependent state sampling time truncated life test Weibull distribution average run length average sample size

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Credit card fraud detection using hybridization of isolation forest with grey wolf optimizer algorithm

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Soft Computing 2024年第17-18期28卷 10215-10233页

作者： Tabrizchi, Hamed Razmara, Jafar Department of Computer Science Faculty of Mathematics Statistics and Computer Science University of Tabriz Tabriz Iran

During recent decades, using credit cards represents a pivotal part of the financial lifeline. Credit cards and online payment gateways are vital elements in the world of world-wide-web. Given the fact that credit cards play an essential role in today's society, the misuse of these cards will lead to significant damages. One of the common ways to deal with these possible damages is using anomaly detection systems. These systems aim to take account of changes in customer and fraudsters’ behavior to detect anomaly patterns. In the current study, we present a model namely IF-GWO to learn fraudulent patterns through analyzing past transactions. The method employs a novel ensemble learning method using isolation forest (IF) and Grey Wolf Optimizer (GWO). The experimental results indicate the priority of our presented fraud-detection system based on a noticeable number of credit card account transactions. Compared to the conventional model used for anomaly detection, the proposed model can detect more fraud accounts with fewer false positives over comparative procedures. Based on a comparison with other models using the dataset contains 284,807 transactions that are made by European cardholders, the proposed model outperformed the other approaches and achieved the highest performance in terms of F-Measure (93.52%), Area under receiver operating characteristic curve (AUC) (94.17%), and G-means (94.10%). © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Anomaly detection

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Deep bed filtration model for cake filtration and erosion

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applied mathematics and Mechanics(English Edition) 2024年第2期45卷 355-372页

作者： L.I.KUZMINA Y.V.OSIPOV A.R.PESTEREV Department of Applied Mathematics HSE UniversityMoscow 101000Russia Department of Computer Science and Applied Mathematics Moscow State University of Civil EngineeringMoscow 129337Russia

Many phenomena in nature and technology are associated with the filtration of suspensions and colloids in porous media. Two main types of particle deposition,namely, cake filtration at the inlet and deep bed filtration throughout the entire porous medium, are studied by different models. A unified approach for the transport and deposition of particles based on the deep bed filtration model is proposed. A variable suspension flow rate, proportional to the number of free pores at the inlet of the porous medium, is considered. To model cake filtration, this flow rate is introduced into the mass balance equation of deep bed filtration. For the cake filtration without deposit erosion,the suspension flow rate decreases to zero, and the suspension does not penetrate deep into the porous medium. In the case of the cake filtration with erosion, the suspension flow rate is nonzero, and the deposit is distributed throughout the entire porous medium. An exact solution is obtained for a constant filtration function. The method of characteristics is used to construct the asymptotics of the concentration front of suspended and retained particles for a filtration function in a general form. Explicit formulae are obtained for a linear filtration function. The properties of these solutions are studied in detail.

关键词： deep bed filtration cake filtration porous medium particle deposition and erosion analytical solution concentration front

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Fuzzy Reliability and Availability of System under a Calendar-based Inspection Involving Multiple Failures and Its Application to Wind Turbine System

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Journal of Systems science and Systems Engineering 2024年第2期33卷 187-206页

作者： Mintu Kumar Himani Pant S.B.Singh Department of Mathematics Statistics and Computer Science G.B.Pant University of AgricultureTechnologyPantnagarUttarakhandIndia

Uncertainty is an important factor that needs to be considered while analyzing the performance of any engineering *** order to quantify uncertainty,fuzzy set theory is frequently used by most of researchers,including energy system *** to the classical reliability theory,component lifetimes have crisp parameters,but due to uncertainty and inaccuracy in data,it is sometimes very difficult to determine the exact values of these parameters in real-world *** overcome this difficulty in the current research,failure and repair rates were taken as triangular fuzzy numbers to determine the fuzzy availability of a system undergoing calendar-based periodic inspection subject to multiple failure modes(FMs).It was assumed that each component in the system had an exponential failure rate and repair rate with fuzzy *** FMs were explicitly taken into account when a functional state of the system was *** FM had a random failure *** the occurrence of any failure,a random time was selected for the relevant corrective repair *** proposed research was studied for one of the major sources of green energy,namely a wind turbine system wherein all the derived propositions have been implemented on it.

关键词： Fuzzy reliability fuzzy availability wind turbine system multiple failure modes triangular fuzzy number

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Context-aware reinforcement learning for cooling operation of data centers with an Aquifer Thermal Energy Storage

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Energy and AI 2024年第3期17卷 233-246页

作者： Lukas Leindals Peter Grønning Dominik Franjo Dominković Rune Grønborg Junker Department of Applied Mathematics and Computer Science Technical University of DenmarkDenmark

Data centers are often equipped with multiple cooling units. Here, an aquifer thermal energy storage (ATES) system has shown to be efficient. However, the usage of hot and cold-water wells in the ATES must be balanced for legal and environmental reasons. Reinforcement Learning has been proven to be a useful tool for optimizing the cooling operation at data centers. Nonetheless, since cooling demand changes continuously, balancing the ATES usage on a yearly basis imposes an additional challenge in the form of a delayed reward. To overcome this, we formulate a return decomposition, Cool-RUDDER, which relies on simple domain knowledge and needs no training. We trained a proximal policy optimization agent to keep server temperatures steady while minimizing operational costs. Comparing the Cool-RUDDER reward signal to other ATES-associated rewards, all models kept the server temperatures steady at around 30 °C. An optimal ATES balance was defined to be 0% and a yearly imbalance of −4.9% with a confidence interval of [−6.2, −3.8]% was achieved for the Cool 2.0 reward. This outperformed a baseline ATES-associated reward of 0 at −16.3% with a confidence interval of [−17.1, −15.4]% and all other ATES-associated rewards. However, the improved ATES balance comes with a higher energy consumption cost of 12.5% when comparing the relative cost of the Cool 2.0 reward to the zero reward, resulting in a trade-off. Moreover, the method comes with limited requirements and is applicable to any long-term problem satisfying a linear state-transition system.

关键词： Data centers ATES Smart energy systems Reinforcement learning Delayed rewards

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Different Methods to Estimate Stress-Strength Reliability Function for Modified Exponentiated Lomax Distribution

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for computer science and mathematics 2025年第2期6卷 99-106页

作者： Al-Rassam, Raya Salim Mohammed, Khalida Ahmed Rashed, Safwan Nathem Department of Statistics & Informatics College of Computer Science & Mathematics University of Mosul Iraq

When ensuring the reliability of device or the suitability of a material, it is necessary to take into consideration the stress cases in the operating environment. This means that the uncertainty about the reality environmental stress must be taken into as random. The stress-strength (S-S) model treated the stress and strength variables as random. In the simplest form of stress-strength model, y represents the stress put on the unit by the operating environment, and the strength of the unit represented by x. A unit is able to perform its required function if its stress imposed on it is less than the strength of the unit. In this paper, the stress-strength reliability estimation for the modified exponentiated Lomax distribution, which is generalization of the Lomax distribution, with an unknown shape parameter and a known scale parameters is studied using different methods. These methods include the maximum likelihood method, Bayesian estimation method under a quadratic loss function, and the least squares method for complete data. The estimators are compared based on Markov Chain Monte Carlo (MCMC) simulations using R-Studio, evaluated by the mean square error (MSE) criteria. The simulation results show that the maximum likelihood estimators are the best in two cases: the first is when the sample sizes are equal and the second is when the shape parameter of the strength variable is greater than the shape parameter of the stress variable. While least squares estimators are the beast if the strength sample size is smaller than the stress sample size. Finally when the strength sample size is greater than the stress sample size, then the best estimators differ between the maximum likelihood estimators and Bayesian estimators. Bayesian estimators become the best when the shape parameter of stress variable is larger than the shape parameter of the strength variable. © 2025 The Author(s).

关键词： Maximum likelihood estimation

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Unsupervised multiplex graph diffusion networks with multi-level canonical correlation analysis for multiplex graph representation learning

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science China(Information sciences) 2025年第3期68卷 66-79页

作者： Sichao FU Qinmu PENG Yange HE Baokun DU Bin ZOU Xiao-Yuan JING Xinge YOU School of Electronic Information and Communications Huazhong University of Science and Technology Platform Operation and Marketing Center JD Retail Faculty of Mathematics and Statistics Hubei Key Laboratory of Applied Mathematics Hubei University School of Computer Science Wuhan University

In recent years, unsupervised multiplex graph representation learning(UMGRL) has received increasing research interest, which aims to learn discriminative node features from the multiplex graphs supervised by data without the guidance of labels. Although these designed UMGRL methods have obtained great success in various graph-related tasks, most existing UMGRL models still have the following issues: highly depending on complex self-supervised strategies(i.e., data augmentation,pretext tasks, and negative pairs sampling), restricted receptive fields, and only aggregating low-frequency information between nodes. In this paper, we propose a simple unsupervised multiplex graph diffusion network(UMGDN) with the aid of multi-level canonical correlation analysis to solve the above issues. Specifically, we first decouple the feature transform and propagation processes of the graph convolution layer to further improve the generalization of the learnable parameters. And then, we propose adaptive diffusion propagation to capture long-range dependency relationships between nodes, not the local neighborhood interactions. Finally, a multi-level canonical correlation analysis loss on both the feature transform and propagation processes is proposed to maximize the correlation of the same node features from multiple graphs for guiding model optimization. Compared to the existing UMGRL models, our proposed UMGDN does not need to introduce any data augmentation, negative pairs sampling techniques, complex pretext tasks, and also adaptively aggregates the optimal frequency information between nodes to generate more robust node embeddings. Extensive experiments on four popular datasets and two graph-related tasks demonstrate the effectiveness of the proposed method.

关键词： unsupervised multiplex graph representation learning graph neural networks node classification node clustering

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Hierarchical GraphCut Phase Unwrapping based on Invariance of Diffeomorphisms Framework

IEEE Open Journal of Signal Processing

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IEEE Open Journal of Signal Processing 2025年 6卷 546-554页

作者： Gao, Xiang Wang, Xinmu Zhao, Zhou Huang, Junqi Gu, Xianfeng David Department of Computer science Stony Brook University NY USA Deparment of Applied Mathematics and Statistics Stony Brook University NY USA

Recent years have witnessed rapid advancements in 3D scanning technologies, with diverse applications spanning VR/AR, digital human creation, and medical imaging. Structured-light scanning with phase-shifting techniques is preferred for its use of non-radiative, low-intensity visible light and high accuracy, making it well suited for human-centric applications such as capturing 4D facial dynamics. A key step in these systems is phase unwrapping, which recovers continuous phase values from measurements that are inherently wrapped modulo 2π. The goal is to estimate the unwrapped phase count k, an integer-valued variable in the equation Φ = ∅ + 2πk, where ∅ is the wrapped phase and Φ is the true phase. However, the presence of noise, occlusions, and piecewise continuous phase functions induced by complex 3D surface geometry makes the inverse reconstruction of the true phase extremely challenging. This is because phase unwrapping is an inherently ill-posed problem: measurements only provide modulo 2π values, and recovering the correct unwrapped phase count requires strong assumptions about the smoothness or continuity of the underlying 3D surface. Existing methods typically involve a trade-off between speed and accuracy: Fast approaches lack precision, while accurate algorithms are too slow for real-time use. To overcome these limitations, this work proposes a novel phase unwrapping framework that reformulates GraphCut-based unwrapping as a pixel-labeling problem. This framework helps significantly improve the estimation of the unwrapped phase count k through the invariance property of diffeomorphisms applied in image space via conformal and optimal transport (OT) maps. An odd number of diffeomorphisms are precomputed from the input phase data, and a hierarchical GraphCut algorithm is applied in each corresponding domain. The resulting label maps are fused via majority voting to efficiently and robustly estimate the unwrapped phase count k at each pixel, using an odd nu

关键词： Transillumination

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