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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Muthukumaran, V. Satheesh Kumar, S. Radhika, E.K. Arpana, G.K. Nalini, R. Department of Mathematics College of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Computer Science School of Applied Sciences REVA University Bangalore India Department of Computer Science Sindhi College Bangalore India Department of ECE East West College of Engineering Bangalore India

ISBN: (纸本)9783031810855

This study suggests a novel soft computing approach for enhancing face recognition performance using the feature learning methodology. The suggested system of Quasi-linear Partial Differential Equations (QPDE) based face recognition model is used to increase the accuracy of facial identification. The webcam first catches the user's image and saves it to the user's computer. The inference model receives the captured image. The input image is pre-processed to enhance the image, and then the image is split into numerous integral images at the inference model. Using speed-up robust features, an intriguing point is constructed for each integral image. The computed interest point is converted into a QPDE based weight matrix by representing the strength of features at all the regions of the face. With the generated weighted matrix of features extracted from the facial image, the method generates a Fast Artificial intelligence (AI) network with many layers. Each layer of the neural network has many neurons with dedicated functions to compute the similarity and feature weight using QPDE. The proposed approach has produced efficient results compared to earlier methods for the Flickr-Faces-HQ database (FFHQ) datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Soft computing

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Partitions into an exact number of parts

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The Ramanujan Journal 2025年第3期67卷 1-19页

作者： Ballantine, Cristina Merca, Mircea Department of Mathematics and Computer Science College of The Holy Cross Worcester USA Department of Mathematical Methods and Models Fundamental Sciences Applied in Engineering Research Center National University of Science and Technology Politehnica Bucharest Bucharest Romania Academy of Romanian Scientists Bucharest Romania

In this paper, we examine the functions $$P_k(n)$$ , which counts the partitions of n into exactly k parts, and $$Q_k(n)$$ , which counts the partitions of n into exactly k distinct parts. These partition functions are closely linked to two classical identities of Euler. We explore this connection and establish several new relationships between $$P_k(n)$$ and $$Q_k(n)$$ . We give both analytic and combinatorial proofs of the theorems.

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RL-Based Control of UAS Subject to Significant Disturbance

RL-Based Control of UAS Subject to Significant Disturbance

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Kousheek Chakraborty Thijs Hof Ayham Alharbat Abeje Mersha Smart Mechatronics and Robotics research group Saxion University of Applied Science Enschede The Netherlands Robotics and Mechatronics research group Faculty of Electrical Engineering Mathematics & Computer Science University of Twente Enschede The Netherlands

ISBN: (数字)9798331513283

ISBN: (纸本)9798331513290

This paper proposes a Reinforcement Learning (RL)-based control framework for position and attitude control of an Unmanned Aerial System (UAS) subjected to significant disturbance that can be associated with an uncertain trigger signal. The proposed method learns the relationship between the trigger signal and disturbance force, enabling the system to anticipate and counteract the impending disturbances before they occur. We train and evaluate three policies: a baseline policy trained without exposure to the disturbance, a reactive policy trained with the disturbance but without the trigger signal, and a predictive policy that incorporates the trigger signal as an observation and is exposed to the disturbance during training. Our simulation results show that the predictive policy outperforms the other policies by minimizing position deviations through a proactive correction maneuver. This work highlights the potential of integrating predictive cues into RL frameworks to improve UAS performance.

关键词： Training Attitude control Simulation Force Reinforcement learning Autonomous aerial vehicles Stability analysis Maintenance Dynamical systems Logistics

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RL-based Control of UAS Subject to Significant Disturbance

arXiv

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arXiv 2025年

作者： Chakraborty, Kousheek Hof, Thijs Alharbat, Ayham Mersha, Abeje Smart Mechatronics and Robotics Research Group Saxion University of Applied Science Enschede Netherlands Robotics and Mechatronics Research Group Faculty of Electrical Engineering Mathematics & Computer Science University of Twente Enschede Netherlands

关键词： Reinforcement learning

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Photovoltaic Module Parameter Estimation by Using Wild Horse Optimizer

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Procedia computer science 2025年 258卷 2808-2816页

作者： Anuj Kumar Tina Sharma Manoj K. Singh Sangeeta Pant Hitesh Kumar Sharma School of Computer Science Engineering & Applications D Y Patil International University (DYPIU) Pune India- 411044 Department of Mathematics Birla Institute of Technology and Science Pilani India School of Computer Science Engineering and Technology Bennett University Greater Noida India- 201310 Department of Applied Sciences Symbiosis Institute of Technology Symbiosis International (Deemed University) (SIU) Pune India- 412115 School of Computer Science University of Petroleum & Energy Studies Dehradun India-248007

The development of highly accurate models is essential for optimizing the performance and efficiency of photovoltaic (PV) modules, which are integral to renewable energy systems. In this context, metaheuristic algorithms have become indispensable tools for tackling complex engineering challenges, particularly in the areas of evaluation, simulation, and parameter estimation. These algorithms excel at finding optimal or near-optimal solutions for problems where traditional methods may struggle due to their complexity or computational intensity. This article presents a novel framework for estimating the parameters of PV modules using the Wild Horse Optimizer (WHO), a recently introduced metaheuristic algorithm known for its robustness and efficiency. Specifically, the framework is applied to the Double Diode Model (DDM) of a solar cell, a widely recognized model for representing the electrical behavior of PV modules. The simulation results clearly illustrate that the proposed framework based on WHO algorithm offers significant improvements in the accuracy and effectiveness of parameter estimation for PV modules, compared to Artificial Bee Swarm Optimization (ABSO), the Flower Pollination Algorithm (FPA), and several others, thereby validating the proposed approach.

关键词： Optimization Photovoltaic (PV) modules Double diode model (DDM) metaheuristics Wild Horse Optimizer (WHO)

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Regression and classification of Windkessel parameters from non-invasive cardiovascular quantities using a fully connected neural network

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Informatics in Medicine Unlocked 2025年 53卷

作者： Gdoura, Ahmed Bernhard, Stefan Departement of Life Science Engineering Technische Hochschule Mittelhessen University of Applied Sciences Giessen Germany Department of Mathematics and Computer Science Free University of Berlin Berlin Germany

Despite their simplicity, three-element Windkessel models (WK-3) provide an effective and straightforward representation of the aortic input impedance. The WK-3 model not only captures valuable information about the mechanical and structural characteristics of the aortic arch but also generates reliable estimations of the central blood pressure (cBP) wave, a significant cardiovascular risk indicator. However, fitting the parameters of the WK-3 model typically requires invasively collected data, which carries substantial risk and high cost for patients. This study aims to enable non-invasive impedance estimation of the WK-3 model using cardiovascular signals. As a proof of concept, we developed and trained a fully connected neural network (FCNN) on an in-silico dataset to predict the WK-3 parameters: characteristic impedance, peripheral arterial resistance, and arterial compliance. These predictions are based on non-invasive parameters, including zero-flow pressure intercept, heart rate, stroke volume, and left ventricular ejection time. The proposed model achieved an overall accuracy of 80% with an average area under the curve (AUC) of 0.91±0.11. The implementation and best-fitting model are available for download from this link. © 2025 The Authors

关键词： Asymptotic pressure BEC Classification Fully connected NN Heart rate LVET Regression Stroke volume WGM Windkessel τ

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Phototropic growth algorithm: A novel metaheuristic inspired from phototropic growth of plants

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Knowledge-Based Systems 2025年 322卷

作者： Bohat, Vijay Kumar Hashim, Fatma A. Batra, Harshit Abd Elaziz, Mohamed Department of Computer Science & Engineering Netaji Subhas University of Technology Dwarka New Delhi110078 India Faculty of Engineering Helwan University Helwan Egypt Applied Science Research Center. Applied Science Private University Amman Jordan Centre of Excellence in AI Netaji Subhas University of Technology Dwarka New Delhi110078 India Department of Mathematics Faculty of Science Zagazig University Zagazig Egypt Faculty of Computer Science and Engineering Galala University Suez 435611 Egypt College of Engineering and Information Technology Ajman University Ajman United Arab Emirates

This paper presents a novel optimization algorithm called the Phototropic Growth Algorithm (PGA), which takes its inspiration from the growth patterns of plant cells in response to sunlight. The proposed algorithm has been evaluated on two benchmark test function suites, the CEC 2017 and CEC 2020, and compared to other established metaheuristic-optimization algorithms based on the quality of the solutions obtained. The effectiveness of PGA is further demonstrated by testing it on six challenging engineering design problems with constraints, showcasing its robustness in solving real-world problems with unknown search spaces. Simulation results show that PGA outperforms other competitive algorithms by consistently generating superior solutions and demonstrates strong potential for solving complex optimization tasks using a nature-inspired approach. © 2025

关键词： Particle swarm optimization (PSO)

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Blockchain Empowered Secure Federated Learning for Consumer IoT Applications in Cloud-Edge Collaborative Environment

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IEEE Transactions on Consumer Electronics 2025年

作者： Kumar, Mohit Samriya, Jitendra Kumar Walia, Guneet Kaur Verma, Prabal Wu, Huaming Gill, Sukhpal Singh National Institute of Technology Department of Information Technology Jalandhar India Indian Institute of Information Technology Computer Science and Engineering Sonepat India National Institute of Technology Department of Information Technology Srinagar India Tianjin University Center for Applied Mathematics Tianjin China Queen Mary University of London School of Electronic Engineering and Computer Science United Kingdom

The growing number of consumer Internet of Things (IoT) gadgets, including smart homes, fitness trackers, connected appliances, and home security systems, is transforming the way we live our daily lives. This has led to the emergence of a collaborative cloud-edge paradigm to leverage resources and services near the end-user, thereby providing prompt response to delay-sensitive real-time applications. Nevertheless, the tremendous amount of data generated by various IoT devices and sent over the network is always an open security challenge. The introduction of Federated Learning (FL) addresses the security and data privacy shortcomings of traditional centralised machine learning. Despite FL's use for data privacy, it must overcome a number of significant challenges, such as privacy concerns, communication overhead, stragglers, and heterogeneity. To solve these challenges, this paper proposes a novel technique for enhancing security in IoT-enabled edge cloud computing networks, utilising blockchain-driven FL and Gaussian Bayesian transfer convolutional neural network architectures for data analysis. Blockchain-driven FL ensures the security and privacy of consumer IoT applications. In comparison to state-of-the-art works, the experimental results achieved throughput of up to 89%, latency of 71%, training accuracy of 91%, validation accuracy of 96%, and network security of 92%. © 1975-2011 IEEE.

关键词： Federated learning

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Improved Compact Genetic Algorithms with Efficient Caching

arXiv

引用

arXiv 2025年

作者： Dutta, Prasanta Mukhopadhyay, Anirban Physics and Applied Mathematics Unit Indian Statistical Institute West Bengal Kolkata700108 India Department of Computer Science and Engineering University of Kalyani West Bengal Kalyani741235 India

Compact Genetic Algorithms (cGAs) are condensed variants of classical Genetic Algorithms (GAs) that use a probability vector representation of the population instead of the complete population. cGAs have been shown to significantly reduce the number of function evaluations required while producing outcomes similar to those of classical GAs. However, cGAs have a tendency to repeatedly generate the same chromosomes as they approach convergence, resulting in unnecessary evaluations of identical chromosomes. This article introduces the concept of caching in cGAs as a means of avoiding redundant evaluations of the same chromosomes. Our proposed approach operates equivalently to cGAs, but enhances the algorithm’s time efficiency by reducing the number of function evaluations. We also present a data structure for efficient cache maintenance to ensure low overhead. The proposed caching approach has an asymptotically constant time complexity on average. The proposed method further generalizes the caching mechanism with higher selection pressure for elitism-based cGAs. We conduct a rigorous analysis based on experiments on benchmark optimization problems using two well-known cache replacement strategies. The results demonstrate that caching significantly reduces the number of function evaluations required while maintaining the same level of performance accuracy. Copyright © 2025, The Authors. All rights reserved.

关键词： Genetic algorithms

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Sparse identification of nonlinear dynamics and Koopman operators with Shallow Recurrent Decoder Networks

arXiv

引用

arXiv 2025年

作者： Gao, Mars Liyao Williams, Jan P. Kutz, J. Nathan Computer Science & Engineering University of Washington SeattleWA United States Mechanical Engineering University of Washington SeattleWA United States Applied Mathematics University of Washington SeattleWA United States Electrical and Computer Engineering University of Washington SeattleWA United States

Spatiotemporal modeling of real-world data poses a challenging problem due to inherent high dimensionality, measurement noise, and expensive data collection procedures. In this paper, we present Sparse Identification of Nonlinear Dynamics with SHallow REcurrent Decoder networks (SINDy-SHRED), a method to jointly solve the sensing and model identification problems with simple implementation, efficient computation, and robust performance. SINDy-SHRED uses Gated Recurrent Units (GRUs) to model the temporal sequence of sensor measurements along with a shallow decoder network to reconstruct the full spatiotemporal field from the latent state space using only a few available sensors. Our proposed algorithm introduces a SINDy-based regularization;beginning with an arbitrary latent state space, the dynamics of the latent space progressively converges to a SINDy-class functional, provided the projection remains within the set. In restricting SINDy to a linear model, the architecture produces a Koopman-SHRED model which enforces a linear latent space dynamics. We conduct a systematic experimental study including synthetic PDE data, real-world sensor measurements for sea surface temperature, and direct video data. With no explicit encoder, SINDy-SHRED and Koopman-SHRED enable efficient training with minimal hyperparameter tuning and laptop-level computing;further, it demonstrates robust generalization in a variety of applications with minimal to no hyperparameter adjustments. Finally, the interpretable SINDy and Koopman models of latent state dynamics enables accurate long-term video predictions, achieving state-of-the-art performance and outperforming all baseline methods considered, including Convolutional LSTM, PredRNN, ResNet, and SimVP. Copyright © 2025, The Authors. All rights reserved.

关键词： Submarine geophysics

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