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Modified Wild Horse Optimizer for system reliability-redundancy allocation problems with cold- standby redundancy strategy

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International Journal of System Assurance Engineering and Management 2025年 1-15页

作者： Pant, Sangeeta Singh, Manoj K. Kumar, Anuj Chaube, Shshank Ram, Mangey Department of Applied Sciences Symbiosis Institute of Technology Symbiosis International (Deemed University) (SIU) Lavale Maharashtra Pune 412115 India School of Computer Science Engineering and Technology Bennett University Greater Noida 201310 India School of Computer Science Engineering & Applications D. Y. Patil International University (DYPIU) Akrudi Maharashtra Pune 411044 India Department of Physical Sciences Jaypee University Anoopshahr Bulandshahr 203390 India Department of Mathematics Computer Science and Engineering Graphic Era Deemed to be University Uttarakhand Dehradun India

The design of a reliable system depends greatly on several key factors, including the allocation of reliability to individual components, the use of redundancy, and the allocation of redundant components to enhance overall system reliability. This article proposes a Modified Wild Horse Optimizer (MWHO) for solving system reliability-redundancy allocation problems (RRAPs) with cold-standby redundancy strategy. The cold-standby redundancy is a common technique used to enhance system reliability by adding backup components that remain idle until a failure occurs. However, creating a trade-off between the number of redundant components and their reliabilities with the objective of maximizing overall system reliability remains a challenging problem. Since components can malfunction even when transitioning from standby to active mode, any issues caused by imperfect switching are also taken into account when evaluating the system's performance. The proposed MWHO algorithm incorporates a novel search strategy and a new update mechanism to deal with mixed-integer non-linear RRAPs. The proposed MWHO algorithm is demonstrated to be effective for solving system RRAPs with cold-standby redundancy strategy, outperforming other optimization algorithms in solution quality and convergence speed to yield higher system reliability values. © The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2025.

关键词： Cold-standby redundancy strategy Imperfect switching Metaheuristics Modified Wild Horse Optimizer Reliability-redundancy allocation problems

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High Capacity Reversible Data Hiding in Encrypted 3D Mesh Models Based on Dynamic Prediction and Virtual Connection

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IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 2996-3010页

作者： Wang, Ke Yao, Ye Shen, Yanzhao Xiao, Fengjun Ren, Yizhi Meng, Weizhi Hangzhou Dianzi University School of Cyberspace Hangzhou310018 China Shandong Institute of Blockchain Jinan250102 China Hangzhou Dianzi University Zhejiang Informatization Development Institute Hangzhou310018 China Lancaster University School of Computing and Communications LancasterLA1 4YW United Kingdom Technical University of Denmark Department of Applied Mathematics and Computer Science Kongens Lyngby2800 Denmark

In recent years, reversible data hiding in encrypted domain (RDH-ED) has garnered considerable interest among researchers, resulting in the development of high-performance methods based on various carriers. However, the challenge of enhancing the data embedding capacity while ensuring reversibility becomes increasingly pronounced when the carrier is a three-dimensional (3D) model. In this paper, a high capacity RDH-ED method based on dynamic prediction and virtual connection for 3D models is proposed. Unlike existing methods that partition the vertices in the model into embeddable and prediction sets, where each vertex can only serve one function, the proposed dynamic prediction mechanism constructs a data embedding order set by leveraging the connectivity relationships between vertices. This allows each vertex within the set to both embed data and provide predictions, significantly increasing the proportion of embeddable vertices. Moreover, the proposed method is the first work to consider independent vertices within the model and integrates a novel virtual connection approach with the dynamic prediction process, enabling all independent vertices to participate in data embedding and prediction, thereby further enhancing the data embedding capacity. Experimental results demonstrated that the proposed method significantly outperforms other state-of-the-art methods in terms of data embedding capacity while ensuring reversibility. © 2004-2012 IEEE.

关键词： Prediction models

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TrueReason: An Exemplar Personalised Learning System Integrating Reasoning with Foundational Models

arXiv

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arXiv 2025年

作者： Bulathwela, Sahan Van Niekerk, Daniel Shipton, Jarrod Perez-Ortiz, Maria Rosman, Benjamin Shawe-Taylor, John Centre for Artificial Intelligence Department of Computer Science University College London United Kingdom School of Computer Science and Applied Mathematics University of The Witwatersrand South Africa

Personalised education is one of the domains that can greatly benefit from the most recent advances in Artificial Intelligence (AI) and Large Language Models (LLM). However, it is also one of the most challenging applications due to the cognitive complexity teaching effectively while personalising the learning experience to suite independent learners. We hypothesise that one promising approach to excelling such demanding use-cases is using a society of minds. In this chapter, we present TrueReason, an exemplar personalised learning system that integrates a multitude of specialised AI models that can mimic micro skills that are composed together by a LLM to operationalise planning and reasoning. The architecture of the initial prototype is presented while describing two micro skills that have been incorporated in the prototype. The proposed system demonstrates the first step in building sophisticated AI systems that can take up very complex cognitive tasks that are demanded by domains such as education. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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L²-convergence of Yosida approximation for semi-linear backward stochastic differential equation with jumps in infinite dimension

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Arab Journal of Mathematical sciences 2025年第1期31卷 82-95页

作者： Abidi, Hani Amami, Rim Pettersson, Roger Trabelsi, Chiraz Department of Computer Science and Applied Mathematics Esprit School of Business Tunis Tunisia Department of Basic Sciences Deanship of Preparatory Year and Supporting Studies Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Department of Mathematics Linnaeus University Vaxjo Sweden Department of Sciences and Technologies Centre Universitaire de Mayotte Mayotte France IMAG Montpellier Montpellier France

Purpose: The main motivation of this paper is to present the Yosida approximation of a semi-linear backward stochastic differential equation in infinite dimension. Under suitable assumption and condition, an L²-convergence rate is established. Design/methodology/approach: The authors establish a result concerning the L²-convergence rate of the solution of backward stochastic differential equation with jumps with respect to the Yosida approximation. Findings: The authors carry out a convergence rate of Yosida approximation to the semi-linear backward stochastic differential equation in infinite dimension. Originality/value: In this paper, the authors present the Yosida approximation of a semi-linear backward stochastic differential equation in infinite dimension. Under suitable assumption and condition, an L²-convergence rate is established. © 2023, Hani Abidi, Rim Amami, Roger Pettersson and Chiraz Trabelsi.

关键词： Backward stochastic differential equation with jumps Gelfand triple Yosida approximation

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Pose Estimation of Spacecraft Using Dual Transformers and Efficient Bayesian Hyperparameter Optimization

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International Journal of Advanced computer science and Applications 2025年第4期16卷 629-644页

作者： Raja, N. Kannaiya Ramesh, Janjhyam Venkata Naga El-Ebiary, Yousef A. Baker Muniyandy, Elangovan Reddy, N. Konda Kumar, Vanipenta Ravi Devarasetty, Prasad School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh Bhopal466114 India Department of CSE Graphic Era Hill University Dehradun248002 India Department of CSE Graphic Era Deemed To Be University Uttarakhand Dehradun248002 India Faculty of Informatics and Computing UniSZA University Malaysia Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602 105 India Applied Science Research Center Applied Science Private University Amman Jordan Department of Engineering Mathematics K L University Greenfields Guntur Dist Andhra Pradesh Vaddeswaram522302 India Department of Mathematics Annamacharya University New Boyanapalli Rajampet India Department of Computer Science and Engineering DVR & Dr HS MIC College of Technology Andhra Pradesh Kanchikacherla India

Spacecraft pose estimation is an essential contribution to facilitating central space mission activities like autonomous navigation, rendezvous, docking, and on-orbit servicing. Nonetheless, methods like Convolutional Neural Networks (CNNs), Simultaneous Localization and Mapping (SLAM), and Particle Filtering suffer significant drawbacks when implemented in space. Such techniques tend to have high computational complexity, low domain generalization capacity for varied or unknown conditions (domain generalization problem), and accuracy loss with noise from the space environment causes such as fluctuating lighting, sensor limitations, and background interference. In order to overcome these challenges, this study suggests a new solution through the combination of a Dual-Channel Transformer Network with Bayesian Optimization methods. The innovation is at the center with the utilization of EfficientNet, augmented with squeeze-and-excitation attention modules, to extract feature-rich representations without sacrificing computational efficiency. The dual-channel architecture dissects satellite pose estimation into two dedicated streams—translational data prediction and orientation estimation via quaternion-based activation functions for rotational precision. Activation maps are transformed into transformer-compatible sequences via 1×1 convolutions, allowing successful learning in the transformer's encoder-decoder system. To maximize model performance, Bayesian Optimization with Gaussian Process Regression and the Upper Confidence Bound (UCB) acquisition function makes the optimal hyperparameter selection with fewer queries, conserving time and resources. This entire framework, used here in Python and verified with the SLAB Satellite Pose Estimation Challenge dataset, had an outstanding Mean IOU of 0.9610, reflecting higher accuracy compared to standard models. In total, this research sets a new standard for spacecraft pose estimation, by marrying the versatility of deep le

关键词： Convolutional neural networks

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Momentum Analysis and Prediction in Tennis Matches: A Data-Driven Approach Using AHP-CRITIC and GBDT Models 25

Momentum Analysis and Prediction in Tennis Matches: A Data-D...

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Proceedings of the 4th International Conference on computer, Artificial Intelligence and Control Engineering

作者： Chengyue Li Yankai Chen Department of Mathematics School of Mathematics and Computer Science Shantou University Shantou Guangdong China Mathematics and Applied Mathematics (Sino US Cooperative Actuarial Science and Risk Management) Ningbo University Ningbo Zhejiang China

ISBN: (纸本)9798400712647

We extracted four key metrics from the "Wimbledon_featured_***" dataset: scoring variance, cumulative winning serves, unforced errors, and physical *** first assigned reasonable weights to these four indicators through AHP-CRITIC, and then summed them up to obtain a momentum calculation model, which was used to calculate the momentum value of each scoring point. Momentum magnitudes of athletes and opponents were then compared, setting fluctuations as binary variables (“0” for defense and “1” for offense). Streaks were quantified using differences in scoring data. The chi-square test was then used to confirm this significant relationship between streaks and momentum fluctuations (p<0.05). The prediction model we developed used the scoring data as the input variable and the magnitude of momentum as the output variable. We first compared the accuracy of the four models, neural network, Markov chain and GBDT, and found that GBDT has 99.8% accuracy and neural network has only 69.9% accuracy, so we chose GBDT as the prediction *** model reflects the cumulative score and service opportunity are important indicators. When our model is used for the dataset “2023-wimbledon-1701”, the accuracy of the model is very high. The sensitivity analysis shows that the model has 68.6% accuracy after excluding key scoring data. A women's tennis tournament data is applied to our model with 95.4% accuracy, which shows the strong generalization ability of our model.

关键词： AHP-CRITIC

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A Novel Approach for Performance Evaluation and Effectiveness of Data-Driven Heart Disease Diagnosis

A Novel Approach for Performance Evaluation and Effectivenes...

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International Conference on Advances and Applications of Artificial Intelligence and Machine Learning, ICAAAIML 2023

作者： Islam, Md Aminul Nag, Anindya Das, Ayontika Faruque, Jobaer Shuvo, Shabbir Ahmed Nur, Abdullah Hafez Rahman, Md Habibur School of Engineering Computing and Mathematics Oxford Brookes University Oxford United Kingdom Department of Computer Science and Engineering Adamas University Kolkata India IRD Dhaka Bangladesh Offenburg University of Applied Sciences Offenburg Germany International Islamic University Chittagong Sonaichhari Bangladesh Department of Mathematics University of Chittagong Chittagong Bangladesh

ISBN: (纸本)9789819795062

Cardiovascular disease (CVD) has emerged as a prominent cause of mortality globally during the past few decades. To receive medical care for heart illness in a clinic or medical center, it is necessary to undergo costly diagnostic tests. Implementing machine learning (ML) algorithms presents a viable approach to monitoring the health of individuals with cardiac conditions, offering a potential solution to mitigate the financial burden of medical expenses. This study employed ten ML algorithms to predict heart disease, which distinguishes itself from others by incorporating three distinct cross-validation approaches and three sample techniques, enhancing our findings’ accuracy. Three different cross-validation strategies have been implemented in this study: holdout cross-validation, tenfold cross-validation, and stratified tenfold cross-validation. Each technique has been applied to every sample technique used in the study. DTC, RFC, KNN, and XGB algorithms exhibited the most favorable outcomes. Our recommended model is the RF Classifier implemented with near-miss under-sampling out of the four models considered. The model under consideration demonstrates the maximum level of accuracy, specifically 97.54%, 97.11%, and 99%, while employing the Extra Tree, Random Forest Classifier, and XGB. The ML model's performance was assessed using metrics like accuracy, error rate, precision, recall, F1-score, ROC AUC curve etc. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cardiology

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Word2Minecraft: Generating 3D Game Levels through Large Language Models

arXiv

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arXiv 2025年

作者： Huang, Shuo Nasir, Muhammad Umair James, Steven Togelius, Julian Department of Computer Science and Engineering Tandon School of Engineering New York University New York10012 United States School of Computer Science and Applied Mathematics University of the Witwatersrand South Africa

We present Word2Minecraft, a system that leverages large language models to generate playable game levels in Minecraft based on structured stories. The system transforms narrative elements—such as protagonist goals, antagonist challenges, and environmental settings—into game levels with both spatial and gameplay constraints. We introduce a flexible framework that allows for the customization of story complexity, enabling dynamic level generation. The system employs a scaling algorithm to maintain spatial consistency while adapting key game elements. We evaluate Word2Minecraft using both metric-based and human-based methods. Our results show that GPT-4Turbo outperforms GPT-4o-Mini in most areas, including story coherence and objective enjoyment, while the latter excels in aesthetic appeal. We also demonstrate the system’s ability to generate levels with high map enjoyment, offering a promising step forward in the intersection of story generation and game design. We open-source the code at https://***/JMZkk/Word2Minecraft/tree/word2mc_v0 Copyright © 2025, The Authors. All rights reserved.

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A Comparative Analysis of IoT Data Using Machine Learning Algorithms 15th

A Comparative Analysis of IoT Data Using Machine Learning Al...

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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： Nagashree, B.A. Satheesh Kumar, S. Muthukumaran, V. Munrathnam, Meram Angeline Kavitha, M. Department of Computer Science and Applications School of CSA REVA University Bangalore India Department of Computer Science School of Applied Sciences REVA University Bangalore India Department of Mathematics College of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Mathematics Rajiv Gandhi University of Knowledge Technologies R.K. Valley Kadapa District Andhra Pradesh Idupulapaya 516330 India Department of Mathematics Voorhees College Vellore 632001 India

ISBN: (纸本)9783031889912

IoT (Internet of Things) is revolutionizing the commercial environment by acquiring vast amounts of data from various sources. However, processing and analyzing this data is challenging due to the large volume of IoT devices. AI-powered IoT creates intelligent machines that mimic human intelligence, aiding decision-making with minimal human input. Both individuals and professionals’ benefit from this combination, as IoT is concerned with communicating online, but AI allows machines to learn from data and experience. This study demonstrates the analysis of IoT data using machine learning algorithms and compares the accuracy of various machine learning algorithms used. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： AI data internet IoT Machine Learning

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Determination of the optimal number of independent components based on similarity measurement testing

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Expert Systems with Applications 2025年 284卷

作者： Lu, Zeyi Yang, Mingshu E, Jianwei Liu, Chengji School of Mathematical Sciences & Center for Applied Mathematics of Guangxi Guangxi Minzu University Nanning530006 China School of Computer Science and Technology Xidian University Xi'an710071 China

Independent component analysis (ICA) has become a prominent statistical and computational method for signals separation and features extraction. ICA model can extract a great quantity of independent components (ICs) from the observed signals, some of which maybe not the pure components due to the random factors. There exist few approaches on determining the optimal number of the ICs accurately. This study proposes a novel method to determine the optimal number of ICs by running ICA separately on the sub-blocks of the observed signals, and determining the number of pure components which are remarkably similar between the blocks. Initially, a two stage sampling method is employed to the observed signals for obtaining two sub-blocks, which are representative of the observed signals. Secondly, a novel similarity measurement is proposed to identify the ICs that correspond to the pure signals between the two sub-blocks. Furthermore, we identify remarkably similar components utilizing the approach inspired by the principle of hypothesis testing, in which the Bonferroni correction is applied to mitigate the risk of Type-I errors. The experimental results on the manmade and real-word signals, including voice signals, electroencephalogram signals and hyperspectral data, verify the effectiveness and superiority of the proposed method in both small sample and sufficient sample scenarios. © 2025 Elsevier Ltd

关键词： Signal sampling

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