检索结果-内蒙古大学图书馆

secretary bird optimization algorithm based on quantum computing and multiple strategies improvement for KELM diabetes classification

引用

SCIENTIFIC REPORTS 2025年第1期15卷 1-24页

作者： Zhu, Yu Zhang, Mingxu Huang, Qinchuan Wu, Xianbo Wan, Li Huang, Ju Chengdu Sport Univ Sch Sports Med & Hlth Chengdu 610041 Peoples R China Hosp Chengdu Univ Tradit Chinese Med Chengdu 620010 Peoples R China

The classification of chronic diseases has long been a prominent research focus in the field of public health, with widespread application of machine learning algorithms. Diabetes is one of the chronic diseases with a high prevalence worldwide and is considered a disease in its own right. Given the widespread nature of this chronic condition, numerous researchers are striving to develop robust machine learning algorithms for accurate classification. This study introduces a revolutionary approach for accurately classifying diabetes, aiming to provide new methodologies. An improved secretary bird optimization algorithm (QHSBOA) is proposed in combination with Kernel Extreme Learning Machine (KELM) for a diabetes classification prediction model. First, the secretary bird optimization algorithm (SBOA) is enhanced by integrating a particle swarm optimization search mechanism, dynamic boundary adjustments based on optimal individuals, and quantum computing-based t-distribution variations. The performance of QHSBOA is validated using the CEC2017 benchmark suite. Subsequently, QHSBOA is used to optimize the kernel penalty parameter \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:C$$\end{document} and bandwidth \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:c$$\end{document} of the KELM. Comparative experiments with other classification models are conducted on diabetes datasets. The experimental results indicate that the QHSBOA-KELM classification model outperforms other comparative models in four evaluation metrics: accuracy (ACC), Matthews correlation coefficient (MCC), sensitivity, and specificity. This approa

关键词： Kernel extreme learning machine secretary bird optimization algorithm Parameter optimization Diabetes classification prediction Quantum computing

来源：评论

学校读者我要写书评

暂无评论

A swin transformer-driven framework for gesture recognition to assist hearing impaired people by integrating deep learning with secretary bird optimization algorithm

引用

AIN SHAMS ENGINEERING JOURNAL 2025年第6期16卷

作者： Assiri, Mohammed S. Selim, Mahmoud M. Prince Sattam Bin Abdulaziz Univ Coll Comp Engn & Sci Dept Comp Sci POB 16273 Al Kharj 3963 Saudi Arabia Prince Sattam Bin Abdulaziz Univ Coll Sci & Humanities Dept Math POB 16273 Al Kharj 3963 Saudi Arabia King Salman Ctr Disabil Res Riyadh 11614 Saudi Arabia Res Fellow Programme INTI Int Univ Persiaran Perdana BBN Putra Nilai 71800 Negeri Sembilan Malaysia

Hand gestures (HG) are the key communication technique for hearing-impaired people, which poses a problem for millions of individuals globally after communicating with those who don't have hearing impairments. The importance of technology in improving accessibility and thus raising the standard of living for persons with hearing impairments is globally acclaimed. Machine learning (ML) is a section of artificial intelligence (AI) that concentrates on developing a method that depends on data. The major problem of HG recognition is that the machine does not identify the human language straightforwardly, and human-machine interaction is required of media for communication, which is determined by machines and, in addition to humans, to assist hearingimpaired individuals and ageing people. Thus, HG recognition as a communication media is necessary to provide instructions to the computer. This paper proposes the Swin Transformer-Driven Framework for Gesture Recognition by Integrating Deep Learning with the secretary bird optimization (STFGR-IDLSBO) methodology. The main intention of the STFGR-IDLSBO methodology is to develop an efficient and robust system for gesture recognition to assist hearing-impaired persons. Initially, the proposed STFGR-IDLSBO method utilizes adaptive bilateral filtering (ABF) in the image pre-processing stage to reduce noise while preserving the edges of the gestures in the captured images. Furthermore, the swin transformer (ST) is a feature extractor that effectively captures multiscale representations and spatial hierarchies from gesture images. The hybrid model integrates the convolutional neural network and bi-directional long short-term memory (CNN-BiLSTM) technique, which is employed for the gesture classification process. Finally, the secretary bird optimizer algorithm (SBOA) is utilized for the optimum hyperparameter tuning of the CNN-BiLSTM classifier. To ensure the enhanced performance of the STFGR-IDLSBO methodology, a wide range simulat

关键词： Swin Transformer Gesture Recognition Hearing-Impaired Person Deep Learning secretary bird optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Multi-Strategy Improved secretary bird optimization algorithm

引用

Journal of Computer and Communications 2025年第1期13卷 90-107页

作者： Fengkai Wang Bo Wang College of Science Shenyang University of Technology Shenyang China

This paper addresses the shortcomings of the Sparrow and Eagle optimization algorithm (SBOA) in terms of convergence accuracy, convergence speed, and susceptibility to local optima. To this end, an improved Sparrow and Eagle optimization algorithm (HS-SBOA) is proposed. Initially, the algorithm employs Iterative Mapping to generate an initial sparrow and eagle population, enhancing the diversity of the population during the global search phase. Subsequently, an adaptive weighting strategy is introduced during the exploration phase of the algorithm to achieve a balance between exploration and exploitation. Finally, to avoid the algorithm falling into local optima, a Cauchy mutation operation is applied to the current best individual. To validate the performance of the HS-SBOA algorithm, it was applied to the CEC2021 benchmark function set and three practical engineering problems, and compared with other optimization algorithms such as the Grey Wolf optimization (GWO), Particle Swarm optimization (PSO), and Whale optimization algorithm (WOA) to test the effectiveness of the improved algorithm. The simulation experimental results show that the HS-SBOA algorithm demonstrates significant advantages in terms of convergence speed and accuracy, thereby validating the effectiveness of its improved strategies.

关键词： secretary bird optimization algorithm Iterative Mapping Adaptive Weight Strategy Cauchy Variation Convergence Speed

来源：评论

学校读者我要写书评

暂无评论

secretary bird optimization algorithm: a new metaheuristic for solving global optimization problems

引用

ARTIFICIAL INTELLIGENCE REVIEW 2024年第5期57卷 123-123页

作者： Fu, Youfa Liu, Dan Chen, Jiadui He, Ling Guizhou Univ Key Lab Adv Mfg Technol Minist Educ Guiyang 550025 Guizhou Peoples R China

This study introduces a novel population-based metaheuristic algorithm called secretary bird optimization algorithm (SBOA), inspired by the survival behavior of secretary birds in their natural environment. Survival for secretary birds involves continuous hunting for prey and evading pursuit from predators. This information is crucial for proposing a new metaheuristic algorithm that utilizes the survival abilities of secretary birds to address real-world optimization problems. The algorithm's exploration phase simulates secretary birds hunting snakes, while the exploitation phase models their escape from predators. During this phase, secretary birds observe the environment and choose the most suitable way to reach a secure refuge. These two phases are iteratively repeated, subject to termination criteria, to find the optimal solution to the optimization problem. To validate the performance of SBOA, experiments were conducted to assess convergence speed, convergence behavior, and other relevant aspects. Furthermore, we compared SBOA with 15 advanced algorithms using the CEC-2017 and CEC-2022 benchmark suites. All test results consistently demonstrated the outstanding performance of SBOA in terms of solution quality, convergence speed, and stability. Lastly, SBOA was employed to tackle 12 constrained engineering design problems and perform three-dimensional path planning for Unmanned Aerial Vehicles. The results demonstrate that, compared to contrasted optimizers, the proposed SBOA can find better solutions at a faster pace, showcasing its significant potential in addressing real-world optimization problems.

关键词： secretary bird optimization algorithm Nature-inspired optimization Heuristic algorithm Exploration and exploitation Engineering design problems

来源：评论

学校读者我要写书评

暂无评论

A multi-strategy enhanced reptile search algorithm for global optimization and engineering optimization design problems

引用

CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2025年第2期28卷 1-41页

作者： Zhou, Liping Liu, Xu Tian, Ruiqing Wang, Wuqi Jin, Guowei Zhejiang Coll Secur Technol Coll Emergency Technol 2555 Ouhai Ave Wenzhou 325016 Zhejiang Peoples R China Shanghai Univ Finance & Econ 777 Guoding Rd Shanghai 200433 Peoples R China Zhejiang Coll Secur Technol Coll New Energy Equipment 2555 Ouhai Ave Wenzhou 325016 Zhejiang Peoples R China

The reptile search algorithm (RSA) is a well-known swarm-based metaheuristic algorithm inspired by the hunting behaviors of crocodiles. To overcome the problems of falling into local optima and premature convergence, this paper proposes a multi-strategy enhanced reptile search algorithm (MRSA), which integrates a novel dynamic evolutionary sense, prey approaching strategy and Cauchy mutation strategy. The prey approaching strategy comes from the secretary bird optimization algorithm and is applied to strengthen the exploration capability of RSA. A comparative performance analysis is conducted using the CEC2005, CEC2017 and CEC2022 benchmark functions. And fifteen algorithms are employed for the performance comparison. The results of numerical, convergence curves, boxplots, Wilcoxon rank-sum test and Friedman ranking confirm the efficacy and stability of proposed MRSA, indicating its superior performance compared to other algorithms. Moreover, seven practical engineering design tasks are used to test the performance of MRSA in real-world optimization problems. The results also show that MRSA can efficiently obtain better optimal solution compared to existing methods.

关键词： Reptile search algorithm Global optimization secretary bird optimization algorithm Cauchy mutation Engineering design problems

来源：评论

学校读者我要写书评

暂无评论

Efficient algorithms for optimal path planning of unmanned aerial vehicles in complex three-dimensional environments

引用

KNOWLEDGE-BASED SYSTEMS 2025年 316卷

作者： Abdel-Basset, Mohamed Mohamed, Reda Sallam, Karam M. Elsayed, Saber Zagazig Univ Fac Comp & Informat Zagazig Egypt Univ Sharjah Dept Comp Sci Sharjah U Arab Emirates Univ New South Wales Sch Syst & Comp Canberra Australia

This paper presents three spherical vector-based optimization techniques, namely the spherical vector-based spider wasp optimizer (SSWO), the spherical vector-based secretary bird optimization algorithm (SSBOA), and the spherical vector-based improved spider wasp optimizer (SISWO), to properly plan UAV trajectories in 3D complicated environments with various threats. SISWO is based on combining some SBOA stages with SWO to benefit from their strengths in dealing with local optima and accelerating convergence speed. Six scenarios generated in Christmas Island, Australia, are used to assess the effectiveness of the proposed algorithms in optimizing four different objectives, including path optimality, threat cost, flight height, and smooth cost. In addition, they are compared to seven recent and well-established algorithms according to several performance metrics. According to the experimental results, both SISWO and SSBOA could outperform all other algorithms in most scenarios, demonstrating that they are more effective at precisely planning the UAV flight path in complex 3-D environments. Quantitatively, in terms of Friedman's mean rank, SISWO could achieve an average rank of 2.04 for all scenarios, followed by SSBOA with 2.27.

关键词： Spider wasp optimizer secretary bird optimization algorithm Spherical coordinate system Path planning Unmanned aerial vehicle

来源：评论

学校读者我要写书评

暂无评论

optimization of Supply and Demand Balancing in Park-Level Energy Systems Considering Comprehensive Utilization of Hydrogen under P2G-CCS Coupling

引用

Energy Engineering 2025年第5期122卷 1919-1948页

作者： Zhiyuan Zhang Yongjun Wu Xiqin Li Minghui Song Guangwu Zhang Ziren Wang Wei Li State Grid Beijing Electric Power Company Beijing102300China State Grid Beijing Mentougou Power Supply Company Beijing102300China School of Economics and Management North China Electric Power UniversityBaoding071003China

The park-level integrated energy system(PIES)is essential for achieving carbon neutrality by managing multi-energy supply and demand while enhancing renewable energy ***,current carbon trading mechanisms lack sufficient incentives for emission reductions,and traditional optimization algorithms often face challenges with convergence and local optima in complex PIES *** address these issues,this paper introduces a low-carbon dispatch strategy that combines a reward-penalty tiered carbon trading model with P2G-CCS integration,hydrogen utilization,and the secretary bird optimization algorithm(SBOA).Key innovations include:(1)A dynamic reward-penalty carbon trading mechanism with coefficients(μ=0.2,λ=0.15),which reduces carbon trading costs by 47.2%(from$694.06 to$366.32)compared to traditional tiered models,incentivizing voluntary emission reductions.(2)The integration of P2G-CCS coupling,which lowers natural gas consumption by 41.9%(from$4117.20 to$2389.23)and enhances CO_(2) recycling efficiency,addressing the limitations of standalone P2G or CCS technologies.(3)TheSBOA algorithm,which outperforms traditionalmethods(e.g.,PSO,GWO)in convergence speed and global search capability,avoiding local optima and achieving 24.39%faster convergence on CEC2005 benchmark functions.(4)A four-energy PIES framework incorporating electricity,heat,gas,and hydrogen,where hydrogen fuel cells and CHP systems improve demand response flexibility,reducing gas-related emissions by 42.1%and generating$13.14 in demand response *** studies across five scenarios demonstrate the strategy’s effectiveness:total operational costs decrease by 14.7%(from$7354.64 to$6272.59),carbon emissions drop by 49.9%(from 5294.94 to 2653.39kg),andrenewable energyutilizationincreases by24.39%(from4.82%to8.17%).These results affirmthemodel’s ability to reconcile economic and environmental goals,providing a scalable approach for low-carbon transitions in industrial parks.

关键词： Park-level integrated energy system P2G-CCS coupling comprehensive utilization of hydrogen rewardpenalty tiered carbon trading mechanism secretary bird optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Gray relational analysis and SBOA-BP for predicting settlement intervals of high-speed railway subgrade

引用

Railway Sciences 2025年第2期4卷 199-212页

作者： Quanpeng He Shaoyuan Li School of Automatization and Electric Engineering Lanzhou Jiaotong UniversityLanzhouChina School of Electronic Information and Electrical Engineering Shanghai Jiao Tong UniversityShanghaiChina

Purpose–The deformation of the roadbed is easily influenced by the external environment to improve the accuracy of high-speed railway subgrade settlement ***/methodology/approach–A high-speed railway subgrade settlement interval prediction method using the secretary bird optimization(SBOA)algorithm to optimize the BP neural network under the premise of gray relational analysis is ***–Using the SBOA algorithm to optimize the BP neural network,the optimal weights and thresholds are obtained,and the best parameter prediction model is *** data were collected from the sensors deployed through the subgrade settlement monitoring system,and the gray relational analysis is used to verify that all four influencing factors had a great correlation to the subgrade settlement,and the collected data are verified using the ***/value–The experimental results show that the SBOA-BP model has higher prediction accuracy than the BP model,and the SBOA-BP model has a wider range of prediction intervals for a given confidence level,which can provide higher guiding value for practical engineering applications.

关键词： Gray relational analysis secretary bird optimization algorithm Backpropagation neural network Subgrade settlement Interval prediction

来源：评论

学校读者我要写书评

暂无评论

Predicting the Performance of Internet of Things Communication Systems Using Finite Element Interpolated Neural Network in Smart Cities

引用

International Journal of Communication Systems 2025年第10期38卷

作者： S. Sathishbabu R. Dhanalakshmi R. Bharathiraja K. Thirunavukkarasu Department of Electronics and Communication Engineering Thanthai Periyar Government Institute of Technology Vellore India Department of Computer Science and Engineering Thanthai Periyar Government Institute of Technology Vellore India

The Internet of Things (IoT) is an essential part of Information and Communications Technology (ICT) for sustainable smart cities because of its capacity to assist sustainability across multiple disciplines. To attain the required quality of IoT communication systems and to enable sustainable progress in smart cities regarding IoT communication systems, it is necessary to avoid fault through constant and dynamic application of network behavior. In this research work, predicting the performance of IoT communication systems using Finite Element Interpolated Neural Network in smart cities (IoT-CS-FEINN-SC) is proposed. Here, the input data is gathered from IoT devices that include various kinds of sensors like visibility, humidity, temperature, pressure, and wind speed. Signed Cumulative Distribution Transform (SCDT) is employed to extract Received Signal Strength (RSS) features as minimum, maximum, and mean. Afterwards, the extracted features are fed to FEINN for predicting the IoT communication system performance in smart cities. The secretary bird optimization algorithm (SBOA) is proposed to enhance the weight parameter of FEINN method that predicts the performance of IoT communication systems precisely. The IoT-CS-FEINN-SC technique achieves 20.36%, 28.42%, and 15.27% better accuracy analyzed with existing techniques: Cloud-assisted IoT intelligent transportation scheme and traffic control scheme in smart city (IoT-TCS-SC), Optimized RNN-dependent performance prediction of IoT and WSN-oriented smart city application utilizing improved honey badger algorithm (RNN-IoT-WSN), and Smart cities: a role of IoT and ML in realizing data-centric smart environs (IoT-ANN-DSE), respectively.

关键词： Finite Element Interpolated Neural Network Internet of Things secretary bird optimization algorithm Signed Cumulative Distribution Transform Smart city communication systems

来源：评论

学校读者我要写书评

暂无评论

Research on Positioning Error Compensation of Rock Drilling Manipulator Based on ISBOA-BP Neural Network

引用

APPLIED SCIENCES-BASEL 2024年第18期14卷 8480页

作者： Xu, Qiaoyu Ju, Wenhao Lin, Yansong Zhang, Tianle Henan Univ Sci & Technol Coll Mech & Elect Engn Luoyang 471000 Peoples R China

In order to solve the problem of the low end positioning accuracy of large hydraulic rock drilling robotic arms due to machining error and the working environment, this paper proposes an end positioning error compensation method based on an Improved secretary bird optimization algorithm (ISBOA) optimized Back Propagation (BP) neural network. Firstly, the good point set strategy is used to initialize the secretary bird population position to make the initial population distribution more uniform and accelerate the convergence speed of the algorithm. Then, the ISBOA is used to optimize the initial weights and biases of the BP neural network, which effectively overcomes the defect of the BP neural network falling into a local optimum. Finally, by establishing the mapping relationship between the joint value of the robot arm and the end positioning error, the error compensation is realized to improve the positioning accuracy of the rock drilling robot arm. The experimental results show that the average positioning error of the rock drilling robotic arm is reduced from 187.972 mm to 28.317 mm, and the positioning accuracy is improved by 84.94%, which meets the engineering requirements.

关键词： rock drilling robotic arm secretary bird optimization algorithm good point set BP neural network error compensation

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：