检索结果-内蒙古大学图书馆

A novel local contrast fusion-based fuzzy model for color image multilevel thresholding using grasshopper optimization

引用

APPLIED SOFT COMPUTING 2019年 81卷 105515-000页

作者： Bhandari, Ashish Kumar Rahul, Kusuma Natl Inst Technol Patna Dept Elect & Commun Engn Patna 800005 Bihar India

The segmentation process is considered the significant step of an image processing system due to its extreme inspiration on the subsequent image analysis. Out of various approaches, thresholding is one of the most popular schemes for image segmentation. In segmentation, image pixels are arranged in various regions based on their intensity levels. In this paper, a straightforward and efficient fusion-based fuzzy model for multilevel color image segmentation using grasshopper optimization algorithm (GOA) has been proposed. Thresholding based segmentation lacks accuracy in segmenting the ambiguous images due to their complex characteristics, uncertainties and inherent fuzziness. However, the fuzzy entropy resolves these problems, but it is unable for segmenting at higher levels and also the complexity level for selecting suitable thresholds is high. The selection of metaheuristic GOA reduces this problem by selecting optimal threshold values. Therefore, to increase the quality of the segmented image, a simple and effective multilevel thresholding method is exploited by using the concept of fusion which is based on the local contrast. Experimental outputs demonstrate that fusion-based multilevel thresholding is better than most specific segmentation methods and can be validated by comparing the different numerical parameters. Experiments on standard daily-life color and satellite images are conducted to prove the effectiveness of the proposed scheme. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Image fusion Local contrast Fuzzy entropy grasshopper optimization algorithm Multi-level image segmentation

来源：评论

学校读者我要写书评

暂无评论

Load forecasting based on grasshopper optimization and a multilayer feed-forward neural network using regressive approach

引用

ENERGY 2020年第0期196卷 117087-000页

作者： Talaat, M. Farahat, M. A. Mansour, Noura Hatata, A. Y. Zagazig Univ Fac Engn Elect Power & Machines PO 44519 Zagazig Egypt Mansoura Univ Fac Engn Elect Engn Dept Mansoura Egypt Shaqra Univ Coll Engn Elect Engn Dept Dawadmi Ar Riyadh Saudi Arabia

This paper introduces a proposed model for mid-term to short-term load forecasting (MTLF;STLF) that can be used to forecast loads at different hours and on different days of each month. The combined MT-STLF model was investigated to aid in power generation and electricity purchase planning. A hybrid model of a multilayer feed-forward neural network (MFFNN) and the grasshopper optimization algorithm (GOA) was introduced to obtain high-accuracy results for load forecasting using the combined MT-STLF model. The MFFNN is prepared by processing the input layer and output layer and finally selecting a suitable number of hidden layers. The main steps in developing the model from the MFFNN include entering the data into the network, training the model and finally implementing the prediction process. The accuracy of the model obtained before using the GOA was lower than that after applying the GOA. Weather factors such as the temperature were used as inputs to the MFFNN during MT-STLF modelling to ensure high accuracy. In the proposed model, the temperature had a clear effect on the forecasted load. Additionally, there was a difference between the maximum and minimum loads in winter and summer months. A regressive model was introduced to determine the relations between the dependent variable (the load) and the independent variables that affect the load, such as the temperature. The regressive model used in the paper highlights the effect of the temperature on the hourly load. The accuracy of the hybrid model is satisfied with deviation error varied between -0.06 and 0.06. Moreover, the performance of the proposed forecasting model has been assessed by three indices;Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) then, compared with other forecasting models considering other optimization algorithms. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Mid-term load forecasting Short-term load forecasting Power generation Multilayer feed-forward neural network grasshopper optimization algorithm Regressive model

来源：评论

学校读者我要写书评

暂无评论

Enhanced Gaussian bare-bones grasshopper optimization: Mitigating the performance concerns for feature selection

引用

EXPERT SYSTEMS WITH APPLICATIONS 2023年 212卷

作者： Xu, Zhangze Heidari, Ali Asghar Kuang, Fangjun Khalil, Ashraf Mafarja, Majdi Zhang, Siyang Chen, Huiling Pan, Zhifang Wenzhou Univ Coll Comp Sci & Artificial Intelligence Wenzhou 325035 Peoples R China Wenzhou Business Coll Sch Informat Engn Wenzhou 325035 Peoples R China Zayed Univ Coll Technol Innovat Abu Dhabi U Arab Emirates Birzeit Univ Dept Comp Sci Birzeit 72439 Palestine Wenzhou Med Univ Affiliated Hosp 1 Wenzhou 325000 Peoples R China King Abdulaziz Univ Fac Comp & Informat Technol Jeddah 21589 Saudi Arabia

As a recent meta-heuristic algorithm, the uniqueness of the grasshopper optimization algorithm (GOA) is to imitate the biological features of grasshoppers for single-objective optimization cases. Despite its advanced optimization ability, the basic GOA has a set of shortcomings that pose challenges in numerous practical scenarios. The GOA core limit is its early convergence to the local optimum and suffering from slow convergence. To mitigate these concerns, this study adopts the elite opposition-based learning and bare-bones Gaussian strategy to extend GOA's global and local search capabilities and effectively balance the exploration and exploitation inclinations. Specifically, elite opposition-based learning can help find better solutions at the early stage of exploration, while the bare-bones Gaussian strategy has an excellent ability to update the search agents. To evaluate the robustness of the proposed Enhanced GOA (EGOA) based on global constrained and unconstrained optimization problems, a straight comparison was made between the proposed EGOA and other meta-heuristics on 30 IEEE CEC2017 benchmark tasks. Moreover, we applied it experimentally to structural design problems and its binary version to the feature selection cases. Findings demonstrate the effectiveness of EGOA and its binary version as an acceptable tool for optimization and feature selection purposes.

关键词： grasshopper optimization algorithm Gaussian bare -bones strategy Elite opposition -based learning Structural design problems Feature selection

来源：评论

学校读者我要写书评

暂无评论

Performance optimization of support vector machine with oppositional grasshopper optimization for acute appendicitis diagnosis

引用

COMPUTERS IN BIOLOGY AND MEDICINE 2022年 143卷 105206-105206页

作者： Xia, Jianfu Wang, Zhifei Yang, Daqing Li, Rizeng Liang, Guoxi Chen, Huiling Heidari, Ali Asghar Turabieh, Hamza Mafarja, Majdi Pan, Zhifang Shanghai Univ Dept Gen Surg Wenzhou Cent Hosp Affiliated Hosp 2 Wenzhou 325000 Zhejiang Peoples R China Zhejiang Prov Peoples Hosp Dept Hepatobiliary Pancreat & Minimally Invas Sur Hangzhou 310014 Peoples R China Wenzhou Polytech Dept Informat Technol Wenzhou 325035 Peoples R China Wenzhou Univ Dept Comp Sci & Artificial Intelligence Wenzhou 325035 Peoples R China Taif Univ Coll Comp & Informat Technol Dept Informat Technol POB 11099 Taif 21944 Saudi Arabia Birzeit Univ Dept Comp Sci Birzeit 72439 Palestine Wenzhou Med Univ Affiliated Hosp 1 Wenzhou 325000 Peoples R China

Preoperative differentiation of complicated and uncomplicated appendicitis is challenging. The research goal was to construct a new intelligent diagnostic rule that is accurate, fast, noninvasive, and cost-effective, distinguishing between complicated and uncomplicated appendicitis. Overall, 298 patients with acute appendicitis from the Wenzhou Central Hospital were recruited, and information on their demographic characteristics, clinical findings, and laboratory data was retrospectively reviewed and applied in this study. First, the most significant variables, including C-reactive protein (CRP), heart rate, body temperature, and neutrophils discriminating complicated from uncomplicated appendicitis, were identified using random forest analysis. Second, an improved grasshopper optimization algorithm-based support vector machine was used to construct the diagnostic model to discriminate complicated appendicitis (CAP) from uncomplicated appendicitis (UAP). The resultant optimal model can produce an average of 83.56% accuracy, 81.71% sensitivity, 85.33% specificity, and 0.6732 Matthews correlation coefficients. Based on existing routinely available markers, the proposed intelligent diagnosis model is highly reliable. Thus, the model can potentially be used to assist doctors in making correct clinical decisions.

关键词： Support vector machine Feature selection grasshopper optimization algorithm Appendicitis diagnosis Opposition-based learning

来源：评论

学校读者我要写书评

暂无评论

A novel application of integrated grasshopper optimization heuristics for attenuation of noise interferences

引用

AIN SHAMS ENGINEERING JOURNAL 2022年第2期13卷 101536-101536页

作者： Khan, Wasim Ullah Raja, Muhammad Asif Zahoor He, Yigang Chaudhary, Naveed Ishtiaq Wuhan Univ Sch Elect Engn & Automat Wuhan 430072 Peoples R China Natl Yunlin Univ Sci & Technol Future Technol Res Ctr 123 Univ RdSect 3 Touliu 64002 Yunlin Taiwan COMSATS Univ Islamabad Dept Elect & Comp Engn Attock Campus Attock 43600 Pakistan Int Islamic Univ Dept Elect Engn Islamabad Pakistan

Novel application of integrated swarming intelligence computing paradigm is exploited for reliable treatment of nonlinear active noise control (ANC) systems using global search capacity of grasshopper optimization algorithm (GOA) combined with local search efficacy of sequential quadratic programming (SQP), i.e., GOA-SQP. The designed optimization mechanism GOA-SQP is applied to minimize the cost function of ANC controller incorporating the nonlinear Volterra filtering having linear/nonlinear primary/secondary paths in case of different noise interferences of sinusoidal, random, and complex random type signals. The comparison of the results through statistical observations in terms of accuracy, convergence and complexity indices reveals that the GOA-SQP based ANC controllers are operative, resilient and viable.(c) 2021 The Authors. Production and hosting by Elsevier B.V. on behalf of Ain Shams University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Active noise control grasshopper optimization algorithm Sequential quadratic programming System identification Volterra filtering

来源：评论

学校读者我要写书评

暂无评论

UAV-Borne LiDAR Crop Point Cloud Enhancement Using grasshopper optimization and Point Cloud Up-Sampling Network

引用

REMOTE SENSING 2020年第19期12卷 3208-3208页

作者： Chen, Jian Zhang, Zichao Zhang, Kai Wang, Shubo Han, Yu China Agr Univ Coll Engn Beijing 100083 Peoples R China China Agr Univ Coll Water Resources & Civil Engn Beijing 100083 Peoples R China

Because of low accuracy and density of crop point clouds obtained by the Unmanned Aerial Vehicle (UAV)-borne Light Detection and Ranging (LiDAR) scanning system of UAV, an integrated navigation and positioning optimization method based on the grasshopper optimization algorithm (GOA) and a point cloud density enhancement method were proposed. Firstly, a global positioning system (GPS)/inertial navigation system (INS) integrated navigation and positioning information fusion method based on a Kalman filter was constructed. Then, the GOA was employed to find the optimal solution by iterating the system noise variance matrix Q and measurement noise variance matrix R of Kalman filter. By feeding the optimal solution into the Kalman filter, the error variances of longitude were reduced to 0.00046 from 0.0091, and the error variances of latitude were reduced to 0.00034 from 0.0047. Based on the integrated navigation, an UAV-borne LiDAR scanning system was built for obtaining the crop point. During offline processing, the crop point cloud was filtered and transformed into WGS-84, the density clustering algorithm improved by the particle swarm optimization (PSO) algorithm was employed to the clustering segment. After the clustering segment, the pre-trained Point Cloud Up-Sampling Network (PU-net) was used for density enhancement of point cloud data and to carry out three-dimensional reconstruction. The features of the crop point cloud were kept under the processing of reconstruction model;meanwhile, the density of the crop point cloud was quadrupled.

关键词： UAV-borne LiDAR scanning system grasshopper optimization algorithm GPS INS integrated navigation point cloud up-sampling network (PU-net) clustering segmentation 3-dimensional reconstruction

来源：评论

学校读者我要写书评

暂无评论

A novel meta-matching approach for ontology alignment using grasshopper optimization

引用

KNOWLEDGE-BASED SYSTEMS 2020年 201卷 106050-106050页

作者： Lv, Zhaoming Peng, Rong Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China

Ontology alignment is a fundamental task to support information sharing and reuse in heterogeneous information systems. Optimizing the combination of matchers by evolutionary algorithms to align ontology is an effective method. However, such methods have two significant shortcomings: weights need to be set manually to combine matchers, and a reference alignment is required during the optimization process. In this paper, a meta-matching approach GSOOM for automatically configuring weights and threshold using grasshopper optimization algorithm (GOA) has been proposed. In this approach, the ontology alignment problem is modeled as optimizing individual fitness of GOA. A fitness function is proposed, which includes two goals: maximizing the number of matching and the similarity score. Since it does not require an expert to provide a reference alignment, it is more suitable for real-world scenarios. To demonstrate the advantages of the approach, we conduct exhaustive experiments tasks on several standard datasets and compare its performance to other state-of-the-art methods. The experimental results illustrate that our approach is more efficiently and is significantly superior to other metaheuristic-based methods. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Ontology matching grasshopper optimization algorithm Ontology alignment evaluation initiative

来源：评论

学校读者我要写书评

暂无评论

Performance optimization of Support Vector Machine with Adversarial grasshopper optimization for Heart Disease Diagnosis and Feature Selection

引用

INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2024年第8期15卷 117-128页

作者： Tang, Nan Wang, Lele Li, Kangming Liu, Zhen Dai, Yanan Hao, Ji Zhang, Qingdui Sun, Huamei Qi, Chunmei Xuzhou Med Univ Affiliated Hosp 2 Dept Cardiol Xuzhou 221000 Jiangsu Peoples R China

The World Health Organization reports that cardiac disorders result in approximately 1.02 million deaths. Over the last years, heart disorders, also known as cardiovascular diseases, have significantly influenced the medical sector due to their immense global impact and high level of danger. Unfortunately, accurate prognosis of heart problems or CD, as well as continuous monitoring of the patient for 24 hours, is unattainable due to the extensive expertise and time required. The management and identification of cardiac disease pose significant challenges, particularly in impoverished or developing nations. Moreover, the absence of adequate medical attention or prompt disease management can result in the individual's demise. This study presents a novel optimization technique for diagnosing cardiac illness utilizing Support Vector Machine (SVM) and grasshopper optimization algorithm (GOA). The primary objective of this approach is to identify the most impactful characteristics and enhance the efficiency of the SVM model. The GOA algorithm, which draws inspiration from the natural movements of grasshoppers, enhances the search for features in the data and effectively reduces the feature set while maintaining prediction accuracy. The initial stage involved pre-processing the ECG data, followed by its classification using several algorithms such as SVM and GOA. The findings demonstrated that the suggested approach has markedly enhanced the effectiveness and precision of heart disease diagnosis through meticulous feature selection and model optimization. This approach can serve as an efficient tool for early detection of heart disease by simplifying the process and enhancing its speed.

关键词： Heart disease predictions Support Vector Machine grasshopper optimization algorithm feature selection

来源：评论

学校读者我要写书评

暂无评论

Power capacity enhancement of hydropower plant through the penetration of solar and wind energy

引用

International Journal of Electrical Power & Energy Systems 2025年 169卷

作者： Ahmad Shah Irshad Ahmad Shah Amin Amir Mohammad Ilham M.H. Elkholy Said Elias Tomonobu Senjyu Department of Electrical and Electronics Engineering Faculty of Engineering University of the Ryukyus Okinawa 903-0213 Japan Department of Energy Engineering Engineering Faculty Kandahar University Kandahar 3801 Afghanistan Da Afghanistan Brashna Sharkat Kandahar 3801 Afghanistan Electrical Power and Machines Faculty of Engineering Zagazig University P.O. 44519 Zagazig Egypt Institute for Risk and Reliability Leibniz University Hannover Germany

Integrating renewable energy sources is crucial for enhancing the power capacity and reliability of existing hydropower plants. This study explores the potential of augmenting hydropower capacity by incorporating solar and wind energy. By leveraging the complementary nature of these renewable sources, the study aims to optimize energy production and reduce dependence on hydro resources during low water availability. A comprehensive analysis, including simulation models and field data, demonstrates the effective harnessing and synchronization of solar and wind energy with hydropower generation. A multi-objective genetic algorithm was employed to optimize this integration, addressing objectives such as maximizing power output, improving energy efficiency, and minimizing environmental impact. This algorithm facilitated the identification of effective strategies for enhancing the performance and capacity of the hybrid renewable energy system. Three key objectives were achieved: reducing the Loss of Power Supply Probability (LPSP) to 0.75 %, calculating the net present cost (NPC) at $4.37 million, and determining the cost of energy (COE) at $0.086 per kilowatt-hour (kWh). These results demonstrate the economic viability and reliability of the integrated system. The power generation capacity of the plant was enhanced by 25 % with integration of PV, and wind energy system. The findings reveal significant improvements in power capacity and grid stability, highlighting the feasibility and benefits of a hybrid renewable energy system. This approach enhances energy security, reduces greenhouse gas emissions by 248,922.4 tons per year, and promotes clean energy use. The study offers valuable insights and guidelines for policymakers and energy planners seeking to implement similar strategies.

关键词： Hybrid Renewable Energy System Solar-Wind-Hydro Integration Energy Cost optimization Net Present Cost Analysis Power Supply Reliability Environmental Sustainability CO 2 Carbon dioxide COE Cost of energy CRF Capital recovery factor DG Diesel generator GA Genetic algorithm GHG Greenhouse gas GHI Global horizontal irradiance GOA grasshopper optimization algorithm HRES Hybrid renewable energy systems IRENA International Renewable Energy Agency LCOE Levelized cost of energy LPSP Loss of power supply probability MILP Mixed-integer linear programming MOGA Multi-objective genetic algorithm MPSO Modified particle swarm optimization NASA National Aeronautics and Space Administration NOCT Nominal operating cell temperature NPC Net present cost O&M Operation and maintenance PHS Pumped hydro storage PV Photovoltaic RE Renewable energy RES Renewable energy sources STC Standard test condition UNFCCC United Nations Framework Convention on Climate Change WT Wind turbine

来源：评论

学校读者我要写书评

暂无评论

A Novel Community Detection algorithm Based on Paring, Splitting and Aggregating in Internet of Things

引用

IEEE ACCESS 2020年 8卷 123938-123951页

作者： Li, Yizhe Xia, Hui Zhang, Rui Hu, Benxu Cheng, Xiangguo Qingdao Univ Coll Comp Sci & Technol Qingdao 266000 Peoples R China Ocean Univ China Coll Informat Sci & Engn Qingdao 266000 Peoples R China

The explosive growth of Internet of Things (i.e., IoT) terminal equipment makes its topology more complex, which leads to the increasing cost of network research. Recently, the implicit community structure is widely used to improve the efficiency of research. However, most of the non-overlapping community detection algorithms have some weakness, such as the large number of community detected and the obvious scale gap between communities. To address these abovementioned problems, we design a novel non-overlapping community detection algorithm, named as Pairing, Splitting and Aggregating algorithm (i.e., PSA). Firstly, in order to improve the accuracy of community division, a new node similarity index is designed to transform the network into a large number of similar node pairs. Secondly, based on the connected branches composed of similar node pairs, the network is further divided into several similar node sets. Thirdly, to balance the scale gap of different communities, the grasshopper optimization algorithm, (i.e., GOA) is introduced to combine the local attribute (i.e., conductance) and global attribute (i.e., modularity) together to aggregate similar node sets into potential (or final) communities. Finally, the experimental results show that PSA not only controls the difference among communities well, but also outperforms the other four popular algorithms in terms of two metrics. Moreover, we propose a community-based resource discovery method (or scheme), named as Community-assisted Short-distance-query Resource Discovery algorithm (i.e., CSRD) to further verify the efficiency of PSA. The results show that the resource discovery efficiency of CSRD using PSA is better compared with other algorithms.

关键词： Community detection grasshopper optimization algorithm Internet of Things non-overlapping community resources discovery

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

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

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

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

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：