检索结果-内蒙古大学图书馆

Fully Elman Neural Network: A Novel Deep Recurrent Neural Network Optimized by an Improved harris hawks algorithm for Classification of Pulmonary Arterial Wedge Pressure

引用

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2022年第5期69卷 1733-1744页

作者： Fetanat, Masoud Stevens, Michael Jain, Pankaj Hayward, Christopher Meijering, Erik Lovell, Nigel H. UNSW Sch Comp Sci & Engn Grad Sch Biomed Engn Sydney NSW 2052 Australia UNSW Grad Sch Biomed Engn Sydney NSW Australia St Vincents Hosp Cardiol Dept Fitzroy Vic Australia Victor Chang Cardiac Res Inst Darlinghurst NSW Australia UNSW Sch Med Sydney NSW Australia

Heart failure (HF) is one of the most prevalent life-threatening cardiovascular diseases in which 6.5 million people are suffering in the USA and more than 23 million worldwide. Mechanical circulatory support of HF patients can be achieved by implanting a left ventricular assist device (LVAD) into HF patients as a bridge to transplant, recovery or destination therapy and can be controlled by measurement of normal and abnormal pulmonary arterial wedge pressure (PAWP). While there are no commercial long-term implantable pressure sensors to measure PAWP, real-time non-invasive estimation of abnormal and normal PAWP becomes vital. In this work, first an improved harris hawks optimizer algorithm called HHO+ is presented and tested on 24 unimodal and multimodal benchmark functions. Second, a novel fully Elman neural network (FENN) is proposed to improve the classification performance. Finally, four novel 18-layer deep learning methods of convolutional neural networks (CNNs) with multi-layer perceptron (CNN-MLP), CNN with Elman neural networks (CNN-ENN), CNN with fully Elman neural networks (CNN-FENN), and CNN with fully Elman neural networks optimized by HHO+ algorithm (CNN-FENN-HHO+) for classification of abnormal and normal PAWP using estimated HVAD pump flow were developed and compared. The estimated pump flow was derived by a non-invasive method embedded into the commercial HVAD controller. The proposed methods are evaluated on an imbalanced clinical dataset using 5-fold cross-validation. The proposed CNN-FENN-HHO+ method outperforms the proposed CNN-MLP, CNN-ENN and CNN-FENN methods and improved the classification performance metrics across 5-fold cross-validation with an average sensitivity of 79%, accuracy of 78% and specificity of 76%. The proposed methods can reduce the likelihood of hazardous events like pulmonary congestion and ventricular suction for HF patients and notify identified abnormal cases to the hospital, clinician and cardiologist for emergency acti

关键词： Convolutional neural networks Classification algorithms Feature extraction Neural networks Heart Hospitals Hafnium Deep learning convolutional neural networks fully Elman neural networks metaheuristics heart failure harris hawks algorithm

来源：评论

学校读者我要写书评

暂无评论

A hybrid harris hawks optimization algorithm with simulated annealing for feature selection

引用

ARTIFICIAL INTELLIGENCE REVIEW 2021年第1期54卷 593-637页

作者： Abdel-Basset, Mohamed Ding, Weiping El-Shahat, Doaa Zagazig Univ Fac Comp & Informat Zagazig 44519 Egypt Nantong Univ Sch Informat Sci & Technol Nantong 226019 Peoples R China

The significant growth of modern technology and smart systems has left a massive production of big data. Not only are the dimensional problems that face the big data, but there are also other emerging problems such as redundancy, irrelevance, or noise of the features. Therefore, feature selection (FS) has become an urgent need to search for the optimal subset of features. This paper presents a hybrid version of the harris hawks Optimization algorithm based on Bitwise operations and Simulated Annealing (HHOBSA) to solve the FS problem for classification purposes using wrapper methods. Two bitwise operations (AND bitwise operation and OR bitwise operation) can randomly transfer the most informative features from the best solution to the others in the populations to raise their qualities. The Simulate Annealing (SA) boosts the performance of the HHOBSA algorithm and helps to flee from the local optima. A standard wrapper method K-nearest neighbors with Euclidean distance metric works as an evaluator for the new solutions. A comparison between HHOBSA and other state-of-the-art algorithms is presented based on 24 standard datasets and 19 artificial datasets and their dimension sizes can reach up to thousands. The artificial datasets help to study the effects of different dimensions of data, noise ratios, and the size of samples on the FS process. We employ several performance measures, including classification accuracy, fitness values, size of selected features, and computational time. We conduct two statistical significance tests of HHOBSA like paired-samples T and Wilcoxon signed ranks. The proposed algorithm presented superior results compared to other algorithms.

关键词： Feature selection harris hawks algorithm k-nearest neighbor Classification Data dimensionality

来源：评论

学校读者我要写书评

暂无评论

Node Localization in WSN and IoT Using harris hawks Optimization algorithm

Node Localization in WSN and IoT Using Harris Hawks Optimiza...

引用

IEEE Global Communications Conference (GLOBECOM)

作者： Mihoubi, Miloud Rahmoun, Abdellatif Lorenz, Pascal Univ Djillali Liabes Sidi Bel Abbes EEDIS Lab Sidi Bel Abbes Algeria Ecole Super Informat LabRI SBA Lab Sidi Bel Abbes Algeria Univ Haute Alsace IRIMAS Lab Colmar France

ISBN: (纸本)9781728181042

During the last decade Wireless Sensor Networks and IoT have taken an overwhelming place in engineering and environmental applications. Thus, with growing interest in large size and complex networks, some critical challenges have to be taken care of such as energy consumption and node localization, especially when dealing with real-time applications. Such factors affect strongly the system performance in sensitive fields like e-health or military applications. The primary purpose is to determine the location of the sensor node that triggers the event. Energy consumption of each node is a serious problem for WSN when it comes to extend the life time of the whole network. In this paper, we propose a multicriteria optimization scheme based on a bio-inspired algorithm called harris hawks Optimization algorithm (HHOA). It is shown that the proposed paradigm is capable of increasing the localization rate, as well as minimizing the energy consumption of the nodes. HHOA populations are able to share information in a multi-agent fashion to compute the trigger's position. Developing this algorithm on a huge WSN with millions of nodes reveals a particularly high performance. To assess this, several experiments in different scenarios are carried out in a decentralized environment of WSN. Finally, a comparative study is carried out also to some recent bio-inspired algorithms.

关键词： Wireless Sensor Network Node Localization Internet Of Thing harris hawks algorithm Energy Consumption

来源：评论

学校读者我要写书评

暂无评论

The harris hawks optimization algorithm, salp swarm algorithm, grasshopper optimization algorithm and dragonfly algorithm for structural design optimization of vehicle components

引用

MATERIALS TESTING 2019年第8期61卷 744-748页

作者： Yildiz, Betul Sultan Yildiz, Ali Riza Uludag Univ Dept Automot Engn Gorukle Bursa Turkey Univ Michigan Ann Arbor MI 48109 USA Mississippi State Univ NSF & DOE Funded Res Projects Starkville MS USA

There is a growing interest in designing lightweight and low-cost vehicles. In this research, the harris hawks optimization algorithm (the HHO), the salp swarm algorithm (SSA), the grasshopper optimization algorithm(GOA), and the dragonfly algorithm (DA) are introduced to solve shape optimization problems in the automotive industry. This research is the first application of the HHO, the SSA, the GOA, and the DA to shape design optimization problems in the literature. In this paper, the HHO, the SSA, and the DA algorithms are used for shape optimization of a vehicle brake pedal to prove how the HHO, the SSA, the GOA, and the DA can be used for solving shape optimization problems. The results show the ability of the HHO, the SSA, the GOA, and the DA to design better optimal components.

关键词： harris hawks algorithm salp swarm algorithm grasshopper optimization algorithm dragonfly algorithm structural optimization

来源：评论

学校读者我要写书评

暂无评论

A novel hybrid algorithm based on harris hawks for tumor feature gene selection

引用

PEERJ COMPUTER SCIENCE 2023年 9卷 e1229页

作者： Liu, Junjian Feng, Huicong Tang, Yifan Zhang, Lupeng Qu, Chiwen Zeng, Xiaomin Peng, Xiaoning Hunan Normal Univ Coll Math & Stat Dept Stat Changsha Hunan Peoples R China Hunan Normal Univ Sch Med Dept Pathol & Pathophysiol Changsha Hunan Peoples R China Jishou Univ Sch Med Dept Biochem & Mol Biol Jishou Hunan Peoples R China Cent South Univ Xiangya Publ Hlth Sch Dept Epidemiol & Hlth Stat Changsha Hunan Peoples R China

Background. Gene expression data are often used to classify cancer genes. In such high-dimensional datasets, however, only a few feature genes are closely related to tumors. Therefore, it is important to accurately select a subset of feature genes with high contributions to cancer classification. Methods. In this article, a new three-stage hybrid gene selection method is proposed that combines a variance filter, extremely randomized tree and harris hawks (VEH). In the first stage, we evaluated each gene in the dataset through the variance filter and selected the feature genes that meet the variance threshold. In the second stage, we use extremely randomized tree to further eliminate irrelevant genes. Finally, we used the harris hawks algorithm to select the gene subset from the previous two stages to obtain the optimal feature gene subset. Results. We evaluated the proposed method using three different classifiers on eight published microarray gene expression datasets. The results showed a 100% classification accuracy for VEH in gastric cancer, acute lymphoblastic leukemia and ovarian cancer, and an average classification accuracy of 95.33% across a variety of other cancers. Compared with other advanced feature selection algorithms, VEH has obvious advantages when measured by many evaluation criteria.

关键词： Gene selection Microarray gene expression harris hawks algorithm Extremely randomized tree

来源：评论

学校读者我要写书评

暂无评论

Research on optimal allocation of flow and head in cascade pumping stations based on harris hawks optimization

引用

WATER SUPPLY 2024年第1期24卷 272-289页

作者： Hou, Xiaopeng Zhang, Leike Liu, Xiaolian Wang, Xueni Tian, Yu Deng, Xianyu Ye, Chen Taiyuan Univ Technol Coll Water Resource Sci & Engn Taiyuan 030024 Peoples R China North China Univ Water Resources & Elect Power Henan Key Lab Water Resources Conservat & Intens U Zhengzhou 450046 Peoples R China China Inst Water Resources & Hydropower Res State Key Lab Simulat & Regulat Water Cycle River Beijing 100038 Peoples R China Design & Res Co Ltd China Water Northeast Invest Changchun 130021 Peoples R China JiMei Univ Coll Harbour & Coastal Engineer Xiamen 361000 Peoples R China

To address the problems of massive energy consumption and low operating efficiency in cascade pumping stations (CPSs), an optimized scheduling model for CPSs with water flow and head constraints was constructed in this study. The harris hawks optimization (HHO) algorithm was employed to solve this model owing to its excellent performance in the field of engineering majorization. Based on this model, an optimal scheduling method for CPSs was proposed and applied to the three-stage pumping station system. The results demonstrate that the optimization schemes based on the HHO algorithm can improve the operational efficiency and annual cost savings under three different pumping flow conditions by 0.16, 0.55, and 0.56%, reducing the annual operating cost by & YEN;22,703, & YEN;74,581, and & YEN;75,356, respectively, relative to the currently used schemes. These results are better than those obtained by the particle swarm optimization (PSO) algorithm and genetic algorithm (GA). Furthermore, in terms of computational time, the optimization method with the HHO algorithm can show an improvement of 8.94-29.74% compared with those of PSO and GA, verifying the feasibility and efficiency of the HHO algorithm in the optimal scheduling for CPSs. Therefore, the proposed method is effective at solving the scheduling problem of *** center dot An optimized scheduling model considering operational efficiency of cascade pumping stations (CPSs) in water transfer systems is *** dot An optimization dispatching method for CPSs based on the harris hawks optimization algorithm is *** dot The effectiveness and superiority of the proposed method are confirmed using an engineering case.

关键词： cascade pumping stations engineering applications harris hawks algorithm optimal scheduling

来源：评论

学校读者我要写书评

暂无评论

A novel hybrid harris hawks-simulated annealing algorithm and RBF-based metamodel for design optimization of highway guardrails

引用

MATERIALS TESTING 2020年第3期62卷 251-260页

作者： Kurtulus, Dies Yildiz, Ali Riza Sait, Sadiq M. Bureerat, Sujin Kaen, Khon Bursa Uludag Univ Dept Automot Engn Bursa Turkey KFUPM Comp Engn Dhahran Saudi Arabia KFUPM Ctr Commun & IT Res Dhahran Saudi Arabia Khon Kaen Univ Dept Mech Engn Khon Kaen Thailand

In this paper, a novel hybrid optimization algorithm is introduced by hybridizing a harris hawks optimization algorithm(HHO) and simulated annealing for the purpose of accelerating its global convergence performance and optimizing structural design problems. This paper is the first research study in which the hybrid harris hawks simulated annealing algorithm (HHOSA) is used for the optimization of design parameters for highway guardrail systems. The HHOSA is evaluated using the well-known benchmark problems such as the three-bar truss problem, cantilever beam problem, and welded beam problem. Finally, a guardrail system that has an H1 containment level as a case study is optimized to investigate the performance of the HHOSA. The guardrail systems are designed with different cross-sections and distances between the posts. TB11 and TB42 crash analyses are performed according to EN 1317 standards. Twenty-five different designs are evaluated considering weight, the guardrail working width, and the acceleration severity index (ASI). As a result of this research, the optimum design of a guardrail is obtained, which has a minimum weight and acceleration severity index value (ASI). The results show that the HHOSA is a highly effective approach for optimizing real-world design problems.

关键词： harris hawks algorithm Simulated annealing crash analysis hybrid optimization algorithm guardrails road safety barriers

来源：评论

学校读者我要写书评

暂无评论

Computing Connected Resolvability of Graphs Using Binary Enhanced harris hawks Optimization

引用

Intelligent Automation & Soft Computing 2023年第5期36卷 2349-2361页

作者： Basma Mohamed Linda Mohaisen Mohamed Amin Mathematics and Computer Science Department Faculty of ScienceMenoufia UniversityShebin Elkom32511Egypt Faculty of Computer and Information Technology King Abdulaziz UniversityJeddah21589Saudi Arabia

In this paper,we consider the NP-hard problem ofﬁnding the minimum connected resolving set of graphs.A vertex set B of a connected graph G resolves G if every vertex of G is uniquely identiﬁed by its vector of distances to the ver-tices in B.A resolving set B of G is connected if the subgraph B induced by B is a nontrivial connected subgraph of *** cardinality of the minimal resolving set is the metric dimension of G and the cardinality of minimum connected resolving set is the connected metric dimension of *** problem is solved heuristically by a binary version of an enhanced harris Hawk Optimization(BEHHO)*** is theﬁrst attempt to determine the connected resolving set *** combines classical HHO with opposition-based learning,chaotic local search and is equipped with an S-shaped transfer function to convert the contin-uous variable into a binary *** hawks of BEHHO are binary encoded and are used to represent which one of the vertices of a graph belongs to the connected resolving *** feasibility is enforced by repairing hawks such that an addi-tional node selected from V\B is added to B up to obtain the connected resolving *** proposed BEHHO algorithm is compared to binary harris Hawk Optimi-zation(BHHO),binary opposition-based learning harris Hawk Optimization(BOHHO),binary chaotic local search harris Hawk Optimization(BCHHO)*** results conﬁrm the superiority of the BEHHO for determining connected metric dimension.

关键词： Connected resolving set binary optimization harris hawks algorithm

来源：评论

学校读者我要写书评

暂无评论

A new hybrid harris hawks-Nelder-Mead optimization algorithm for solving design and manufacturing problems

引用

MATERIALS TESTING 2019年第8期61卷 735-743页

作者： Yildiz, Ali Riza Yildiz, Betul Sultan Sait, Sadiq M. Bureerat, Sujin Pholdee, Nantiwai Uludag Univ Dept Automot Engn Gorukle Bursa Turkey Univ Michigan Ann Arbor MI 48109 USA Mississippi State Univ Ctr Adv Vehicular Syst NSF & DOE Funded Res Projects Starkville MS USA KFUPM Comp Engn Dhahran Saudi Arabia KFUPM Ctr Commun & IT Res Dhahran Saudi Arabia Khon Kaen Univ Dept Mech Engn Khon Kaen Thailand

In this paper, a novel hybrid optimization algorithm (H-HHONM) which combines the Nelder-Mead local search algorithm with the harris hawks optimization algorithm is proposed for solving real-world optimization problems. This paper is the first research study in which both the harris hawks optimization algorithm and the H-HHONM are applied for the optimization of process parameters in milling operations. The H-HHONM is evaluated using well-known benchmark problems such as the three-bar truss problem, cantilever beam problem, and welded beam problem. Finally, a milling manufacturing optimization problem is solved for investigating the performance of the H-HHONM. Additionally, the salp swarm algorithm is used to solve the milling problem. The results of the H-HHONM for design and manufacturing problems solved in this paper are compared with other optimization algorithms presented in the literature such as the ant colony algorithm, genetic algorithm, particle swarm optimization algorithm, simulated annealing algorithm, artificial bee colony algorithm, teaching learning-based optimization algorithm, cuckoo search algorithm, multi-verse optimization algorithm, harris hawks optimization optimization algorithm, gravitational search algorithm, ant lion optimizer, moth-flame optimization algorithm, symbiotic organisms search algorithm, and mine blast algorithm. The results show that H-HHONM is an effective optimization approach for optimizing both design and manufacturing optimization problems.

关键词： harris hawks algorithm nelder mead hybrid optimization milling design

来源：评论

学校读者我要写书评

暂无评论

Reliable Machine Learning Model for IIoT Botnet Detection

引用

IEEE ACCESS 2023年 11卷 49319-49336页

作者： Taher, Fatma Abdel-Salam, Mahmoud Elhoseny, Mohamed El-Hasnony, Ibrahim M. Zayed Univ Coll Technol Innovat Dubai U Arab Emirates Mansoura Univ Fac Comp & Informat Sci Mansoura 35516 Egypt Univ Sharjah Coll Comp & Informat Sharjah U Arab Emirates

Due to the growing number of Industrial Internet of Things (IoT) devices, network attacks like denial of service (DoS) and floods are rising for security and reliability issues. As a result of these attacks, IoT devices suffer from denial of service and network disruption. Researchers have implemented different techniques to identify attacks aimed at vulnerable Industrial Internet of Things (IoT) devices. In this study, we propose a novel features selection algorithm FGOA-kNN based on a hybrid filter and wrapper selection approaches to select the most relevant features. The novel approach integrated with clustering rank the features and then applies the Grasshopper algorithm (GOA) to minimize the top-ranked features. Moreover, a proposed algorithm, IHHO, selects and adapts the neural network's hyper parameters to detect botnets efficiently. The proposed harris hawks algorithm is enhanced with three improvements to improve the global search process for optimal solutions. To tackle the problem of population diversity, a chaotic map function is utilized for initialization. The escape energy of hawks is updated with a new nonlinear formula to avoid the local minima and better balance between exploration and exploitation. Furthermore, the exploitation phase of HHO is enhanced using a new elite operator ROBL. The proposed model combines unsupervised, clustering, and supervised approaches to detect intrusion behaviors. This combination can enhance the accuracy and robustness of the proposed model by identifying the most relevant features and detecting known and unknow botnet activity. The N-BaIoT dataset is utilized to validate the proposed model. Many recent techniques were used to assess and compare the proposed model's performance. The result demonstrates that the proposed model is better than other variations at detecting multiclass botnet attacks.

关键词： Botnet features selection grasshopper algorithm harris hawks algorithm security botnet detection reliability the Industrial Internet of Things chaotic map filter wrapper clustering

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：