检索结果-内蒙古大学图书馆

A synergistic approach for enhanced eye blink detection using wavelet analysis, autoencoding and crow-search optimized k-NN algorithm

引用

SCIENTIFIC REPORTS 2025年第1期15卷 1-21页

作者： Chandralekha, M. Jayadurga, N. Priyadharshini Chen, Thomas M. Sathiyanarayanan, Mithileysh Saleem, Kasif Orgun, Mehmet A. Amrita Vishwa Vidyapeetham Amrita Sch Comp Dept Comp Sci & Engn Chennai India City Univ London Sch Sci & Technol London England MIT Sq Res & Innovat London England King Saud Univ Coll Appl Studies & Community Serv Dept Comp Sci & Engn Riyadh Saudi Arabia Macquarie Univ Dept Comp N Ryde NSW 2109 Australia

This research endeavor introduces a state-of-the-art, assimilated approach for eye blink detection from Electroencephalography signals. It combines the prominent strategies of wavelet analysis, autoencoding, and a crow-search-optimized k-Nearest Neighbors to enhance the performance of eye blink detection from EEG signals. This procedure is initiated by escalating the robustness of EEG data through jittering, which integrates noise into the dataset. Consequently, the wavelet transform is highly demanded during feature extraction in identifying the essential time-frequency components of the signals. These features are further distilled using an autoencoder to provide a dense, yet informative representation. Prior to introducing these features into the machine learning system, they were adjusted. Evidently, the hyperparameters of the k-Nearest Neighbors model have been fine-tuned using crow search algorithm, inspired by the hunting characteristics of crows. This optimization method actively samples the search space to balance exploration and exploitation to identify the optimal configuration for the model. The k-NN model that has been optimized using the proposed method demonstrates significantly higher performance in the eye blink detection process in comparison to the deep learning models when equipped with decorous feature extraction and fine tuning. The effectiveness of the developed system has been ascertained according to the assessment indices such as accuracy, classification reports, and confusion matrix. Thus, the present work offers an optimal method of detecting the eye blink from the EEG signals assisting in the development of further EEG applications including user interfaces, fatigue level identification, and neurological disorders analysis through the enhancement of signal processing and optimization methods. It becomes evident after a detailed evaluation that conventional machine learning algorithms if implemented with correct feature extraction and fin

关键词： Eye blink detection Electroencephalogram Wavelet analysis Autoencoder crow search algorithm k-Nearest neighbors Feature extraction Signal processing

来源：评论

学校读者我要写书评

暂无评论

Hybrid crow search and uniform crossover algorithm-based clustering for top-N recommendation system

引用

NEURAL COMPUTING & APPLICATIONS 2021年第12期33卷 7145-7164页

作者： El-Ashmawi, Walaa H. Ali, Ahmed F. Slowik, Adam Suez Canal Univ Dept Comp Sci Fac Comp & Informat Ismailia Egypt Misr Int Univ Fac Comp Sci Cairo Egypt Koszalin Univ Technol Dept Elect & Comp Sci Koszalin Poland

Recommender systems (RSs) have gained immense popularity due to their capability of dealing with a huge amount of information available in various domains. They are considered to be information filtering systems that make predictions or recommendations to users based on their interests. One of the most common recommender system techniques is user-based collaborative filtering. In this paper, we follow this technique by proposing a new algorithm which is called hybrid crow search and uniform crossover algorithm (HCSUC) to find a set of feasible clusters of similar users to enhance the recommendation process. Invoking the genetic uniform crossover operator in the standard crow search algorithm can increase the diversity of the search and help the algorithm to escape from trapping in local minima. The top-N recommendations are presented for the corresponding user according to the most feasible cluster's members. The performance of the HCSUC algorithm is evaluated using the Jester dataset. A set of experiments have been conducted to validate the solution quality and accuracy of the HCSUC algorithm against the standard particle swarm optimization (PSO), African buffalo optimization (ABO), and the crow search algorithm (CSA). In addition, the proposed algorithm and the other meta-heuristic algorithms are compared against the collaborative filtering recommendation technique (CF). The results indicate that the HCSUC algorithm has obtained superior results in terms of mean absolute error, root means square errors and in minimization of the objective function.

关键词： crow search algorithm Uniform crossover Recommendation system User-based collaborative filtering

来源：评论

学校读者我要写书评

暂无评论

Research on crow swarm intelligent search optimization algorithm based on surrogate model

引用

JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 2020年第10期34卷 4043-4049页

作者： Xu, Huanwei Liu, Liangwen Zhang, Miao Univ Elect Sci & Technol China Chengdu 611731 Peoples R China

A large amount of calculation exists in a complex engineering optimization problem. The swarm intelligence algorithm can improve calculation efficiency and accuracy of complex engineering optimization. In the existing research, the surrogate model and the swarm intelligence algorithm are only two independent tools to solve the optimization problem. In this paper, we propose the surrogate-assisted crow swarm intelligent search optimization algorithm (SACSA) by combining the characteristics of swarm intelligence algorithm and surrogate model. The proposed algorithm utilizes the initial samples to construct the surrogate model, and then the improved crow search algorithm (CSA) is applied to obtain optimal solution. Finally, the proposed algorithm is compared with EGO, MSSR, ARSM-ISES, AMGO and SEUMRE, MPS, HAM algorithms. The comparison results show that the proposed algorithm can find a global optimal solution with fewer samples and is beneficial to improving the efficiency and accuracy of calculation.

关键词： crow search algorithm Kriging Optimization algorithm Surrogate model

来源：评论

学校读者我要写书评

暂无评论

Hybrid crow search-Ant Colony Optimization algorithm for Capacitated Vehicle Routing Problem 1

引用

2nd International Conference on Soft Computing Systems (ICSCS)

作者： Dhanya, K. M. Kanmani, Selvadurai Hanitha, G. Abirami, S. Pondicherry Engn Coll Dept Comp Sci & Engn Pondicherry India Pondicherry Engn Coll Dept Informat Technol Pondicherry India

ISBN: (数字)9789811319365

ISBN: (纸本)9789811319365;9789811319358

Capacitated Vehicle Routing Problem (CVRP) is a NP-Hard problem in which the optimal set of paths taken by the vehicles is determined under the capacity constraint. Ant Colony Optimization (ACO) is a metaheuristic method incorporating the ant's ability to find the shortest path from source to destination using the concept of pheromone trails. It has been used to solve CVRP. However, it exhibits stagnation property, due to which, the exploration probability of new route is reduced. crow search algorithm (CSA) is a recently developed metaheuristic method inspired from crow's food hunting behavior. This paper provides a hybrid relay algorithm which involves ACO and CSA to solve CVRP. The hybridization is done to get a consistent solution with optimal execution time. The experimentation with Augerat instances shows betterment in the optimal solution at the earliest time.

关键词： crow search algorithm Ant Colony Optimization Metaheuristic Capacitated Vehicle Routing Problem Hybridization Optimal route distance

来源：评论

学校读者我要写书评

暂无评论

A crow search-Based Genetic algorithm for Solving Two-Dimensional Bin Packing Problem 1

引用

42nd German Conference on Artificial Intelligence (KI)

作者： Laabadi, Soukaina Naimi, Mohamed El Amri, Hassan Achchab, Boujemaa ENS Hassan II Univ Lab Math & Applicat Casablanca Morocco ENSA Hassan I Univ Lab Anal Modeling Syst & Decis Support Berrechid Morocco

ISBN: (数字)9783030301798

ISBN: (纸本)9783030301798;9783030301781

The two-dimensional bin packing problem (2D-BPP) consists of packing, without overlapping, a set of rectangular items with different sizes into smallest number of rectangular containers, called "bins", having identical dimensions. According to the real-word requirements, the items may either have a fixed orientation or they can be rotated by 90 degrees. In addition, it may or not be subjugate to the guillotine cutting. In this article, we consider the two-dimensional bin packing problem with fixed orientation and free cutting. In fact, we propose a hybrid approach by combining two bio-inspired algorithms that are the crow search algorithm (CSA) and the genetic algorithm (GA) to solve the considered problem. So, the main idea behind this hybridization is to expect reaching a sort of cooperative synergy between the operators of the two combined algorithms. That is, the CSA is discretized and adapted to the 2D-BPP context, while using genetic operators to improve individuals (i.e. crows) adaptation. The average performance of the proposed hybrid approach is evaluated on the standard benchmark instances of the considered problem and compared with two other bio-inspired algorithms having closely similar nature;namely standard genetic algorithm and binary particle swarm optimization algorithm. The obtained results are very promising.

关键词： Two-dimensional bin packing problem Bio-inspired algorithms crow search algorithm Genetic algorithm Hybridization

来源：评论

学校读者我要写书评

暂无评论

A novel hybrid path planning method for sweep coverage of multiple UAVs

引用

JOURNAL OF SUPERCOMPUTING 2025年第1期81卷 1-40页

作者： Ozdag, Recep Van Yuzuncu Yil Univ Fac Engn Dept Comp Engn Van Turkiye

Sweep coverage (SC) is an NP-hard problem that needs to be solved with a small number of mobile sensors (sensor nodes) to monitor or observe targets in wireless sensor networks (WSNs) over a period of time. One of the proposed solutions to optimize the lifetime of WSNs is to monitor targets in the area of interest (AoI) by path planning of the sensor nodes. Considering the applications of unmanned aerial vehicles (UAVs) in military and civilian fields, in this study, we model multiple UAVs to solve the SC problem based on non-uniformly distributed targets in the AoI. In this modeling, we conduct effective coverage path planning for UAVs by performing an SC task for targets in the AoI with multiple asynchronously flying UAVs. To this end, we propose a new approach, which we call Weighted Targets Sweep Coverage, based on the metaheuristic crow search algorithm and the heuristic Greedy algorithm. In our proposed approach, we define a new objective function by considering the UAV task completion time and the AoI coverage rate as metrics. In the Monte Carlo simulations that we perform to measure the performance, we run 5 different scenarios considering the number of targets defined for AoI and the alpha parameters defined for the objective function. The results of the scenario studies show that the proposed approach outperforms the other algorithms by up to 21% and 50% in terms of UAV task completion time and AoI coverage rate, respectively.

关键词： Coverage path planning Sweep coverage crow search algorithm Greedy algorithm Unmanned aerial vehicle Optimization

来源：评论

学校读者我要写书评

暂无评论

Small Signal Analysis of Gate Controlled Series Capacitor based Tidal Generation using Hybrid crow search-Nelder Mead algorithm 3

Small Signal Analysis of Gate Controlled Series Capacitor ba...

引用

3rd IEEE International Conference on Smart Technologies for Power, Energy and Control, STPEC 2023

作者： Kumar, Akshay Prabhu, M.S. Zeno, Aldrich National Sun Yat-sen University Department of Electrical Engineering Kaohsiung City Taiwan Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Electrical and Electronics Engineering India Norwegian University of Science and Technology Department of Electrical Energy Norway

ISBN: (纸本)9798350304732

In this paper, dynamic and steady-state stability enhancement of tidal turbine based direct driven permanent magnet synchronous generator with gate controlled series capacitor (GCSC) is presented. To accomplish this objective, a reserve power margin is created on tidal power generation (TPG) using speed control and pitch angle control strategies to deliver supplementary active power to the system in case of system events. Also, GCSC is added with TPG to damp the signal oscillation quickly and provides uninterrupted capacitive reactance without the threat of resonance. Parameters of GCSC are optimized using hybrid crow search and nelder mead algorithm. Dynamic simulation based on time domain is carried out on nonlinear model to find dynamic stability under different system disturbances whereas eigenvalue analysis based on frequency domain is performed to define steady-state steadiness under different tidal speeds. The proposed investigation is confirmed on the multi-machine system. © 2023 IEEE.

关键词： crow search algorithm gate controlled series capacitor nelder mead algorithm tidal power generation

来源：评论

学校读者我要写书评

暂无评论

A Novel Hybrid crow search Arithmetic Optimization algorithm for Solving Weighted Combined Economic Emission Dispatch with Load-Shifting Practice

引用

algorithmS 2024年第7期17卷 313页

作者： Dey, Bishwajit Sharma, Gulshan Bokoro, Pitshou N. Univ Johannesburg Dept Elect Engn Technol ZA-2006 Johannesburg South Africa

The crow search arithmetic optimization algorithm (CSAOA) method is introduced in this article as a novel hybrid optimization technique. This proposed strategy is a population-based metaheuristic method inspired by crows' food-hiding techniques and merged with a recently created simple yet robust arithmetic optimization algorithm (AOA). The proposed method's performance and superiority over other existing methods is evaluated using six benchmark functions that are unimodal and multimodal in nature, and real-time optimization problems related to power systems, such as the weighted dynamic economic emission dispatch (DEED) problem. A load-shifting mechanism is also implemented, which reduces the system's generation cost even further. An extensive technical study is carried out to compare the weighted DEED to the penalty factor-based DEED and arrive at a superior compromise option. The effects of CO2, SO2, and NOx are studied independently to determine their impact on system emissions. In addition, the weights are modified from 0.1 to 0.9, and the effects on generating cost and emission are investigated. Nonparametric statistical analysis asserts that the proposed CSAOA is superior and robust.

关键词： combined economic emission dispatch (CEED) load shifting demand side management crow search algorithm arithmetic optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

A feature selection algorithm for intrusion detection system based on the enhanced heuristic optimizer

引用

EXPERT SYSTEMS WITH APPLICATIONS 2025年 265卷

作者： Yu, Hongchen Zhang, Wei Kang, Chunying Xue, Yankun Hangzhou Dianzi Univ Sch Cyberspace Baiyang St Hangzhou 310018 Peoples R China Univ Heilongjiang Modern Educ Technol Ctr Harbin 150000 Heilongjiang Peoples R China Univ Heilongjiang Sch Data Sci & Technol Harbin 150000 Heilongjiang Peoples R China

With the rapid development of network technology, the dramatic growth of network traffic has also led to a large number of irrelevant features and noise, which affect the performance of network intrusion detection systems. Feature selection has thus become a key aspect in building these systems. In this paper, an enhanced heuristic optimization algorithm (EHO) is proposed, demonstrating excellent global convergence and superior search capabilities. The CEC standard test functions are used to evaluate the effectiveness of the algorithm. Experimental results show that the proposed algorithm has a faster convergence speed and stronger exploration ability when dealing with multimodal problems, significantly outperforming CSA, CSO, EFA, BWO, and RIME methods. Additionally, a wrapper feature selection method based on the optimization algorithm is proposed, and the algorithm's performance is evaluated using three public datasets (NSL_KDD, UNSW_NB15, and CICIDS2018). The results indicate that the proposed method outperforms existing feature selection algorithms in terms of accuracy, precision, recall, and F1-score, achieving 90.95%, 93.39%, and 98.50% accuracy on the NSL_KDD, UNSW_NB15, and CIC-IDS2018 datasets, respectively.

关键词： Intrusion detection system crow search algorithm Feature selection Network attacks

来源：评论

学校读者我要写书评

暂无评论

Optimizing sustainable desalination plants with advanced ML-based uncertainty analysis

引用

APPLIED SOFT COMPUTING 2025年 169卷

作者： Abba, Sani I. Usman, Jamilu Bafaqeer, Abdullah Salami, Babatunde A. Lawal, Zaharaddeen Karami Lawal, Abdulmajid Usman, A. G. Aljundi, Isam H. Prince Mohammad Bin Fahd Univ Dept Civil Engn Al Khobar 31952 Saudi Arabia King Fahd Univ Petr & Minerals Interdisciplinary Res Ctr Refining & Adv Chem Dhahran 31261 Saudi Arabia Cardiff Metropolitan Univ Cardiff Sch Management Llandaff Campus Cardiff CF5 2YB Wales Univ Brunei Darussalam Fac Integrated Technol BE-1410 Gadong Brunei King Fahd Univ Petr & Minerals Interdisciplinary Res Ctr Commun Syst & Sensing Dhahran 31261 Saudi Arabia Near East Univ Operat Res Ctr Healthcare Nicosia Turkiye King Fahd Univ Petr & Minerals Dept Chem Engn Dhahran 31261 Saudi Arabia King Fahd Univ Petr & Minerals Interdisciplinary Res Ctr Membranes & Water Secur Dhahran 31261 Saudi Arabia

Reliable, cutting-edge environmental research and innovative solutions to critical environmental challenges are essential for a sustainable future. The desalination industry is increasingly interested in predicting the performance of hybrid nanofiltration/reverse osmosis (NF/RO) systems for treating very saline brine to recover valuable resources. This study aimed to optimize the effectiveness of a Long-Short-Term Memory (LSTM) model by applying two distinct metaheuristic optimization techniques: Genetic algorithm (GA) and crow search algorithm (CSA). The focus was on leveraging these algorithms for uncertainty analysis and forecasting in hybrid NF/RO desalination processes within Saudi Arabia. Furthermore, Particle Swarm Optimization (PSO) was utilized to determine the most suitable data models, consisting of three and four parameters, for this modelling purpose. Statistical tests based on analysis of variance (ANOVA) test (ANOVA F-test and the Welch F-test), covariance analysis (covariance matrix, t-statistics, and p-values), and pairwise Granger causality tests were conducted. Statistical methods and visual techniques were employed to assess and compare the precision of the LSTM model when integrated with the metaheuristic algorithms (LSTM-GA and LSTM-CSA) against the standalone LSTM model. The results demonstrated that the metaheuristic algorithms provided higher accuracy than the standalone LSTM model in both data models. In hybrid NF/RO desalination prediction, the LSTM-GA (MAE=0.13), LSTM-CSA (MAE=0.14), and LSTM (0.20) models achieved increasingly higher accuracy. Besides, the Monte Carlo method was utilized to evaluate the uncertainty linked with the predictions. The findings indicated that the LSTM model integrated with the GA (LSTM-GA) demonstrated the slightest uncertainty in its predictions.

关键词： NF/RO desalination Genetic algorithm crow search algorithm Uncertainty analysis Particle swarm optimization

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：