检索结果-内蒙古大学图书馆

Feature Selection in GPCR Classification Using bat algorithm

INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS 2020年第1期19卷

作者： Bekhouche, Safia Ben Ali, Yamina Mohamed Badji Mokhtar Univ Dept Comp Sci Annaba 23000 Algeria Badji Mokhtar Univ LRI Annaba 23000 Algeria

G-Protein-Coupled Receptors (GPCR) are the large family of protein membrane;and until now some of them still remain orphans. Predicting GPCR functions is a challenging task, it depends closely to their classification, which requires a digital representation of each protein chain as an attribute vector. A major problem of GPCR databases is their great number of features which can produce combinatorial explosion and increase the complexity of classification algorithms. Feature selection techniques are used to deal with this problem by minimizing features space dimension, and keeping the most relevant ones. In this paper, we propose to use the bat algorithm for extracting the pertinent features and to improve the classification results. We compared the results obtained by our system with two other bio-inspired algorithms, Evolutionary algorithm and PSO search. Metrics quality measures used for comparison are Error Rate, Accuracy, MCC and F-measure. Experimental results indicate that our system is more efficient.

关键词： GPCR feature selection bat algorithm feature extraction strategies classification local descriptors data mining algorithms protein representation methods

来源：评论

学校读者我要写书评

暂无评论

bat algorithm Method for Automatic Determination of Color and Contrast of Modified Digital Images 44

Bat Algorithm Method for Automatic Determination of Color an...

引用

44th Annual IEEE-Computer-Society International Conference on Computers, Software, and Applications (COMPSAC)

作者： Galvez, Akemi Iglesias, Andres Osaba, Eneko Del Ser, Javier Toho Univ Funabashi Chiba Japan Univ Cantabria Santander Spain Basque Res & Technol Alliance BRTA TECNALIA Derio Spain Univ Basque Country Bilbao Spain

ISBN: (纸本)9781728173030

This paper presents a new artificial intelligence-based method to address the following problem: given an initial digital image (source image), and a modification of the image (mod image) obtained from the source through a color map and visual attributes assumed to be unknown, determine suitable values for color map and contrast such that, when applied to the mod image, a similar image to the source is obtained. This problem has several applications in the fields of image restoration and cleaning. Our approach is based on the application of a powerful swarm intelligence method called bat algorithm. The method is tested on an illustrative example of the digital image of a famous oil painting. The experimental results show that the method performs very well, with a similarity error rate between the source and the reconstructed images of only 8.37%.

关键词： swarm intelligence bat algorithm image proessing digital image visual attributes

来源：评论

学校读者我要写书评

暂无评论

A novel approach for text categorization by applying hybrid genetic bat algorithm through feature extraction and feature selection methods

引用

EXPERT SYSTEMS WITH APPLICATIONS 2022年 202卷 1页

作者： Eliguzel, Nazmiye Cetinkaya, Cihan Dereli, Tuerkay Gaziantep Univ Ind Engn TR-27310 Gaziantep Turkey Adana Alparslan Turkes Sci & Technol Univ Dept Management Informat Syst TR-01250 Adana Turkey Hasan Kalyoncu Univ Off President Gaziantep Turkey

Due to the rapid incline in the number of documents along with social media usage, text categorization has become an important concept. There are tasks required to be fulfilled during the text categorization, such as extracting useful data from different perspectives, reducing the high feature space dimension, and improving effectiveness. In order to accomplish these tasks, feature selection, and feature extraction gain importance. This paper investigates how to solve feature selection and extraction problems. Also, this study aims to decide which topics are the focus of a document. Moreover, the Twitter data-set is utilized as a document and an Uncapacitated P-Median Problem (UPMP) is applied to make clustering. In this study, UPMP is used on Twitter data collection for the first time to collect clustered tweets. Therefore, a novel hybrid genetic bat algorithm (HGBA) is proposed to solve the UPMP for our case. The proposed novel approach is applied to analyze the Twitter data-set of the Nepal earthquake. The first part of the analysis includes the data pre-processing stage. The Latent Dirichlet Allocation (LDA) method is applied to the pre-processed text. After that, a similarity (distance) matrix is generated by utilizing the Jensen Shannon Divergence (JSD) model. The study's main goal is to use Twitter to assess the needs of victims during and after a disaster. To evaluate the applicability of the proposed approach, experiments are conducted on the OR-Library data-set. The results demonstrate that the proposed approach successfully extracts topics and categorizes text.

关键词： bat algorithm Feature extraction Feature selection Genetic algorithm Uncapacitated P-median problem Text categorization

来源：评论

学校读者我要写书评

暂无评论

Hybrid Cloud Resource Provisioning (HCRP) algorithm for Optimal Resource Allocation Using MKFCM and bat algorithm

引用

WIRELESS PERSONAL COMMUNICATIONS 2020年第2期111卷 1171-1185页

作者： Kalaiselvi, S. Selvi, C. S. Kanimozhi Kongu Engn Coll Dept Comp Technol Erode Tamil Nadu India Kongu Engn Coll Dept Comp Sci & Engn Erode Tamil Nadu India

The basic purpose of resource allocation is to make the most efficient allocation of available resources. It contains resources and the number of tasks. The proposed methodology has two types there are resource discovery and resource allocation. The Multiple Kernel Fuzzy C Means Clustering algorithm (MKFCM) is utilized for resource discovery process. Depends on the MKFCM algorithm the recommended method is group the available resources. Thereafter the resources are allocated with the help of a hybrid optimization technique. Here, resource provisioning algorithm is hybrid with bat algorithm for hybridization approach. The experimental analysis of the proposed mechanism is evaluated based on cost value, memory utilization and time. The prospective strategies have been experimented using the Cloud simulator with Java as the working platform.

关键词： Resource discovery Resource allocation Multiple Kernel Fuzzy C Means Clustering Cloud resource provisioning bat algorithm

来源：评论

学校读者我要写书评

暂无评论

Optimal design of damage tolerant composite using ply angle dispersion and enhanced bat algorithm

引用

NEURAL COMPUTING & APPLICATIONS 2020年第8期32卷 3387-3406页

作者： Reddy, M. Prashanth Mukherjee, Shuvajit Ganguli, Ranjan Indian Inst Sci Dept Aerosp Engn Bangalore 560012 Karnataka India

In this work, minimum weight optimization of laminated composite is performed using a newly developed enhanced bat algorithm (EBA). bat algorithm (BA) is a recently developed swarm-based optimization technique which is inspired by the echolocation behavior of bats. The standard BA shows premature convergence and reduced convergence speeds under some conditions. So, the EBA is used to perform the design optimization of laminated composites. The laminate analysis based on classical laminate theory is utilized for the stress calculations. Tsai-Wu failure curve is considered as the constraint in this constrained optimization problem. Number of plies at each orientation angle are considered as the design variables. The design optimization has been carried out for both conventional and unconventional (dispersed plies) stacking sequences considering different loading configurations: uniaxial tension, biaxial tension with and without shear loadings. Ply angles dispersed in the range of 5 circle\-85 circle 25 circle-65 circle and 0 circle\-90 circle} at intervals of 5 circle are considered for the unconventional stacking sequence to increase damage tolerance. In addition, a new mathematical function is proposed to measure the dispersion of ply angles in the laminate called the dispersion function. Also, the performance of EBA is compared with standard BA in the optimum weight design of composite laminates.

关键词： Laminated composite bat algorithm Tsai-Wu failure curve Constrained optimization Stacking sequence Damage tolerance

来源：评论

学校读者我要写书评

暂无评论

Data Clustering Method Based on Improved bat algorithm With Six Convergence Factors and Local Search Operators

引用

IEEE ACCESS 2020年 8卷 80536-80560页

作者： Zhu, L. F. Wang, J. S. Wang, H. Y. Guo, S. S. Guo, M. W. Xie, W. Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114051 Peoples R China Univ Sci & Technol Liaoning Natl Financial Secur & Syst Equipment Engn Res Ct Anshan 114051 Peoples R China

Clustering as an unsupervised learning method is a process of dividing a data object or observation object into a subset, that is to classify the data through observation learning instead of example learning without the guidance of the prior class label information. bat algorithm (BA) is a swarm intelligence optimization algorithm inspired by bat's ultrasonic echo localization foraging behavior, but it has the disadvantages of being easily trapped into local minima and not being highly accurate. So an improved bat algorithm was proposed. In the global search, a Gaussian-like convergence factor is added, and five different convergence factors are proposed to improve the global optimization ability of the algorithm. In the local search, the hunting mechanism of the whale optimization algorithm (WOA) and the sine position updating strategy are adopted to improve the local optimization ability of the algorithm. This paper compares the clustering effect of the improved bat algorithm with bat algorithm, flower pollination algorithm (FPA), harmony search (HS) algorithm, whale optimization algorithm and particle swarm optimization (PSO) algorithm on seven real data sets under six different convergence factors. The simulation results show that the clustering effect of the improved bat algorithm is superior to other intelligent optimization algorithms.

关键词： Clustering bat algorithm convergence factor hunting mechanism sinusoidal position update strategy

来源：评论

学校读者我要写书评

暂无评论

A novel scanning bat algorithm strategy for maximum power point tracker of partially shaded photovoltaic energy systems

引用

AIN SHAMS ENGINEERING JOURNAL 2020年第4期11卷 1093-1103页

作者： Eltamaly, Ali M. Al-Saud, M. S. Abokhalil, Ahmed G. King Saud Univ Sustainable Energy Technol Ctr Riyadh 11421 Saudi Arabia Mansoura Univ Elect Engn Dept Mansoura Egypt King Saud Univ Coll Engn Elect Engn Dept Riyadh 11421 Saudi Arabia Majmaah Univ Elect Engn Dept Almajmaah 11952 Saudi Arabia Assiut Univ Elect Engn Dept Assiut 71515 Egypt

bat algorithm (BA) is used to track the global peak (GP) of photovoltaic (PV) energy systems due to its fast convergence. Meanwhile, BA has many limitations when it is used as maximum power point tracker (MPPT) of PV systems like the need for re-initialization, and the problem of premature convergence. These problems are solved in this paper by injecting one flying bat to search for greater power than the value of current global best to avoid reinitialization of bats. This proposed strategy sends the flying bat to positions of anticipated peaks when the generated power is changed with certain value to remove the need for reinitialization with highest confidence of capturing the GP. Moreover, the convergence time is reduced by at least 566% compared to the state of the art strategy. The results obtained from this novel strategy showed its superiority in tracking the GP under all operating conditions. (c) 2020 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, 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： Photovoltaic MPPT Partial shading bat algorithm Initialization Scanning bat

来源：评论

学校读者我要写书评

暂无评论

A robust digital image watermarking scheme based on bat algorithm optimization and SURF detector in SWT domain

引用

MULTIMEDIA TOOLS AND APPLICATIONS 2020年第29-30期79卷 21653-21677页

作者： Pourhadi, Ali Mahdavi-Nasab, Homayoun Islamic Azad Univ Dept Elect Engn Najafabad Branch Najafabad Iran Islamic Azad Univ Digital Proc & Machine Vis Res Ctr Najafabad Branch Najafabad Iran

This paper presents an optimized robust digital image watermarking scheme based on Stationary Wavelet Transform (SWT) using bat optimization algorithm (BA) and Speed-Up Robust Feature (SURF). The proposed scheme applies high-frequency coefficients of the SWT of the host image in the BA framework to optimize watermark strength factors in the embedding process, considering relevant attacks. On the final step of this process, the SURF detector is employed on the watermarked image for getting point features used for geometric distortion correction. For watermark extracting, the primary step is to correct probable geometrical distortions, utilizing the SURF rotation and scaling invariance property, and the procedure goes on by executing the reverse of embedding phase steps. For evaluating the capabilities of the proposed algorithm, different types of image processing operations such as Gaussian filtering, scaling, rotation and salt and pepper, Poison, speckle, and Gaussian noise, have been used as attacks. According to the experimental results, the proposed combination of techniques exhibits an overall superior performance in both imperceptibility and robustness metrics in various situations compared to state-of-the-art and relevant methods.

关键词： Image watermarking Stationary wavelet transform Speed-up robust feature bat algorithm Arnold transform

来源：评论

学校读者我要写书评

暂无评论

Fuzzy Extended Krill Herd Optimization with Quantum bat algorithm for Cluster Based Routing in Mobile Adhoc Networks

引用

淡江理工学刊 2022年第4期25卷 733-740页

作者： Maganti Srinivas M.Ramesh Patnaik

MANET integrates a set of autonomous mobile nodes which move independently and send data through wireless links. Clustering and routing are the commonly employed energy efficient techniques, which can be treated as an NP hard problem and is resolved by computational intelligence algorithms. The mobility of the nodes leads to repeated link failures and low energy efficiency. In order to achieve high energy efficiency and network connectivity, this paper presents a new Fuzzy Extended Krill Herd Optimization with Quantum bat algorithm (FEKHO-QBA) for Cluster Based Routing in MANET. The presented model uses FEKHO algorithm by integrating the concepts of fuzzy logic and KHO algorithm for clustering process and effective selection of cluster heads (CHs). Besides, the QBA is applied as a routing technique to determine the optimal paths to the destination nodes. The QBA involves the features of faster convergence rate, easier to implement, and improved accurateness. The application of FEKHO-QBA algorithm offers maximum energy efficiency and network longevity. For determining the effectual performance of the FEKHO-QBA algorithm, a set of different experiments were carried out and highlighted the supremacy over the compared methods interms of different performance measures.

关键词： MANET Clustering Routing Krill herd algorithm bat algorithm

来源：评论

学校读者我要写书评

暂无评论

Modified hybrid bat algorithm with genetic crossover operation and smart inertia weight for multilevel image segmentation

引用

APPLIED SOFT COMPUTING 2020年 90卷 106157-000页

作者： Yue, Xiaofeng Zhang, Hongbo Changchun Univ Technol Sch Mechatron Engn Changchun 130012 Peoples R China

Multilevel thresholding is one of the most commonly used methods in image segmentation. However, the exhaustive search method is computationally expensive for selecting the optimal thresholds. Therefore, a hybrid bat algorithm with genetic crossover operation and smart inertia weight (SGA-BA) is proposed to choose the optimal thresholds. Furthermore, between-class variance (the Otsu method) and Kapur's entropy are used as objective functions. In the novel SGA-BA, the smart inertia weight balances the SGA-BA's exploration and exploitation based on the number of iterations and fitness values. Moreover, the local search capability of SGA-BA is strengthened by the crossover operation of the genetic algorithm. Meanwhile, the random vector is replaced by the beta distribution, which updates the frequency of bat in a smart way. The proposed SGA-BA was evaluated by a set of benchmark images with various levels of thresholds. Additionally, SGA-BA was compared with some well-known and recent heuristic algorithms, such as the genetic algorithm (GA), gravitational search algorithm (GSA), particle swarm optimization (PSO), whale optimization algorithm (WOA), improved salp swarm algorithm (LSSA) and basic bat algorithm (BA). The experimental results show that the proposed SGA-BA provides better outcomes than the other algorithms. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Multilevel image segmentation bat algorithm Smart inertia weight Crossover operation Beta distribution

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：