检索结果-内蒙古大学图书馆

A novel firefly algorithm approach for efficient feature selection with COVID-19 dataset

MICROPROCESSORS AND MICROSYSTEMS 2023年第1期98卷 104778页

作者： Bacanin, Nebojsa Venkatachalam, K. Bezdan, Timea Zivkovic, Miodrag Abouhawwash, Mohamed Singidunum Univ Danijelova 32 Belgrade 11000 Serbia Univ Hradec Kralove Fac Sci Dept Appl Cybernet Hradec Kralove 50003 Czech Republic Mansoura Univ Fac Sci Dept Math Mansoura 35516 Egypt Michigan State Univ Dept Computat Math Sci & Engn CMSE E Lansing MI 48824 USA

Feature selection is one of the most important challenges in machine learning and data science. This process is usually performed in the data preprocessing phase, where the data is transformed to a proper format for further operations by machine learning algorithm. Many real-world datasets are highly dimensional with many irrelevant, even redundant features. These kinds of features do not improve classification accuracy and can even shrink down performance of a classifier. The goal of feature selection is to find optimal (or sub-optimal) subset of features that contain relevant information about the dataset from which machine learning algorithms can derive useful conclusions. In this manuscript, a novel version of firefly algorithm (FA) is proposed and adapted for feature selection challenge. Proposed method significantly improves performance of the basic FA, and also outperforms other state-of-the-art metaheuristics for both, benchmark bound-constrained and practical feature selection tasks. Method was first validated on standard unconstrained benchmarks and later it was applied for feature selection by using 21 standard University of California, Irvine (UCL) datasets. Moreover, presented approach was also tested for relatively novel COVID-19 dataset for predicting patients health, and one microcontroller microarray dataset. Results obtained in all practical simulations attest robustness and efficiency of proposed algorithm in terms of convergence, solutions' quality and classification accuracy. More precisely, the proposed approach obtained the best classification accuracy on 13 out of 21 total datasets, significantly outperforming other competitor methods.

关键词： firefly algorithm Swarm intelligence Quasi-reflection-based learning Feature selection Genetic operators COVID-19 dataset

来源：评论

学校读者我要写书评

暂无评论

Detecting distributed denial of service (DDoS) in SD-IoT environment with enhanced firefly algorithm and convolution neural network

引用

OPTICAL AND QUANTUM ELECTRONICS 2023年第5期55卷 1-16页

作者： Sivanesan, N. Archana, K. S. Vels Inst Sci Technol & Adv Studies VISTAS Dept Comp Sci & Engn Chennai India

The Internet of Things (IoT) devices have become part of today's human life and the amplified use of smart phones as well as number of IoT devices in everyday life has made network security more important. These IoT devices are less secure and often abandoned making them a easy target of DDoS attacks which caused by extreme network packet flow threaten vital network services. The DoS is a single-server attack in which the DDoS is a multi-server attack and IoT devices are managed through Software-defined networks (SDN). This research is proposed to improve the firefly method in optimizing the Convolution neural network (CNN) for detecting DDoS attacks in Software Defined-IoT (SD-IoT). The suggested SD-IoT framework has been structured into three layers, namely application layer, control layer and user infrastructure layer. The SDN-IoT architecture layer model, Security apps identify DDoS attack in Application layer C-DAD attack detection. The second layer manages the SD-IoT network with the SDNWISE controller and the IoT controller. The infrastructure layer contains SOFS (Sensor Open Flow Switch) and IoT devices. However, this proposal has considered each firefly as a single hyperparameter and by updating the position of firefly periodically results in the reduction of the search iteration. Hence, the proposed model is evaluated by Root mean square and used for measuring the training accuracy of the proposed model. DDoS attacks use a backtracking technique to pinpoint the source, which increases response time. Four CNN models by varying layers and parameters has implemented to improve DDoS detection yielded results with 98% accuracy.

关键词： Internet of Things Software-defined networks Denial of service Distributed denial of services Convolution neural network firefly algorithm

来源：评论

学校读者我要写书评

暂无评论

Improved rough K-means clustering algorithm based on firefly algorithm

引用

INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS 2023年第1期17卷 1-12页

作者： Ye, Tingyu Ye, Jun Wang, Lei Nanchang Inst Technol Sch Informat Engn Nanchang 330000 Peoples R China

The rough K-means clustering algorithm has a strong ability to deal with data with uncertain boundaries. However, this algorithm also has limitations such as sensitivity to initial data selection, as well as it use of fixed weights and thresholds, which results in unstable clustering results and decreased accuracy. In response to this problem, combined with the firefly algorithm, the original algorithm has been improved from three aspects. Firstly, based on the ratio of the number of objects in the dataset to the product of the difference of the objects in the dataset, a more reasonable method of dynamically adjusting the weights of approximation and boundary set is designed. Secondly, a method of adaptively realising the threshold associated with the number of iterations is given. Then, by constructing a new objective function, and take the objective function value as the firefly brightness intensity to perform the search and update iteration of the initial cluster centre point, the optimal solution obtained by each iteration of firefly is taken as the initial centre position of the algorithm. Experiment result shows that the new algorithm has improved the clustering effect.

关键词： rough K-means algorithm firefly algorithm cluster centre lower approximation and boundary set objective function

来源：评论

学校读者我要写书评

暂无评论

Weight Optimization Based on firefly algorithm for Analogy-based Effort Estimation

引用

INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第6期14卷 617-628页

作者： AlMutlaq, Ayman Jalal Jawawi, Dayang N. A. Arbain, Adila Firdaus Binti Univ Teknol Malaysia Fac Engn Sch Comp Dept Software Engn Johor Baharu Malaysia

Proper cost estimation is one of the vital tasks that must be achieved for software project development. Owing to the complexity and uncertainties of the software development process, this task is ambiguous and difficult. Recently, analogy based estimation (ABE) has become one of the popular approaches in this field due to its effectiveness and practicability in comparing completed projects and new projects in estimating the development effort. However, in spite of its many achievements, this method is not capable to guarantee accurate estimation confronting the complex relation between independent features and software effort. In such a case, the performance of the ABE can be improved by efficient feature weighting. This study introduces an enhanced software estimation method by integrating the firefly algorithm (FA) with the ABE method for improving software development effort estimation (SDEE). The proposed model can provide accurate identification of similar projects by optimising the performances of the similarity function in the estimation process in which the most relevant weights are assigned to project features for obtaining the more accurate estimates. A series of experiments were carried out using six real-world datasets. The results based on the statistical analysis showed that the integration of the FA and ABE significantly outperformed the existing analogy-based approaches especially for the ISBSG dataset.

关键词： Analogy-based estimation firefly algorithm software cost estimation weight optimization

来源：评论

学校读者我要写书评

暂无评论

Optimal fog node selection based on hybrid particle swarm optimization and firefly algorithm in dynamic fog computing services

引用

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第1期121卷

作者： Ogundoyin, Sunday Oyinlola Kamil, Ismaila Adeniyi Univ Ibadan Dept Elect & Elect Engn Secur Privacy & Commun SPCOM Res Grp Ibadan Nigeria

Fog computing is a qualifiedly roseate technology introduced to support latency-sensitive and mission-critical applications by bringing resources closer to the end users. To exploit the full potentials of this auspicious technology, it is important to select optimal fog nodes for secured service provisioning. Therefore, in this work, an optimal fog node selection is formulated as a multi-objective optimization problem. To solve this problem, an efficient selection strategy based on improved particle swarm optimization (PSO) and modified firefly algorithm (FA) is developed. This study considers two important objectives that have not been optimized in the previous works: trust and rate of sojourn, in addition to remaining node capacity and energy consumption. The linear weighted-sum approach is used to aggregate the individual objective functions, after which the Best-worst method (BWM) is used to determine the weight vector of the aggregated function. The impact of the variation in the weight values of the most sensitive objective indicate that in the worst-case scenario, the proposed model is robust and insensitive with an improvement of about 42.16%-50.02% over the related methods. Moreover, we conduct an experimental simulation of the proposed selection strategy as well as comparative analysis with the state-of-the-art algorithms using six performance metrics. The results show that the proposed hybrid PSO-FA achieves a higher accuracy and faster convergence. The proposed solution also records an improvement of about 18.64%-69.45% in resource utilization, 10.94%-40.45% in energy consumption, 12.50%-75% in trust violation, 4.94%-31.12% in computation delay, and 24.71%-37.7% in makespan over the related advanced algorithms.

关键词： Trust Rate of sojourn Adaptive weighting PSO firefly algorithm BWM

来源：评论

学校读者我要写书评

暂无评论

Improving the Detection of Kidney Tumours using firefly algorithm Optimisation 2

Improving the Detection of Kidney Tumours using Firefly Algo...

引用

2nd International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI)

作者： Rajagopal, K. Kumari, V. Sheeja Saraswathy, S. Ponmaniraj, S. Kumar, V. Suresh Deepa, A. SIMATS Univ Saveetha Inst Med & Tech Sci Dept Comp Sci & Engn Chennai Tamil Nadu India Sri Sairam Engn Coll Dept Comp Sci & Business Syst Chennai Tamil Nadu India SIMATS Univ Inst ECE Saveetha Inst Med & Tech Sci Dept Computat Intelligence Chennai Tamil Nadu India Saveetha Engn Coll Dept Comp Sci & Engn Chennai Tamil Nadu India

ISBN: (纸本)9798331540661;9798331540678

Accurate and timely diagnosis of kidney tumors is crucial for effective treatment. This study proposes a novel approach utilizing deep learning techniques and the firefly algorithm (FA) to enhance kidney tumor segmentation. By optimizing feature selection and CNN hyperparameters, FA improves the accuracy and efficiency of the detection system. The proposed method involves pre-processing medical images, extracting features using CNNs, and fine-tuning the model with FA. Experiments on a public dataset demonstrate significant improvements in classification metrics compared to traditional methods. The FA-optimized model provides clinicians with a valuable tool for accurate kidney tumor detection and classification, leading to improved patient outcomes.

关键词： Kidney tumor detection firefly algorithm CNN hyperparameter tuning image preprocessing

来源：评论

学校读者我要写书评

暂无评论

A multi-objective firefly algorithm combining logistic mapping and cross-variation

引用

INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS 2023年第3期18卷 255-265页

作者： Pan, Ningkang Lv, Li Fan, Tanghuai Kang, Ping Nanchang Inst Technol Sch Informat Engn Nanchang 330099 Peoples R China

In the process of evolution, the multi-objective firefly algorithm (MOFA) has low optimisation accuracy and is prone to premature convergence, resulting in poor distribution and convergence of the population. To solve this problem, a multi-objective firefly algorithm (MOFA-LC) combining logistic mapping and cross-mutation was proposed. To improve the distribution of the population, the initial population with good ergodicity and uniformity was generated by logistic mapping. To improve population convergence, Levy flights and non-dominated sorting are used to improve the position updating formula. After the individual position updating, the cross-mutation method in the genetic algorithm can be used to improve the optimisation accuracy of the algorithm and make it jump out of the local optimal, overcome the intelligent convergence of the algorithm, and maintain the convergence of the population. In the experimental part, two typical test functions are selected to plot the IGD convergence curves of MOFA-LC and 11 recent multi-objective optimisation algorithms. The results show that MOFA-LC has obvious advantages over other algorithms.

关键词： multi-objective optimisation firefly algorithm logistic mapping Levy flights non-dominated sorting cross variation

来源：评论

学校读者我要写书评

暂无评论

An efficient IoT task scheduling algorithm in cloud environment using modified firefly algorithm

引用

International Journal of Information Technology (Singapore) 2025年第1期17卷 179-188页

作者： Qasim, Mohammad Sajid, Mohammad Department of Computer Science Aligarh Muslim University Aligarh India

Internet and communication technologies now rely heavily on cloud computing, making it an indispensable component and allowing users to access infrastructure, platforms, and applications on a flexible, scalable, on-demand via a pay-per-use model. As an essential component of cloud computing, the scheduling algorithm controls many virtualized resources and plays a crucial role in providing efficient cloud services. Assigning Internet-of-Things (IoT) tasks to virtual machines (VMs) is one of the most significant issues and falls in the category of NP-Hard problems. This paper proposes a scheduler based on the firefly algorithm (FFA) to solve the IoT task scheduling problem in cloud computing efficiently. The modified firefly algorithm-based scheduler employs the transfer function (TF) and the Quantization technique to schedule IoT tasks on VMs in a cloud environment to minimize the makespan of IoT tasks. The proposed algorithm has been compared with the well-known Harris Hawks Optimization (HHO) and Differential Evolution (DE) algorithms. The simulation study demonstrates that the proposed algorithm outperforms HHO and DE algorithms regarding convergence speed and result quality. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： Cloud Computing firefly algorithm Internet-of-things Makespan Quantization method Scheduling Transfer function

来源：评论

学校读者我要写书评

暂无评论

I/F-Race tuned firefly algorithm and particle swarm optimization for K-medoids-based clustering

引用

EVOLUTIONARY INTELLIGENCE 2023年第1期16卷 351-373页

作者： Khennak, Ilyes Drias, Habiba Drias, Yassine Bendakir, Faysal Hamdi, Samy USTHB Lab Res Artificial Intelligence Algiers Algeria Algiers Univ Algiers Algeria

Clustering is still one of the most common unsupervised learning techniques in data mining since it allows the discovery of meaningful and interesting patterns, knowledge, rules and associations from large-scale datasets. K-medoids, a variant of K-means, is a popular clustering method that attempts to find the optimal combination of K medoids from among a set of potential combinations. It has been successfully applied to solve various real-life problems owing to its simplicity and effectiveness. Nevertheless, due to the exponential number of possible combinations of K medoids, it is extremely challenging to produce the optimal one within a reasonable amount of time. Therefore, in this work, we propose to formulate the problem of K-medoids clustering as an optimization problem and then combine two effective and powerful Swarm Intelligence (SI) algorithms, namely firefly algorithm (FA) and Particle Swarm Optimization (PSO), to select the appropriate combination of K medoids. We extensively evaluate the proposed FA-PSO for K-medoids-based clustering, abbreviated as FA-PSO-KMED, using 10 UCI datasets. We first use the Iterated F-Race (I/F-Race) algorithm to determine the optimal parameter settings for FA and PSO. Then, we compare the results of the proposed FA-PSO-KMED with those obtained using the well-known state-of-the-art K-medoids-based clustering algorithms: PAM, CLARA and CLARANS. We also compare the results with 11 popular swarm intelligence algorithms: PSO, ABC, CS, FA, BA, APSO, EHO, HHO, SMA, AO and RSA. Experimental results and statistical analysis show that the proposed FA-PSO-KMED is very promising and demonstrates a significant improvement over the other clustering algorithms.

关键词： Clustering K-medoids Swarm intelligence firefly algorithm Particle swarm optimization Iterated F-Race algorithm

来源：评论

学校读者我要写书评

暂无评论

Multi-criteria- Recommendations using Autoencoder and Deep Neural Networks with Weight Optimization using firefly algorithm

引用

INTERNATIONAL JOURNAL OF ENGINEERING 2023年第1期36卷 130-138页

作者： Spoorthy, G. Sanjeevi, S. G. NIT Warangal Dept CSE Warangal India

Demand for personalized recommendation systems elevated recently by e-commerce, news portals etc., to grab the customer interest on the sites. Collaborative filtering proves to be powerful technique but it always suffers from data sparsity, cold-start and robustness issues. These issues have been tackled by some approaches resulting in higher accuracy. Few of them take user profiles, item attributes and rating time as the side information along with ratings to give interpretative personalized recommendations. These type of approaches tries to find which factors mainly impacted the user to rate an item. Another approach extends the single-criteria ratings of collaborative filtering to multi-criteria ratings. Our approach exploits non-linear interpretative recommendations by exploring Multi-criteria ratings by combination of Autoencoders with dropout layer and firefly algorithm optimized weights for deep neural networks. Our approach solves data sparsity, scalability issues and fetch accurate recommendations. Experimental evaluations have been done using Yahoo! Movie and MovieLens datasets. Our approach outperforms in robustness and accuracy with respect to previous research works.

关键词： Multi-Criteria Recommendation Systems Autoencoder firefly algorithm Deep Neural Networks Deep Learning

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：