检索结果-内蒙古大学图书馆

Analysis and classification of arrhythmia types using improved firefly optimization algorithm and autoencoder model

MULTIAGENT AND GRID SYSTEMS 2023年第1期19卷 43-60页

作者： Sinnoor, Mala Janardhan, Shanthi Kaliyil Dr Ambedkar Inst Technol Dept Elect & Commun Bengaluru India Dr Ambedkar Inst Technol Dept Med Elect Bengaluru India

In the present scenario, Electrocardiogram (ECG) is an effective non-invasive clinical tool, which reveals the functionality and rhythm of the heart. The non-stationary nature of ECG signal, noise existence, and heartbeat abnormality makes it difficult for clinicians to diagnose arrhythmia. The most of the existing models concentrate only on classification accuracy. In this manuscript, an automated model is introduced that concentrates on arrhythmia type classification using ECG signals, and also focuses on computational complexity and time. After collecting the signals from the MIT-BIH database, the signal transformation and decomposition are performed by Multiscale Local Polynomial Transform (MLPT) and Ensemble Empirical Mode Decomposition (EEMD). The decomposed ECG signals are given to the feature extraction phase for extracting features. The feature extraction phase includes six techniques: standard deviation, zero crossing rate, mean curve length, Hjorth parameters, mean Teager energy, and log energy entropy. Next, the feature dimensionality reduction and arrhythmia classification are performed utilizing the improved firefly optimization algorithm and autoencoder. The selection of optimal feature vectors by the improved firefly optimization algorithm reduces the computational complexity to linear and consumes computational time of 18.23 seconds. The improved firefly optimization algorithm and autoencoder model achieved 98.96% of accuracy in the arrhythmia type classification, which is higher than the comparative models.

关键词： Arrhythmia classification autoencoder electrocardiogram (ECG) ensemble empirical mode decomposition firefly optimization algorithm multiscale local polynomial transform

来源：评论

学校读者我要写书评

暂无评论

Exponential gannet firefly optimization algorithm enabled deep learning for diabetic retinopathy detection

引用

BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2024年第PartA期87卷

作者： Prabhakar, Telagarapu Rao, T. V. Madhusudhana Maram, Balajee Chigurukota, Dhanunjayarao GMR Inst Technol Dept ECE Vizianagaram 532127 Andhra Pradesh India Vignans Inst Informat Technol Dept Comp Sci & Engn Visakhapatnam India Chandigarh Univ Dept Comp Sci & Engn Mohali 140055 Punjab India Gokaraju Lailavathi Womens Engn Coll Dept CSE Hyderabad India

The diabetes complication which causes various damage to the human eye lead to complete blindness is called diabetic retinopathy. The investigation of the optimization-based Deep Learning (DL) approach is introduced for the detection of diabetic retinopathy using fundus images. Here, the fundus images are pre-processed initially using a median filter and Region of Interest (RoI) extraction, to remove the noise in the image. U-Net is used for lesion segmentation and trained using the introduced Gannet Pelican optimization algorithm (GPOA) to identify various types of lesions where GPOA is the integration of the Gannet optimization algorithm (GOA) and Pelican optimization algorithm (POA). The data augmentation process is carried out using flipping, rotation, shearing, cropping, and translation of fundus images, and the data-augmented fundus image is allowed for a feature extraction process where the image and vector-based features of fundus images are extracted. In addition, Deep Q Network (DQN) is used for the detection of diabetic retinopathy and is trained using the introduced Exponential Gannet Pelican optimization algorithm (EGFOA). The EGFOA is the combination of Exponentially Weighted Moving Average (EWMA), Gannet optimization algorithm (GOA), and firefly optimization algorithm (FFA). Experimental outcomes achieved a maximum of 91.6% of accuracy, 92.2% of sensitivity, and 91.9% of specificity.

关键词： Diabetic retinopathy Gannet optimization algorithm firefly optimization algorithm Pelican optimization algorithm RoI extraction Lesion segmentation

来源：评论

学校读者我要写书评

暂无评论

Performance investigation of an active free-piston Stirling engine using artificial neural network and firefly optimization algorithm

引用

HELIYON 2024年第7期10卷 e28387页

作者： Masoumi, A. P. Tavakolpour-Saleh, A. R. Bagherian, V. Shiraz Univ Technol Dept Mech & Aerosp Engn Shiraz Iran Shiraz Univ Dept Mech Engn Shiraz Iran

The aim of this study is to explore the characteristics of an active Free -Piston Stirling Engine (AFPSE) through the use of machine learning methods. Due to the time -intensive nature of extracting simulation results from complex thermal equations, an Artificial Neural Network (ANN) is utilized to expedite the process. To construct a nonlinear model, 5000 samples are extracted from simulation results. Input parameters included in the model are the hot and cold source temperatures, the voltage given to the DC motor, spring stiffness, and the mass of the power piston, while output parameters are the amplitude and frequency of power piston displacement. The proposed ANN model structure comprises two hidden layer with 10 and 20 neurons, respectively, indicating the applicability of the ANN model in estimating significant parameters of AFPSE in a shorter amount of time. The firefly optimization algorithm is utilized to determine the unknown input parameters of ANN and maximize the output power. Results indicate that a maximum output power of 23.07 W can be attained by applying 8.5 V voltage on the DC motor. This study highlights the potential of machine learning techniques to explore the primary features of AFPSE.

关键词： Active free piston stirling engine Artificial neural network firefly optimization algorithm Nonlinear model

来源：评论

学校读者我要写书评

暂无评论

Mutation reduction in software mutation testing using firefly optimization algorithm

引用

DATA TECHNOLOGIES AND APPLICATIONS 2020年第4期54卷 461-480页

作者： Shomali, Nasrin Arasteh, Bahman Islamic Azad Univ Dept Comp Engn Tabriz Branch Tabriz Iran

Purpose For delivering high-quality software applications, proper testing is required. A software test will function successfully if it can find more software faults. The traditional method of assessing the quality and effectiveness of a test suite is mutation testing. One of the main drawbacks of mutation testing is its computational cost. The research problem of this study is the high computational cost of the mutation test. Reducing the time and cost of the mutation test is the main goal of this study. Design/methodology/approach With regard to the 80-20 rule, 80% of the faults are found in 20% of the fault-prone code of a program. The proposed method statically analyzes the source code of the program to identify the fault-prone locations of the program. Identifying the fault-prone (complex) paths of a program is an NP-hard problem. In the proposed method, a firefly optimization algorithm is used for identifying the most fault-prone paths of a program;then, the mutation operators are injected only on the identified fault-prone instructions. Findings The source codes of five traditional benchmark programs were used for evaluating the effectiveness of the proposed method to reduce the mutant number. The proposed method was implemented in Matlab. The mutation injection operations were carried out by MuJava, and the output was investigated. The results confirm that the proposed method considerably reduces the number of mutants, and consequently, the cost of software mutation-test. Originality/value The proposed method avoids the mutation of nonfault-prone (simple) codes of the program, and consequently, the number of mutants considerably is reduced. In a program with n branch instructions (if instruction), there are 2n execution paths (test paths) that the data and codes into each of these paths can be considered as a target of mutation. Identifying the error-prone (complex) paths of a program is an NP-hard problem. In the proposed method, a firefly optimization algo

关键词： Software testing Mutation testing Mutation reduction firefly optimization algorithm Mutation score

来源：评论

学校读者我要写书评

暂无评论

Comparative analysis of hybrid models of firefly optimization algorithm with support vector machines and multilayer perceptron for predicting soil temperature at different depths

引用

ENGINEERING APPLICATIONS OF COMPUTATIONAL FLUID MECHANICS 2020年第1期14卷 939-953页

作者： Shamshirband, Shahaboddin Esmaeilbeiki, Fatemeh Zarehaghi, Davoud Neyshabouri, Mohammadreza Sannadianfard, Saeed Ghorbani, Mohammad Ali Mosavi, Amir Nabipour, Narjes Chau, Kwok-Wing Ton Duc Thang Univ Dept Management Sci & Technol Dev Ho Chi Minh City Vietnam Ton Duc Thang Univ Fac Informat Technol Ho Chi Minh City Vietnam Univ Tabriz Dept Soil Sci Tabriz Iran Univ Tabriz Dept Water Engn Tabriz Iran Tech Univ Dresden Fac Civil Engn Dresden Germany Bauhaus Univ Weimar Inst Struct Mech Weimar Germany Thuringian Inst Sustainabil & Climate Protect Jen Jena Germany Obuda Univ Budapest Dept Automat Budapest Hungary J Selye Univ Dept Math Komarno Slovakia Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Hong Kong Polytech Univ Dept Civil & Environm Engn Hong Kong Peoples R China

This research aims to model soil temperature (ST) using machine learning models of multilayer perceptron (MLP) algorithm and support vector machine (SVM) in hybrid form with the firefly optimization algorithm, i.e. MLP-FFA and SVM-FFA. In the current study, measured ST and meteorological parameters of Tabriz and Ahar weather stations in a period of 2013-2015 are used for training and testing of the studied models with one and two days as a delay. To ascertain conclusive results for validation of the proposed hybrid models, the error metrics are benchmarked in an independent testing period. Moreover, Taylor diagrams utilized for that purpose. Obtained results showed that, in a case of one day delay, except in predicting ST at 5 cm below the soil surface (ST5(cm)) at Tabriz station, MLP-FFA produced superior results compared with MLP, SVM, and SVM-FFA models. However, for two days delay, MLP-FFA indicated increased accuracy in predicting ST(5cm)and ST(20cm)of Tabriz station and ST(10cm)of Ahar station in comparison with SVM-FFA. Additionally, for all of the prescribed models, the performance of the MLP-FFA and SVM-FFA hybrid models in the testing phase was found to be meaningfully superior to the classical MLP and SVM models.

关键词： firefly optimization algorithm soil temperature artificial neural networks hybrid machine learning prediction

来源：评论

学校读者我要写书评

暂无评论

firefly optimization algorithm for the Reactive Power Control of an Isolated Wind-Diesel System

引用

ELECTRIC POWER COMPONENTS AND SYSTEMS 2017年第13期45卷 1413-1425页

作者： Kassem, Ahmed M. Abdelaziz, Almoataz Y. Sohag Univ Fac Engn Dept Elect Engn Sohag Egypt Ain Shams Univ Fac Engn Elect Power & Machines Dept Cairo 11517 Egypt

This paper investigates the application of firefly optimization algorithm to design an optimal control for voltage stability of a stand-alone hybrid renewable generation unit based on reactive power control. The studied renewable generation unit mainly consists of a permanent magnet induction generator driven by wind turbine and a synchronous generator driven by diesel engine. A STATCOM is used to stabilize the terminal load bus voltage via compensating of reactive power. The main control objective aims to stabilize the terminal load voltage against any disturbances in load reactive power and/or input wind power by adjusting the total system reactive power. This is accomplished by controlling STATCOM phase angle and hence to control the load bus voltage and also by controlling the excitation voltage of the synchronous generator. The proposed renewable energy power system based on the proposed optimal controller has been tested through step change in input wind power and load reactive power. The system performance based on the proposed control is compared with model predictive control, a robust H-infinity control, and a classical PI control.

关键词： firefly optimization algorithm permanent magnet induction generator hybrid wind/diesel power unit STATCOM reactive-power control

来源：评论

学校读者我要写书评

暂无评论

Placement optimization of wireless mesh routers using firefly optimization algorithm

Placement optimization of wireless mesh routers using firefl...

引用

International Conference on Smart Communications in Network Technologies (SaCoNeT)

作者： Sayad, Lamri Aissani, Djamil Bouallouche-Medjkoune, Louiza Univ Msila Comp Sci Dept Msila Algeria Univ Bejaia LAMOS Res Unit Bejaia Algeria

ISBN: (纸本)9781538694930

This paper addresses the problem of optimal router nodes placement (RNP) in a wireless mesh network. This issue consists to determine the optimal positions of mesh routers that allow the optimization of the network performance with regards to client coverage and network connectivity. To solve this issue, a bio-inspired algorithm, called firefly optimization algorithm, has been applied since it is an NP-hard issue. The obtained results demonstrate the effectiveness of our proposed approach when compared to the existing genetic algorithm.

关键词： Router placement Node placement Wireless mesh network firefly optimization algorithm Metaheuristic

来源：评论

学校读者我要写书评

暂无评论

Multi-class classification with modified firefly optimization algorithm

JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GA...

引用

JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY 2017年第4期32卷 1097-1107页

作者： Aydilek, Ibrahim Berkan Harran Univ Muhendisl Fak Bilgisayar Muhendisligi Bolumu Osmanbey Kampusu TR-63300 Sanliurfa Turkey

In recent years, nature inspired, modern swarm intelligence optimization algorithms have become more popular. One of these optimization techniques is firefly algorithm. firefly algorithm works based on the principle in the nature, according to its light spreading, the less bright fireflies are directed towards the brighter one. In the nature, intensity of light decreases during spreading, due to suffering a certain amount of absorption according to environment kind and distance to go. Therefore, in this paper we presented a modified firefly algorithm which takes into account changings of the environment situations. Proposed method makes improvement on standard firefly algorithm and is applied for classification on iris, car and zoo multi-class datasets which are frequently used in literature. Classification rules are obtained corresponding to each class label and rule-based classification is done. Classification accuracy is compared with other known classification methods C4.5, PART and Naive-Bayes. The obtained experimental test results are showed that proposed classification method has quite adequate and successful results.

关键词： firefly optimization algorithm rule extraction rule based classification

来源：评论

学校读者我要写书评

暂无评论

Prediction of gas flow rates from gas condensate reservoirs through wellhead chokes using a firefly optimization algorithm

引用

JOURNAL OF NATURAL GAS SCIENCE AND ENGINEERING 2017年 45卷 256-271页

作者： Ghorbani, Hamzeh Moghadasi, Jamshid Wood, David A. Islamic Azad Univ Omidiyeh Branch Young Researchers Club Omidiyeh Iran Petr Univ Technol Dept Petr Engn Ahvaz Iran DWA Energy Ltd Lincoln England

Numerous empirical correlation models for predicting wellhead flow rates have been proposed. Here we apply a recently developed model based upon extensive data from the Ghawar Field (Saudi Arabia) to the Pazanan 1 retrograde gas-condensate field (Aghajari, Iran). A firefly optimization algorithm is applied to select the optimum coefficient values for that model by minimizing the mean square error between measured and predicted gas flow rates from a wellhead-test data set. The input data to calculate gas flow rate includes choke diameter, gas specific gravity, flowing fluid temperature, upstream and downstream pressure. The models prediction accuracy depends upon the coefficient values applied in its formula. The firefly optimization model was tested with various sensitivity cases applying different values to the key control variables gamma and N (number of fireflies in the population). Optimum results in terms of minimum mean square error and rapid convergence was achieved with the control variable values gamma = 2 and N = 40. The optimum case achieved with low error values and a level of accuracy that is significantly better than the predictions for dataset using the coefficient values applied to the Ghawar field, suggesting that such model coefficients need to be optimized on a field-by-field basis. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Gas condensate reservoir performance firefly optimization algorithm Wellhead choke gas flow modelling Gas-flow prediction and optimization firefly optimization implementation workflow

来源：评论

学校读者我要写书评

暂无评论

firefly optimization algorithm effect on support vector regression prediction improvement of a modified labyrinth side weir's discharge coefficient

引用

APPLIED MATHEMATICS AND COMPUTATION 2016年 274卷 14-19页

作者： Zaji, Amir Hossein Bonakdari, Hossein Khodashenas, Saeed Reza Shamshirband, Shahaboddin Razi Univ Dept Civil Engn Kermanshah Iran Ferdowsi Univ Mashhad Water Engn Dept Mashhad Iran Univ Malaya Fac Comp Sci & Informat Technol Dept Comp Syst Kuala Lumpur 50603 Malaysia

A principal step in designing dividing hydraulic structures entails determining the side weir discharge coefficient. In this study, firefly optimization-based Support Vector Regression (SVR-FF) is introduced and examined in terms of predicting the discharge coefficient of a modified labyrinth side weir. Ten non-dimensional parameters of various geometrical and hydraulic conditions are defined as the input parameters for the SVR-FF and the side weir discharge coefficient is defined as the output. Improvements in SVR prediction accuracy are determined by comparing SVR-FF with the traditional SVR model. The results indicate that the SVR-FF model with RMSE of 0.035 is about 10% more accurate than SVR with RMSE of 0.039. Thus, combining the firefly optimization algorithm with SVR increases the prediction model performance. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Discharge coefficient firefly optimization algorithm Modified labyrinth side weir Neural network Support vector regression

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

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

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：