A variable weight‐based hybrid approach for multi‐attribute group decision making under interval‐valued intuitionistic fuzzy sets

Liu, Sen; Wei, Yunxie; Chan, Felix T. S.; Niu, Ben

doi:10.1002/int.22329

Cited by 119 publications

(75 citation statements)

References 87 publications

(137 reference statements)

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“…The spatial analysis of LS indicates that subsidence specifically occurred in the flat areas particularly in the alluvial deposited land and agricultural areas located in arid regions (Herrera-García et al, 2021). A present time, ML algorithms and their ensemble methods have been applied in various fields for the susceptibility mapping and it has been shown to be effective in terms of predictive performance (Nguyen et al, 2019;Arabameri et al, 2020c;Feng et al, 2020;Liu et al, 2020;Zhang et al, 2020a;Saha et al, 2021b). Particularly, ensemble models always enhanced the output result by integrated the several ML algorithms (Mojaddadi et al, 2017;Arabameri et al, 2020d;Saha et al, 2021a).…”

Section: Discussionmentioning

confidence: 99%

Performance Evaluation of GIS-Based Novel Ensemble Approaches for Land Subsidence Susceptibility Mapping

et al. 2021

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The optimal prediction of land subsidence (LS) is very much difficult because of limitations in proper monitoring techniques, field-base surveys and knowledge related to functioning and behavior of LS. Thus, due to the lack of LS susceptibility maps it is almost impossible to identify LS prone areas and as a result it influences severe economic and human losses. Hence, preparation of LS susceptibility mapping (LSSM) can help to prevent natural and human catastrophes and reduce the economic damages significantly. Machine learning (ML) techniques are becoming increasingly proficient in modeling purpose of such kinds of occurrences and they are increasing used for LSSM. This study compares the performances of single and hybrid ML models to preparation of LSSM for future prediction of performance analysis. In this study, the spatial prediction of LS was assessed using four ML models of maximum entropy (MaxEnt), general linear model (GLM), artificial neural network (ANN) and support vector machine (SVM). Alongside, the possible numbers of novel ensemble models were integrated through the aforementioned four ML models for optimal analysis of LSSM. An inventory LS map was prepared based on the previous occurrences of LS points and the dataset were divvied into 70:30 ratios for training and validating of the modeling process. To identify the robust and best LSSMs, receiver operating characteristic-area under curve (ROC-AUC) curve was employed. The ROC-AUC result indicated that ANN model gives the highest ROC-AUC (0.924) in training accuracy. The highest AUC (0.823) of the LSSMs was determined based on validation datasets identified by SVM followed by ANN-SVM (0.812).

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Section: Discussionmentioning

confidence: 99%

Performance Evaluation of GIS-Based Novel Ensemble Approaches for Land Subsidence Susceptibility Mapping

et al. 2021

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“…Consequently, the necessity of designing an accurate (Liu et al 2021 ; Yang and Sowmya 2015 ; Zhang et al 2020b , c ) and real-time detector (Ran et al 2020 ; Wang 2020 ; Zuo et al 2015 ) has become more prominent. Besides, this review on COVID19 detection systems shows that most of the existing deep learning-based systems have used deep CNN-based networks (Li et al 2019a ; Ma et al 2019 ; Xu et al 2020 ; Yang et al 2020a , 2021 ); thereby, we propose to employ the ability of deep CNN as a COVID 19 detector.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Evolving deep convolutional neutral network by hybrid sine–cosine and extreme learning machine for real-time COVID19 diagnosis from X-ray images

Khishe²,

Mohammadi

et al. 2021

Soft Comput

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The COVID19 pandemic globally and significantly has affected the life and health of many communities. The early detection of infected patients is effective in fighting COVID19. Using radiology (X-Ray) images is, perhaps, the fastest way to diagnose the patients. Thereby, deep Convolutional Neural Networks (CNNs) can be considered as applicable tools to diagnose COVID19 positive cases. Due to the complicated architecture of a deep CNN, its real-time training and testing become a challenging problem. This paper proposes using the Extreme Learning Machine (ELM) instead of the last fully connected layer to address this deficiency. However, the parameters’ stochastic tuning of ELM’s supervised section causes the final model unreliability. Therefore, to cope with this problem and maintain network reliability, the sine–cosine algorithm was utilized to tune the ELM’s parameters. The designed network is then benchmarked on the COVID-Xray-5k dataset, and the results are verified by a comparative study with canonical deep CNN, ELM optimized by cuckoo search, ELM optimized by genetic algorithm, and ELM optimized by whale optimization algorithm. The proposed approach outperforms comparative benchmarks with a final accuracy of 98.83% on the COVID-Xray-5k dataset, leading to a relative error reduction of 2.33% compared to a canonical deep CNN. Even more critical, the designed network’s training time is only 0.9421 ms and the overall detection test time for 3100 images is 2.721 s.

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“…Mathematical Problems in Engineering [59][60][61], whale optimization algorithm (WOA) [62][63][64], grey wolf optimizer (GWO) [65,66], bacterial foraging optimization (BFO) [67], and grasshopper optimization algorithm (GOA) [68]. e aim of optimization is to determine a suitable value for one or more parameters between all possible values for them in order to minimize or maximize a function and it can be applied to find feasible answer to many potential real-life applications such as deployment optimization [69], adaptive control concepts [35,[70][71][72], computer vision techniques [73], transportation networks [74], image and video processing [75][76][77][78][79][80], decision-making approaches [81][82][83], power allocation systems [84], sensor fusion approaches [85], monitoring systems [86][87][88][89], and deep learning models [19,[90][91][92][93]. e PSO algorithm as an optimization algorithm is a social interaction model between independent particles that use their social knowledge to find the minimum and maximum value of a function [15].…”

Section: Particle Swarm Optimization (Pso) Algorithmmentioning

confidence: 99%

Short-Term Load Forecasting Using Neural Network and Particle Swarm Optimization (PSO) Algorithm

Chafi

Afrakhte

2021

Mathematical Problems in Engineering

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Electrical load forecasting plays a key role in power system planning and operation procedures. So far, a variety of techniques have been employed for electrical load forecasting. Meanwhile, neural-network-based methods led to fewer prediction errors due to their ability to adapt properly to the consuming load's hidden characteristic. Therefore, these methods were widely accepted by the researchers. As the parameters of the neural network have a significant impact on its performance, in this paper, a short-term electrical load forecasting method using neural network and particle swarm optimization (PSO) algorithm is proposed, in which some neural network parameters including learning rate and number of hidden layers are determined in order to forecast electrical load using the PSO algorithm precisely. Then, the neural network with these optimized parameters is used to predict the short-term electrical load. In this method, a three-layer feedforward neural network trained by backpropagation algorithm is used beside an improved gbest PSO algorithm. Also, the neural network prediction error is defined as the PSO algorithm cost function. The proposed approach has been tested on the Iranian power grid using MATLAB software. The average of three indices beside graphical results has been considered to evaluate the performance of the proposed method. The simulation results reflect the capabilities of the proposed method in accurately predicting the electrical load.

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A variable weight‐based hybrid approach for multi‐attribute group decision making under interval‐valued intuitionistic fuzzy sets

Cited by 119 publications

References 87 publications

Performance Evaluation of GIS-Based Novel Ensemble Approaches for Land Subsidence Susceptibility Mapping

Performance Evaluation of GIS-Based Novel Ensemble Approaches for Land Subsidence Susceptibility Mapping

RETRACTED ARTICLE: Evolving deep convolutional neutral network by hybrid sine–cosine and extreme learning machine for real-time COVID19 diagnosis from X-ray images

Short-Term Load Forecasting Using Neural Network and Particle Swarm Optimization (PSO) Algorithm

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