Multiple attribute group decision-making method based on extended bipolar fuzzy MABAC approach

Jana, Chiranjibe

doi:10.1007/s40314-021-01606-3

Cited by 40 publications

(20 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In constructing these experiments, we referred to other prior research that planned experiments similar to this study and suggested improvements in the existing models and evaluation performances (Jana, 2021a ; Liu et al, 2019 ; Jana, 2021b ; Jana & Ghosh, 2018 ; Jana & Pal, 2021 ; Jana et al, 2021 ). These studies effectively improved decision-making and prediction models, which are similar to the proposed models in this study.…”

Section: Prediction Based On Network Analysis Resultsmentioning

confidence: 99%

Improving data efficiency for analyzing global exchange rate fluctuations based on nonlinear causal network-based clustering

2022

View full text Add to dashboard Cite

This study used information theory and network theory to predict the fluctuations of currency values of the machine learning model. For experiments, we calculate the causal relationships between currencies using loarithmic return (log-return) and entropic value-at-risk (EVaR) values of gold price per troy ounce in 48 currencies over 25 years. To quantify the causal relationships, we used the concept of transfer entropy. After quantifying their information flow, we modeled and analyzed those nonlinear causal relationships as a network. The network analysis results confirmed that information flow-based nonlinear causal relationships differed from the commonly-known key currency order. Then, we classified currencies using hierarchical clustering methods based on the configured networks. We predicted fluctuations in currency values using machine learning algorithms based on network topology-based information. As a result, we show that using the data columns in the same communities based on statistically significant nonlinear causal relationships can improve most machine-learning-based fluctuations of currency values for various countries from the perspective of data efficiency.

show abstract

Section: Prediction Based On Network Analysis Resultsmentioning

confidence: 99%

Improving data efficiency for analyzing global exchange rate fluctuations based on nonlinear causal network-based clustering

2022

View full text Add to dashboard Cite

show abstract

“…Finally, the proposed system is compared with the current methods. Future research can benefit from the papers findings 44–48 …”

Section: Discussionmentioning

confidence: 99%

Extension of GRA method for multiattribute group decision making problem under linguistic Pythagorean fuzzy setting with incomplete weight information

Khan¹,

Jana

Khan

et al. 2022

Int J of Intelligent Sys

Self Cite

View full text Add to dashboard Cite

show abstract

“…Machine learning techniques are finding their applications in several fields, and various methods are evolving [26][27][28][29][30][31]. Optuna is an automatic hyper-parameter software framework for the optimization process, and it automatically finds optimal hyper-parameter values by making use of different samplers, such as grid search, random, Bayesian, and evolutionary algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

Annealing of Monel 400 Alloy Using Principal Component Analysis, Hyper-parameter Optimization, Machine Learning Techniques, and Multi-objective Particle Swarm Optimization

Chintakindi

Al-Samhan

Abidi

et al. 2022

Int J Comput Intell Syst

View full text Add to dashboard Cite

The purpose of this paper is to investigate the effect of the annealing process at 1000 °C on machining parameters using contemporary techniques such as principal component analysis (PCA), hyper-parameter optimization by Optuna, multi-objective particle swarm optimization, and theoretical validation using the machine learning method. Results after annealing show that there will be a reduction in surface roughness values by 19.61%, tool wear by 6.3%, and an increase in the metal removal rate by 14.98%. The PCA results show that the feed is more significant than the depth of cut and speed. The higher value of the composite primary component will represent optimal factors such as speed of 80, feed of 0.2 and depth of cut of 0.3, and values of principal components like surface roughness (Ψ1 = 64.5), tool wear (Ψ2 = 22.3) and metal removal rate (Ψ3 = 13.2). Hyper-parameter optimization represents speed is directly proportional to roughness, tool wear, and metal removal rate, while feed and depth of cut are inversely proportional. The optimization history plot will be steady, and the prediction accuracy will be 96.96%. Machine learning techniques are employed through the Python language using Google Colab. The estimated values from the decision tree method for surface roughness and tool wear predictions using the AdaBoost algorithm match well with actual values. As per MOPSO (multi-objective particle swarm optimization), the predicted responses are as follows; surface roughness (2.5 μm, 100, 02, 0.45), tool wear (0.31 mm, 40, 0.40, 0.60), and MRR (material removal rate) (5145 mm3/min, 100, 0.4, 0.15). As validated by experimentation, there are small variations as the surface roughness varied by 1.56%, tool wear by 6.8%, and MRR by 2.57%.

show abstract

Multiple attribute group decision-making method based on extended bipolar fuzzy MABAC approach

Cited by 40 publications

References 45 publications

Improving data efficiency for analyzing global exchange rate fluctuations based on nonlinear causal network-based clustering

Improving data efficiency for analyzing global exchange rate fluctuations based on nonlinear causal network-based clustering

Extension of GRA method for multiattribute group decision making problem under linguistic Pythagorean fuzzy setting with incomplete weight information

Annealing of Monel 400 Alloy Using Principal Component Analysis, Hyper-parameter Optimization, Machine Learning Techniques, and Multi-objective Particle Swarm Optimization

Contact Info

Product

Resources

About