A novel TODIM‐VIKOR approach based on entropy and Jensen–Tsalli divergence measure for picture fuzzy sets in a decision‐making problem

Arya, Vikas; Kumar, Satish

doi:10.1002/int.22289

Cited by 58 publications

(17 citation statements)

References 68 publications

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“…Ma et al established a sitting posture recognition system based on triaxial accelerometers, transformed the acceleration data into feature vectors for component analysis, and used SVM and K ‐means clustering to classify sitting postures, and experimentally demonstrated the superiority of the SVM algorithm on sitting posture classification 29 . Arya and Kumar designed a home monitoring and assessment model for the activities of the elderly 10 . A back‐propagation (BP) neural network‐based model was designed using the triaxial accelerometer and pressure sensor data, and the validity of the model was experimentally demonstrated 30 .…”

Section: Related Workmentioning

confidence: 99%

“…9 In 2017, Arya's team extracted 15 human skeleton joint data through Kinect sensors, used ConvNets to input RGB-D video frames for pose recognition, and used SVM classifiers for high-precision pose estimation, avoiding the tedious preprocessing of scenes upfront. 10 Ding designed a linear subspace based on Grassmannian manifold using the 3D rigid body relationship matrix (RMRB3D) established by the rotational motion of the human body pose, extracted the pose and generated symbolic sequences by spectral clustering between points, and finally established action sequences by dynamic time warping and HMM up to 72%. In 2018, Munoz et al designed a Kinect-based multimodal learning analysis model as AdaBoost, for behavior recognition by detecting students' learning state through body and gesture postures.…”

Section: Image-based Gesture Recognitionmentioning

confidence: 99%

“…29 Arya and Kumar designed a home monitoring and assessment model for the activities of the elderly. 10 A back-propagation (BP) neural network-based model was designed using the triaxial accelerometer and pressure sensor data, and the validity of the model was experimentally demonstrated. 30 Gravina's team designed an information fusion-based Dempster-Shafer (D-S) theory using body sensor networks (BSNs), and the usability of D-S in the field of posture detection was experimentally demonstrated.…”

Section: Sensor-based Gesture Recognitionmentioning

confidence: 99%

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A fully connected deep learning approach to upper limb gesture recognition in a secure FES rehabilitation environment

Liu

Jiang

et al. 2021

Int J Intell Syst

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Stroke is one of the leading causes of death and disability in the world. The rehabilitation of Patients' limb functions has great medical value, for example, the therapy of functional electrical stimulation (FES) systems, but suffers from effective rehabilitation evaluation. In this paper, six gestures of upper limb rehabilitation were monitored and collected using microelectromechanical systems sensors, where data stability was guaranteed using data preprocessing methods, that is, deweighting, interpolation, and feature extraction. A fully connected neural network has been proposed investigating the effects of different hidden layers, and determining its activation functions and optimizers. Experiments have depicted that a three‐hidden‐layer model with a softmax function and an adaptive gradient descent optimizer can reach an average gesture recognition rate of 97.19%. A stop mechanism has been used via recognition of dangerous gesture to ensure the safety of the system, and the lightweight cryptography has been used via hash to ensure the security of the system. Comparison to the classification models, for example, k‐nearest neighbor, logistic regression, and other random gradient descent algorithms, was conducted to verify the outperformance in recognition of upper limb gesture data. This study also provides an approach to creating health profiles based on large‐scale rehabilitation data and therefore consequent diagnosis of the effects of FES rehabilitation.

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Section: Related Workmentioning

confidence: 99%

Section: Image-based Gesture Recognitionmentioning

confidence: 99%

Section: Sensor-based Gesture Recognitionmentioning

confidence: 99%

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A fully connected deep learning approach to upper limb gesture recognition in a secure FES rehabilitation environment

Liu

Jiang

et al. 2021

Int J Intell Syst

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“…Hence, Garai et al 30,31 approach depict limitations as such this method will not be a good choice in decision making problems. Further, the decision by the current method is equivalent to that of Biswas et al 28 T A B L E = (1, 4, 4, 7), (0, 4, 4, 8), (1,4,4,7) and 〈 〉 ͠ b = (2,4,4,6), (1,4,4,7), (2,4,4,6) . A comparative study of the methods by Deli and Subas, 27 Biswas et al 28 and with the current method is done.…”

Section: 6mentioning

confidence: 99%

“…Another such generalization of fuzzy numbers, in fact, IFNs are picture fuzzy numbers and neutrosophic numbers, which incorporates the indeterminacy-membership apart from the truth-membership and falsitymembership functions. As such many works are done on the application of these numbers in various decision making problems, namely, Si et al, 5 Riaz et al, 6 Arya and Kumar, 7 Ates and Akay, 8 and so forth. The generalization of neutrosophic numbers is possible through the pioneering work by Smarandache.…”

Section: Introductionmentioning

confidence: 99%

Ordering single‐valued neutrosophic numbers based on flexibility parameters and its reasonable properties

et al. 2021

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Undoubtedly, most of the information conveyed is full of impreciseness or vagueness. In such situation, fuzzy set theory is a proper tool to handle these types of impreciseness or vagueness. Fuzzy numbers are also of different types based on the presence of uncertainty. Neutrosphic numbers are one type of fuzzy number that consists of three types of membership functions, namely, truth, indeterminacy and falsity. In fact, neutrosophic numbers are generalization of intuitionistic fuzzy numbers. These numbers might be used in decision making problems, where it is essential to rank those numbers. So, ordering of these numbers becomes a new trends with more challenges. In this paper, an innovative ranking approach of single-valued neutrosophic number has been put forwarded. The method is based on value, ambiguity and θ function. Value and ambiguity are defined on three functions of the neutrosophic numbers. Further, the reliability of the proposed method is established by considering the reasonable properties of ranking method. One important aspect of ranking neutrosophic numbers is to consider the consistency in ordering the neutrosophic numbers and their corresponding images. It is worth to mention that the current approach consistently ranks

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Intuitionistic fuzzy MABAC method based on cumulative prospect theory for multiple attribute group decision making

Zhao

Wei

Chen

et al. 2021

Int J of Intelligent Sys

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Intuitionistic fuzzy sets are often employed to depict complex and uncertain environments. The purpose of this article is to construct a new approach for dealing with multiattribute group decision making (MAGDM) problems. In this paper, we introduce the concept of cumulative prospect theory (CPT) into the original multiattributive border approximation area comparison (MABAC) method and create the intuitionistic fuzzy MABAC method based on CPT (CPT‐IF‐MABAC method). The new CPT‐IF‐MABAC method not only has fairly strong operability, but also inherits the characteristic of CPT that considers the influence of decision makers' attitude. Furthermore, this new model mixes together the determination method of attribute weight and alternative. At the end, the stability and availability of this method are demonstrated through an application instance about cold chain and the comparison with the existing methods, such as IF‐MULTIMOORA method and IF‐VIKOR method. In the future, we devote ourselves to explore more applications of this new proposed method and other more effective solutions for MAGDM.

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A novel TODIM‐VIKOR approach based on entropy and Jensen–Tsalli divergence measure for picture fuzzy sets in a decision‐making problem

Abstract: The picture fuzzy set (PFS) has grown huge attention in the research area of uncertain information from the last few years. Information measures have been widely studied in various fuzzy environments.

Cited by 58 publications

References 68 publications

A fully connected deep learning approach to upper limb gesture recognition in a secure FES rehabilitation environment

A fully connected deep learning approach to upper limb gesture recognition in a secure FES rehabilitation environment

Ordering single‐valued neutrosophic numbers based on flexibility parameters and its reasonable properties

Intuitionistic fuzzy MABAC method based on cumulative prospect theory for multiple attribute group decision making

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