23 research outputs found
A Method Based on Intuitionistic Fuzzy Dependent Aggregation Operators for Supplier Selection
Recently, resolving the decision making problem of evaluation and ranking the potential suppliers have become as a key strategic factor for business firms. In this paper, two new intuitionistic fuzzy aggregation operators are developed: dependent intuitionistic fuzzy ordered weighed averaging (DIFOWA) operator and dependent intuitionistic fuzzy hybrid weighed aggregation (DIFHWA) operator. Some of their main properties are studied. A method based on the DIFHWA operator for intuitionistic fuzzy multiple attribute decision making is presented. Finally, an illustrative example concerning supplier selection is given
Exploiting Reliability-Guided Aggregation for the Assessment of Curvilinear Structure Tortuosity
The study on tortuosity of curvilinear structures in medical images has been significant in support of the examination and diagnosis for a number of diseases. To avoid the bias that may arise from using one particular tortuosity measurement, the simultaneous use of multiple measurements may offer a promising approach to produce a more robust overall assessment. As such, this paper proposes a data-driven approach for the automated grading of curvilinear structures’ tortuosity, where multiple morphological measurements are aggregated on the basis of reliability to form a robust overall assessment. The proposed pipeline starts dealing with the imprecision and uncertainty inherently embedded in empirical tortuosity grades, whereby a fuzzy clustering method is applied on each available measurement. The reliability of each measurement is then assessed following a nearest neighbour guided approach before the final aggregation is made. Experimental results on two corneal nerve and one retinal vessel data sets demonstrate the superior performance of the proposed method over those where measurements are used independently or aggregated using conventional averaging operators
Leverage Business Analytics and OWA to Recommend Appropriate Projects in Crowdfunding Platform
Nowadays, crowdfunding is becoming more and more popular. Many studies have been published on the crowdfunding platform from different perspectives. However, among all these studies, few are concerned about the recommendation methods, which, in effect, are highly beneficial to crowdfunding websites and the participants. Having considered the situation talked above, this paper works out the several features from the relative projects of user’s current browsing project. Then we give different weights to each feature based on selective attention phenomenon, and adopt the method of OWA operator to calculate the final score of each relative project and accomplish our model by picking out the four projects with different outstanding characteristics. Finally, according to the statistics on China’s famous crowdfunding website, we conducted a group of contrast experiments and eventually testified that our proposed model could, to some extent, help classify and give recommendation effectively. Furthermore, the results of this research can give guidance to the management of crowdfunding websites and they are also very significant advices for the future crowdfunding website development
OWA Operators in Generalized Distances
Different types of aggregation operators such as the ordered weighted quasi-arithmetic mean (Quasi-OWA) operator and the normalized Hamming distance are studied. We introduce the use of the OWA operator in generalized distances such as the quasi-arithmetic distance. We will call these new distance aggregation the ordered weighted quasi-arithmetic distance (Quasi-OWAD) operator. We develop a general overview of this type of generalization and study some of their main properties such as the distinction between descending and ascending orders. We also consider different families of Quasi-OWAD operators such as the Minkowski ordered weighted averaging distance (MOWAD) operator, the ordered weighted averaging distance (OWAD) operator, the Euclidean ordered weighted averaging distance (EOWAD) operator, the normalized quasi-arithmetic distance, et
State Estimation Fusion for Linear Microgrids over an Unreliable Network
Microgrids should be continuously monitored in order to maintain suitable voltages over
time. Microgrids are mainly monitored remotely, and their measurement data transmitted through
lossy communication networks are vulnerable to cyberattacks and packet loss. The current study
leverages the idea of data fusion to address this problem. Hence, this paper investigates the effects of
estimation fusion using various machine-learning (ML) regression methods as data fusion methods
by aggregating the distributed Kalman filter (KF)-based state estimates of a linear smart microgrid
in order to achieve more accurate and reliable state estimates. This unreliability in measurements
is because they are received through a lossy communication network that incorporates packet loss
and cyberattacks. In addition to ML regression methods, multi-layer perceptron (MLP) and dependent
ordered weighted averaging (DOWA) operators are also employed for further comparisons.
The results of simulation on the IEEE 4-bus model validate the effectiveness of the employed ML
regression methods through the RMSE, MAE and R-squared indices under the condition of missing
and manipulated measurements. In general, the results obtained by the Random Forest regression
method were more accurate than those of other methods.This research was partially funded by public research projects of Spanish Ministry of
Science and Innovation, references PID2020-118249RB-C22 and PDC2021-121567-C22 - AEI/10.13039/
501100011033, and by the Madrid Government (Comunidad de Madrid-Spain) under the Multiannual
Agreement with UC3M in the line of Excellence of University Professors, reference EPUC3M17
Short-term intra-subject variation in exhaled volatile organic compounds (VOCs) in COPD patients and healthy controls and its effect on disease classification
Exhaled volatile organic compounds (VOCs) are of interest for their potential to diagnose disease non-invasively. However, most breath VOC studies have analyzed single breath samples from an individual and assumed them to be wholly consistent representative of the person. This provided the motivation for an investigation of the variability of breath profiles when three breath samples are taken over a short time period (two minute intervals between samples) for 118 stable patients with Chronic Obstructive Pulmonary Disease (COPD) and 63 healthy controls and analyzed by gas chromatography and mass spectroscopy (GC/MS). The extent of the variation in VOC levels differed between COPD and healthy subjects and the patterns of variation differed for isoprene versus the bulk of other VOCs. In addition, machine learning approaches were applied to the breath data to establish whether these samples differed in their ability to discriminate COPD from healthy states and whether aggregation of multiple samples, into single data sets, could offer improved discrimination. The three breath samples gave similar classification accuracy to one another when evaluated separately (66.5% to 68.3% subjects classified correctly depending on the breath repetition used). Combining multiple breath samples into single data sets gave better discrimination (73.4% subjects classified correctly). Although accuracy is not sufficient for COPD diagnosis in a clinical setting, enhanced sampling and analysis may improve accuracy further. Variability in samples, and short-term effects of practice or exertion, need to be considered in any breath testing program to improve reliability and optimize discrimination
Determine OWA operator weights using kernel density estimation
Some subjective methods should divide input values into local
clusters before determining the ordered weighted averaging
(OWA) operator weights based on the data distribution characteristics
of input values. However, the process of clustering input values
is complex. In this paper, a novel probability density based
OWA (PDOWA) operator is put forward based on the data distribution
characteristics of input values. To capture the local cluster
structures of input values, the kernel density estimation (KDE) is
used to estimate the probability density function (PDF), which fits
to the input values. The derived PDF contains the density information
of input values, which reflects the importance of input
values. Therefore, the input values with high probability densities
(PDs) should be assigned with large weights, while the ones with
low PDs should be assigned with small weights. Afterwards, the
desirable properties of the proposed PDOWA operator are investigated.
Finally, the proposed PDOWA operator is applied to handle
the multicriteria decision making problem concerning the evaluation
of smart phones and it is compared with some existing
OWA operators. The comparative analysis shows that the proposed
PDOWA operator is simpler and more efficient than the
existing OWA operator