Two‐parametric generalized fuzzy knowledge measure and accuracy measure with applications

2021 ◽  
Author(s):  
Surender Singh ◽  
Abdul Haseeb Ganie
Keyword(s):  
Knowledge Measure ◽  
Accuracy Measure ◽  
Fuzzy Knowledge

scholarly journals On Generalized Intuitionistic Fuzzy Knowledge Measure and its Application in MADM using Combined Weights

2019 ◽  
Vol 8 (2) ◽  
pp. 1536-1542
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Intuitionistic Fuzzy Set ◽  
Attribute Weights ◽  
Intuitionistic Fuzzy ◽  
Knowledge Measure ◽  
Multi Attribute Decision Making ◽  
Generalized Knowledge ◽  
Fuzzy Knowledge

The role of the fuzzy sets is eminent in the study of the ambiguous type of information. Generalizations and extensions of fuzzy sets have led to a broader analysis of these types of researches. In this communication, we suggest a generalized knowledge measure of an intuitionistic fuzzy set (IFS). We compare several existing intuitionistic fuzzy entropies and knowledge measures with our proposed intuitionistic fuzzy (IF) knowledge measure which indicates that the proposed IF-knowledge measure has a greater ability in discriminating different IFSs. We also apply our proposed intuitionistic fuzzy knowledge measure in multi-attribute decision-making (MADM) problem by using combined weights i.e., objective and subjective attribute weights.

Conceptual, Procedural, and Flexibility Knowledge Measure

2011 ◽  
Author(s):  
Michael Schneider ◽  
Bethany Rittle-Johnson ◽  
Jon R. Star
Keyword(s):  
Knowledge Measure

scholarly journals Quantum algorithm for quicker clinical prognostic analysis: an application and experimental study using CT scan images of COVID-19 patients

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Kinshuk Sengupta ◽  
Praveen Ranjan Srivastava
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Image Classification ◽  
Machine Learning Algorithms ◽  
Classification Task ◽  
Clinical Image ◽  
Prototype Model ◽  
Learning Models ◽  
Accuracy Measure ◽  
Quantum Machine Learning

Abstract Background In medical diagnosis and clinical practice, diagnosing a disease early is crucial for accurate treatment, lessening the stress on the healthcare system. In medical imaging research, image processing techniques tend to be vital in analyzing and resolving diseases with a high degree of accuracy. This paper establishes a new image classification and segmentation method through simulation techniques, conducted over images of COVID-19 patients in India, introducing the use of Quantum Machine Learning (QML) in medical practice. Methods This study establishes a prototype model for classifying COVID-19, comparing it with non-COVID pneumonia signals in Computed tomography (CT) images. The simulation work evaluates the usage of quantum machine learning algorithms, while assessing the efficacy for deep learning models for image classification problems, and thereby establishes performance quality that is required for improved prediction rate when dealing with complex clinical image data exhibiting high biases. Results The study considers a novel algorithmic implementation leveraging quantum neural network (QNN). The proposed model outperformed the conventional deep learning models for specific classification task. The performance was evident because of the efficiency of quantum simulation and faster convergence property solving for an optimization problem for network training particularly for large-scale biased image classification task. The model run-time observed on quantum optimized hardware was 52 min, while on K80 GPU hardware it was 1 h 30 min for similar sample size. The simulation shows that QNN outperforms DNN, CNN, 2D CNN by more than 2.92% in gain in accuracy measure with an average recall of around 97.7%. Conclusion The results suggest that quantum neural networks outperform in COVID-19 traits’ classification task, comparing to deep learning w.r.t model efficacy and training time. However, a further study needs to be conducted to evaluate implementation scenarios by integrating the model within medical devices.

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