An Introduction to Information Sets with an Application to Iris Based Authentication

Hanmandlu, M.; Bansal, Monika; Vasikarla, Shantaram

doi:10.4236/jmp.2020.111008

Cited by 10 publications

(5 citation statements)

References 43 publications

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“…The basic information value

I_{x} μ_{x}

is considered the unit of Information. The properties of the information set and different features derived from it can be found in References 34–36. Having defined the information value associated with each attribute value, we return to the problem under consideration.…”

Section: The Proposed Methodologymentioning

confidence: 99%

“…= d (.) = 0,

c (() .)

μ_{z}

which is the membership function of

I_{z}

in Equation (), it becomes the Hanman transform 34 :

H ((), I_{z}) goodbreak= \frac{1}{n} \sum_{z = 1}^{n} ({}, I_{z} e^{- μ_{z} I_{z}}) .

…”

Section: The Proposed Methodologymentioning

confidence: 99%

“…where the parameters a : ð Þ, b : ð Þ, and d : ð Þ are considered as variables, unlike in Equation ( 3) where they are constants. Now with the substitution of values a : 26), it becomes the Hanman transform 34 :…”

Section: Development Of Pervasive Learning Modelmentioning

confidence: 99%

See 2 more Smart Citations

Brain tumor detection and patient survival prediction using U‐Net and regression model

Asthana

Hanmandlu²,

Vashisth

2022

Int J Imaging Syst Tech

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Brain tumor segmentation is necessitated to ascertain the severity of tumor growth in a brain for possible treatment planning. In this work, we attempt the development of U‐Net‐based semantic segmentation of brain tumors. This network model is trained and tested on three MRI datasets: Brats 2018, Brats 2019, and Brats 2020. The trained U‐Net yields the dice scores of 0.893, 0.837, and 0.753 on Brats 2018; 0.912, 0.891, and 0.808 on Brats 2019, and of 0.917, 0.894, and 0.811 on Brats 2020 for the complete tumor, tumor core, and enhancing tumor respectively. This paper also presents a novel formulation of a regression model based on an Information set to predict the survival rates of patients affected with a brain tumor. The weights of the regression model are learned using the pervasive learning model based on the pervasive information set. The overall survival rates of patients are predicted using the proposed regression model on the three datasets for which High‐Grade Gliomas subjects are considered, and the model achieves the accuracies of 64.2%, 59.8%, and 60.5% on Brats 2018, 2019, and 2020 datasets respectively.

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“…The basic information value

I_{x} μ_{x}

Section: The Proposed Methodologymentioning

confidence: 99%

“…= d (.) = 0,

c (() .)

μ_{z}

which is the membership function of

I_{z}

in Equation (), it becomes the Hanman transform 34 :

H ((), I_{z}) goodbreak= \frac{1}{n} \sum_{z = 1}^{n} ({}, I_{z} e^{- μ_{z} I_{z}}) .

…”

Section: The Proposed Methodologymentioning

confidence: 99%

See 1 more Smart Citation

Brain tumor detection and patient survival prediction using U‐Net and regression model

Asthana

Hanmandlu²,

Vashisth

2022

Int J Imaging Syst Tech

Self Cite

View full text Add to dashboard Cite

show abstract

“…properties are given in [30] and [31]. It is similar to (44) except that its parameters are variables.…”

Section: Definition Of the Information Setmentioning

confidence: 99%

“…To derive this, we make use of the non-normalized Hanman-Anirban entropy function in the possibilistic domain. It is defined as: By choosing the real parameters the exponential gain function can be converted into a Gaussian membership function [30] involving a fuzzifier for the greyscale mammogram image I as follows: I(Ref ), i.e. reference value of the image is taken as the average value and f 2 h (Ref) is the fuzzifier or spread function that is similar to the variance taken from [29].…”

Section: Conflict Of Interestmentioning

confidence: 99%

Formulation of probability-based pervasive information set features and Hanman transform classifier for the categorization of mammograms

Dabass

Hanmandlu²,

Vig

2021

SN Appl. Sci.

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With aim of detecting breast cancer at the early stages using mammograms, this study presents the formulation of five feature types by extending the information set to encompass the concept of an intuitionist fuzzy set. The resulting pervasive information set gives not only the certainty of the pixel intensities of mammograms to a class but also the deficiency in the fuzzy modeling referred to as the hesitancy. The generalized adaptive Hanman Anirban fuzzy entropy function is shown to be equivalent to the hesitancy entropy function. The probability-based fuzzy Hanman transform and the pervasive Information with probability taking the role of hesitancy degree help derive the above five feature types termed as probability-based pervasive Information set features. The effectiveness of each feature type is demonstrated on the mini-MIAS and DDSM databases for the multi-class categorization of mammograms using the Hanman transform classifier. The statistical analysis by ANOVA test proves that the features are statistically significant and the experimental results are shown to be clinically relevant by the expert radiologists. The performance of the five feature types is either superior to or equal to that of some deep learning architectures on comparison but they outperform the state-of-the-art literature methods in the classification of breast cancer using mammograms.

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Novel competitive-cooperative learning models (cclms) based on higher order information sets

Grover

Hanmandlu²

2020

Appl Intell

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An Introduction to Information Sets with an Application to Iris Based Authentication

Cited by 10 publications

References 43 publications

Brain tumor detection and patient survival prediction using U‐Net and regression model

Brain tumor detection and patient survival prediction using U‐Net and regression model

Formulation of probability-based pervasive information set features and Hanman transform classifier for the categorization of mammograms

Novel competitive-cooperative learning models (cclms) based on higher order information sets

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