Mathematical Methods in Interdisciplinary Sciences E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 802

Veröffentlichungsjahr: 2020

Beschreibung

Brings mathematics to bear on your real-world, scientific problems Mathematical Methods in Interdisciplinary Sciences provides a practical and usable framework for bringing a mathematical approach to modelling real-life scientific and technological problems. The collection of chapters Dr. Snehashish Chakraverty has provided describe in detail how to bring mathematics, statistics, and computational methods to the fore to solve even the most stubborn problems involving the intersection of multiple fields of study. Graduate students, postgraduate students, researchers, and professors will all benefit significantly from the author's clear approach to applied mathematics. The book covers a wide range of interdisciplinary topics in which mathematics can be brought to bear on challenging problems requiring creative solutions. Subjects include: * Structural static and vibration problems * Heat conduction and diffusion problems * Fluid dynamics problems The book also covers topics as diverse as soft computing and machine intelligence. It concludes with examinations of various fields of application, like infectious diseases, autonomous car and monotone inclusion problems.

Leseprobe

Table of Contents

Cover

Notes on Contributors

Preface

Acknowledgments

1 Connectionist Learning Models for Application Problems Involving Differential and Integral Equations

1.1 Introduction

1.2 Methodology for Differential Equations

1.3 Methodology for Solving a System of Fredholm Integral Equations of Second Kind

1.4 Numerical Examples and Discussion

1.5 Conclusion

References

2 Deep Learning in Population Genetics: Prediction and Explanation of Selection of a Population

2.1 Introduction

2.2 Literature Review

2.3 Dataset Description

2.4 Objective

2.5 Relevant Theory, Results, and Discussions

2.6 Conclusion

References

3 A Survey of Classification Techniques in Speech Emotion Recognition

3.1 Introduction

3.2 Emotional Speech Databases

3.3 SER Features

3.4 Classification Techniques

3.5 Difficulties in SER Studies

3.6 Conclusion

References

4 Mathematical Methods in Deep Learning

4.1 Deep Learning Using Neural Networks

4.2 Introduction to Neural Networks

4.3 Other Activation Functions (Variant Forms of ReLU)

4.4 Backpropagation Algorithm

4.5 Performance and Accuracy

4.6 Results and Observation

References

5 Multimodal Data Representation and Processing Based on Algebraic System of Aggregates

5.1 Introduction

5.2 Basic Statements of ASA

5.3 Operations on Aggregates and Multi‐images

5.4 Relations and Digital Intervals

5.5 Data Synchronization

5.6 Fuzzy Synchronization

5.7 Conclusion

References

6 Nonprobabilistic Analysis of Thermal and Chemical Diffusion Problems with Uncertain Bounded Parameters

6.1 Introduction

6.2 Preliminaries

6.3 Finite Element Formulation for Tapered Fin

6.4 Radon Diffusion and Its Mechanism

6.5 Radon Diffusion Mechanism with TFN Parameters

6.6 Conclusion

References

7 Arbitrary Order Differential Equations with Fuzzy Parameters

7.1 Introduction

7.2 Preliminaries

7.3 Arbitrary Order Integral and Derivative for Fuzzy‐Valued Functions

7.4 Generalized Fuzzy Laplace Transform with Respect to Another Function

References

8 Fluid Dynamics Problems in Uncertain Environment

8.1 Introduction

8.2 Preliminaries

8.3 Problem Formulation

8.4 Methodology

8.5 Application of HPM and HPTM

8.6 Results and Discussion

8.7 Conclusion

References

9 Fuzzy Rough Set Theory‐Based Feature Selection: A Review

9.1 Introduction

9.2 Preliminaries

9.3 Fuzzy Rough Set‐Based Attribute Reduction

9.4 Approaches for Semisupervised and Unsupervised Decision Systems

9.5 Decision Systems with Missing Values

9.6 Applications in Classification, Rule Extraction, and Other Application Areas

9.7 Limitations of Fuzzy Rough Set Theory

9.8 Conclusion

References

10 Universal Intervals: Towards a Dependency‐Aware Interval Algebra

10.1 Introduction

10.2 The Need for Interval Computations

10.3 On Some Algebraic and Logical Fundamentals

10.4 Classical Intervals and the Dependency Problem

10.5 Interval Dependency: A Logical Treatment

10.6 Interval Enclosures Under Functional Dependence

10.7 Parametric Intervals: How Far They Can Go

10.8 Universal Intervals: An Interval Algebra with a Dependency Predicate

10.9 The S‐Field Algebra of Universal Intervals

10.10 Guaranteed Bounds or Best Approximation or Both?

Supplementary Materials

Acknowledgments

References

11 Affine‐Contractor Approach to Handle Nonlinear Dynamical Problems in Uncertain Environment

11.1 Introduction

11.2 Classical Interval Arithmetic

11.3 Interval Dependency Problem

11.4 Affine Arithmetic

11.5 Contractor

11.6 Proposed Methodology

11.7 Numerical Examples

11.8 Conclusion

References

12 Dynamic Behavior of Nanobeam Using Strain Gradient Model

12.1 Introduction

12.2 Mathematical Formulation of the Proposed Model

12.3 Review of the Differential Transform Method (DTM)

12.4 Application of DTM on Dynamic Behavior Analysis

12.5 Numerical Results and Discussion

12.6 Conclusion

Acknowledgment

References

13 Structural Static and Vibration Problems

13.1 Introduction

13.2 One‐parameter Groups

13.3 Infinitesimal Transformation

13.4 Canonical Coordinates

13.5 Algorithm for Lie Symmetry Point

13.6 Reduction of the Order of the ODE

13.7 Solution of First‐Order ODE with Lie Symmetry

13.8 Identification

13.9 Vibration of a Microcantilever Beam Subjected to Uniform Electrostatic Field

13.10 Contact Form for the Equation

13.11 Reducing in the Order of the Nonlinear ODE Representing the Vibration of a Microcantilever Beam Under Electrostatic Field

13.12 Nonlinear Pull‐in Voltage

13.13 Nonlinear Analysis of Pull‐in Voltage of Twin Microcantilever Beams

13.14 Nonlinear Analysis of Pull‐in Voltage of Twin Microcantilever Beams of Different Thicknesses

References

14 Generalized Differential and Integral Quadrature: Theory and Applications

14.1 Introduction

14.2 Differential Quadrature

14.3 General View on Differential Quadrature

14.4 Generalized Integral Quadrature

14.5 General View: The Two‐Dimensional Case

References

15 Brain Activity Reconstruction by Finding a Source Parameter in an Inverse Problem

15.1 Introduction

15.2 Methodology

15.3 Implementation

15.4 Numerical Results and Discussion

15.5 Conclusion

References

16 Optimal Resource Allocation in Controlling Infectious Diseases

16.1 Introduction

16.2 Mobility‐Based Resource Distribution

16.3 Connection–Strength Minimization

16.4 Risk Minimization

16.5 Conclusion

References

17 Artificial Intelligence and Autonomous Car

17.1 Introduction

17.2 What Is Artificial Intelligence?

17.3 Natural Language Processing

17.4 Robotics

17.5 Image Processing

17.6 Problem Solving

17.7 Optimization

17.8 Autonomous Systems

17.9 Conclusion

References

18 Different Techniques to Solve Monotone Inclusion Problems

18.1 Introduction

18.2 Preliminaries

18.3 Proximal Point Algorithm

18.4 Splitting Algorithms

18.5 Inertial Methods

18.6 Numerical Experiments

References

Index

End User License Agreement

List of Tables

Chapter 1

Table 1.1 Comparison between analytical and LgNN results (Example 1.1).

Table 1.2 Comparison between analytical and LgNN results (Example 1.2).

Table 1.3 Comparison between exact and ANN solutions.

Table 1.4 Comparison between exact and ANN solutions.

Table 1.5 Comparison between exact and ANN solutions.

Chapter 2

Table 2.1 automl results.

Table 2.2 Accuracy on test data.

Chapter 3

Table 3.1 List of literatures on SER grouped by classification models used.

Chapter 5

Table 5.1 Allen's interval algebra relations.

Chapter 6

Table 6.1 Finite difference schemes for PDEs.

Table 6.2 Fuzzy parameters (triangular fuzzy number).

Table 6.3 Nodal temperatures of tapered fin (crisp value).

Table 6.4 Nodal temperatures of tapered fin (triangular fuzzy values).

Table 6.5 : Radon concentration

c

(4,

t

,

α

,

β

)

of Eq. (6.24) by EFDM....

Chapter 8

Table 8.1 Some properties of the considered fluid and nanoparticles [27].

Table 8.2 Velocity profile when

Ha = 0, Re = 110, ω = 30, φ = 0

...

Table 8.3 Comparison of velocity profile of present results with existing res...

Table 8.4 Solution bounds for SA–Cu nanofluid when

Ha = 100, Re = 50, ω = 30

...

Chapter 9

Table 9.1 Fuzzy rough set‐assisted feature selection techniques based on the ...

Table 9.2 Fuzzy rough set‐assisted feature selection techniques based on the ...

Chapter 10

Table 10.1 InCLosure result for different numbers of subdivisions.

Chapter 11

Table 11.1 Comparison of enclosures obtained using IA, AA, and SIVIA.

Chapter 12

Table 12.1 Implementation of DTM on some basic functions [36].

Table 12.2 Implementation of DTM on boundary conditions [36].

Table 12.3 Comparison of present results with Wang et al. [38] for SS case.

Table 12.4 Comparison of present results with Wang et al. [38] for CC case.

Table 12.5 Effect of small‐scale parameter on frequency parameters for SS cas...

Table 12.6 Effect of small‐scale parameters on frequency parameters for CC ca...

Table 12.7 Effect of length‐scale parameter on frequency parameters for SS ca...

Table 12.8 Effect of length‐scale parameter on frequency parameters for CC ca...

Chapter 13

Table 13.1 Pull‐in voltage in linear and nonlinear analysis.

Table 13.2 Pull‐in distance.

Chapter 14

Table 14.1 List of the classic orthogonal polynomials used as basis functions...

Table 14.2 List of the most common basis functions for the functional approxi...

Chapter 15

Table 15.1 A comparison between absolute error obtained by the methods of [1]...

Table 15.2 A comparison between absolute error obtained by the methods of [1]...

Table 15.3 A comparison between absolute error obtained by the methods of [1]...

Table 15.4 A comparison between absolute error obtained by the methods of [1]...

Table 15.5 A comparison between the presented method and the method of [23] i...

Table 15.6 Table of obtaining accuracy for unknown function by using

time st...

Table 15.7 Table of obtaining accuracy for discovering the source parameter b...

Table 15.8 Table of obtaining results for different time steps and different ...

Chapter 16

Table 16.1 Yearly dengue‐infected ratio–Colombo MOH areas.

List of Illustrations

Chapter 1

Figure 1.1 General model of ANN.

Figure 1.2 Sigmoidal function.

Figure 1.3 Structure of FLANN model.

Figure 1.4 Structure of single‐layer Laguerre neural network.

Figure 1.5 ANN model for solving linear systems.

Figure 1.6 Plot of analytical and LgNN results (Example 1.1).

Figure 1.7 Error plot between analytical and LgNN results (Example 1.1).

Figure 1.8 Plot of analytical and LgNN results (Example 1.2).

Figure 1.9 Error plot between analytical and LgNN results (Example 1.2).

Figure 1.10 Time series plot by RKM (Example 1.3).

Figure 1.11 Time series plot by LgNN (Example 1.3).

Figure 1.12 Phase plane plot for RKM.

Figure 1.13 Phase plane plot for LgNN.

Figure 1.14 Convergence plot of Eq. (1.29).

Figure 1.15 Convergence plot of Eq. (1.33).

Figure 1.16 Convergence plot of Eq. (1.37).

Chapter 2

Figure 2.1 Proportional frequency distribution of hard sweep and neutral reg...

Figure 2.2 Spatial distribution of the two classes of selection.

Figure 2.3 Importance of the predictors.

Chapter 3

Figure 3.1 Categories of classifiers used in SER along with some examples.

Figure 3.2 Schematic diagram of an HMM, where

and

are the states and obs...

Chapter 4

Figure 4.1 Shows the schematic representation of a single hidden layer neura...

Figure 4.2 Shows the schematic representation of a single neuron of the hidd...

Figure 4.3 Shows the graphical representation of Sigmoid function.

Figure 4.4 Shows the graphical representation of tanh function.

Figure 4.5 Shows the graphical representation of ReLU function.

Figure 4.6 Shows the schematic representation of a standard back propagation...

Chapter 5

Figure 5.1 An example of two coinciding discrete intervals.

Figure 5.2 The subcases for

when

.

and

are elements of the time tuple...

Figure 5.3 The subcases for

when

.

is an element of the time tuple

;

Figure 5.4 The subcases for

when

.

and

are elements of the time tuple...

Figure 5.5 Fuzzy synchronization cases.

and

are elements of the time tup...

Chapter 6

Figure 6.1 Triangular fuzzy number (TFN).

Figure 6.2 Model diagram of tapered fin.

Figure 6.3 Tapered fin having two nodes.

Figure 6.4 Two‐element discretization of tapered fin.

Figure 6.5 Nodal temperatures of tapered fin (crisp values).

Figure 6.6 Nodal temperatures of tapered fin (left and right values).

Figure 6.7 Crisp numerical solution of Eq. (6.24) by EFDM at different times...

Figure 6.8 Fuzzy numerical solution of Eq. (6.24) by EFDM at different times...

Figure 6.9 Radon concentration at

c

(4,500

s

,

α

,

β

)

.

Figure 6.10 Radon concentration at

c

(4,12 000

s

,

α

,

β

)

.

Chapter 8

Figure 8.1 Geometry of the problem.

Figure 8.2 Residual error plot for different numbers of terms in series solu...

Figure 8.3 Effect of nanoparticle volume fraction on velocity profile for SA...

Figure 8.4 Effect of Hartmann number on the velocity profile for SA–Cu nanof...

Figure 8.5 Effect of Reynolds number on the velocity profile for SA–Cu nanof...

Figure 8.6 Fuzzy plot of the velocity profile for SA–Cu nanofluid when

and...

Figure 8.7 Comparison of solution of CWBK equations (8.10) and (8.11) with e...

Figure 8.8 Comparison of solution of CWBK equations (8.10) and (8.11) with e...

Figure 8.9 Solution of interval CWBK equations (8.14) and (8.15) along with ...

Figure 8.10 Solution of interval CWBK equations (8.14) and (8.15) along with...

Chapter 9

Figure 9.1 Rough set approximations.

Figure 9.2 Various application areas of feature selection.

Chapter 11

Figure 11.1 Contractor of

[

s

]

.

Figure 11.2 Set inversion via interval analysis (SIVIA).

Figure 11.3 SIVIA of circle with width

[3, 4]

.

Figure 11.4 Solution enclosure plot of Example 11.3.

Figure 11.5 Solution enclosure plot of Example 11.4 (Rayleigh equation).

Figure 11.6 Solution enclosure plot of Example 11.5 (Van der Pol–Duffing equ...

Figure 11.7 Solution enclosure plot of Example 11.6 (Lane–Emden equation).

Chapter 12

Figure 12.1 Frequency parameter vs. number of terms for SS boundary conditio...

Figure 12.2 Frequency parameter vs. number of terms for CC boundary conditio...

Figure 12.3 Frequency parameter vs. small‐scale parameter for SS boundary co...

Figure 12.4 Frequency parameter vs. small‐scale parameter for CC boundary co...

Figure 12.5 Frequency ratio vs. small‐scale parameter for SS boundary condit...

Figure 12.6 Frequency ratio vs. small‐scale parameter for CC boundary condit...

Figure 12.7 Frequency parameter vs. length‐scale parameter for SS boundary c...

Figure 12.8 Frequency parameter vs. length‐scale parameter for CC boundary c...

Chapter 13

Figure 13.1 Fixed free beam subjected to distributed uniform load (electrost...

Figure 13.2 The schematic of a mass‐spring‐damper system of a beam.

Figure 13.3 The pull‐in voltage for different gap distances and various mode...

Figure 13.4 Time response of the system near the pull‐in voltage for the fou...

Figure 13.5 Phase portrait of the four models for potentials near the pull‐i...

Figure 13.6 Dependency of the deflection with the applied voltage for the fo...

Figure 13.7 Variation of the resonant frequency of the system for the four m...

Figure 13.8 Two identical microcantilever beams.

Figure 13.9 Twin microcantilever beams under electrostatic filed.

Figure 13.10 The variation of the deflection for one‐beam and two‐beam scena...

Figure 13.11 Phase diagram of beam.

Figure 13.12 Nonsymmetric twin set microcantilever beams (exhibit different ...

Figure 13.13 The schematic view of a mass‐spring‐damper system of twin micro...

Figure 13.14 The deviation of the deflection vs. time for the thin beam (upp...

Figure 13.15 Phase diagram of the thin beam (upper beam) – changes in veloci...

Figure 13.16 The deviation of the deflection vs. time for the thick beam (lo...

Figure 13.17 Phase diagram of the thick beam (lower beam) – unsteady changes...

Chapter 14

Figure 14.1 Integral of

f

(

x

) within a closed interval [

a

,

b

].

Figure 14.2 One‐dimensional representation of the problem.

Figure 14.3 Accuracy of the first four derivatives of the function

x

5

by the...

Figure 14.4 Accuracy of the first four derivatives of the function

x

5

by the...

Figure 14.5 Accuracy of the first four derivatives of the function

x

5

by the...

Figure 14.6 Accuracy of the first four derivatives of the function

x

5

by the...

Figure 14.7 Accuracy of the first four derivatives of the function

by the ...

Figure 14.8 Accuracy of the first four derivatives of the function

by the ...

Figure 14.9 Accuracy of the first four derivatives of the function

by the ...

Figure 14.10 Accuracy of the first four derivatives of the function

using ...

Figure 14.11 Accuracy of the first four derivatives of the function

cos(

x

)

u...

Figure 14.12 Accuracy of the first four derivatives of the function

cos(

x

)

b...

Figure 14.13 Accuracy of the first four derivatives of the function

cos(

x

)

b...

Figure 14.14 Accuracy of the first four derivatives of the function

cos(

x

)

b...

Figure 14.15 Accuracy for the integral of the function

x

5

by the use of diff...

Figure 14.16 Accuracy for the integral of the function

by the use of diffe...

Figure 14.17 Accuracy for the integral of the function

cos(

x

)

by the use of ...

Figure 14.18 Accuracy for the integral of the function

by the use of diffe...

Figure 14.19 Accuracy for the integral of the function

x

5

for different appr...

Figure 14.20 Accuracy for the integral of the function

for different appro...

Figure 14.21 Accuracy for the integral of the function

cos(

x

)

for different ...

Figure 14.22 Grid point order adopted to define the vector

π

.

Figure 14.23 Accuracy of the numerical derivatives of the function

f

1

(

x

,

y

)

...

Figure 14.24 Accuracy of the numerical derivatives of the function

f

2

(

x

,

y

)

...

Figure 14.25 Accuracy of the numerical derivatives of the function

f

3

(

x

,

y

)

...

Figure 14.26 Accuracy of the numerical derivatives of the function

f

4

(

x

,

y

)

...

Figure 14.27 Accuracy of the numerical derivatives of the function

f

5

(

x

,

y

)

...

Figure 14.28 Accuracy of the numerical integrals of the function

f

1

(

x

,

y

)

vs...

Figure 14.29 Accuracy of the numerical integrals of the function

f

2

(

x

,

y

)

vs...

Figure 14.30 Accuracy of the numerical integrals of the function

f

3

(

x

,

y

)

vs...

Figure 14.31 Accuracy of the numerical integrals of the function

f

4

(

x

,

y

)

vs...

Figure 14.32 Accuracy of the numerical integrals of the function

f

5

(

x

,

y

)

vs...

Chapter 15

Figure 15.1 The graph of problem modeling procedure for the brain activities...

Figure 15.2 Obtaining errors in approximating

, for test problem 1 with dif...

Figure 15.3 The graph of (a)

and (b)

for the various numbers of collocat...

Figure 15.4 Obtaining errors in approximating

, for test problem 2 with a d...

Figure 15.5 The graph of (a)

and (b)

for the various numbers of collocat...

Figure 15.6 The graph of (a)

and (b)

for the various numbers of collocat...

Figure 15.7 Obtaining errors in approximating

, for test problem 3 with

a...

Figure 15.8 Obtaining errors in approximating

for test problem 3 with

an...

Figure 15.9 The graph of (a)

and (b)

for the various numbers of collocat...

Figure 15.10 Obtaining errors in approximating

for test problem 4 with

a...

Figure 15.11 Obtaining errors in approximating

for test problem 4 with

a...

Figure 15.12 Obtaining errors in approximating

for test problem 5 with

,

Figure 15.13 Obtaining errors in approximating

, for test problem 5 with

,...

Figure 15.14 Obtaining errors in approximating

for test problem 5 with

,

Chapter 16

Figure 16.1 Solution for infected population with respect to

.

Figure 16.2 Function

with respect to

.

Figure 16.3 Comparison of resource allocation percentages (allocation by inf...

Figure 16.4 (a) Allocation by infectious percentage and (b) optimal allocati...

Figure 16.5 Example graph on four subregions.

Figure 16.6 Comparison of resource allocation percentages (allocation by inf...

Figure 16.7 (a) Allocation by infectious percentage. (b) Optimal allocation....

Figure 16.8 Example graph on eight individuals.

Figure 16.9 Variation of high‐risk population percentage of susceptible popu...

Figure 16.10 Variation of total risk percentage with isolation percentage of...

Figure 16.11 Total deviation of goals with isolation. (

%= isolation per inf...

Chapter 17

Figure 17.1 General usage of artificial intelligence.

Figure 17.2 Steps of problem‐solving process.

Figure 17.3 Examples of LIDAR views [7].

Figure 17.4 (a) Ensco, (b) Spirit of Las Vegas, (c) Overbot, and (d) Cajunbo...

Figure 17.5 The structure of autonomous car.

Figure 17.6 A simple example of object detection.

Chapter 18

Figure 18.1 Semilog graph between number of iterations and iteration value....

Figure 18.2 The semilog graphs are plotted between number of iteration vs. c...

Figure 18.3 The semilog graphs are plotted between number of iterations and ...

Guide

Cover

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Mathematical Methods in Interdisciplinary Sciences

Edited bySnehashish Chakraverty

This edition first published 2020

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Library of Congress Cataloging‐in‐Publication Data

Names: Chakraverty, Snehashish, editor.

Title: Mathematical methods in interdisciplinary sciences / edited by

    Snehashish Chakraverty.

Description: Hoboken, NJ : Wiley, 2020. | Includes bibliographical

    references and index.

Identifiers: LCCN 2020001570 (print) | LCCN 2020001571 (ebook) | ISBN

    9781119585503 (cloth) | ISBN 9781119585619 (adobe pdf) | ISBN

    9781119585657 (epub)

Subjects: LCSH: Science‐‐Mathematics. | Mathematical analysis.

Classification: LCC Q175.32.M38 M38 2020 (print) | LCC Q175.32.M38

    (ebook) | DDC 501/.51–dc23

LC record available at https://lccn.loc.gov/2020001570

LC ebook record available at https://lccn.loc.gov/2020001571

Cover design by Wiley

Cover image: © Sharlotta/Shutterstock

Notes on Contributors

Snehashish Chakraverty has an experience of 29 years as a researcher and teacher. Presently, he is working in the Department of Mathematics (Applied Mathematics Group), National Institute of Technology Rourkela, Odisha, as a senior (HAG) professor. Before this, he was with CSIR‐Central Building Research Institute, Roorkee, India. After completing graduation from St. Columba's College (Ranchi University), his career started from the University of Roorkee (Now, Indian Institute of Technology Roorkee) and did MSc (Mathematics) and MPhil (Computer Applications) from there securing the first position in the university. Dr. Chakraverty received his PhD from IIT Roorkee in 1992. Thereafter, he did his postdoctoral research at the Institute of Sound and Vibration Research (ISVR), University of Southampton, UK, and at the Faculty of Engineering and Computer Science, Concordia University, Canada. He was also a visiting professor at Concordia and McGill Universities, Canada, during 1997–1999 and a visiting professor of University of Johannesburg, South Africa, during 2011–2014. He has authored/coauthored 16 books, published 333 research papers (till date) in journals and conferences, 2 more books are in press, and 2 books are ongoing. He is in the editorial boards of various International Journals, Book Series, and Conferences. Prof. Chakraverty is the chief editor of the “International Journal of Fuzzy Computation and Modelling” (IJFCM), Inderscience Publisher, Switzerland (http://www.inderscience.com/ijfcm), associate editor of “Computational Methods in Structural Engineering, Frontiers in Built Environment,” and happens to be the editorial board member of “Springer Nature Applied Sciences,” “IGI Research Insights Books,” “Springer Book Series of Modeling and Optimization in Science and Technologies,” “Coupled Systems Mechanics (Techno Press),” “Curved and Layered Structures (De Gruyter),” “Journal of Composites Science (MDPI),” “Engineering Research Express (IOP),” and “Applications and Applied Mathematics: An International Journal.” He is also the reviewer of around 50 national and international journals of repute, and he was the President of the Section of Mathematical Sciences (including Statistics) of “Indian Science Congress” (2015–2016) and was the Vice President – “Orissa Mathematical Society” (2011–2013). Prof. Chakraverty is a recipient of prestigious awards, viz. Indian National Science Academy (INSA) nomination under International Collaboration/Bilateral Exchange Program (with Czech Republic), Platinum Jubilee ISCA Lecture Award (2014), CSIR Young Scientist (1997), BOYSCAST (DST), UCOST Young Scientist (2007, 2008), Golden Jubilee Director's (CBRI) Award (2001), INSA International Bilateral Exchange Award (2010–2011 [selected but could not undertake], 2015 [selected]), Roorkee University Gold Medals (1987, 1988) for first positions in MSc and MPhil (Computer Applications), etc. He has already guided 15 PhD students and 9 are ongoing. Prof. Chakraverty has undertaken around 16 research projects as the principle investigator funded by international and national agencies totaling about ₹1.5 crores. A good number of international and national conferences, workshops, and training programs have also been organized by him. His present research area includes differential equations (ordinary, partial, and fractional), Numerical Analysis and Computational Methods, Structural Dynamics (FGM, Nano) and Fluid Dynamics, Mathematical Modeling and Uncertainty Modeling, Soft Computing and Machine Intelligence (Artificial Neural Network, Fuzzy, Interval, and Affine Computations).

Susmita Mallreceived her PhD degree in Mathematics from the National Institute of Technology Rourkela, Rourkela, Odisha, India, in 2016. Currently, she is a postdoctoral fellow in National Institute of Technology, Rourkela – 769 008, Odisha, India. She has been awarded Women Scientist Scheme‐A (WOS‐A) fellowship, under Department of Science and Technology (DST), Government of India, to undertake her PhD studies and Post Doc. Her current research interest includes mathematical modeling, artificial neural network, differential equations, and numerical analysis.

Sumit Kumar Jeswal received the undergraduation degree from G.M. College, Sambalpur, India, the MSc degree in mathematics from Utkal University, Bhubaneswar, India, and the MTech degree in computer science and data processing from Indian Institute of Technology Kharagpur, Kharagpur, India. He is currently pursuing the PhD degree with the Department of Mathematics, National Institute of Technology Rourkela, Rourkela, India. His current research interests include artificial neural network, fuzzy and interval analysis, and numerical analysis.

Romila Ghosh did her master's graduate in statistics from Amity University, India. At present, she is working with the Data Sciences team in one of the reputed Big 4 consulting MNCs. Her areas of interest include biostatistics, population genetics, and data science.

Satyakama Paul is a PhD and postdoctorate in computational intelligence from the University of Johannesburg, South Africa. At present, he is a senior data scientist of a consulting firm in Kolkata, India. His research interests lie in deep learning and its application in various domains such as finance, medical image processing, etc.

Tanmoy Roy is doing his doctoral studies from the University of Johannesburg. His primary research interest is speech emotion recognition (SER), where he is applying the latest machine learning and deep learning techniques to solve the SER problem. His publications include one journal paper in an Elsevier journal named “Communications in Nonlinear Science and Numerical Simulations” and two conference papers, one IEEE and another Elsevier. Apart from that, he has also submitted one book chapter with Springer. Before his doctoral studies, he was an experienced software programmer and worked as an associate consultant at SIEMENS.

Tshilidzi Marwala received his PhD from the University of Cambridge in Artificial Intelligence. He is presently the Vice Chancellor and Principal of the University of Johannesburg. University of Johannesburg is making remarkable progress under his energetic and dynamic leadership. After his PhD, he became a postdoctoral research associate at the University of London's Imperial College of Science, Technology, and Medicine, where he worked on intelligence software. He was a visiting fellow at Harvard University and Wolfson College, Cambridge. His research interests include the theory and application of artificial intelligence to engineering, computer science, finance, economics, social science, and medicine. He has made fundamental contributions to engineering science including the development of the concept of pseudo‐modal energies, proposing the theory of rational counterfactual thinking, rational opportunity cost, and the theory of flexibly bounded rationality. He was a coinventor of the innovative methods of radiation imaging with Megan Jill Russell and invented the artificial larynx with David Rubin. His publication list is long, which includes important patents, books, and articles. He has published his work in reputed journals and conferences. His work has been cited by many organizations, including the International Standards Organizations (ISO) and NASA. He collaborates with researchers in South Africa, the United States of America, England, France, Sweden, and India. He regularly acts as a reviewer for international journals. He has received numerous awards and honors including winning the National Youth Science Olympiad, and because of this, he attended the 1989 London International Youth Science Forum, the Order of Mapungubwe, Case Western Reserve University Professional Achievement Award, The Champion of Research Capacity Development and Transformation at SA Higher Education Institutions, NSTF Award, Harvard‐South Africa Fellowship, TWAS‐AAS‐Microsoft Award, and ACM Distinguished Member Award.

Srinivasa Manikant Upadhyayula is the lead analyst for machine learning and automation team at CRISIL Global Research & Analytics with an extensive experience of over a decade in financial services and technology across investment banking, private banking, risk, and finance. He is passionately pursuing an active innovation roadmap to enable artificial intelligence (AI)‐based products in financial risk and analytics. He is pioneering initiatives that apply AI, ML, NLP, and big data platforms to automate business processes and control functions, validate model risk scenarios, and proactively manage operational and reputational risk for various large banks.

He holds an MBA in finance with honors from Great Lakes Institute of Management and Bachelor's in electronics and communication engineering from Sri Chandrasekharendra Saraswathi Viswa Mahavidyalaya (SCSVMV) University.

Kannan Venkataramanan is a senior quantitative analyst for risk and analytics at CRISIL Global Research & Analytics, which has a broad mandate to build and implement ML‐ and AI‐based automation solutions across clients. He is involved in driving efforts to apply word embedding techniques and NLP algorithms to streamline market news analysis as well as automate financial insights for investments banks. Before CRISIL, he was a decision scientist at MuSigma Inc., where he was involved in analyzing big data, identifying anomalies and patterns, and designing business strategies to solve complex, real‐time problems.

Earlier, he completed his bachelor in mechatronics engineering with a gold medal from Sastra University. Also, he has published a couple of research papers on Euler's totient function and Jacobsthal numbers.

Yevgeniya S. Sulema is an associated professor at the Computer Systems Software Department and a vice‐dean at the Faculty of Applied Mathematics of the National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute.” She received her PhD degree from the National Technical University of Ukraine “Kyiv Polytechnic Institute” in 1999. She is a member of the editorial advisory board of Systemics, Cybernetics and Informatics Journal and a member of the program committees at several international conferences. She is a member of the International Institute of Informatics and Systemics. The research in her laboratory of Multimedia, Mulsemedia, and Immersive Technologies covers multimodal data representation and processing, mulsemedia and immersive applications, image and audio processing, and multimedia data protection methods. She is an author of more than 130 scientific publications. She participates in numerous European and national research projects. She teaches the course on multimedia technologies and the course on digital signal and image processing to Master program students and the course on mathematical and algorithmic fundamentals of computer graphics to Bachelor program students at her university. She also gave lectures on multimedia, mulsemedia, and immersive technologies to students at several European universities as a guest lecturer within academic mobility projects.

Etienne E. Kerre was born in Zele, Belgium on 8 May 1945. He obtained his MSc degree in mathematics in 1967 and his PhD in mathematics in 1970 from Ghent University. Since 1984, he has been a lector and since 1991 a full‐time professor at Ghent University. In 2010, he became a retired professor. He is a referee for more than 80 international scientific journals and also a member of the editorial board of international journals and conferences on fuzzy set theory. He was an honorary chairman at various international conferences. In 1976, he founded the Fuzziness and Uncertainty Modeling Research Unit (FUM), and since then, his research has been focused on the modeling of fuzziness and uncertainty and has resulted in a great number of contributions in fuzzy set theory and its various generalizations. Especially, the theories of fuzzy relational calculus and of fuzzy mathematical structures owe a very great deal of him. Over the years, he has also been a promoter of 30 PhDs on fuzzy set theory. His current research interests include fuzzy and intuitionistic fuzzy relations, fuzzy topology, and fuzzy image processing. He has authored or coauthored 25 books, and more than 500 papers appeared in international refereed journals and proceedings.

Sukanta Nayak is an assistant professor of mathematics at Amrita Vishwa Vidyapeetham, Coimbatore, India. Before coming to Amrita Vishwa Vidyapeetham, he was a postdoctoral research fellow at University of Johannesburg, South Africa. He received PhD in mathematics from the National Institute of Technology Rourkela in 2016. Dr. Nayak has received Global Excellence Stature Postdoctoral Fellowship in 2016 and postgraduate scholarship by Government of Odisha in 2008. In addition, he qualified all India Graduate Aptitude Test in Engineering (GATE) and awarded as best presentation of Department of Mathematics at Research Scholar Week (RSW‐2015), NIT Rourkela in 2015. His research interests span uncertainty modeling and investigation of various diffusion problems, viz., heat transfer and neutron diffusion. He is also an author of the book entitled “Neutron Diffusion: Concepts and Uncertainty Analysis for Engineers and Scientists” of CRC Press (Taylor and Francis) and “Interval Finite Element Method with MATLAB” of Academic Press (Elsevier), as well as he has several book chapters and numerous articles in academic journals.

Tharasi Dilleswar Rao is presently a PhD scholar at the National Institute of Technology (NIT) Rourkela, Odisha, India. He received his MSc degree from Osmania University, Telangana, in 2013. Then, he worked as a faculty from 2014 to 2015. Then, he started his research work as a PhD student at NIT Rourkela in 2015. His research area includes numerical methods, fuzzy/interval uncertainty, and modeling radon transport mechanisms. He has four published/accepted papers in reputed journals, two book chapters (accepted) in Springer, and he is a coauthor of a book entitled “Advanced Numerical Methods and Semi‐Analytical Methods for Differential Equations” in John Wiley and Sons till now. He also has two conference papers.

Tofigh Allahviranloo is a senior full‐time professor in applied mathematics at Bahcesehir World International University (BAU) in Istanbul.

As a trained mathematician and computer scientist, Tofigh has developed a passion for multi‐ and interdisciplinary research. He is not only deeply involved in fundamental research in fuzzy applied mathematics, especially fuzzy differential equations, but he also aims at innovative applications in the applied biological sciences.

Soheil Salahshour is an Assistant Professor of Applied Mathematics at Bahcesehir University, Istanbul, Turkey. He not only pursues fundamental research in fuzzy applied mathematics, especially fuzzy differential equations of arbitrary order, but also develops innovative applications in the applied sciences like as mechanical, electrical and industrial engineering.

Karunakar Perumandla is working as an assistant professor in the Department of Mathematics, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Chennai Campus, India. He has submitted his PhD thesis on “Shallow Water Wave Equations with Uncertain Parameters” to National Institute of Technology Rourkela, Odisha in 2019. He received his MSc degree from NIT Warangal in 2009. His research area includes numerical methods, fuzzy/interval uncertainty, and shallow water wave equations. He has eight published/accepted papers in reputed journals, a book and two book chapters in Springer till now. Also, he has three conference papers. He is the editorial assistant of International Journal of Fuzzy Computation and Modelling (Inderscience publisher).

Uddhaba Biswal is presently a PhD scholar at National Institute of Technology (NIT) Rourkela, Odisha, India. He received his BSc degree from Rajendra (Auto.) college, Balangir, Odisha, India, in 2014 and MSc degree from Pondicherry University, Puducherry, India, in 2016. His research area includes fluid dynamics, fuzzy/interval uncertainty, and numerical analysis.

Tanmoy Som is serving as a professor in the Department of Mathematical Sciences, Indian Institute of Technology (Banaras Hindu University), Varanasi, India, since last 10 years and has more than 30 years of teaching and research experience. He has done PhD from Institute of Technology, Banaras Hindu University, Varanasi, India, in 1986. His main research areas are “fixed point theory, fuzzy and soft set theory, image processing and mathematical modeling.” Around 125 research publications are there to his credit in reputed journals and edited book chapters of National and International repute. He has delivered several invited talks at National and International conferences/workshops/refresher courses, which includes talks at Texas A&M University, Kingsville, USA, Naresuan University, Thailand, and University of California, Berkeley. Thirteen scholars have completed PhD under his supervision. He is also a reviewer/editorial board member/guest editor of few reputed journals. He has organized quite a few national/international level conferences/workshops.

Shivam Shreevastava received his BSc (Hons) from Institute of Science, Banaras Hindu University, Varanasi, India, in 2011 and MSc in mathematics from Indian Institute of Technology, Kanpur, in 2013. He has completed his PhD from the Department of Mathematical Sciences, Indian Institute of Technology (BHU), Varanasi, India, in 2018. His research area focuses on applications of fuzzy set theory in decision making problems. He has several research publications in reputed journals in the last few years.

Anoop Kumar Tiwari has completed his BSc, MSc, and PhD degree from Department of Computer Science and Engineering, Dr. K. N. Modi University, Tank, Rajasthan, India. His research interests are machine learning, bioinformatics, and medical image processing. He has quite a few good research publications in the above areas to his credit.

Shivani Singh received her BSc in 2013 and MSc in mathematics in 2015 from PPN PG College, Kanpur, UP. She is currently pursuing her PhD in DST‐Centre for Interdisciplinary Mathematical Sciences, Institute of Science, Banaras Hindu University, Varanasi, India. Her research area focuses on fuzzy rough set‐based techniques for knowledge discovery process.

Hend Dawood is a senior lecturer of computational mathematics in the Department of Mathematics at Cairo University. Her current research interests include algebraic systems of interval mathematics, ordered algebraic structures and algebraic logic, computable analysis, nonclassical logics, formal fuzzy logics, automatic differentiation, and uncertainty quantification. Dr. Dawood is a member of the IEEE 1788 committee for standardizing interval arithmetic, the Cairo University Interval Arithmetic Research Group (CUIA), and the Egyptian Mathematical Society (EMS). She also serves in the editorial board of a number of international journals of repute in the field of computational mathematics.

Yasser Dawood is currently the head of the Department of Mathematical and Computational Urbanism, Go Green Architects, Giza, Egypt. He has over 20 years of experience in the fields of computational and applied mathematics. His current research interests include algebraic logic, nonclassical logics, formal fuzzy logics, reasoning under uncertainty, and formal representations of uncertainty, with emphasis on applying algebraic and logical methods to engineering and real‐world problems. Dr. Dawood serves as a consultant for many Egyptian and international companies and institutes. He also serves in the editorial and reviewing boards of a number of international journals of repute in the field of applied and computational mathematics.

Nisha Rani Mahato is a PhD scholar at the Department of Mathematics, National Institute of Technology, Rourkela, Odisha. She completed her MSc degree in mathematics at the National Institute of Technology, in 2011. She was awarded Raman–Charpak Fellowship‐2016 (RCF‐2016) by CEFIPRA, New Delhi. Also, she was awarded the best paper at the 38th Annual Conference of Orissa Mathematical Society in 2011 and best poster in mathematics at Research Scholar Week 2018, NIT Rourkela. Further, she has participated in various conferences/workshops and published a few research papers, two book chapters, and two books. Her current research areas include interval computing, fuzzy set theory, interval/fuzzy eigenvalue problems, interval/fuzzy simultaneous equations, and interval/fuzzy differential equations.

Saudamini Rout is currently pursuing her PhD degree under the supervision of Prof. S. Chakraverty at the Department of Mathematics, National Institute of Technology Rourkela, Rourkela, Odisha, India. She completed her MPhil degree in Mathematics in 2016, MSc degree in Mathematics in 2015, and BSc degree in 2013 from Ravenshaw University, Cuttack, Odisha, India. She was awarded a gold medal for securing first position in the university for MSc degree. Her current research interests include fuzzy set theory, interval analysis, affine arithmetic, uncertain linear, and/or nonlinear static and dynamic problems and numerical analysis.

Subrat Kumar Jena is currently working as a research fellow at the Department of Mathematics (MA), National Institute of Technology Rourkela. He is also working in Defence Research and Development Organisation (DRDO) sponsored project entitled “Vibrations of Functionally Graded Nanostructural Members” in collaboration with Defence Metallurgical Research Laboratory (DMRL). Subrat does research in structural dynamics, nano vibration, applied mathematics, computational methods, and numerical analysis, differential equations, fractional differential equations, mathematical modeling, uncertainty modelling, soft computing, etc. He has published 13 research papers (till date) in journals, 4 conference papers, and 3 book chapters. He has also served as the reviewer for International Journal of Fuzzy Computation and Modelling (IJFCM), and Computational Methods in Structural Engineering. Subrat has attended one national conference and one GIAN course on “Structural Dynamics.” He has also been continuing collaborative research works with renowned researchers from Italy, Estonia, Turkey, Iran, etc.

Rajarama Mohan Jena is currently working as a senior research fellow at the Department of Mathematics (MA), National Institute of Technology Rourkela. He is an INSPIRE (Innovation in Science Pursuit for Inspired Research) fellow of Department of Science of Technology, Ministry of Science and Technology, Govt. of India, and doing his research under this fellowship scheme. Rajarama does research in fractional dynamical systems, applied mathematics, computational methods, numerical analysis, partial differential equations, fractional differential equations, uncertainty modeling, and soft computing. He has published 12 research papers (till date) in journals, 1 conference paper, and 1 book chapter. Rajarama also served as the reviewer for International Journal of Fuzzy Computation and Modelling (IJFCM), Abstract and Applied Analysis, and SN Applied Sciences. He has attended one national conference. He has been continuing collaboration works with renowned researchers from Turkey, Canada, Iran, Egypt, Nigeria, etc.

M. Amin Changizi received the PhD degree from Concordia University, Montréal, Canada, in 2011. He is currently a senior mechanical engineer researcher in Mohawk Innovative Technology. He is working in turbomachine and bearing industry. Previously, he was a knowledge engineer with Intelliquip, USA. He was working on pump industry, from 2014 to 2018. Before Intelliquip, he was with Carrier Corporation, USA, as a staff engineer, from 2012 to 2013. Before Carrier Corporation, he was a part‐time faculty member with Concordia University, Montréal, from 2008 to 2011. He has been a full‐time faculty member with Tabriz Azad University, Tabriz, for seven years, and an engineer in several different positions in PETCO pump industry. His research interests are theoretical nonlinear mechanics, Lie symmetry method with applications on nonlinear differential equations and nonlinear behaviors of MEMS devices.

Ion Stiharu is graduated as a diplomat engineer of the Polytechnic University of Bucharest – Romania – in 1979 and PhD in 1989 from the same institution. In 1991, he joined Concordia University in Montreal in the Department of Mechanical, Industrial, and Aerospace Engineering. His research interests are focusing on microsystems performance, microfabrication, and applications. He has supervised and co‐supervised 31 PhD students who successfully graduated and he authored and coauthored more than 100 journal publications and over 250 conference and presentations. He is one of the editors of “Micromachines” journal and he is part of the editorial board of few journals. He is a fellow of the ASME and CSME.

Francesco Tornabene was born in Bologna, 13 January 1978, and received a degree in mechanical engineering (Course of Studies in Structural Mechanics) on 23 July 2003. He submitted a thesis under the title (in Italian) Dynamic Behavior of Cylindrical Shells: Formulation and Solution. He obtained the first position in the competition for admission to the PhD in structural mechanics in December 2003. He is the winner of the scholarship Carlo Felice Jodi for a degree in structural mechanics in 2004. He did PhD in structural mechanics on 31 May 2007. He is the author of 11 books. Some of them entitled Meccanica delle Strutture a Guscio in Materiale Composito. Il metodo Generalizzato di Quadratura Differenziale, 2012; Mechanics of Laminated Composite Doubly‐Curved Shell Structures. The Generalized Differential Quadrature Method and the Strong Formulation Finite Element Method, 2014; Laminated Composite Doubly‐Curved Shell Structures I. Differential Geometry. Higher‐Order Structural Theories, 2016; Laminated Composite Doubly‐Curved Shell Structures II. Differential and Integral Quadrature. Strong Formulation Finite Element Method, 2016; and Anisotropic Doubly‐Curved Shells. Higher‐Order Strong and Weak Formulations for Arbitrarily Shaped Shell Structures, 2018. He is an author of more than 190 research papers since 2004. He is an assistant professor at the University of Bologna from 2012 to 2018 and from 2018 until now at the University of Salento. His research focuses on structural mechanics, mechanics of solids and structures, computational mechanics, composite and innovative materials.

Rossana Dimitri is an associate professor at the School of Engineering, Department of Innovation Engineering, University of Salento, Lecce, Italy. She received from the University of Salento, a MSc degree in “materials engineering” in 2004, a PhD degree in “materials and structural engineering” in 2009, and a PhD degree in “industrial and mechanical engineering” in 2013. In 2005, she received from the University of Salento the “Best M. Sc. Thesis Price 2003–2004” in memory of Eng. Gabriele De Angelis; in 2013, she was awarded by the Italian Group for Computational Mechanics (GIMC) for the Italian selection of the 2013 ECCOMAS PhD Award. Her current interests include structural mechanics, solid mechanics, damage and fracture mechanics, contact mechanics, isogeometric analysis, high‐performance finite elements, consulting in applied technologies, and technology transfer. During 2011 and 2012, she was a visiting scientist with a fellowship at the Institut für Kontinuumsmechanik Gottfried Wilhelm Leibniz Universität Hannover to study interfacial problems with isogeometric approaches. From 2013 to 2019, she was a researcher at the University of Salento, working on the computational mechanical modeling of structural interfaces, and on the comparative assessment of some advanced numerical collocation methods with lower computational cost for fracturing problems and structural mechanics of composite plates and shells, with complex geometry and material. She also collaborates, as a reviewer, with different prestigious international journals.

Jamal Amani Rad received his PhD degree in numerical analysis (scientific computing) from Shahid Beheshti University (SBU) in 2015. His doctoral dissertation was focused on numerical algorithms to evaluate financial option models. After graduation, he joined the Department of Cognitive Modeling, Institute for Cognitive and Brain Sciences, Shahid Beheshti University, as a postdoctoral fellow and is currently an assistant professor focused on the development of mathematical methods as tools in cognitive modeling.

Amir Hosein Hadian‐Rasanan received the BSc degree in computer sciences from Shahid Beheshti University in 2019 and he is currently an MSc student of cognitive sciences in the Institute for Cognitive and Brain Sciences at Shahid Beheshti University. His research interests are brain data analysis using tensor methods and modeling of human decision making using fractional dynamics.

S.S.N. Perera has 20 years of research and teaching experience. He received his PhD in mathematical modeling from the TU Kaiserslautern, Germany, and University of Colombo, Sri Lanka (2008), under the DAAD scholarship program, and his MSc from ICTP/SISSA, Trieste, Italy (2004). Presently, he is the head of Department of Mathematics, University of Colombo. His current research interests are on multidisciplinary areas that bridge applied mathematics, numerics and natural sciences together. He is working in the area of mathematical modeling on epidemiology, biology, ecology, and finance/actuarial science. He has published 57 research papers in leading national and international journals and over 100 publications local/international conferences and symposia through collaborative research work. He has published eight book chapters, three books, and has edited one book. He is a member of the editorial board of the Journal of National Science Foundation, which is the only Sri Lankan journal in the science citation index. He served as the keynote speaker/plenary speaker/invited speaker/resource person in more than 30 international conferences/symposia/workshops. He was awarded many national and international awards in recognition of research excellence.

A.C. Mahasinghe received BSc degree in mathematics with a first class honors from University of Colombo and completed a collaborative PhD program with the School of Physics, University of Western Australia in Perth. Later, he worked as a postdoctoral research fellow at the Department of Computer Science, University of Auckland. He is currently teaching at the Department of Mathematics, University of Colombo, where he is the deputy director of the Centre for Mathematical Modelling. His works are mainly on mathematical aspects of quantum walks, quantum algorithms, and computational mathematics. He has published several papers in theoretical physics and computer science journals, given talks at several international conferences, and won national awards for scientific publications.

K.K.W.H. Erandi received her BSc honors in mathematics from University of Colombo. She is currently pursuing her PhD at the same institution. She is a former visiting fellow on dynamical modelling at the Department of Mathematics, University of Koblenz.

Merve Arıtürk is a research assistant of Software Engineering Department at Bahcesehir University in Istanbul. She received her bachelor's degree from Baskent University and master's degree from Bahcesehir University. She is now a PhD candidate at Yıldız Technical University. Her research interests are artificial intelligence, autonomous systems and vehicles, deep learning, and natural language processing.

Sırma Yavuz is an associate professor of computer engineering at Yıldız Technical University. She received her BE, ME, and PhD from Yıldız Technical University. Her research interests include autonomous robots, real‐time robot localization, mapping, exploration, and navigation. For the past five years, she has been teaching courses on deep learning and robotics, in addition to supervising the research of graduate students in these fields. She has also served in numerous capacities successfully designing, implementing, and leading deep learning‐based projects especially in real‐world robotic applications. She is a founder of Probabilistic Robotics Group at Yıldız Technical University. With her colleagues and students, she has developed teams of real and virtual rescue robots, which have participated in RoboCup championships and won a number of top awards. She is a member of IEEE and ACM.

Pankaj Gautam received his BSc and MSc degrees from the University of Allahabad, Allahabad, in 2013 and 2015, respectively. He is currently pursuing his PhD in the Department of Mathematical Sciences, Indian Institute of Technology (BHU), Varanasi, India, since July 2016. His research area focuses on “approximation of some nonlinear problems in Hilbert spaces and its applications.” He has attended several workshops and presented his work in several international conferences with one held in the Technical University of Berlin, Germany. He is an active member of European Mathematical Society, Working Group on Generalized Convexity, Society for Industrial and Applied Mathematics, Institute of Mathematics and its Applications.

Avinash Dixit received his BSc degree from University of Allahabad, Allahabad, India, in 2013 and MSc degree in mathematics from University of Allahabad, Allahabad, in 2015. He is pursuing his PhD in the Department of Mathematical Sciences, Indian Institute of Technology (BHU), Varanasi, India. His research area focuses on “fixed point theory and its application to machine learning and image processing.” He has few research publications in reputed journals in last few years.

D.R. Sahu is a professor at the Department of Mathematics, Institute of Science, Banaras Hindu University, Varanasi. He has more than 25 years of research and teaching experience. Professor Sahu has published more than 120 research papers in reputed journals and conferences and has more than 1900 citations. He is a coauthor of a book on “Fixed Point Theory for Lipschitzian‐Type Mappings with Applications: Topological Fixed Point Theory and Its Applications, Springer, New York, 2009.” He has been a referee and an editor of many mathematical communities including Applied Mathematics Letters, Nonlinear Analysis, Journal of Mathematical Analysis and Applications, Fixed Point Theory and Applications, International Journal of Mathematics and Mathematical Sciences, Journal of Inequalities and Applications, Computers and Mathematics with Applications, International Journal of Nonlinear Operators Theory and Applications, JOTA, AAA, etc. His fields of interest include fixed point theory, operator theory, best approximation theory, variational inequality theory, and Newton‐like methods for solving generalized operator equations. Currently, he is working on numerical techniques for some engineering problems.

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