Table Of ContentStudies in Computational Intelligence 1072
Aboul Ella Hassanien
Deepak Gupta
Anuj Kumar Singh
Ankit Garg Editors
Explainable Edge
AI: A Futuristic
Computing
Perspective
Studies in Computational Intelligence
Volume 1072
Series Editor
Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland
The series “Studies in Computational Intelligence” (SCI) publishes new develop-
ments and advances in the various areas of computational intelligence—quickly and
with a high quality. The intent is to cover the theory, applications, and design methods
of computational intelligence, as embedded in the fields of engineering, computer
science, physics and life sciences, as well as the methodologies behind them. The
series contains monographs, lecture notes and edited volumes in computational
intelligence spanning the areas of neural networks, connectionist systems, genetic
algorithms, evolutionary computation, artificial intelligence, cellular automata, self-
organizing systems, soft computing, fuzzy systems, and hybrid intelligent systems.
Of particular value to both the contributors and the readership are the short publica-
tion timeframe and the world-wide distribution, which enable both wide and rapid
dissemination of research output.
This series also publishes Open Access books. A recent example is the book
Swan, Nivel, Kant, Hedges, Atkinson, Steunebrink: The Road to General Intelligence
https://link.springer.com/book/10.1007/978-3-031-08020-3
Indexed by SCOPUS, DBLP, WTI Frankfurt eG, zbMATH, SCImago.
All books published in the series are submitted for consideration in Web of Science.
· ·
Aboul Ella Hassanien Deepak Gupta
·
Anuj Kumar Singh Ankit Garg
Editors
Explainable Edge AI:
A Futuristic Computing
Perspective
Editors
Aboul Ella Hassanien Deepak Gupta
Scientific Research Group in Egypt (SRGE) Department of Computer Science
Cairo University and Engineering
Giza, Egypt Maharaja Agrasen Institute of Technology
New Delhi, Delhi, India
Anuj Kumar Singh
School of Computing Ankit Garg
University of Engineering & Technology School of Computing
(UETR) University of Engineering & Technology
Roorkee, Uttarakhand, India (UETR)
Roorkee, Uttarakhand, India
ISSN 1860-949X ISSN 1860-9503 (electronic)
Studies in Computational Intelligence
ISBN 978-3-031-18291-4 ISBN 978-3-031-18292-1 (eBook)
https://doi.org/10.1007/978-3-031-18292-1
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature
Switzerland AG 2023
This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether
the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse
of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and
transmission or information storage and retrieval, electronic adaptation, computer software, or by similar
or dissimilar methodology now known or hereafter developed.
The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication
does not imply, even in the absence of a specific statement, that such names are exempt from the relevant
protective laws and regulations and therefore free for general use.
The publisher, the authors, and the editors are safe to assume that the advice and information in this book
are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or
the editors give a warranty, expressed or implied, with respect to the material contained herein or for any
errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional
claims in published maps and institutional affiliations.
This Springer imprint is published by the registered company Springer Nature Switzerland AG
The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Preface
Explainable artificial intelligence (XAI) derives notions from different fields
including cognitive sciences, philosophy, and psychology to yield a range of proce-
dural methodologies that can produce explainable results for the end-users not having
enough background on artificial intelligence. The main emphasis has been placed
toward the progression of XAI that encompasses the human end-user in the cycle
and, therefore, becomes human-centric. XAI-based intelligent systems will facili-
tate enhanced prediction accuracy with comprehensible decision and traceability of
actions performed and have significant impact in the development of the society
at large. In the futuristic computing scenario, the goal of explainable edge AI will
be to execute the AI tasks and produce explainable results at the edge. The issues
of transparency, fairness, accountability, explainability, interpretability, data fusion,
and comprehensibility that are significant for the edge AI are being addressed here
through explainable models and techniques. The book has been organized in ten
chapters highlighting the different technological aspects of explainable edge AI.
Chapter 1 of the book provides a brief overview of explainable artificial intel-
ligence (XAI) by covering almost every aspect of XAI. It covers the principles,
techniques, current state of the art, benefits, and applications of XAI. It can be
inferred from the chapter that XAI is a great development in AI due to its transparent
nature. The chapter also addressed the challenges in XAI, as well as the field’s future
potential. Chapter 2 of the book serves as a learning platform for academics and
practitioners who are interested in essential parts of the new and quickly increasing
field of XAI. The need of trust and transparency in AI and the principal applica-
tion domain of XAI have been primarily discussed in this chapter. Chapter 3 epito-
mizes contemporary developments in explainable AI that describes explainability in
machine learning, constituting a fiction definition of explainable machine learning
that envelopes such prior conceptual propositions with a considerable focus on the
audience for which the explainability is needed.
A wide and insightful view of XAI and its application in various fields has been
presented in Chap. 4 of this book. This chapter also includes the future scope of this
technology and the need for the growth of this type of technology. Chapter 5 presents
recent challenges on edge AI and its numerous applications. This chapter provides
v
vi Preface
a thorough analysis of edge computing-related AI techniques and tools, sometimes
known as edge AI. The objective is to offer a hypothetical roadmap that may unite
important players and enablers in order to the development of edge AI.
Chapter 6 highlights the usage of XAI in healthcare which is a critical application
for the human community. ‘How XAI improves user trust in high-risk decisions’ has
been elaborated in this chapter. Significant AI recommendations, such as surgical
treatments or hospitalizations, requiring explanation from providers and patients have
been discussed here. Chapter 7 focuses on describing the role of explainable edge AI
to resolve real-time problems. Various issues in the applicability of explainable edge
AI for real-time implementations have been highlighted in this chapter. Chapter 8
focuses on different data models of fusion, discusses a framework for AI and data
fusion at the edge, and identifies potential challenges and possible solutions in this
regard. While doing so, the chapter intends to cover the fundamentals of explainability
in AI, the need to convert the black box system into a transparent one, and the
associated opportunities for explainable artificial intelligence.
In Chap. 9 of this book, a novel technique in data fusion model with security
and data optimization technique in edge computing has been presented. Here, the
proposed data fusion is carried out using secure sequential discriminant autoencoder
in which the improvement of data accuracy, as well as for the maximizing of edge
cloud-based sensor networks lifespan. The fusion of edge cloud data has been carried
out using discriminant autoencoder which is integrated with distributed edge cloud
users, where the security of the network has been enhanced using secure sequential
fuzzy-based trust model. The integration of edge computing framework with the
sensor network that can aid in the data collection, dissemination, and decision making
has been performed in Chap. 10 of the book. This chapter proposes a LSTM-based
strategy for an edge computing-enabled WSN to determine the status of the node
depending on the network parameters such as number of communications, number
of packets transmitted, and initial energy of the nodes. The proposed protocol is
implemented using TensorFlow and Keras libraries in Python language.
A wide range of topics related to the integration of XAI and edge AI have been
presented in this book. We are sure that the book will provide a comprehensive
knowledge about explainability in edge AI to the readers.
Giza, Egypt Aboul Ella Hassanien
New Delhi, India Deepak Gupta
Roorkee, India Anuj Kumar Singh
Roorkee, India Ankit Garg
Contents
1 Explainable Artificial Intelligence: Concepts and Current
Progression ................................................... 1
Kirti Kangra and Jaswinder Singh
2 Explainable Artificial Intelligence (XAI): Understanding
and Future Perspectives ....................................... 19
Megha Gupta
3 Explainable Artificial Intelligence (XAI): Conception,
Visualization and Assessment Approaches Towards Amenable
XAI .......................................................... 35
Tasleem Nizam and Sherin Zafar
4 Explainable AI (XAI): A Survey of Current and Future
Opportunities ................................................. 53
Meet Kumari, Akshit Chaudhary, and Yogendra Narayan
5 Recent Challenges on Edge AI with Its Application: A Brief
Introduction .................................................. 73
Kapil Joshi, Harishchander Anandaram, Manisha Khanduja,
Rajesh Kumar, Vikrant Saini, and Yasmin Makki Mohialden
6 Explainable Artificial Intelligence in Health Care: How XAI
Improves User Trust in High-Risk Decisions ..................... 89
Sheeba Praveen and Kapil Joshi
7 Role of Explainable Edge AI to Resolve Real Time Problem ....... 101
Ambeshwar Kumar, T. M. Rajesh, Manikandan Ramachandran,
and Deepak Gupta
8 Explainable Data Fusion on Edge: Challenges
and Opportunities ............................................. 117
Shweta Sinha and Priyanka Vashisht
vii
viii Contents
9 Trust Model Based Data Fusion in Explainable Artificial
Intelligence for Edge Computing Using Secure Sequential
Discriminant Auto Encoder with Lightweight Optimization
Algorithm .................................................... 139
D. Prabakar, M. Sundarrajan, S. Prasath Alias Surendhar,
Manikandan Ramachandran, and Deepak Gupta
10 A Deep Learning Based Target Coverage Protocol for Edge
Computing Enabled Wireless Sensor Networks .................. 161
Pooja Chaturvedi, A. K. Daniel, and Umesh Bodkhe
About the Editors
Prof. Aboul Ella Hassanien is Founder and Head of the Egyptian Scientific Research
Group (SRGE) and Professor of Information Technology at the Faculty of Computer
and Artificial Intelligence, Cairo University. Professor Hassanien is an ex-dean of the
Faculty of Computers and Information, Beni Suef University. Professor Hassanien
has more than 800 scientific research papers published in prestigious international
journals and over 40 books covering such diverse topics as data mining, medical
images, intelligent systems, social networks, and smart environment. Prof. Hassanien
won several awards, including the Best Researcher of the Youth Award of Astronomy
and Geophysics of the National Research Institute, Academy of Scientific Research
(Egypt, 1990). He was also granted a Scientific Excellence Award in Humanities from
the University of Kuwait for the 2004 Award and received the Scientific University
Award (Cairo University, 2013). Also, he was honored in Egypt as the best researcher
at Cairo University in 2013. He also received the Islamic Educational, Scientific and
Cultural Organization (ISESCO) Prize on Technology (2014) and received the State
Award for excellence in engineering sciences 2015. He was awarded the Medal of
Sciences and Arts of the first class by the President of the Arab Republic of Egypt,
2017.
Deepak Gupta received a B.Tech. degree in 2006 from the Guru Gobind Singh
Indraprastha University, Delhi, India. He received an M.E. degree in 2010 from
Delhi Technological University, India, and Ph.D. degree in 2017 from Dr. A. P.
J. Abdul Kalam Technical University (AKTU), Lucknow, India. He completed his
post-doc from the National Institute of Telecommunications (Inatel), Brazil, in 2018.
He has co-authored more than 207 journal articles, including 168 SCI papers and
45 conference articles. He has authored/edited 60 books, published by IEEE-Wiley,
Elsevier, Springer, Wiley, CRC Press, DeGruyter, and Katsons. He has filed four
Indian patents. He is the convener of the ICICC, ICDAM, ICCCN, ICIIP, and DoSCI
Springer conferences series. He is Associate Editor of Computer and Electrical
Engineering, Expert Systems, Alexandria Engineering Journal, Intelligent Decision
Technologies. He is the recipient of the 2021 IEEE System Council Best Paper Award.
He has been featured in the list of top 2% scientist/researcher databases worldwide.
ix
