Enabling AI Applications in Data Science

644 Pages·2021·20.711 MB·English

by  Aboul-Ella Hassanien, Mohamed Hamed N. Taha, Nour Eldeen M. Khalifa

Studies in Computational Intelligence 911
Aboul-Ella Hassanien
Mohamed Hamed N. Taha
Nour Eldeen M. Khalifa   Editors
Enabling AI
Applications
in Data
Science
Studies in Computational Intelligence
Volume 911
Series Editor
Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland
The series “Studies in Computational Intelligence” (SCI) publishes new develop-
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Aboul-Ella Hassanien
(cid:129)
Mohamed Hamed N. Taha
(cid:129)
Nour Eldeen M. Khalifa
Editors
Enabling AI Applications
in Data Science
123
Editors
Aboul-Ella Hassanien MohamedHamed N.Taha
Faculty of Computers Faculty of Computers
andArtificialIntelligence andArtificialIntelligence
CairoUniversity CairoUniversity
Giza, Egypt Giza, Egypt
Chairof the scientificResearch
Group inEgypt
CairoUniversity
Giza, Egypt
Nour EldeenM.Khalifa
Faculty of Computers
andArtificialIntelligence
CairoUniversity
Giza, Egypt
ISSN 1860-949X ISSN 1860-9503 (electronic)
Studies in Computational Intelligence
ISBN978-3-030-52066-3 ISBN978-3-030-52067-0 (eBook)
https://doi.org/10.1007/978-3-030-52067-0
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Preface
ArtificialIntelligenceandDataSciencearethemostusefultechnologiesthatcould
powerfullyimprovethehumanlife.Thiswillbeachievedbymergingbothsciences
in order to solve real complex problems in various fields. This book provides a
detailed overview on the latest advancements and applications in the field of
Artificial Intelligence and Data Science. AI applications have achieved great
accuracyandperformancewiththehelpoftheadvancementsindataprocessingand
storage. AI applications also gained power through the amount and quality of data
which is the main core of data science. This book is aimed to introduce the state
of the art in research on Artificial Intelligence with the Data Science. We accepted
28 chapters. The accepted chapters covered the following four parts:
(cid:129) Part I—Artificial Intelligence and Optimization
(cid:129) Part II—Big Data and Artificial Intelligence Applications
(cid:129) Part III—IOT within Artificial Intelligence and Data Science
(cid:129) Part IV—Artificial Intelligence and Security
We thank and acknowledge all persons who were involved in all the stages of
publishing.Thatincludes(authors,reviewers,andpublishingteam).Weprofoundly
revalue their engagement and sustenance that was essential for the success of the
“Enabling AI applications in Data Science” edited book. We hope the readers
would equally love the chapters and their contents and appreciate the efforts that
have gone into bringing it to reality.
Giza, Egypt Aboul-Ella Hassanien
Mohamed Hamed N. Taha
Nour Eldeen M. Khalifa
v
Contents
Artificial Intelligence and Optimization
Stochastic SPG with Minibatches. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Andrei Pătraşcu, Ciprian Păduraru, and Paul Irofti
The Human Mental Search Algorithm for Solving Optimisation
Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
Seyed Jalaleddin Mousavirad, Gerald Schaefer,
and Hossein Ebrahimpour-Komleh
Reducing Redundant Association Rules Using Type-2 Fuzzy Logic . . . . 49
Eman Abd El Reheem, Magda M. Madbouly, and Shawkat K. Guirguis
Identifiability of Discrete Concentration Graphical Models
with a Latent Variable. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
Mohamed Sharaf
An Automatic Classification of Genetic Mutations by Exploring
Different Classifiers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
Badal Soni, K. Suganya Devi, and Angshuman Bora
Towards Artificial Intelligence: Concepts, Applications,
and Innovations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
Djamel Saba, Youcef Sahli, Rachid Maouedj, Abdelkader Hadidi,
and Miloud Ben Medjahed
Big Data and Artificial Intelligence Applications
In Depth Analysis, Applications and Future Issues of Artificial
Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
Badal Soni, Prachi Mathur, and Angshuman Bora
Big Data and Deep Learning in Plant Leaf Diseases Classification
for Agriculture. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185
Mohamed Loey
vii
viii Contents
Machine Learning Cancer Diagnosis Based on Medical Image Size
and Modalities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201
Walid Al-Dhabyani and Aly Fahmy
Edge Detector-Based Hybrid Artificial Neural Network Models
for Urinary Bladder Cancer Diagnosis. . . . . . . . . . . . . . . . . . . . . . . . . . 225
Ivan Lorencin, Nikola Anđelić, Sandi Baressi Šegota, Jelena Musulin,
Daniel Štifanić, Vedran Mrzljak, Josip Španjol, and Zlatan Car
Predicting Building-Related Carbon Emissions: A Test of Machine
Learning Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 247
Emmanuel B. Boateng, Emmanuella A. Twumasi, Amos Darko,
Mershack O. Tetteh, and Albert P. C. Chan
Artificial Intelligence System for Breast Cancer Screening Based
on Malignancy-Associated Changes in Buccal Epithelium . . . . . . . . . . . 267
A. V. Andreichuk, N. V. Boroday, K. M. Golubeva, and D. A. Klyushin
The Role of Artificial Intelligence in Company’s Decision Making . . . . 287
Djamel Saba, Youcef Sahli, and Abdelkader Hadidi
The Virtues and Challenges of Implementing Intelligent
‘Student-Success-Centered’ System . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315
Fatema Abdulrasool
Boosting Traditional Healthcare-Analytics with Deep Learning AI:
Techniques, Frameworks and Challenges. . . . . . . . . . . . . . . . . . . . . . . . 335
Prabha Susy Mathew and Anitha S. Pillai
Factors Influencing Electronic Service Quality on Electronic Loyalty
in Online Shopping Context: Data Analysis Approach. . . . . . . . . . . . . . 367
Ahlam Al-Khayyal, Muhammad Alshurideh, Barween Al Kurdi,
and Said A. Salloum
IOT within Artificial Intelligence and Data Science
IoT Sensor Data Analysis and Fusion . . . . . . . . . . . . . . . . . . . . . . . . . . 381
Mohamed Sohail
Internet of Things Sensor Data Analysis for Enhanced Living
Environments: A Literature Review and a Case Study Results
on Air Quality Sensing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 397
Gonçalo Marques
Data Science and AI in IoT Based Smart Healthcare: Issues,
Challenges and Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 415
Sohail Saif, Debabrata Datta, Anindita Saha, Suparna Biswas,
and Chandreyee Chowdhury
Contents ix
IoT Sensor Data Analysis and Fusion Applying Machine Learning
and Meta-Heuristic Approaches. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 441
Anindita Saha, Chandreyee Chowdhury, Mayurakshi Jana,
and Suparna Biswas
Geo-Spatiotemporal Intelligence for Smart Agricultural
and Environmental Eco-Cyber-Physical Systems . . . . . . . . . . . . . . . . . . 471
Babak Majidi, Omid Hemmati, Faezeh Baniardalan, Hamid Farahmand,
Alireza Hajitabar, Shahab Sharafi, Khadije Aghajani, Amir Esmaeili,
and Mohammad Taghi Manzuri
SaveMeNow.AI:AMachineLearningBasedWearableDeviceforFall
Detection in a Workplace. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 493
Emiliano Anceschi, Gianluca Bonifazi, Massimo Callisto De Donato,
Enrico Corradini, Domenico Ursino, and Luca Virgili
Artificial Intelligence and Security
Fraud Detection in Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 517
Paul Irofti, Andrei Pătraşcu, and Andra Băltoiu
Biomorphic Artificial Intelligence: Achievements and Challenges . . . . . 537
D. O. Chergykalo and D. A. Klyushin
Medical Data Protection Using Blind Watermarking Technique . . . . . . 557
Abdallah Soualmi, Adel Alti, and Lamri Laouamer
An Artificial Intelligence Authentication Framework to Secure
Internet of Educational Things. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 577
Ahmed A. Mawgoud, Mohamed Hamed N. Taha,
and Nour Eldeen M. Khalifa
A Malware Obfuscation AI Technique to Evade Antivirus Detection
in Counter Forensic Domain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 597
Ahmed A. Mawgoud, Hussein M. Rady, and Benbella S. Tawfik
An Artificial Intelligence Resiliency System (ARS). . . . . . . . . . . . . . . . . 617
Ali Hussein, Ali Chehab, Ayman Kayssi, and Imad H. Elhajj
Artificial Intelligence and Optimization