Classification Applications with Deep Learning and Machine Learning Technologies

287 Pages·2022·13.587 MB·English

by  Laith Abualigah

Studies in Computational Intelligence 1071
Laith Abualigah   Editor
Classification
Applications with Deep
Learning and Machine
Learning Technologies
Studies in Computational Intelligence
Volume 1071
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Laith Abualigah
Editor
Classification Applications
with Deep Learning
and Machine Learning
Technologies
Editor
Laith Abualigah
Hourani Center for Applied Scientific
Research
Al-Ahliyya Amman University
Amman, Jordan
Faculty of Information Technology
Middle East University
Amman, Jordan
School of Computer Sciences
Universiti Sains Malaysia
George Town, Pulau Pinang, Malaysia
ISSN 1860-949X ISSN 1860-9503 (electronic)
Studies in Computational Intelligence
ISBN 978-3-031-17575-6 ISBN 978-3-031-17576-3 (eBook)
https://doi.org/10.1007/978-3-031-17576-3
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Preface
Nowadays, with the considerable growth in deep learning and machine learning clas-
sification approaches ranging from many real-world problems such as Artocarpus
Classification, Rambutan Classification, Mango Varieties Classification, Salak Clas-
sification, Image Processing, Identification for Sapodilla Transfer Learning Tech-
niques, Classification of Jackfruit Artocarpus integer and Artocarpus heterophyllus,
Markisa/Passion Fruit Classification, Big Data Classification, and Arabic text classifi-
cation. Deep learning and machine learning have become indispensable technologies
in the current time, and this is the era of artificial intelligence. These techniques find
their marks in data analysis, text mining, classification problems, computer vision,
image analysis, pattern recognition, medicine, etc. There is a continuous flow of data,
so it is impossible to manage and analyze these data manually. The outcome depends
on the processing of high-dimensional data. Most of it is irregular and unordered,
present in various forms like text, images, videos, audio, graphics, etc. Fruit image
recognition systems are used to classify different types of fruits and to differentiate
different fruit variants of a single fruit type. Rambutan is an exotic fruit mainly in the
Southeast Asian region and prevalent fruit in Malaysia. It comes in different varieties
or cultivars. These cultivars appear to look alike in the naked eyes. Hence, an image
recognition system powered by deep learning methods can be applied in classifying
rambutan cultivars accurately. Currently, sorting and classifying mango cultivars are
manually done by observing the features or attributes of mango like size, skin color,
shape, sweetness and flesh color. Generally, experienced taxonomy experts can iden-
tify different species. However, it is not easy to distinguish these mangoes for most
people. Nowadays, society is advancing in science and technology. There is a lot of
technology that could solve the problem, which can make it easy for people to distin-
guish the cultivar. The solution we would like to propose to solve the concern is the
computer vision technique. Artificial intelligence trains computers to interpret and
understand the visual world like images and video. Deep learning, also known as deep
neural networks or deep neural understanding, is used to process the data and create
patterns by imitating the human brain to decide. It uses neurocodes that are linked
together within the hierarchical neural network to analyze the incoming data. Image
recognition is one of the most popular deep learning applications that help many
v
vi Preface
fields, especially in fruit agriculture, to identify the classification of the fruit. This
book proposal intends to bring together researchers and developers from academic
fields and industries worldwide working in the broad areas of deep learning and
machine learning community-wide discussion of ideas that will influence and foster
continued research in this field to better humanity. This book emphasizes bringing in
front some of the technology-based revolutionary solutions that make the classifica-
tion process more efficient. It also provides deep insight into classification techniques
by capturing information from the given chapters.
Amman, Jordan Laith Abualigah
Contents
Artocarpus Classification Technique Using Deep Learning Based
Convolutional Neural Network ..................................... 1
Lee Zhi Pen, Kong Xian Xian, Ching Fum Yew, Ong Swee Hau,
Putra Sumari, Laith Abualigah, Absalom E. Ezugwu,
Mohammad Al Shinwan, Faiza Gul, and Ala Mughaid
Rambutan Image Classification Using Various Deep Learning
Approaches ....................................................... 23
Nur Alia Anuar, Loganathan Muniandy, Khairul Adli Bin Jaafar,
Yi Lim, Al Lami Lamyaa Sabeeh, Putra Sumari,
Laith Abualigah, Mohamed Abd Elaziz, Anas Ratib Alsoud,
and Ahmad MohdAziz Hussein
Mango Varieties Classification-Based Optimization with Transfer
Learning and Deep Learning Approaches ............................ 45
Chen Ke, Ng Tee Weng, Yifan Yang, Zhang Ming Yang,
Putra Sumari, Laith Abualigah, Salah Kamel, Mohsen Ahmadi,
Mohammed A. A. Al-Qaness, Agostino Forestiero,
and Anas Ratib Alsoud
Salak Image Classification Method Based Deep Learning
Technique Using Two Transfer Learning Models ..................... 67
Lau Wei Theng, Moo Mei San, Ong Zhi Cheng, Wong Wei Shen,
Putra Sumari, Laith Abualigah, Raed Abu Zitar, Davut Izci,
Mehdi Jamei, and Shadi Al-Zu’bi
Image Processing Identification for Sapodilla Using Convolution
Neural Network (CNN) and Transfer Learning Techniques ............ 107
Ali Khazalah, Boppana Prasanthi, Dheniesh Thomas,
Nishathinee Vello, Suhanya Jayaprakasam, Putra Sumari,
Laith Abualigah, Absalom E. Ezugwu, Essam Said Hanandeh,
and Nima Khodadadi
vii
viii Contents
Comparison of Pre-trained and Convolutional Neural Networks
for Classification of Jackfruit Artocarpus integer and Artocarpus
heterophyllus ...................................................... 129
Song-Quan Ong, Gomesh Nair, Ragheed Duraid Al Dabbagh,
Nur Farihah Aminuddin, Putra Sumari, Laith Abualigah,
Heming Jia, Shubham Mahajan, Abdelazim G. Hussien,
and Diaa Salama Abd Elminaam
Markisa/Passion Fruit Image Classification Based Improved Deep
Learning Approach Using Transfer Learning ........................ 143
Ahmed Abdo, Chin Jun Hong, Lee Meng Kuan,
Maisarah Mohamed Pauzi, Putra Sumari, Laith Abualigah,
Raed Abu Zitar, and Diego Oliva
Enhanced MapReduce Performance for the Distributed Parallel
Computing: Application of the Big Data ............................. 191
Nathier Milhem, Laith Abualigah, Mohammad H. Nadimi-Shahraki,
Heming Jia, Absalom E. Ezugwu, and Abdelazim G. Hussien
A Novel Big Data Classification Technique for Healthcare
Application Using Support Vector Machine, Random Forest
and J48 ........................................................... 205
Hitham Al-Manaseer, Laith Abualigah, Anas Ratib Alsoud,
Raed Abu Zitar, Absalom E. Ezugwu, and Heming Jia
Comparative Study on Arabic Text Classification: Challenges
and Opportunities ................................................. 217
Mohammed K. Bani Melhem, Laith Abualigah, Raed Abu Zitar,
Abdelazim G. Hussien, and Diego Oliva
Pedestrian Speed Prediction Using Feed Forward Neural Network ..... 225
Abubakar Dayyabu, Hashim Mohammed Alhassan, and Laith Abualigah
Arabic Text Classification Using Modified Artificial Bee Colony
Algorithm for Sentiment Analysis: The Case of Jordanian Dialect ...... 243
Abdallah Habeeb, Mohammed A. Otair, Laith Abualigah,
Anas Ratib Alsoud, Diaa Salama Abd Elminaam, Raed Abu Zitar,
Absalom E. Ezugwu, and Heming Jia
Artocarpus Classification Technique
Using Deep Learning Based
Convolutional Neural Network
Lee Zhi Pen, Kong Xian Xian, Ching Fum Yew, Ong Swee Hau,
Putra Sumari, Laith Abualigah, Absalom E. Ezugwu,
Mohammad Al Shinwan, Faiza Gul, and Ala Mughaid
Abstract There are many species of Artocarpus fruits in Malaysia, which have
different market potentials. This study classifies 4 species of Artocarpus fruits using
deep learning approach, which is Convolutional Neural Network (CNN). A new
proposed CNN model is compared with pre-trained models, i.e., VGG-16, ResNet50,
and Xception. Effects of variables, i.e., hidden layers, perceptrons, filter number,
optimizers, and learning rate, on the proposed model are also investigated in this
study. The best performing model in this study is the new proposed model with 2
CNN layers (12, 96 filters) and 6 dense layers with 147 perceptrons, achieving an
accuracy of 87%.
· · ·
Keywords Deep learning Transfer learning Convolutional neural network
·
Fruit classification Artocarpus
B
L. Z. Pen · K. Xian Xian · C. F. Yew · O. S. Hau · P. Sumari · L. Abualigah ( )
School of Computer Sciences, Universiti Sains Malaysia, 11800 George Town, Pulau Pinang,
Malaysia
e-mail: [e-mail]
L. Abualigah
Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman 11328,
Jordan
Faculty of Information Technology, Middle East University, Amman 11831, Jordan
A. E. Ezugwu
School of Computer Science, University of KwaZulu-Natal, Pietermaritzburg Campus,
Pietermaritzburg 3201, South Africa
M. A. Shinwan
Faculty of Information Technology, Applied Science Private University, Amman 11931, Jordan
F. Gul
Department of Electrical Engineering, Air University, Aerospace and Aviation Campus, Kamra,
Attock 43600, Pakistan
A. Mughaid
Department of Information Technology, Faculty of Prince Al-Hussien Bin Abdullah for IT, The
Hashemite University, PO Box 330127, Zarqa 13133, Jordan
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 1
L. Abualigah (ed.), Classification Applications with Deep Learning and Machine Learning
Technologies, Studies in Computational Intelligence 1071,
https://doi.org/10.1007/978-3-031-17576-3_1