Skip to main content
SpringerLink
Log in

Classification Applications with Deep Learning and Machine Learning Technologies

  • Book
  • © 2023

Overview

Editors:
  1. Laith Abualigah

    1. Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, Jordan

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  • Presents recent research in Classification Applications with Deep Learning and Machine Learning Technologies
  • Brings together outstanding research and recent developments in the broad areas of Deep Learning and Machine Learning
  • Written by experts in the field

Part of the book series: Studies in Computational Intelligence (SCI, volume 1071)

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

eBook USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

About this book

This book is very beneficial for early researchers/faculty who want to work in deep learning and machine learning for the classification domain. It helps them study, formulate, and design their research goal by aligning the latest technologies studies’ image and data classifications. The early start-up can use it to work with product or prototype design requirement analysis and its design and development.

Similar content being viewed by others

Keywords

Table of contents (12 chapters)

  1. Front Matter

    Pages i-viii
    Download chapter PDF

  2. 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 et al.
    Pages 1-21

  3. Rambutan Image Classification Using Various Deep Learning Approaches

    • Nur Alia Anuar, Loganathan Muniandy, Khairul Adli Bin Jaafar, Yi Lim, Al Lami Lamyaa Sabeeh, Putra Sumari et al.
    Pages 23-43

  4. Mango Varieties Classification-Based Optimization with Transfer Learning and Deep Learning Approaches

    • Chen Ke, Ng Tee Weng, Yifan Yang, Zhang Ming Yang, Putra Sumari, Laith Abualigah et al.
    Pages 45-65

  5. Salak Image Classification Method Based Deep Learning Technique Using Two Transfer Learning Models

    • Lau Wei Theng, Moo Mei San, Ong Zhi Cheng, Wong Wei Shen, Putra Sumari, Laith Abualigah et al.
    Pages 67-105

  6. Image Processing Identification for Sapodilla Using Convolution Neural Network (CNN) and Transfer Learning Techniques

    • Ali Khazalah, Boppana Prasanthi, Dheniesh Thomas, Nishathinee Vello, Suhanya Jayaprakasam, Putra Sumari et al.
    Pages 107-127

  7. Comparison of Pre-trained and Convolutional Neural Networks for Classification of Jackfruit Artocarpus integer and Artocarpus heterophyllus

    • Song-Quan Ong, Gomesh Nair, Ragheed Duraid Al Dabbagh, Nur Farihah Aminuddin, Putra Sumari, Laith Abualigah et al.
    Pages 129-141

  8. Markisa/Passion Fruit Image Classification Based Improved Deep Learning Approach Using Transfer Learning

    • Ahmed Abdo, Chin Jun Hong, Lee Meng Kuan, Maisarah Mohamed Pauzi, Putra Sumari, Laith Abualigah et al.
    Pages 143-189

  9. Enhanced MapReduce Performance for the Distributed Parallel Computing: Application of the Big Data

    • Nathier Milhem, Laith Abualigah, Mohammad H. Nadimi-Shahraki, Heming Jia, Absalom E. Ezugwu, Abdelazim G. Hussien
    Pages 191-203

  10. A Novel Big Data Classification Technique for Healthcare Application Using Support Vector Machine, Random Forest and J48

    • Hitham Al-Manaseer, Laith Abualigah, Anas Ratib Alsoud, Raed Abu Zitar, Absalom E. Ezugwu, Heming Jia
    Pages 205-215

  11. Comparative Study on Arabic Text Classification: Challenges and Opportunities

    • Mohammed K. Bani Melhem, Laith Abualigah, Raed Abu Zitar, Abdelazim G. Hussien, Diego Oliva
    Pages 217-224

  12. Pedestrian Speed Prediction Using Feed Forward Neural Network

    • Abubakar Dayyabu, Hashim Mohammed Alhassan, Laith Abualigah
    Pages 225-241

  13. Arabic Text Classification Using Modified Artificial Bee Colony Algorithm for Sentiment Analysis: The Case of Jordanian Dialect

    • Abdallah Habeeb, Mohammed A. Otair, Laith Abualigah, Anas Ratib Alsoud, Diaa Salama Abd Elminaam, Raed Abu Zitar et al.
    Pages 243-288
Back to top

Editors and Affiliations

  • Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, Jordan

    Laith Abualigah

Bibliographic Information

  • Book Title: Classification Applications with Deep Learning and Machine Learning Technologies

  • Editors: Laith Abualigah

  • Series Title: Studies in Computational Intelligence

  • DOI: https://doi.org/10.1007/978-3-031-17576-3

  • Publisher: Springer Cham

  • eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023

  • Hardcover ISBN: 978-3-031-17575-6Published: 17 November 2022

  • Softcover ISBN: 978-3-031-17578-7Published: 18 November 2023

  • eBook ISBN: 978-3-031-17576-3Published: 16 November 2022

  • Series ISSN: 1860-949X

  • Series E-ISSN: 1860-9503

  • Edition Number: 1

  • Number of Pages: VIII, 288

  • Number of Illustrations: 34 b/w illustrations, 201 illustrations in colour

  • Topics: Computational Intelligence, Machine Learning, Big Data

Publish with us

Policies and ethics

Back to top

Search

Navigation