Abualigah Classification Applications with Deep Learning and Machine Learning Technologies

Erscheinungsjahr 2022
ISBN: 978-3-031-17576-3
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 1071, 288 Seiten, eBook

Reihe: Studies in Computational Intelligence

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.

Zielgruppe

Research

Autoren/Hrsg.

Abualigah, Laith

Inhaltsverzeichnis

Artocarpus Classification Technique using Deep Learning based Convolutional Neural Network.- Rambutan Image Classification using Various Deep Learning Approaches.- Mango Varieties Classification-based Optimization with Transfer Learning and Deep Learning Approaches.- Salak Image Classification Method based Deep Learning Technique using Two Transfer Learning Models.- Image Processing Identification for Sapodilla Using Convolution Neural Network (CNN) and Transfer Learning Techniques.- Comparison of Pre-trained and Convolutional Neural Networks for Classification of Jackfruit Artocarpus Integer and Artocarpus Heterophyllus.- Markisa/Passion Fruit Image Classification based Improved Deep Learning Approach using Transfer Learning.- Enhanced MapReduce Performance for the Distributed Parallel Computing: Application of the Big Data.- A Novel Big Data Classification Technique for Healthcare Application using Support Vector Machine, Random Forest and J48.- Comparative Study on Arabic Text Classification: Challenges and Opportunities.- Pedestrian Speed Prediction Using Feed Forward Neural Network.- Arabic Text Classification using Modified Artificial Bee Colony Algorithm for Sentiment Analysis: The Case of Jordanian Dialect.