Special Issue "Applications of Machine Learning for Big Data"

Submission Deadline: 31 August 2021 (closed)
Guest Editors
Dr. Mohan Prakash, Karpagam College of Engineering, Coimbatore, India.
Dr. Nithiyananthan Kannan, King Abdulaziz University, Rabigh, KSA.

Summary

In recent years, the Machine Learning and Big Data are the hot research topics both in terms of theory and applications. Big Data is one of the core foundational pillars. Big Data application provides important challenges that can addressed only with Machine Learning Tecniques. This special issue focuses on the latest development in Machine Learning foundation of Big Data, as well as the synergy between the Big Data and Machine Learning. We welcome the new research ideas and developments in mathematics and computing that are relevant for Big Data from Machine Learning perspective including foundation, systems, innovative application and other research contribution.


Keywords
Augmented Reality
Cloud Data Storage using Machine Learning
Computer Vision
Deep Learning
IoT Data analytics and Big Data
Large-scale processing and distributed/parallel computing
Online Recommender Systems
Real-Time Anomaly, Failure, image manipulation and fake detection
Semi-supervised and weakly supervised learning
Streaming Analytics

Published Papers
  • A Hybrid Deep Learning-Based Unsupervised Anomaly Detection in High Dimensional Data
  • Abstract Anomaly detection in high dimensional data is a critical research issue with serious implication in the real-world problems. Many issues in this field still unsolved, so several modern anomaly detection methods struggle to maintain adequate accuracy due to the highly descriptive nature of big data. Such a phenomenon is referred to as the “curse of dimensionality” that affects traditional techniques in terms of both accuracy and performance. Thus, this research proposed a hybrid model based on Deep Autoencoder Neural Network (DANN) with five layers to reduce the difference between the input and output. The proposed model was applied to a… More
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