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.


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.

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
  • Prediction of Cardiovascular Disease Using Machine Learning Technique—A Modern Approach
  • Abstract Cardio Vascular disease (CVD), involving the heart and blood vessels is one of the most leading causes of death throughout the world. There are several risk factors for causing heart diseases like sedentary lifestyle, unhealthy diet, obesity, diabetes, hypertension, smoking and consumption of alcohol, stress, hereditary factory etc. Predicting cardiovascular disease and improving and treating the risk factors at an early stage are of paramount importance to save the precious life of a human being. At present, the highly stressful life with bad lifestyle activities causes heart disease at a very young age. The main aim of this research is… More
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  • Piezoresistive Prediction of CNTs-Embedded Cement Composites via Machine Learning Approaches
  • Abstract Conductive cementitious composites are innovated materials that have improved electrical conductivity compared to general types of cement, and are expected to be used in a variety of future infrastructures with unique functionalities such as self-heating, electromagnetic shielding, and piezoelectricity. In the present study, machine learning methods that have been recently applied in various fields were proposed for the prediction of piezoelectric characteristics of carbon nanotubes (CNTs)-incorporated cement composites. Data on the resistivity change of CNTs/cement composites according to various water/binder ratios, loading types, and CNT content were considered as training values. These data were applied to numerous machine learning techniques… More
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  • 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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