Special Issue "Emerging Applications of Artificial Intelligence, Machine learning and Data Science"

Submission Deadline: 25 October 2021
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Guest Editors
Dr. Dharmendra Singh Rajput, Vellore Institute of Technology, India.
Dr. Syed Muzamil Basha, Sri Krishna College of Engineering and Technology Coimbatore, India.
Dr. Dr. Thippa Reddy G, Vellore Institute of Technology, India.
Dr. Saurabh Singh, Dingguk University, South Korea.

Summary

Today, artificial intelligence (AI) is the mainstream technology for all the companies, unicorns, and industries those are aiming to improvise to increase revenue. AI is the buzz word of the century, but most of them don't understand it well. Before understanding AI, understanding its subsets is important. Machine Learning (ML) and Deep Learning (DL). ML is an algorithmic approach to train & teach the machine to perform predictive tasks and test them to improve based on their performance. DL on the other hand structures algorithms in layers to create neural networks that can make the decisions on their own.

Even though humanity is ready to switch from gasoline to electricity, Data is the fuel for all, even to decide the next leader. There is no demand, it's is been flooded on daily basis. Processing those may bring peace or war, it depends on the purposes.

Data Science and its contribution to the field of AI is enormous. The research encompasses statistics, mathematics, and computer concepts to deal with big data to visualize the insights that may change the decision and may shape the future. The power of AI is intangible. Recognizing its potential and understanding it is important.


Keywords
• Artificial Intelligence
• Machine learning
• Deep Learning
• Data Science
• Industry 4.0

Published Papers
  • An Approach Using Fuzzy Sets and Boosting Techniques to Predict Liver Disease
  • Abstract The aim of this research is to develop a mechanism to help medical practitioners predict and diagnose liver disease. Several systems have been proposed to help medical experts by diminishing error and increasing accuracy in diagnosing and predicting diseases. Among many existing methods, a few have considered the class imbalance issues of liver disorder datasets. As all the samples of liver disorder datasets are not useful, they do not contribute to learning about classifiers. A few samples might be redundant, which can increase the computational cost and affect the performance of the classifier. In this paper, a model has been… More
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  • Performance Comparison of Deep CNN Models for Detecting Driver’s Distraction
  • Abstract According to various worldwide statistics, most car accidents occur solely due to human error. The person driving a car needs to be alert, especially when travelling through high traffic volumes that permit high-speed transit since a slight distraction can cause a fatal accident. Even though semi-automated checks, such as speed detecting cameras and speed barriers, are deployed, controlling human errors is an arduous task. The key causes of driver’s distraction include drunken driving, conversing with co-passengers, fatigue, and operating gadgets while driving. If these distractions are accurately predicted, the drivers can be alerted through an alarm system. Further, this research… More
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  • Early Tumor Diagnosis in Brain MR Images via Deep Convolutional Neural Network Model
  • Abstract Machine learning based image analysis for predicting and diagnosing certain diseases has been entirely trustworthy and even as efficient as a domain expert’s inspection. However, the style of non-transparency functioning by a trained machine learning system poses a more significant impediment for seamless knowledge trajectory, clinical mapping, and delusion tracing. In this proposed study, a deep learning based framework that employs deep convolution neural network (Deep-CNN), by utilizing both clinical presentations and conventional magnetic resonance imaging (MRI) investigations, for diagnosing tumors is explored. This research aims to develop a model that can be used for abnormality detection over MRI data… More
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  • Multi Sensor-Based Implicit User Identification
  • Abstract Smartphones have ubiquitously integrated into our home and work environments, however, users normally rely on explicit but inefficient identification processes in a controlled environment. Therefore, when a device is stolen, a thief can have access to the owner’s personal information and services against the stored passwords. As a result of this potential scenario, this work proposes an automatic legitimate user identification system based on gait biometrics extracted from user walking patterns captured by smartphone sensors. A set of preprocessing schemes are applied to calibrate noisy and invalid samples and augment the gait-induced time and frequency domain features, then further optimized… More
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