Special Issue "Applications and Methodologies of Artificial Intelligence in Big Data Analysis"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 31 December 2020.

Special Issue Editors

Prof. Prasan Kumar Sahoo
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Guest Editor
Department of Computer Science and Information Engineering, Chang Gung University, Guishan 33302, Taiwan
Interests: Artificial Intelligence, Deep Learning, Big Data analysis, Medical image data analysis, Prediction model design, IoT, Fog and Edge Computing
Prof. Omar BOUSSAID
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Guest Editor
Laboratoire ERIC, Institut de Communication, Université de Lyon, Lyon 2, 5 avenue Pierre Mendès, 69676 Bron Cedex, France
Interests: Business Intelligence & Big Data Management, Business Analytics & Data Discovery, Distributed Data Warehousing, Intensive Queries in Big Data Warehouses, MapReduce and Spark technologies, Text Warehousing & OLAP Text Cubes, Semantic OLAP & Social OLAP, Analysis of social graphs cubes, Community detection and evaluation
Prof. Nader F. Mir
Website
Guest Editor
Department of Electrical Engineering, Charles W. Davidson College of Engineering, San Jose State University, One Washington Square, 95192, San Jose, CA, USA
Interests: Computer and Communication Networks, TCP/IP Internet, Client-Server, WEB, Traffic Load Balancing, VoIP, Video and Streaming over IP, Multimedia Networks, Design of Networking Equipments, Modems, Switches and Routers, Wireless and Mobile Networks and Wireless Sensor Networks

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) and Big Data Analytics are two of the highly promising technologies considered by data scientists and big corporations. The emergence of robotics has introduced an autonomy, which requires no human intervention in the implementation of the decisions. Deep learning is considered an advanced version of AI through which various machines can send or receive data and learn new concepts by analyzing the data. Big data helps organizations in analyzing their existing data and in drawing meaningful insights from the same. The exponential growth of technologies like AI and big data analytics can be used for anomaly detection, pattern recognition, and industrial fault detection, and can boost market analysis insights. It is a need of the time to design efficient algorithms, models and methodologies of AI to analyze the big data generated from various sources, such as industry, healthcare, medical and the financial market.

The topics of interest in this SI include, but are not limited to:

  • Applications of machine learning
  • Deep learning algorithms
  • IoT and smart city big data analysis
  • Imaging big data analysis
  • Natural language processing and speech recognition
  • Medical imaging data analysis
  • Applications of deep learning in image data analysis
  • Pattern recognition and applications of AI
Prof. Prasan Kumar Sahoo
Prof. Omar BOUSSAID
Prof. Nader F. Mir
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Artificial Intelligence
  • Machine Learning
  • Deep Learning
  • Big Data
  • Internet of Things
  • Language Processing
  • Image analysis

Published Papers (2 papers)

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Research

Open AccessFeature PaperArticle
A Machine Learning and Integration Based Architecture for Cognitive Disorder Detection Used for Early Autism Screening
Electronics 2020, 9(3), 516; https://doi.org/10.3390/electronics9030516 - 21 Mar 2020
Abstract
About 15% of the world’s population suffers from some form of disability. In developed countries, about 1.5% of children are diagnosed with autism. Autism is a developmental disorder distinguished mainly by impairments in social interaction and communication and by restricted and repetitive behavior. [...] Read more.
About 15% of the world’s population suffers from some form of disability. In developed countries, about 1.5% of children are diagnosed with autism. Autism is a developmental disorder distinguished mainly by impairments in social interaction and communication and by restricted and repetitive behavior. Since the cause of autism is still unknown, there have been many studies focused on screening for autism based on behavioral features. Thus, the main purpose of this paper is to present an architecture focused on data integration and analytics, allowing the distributed processing of input data. Furthermore, the proposed architecture allows the identification of relevant features as well as of hidden correlations among parameters. To this end, we propose a methodology able to integrate diverse data sources, even data that are collected separately. This methodology increases the data variety which can lead to the identification of more correlations between diverse parameters. We conclude the paper with a case study that used autism data in order to validate our proposed architecture, which showed very promising results. Full article
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Open AccessArticle
An Efficient and Unique TF/IDF Algorithmic Model-Based Data Analysis for Handling Applications with Big Data Streaming
Electronics 2019, 8(11), 1331; https://doi.org/10.3390/electronics8111331 - 11 Nov 2019
Cited by 5
Abstract
As the field of data science grows, document analytics has become a more challenging task for rough classification, response analysis, and text summarization. These tasks are used for the analysis of text data from various intelligent sensing systems. The conventional approach for data [...] Read more.
As the field of data science grows, document analytics has become a more challenging task for rough classification, response analysis, and text summarization. These tasks are used for the analysis of text data from various intelligent sensing systems. The conventional approach for data analytics and text processing is not useful for big data coming from intelligent systems. This work proposes a novel TF/IDF algorithm with the temporal Louvain approach to solve the above problem. Such an approach is supposed to help the categorization of documents into hierarchical structures showing the relationship between variables, which is a boon to analysts making essential decisions. This paper used public corpora, such as Reuters-21578 and 20 Newsgroups for massive-data analytic experimentation. The result shows the efficacy of the proposed algorithm in terms of accuracy and execution time across six datasets. The proposed approach is validated to bring value to big text data analysis. Big data handling with map-reduce has led to tremendous growth and support for tasks like categorization, sentiment analysis, and higher-quality accuracy from the input data. Outperforming the state-of-the-art approach in terms of accuracy and execution time for six datasets ensures proper validation. Full article
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