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Information
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Advanced Information Technology, Big Data and Artificial Intelligence
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Advanced Information Technology, Big Data and Artificial Intelligence

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Applications".

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 5825

Special Issue Editors

Dr. Yinong Chen

E-Mail Website
Guest Editor
Computer Science and Engineering, Arizona State University, Tempe, AZ 85287-8809, USA
Interests: service-oriented computing; IoT; robotics; AI in education
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jian Wang

E-Mail Website
Guest Editor
College of Sciences, China University of Petroleum (East China), Qingdao, China
Interests: machine learning; computational mathematics; neural networks; optimization

Special Issue Information

Dear Colleagues,

This Special Issue encourages authors from academia and industry to submit new research results in Advanced Information Technology, Big Data, and Artificial Intelligence. It provides opportunities for the delegates to exchange new ideas and application experiences, to establish business or research relations, and to find global partners for future collaboration.

This Special Issue will also present extended versions of selected papers presented at the 10th Joint International Information Technology and Artificial Intelligence Conference (ITAIC 2022). The aim of ITAIC 2022 is to provide a platform for researchers, engineers, academicians, as well as industrial professionals from all over the world to present their research results and development activities in Information Technology and Artificial Intelligence.

Dr. Yinong Chen
Prof. Dr. Jian Wang
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 submissions that pass pre-check are 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. Information 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 1600 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

  • Information technology
  • Artificial intelligence
  • Machine learning
  • Big data
  • Computational methods and algorithms
  • Internet of Things

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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Research

14 pages, 1029 KiB  
Article
A Spark-Based Artificial Bee Colony Algorithm for Unbalanced Large Data Classification
by Jamil Al-Sawwa and Mohammad Almseidin
Information 2022, 13(11), 530; https://doi.org/10.3390/info13110530 - 8 Nov 2022
Cited by 2 | Viewed by 2114
Abstract
With the rapid development of internet technology, the amount of collected or generated data has increased exponentially. The sheer volume, complexity, and unbalanced nature of this data pose a challenge to the scientific community to extract meaningful information from this data within a [...] Read more.
With the rapid development of internet technology, the amount of collected or generated data has increased exponentially. The sheer volume, complexity, and unbalanced nature of this data pose a challenge to the scientific community to extract meaningful information from this data within a reasonable time. In this paper, we implemented a scalable design of an artificial bee colony for big data classification using Apache Spark. In addition, a new fitness function is proposed to handle unbalanced data. Two experiments were performed using the real unbalanced datasets to assess the performance and scalability of our proposed algorithm. The performance results reveal that our proposed fitness function can efficiently deal with unbalanced datasets and statistically outperforms the existing fitness function in terms of G-mean and F1-Score. In additon, the scalability results demonstrate that our proposed Spark-based design obtained outstanding speedup and scaleup results that are very close to optimal. In addition, our Spark-based design scales efficiently with increasing data size. Full article
(This article belongs to the Special Issue Advanced Information Technology, Big Data and Artificial Intelligence)
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19 pages, 1150 KiB  
Article
Comparing Worldwide, National, and Independent Notifications about Adverse Drug Reactions Due to COVID-19 Vaccines
by Francesco Branda and Davide Tosi
Information 2022, 13(7), 329; https://doi.org/10.3390/info13070329 - 8 Jul 2022
Viewed by 3064
Abstract
The rapid development of effective vaccines against COVID-19 is an extraordinary achievement. However, no medical product can ever be considered risk-free. Several countries have a pharmacovigilance system that detects, assesses, understands, and prevents possible adverse effects of a drug. To benefit from such [...] Read more.
The rapid development of effective vaccines against COVID-19 is an extraordinary achievement. However, no medical product can ever be considered risk-free. Several countries have a pharmacovigilance system that detects, assesses, understands, and prevents possible adverse effects of a drug. To benefit from such huge data sources, specialists and researchers need advanced big data analysis tools able to extract value and find valuable insights. This paper defines a general framework for a pharmaceutical data analysis application that provides a predefined (but extensible) set of functions for each data processing step (i.e., data collection, filtering, enriching, analysis, and visualization). As a case study, we present here an analysis of the potential side effects observed following the administration of the COVID-19 vaccines. The experimental evaluation shows that: (i) most adverse events can be classified as non-serious and concern muscle/joint pain, chills and nausea, headache, and fatigue; (ii) the notification rate is higher in the age group 20–39 years and decreases in older age groups and in very young people. Full article
(This article belongs to the Special Issue Advanced Information Technology, Big Data and Artificial Intelligence)
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