Special Issue "Computer Vision, Deep Learning and Machine Learning with Applications"

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Big Data and Augmented Intelligence".

Deadline for manuscript submissions: 30 April 2021.

Special Issue Editors

Prof. Dr. Remus Brad
Website
Guest Editor
Computer Science and Electrical Engineering Department, Lucian Blaga University of Sibiu, Bulevardul Victoriei 10, 550024 Sibiu, Romania
Interests: image processing; pattern recognition; computer vision; textile engineering; medical imaging
Dr. Arpad Gellert
Website
Co-Guest Editor
Computer Science and Electrical Engineering Department, Lucian Blaga University of Sibiu, Emil Cioran 4, 550025 Sibiu, Romania
Interests: image processing; smart buildings; smart factories; web mining; computer architecture

Special Issue Information

Dear Colleagues,

In recent years, interest toward automation, both in industrial or domestic applications, has increased with the development of new methods and with the growth of computer processing capabilities. One of the major pillars of today’s research goes beyond image processing, in the more generous concept of computer vision. Nevertheless, the latest evolutions in neural networks and machine learning have added more valences and applications to the domain, starting from industrial process control to the challenging self-driving in automotive, passing from medical imaging, information retrieval and digital forensics.

The scope of this Special Issue is to collect the latest works in the fields of computer vision, machine learning, and deep learning and gather researchers from different areas who are struggling to solve the provoking and demanding topics proposed by the industry and an ever-changing world. Potential topics include but are not limited to:

  • Image processing and applications;
  • Medical imaging;
  • Autonomous vehicles;
  • Convolutional neural networks;
  • Digital forensics;
  • Smart home;
  • Smart city;
  • Industrial quality control.

 

Prof. Dr. Remus Brad

Dr. Arpad Gellert
Guest Editor

Prof. Dr. Remus Brad
Dr. Arpad Gellert
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. Future Internet 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 1000 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

  • image processing
  • computer vision
  • deep learning
  • artificial intelligence
  • machine learning
  • convolutional neural networks
  • automation
  • smart home
  • smart city
  • medical imaging

Published Papers (1 paper)

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Research

Open AccessArticle
Comparison of Machine Learning and Deep Learning Models for Network Intrusion Detection Systems
Future Internet 2020, 12(10), 167; https://doi.org/10.3390/fi12100167 - 30 Sep 2020
Abstract
The development of robust anomaly-based network detection systems, which are preferred over static signal-based network intrusion, is vital for cybersecurity. The development of a flexible and dynamic security system is required to tackle the new attacks. Current intrusion detection systems (IDSs) suffer to [...] Read more.
The development of robust anomaly-based network detection systems, which are preferred over static signal-based network intrusion, is vital for cybersecurity. The development of a flexible and dynamic security system is required to tackle the new attacks. Current intrusion detection systems (IDSs) suffer to attain both the high detection rate and low false alarm rate. To address this issue, in this paper, we propose an IDS using different machine learning (ML) and deep learning (DL) models. This paper presents a comparative analysis of different ML models and DL models on Coburg intrusion detection datasets (CIDDSs). First, we compare different ML- and DL-based models on the CIDDS dataset. Second, we propose an ensemble model that combines the best ML and DL models to achieve high-performance metrics. Finally, we benchmarked our best models with the CIC-IDS2017 dataset and compared them with state-of-the-art models. While the popular IDS datasets like KDD99 and NSL-KDD fail to represent the recent attacks and suffer from network biases, CIDDS, used in this research, encompasses labeled flow-based data in a simulated office environment with both updated attacks and normal usage. Furthermore, both accuracy and interpretability must be considered while implementing AI models. Both ML and DL models achieved an accuracy of 99% on the CIDDS dataset with a high detection rate, low false alarm rate, and relatively low training costs. Feature importance was also studied using the Classification and regression tree (CART) model. Our models performed well in 10-fold cross-validation and independent testing. CART and convolutional neural network (CNN) with embedding achieved slightly better performance on the CIC-IDS2017 dataset compared to previous models. Together, these results suggest that both ML and DL methods are robust and complementary techniques as an effective network intrusion detection system. Full article
(This article belongs to the Special Issue Computer Vision, Deep Learning and Machine Learning with Applications)
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