Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.5
2.4
Journals
Mathematics
Special Issues
Anomaly and Novelty Detection and Explainability
share announcement

Anomaly and Novelty Detection and Explainability

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 7278

Special Issue Editor

Prof. Dr. Radu Tudor Ionescu

E-Mail Website
Guest Editor
Department of Computer Science, University of Bucharest, 14 Academiei, 010014 Bucharest, Romania
Interests: artificial intelligence; machine learning; computer vision; image processing; text mining; computational linguistics; medical imaging
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

An anomaly is an observation or event that is rare or very different from known or familiar observations or events, while a novelty is a new type of observation not discovered in the learning process. Recognizing, detecting and explaining anomalies and novelties have come to form an area of active research in many domains, including machine learning, computer vision, natural language processing, fraud prevention, cybersecurity and medicine. In these domains, successful and early detection of anomalies and novelties is essential. For example, anomaly detection methods can be applied to:

  • Intrusion detection to protect computer networks and data centers from enemy attacks in the cyberspace.
  • Fraud detection and anti-money laundering activities in order to prevent damages of millions of dollars.
  • Abnormal event detection in video to enable immediate intervention by first responders in case of fights, traffic accidents, explosions, etc.
  • Industrial inspection to identify defects and verify the conformity of products and parts.

Anomalies and novelties are very rare, and, in most cases, such observations are not available at training time. This means that machine learning methods based on traditional supervision are ruled out. The main problem in anomaly and novelty detection is to design artificial intelligence systems that are able to characterize the nature of anomalies and novelties, without seeing such observations at training time. An equally important problem is the design of artificial intelligence systems that are able to explain the decision-making process. Additional problems are related to (i) the difficulty of learning latent representations with deep neural models that disentangle normal and abnormal observations, (ii) the need to formulate new fundamental theories to clarify what anomalies can be detected, and (iii) the evaluation of proposed models in realistic scenarios. This Special Issue aims to gather articles addressing the problems enumerated above, as well as other problems related to anomaly and novelty detection.

Prof. Dr. Radu Tudor Ionescu
Guest Editor

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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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

  • anomaly detection
  • novelty detection
  • abnormal event detection
  • novelty learning
  • outlier detection
  • outlier removal
  • explainable AI
  • model explainability

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

21 pages, 7100 KiB  
Article
OASIS-Net: Morphological Attention Ensemble Learning for Surface Defect Detection
by Younggi Hong and Seok Bong Yoo
Mathematics 2022, 10(21), 4114; https://0-doi-org.brum.beds.ac.uk/10.3390/math10214114 - 04 Nov 2022
Cited by 2 | Viewed by 1575
Abstract
Surface defect detection systems, which have advanced beyond conventional defect detection methods, lower the risk of accidents and increase working efficiency and productivity. Most fault detection techniques demand extra tools, such as ultrasonic sensors or lasers. With the advancements, these techniques can be [...] Read more.
Surface defect detection systems, which have advanced beyond conventional defect detection methods, lower the risk of accidents and increase working efficiency and productivity. Most fault detection techniques demand extra tools, such as ultrasonic sensors or lasers. With the advancements, these techniques can be examined without additional tools. We propose a morphological attention ensemble learning for surface defect detection called OASIS-Net, which can detect defects of three kinds (crack, efflorescence, and spalling) at the bounding box level. Based on the morphological analysis of each defect, OASIS-Net offers specialized loss functions for each defect that can be examined. Specifically, high-frequency image augmentation, connectivity attention, and penalty areas are used to detect cracks. It also compares the colors of the sensing objects and analyzes the image histogram peaks to improve the efflorescence-verification accuracy. Analyzing the ratio of the major and minor axes of the spalling through morphological comparison reveals that the spalling-detection accuracy improved. Defect images are challenging to obtain due to their properties. We labeled some data provided by AI hub and some concrete crack datasets and used them as custom datasets. Finally, an ensemble learning technique based on multi-task classification is suggested to learn and apply the specialized loss of each class to the model. For the custom dataset, the accuracy of the crack detection increased by 5%, the accuracy of the efflorescence detection increased by 4.4%, and the accuracy of the spalling detection increased by 6.6%. The experimental results reveal that the proposed network outperforms the previous state-of-the-art methods. Full article
(This article belongs to the Special Issue Anomaly and Novelty Detection and Explainability)
►▼ Show Figures

Figure 1

16 pages, 2222 KiB  
Article
Credit Card Fraud Detection with Autoencoder and Probabilistic Random Forest
by Tzu-Hsuan Lin and Jehn-Ruey Jiang
Mathematics 2021, 9(21), 2683; https://0-doi-org.brum.beds.ac.uk/10.3390/math9212683 - 22 Oct 2021
Cited by 28 | Viewed by 3829
Abstract
This paper proposes a method, called autoencoder with probabilistic random forest (AE-PRF), for detecting credit card frauds. The proposed AE-PRF method first utilizes the autoencoder to extract features of low-dimensionality from credit card transaction data features of high-dimensionality. It then relies on the [...] Read more.
This paper proposes a method, called autoencoder with probabilistic random forest (AE-PRF), for detecting credit card frauds. The proposed AE-PRF method first utilizes the autoencoder to extract features of low-dimensionality from credit card transaction data features of high-dimensionality. It then relies on the random forest, an ensemble learning mechanism using the bootstrap aggregating (bagging) concept, with probabilistic classification to classify data as fraudulent or normal. The credit card fraud detection (CCFD) dataset is applied to AE-PRF for performance evaluation and comparison. The CCFD dataset contains large numbers of credit card transactions of European cardholders; it is highly imbalanced since its normal transactions far outnumber fraudulent transactions. Data resampling schemes like the synthetic minority oversampling technique (SMOTE), adaptive synthetic (ADASYN), and Tomek link (T-Link) are applied to the CCFD dataset to balance the numbers of normal and fraudulent transactions for improving AE-PRF performance. Experimental results show that the performance of AE-PRF does not vary much whether resampling schemes are applied to the dataset or not. This indicates that AE-PRF is naturally suitable for dealing with imbalanced datasets. When compared with related methods, AE-PRF has relatively excellent performance in terms of accuracy, the true positive rate, the true negative rate, the Matthews correlation coefficient, and the area under the receiver operating characteristic curve. Full article
(This article belongs to the Special Issue Anomaly and Novelty Detection and Explainability)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop