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Applications of Artificial Intelligence Using Real Data
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Applications of Artificial Intelligence Using Real Data

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

Deadline for manuscript submissions: closed (17 January 2022) | Viewed by 5562

Special Issue Editors

Dr. Nuno Cavalheiro Marques

E-Mail Website
Guest Editor
DI-FCT & NOVA LINCS, Universidade NOVA, Lisboa, Portugal
Interests: ubiquitous data mining; data mining in finance; deep learning; text mining
Dr. Bruno Silva

E-Mail Website
Guest Editor
DSI & Sustain.RD, ESTSetúbal/IPS, Setúbal, Portugal
Interests: machine learning; artificial neural networks; stream mining

Special Issue Information

Dear Colleagues,

The topic “data is the new oil” has been discussed in editorials from Forbes to The Economist. The continuous and non-continuous information data sources are both advantageous and a challenge to the future economy and society. The artificial intelligence and machine learning algorithms used to process such data require acceptable accuracy rates for specific tasks. Yet, over time, the monitoring accuracy, processing overhead and transparency of such algorithms still warrant careful analysis for real applications. Data-driven methods can also be unintentionally contaminated by bias or undermined by concept drift within dynamical data streams. The goal of this Special Issue in the journal of Information is to map how artificial intelligence algorithms can be applied to real problems and address such issues. Specific focus will be given to applications in areas such as medicine, education, IoT, and finance. Original research work, significantly extended versions of conference papers, and review papers are welcome. Topics of interest include, but are not limited to, the following:

  • AI in medicine and healthcare;
  • Data mining in genomics;
  • AI-powered therapy;
  • Data mining using epidemiological data;
  • Data mining in education;
  • Mining public data sources;
  • IoT and data stream mining;
  • Cybersecurity and data mining;
  • AI in stock markets;
  • AI in industry and services;
  • AI in finance, marketing, and production.

Dr. Nuno Cavalheiro Marques
Dr. Bruno Silva
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

  • artificial intelligence in medicine
  • artificial intelligence in finance
  • artificial intelligence and Internet of Things
  • data streams
  • machine learning data visualization
  • interpretable machine learning

Published Papers (2 papers)

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Research

12 pages, 6233 KiB  
Article
Adversarial Attacks Impact on the Neural Network Performance and Visual Perception of Data under Attack
by Yakov Usoltsev, Balzhit Lodonova, Alexander Shelupanov, Anton Konev and Evgeny Kostyuchenko
Information 2022, 13(2), 77; https://0-doi-org.brum.beds.ac.uk/10.3390/info13020077 - 05 Feb 2022
Viewed by 2583
Abstract
Machine learning algorithms based on neural networks are vulnerable to adversarial attacks. The use of attacks against authentication systems greatly reduces the accuracy of such a system, despite the complexity of generating a competitive example. As part of this study, a white-box adversarial [...] Read more.
Machine learning algorithms based on neural networks are vulnerable to adversarial attacks. The use of attacks against authentication systems greatly reduces the accuracy of such a system, despite the complexity of generating a competitive example. As part of this study, a white-box adversarial attack on an authentication system was carried out. The basis of the authentication system is a neural network perceptron, trained on a dataset of frequency signatures of sign. For an attack on an atypical dataset, the following results were obtained: with an attack intensity of 25%, the authentication system availability decreases to 50% for a particular user, and with a further increase in the attack intensity, the accuracy decreases to 5%. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence Using Real Data)
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18 pages, 2779 KiB  
Article
Financial Volatility Forecasting: A Sparse Multi-Head Attention Neural Network
by Hualing Lin and Qiubi Sun
Information 2021, 12(10), 419; https://0-doi-org.brum.beds.ac.uk/10.3390/info12100419 - 14 Oct 2021
Cited by 6 | Viewed by 2230
Abstract
Accurately predicting the volatility of financial asset prices and exploring its laws of movement have profound theoretical and practical guiding significance for financial market risk early warning, asset pricing, and investment portfolio design. The traditional methods are plagued by the problem of substandard [...] Read more.
Accurately predicting the volatility of financial asset prices and exploring its laws of movement have profound theoretical and practical guiding significance for financial market risk early warning, asset pricing, and investment portfolio design. The traditional methods are plagued by the problem of substandard prediction performance or gradient optimization. This paper proposes a novel volatility prediction method based on sparse multi-head attention (SP-M-Attention). This model discards the two-dimensional modeling strategy of time and space of the classic deep learning model. Instead, the solution is to embed a sparse multi-head attention calculation module in the network. The main advantages are that (i) it uses the inherent advantages of the multi-head attention mechanism to achieve parallel computing, (ii) it reduces the computational complexity through sparse measurements and feature compression of volatility, and (iii) it avoids the gradient problems caused by long-range propagation and therefore, is more suitable than traditional methods for the task of analysis of long time series. In the end, the article conducts an empirical study on the effectiveness of the proposed method through real datasets of major financial markets. Experimental results show that the prediction performance of the proposed model on all real datasets surpasses all benchmark models. This discovery will aid financial risk management and the optimization of investment strategies. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence Using Real Data)
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