Special Issue "10th Anniversary of Applied Sciences: Invited Papers in Computing and Artificial Intelligence Section"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 31 October 2020.

Special Issue Editor

Prof. Dr. Andrea Prati
Website SciProfiles
Guest Editor
Department of Engineering and Architecture, University of Parma, Parco Area delle Scienze, 181/A 43124 Parma, Italy
Interests: video surveillance; mobile vision; visual sensor networks; machine vision; multimedia and video processing; performance analysis of multimedia computer architectures
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Special Issue Information

Dear Colleagues,

The Section “Computing and Artificial Intelligence” of Applied Sciences covers a set of emerging research topics related to computer science and artificial intelligence. It is well known that artificial intelligence (as a general term) has gained a lot of attention worldwide both in academia and in industry. Applications are numerous and range from computer vision to natural language processing, to IoT and blockchain, to robotics and Industry 4.0, etc. On the one hand, many baseline techniques are now mature enough to reach common daily applications; nevertheless, so much is still under discovery and development, making this field one of the keystones for the future.

This Special Issue intends to gather moderate-sized review papers featuring important and recent developments or achievements of computing and artificial intelligence with a special emphasis on recently discovered techniques or applications. The authors are well-known experts in their domain who are invited to submit their contribution at any moment from now to the end of October 2020. The papers can cover either experimental or theoretical aspects or both. Machine and deep learning, applied artificial intelligence, IoT and fog computing, distributed systems and blockchain, computer vision and pattern recognition, natural language processing, etc. are among the main topics.

Prof. Dr. Andrea Prati
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 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. Applied Sciences 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 1800 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.

Published Papers (2 papers)

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Research

Open AccessArticle
Ocular Biometrics Recognition by Analyzing Human Exploration during Video Observations
Appl. Sci. 2020, 10(13), 4548; https://doi.org/10.3390/app10134548 - 30 Jun 2020
Abstract
Soft biometrics provide information about the individual but without the distinctiveness and permanence able to discriminate between any two individuals. Since the gaze represents one of the most investigated human traits, works evaluating the feasibility of considering it as a possible additional soft [...] Read more.
Soft biometrics provide information about the individual but without the distinctiveness and permanence able to discriminate between any two individuals. Since the gaze represents one of the most investigated human traits, works evaluating the feasibility of considering it as a possible additional soft biometric trait have been recently appeared in the literature. Unfortunately, there is a lack of systematic studies on clinically approved stimuli to provide evidence of the correlation between exploratory paths and individual identities in “natural” scenarios (without calibration, imposed constraints, wearable tools). To overcome these drawbacks, this paper analyzes gaze patterns by using a computer vision based pipeline in order to prove the correlation between visual exploration and user identity. This correlation is robustly computed in a free exploration scenario, not biased by wearable devices nor constrained to a prior personalized calibration. Provided stimuli have been designed by clinical experts and then they allow better analysis of human exploration behaviors. In addition, the paper introduces a novel public dataset that provides, for the first time, images framing the faces of the involved subjects instead of only their gaze tracks. Full article
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Open AccessFeature PaperArticle
COVID-19: A Comparison of Time Series Methods to Forecast Percentage of Active Cases per Population
Appl. Sci. 2020, 10(11), 3880; https://doi.org/10.3390/app10113880 - 03 Jun 2020
Abstract
The ongoing COVID-19 pandemic has caused worldwide socioeconomic unrest, forcing governments to introduce extreme measures to reduce its spread. Being able to accurately forecast when the outbreak will hit its peak would significantly diminish the impact of the disease, as it would allow [...] Read more.
The ongoing COVID-19 pandemic has caused worldwide socioeconomic unrest, forcing governments to introduce extreme measures to reduce its spread. Being able to accurately forecast when the outbreak will hit its peak would significantly diminish the impact of the disease, as it would allow governments to alter their policy accordingly and plan ahead for the preventive steps needed such as public health messaging, raising awareness of citizens and increasing the capacity of the health system. This study investigated the accuracy of a variety of time series modeling approaches for coronavirus outbreak detection in ten different countries with the highest number of confirmed cases as of 4 May 2020. For each of these countries, six different time series approaches were developed and compared using two publicly available datasets regarding the progression of the virus in each country and the population of each country, respectively. The results demonstrate that, given data produced using actual testing for a small portion of the population, machine learning time series methods can learn and scale to accurately estimate the percentage of the total population that will become affected in the future. Full article
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