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Mathematical Modelling in Science and Engineering

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A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Mathematics".

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 13270

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Special Issue Editor

Prof. Dr. Nikita Andriyanov

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Guest Editor

Data Analysis and Machine Learning Department, Financial University under the Government of the Russian Federation, 125993 Moscow, Russia
Interests: artificial intelligence; machine learning; deep learning; computer vision; object detection; pattern recognition; data science; mathematical modeling; random processes
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear colleagues,

Recently, artificial intelligence systems have seen widespread use in various branches of engineering and technology. Indeed, data mining not only helps to solve complex technical problems, but in some cases can do it more efficiently than conventional methods.

Despite the existence of models and algorithms of various complexity, the success of solving data mining analysis problems largely depends on the initial data. Most methods are built on the assumption that the data is normal (i.e., distributions must be symmetrical). Data standardization transformations may also be required that result in a zero-symmetric distribution. In this regard, the property of symmetry plays an important role in solving applied problems of science and technology.

Among the applied engineering tasks of using artificial intelligence are the identification of persons for organizing access, the recognition of speech messages in voice assistants, maintaining a comfortable temperature in the house, etc. At the same time, all of the listed tasks are closely related to scientific research, including the development and analysis of mathematical models and numerical methods, which on the one hand can be considered as universal algorithms for data analysis, and on the other hand will find their niche in applied scientific and engineering problems. In our Special Issue of Symmetry, we expect articles related to the solution of computer vision problems, natural language processing, and robotic solutions to applied technical problems.

Prof. Dr. Nikita Andriyanov
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. Symmetry 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 2400 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
Machine Learning
Computer Vision
Machine Vision
Natural Language Processing
Mathematical modeling
Data Science
Program Complexes
Numerical Methods
Applied Engineering
Applied Sciences
Internet of Things

Published Papers (3 papers)

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Research

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30 pages, 4379 KiB

Open AccessArticle

Predictive Modelling of Statistical Downscaling Based on Hybrid Machine Learning Model for Daily Rainfall in East-Coast Peninsular Malaysia

by Nurul Ainina Filza Sulaiman, Shazlyn Milleana Shaharudin, Shuhaida Ismail, Nurul Hila Zainuddin, Mou Leong Tan and Yusri Abd Jalil

Symmetry 2022, 14(5), 927; https://0-doi-org.brum.beds.ac.uk/10.3390/sym14050927 - 02 May 2022

Cited by 9 | Viewed by 2214

Abstract

In recent years, climate change has demonstrated the volatility of unexpected events such as typhoons, flooding, and tsunamis that affect people, ecosystems and economies. As a result, the importance of predicting future climate has become even direr. The statistical downscaling approach was introduced as a solution to provide high-resolution climate projections. An effective statistical downscaling scheme aimed to be developed in this study is a two-phase machine learning technique for daily rainfall projection in the east coast of Peninsular Malaysia. The proposed approaches will counter the emerging issues. First, Principal Component Analysis (PCA) based on a symmetric correlation matrix is applied in order to rectify the issue of selecting predictors for a two-phase supervised model and help reduce the dimension of the supervised model. Secondly, two-phase machine learning techniques are introduced with a predictor selection mechanism. The first phase is a classification using Support Vector Classification (SVC) that determines dry and wet days. Subsequently, regression estimates the amount of rainfall based on the frequency of wet days using Support Vector Regression (SVR), Artificial Neural Networks (ANNs) and Relevant Vector Machines (RVMs). The comparison between hybridization models’ outcomes reveals that the hybrid of SVC and RVM reproduces the most reasonable daily rainfall prediction and considers high-precipitation extremes. The hybridization model indicates an improvement in predicting climate change predictions by establishing a relationship between the predictand and predictors. Full article

(This article belongs to the Special Issue Mathematical Modelling in Science and Engineering)

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18 pages, 3554 KiB

Open AccessArticle

Spatial-Temporal Epidemiology of COVID-19 Using a Geographically and Temporally Weighted Regression Model

by Sifriyani Sifriyani, Mariani Rasjid, Dedi Rosadi, Sarifuddin Anwar, Rosa Dwi Wahyuni and Syatirah Jalaluddin

Symmetry 2022, 14(4), 742; https://0-doi-org.brum.beds.ac.uk/10.3390/sym14040742 - 04 Apr 2022

Cited by 11 | Viewed by 2300

Abstract

This article describes the application of spatial statistical epidemiological modeling and its inference and applies it to COVID-19 case data, looking at it from a spatial perspective, and considering time-series data. COVID-19 cases in Indonesia are increasing and spreading in all provinces, including Kalimantan. This study uses applied mathematics and spatiotemporal analysis to determine the factors affecting the constant rise of COVID-19 cases in Kalimantan. The spatiotemporal analysis uses the Geographically Temporally Weighted Regression (GTWR) model by developing a spatial and temporal interaction distance function. The GTWR model was applied to data on positive COVID-19 cases at a scale of 56 districts/cities in Kalimantan between the period of January 2020 and August 2021. The purpose of the study was to determine the factors affecting the cumulative increase in COVID-19 cases in Kalimantan and map the spatial distribution for 56 districts/cities based on the significant predictor variables. The results of the study show that the GTWR model with the development of a spatial and temporal interaction distance function using the kernel Gaussian fixed bandwidth function is a better model compared to the Ordinary Least Squares (OLS) model. According to the significant variables, there are various factors affecting the rise in cases of COVID-19 in the region of Kalimantan, including the number of doctors, the number of TB cases, the percentage of elderly population, GRDP, and the number of hospitals. The highest factors that affect COVID-19 cases are the high number of TB cases, population density, and the lack of health services. Furthermore, an area map was produced on the basis of the significant variables affected by the rise in COVID-19 cases. The results of the study provide local governments with decision-making recommendations to overcome COVID-19-related issues in their respective regions. Full article

(This article belongs to the Special Issue Mathematical Modelling in Science and Engineering)

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Review

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35 pages, 1384 KiB

Open AccessReview

Data-Driven Analytics Leveraging Artificial Intelligence in the Era of COVID-19: An Insightful Review of Recent Developments

by Abdul Majeed and Seong Oun Hwang

Symmetry 2022, 14(1), 16; https://0-doi-org.brum.beds.ac.uk/10.3390/sym14010016 - 23 Dec 2021

Cited by 14 | Viewed by 7799

Abstract

This paper presents the role of artificial intelligence (AI) and other latest technologies that were employed to fight the recent pandemic (i.e., novel coronavirus disease-2019 (COVID-19)). These technologies assisted the early detection/diagnosis, trends analysis, intervention planning, healthcare burden forecasting, comorbidity analysis, and mitigation and control, to name a few. The key-enablers of these technologies was data that was obtained from heterogeneous sources (i.e., social networks (SN), internet of (medical) things (IoT/IoMT), cellular networks, transport usage, epidemiological investigations, and other digital/sensing platforms). To this end, we provide an insightful overview of the role of data-driven analytics leveraging AI in the era of COVID-19. Specifically, we discuss major services that AI can provide in the context of COVID-19 pandemic based on six grounds, (i) AI role in seven different epidemic containment strategies (a.k.a non-pharmaceutical interventions (NPIs)), (ii) AI role in data life cycle phases employed to control pandemic via digital solutions, (iii) AI role in performing analytics on heterogeneous types of data stemming from the COVID-19 pandemic, (iv) AI role in the healthcare sector in the context of COVID-19 pandemic, (v) general-purpose applications of AI in COVID-19 era, and (vi) AI role in drug design and repurposing (e.g., iteratively aligning protein spikes and applying three/four-fold symmetry to yield a low-resolution candidate template) against COVID-19. Further, we discuss the challenges involved in applying AI to the available data and privacy issues that can arise from personal data transitioning into cyberspace. We also provide a concise overview of other latest technologies that were increasingly applied to limit the spread of the ongoing pandemic. Finally, we discuss the avenues of future research in the respective area. This insightful review aims to highlight existing AI-based technological developments and future research dynamics in this area. Full article

(This article belongs to the Special Issue Mathematical Modelling in Science and Engineering)

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