Special Issue "Data Science and Big Data in Biology, Physical Science and Engineering"

A special issue of Technologies (ISSN 2227-7080).

Deadline for manuscript submissions: 30 September 2022.

Special Issue Editor

Prof. Dr. Mohammed Mahmoud
E-Mail Website
Guest Editor
Department of Computer Science and Engineering, Oakland University, Rochester, MI, USA
Interests: data science; big data; machine learning; deep learning; Artificial Intelligence (AI); cybersecurity
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nowadays, Big Data analysis represents one of the most important contemporary areas of development and research. Tremendous amounts of data are generated every single day from digital technologies and modern information systems, such as cloud computing and Internet of Things (IoT) devices. Analysis of these enormous amounts of data has become of crucial significance and requires a great deal of effort in order to extract valuable knowledge for decision-making which, in turn, will make important contributions in both academia and industry.

Big Data and data science have emerged due to the significant need for generating, storing, organising and processing immense amounts of data. Data scientists strive to use artificial intelligence (AI) and machine learning (ML) approaches and models to allow computers to detect and identify what the data represents and be able to detect patterns more quickly, efficiently and reliably than humans.

The goal behind this Special Issue is to explore and discuss various principles, tools and models in the context of data science, besides the diverse and varied concepts and techniques relating to Big Data in biology, chemistry, biomedical engineering, physics, mathematics and other areas that work with Big Data.

Prof. Dr. Mohammed Mahmoud
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. Technologies is an international peer-reviewed open access quarterly 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 1400 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.

Prof. Dr. Mohammed Mahmoud
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. Technologies is an international peer-reviewed open access quarterly 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 1400 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

  • Data science
  • Big Data
  • Machine learning
  • Artificial intelligence

Published Papers (2 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Review

Article
A Novel Ensemble Machine Learning Approach for Bioarchaeological Sex Prediction
Technologies 2021, 9(2), 23; https://0-doi-org.brum.beds.ac.uk/10.3390/technologies9020023 - 01 Apr 2021
Viewed by 829
Abstract
I present a novel machine learning approach to predict sex in the bioarchaeological record. Eighteen cranial interlandmark distances and five maxillary dental metric distances were recorded from n = 420 human skeletons from the necropolises at Alfedena (600–400 BCE) and Campovalano (750–200 BCE [...] Read more.
I present a novel machine learning approach to predict sex in the bioarchaeological record. Eighteen cranial interlandmark distances and five maxillary dental metric distances were recorded from n = 420 human skeletons from the necropolises at Alfedena (600–400 BCE) and Campovalano (750–200 BCE and 9–11th Centuries CE) in central Italy. A generalized low rank model (GLRM) was used to impute missing data and Area under the Curve—Receiver Operating Characteristic (AUC-ROC) with 20-fold stratified cross-validation was used to evaluate predictive performance of eight machine learning algorithms on different subsets of the data. Additional perspectives such as this one show strong potential for sex prediction in bioarchaeological and forensic anthropological contexts. Furthermore, GLRMs have the potential to handle missing data in ways previously unexplored in the discipline. Although results of this study look promising (highest AUC-ROC = 0.9722 for predicting binary male/female sex), the main limitation is that the sexes of the individuals included were not known but were estimated using standard macroscopic bioarchaeological methods. However, future research should apply this machine learning approach to known-sex reference samples in order to better understand its value, along with the more general contributions that machine learning can make to the reconstruction of past human lifeways. Full article
Show Figures

Figure 1

Review

Jump to: Research

Review
Big Data in Biodiversity Science: A Framework for Engagement
Technologies 2021, 9(3), 60; https://0-doi-org.brum.beds.ac.uk/10.3390/technologies9030060 - 17 Aug 2021
Viewed by 657
Abstract
Despite best efforts, the loss of biodiversity has continued at a pace that constitutes a major threat to the efficient functioning of ecosystems. Curbing the loss of biodiversity and assessing its local and global trends requires a vast amount of datasets from a [...] Read more.
Despite best efforts, the loss of biodiversity has continued at a pace that constitutes a major threat to the efficient functioning of ecosystems. Curbing the loss of biodiversity and assessing its local and global trends requires a vast amount of datasets from a variety of sources. Although the means for generating, aggregating and analyzing big datasets to inform policies are now within the reach of the scientific community, the data-driven nature of a complex multidisciplinary field such as biodiversity science necessitates an overarching framework for engagement. In this review, we propose such a schematic based on the life cycle of data to interrogate the science. The framework considers data generation and collection, storage and curation, access and analysis and, finally, communication as distinct yet interdependent themes for engaging biodiversity science for the purpose of making evidenced-based decisions. We summarize historical developments in each theme, including the challenges and prospects, and offer some recommendations based on best practices. Full article
Show Figures

Figure 1

Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Current Advances in Radiographic Methods for Personal Iden-tification of Unknown Decedents
Authors: Sharon M. Derrick 1, Ruby Mehrubeoglu 2, and Longzhuang Li 3
Affiliation: 1 Texas A&M University-Corpus Christi 1; [email protected] 2 Texas A&M University-Corpus Christi 2; [email protected] 3 Texas A&M University-Corpus Christi 3; [email protected] * Correspondence: [email protected]; Tel.: (361-825-3637, smd)
Abstract: Forensic practitioners and researchers have been cognizant for decades that quantitative and ac-cessible methods of decedent identification, processed promptly within the medical examin-er/coroner office, are needed but sorely lacking. The most available on-site use of biometric technology has been fingerprint comparison through AFIS,1 a massive repository of fingerprint data, but AFIS is not useful if the person has no fingerprints in the system or if the decedent’s hands are decomposed/skeletal. Rapid advances in radiographic technology and software over the last decade, in conjunction with an increase in digital X-ray machines and CT instrumenta-tion purchased as standard equipment in medical examiner/coroner offices, have elicited a plethora of research into new quantitative methods of radiographic identification. Large data-bases of digital antemortem and postmortem standard radiograph and CT images, which can be de-identified for research purposes, are available for access by these researchers. A review of major papers published from 2010-2021 describing novel radiographic comparison methods provides an encouraging view of the present and future use of radiography in forensic identifi-cation.
Keywords: forensic; identification; radiography

Back to TopTop