Special Issue "Sustainable Data Science and Machine Learning for Business, Research and Innovation"

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Management".

Deadline for manuscript submissions: closed (30 September 2020).

Special Issue Editor

Prof. Dr. Miltiadis D. Lytras
E-Mail Website
Guest Editor
1. School of Business, Deree—The American College of Greece, 6 Gravias Street GR-153 42 Aghia Paraskevi Athens, Greece
2. Effat University, Jeddah, Saudi Arabia
Interests: cognitive computing; artificial intelligence; data science; bioinformatics; innovation; big data research; data mining; emerging technologies; information systems; technology driven innovation; knowledge management; semantic web
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Information systems and data science have contributed significantly to Sustainability Research in the last decade. The latest arrivals in the domains, including cyberphysical systems, artificial intelligence, edge computing, quantum computing, and sentiment and behavioral analysis, provide a brand-new context for Sustainability Research. 

To this end, the Guest Editors of this Special Issue seek papers that address, but are not limited to, the following issues and aspects related to the diverse aspects of Sustainable Data Science Research:

Data Science Topics

  • Statistics research for data processing;
  • Linear algebra algorithms for data science;
  • Programming solutions;
  • Machine learning;
  • Data mining;
  • Data visualization;
  • R mining applications;
  • Building recommendation engines and deep learning models;
  • Smart cities applications and data science;

Analytics Research for Sustainability

  • Dashboards and decision making analytics;
  • Emerging platforms, infrastructures, systems;
  • Sophisticated reasoning/natural language processing/speech recognition/human computer interaction;
  • KPI research;

Industry Applications and Tools

  • SAP solutions;
  • Tableau case studies;
  • R-mining exemplar data mining;
  • Dashboards for smart cities;

Policy Making for Data Science and Regional Studies

  • Data science in the Middle East;
  • Policy making for sustainable data science;
  • Policies for skills and competencies management related to data science;

R&D Projects Dissemination

  • Horizon 2020 projects;
  • Vision 2030 Saudi Arabia;
  • Digital transformation.

References

Chen, M.-Y.; Lytras, M.D.; Sangaiah, A.K. Anticipatory computing: Crowd intelligence from social network and big data. Comput. Hum. Behav. 2019, 101, 350–351.

Lytras, M.D.; Visvizi, A. Big data and their social impact: Preliminary study. Sustainability 2019, 11, 5067.

Lytras, M.D.; Chui, K.T. The recent development of artificial intelligence for smart and sustainable energy systems and applications. Energies 2019, 12, 3108.

Lytras, M.D.; Chui, K.T.; Visvizi, A. Data analytics in smart healthcare: The recent developments and beyond. Appl. Sci. 2019, 9, 2812.

Lytras, M.D.; Hassan, S.-U.; Aljohani, N.R. Linked open data of bibliometric networks: analytics research for personalized library services. Libr. Hi Tech. 2019, 37, 2–7.

Lytras, M.; Visvizi, A.; Damiani, E.; Mathkour, H. The cognitive computing turn in education: Prospects and application. Comput. Hum. Behav. 2019, 92, 446–449.

Arafat, S.; Aljohani, N.; Abbasi, R.; Hussain, A.; Lytras, M. Connections between e-learning, web science, cognitive computation and social sensing, and their relevance to learning analytics: A preliminary study. Comput. Hum. Behav. 2019, 92, 478–486.

Lytras, M.D.; Visvizi, A.; Sarirete, A. Clustering smart city services: Perceptions, expectations, responses. Sustainability 2019, 11, 1669.

Zhang, X.; Lytras, M.D.; Aljohani, N.R. Cognitive computing alternate research track chairs' welcome. In Proceedings of The 26th International World Wide Web Conference 2017, Perth, Australia, 3–7 April 2017.

Chui, K.T.; Liu, R.W.; Lytras, M.D.; Zhao, M. Big data and IoT solution for patient behaviour monitoring.

Behav. Inf. Technol. 2019, 38, 940–949.

Chui, K.T.; Lytras, M.D.; Visvizi, A. Energy sustainability in smart cities: Artificial intelligence, smart monitoring, and optimization of energy consumption. Energies 2018, 11, 2869.

Spruit, M.; Lytras, M. Applied data science in patient-centric healthcare: Adaptive analytic systems for empowering physicians and patients. Telemat. Inform. 2018, 35, 643–653.

Prof. Dr. Miltiadis D. Lytras
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. Sustainability 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 1900 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
  • Edge computing
  • Cyberphysical systems
  • Artificial intelligence
  • Cognitive computing
  • Machine learning
  • Deep learning
  • Big data
  • Data analytics
  • Visual analytics
  • Case-studies
  • Conceptual approaches
  • International collaboration

Published Papers (1 paper)

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Research

Open AccessArticle
The King Abdulaziz University (KAU) Pandemic Framework: A Methodological Approach to Leverage Social Media for the Sustainable Management of Higher Education in Crisis
Sustainability 2020, 12(11), 4367; https://doi.org/10.3390/su12114367 - 26 May 2020
Cited by 10 | Viewed by 1856
Abstract
The recent pandemic has raised significant challenges worldwide. In higher education, the necessity to adopt efficient strategies to sustain education during the crisis is mobilizing diverse, complementary, and integrative action in response. In this research article, we rise to the challenge of designing [...] Read more.
The recent pandemic has raised significant challenges worldwide. In higher education, the necessity to adopt efficient strategies to sustain education during the crisis is mobilizing diverse, complementary, and integrative action in response. In this research article, we rise to the challenge of designing and implementing a transparent strategy for social media awareness at King Abdulaziz University (KAU). We introduce a framework for social media impact, termed the KAU Pandemic Framework. This includes the factors with the most important role in enhancing the deployment of social media in crisis in order to minimize the negative impact on education’s sustainability. We used a mixed-methods approach, integrating quantitative statistical analyses of social media data and online surveys and qualitative interviews in such a way as to construct a comprehensive framework. The results show that a methodological framework can be justified and that Twitter contributes significantly to six areas: administrative resilience; education sustainability; community responsibility; positive sentiment; community bonds; and delivery of promised value. The components of our proposed methodological framework integrate five pillars of the strategic adoption of social media: social media governance; social media resilience; social media utilization; decision-making capability; and institutional strategy. Finally, we show that the KAU Pandemic Framework can be used as strategic decision-making tool for the analysis of the gaps and inefficiencies in any social media plan that is deployed and the management challenges arising from the pandemic. Full article
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