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) | Viewed by 8163
Special Issue Editor
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, Collections and Topics 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
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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
