CALL FOR PAPERS

The International Conference on Data-Driven Analytics is a cutting-edge event that brings together experts, researchers, and industry leaders to showcase the latest advancements in information technology and data science. The conference will have a technical program structured around a series of sessions featuring keynote speeches, panels, and presentations by leading experts and researchers in their respective fields. The program will include a diverse range of topics related to data-driven analytics, such as Big Data, Data Mining, Machine Learning, Data Security, and other aspects of data processing.

The conference is intended for a wide audience that includes academics, researchers, professionals, and industry leaders in the fields of Fintech, E-health, Industry 4.0, educational systems and Agriculture. The program is designed to appeal to those who are interested in the latest advancements in information technology and data science and who are seeking to expand their knowledge and stay up to date with the latest trends and developments.

Attendees will have the chance to engage in lively discussions and hands-on workshops, led by experts in their respective fields. The conference will feature a diverse range of speakers, including both academic researchers and industry professionals, who will share their knowledge and expertise on the latest developments in data-driven analytics. The technical program will be organized into several sessions, each focused on a particular topic, and attendees will have the opportunity to choose which sessions they wish to attend.

In addition to the technical program, the conference also offers attendees the opportunity to participate in a gala dinner and cultural visit, while adhering to a well-managed schedule. The conference provides a platform for networking and collaboration, as well as a chance to explore cutting-edge technologies and their applications across various domains.

Tracks : The conference will be structured around six key areas

1. Data-driven models, Algorithms and Frameworks

This area will track advanced data-based models, methods and frameworks to efficiently extract relevant insights. Topics of interest include but are not limited to:

Big data trends to process huge heterogeneous data
Data science models
Data integration architectures
Inferences models
Descriptive analytics methods
Predictive analytics methods
Prescriptive analytics methods

2.Data-driven analytics for Fintech

This area will cover topics related to the application of data-driven analytics in financial technology. Topics of interest include but are not limited to:

The microstructure of modern financial markets: algorithmic / high frequency trading, market liquidity, dark trading, blockchain settlements etc.
Behavioural economics in financial technology;
FinTech and decision making;
Alternative data (structured and unstructured datasets);
Peer to peer lending and investment strategy;
New exchange traded financial derivatives;
Financial stability risks from the development of FinTech ;
FinTech regulation (RegTech);
Open banking , insurTech, SupTech, PayTech and embedded FinTech;
Micro FinTech and Financial Inclusion;
FinTech and Sustainable Finance;
FinTech and Gender

3.Data-driven analytics for E-health

This area will cover topics related to the use of data-driven analytics in healthcare. Topics of interest include but are not limited to:

Drug discovery;
Computational biology;
Health informatics;
Telehealth;
Sensor informatics and medical imaging;
QSAR / QSPR modeling;
Disease diagnosis and treatment prediction;
Medical image and signal analysis;
Healthcare resource allocation and optimization;
Electronic health records analysis;
Patient outcome prediction.

4.Data-driven analytics for Industry 4.0

This area will cover topics related to the application of data-driven analytics in the context of Industry 4.0. Topics of interest include but are not limited to:

Data-driven decision making in Industry 4.0
Data analytics for smart manufacturing
Data analytics for supply chain optimization
Big data analytics for Industry 4.0
Data-driven quality control
Data analytics for energy management in Industry 4.0
Data analytics for predictive maintenance
Machine learning for Industry 4.0

5. Data-driven analytics for Education

This area will cover topics related to the application of data-driven analytics in the field of education. Topics of interest include but are not limited to:

Artificial Intelligence in Education;
Learning Analytics for adaptive learning;
Scalable Data Driven architectures for Smart Learning;
e-Assessment;
Development of AI and new roles for teachers;
Best practices and case studies on smart teaching and learning.

6.Data-driven analytics for Agriculture

This area will cover topics related to the application of data-driven analytics in the field of Agriculture. Topics of interest include but are not limited to:
Predicting and mitigating climate change impact through data analytics
Predictive modeling of crop yield and quality using sensor data
Crop health monitoring with satellite and drone imagery and sensor data
Holistic farm management through data integration
Visualization tools for complex agricultural data
Resource management through data-driven analytics
Ethical and privacy considerations in agricultural data collection and use
Sharing and integration of agricultural data across stakeholders
Evaluation and improvement of agricultural data platforms and tools
Data-driven solutions for global food security
Blockchain for transparency in agricultural supply chains

Submission Guidelines :

Only original contributions will be accepted. Papers must be written in English and not have been published before, and not be under review for any other conference or publication.

Manuscripts should respect IEEE template (6 pages) including figures, tables, and references.

https://www.ieee.org/conferences/publishing/templates.html

All papers accepted will be published in the conference proceedings and are expected to be published by IEEE Xplore, subject to meeting IEEE Xplore's scope and quality requirements.

The conference will check plagiarism for all the articles before prior publication, if the plagiarism rate is exceeding 25%, the article will be rejected and the author will be informed accordingly.

Authors of selected papers will be invited to extend the paper for expected publication in international journals and in edited books indexed by SCOPUS.

Workshops:

As part of the conference, we are proud to offer six informative and engaging workshops. These workshops will cover a range of cutting-edge topics, including and not limited to Data Science, Deep Learning, Trading, Learning Analytics, and Greentech.
The call for workshop proposals will be launched soon.

General chairs :

Dr. Kurosh Madani, University of Paris-Est Créteil Val de Marne (UPEC), France
Dr. Rui Marques, University of Aveiro (UA), Portugal
Dr. Dalel Kanzari, University of Sousse, Tunisia
Dr. Mohamed Essalih, University of Cadi Ayyad, Morocco

Steering committee :

Dr. João Batista, University of Aveiro, Portugal
Dr. Rui Marques , University of Aveiro, Portugal
Dr. Kurosh Madani, University of Paris-Est Créteil Val de Marne, France
Dr. Dalel Kanzari, University of Sousse, Tunisia
Dr. Essalih Mohamed, University of Cadi Ayyad, Morocco
Dr. Othmane Alaoui Fdili, University of Cadi Ayyad, Morocco
Dr. Maha Khemaja, University of Sousse, Tunisia

TPC chair :

Dr. Kurosh Madani, University of Paris-Est Créteil Val de Marne (UPEC), France

TPC :

Dr. João Carvalho, University of Aveiro, Portugal
Dr. Dora Simões, University of Aveiro, Portugal
Dr. Mohsen Maraoui, University of Monastir, Tunisia
Dr. Imen berguiga, Sousse University, Tunisia
Dr. Hanine Mohamed, University of Chouaib Doukkali, Morocco
Dr. El Bhiri Brahim, EMSI Rabat, Morocco

Track chairs :

Data-driven models, Algorithms and Frameworks

Dr. Salma Mouline, University of Mohammed V, Morocco
Dr. Sami Achour, University of Sousse, Tunisia

Data-driven analytics for Fintech

Dr. Yosra Ben Said, University of Sfax, Tunisia
Dr. Sonia Makni, University of Sousse, Tunisia

Data-driven analytics for E-health

Dr. Minaoui Khalid, University of Mohammed V, Morocco
Dr. André Monteiro, University of Aveiro, Portugal

Data-driven analytics for Industry 4.0

Dr. Jamal Bakkas, University of Cadi Ayyad, Morocco
Dr. Imran Ashraf, University of Gyeongsan, Republic of Korea

Data-driven analytics for Education

Dr. Abderrahman Chekry, University of Cadi Ayyad, Morocco
Dr. Dora Simões, University of Aveiro, Portugal

Data-driven analytics for Agriculture

Dr. Soufiane Hourri, Cadi Ayyad University, Morocco
Dr. Karim Fathallah, University of Manar, Tunisia

Contact information:

If you have any questions or queries on ADACIS’23 please send email to adacis2023@uca.ac.ma