Special Issue for Selected Papers From the 2023 IEEE International Conference on Advances in Data-Driven Analytics and Intelligent Systems

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".

Deadline for manuscript submissions: closed (30 November 2024) | Viewed by 2440

Special Issue Editors


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Guest Editor
1. The Research Laboratory in PSSII, University of Cadi Ayyad, Marrakech, Morocco
2. The Research Laboratory in Computer Science and Telecommunications, Mohammed V University, Rabat, Morocco
Interests: data-driven analytics; e-Health; deep learning; AI

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Guest Editor
UPEC - Laboratoire Images, Signaux et Systèmes Intelligents (LISSI) - EA 3956, University of Paris-Est Créteil Val de Marne, 94000 Créteil, France
Interests: data-driven analytics; Industry 4.0; deep learning; AI
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Guest Editor
Higher Institute of Accounting and Administration of Aveiro, University of Aveiro, 3810-500 Aveiro, Portugal
Interests: information systems; artificial intelligence; blockchain for accounting and auditing; ICT for auditing; e-auditing; continuous auditing; fraud detection systems
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Higher Institute of Applied Sciences and Technology of Sousse, University of Sousse, Sousse, Tunisia
Interests: data-driven analytics; education; deep learning; AI

Special Issue Information

Dear Colleagues,

The special issue will include the papers from The 2023 IEEE International Conference on Advances in Data-Driven Analytics and Intelligent Systems, to be held in Marrakech, Morocco during November, 23rd–25th, 2023. The website is: https://www.adacis-conf.com/index.php. The aim and scope of this conference are to bring together leading experts and researchers of data-driven analytics, such as big data, data mining, machine learning, data security, and other aspects of data processing.

This special issue will cover the following 4 topics from the conference:

  • Data-driven models, algorithms, and frameworks;
  • Data-driven analytics for Fintech;
  • Data-driven analytics for E-health;
  • Data-driven analytics for Industry 4.0.

Dr. Essalih Mohamed
Dr. Kurosh Madani
Dr. Rui Pedro Marques
Dr. Dalel Kanzari
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 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

  • deep learning
  • artificial intelligence
  • computer sciences
  • data-driven analytics for fintech
  • data-driven analytics for e-Health
  • data-driven analytics for Industry 4.0

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Published Papers (1 paper)

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Research

36 pages, 7846 KiB  
Article
Reference Architecture for the Integration of Prescriptive Analytics Use Cases in Smart Factories
by Julian Weller, Nico Migenda, Yash Naik, Tim Heuwinkel, Arno Kühn, Martin Kohlhase, Wolfram Schenck and Roman Dumitrescu
Mathematics 2024, 12(17), 2663; https://doi.org/10.3390/math12172663 - 27 Aug 2024
Cited by 1 | Viewed by 2092
Abstract
Prescriptive analytics plays an important role in decision making in smart factories by utilizing the available data to gain actionable insights. The planning, integration and development of such use cases still poses manifold challenges. Use cases are still being implemented as standalone versions; [...] Read more.
Prescriptive analytics plays an important role in decision making in smart factories by utilizing the available data to gain actionable insights. The planning, integration and development of such use cases still poses manifold challenges. Use cases are still being implemented as standalone versions; the existing IT-infrastructure is not fit for integrative bidirectional decision communication, and implementations only reach low technical readiness levels. We propose a reference architecture for the integration of prescriptive analytics use cases in smart factories. The method for the empirically grounded development of reference architectures by Galster and Avgeriou serves as a blueprint. Through the development and validation of a specific IoT-Factory use case, we demonstrate the efficacy of the proposed reference architecture. We expand the given reference architecture for one use case to the integration of a smart factory and its application to multiple use cases. Moreover, we identify the interdependency among multiple use cases within dynamic environments. Our prescriptive reference architecture provides a structured way to improve operational efficiency and optimize resource allocation. Full article
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