de Recherche et d’Innovation
en Cybersécurité et Société
Saini, H. K.; Rani, S.; Ouaissa, M.; Ouaissa, M.; Houda, Z. A. El; Moudoud, H.
CRC Press, 2025, ISBN: 978-100364032-5 (ISBN); 978-104107046-7 (ISBN), (Journal Abbreviation: Digit. Forensics in Next-Gener. Internet of Med. Things: Balanc. Secur. and Sustain. Pages: 283 Publication Title: Digit. Forensics in Next-Gener. Internet of Med. Things: Balanc. Secur. and Sustain.).
Abstract | Links | BibTeX | Tags: Case-studies, Computer forensics, Forensic Techniques, Internet of things, Machine data, Medical computing, Medical data, Medical practitioner, Network security, Next generation Internet, Patient data, Real-world, Security challenges, Security solutions, sustainable development
@book{sainiDigitalForensicsNextGeneration2025,
title = {Digital Forensics in Next-Generation Internet of Medical Things: Balancing Security and Sustainability},
author = {H. K. Saini and S. Rani and M. Ouaissa and M. Ouaissa and Z. A. El Houda and H. Moudoud},
url = {https://www.scopus.com/pages/publications/105024400792?origin=resultslist},
doi = {10.1201/9781003640325},
isbn = {978-100364032-5 (ISBN); 978-104107046-7 (ISBN)},
year = {2025},
date = {2025-01-01},
publisher = {CRC Press},
series = {Digital Forensics in Next-Generation Internet of Medical Things: Balancing Security and Sustainability},
abstract = {This book provides a comprehensive exploration of the security challenges and solutions with digital sustainability in the rapidly evolving digital landscape of digital forensics. It explores the details of protecting Internet of Medical Things (IoMT) environments, where the medical data, patient data, and machine data are at high risk with the digital experiences. The book seeks to provide researchers, medical practitioners, and IT specialists with important information. It aims to set the stage for a future in which security and efficiency in IoMT smoothly blend through real-world case studies. Key themes cover IoMT-specific forensic techniques, the difficulties of striking a balance between environmental responsibility and security, and creative solutions that combine the two viewpoints. © 2026 selection and editorial matter, Hemant Kumar Saini, Sita Rani, Mariya Ouaissa, Mariyam Ouaissa, Zakaria Abou El Houda, and Hajar Moudoud. All rights reserved.},
note = {Journal Abbreviation: Digit. Forensics in Next-Gener. Internet of Med. Things: Balanc. Secur. and Sustain.
Pages: 283
Publication Title: Digit. Forensics in Next-Gener. Internet of Med. Things: Balanc. Secur. and Sustain.},
keywords = {Case-studies, Computer forensics, Forensic Techniques, Internet of things, Machine data, Medical computing, Medical data, Medical practitioner, Network security, Next generation Internet, Patient data, Real-world, Security challenges, Security solutions, sustainable development},
pubstate = {published},
tppubtype = {book}
}
Damadi, M. S.; Davoust, A.
Fairness in Socio-Technical Systems: A Case Study of Wikipedia Journal Article
In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14199 LNCS, pp. 84–100, 2023, ISSN: 03029743, (ISBN: 9783031421402).
Abstract | Links | BibTeX | Tags: Algorithmics, Bias, Case-studies, Causal relationships, Cultural bias, Fairness, Gender bias, Machine learning, Machine-learning, Parallel processing systems, Sociotechnical systems, Wikipedia
@article{damadi_fairness_2023,
title = {Fairness in Socio-Technical Systems: A Case Study of Wikipedia},
author = {M. S. Damadi and A. Davoust},
editor = {Alvarez C. Marutschke D.M. Takada H.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172720004&doi=10.1007%2f978-3-031-42141-9_6&partnerID=40&md5=172c8c6ae5b09536efdf983e9be965e7},
doi = {10.1007/978-3-031-42141-9_6},
issn = {03029743},
year = {2023},
date = {2023-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {14199 LNCS},
pages = {84–100},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {Wikipedia content is produced by a complex socio-technical systems (STS), and exhibits numerous biases, such as gender and cultural biases. We investigate how these biases relate to the concepts of algorithmic bias and fairness defined in the context of algorithmic systems. We systematically review 75 papers describing different types of bias in Wikipedia, which we classify and relate to established notions of harm and normative expectations of fairness as defined for machine learning-driven algorithmic systems. In addition, by analysing causal relationships between the observed phenomena, we demonstrate the complexity of the socio-technical processes causing harm. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.},
note = {ISBN: 9783031421402},
keywords = {Algorithmics, Bias, Case-studies, Causal relationships, Cultural bias, Fairness, Gender bias, Machine learning, Machine-learning, Parallel processing systems, Sociotechnical systems, Wikipedia},
pubstate = {published},
tppubtype = {article}
}
Nabelsi, V.; Gagnon, S.
Detecting constraints in supply chain reengineering projects: Case study of data and process integration in a hospital pharmacy Proceedings Article
In: A., Zaremba M. Sasiadek J. Dolgui (Ed.): IFAC-PapersOnLine, pp. 106–111, 2015, ISBN: 24058963 (ISSN), (Journal Abbreviation: IFAC-PapersOnLine).
Abstract | Links | BibTeX | Tags: Administrative data processing, Artificial intelligence, Business Process, Business process management, Business process management (BPM), Business process re-engineering, Case-studies, Data integration, Data mining, Data models, Data structures, Data warehouses, Decision support system, Decision support system (dss), Decision support systems, Enterprise resource management, Extract transform and load, Extract Transform and Load (ETL), Hospitals, Information management, Integration, Process management, Project case, Re-engineering projects, Reengineering, Supply chain management, Supply Chain Management (SCM), Supply chain managements (SCM), System architectures, Verification method, Verification of information system
@inproceedings{nabelsi_detecting_2015,
title = {Detecting constraints in supply chain reengineering projects: Case study of data and process integration in a hospital pharmacy},
author = {V. Nabelsi and S. Gagnon},
editor = {Zaremba M. Sasiadek J. Dolgui A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84953887968&doi=10.1016%2fj.ifacol.2015.06.066&partnerID=40&md5=ce9be2cbe2fdcfc4872793c13f4228a2},
doi = {10.1016/j.ifacol.2015.06.066},
isbn = {24058963 (ISSN)},
year = {2015},
date = {2015-01-01},
booktitle = {IFAC-PapersOnLine},
volume = {28},
number = {3},
pages = {106–111},
abstract = {This paper discusses how messy data may be a hidden failure factor that Business Process Reengineering (BPR) projects typically cannot detect during the planning phase. Our case study deals with Supply Chain Management (SCM) within two major urban hospitals, involving $2 million in minimum stocks for drug inventory. Our project addresses the feasibility of the hospital's data warehousing integration, especially at the stage of Extract, Transform, and Load (ETL). We conclude with a proposed system architecture audit and verification method that may serve to guide reengineering project planning and execution. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.},
note = {Journal Abbreviation: IFAC-PapersOnLine},
keywords = {Administrative data processing, Artificial intelligence, Business Process, Business process management, Business process management (BPM), Business process re-engineering, Case-studies, Data integration, Data mining, Data models, Data structures, Data warehouses, Decision support system, Decision support system (dss), Decision support systems, Enterprise resource management, Extract transform and load, Extract Transform and Load (ETL), Hospitals, Information management, Integration, Process management, Project case, Re-engineering projects, Reengineering, Supply chain management, Supply Chain Management (SCM), Supply chain managements (SCM), System architectures, Verification method, Verification of information system},
pubstate = {published},
tppubtype = {inproceedings}
}
Nabelsi, V.; Gagnon, S.
Detecting constraints in supply chain reengineering projects: Case study of data and process integration in a hospital pharmacy Proceedings Article
In: A., Zaremba M. Sasiadek J. Dolgui (Ed.): IFAC-PapersOnLine, pp. 106–111, 2015, ISBN: 24058963 (ISSN), (Journal Abbreviation: IFAC-PapersOnLine).
Abstract | Links | BibTeX | Tags: Administrative data processing, Artificial intelligence, Business Process, Business process management, Business process management (BPM), Business process re-engineering, Case-studies, Data integration, Data mining, Data models, Data structures, Data warehouses, Decision support system, Decision support system (dss), Decision support systems, Enterprise resource management, Extract transform and load, Extract Transform and Load (ETL), Hospitals, Information management, Integration, Process management, Project case, Re-engineering projects, Reengineering, Supply chain management, Supply Chain Management (SCM), Supply chain managements (SCM), System architectures, Verification method, Verification of information system
@inproceedings{nabelsiDetectingConstraintsSupply2015,
title = {Detecting constraints in supply chain reengineering projects: Case study of data and process integration in a hospital pharmacy},
author = {V. Nabelsi and S. Gagnon},
editor = {Zaremba M. Sasiadek J. Dolgui A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84953887968&doi=10.1016%2fj.ifacol.2015.06.066&partnerID=40&md5=ce9be2cbe2fdcfc4872793c13f4228a2},
doi = {10.1016/j.ifacol.2015.06.066},
isbn = {24058963 (ISSN)},
year = {2015},
date = {2015-01-01},
booktitle = {IFAC-PapersOnLine},
volume = {28},
number = {3},
pages = {106–111},
abstract = {This paper discusses how messy data may be a hidden failure factor that Business Process Reengineering (BPR) projects typically cannot detect during the planning phase. Our case study deals with Supply Chain Management (SCM) within two major urban hospitals, involving $2 million in minimum stocks for drug inventory. Our project addresses the feasibility of the hospital's data warehousing integration, especially at the stage of Extract, Transform, and Load (ETL). We conclude with a proposed system architecture audit and verification method that may serve to guide reengineering project planning and execution. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.},
note = {Journal Abbreviation: IFAC-PapersOnLine},
keywords = {Administrative data processing, Artificial intelligence, Business Process, Business process management, Business process management (BPM), Business process re-engineering, Case-studies, Data integration, Data mining, Data models, Data structures, Data warehouses, Decision support system, Decision support system (dss), Decision support systems, Enterprise resource management, Extract transform and load, Extract Transform and Load (ETL), Hospitals, Information management, Integration, Process management, Project case, Re-engineering projects, Reengineering, Supply chain management, Supply Chain Management (SCM), Supply chain managements (SCM), System architectures, Verification method, Verification of information system},
pubstate = {published},
tppubtype = {inproceedings}
}