Invited Speakers


Assoc. Prof. Yousef Farhaoui

Assoc. Prof. Yousef Farhaoui

Moulay Ismail University, Morocco

Prof. Dr. Yousef FARHAOUI, is a Professor at Moulay Ismail University, Faculty of sciences and Techniques, Morocco. Chair of IDMS Team, Director of STI laboratory. Local Publishing and Research Coordinator, Cambridge International Academics in United Kingdom. He obtained his Ph.D. degree in Computer Security from Ibn Zohr University of Science. His research interests include learning, e-learning, computer security, big data analytics, and business intelligence. Farhaoui has three books in computer science. He is a coordinator and member of the organizing committee and also a member of the scientific committee of several international congresses, and is a member of various international associations. He has authored 7 Book and many Book Chapters with Reputed Publishers such as Springer and IGI. He is served as a Reviewer for IEEE, IET, Springer, Inder science and Elsevier Journals. He is also the Guest Editor of many Journals with Wiley, Springer, Inder science, etc. He has been the General Chair, Session Chair, and Panelist in Several Conferences. He is Senior Member of IEEE, IET, ACM and EAI Research Group.

Title: Securing E-Business Ecosystems: AI-Driven Approaches to Cybersecurity and Risk Management

Abstract:  The rapid expansion of e-business ecosystems has created unprecedented opportunities for innovation, efficiency, and global connectivity. Yet, this digital growth also exposes organizations to increasingly sophisticated cyber threats, including financial fraud, phishing, and large-scale distributed denial-of-service (DDoS) attacks. Traditional security methods are no longer sufficient to guarantee the confidentiality, integrity, and availability of critical business data and services. This presentation examines AI-driven approaches to cybersecurity and risk management within e-business ecosystems. It demonstrates how machine learning, deep learning, and natural language processing (NLP) can be leveraged to detect anomalies, predict threats, and automate incident response. It also explores the integration of AI into risk management frameworks to enable proactive identification and prioritization of vulnerabilities. Practical case studies are discussed, including fraud detection in e-commerce and fintech, blockchain-enabled supply chain protection, and secure handling of sensitive healthcare data. The talk emphasizes the shift from reactive defense to proactive and intelligent security mechanisms, highlighting the importance of explainable AI (XAI), ethical considerations, and governance. By combining AI with emerging technologies, organizations can build resilient, trustworthy, and future-ready e-business ecosystems.

 

Prof. Prashant Sharma

Prof. Prashant Sharma

O. P. Jindal Global University, India

Prof. (Dr.) Prashant Sharma is working as Professor & Vice Dean at the Jindal School of Banking & Finance (JSBF), O. P. Jindal Global University, Sonipat. Prior to joining JSBF, he was associated with Jaipuria Noida as Associate Professor and Chairperson (Admissions), IIHMR University Jaipur as Associate Professor and Coordinator (PhD Programme), Jaipuria Institute of Management Jaipur as Assistant Professor (Finance) and Programme Director (PGDM & FPM), and the National Institute of Financial Management (An institute of the Ministry of Finance, Government of India) as a full-time research fellow. At NIFM, he was part of the study team that conducted research on “Unaccounted Income and Wealth in India and Abroad,” popularly known as the Black Money Project, sponsored by CBDT, Ministry of Finance, Government of India. With close to 10 years of teaching and research experience, Prof. Sharma is a Fellow of the National Institute of Financial Management, Faridabad. He has completed a postgraduate degree in finance specialisation from the School of Management, Gautam Buddha University, Greater Noida, and graduated in the field of Mathematics from Dr. B. R. Ambedkar University, Agra. He is a keen researcher, experienced trainer, and dedicated teacher who has an interest in and expertise in the fields of finance and data analytics. He has expertise in various computational tools such as R, SPSS, E-Views, Python, etc. Apart from data analytics, his areas of research and teaching are asset pricing dynamics, corporate finance, capital markets, and econometrics. He has published numerous papers in Scopus and ABDC-indexed journals and also contributed significantly to various sponsored research projects funded by agencies of repute, including CBDT, the Airport Authority of India, and ICSSR. Prof. Sharma has also presented research papers at various national and international conferences and has been shortlisted to participate in various workshops sponsored by the Government of India, AICTE, ISI Calcutta, etc. He is also the recipient of various awards, such as the Best Faculty Award and the Best Research Methodology Award, for his contributions to teaching and research.

Title: Fintech Adoption and Financial Performance: The Unrecognized Contributions of Supply Chain Finance and Supply Chain Risk

Abstract: This study is the first to look at supply chain finance’s mediating role in elucidating how fintech adoption leads to improved financial performance. By assessing supply chain finance practices in small and medium enterprises in emerging economies, such as India, in this article, we aim to bridge the knowledge gap and guarantee the influence of fintech adoption in attaining improved financial performance. The original data were gathered from 270 workers in North India who worked for various small and medium enterprises. The study found that supply chain finance mediates the favorable association between fintech adoption and financial performance. A crucial moderator in the relationship between fintech adoption and financial performance was supply chain risk. The results indicate several applicable ramifications, including the use of fintech by companies, which is eventually enhanced by effective supply chain risk management to improve supply chain finance practices.

 

Prof. Patrícia Bonini

Prof. Patrícia Bonini

Santa Catarina State University, Brazil

Bio: Patricia Bonini is an Associate Professor of Economics at the Universidade do Estado de Santa Catarina (UDESC), with a PhD in Economics from the University of Birmingham (2001), an MSc in Economics from the University of Brasília (1995), and a BA in Economics from UNICAMP (1991). Her research trajectory combines macroeconomic theory, labor economics, and human capital with a strong focus on STEM workforce analysis and gender inequalities in the labor market. She has coordinated and executed multiple funded research projects, including: STEM and gender choices in Brazil (2024–ongoing) – surveys and statistical analysis of women’s career choices in engineering and ICT. STEM wage premium and new occupations (2016–2023) – decomposition analyses of salary differentials using RAIS/INEP data. Gender gaps in IT wages (2014–2016) – identifying conditions under which women capture occupational wage premiums.

Title: Human Capital as the Bottleneck of Digital Transformation: Regional STEM Workforce Challenges

Abstract: The digital transformation of economies relies not only on technological adoption but also on the availability of a skilled and diverse STEM workforce. This presentation examines comparative evidence from South America and Asia to assess which regions are best prepared to sustain AI-enabled business innovation, Big Data strategies, and digital logistics. Using UNESCO, World Bank, OECD, and WEF data, the analysis highlights how Asia’s coordinated policies and massive STEM graduate output provide scale advantages, while South America faces bottlenecks due to fragmented policies, low researcher densities, and persistent gender inequalities. The discussion emphasizes that the future of e-business depends critically on human capital formation and inclusivity. Policy recommendations address how governments and firms can bridge the workforce gap to avoid widening the global digital divide.