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International Business Analytics Conference for Academic and Industry Professionals 2025

Authors: Saumya Padhi;

International Business Analytics Conference for Academic and Industry Professionals 2025

Abstract

Conference at a Glance The International Business Analytics Conference (IBAC) successfully hosted its inaugural event at SUNY Fredonia on May 3-4, 2024. This landmark conference established a unique platform designed to bridge the gap between academic scholarship and industry practice in the field of business and data analytics through meaningful Academia-Industry partnerships. By convening industry professionals and academic experts, IBAC fostered valuable dialogue and collaboration within the dynamic and rapidly evolving discipline of business analytics. The conference welcomed submissions across a broad spectrum of topics related to both research and industry applications in business and data analytics. Areas of focus included, but were not limited to, Accounting, Business and Management, Computer Science/Management Information Systems, Economics, Finance and Insurance, Education, Healthcare, Marketing, Music and Entertainment, and Sport Management. In its first year, IBAC attracted 189 participants, demonstrating strong interest and confirming the demand for a forum of this kind. Encouraged by this positive response, the conference is set to become an annual event. The 2025 IBAC conference is scheduled for May 8-9, with plans to further expand participation and engagement. We are pleased to witness growing support from local and regional businesses, whose involvement continues to strengthen the conference’s impact and reach. Additionally, IBAC 2025 will introduce two new and highly anticipated components: Undergraduate Poster Sessions and an Analytics Career Fair, both of which have generated considerable enthusiasm from students and industry partners alike. For example, submission specifically addressed research or current business practices in: Ø Data-driven decision-making Ø Predictive and prescriptive analytics Ø Artificial Intelligence and Machine Learning in business Ø Big data analytics and its applications Ø Business Intelligence and data visualization Ø Data mining and pattern recognition Ø Text and sentiment analysis in business Ø Supply chain analytics and logistics optimization Ø Marketing and customer analytics Ø Risk management and fraud detection Ø Ethics and privacy in business analysis Proceedings and Publication Opportunity: We are pleased to announce that this year, IBAC is collaborating with Empirical Economics Letters (indexed by the Australian Business Deans Council, Rating: C, http://www.eel.my100megs.com/) to publish all conference proceedings. In addition, authors will have the opportunity to submit their articles for publication in this journal under a special issue edited by Drs. Kaustav Misra and Justin Mindzek.

Keywords

IoT, IDS, Anomaly Detection, Machine Learning, Ensemble Learning, XGBoost, Random Forest, Edge-IIoTset, Cybersecurity, IoT, Intrusion Detection System, Anomaly Detection, Machine Learning, Ensemble Learning, XGBoost, Random Forest, Edge-IIoTset, Cybersecurity, Real-time Detection

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    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green