IEEE Educational Events

AI/ML in Supply Chain Decision Making
AI/ML in Supply Chain Decision Making 150 150 ieeeeduweek

Machine learning is transforming supply chain management by  enabling businesses to make faster, data-driven decisions in an
increasingly complex and volatile market. By analyzing vast amounts of historical and real-time data, ML helps companies improve demand
forecasting, ensuring better alignment between supply and customer needs while reducing stockouts and overstocking. It also plays a
critical role in optimizing inventory by balancing stock levels, cutting down holding costs, and enhancing overall efficiency. Supplier
evaluation is another area where ML proves invaluable, as it helps assess pricing trends, delivery performance, and quality metrics to
identify the most reliable partners. Additionally, logistics and transportation benefit significantly from ML-driven route and schedule
optimization, reducing fuel costs and improving delivery times. By integrating machine learning into supply chain operations, businesses
can enhance agility, reduce inefficiencies, and gain a competitive edge in an increasingly dynamic market.

Smarter Demand Forecasting for Fast Fashion: Hybrid Models for a Dynamic Market
Smarter Demand Forecasting for Fast Fashion: Hybrid Models for a Dynamic Market 150 150 ieeeeduweek

Forecasting the demand for new fashion products in the fast fashion industry is a com-

plex task due to its dynamic nature, short product life cycles, and limited historical data.

Traditional forecasting models often fail, leading to inefficiencies such as overproduc-

tion or underproduction. This paper reviews key challenges and explores innovative

machine learning (ML) and artificial intelligence (AI)-based models to improve fore-

cast accuracy. We propose a hybrid AI-driven approach that integrates structured and

unstructured data sources, real-time monitoring, and ensemble models to address fore-

cast limitations in the fast fashion industry.

AI One year on from AI to Artificial Stupidity
AI One year on from AI to Artificial Stupidity 150 150 ieeeeduweek

 Talk covered the AI incorporation into his tertiary subjects at the College of New Jersey.  

Enhancing STEM Education through AI-Assisted Tutoring
Enhancing STEM Education through AI-Assisted Tutoring 150 150 ieeeeduweek

AI-Assisted Tutoring represents a transformative approach to education in Science, Technology, Engineering, and Mathematics (STEM) fields. This innovative educational model integrates artificial intelligence (AI) technologies to create personalized learning experiences, addressing the unique needs and learning styles of each student. The core of AI-assisted tutoring lies in its ability to analyze vast amounts of data, including student performance metrics, learning preferences, and engagement levels, to tailor educational content and methodologies.

The system employs algorithms and machine learning techniques to identify knowledge gaps, predict learning outcomes, and adapt instructional strategies in real-time. This dynamic approach ensures that students receive targeted support in areas where they need it most, promoting deeper understanding and retention of complex STEM concepts. Moreover, AI-assisted tutoring can simulate one-on-one interaction, providing immediate feedback and guidance, thereby fostering a supportive and responsive learning environment.

The integration of AI in STEM education has the potential to revolutionize traditional teaching paradigms by making learning more accessible, efficient, and effective. It can bridge educational gaps, enhance student performance, and inspire a generation of learners to excel in STEM disciplines. This abstract explores the potential of AI-assisted tutoring to transform STEM education, highlighting its benefits, challenges, and future prospects in nurturing the next wave of innovators and problem-solvers.

 

A Call to Action: Uniting Tech Innovators, Policy Leaders & Businesses to Safeguard Digital Privacy
A Call to Action: Uniting Tech Innovators, Policy Leaders & Businesses to Safeguard Digital Privacy 150 150 ieeeeduweek

The presentation discusses the challenges in achieving digital privacy and the important role of technologists and policymakers while encouraging engagement from individuals and businesses.

When talking about the complexity and the importance of digital privacy, there is currently a balancing act between privacy rights and technological advancements. As technologies in digital privacy, like encryption techniques, continue to evolve, it will be essential for policymakers, businesses, and individuals to work together ensuring that personal data is protected, and privacy rights are upheld in the digital age.  In building trust, collaboration among technologists, policymakers and businesses must effectively address digital privacy challenges and protect consumer rights.

To help bridge the digital privacy and communication gap between technologists and policymakers, as well as building trust among the public at large and businesses, this presentation will also include discussion on the IEEE Digital Privacy Model.  The model is intended not to provide a prescription and implementation of solutions but serves as a vehicle for productive discussion on covering the broad and dynamic aspects of digital privacy issues.

To learn more about the work and how you can get involved with IEEE Digital Privacy Initiative and/or to keep apprised of future events such as the Reframing Privacy in the Digital World workshop, please visit:  https://digitalprivacy.ieee.org 

Revolutionize Electronic Design Automation (EDA) with AI: Unlocking the Power of Generative Adversarial Networks in High-Speed Receiver Modeling
Revolutionize Electronic Design Automation (EDA) with AI: Unlocking the Power of Generative Adversarial Networks in High-Speed Receiver Modeling 150 150 ieeeeduweek

Join us for an exciting virtual event on “AI for Electronic Design Automation,” where we will delve into the revolutionary applications of machine learning and artificial intelligence in the field of electronic design. Our featured speaker, Priyank Kashyap, a Hardware Engineering Specialist at Hewlett-Packard Enterprise, will showcase the use of generative adversarial networks in modeling high-speed receivers, addressing complex multi-physics problems like thermal analysis of PCBs and 3D-ICs, and exploring the potential of current models like LLMs in electronic design flows. This event promises to be a captivating deep dive into the cutting-edge technologies shaping the future of electronic design!

 

Webinar on the data science application in engineering practices: A future outlook
Webinar on the data science application in engineering practices: A future outlook 150 150 ieeeeduweek

Genuinely computer science, algorithms, programming, data, big data, artificial intelligence, machine learning, and data science are leading today’s society innovations and technologies. Therefore, understanding of properly tacking essential practical among countless topics for engineering students to apply in real-world applications is known as essential. This event will explore examples of data scient applications and real-world applications for students.

MACHINE LEARNING APLICADO A LA INGENIERÍA USANDO BASADO EN PYTHON, (DIA 3)
MACHINE LEARNING APLICADO A LA INGENIERÍA USANDO BASADO EN PYTHON, (DIA 3) 150 150 ieeeeduweek

Machine learning aplicado a la ingeniería usando basado en Python

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