This event will feature two insightful talks that together provide both a broad overview of the rapidly evolving public blockchain landscape and a hands-on roadmap for developers building the platforms of tomorrow. The first talk, Emerging Trends in the Public Blockchain Ecosystem will examine the latest developments shaping open networks such as stablecoins, NFTs, DAOs, Decentralized Physical Infrastructure (DePIN), Real-World Assets (RWAs), and AI-driven interactions. Attendees will gain an understanding of the broader economic and technological forces guiding public blockchain innovation. Following this, Blockchain Developer Roadmap – 2025 will shift the focus to practical career growth and skill-building for developers. This talk will outline key technologies, programming languages, interoperability solutions, and best practices in security and compliance, offering a clear path to successfully navigating the complex environment of next-generation blockchain development. Together, these sessions provide a comprehensive perspective on where the public blockchain ecosystem is headed and how developers can best prepare to build meaningful, secure, and innovative applications that will drive the industry forward. Co-sponsored by: FreeCodingSchool.org Speaker(s): Dr Ramesh Ramadoss, Revanth R Airre Agenda: 9:00 – 09:05 AM: Welcome Address & Introduction 9:05 – 9:35 AM: Talk 1 – "Emerging Trends in the Public Blockchain Ecosystem" By Ramesh 9:35 – 9:40 AM: Audience Q&A Session with Ramesh 9:40 – 10:10 AM: Talk 2 – Blockchain Developer Roadmap – 2025 by Revanth 10:10 – 10:15 AM: Audience Q&A Session with Revanth 10:15 AM – 10:30 AM: Closing Remarks & Future Events Preview 10:30 PM: Event Concludes Virtual: https://events.vtools.ieee.org/m/453000

[] Free Registration (with a Zoom account; you can get one for free if you don't already have it): https://sjsu.zoom.us/meeting/register/tZcsc-CoqjwpG9aPDHfg6Axqvn90i4uQRmqr Synopsis: For a long time, the AI/ML community relied on traditional evaluation metrics such as the confusion matrix, accuracy, precision, and recall for assessing the performance of machine learning models. However, the rapidly evolving field has been raising several ethical concerns, which calls for a more comprehensive evaluation scheme. In easy-to-understand language, this talk will delve into the quantitative analysis of model performance, emphasizing the critical importance of explainability. As ML models become increasingly complex and pervasive, understanding their decision-making processes is paramount. We'll explore various performance metrics, their limitations, and the growing need for transparency. Topics covered include Cohen’s Kappa Statistic, Matthew's correlation coefficient (MCC), Confusion Matrix, Precision, Recall, G-measure, ROC Curve, Youden's J statistic, Type II Adversarial attack, R-squared, LIME, SHAP, and more. Speaker(s): Dr. Vishnu S. Pendyala Virtual: https://events.vtools.ieee.org/m/442073