The increase in popularity of machine learning models and algorithms has brought up methods for increasing the efficiency of processes in various industries and new use cases. From corporations to individuals, machine learning is applicable in a wide range of settings. One such application is using machine learning models for stock price forecasting. This area is often studied. However, it still needs to solve problems due to the high complexity and volatility that technical factors and sentiment-analysis models try to capture. Based on this premise, we suggested building up our previous experience building short-term forecasting models using machine learning models. The research project aims to develop practical algorithmic trading algorithms based on accurate short-term forecasts for financial time series using machine learning models. The project focuses on these research activities:- Building a database containing the targeted financial and other time series is valuable input to the machine learning models. This streamlines the process of acquiring financial data required for training the machine learning model by adding a web UI for configuring data collection automation and extracting required data.- A short-term forecasting model based on machine learning is developed using the various time-series data from the data warehouse. Machine learning algorithms such as neural networks, random forecasts, support vector regression, XGBoost, and long short-term memory (LSTM) are evaluated with several performance criteria to identify the most accurate model from a short-term trading perspective. After the performance analysis based on several metrics, XGBoost was chosen for further development.We will demonstrate how to evaluate the quality of the collected data, the metrics of the machine learning model’s prediction, and their integration.Co-sponsored by: IEEE Okanagan College Student BranchSpeaker(s): Albert Wong, Gaétan Hains, Ajitesh Parihar Agenda: 1. Welcome from the IEEE Okanagan Subsection, IEEE Okanagan College Student Branch and Computer Science Department: Dr. Youry Khmelevsky, Chair (9:30 am – 9:35 am)2. Invited speaker from the University of Paris East Creteil: Dr. Gaétan Hains (9:35 am – 10:15 am)3. Invited speakers from Langara College: Dr. Albert Wong & Andres C. Viloria Garcia (10:15 am – 11:00 am)      Machine Learning algorithms for stock price forecasting      Model building process and methodology      Normalization and performance evaluation      Iterations and results4. Coffee Break (11:00 am - 11:15 am)5. Student research project presentation and demonstration (11:15 am - 12:30 pm)      Database design      Data collection process automation      Extraction, Transformation and Loading Process      UI development for data configurations and extraction      Integration prototype using Jupyter notebooksWe will demonstrate how to evaluate the quality of the collected data, the metrics of the machine learning model’s prediction, and their integration.Room: 310, Bldg: E, 1000 KLO Rd., Kelowna, British Columbia, Canada, V1Y 4X8, Virtual: https://events.vtools.ieee.org/m/428090

The digital landscape is amid an evolution that could transform our world in ways we probably have not even envisioned. Artificial Intelligence (AI) has emerged as a powerful weapon, transforming every facet of our lives while society, organizations and governments all over the world scramble on how best to regulate it.Since the launch of ChatGPT about 18 months ago, cybercriminals lurking in the shadows of the dark web are constantly devising ways to take advantage of the technology to get around safety and ethical guardrails or use it for more sophisticated malicious activities in the shadowy world of cybercrime.This talk explores the evil realm of the Internet (aka the Dark Web) where unethical and criminal activities flourish and the interplay with cybersecurity, cyberwarfare, and cybercrime. This talk will also highlight the essential undertakings in the unavoidable and perpetual battle against cybercrime that must be fought by all.Speaker(s): Kay, Agenda: 7:00PM - Introduction of IEEE Hamilton Section7:15PM - Presentation8:15PM - Q&A8:30PM - Presentation EndVirtual: https://events.vtools.ieee.org/m/425904

The latest McKinsey Global Survey on AI in May of 2024 concludes that 2023 was the year the world discovered generative AI (GenAI) and 2024 is the year organizations truly began using—and deriving business value from—this new technology. An incredible 65 percent of respondents report that their organizations are regularly using GenAI, which is nearly double the percentage from the previous survey just ten months before. Respondents’ expectations for gen AI’s impact remain as high with three-quarters predicting that gen AI will lead to significant or disruptive change in their industries in the years ahead.This session is an invaluable opportunity for business leaders like you to overcome your hesitation and uncertainty of AI. Mastering AI tools and techniques is not just about keeping pace with technology; it's about leveraging these advanced resources to fill in knowledge gaps, enhance decision-making, and drive productivity.Learning to utilize AI not only streamlines operations but also significantly boosts your capacity to deliver increased business outcomes, making you more valuable and a strategic asset to your organization. You will leave equipped with practical, actionable strategies that you can implement immediately, turning your uncertainty into confidence and empowering you to leverage AI effectively in your daily tasks.Speaker(s): Paul CarneyVirtual: https://events.vtools.ieee.org/m/423706

The Montreal Chapters of the IEEE Control Systems (CS) and Systems, Man & Cybernetics (SMC) cordially invite you to attend the following in-person talk, to be given by Dr. Jesús Rodríguez Molina from the Technical University of Madrid.Co-sponsored by: Concordia UniversitySpeaker(s): Dr. Jesús Rodríguez MolinaRoom: EV002.184, Bldg: EV Building, Concordia University , Montreal, Quebec, Canada, H3G 1M8

[]Join the IEEE Toronto Instrumentation & Measurement – Robotics & Automation Joint Chapter for a talk on the Walking with Robots, presented by Dr. Amy R. Wu from Queen's University.Wednesday, July 31, 2024 @ 5:00 – 6:00 PMAbstract: A world embedded with robots seems inevitable. One challenge, however, is designing robots to work with us, not against us. It is unclear how assistive robots, such as robot companions, would affect activities of human daily living, such as walking. To inform the design of controllers that enable seamless human-robot interaction during walking tasks, we first sought to determine whether legged robots could affect human walking behavior. Robots have the potential to greatly improve human lives, but we should ensure they are designed to interact well with us to minimize mechanical and possibly energetically related consequences from human-robot interaction.In this talk, Dr. Amy R. Wu will discuss two studies they conducted with human participants and quadrupedal robots walking side-by-side. They compared human behavior with and without the robot and found that legged robots can affect human gait even without physical interactions, which has implications for the design of robot companions or monitoring robots.Speaker(s): Amy R. Wu, PhD, P.L.EngVirtual: https://events.vtools.ieee.org/m/425309