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DTSTART;TZID=America/New_York:20220426T180000
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DTSTAMP:20261108T062114
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SUMMARY:Fake News Detection – Students Research in ML and DL at Durham College
DESCRIPTION:The term “fake news” was pretty much unknown and unpopular a few decades ago\, but it has emerged as a massive monster in the digital era of social media. Fake news is spreading like wildfire these days\, and people share it without confirming it. Often\, it is to promote or enforce specific views\, and it is carried out through political agendas. Fake news refers to news that may or may not be correct and is widely disseminated via social media and other internet platforms. \nIn this digital age\, it is not easy to tackle the spread of fake news\, where thousands of information-sharing sites via fake news or misinformation can be shared. It has become a greater issue as AI advances\, bringing with it artificial bots that may be used to create and propagate fake news. The problem is critical because many individuals believe anything they read on the internet\, and those who are inexperienced or new to digital technologies are vulnerable to being misled. Fraud is another issue that can arise as a result of spam or harmful emails and communications.\nFake news has grown in popularity and spread as a result of recent political events. Humans are inconsistent\, if not outright terrible detectors of fake news\, as evidenced by the pervasive effects of the widespread onset of fake news. As a result\, efforts have been made to automate detecting fake news. The most prominent of these attempts are “blacklists” of unreliable sources and authors. While these technologies are useful we need to account for more complex instances when trusted sources and authors leak fake news in order to provide a complete end-to-end solution. As a result\, the goal of this project was to develop a tool that used machine learning and natural language processing techniques to recognize the language patterns that distinguish fake and true news. The outcomes of this project show that machine learning can be effective in this situation. We developed a model that detects a variety of intuitive indicators of real and fake news and an application to aid in the visual representation of the classification decision. We aim to give users the ability to classify news as fake or real and verify the website legitimacy that published it. \nSpeaker(s): Roshna Babu\, Abraham Mathew\, Neha Joseph\nToronto\, Ontario\, Canada\, Virtual: https://events.vtools.ieee.org/m/312334
URL:https://www.ieeetoronto.ca/event/fake-news-detection-students-research-in-ml-and-dl-at-durham-college/
LOCATION:Toronto\, Ontario\, Canada\, Virtual: https://events.vtools.ieee.org/m/312334
CATEGORIES:Magnetics,Women in Engineering
ORGANIZER;CN="Reza Dibaj":MAILTO:reza.dibaj@ieee.org
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DTSTART;TZID=America/New_York:20220427T180000
DTEND;TZID=America/New_York:20220427T190000
DTSTAMP:20261108T062114
CREATED:20220425T202533Z
LAST-MODIFIED:20220429T070736Z
UID:10000522-1651082400-1651086000@www.ieeetoronto.ca
SUMMARY:Credit Card Fraud Detection – Students Research in ML and DL at Durham College
DESCRIPTION:With the new trend of Online Shopping and Online Platforms for transactions\, the number of Credit Card based transactions increased tremendously. However\, there have been a lot of cases where illegal use of Debit/Credit Cards for making Fraudulent Transactions. Credit card companies have been paying a lot of attention to providing the best service for their customers by having process enhancements and pro-actively looking into transactions before making them through. Global financial losses related to payment cards are estimated to reach $34.66 billion in 2022\, according to The Nilson Report\, a newsletter that tracks the payment industry. Related to the negative impacts of credit card fraud activities\, and financial and product losses\, it’s easy for merchants and users to feel victimized and helpless. Machine Learning Models can work well in detecting such Fraudulent actions when they are trained on a large quantity of historical data and then fine-tuned depending on validation and evaluation metrics. \nSpeaker(s): Priyanka Singh\, Devy Ratnasari\, Gopika Shaji\, Oluwole Ayodele\, Saurav Bisht\,\nToronto\, Ontario\, Canada\, Virtual: https://events.vtools.ieee.org/m/312336
URL:https://www.ieeetoronto.ca/event/credit-card-fraud-detection-students-research-in-ml-and-dl-at-durham-college/
LOCATION:Toronto\, Ontario\, Canada\, Virtual: https://events.vtools.ieee.org/m/312336
CATEGORIES:Magnetics,Women in Engineering
ORGANIZER;CN="Reza Dibaj":MAILTO:reza.dibaj@ieee.org
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220428T180000
DTEND;TZID=America/New_York:20220428T190000
DTSTAMP:20261108T062114
CREATED:20220425T202839Z
LAST-MODIFIED:20220429T070737Z
UID:10000524-1651168800-1651172400@www.ieeetoronto.ca
SUMMARY:Text Summarization of Transcripts from Online Meetings – Students Research in ML and DL at Durham College
DESCRIPTION:Text Summarization is a technique for generating a concise and precise summary of voluminous texts while focusing on the sections that convey useful information without losing the overall meaning. It aims to transform lengthy documents into shortened versions\, which could be difficult and costly to undertake if done manually. With the current explosion of data circulating in digital space\, primarily unstructured textual data\, there is a need to develop tools that allow people to get insights from them quickly. In situations where it is essential to keep track of what is being spoken\, such as during an online lecture\, taking notes is a popular activity used by many. The art of notetaking does not involve making notes of every single word that is spoken but comprehensive outlines of what is discussed. The key to good notetaking lies in making concise yet informative summaries. In this seminar\, we will be discussing how we have tried to address the difficulties of notetaking by building an application that produces notes based on transcripts generated by the Automatic Speech Recognition (ASR) technology of the meeting platforms. We experimented with six summarization models for this application\, including transformer-based models pre-trained on large corpora. The datasets used for this application are the transcripts dataset acquired from online meeting platforms and the Extreme Summarization (XSum) dataset. We evaluated the models using Rouge metrics (Rouge-1\, Rouge-2\, and Rouge-L) and selected the best-performing model as the final model. We have built a bot that utilizes Telegram’s API and shares the generated summaries via group chat with the users. \nSpeaker(s): Manoj Varma Alluri\, Navaneeth Jawahar\, Sharath Kumar Prabhu\, Jeel Jani\, Shravya Sandupata\nToronto\, Ontario\, Canada\, Virtual: https://events.vtools.ieee.org/m/312337
URL:https://www.ieeetoronto.ca/event/text-summarization-of-transcripts-from-online-meetings-students-research-in-ml-and-dl-at-durham-college/
LOCATION:Toronto\, Ontario\, Canada\, Virtual: https://events.vtools.ieee.org/m/312337
CATEGORIES:Magnetics,Women in Engineering
ORGANIZER;CN="Reza Dibaj":MAILTO:reza.dibaj@ieee.org
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