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DTSTART;TZID=America/New_York:20220512T160000
DTEND;TZID=America/New_York:20220512T170000
DTSTAMP:20260415T191958
CREATED:20220518T191802Z
LAST-MODIFIED:20220524T100552Z
UID:10000530-1652371200-1652374800@www.ieeetoronto.ca
SUMMARY:Visualization Techniques in Text Summarization of Online Transcripts – Students’ Research in ML and DL at Durham College
DESCRIPTION:Text summarization is a method for generating a summary of long texts by focusing on the sections that contain essential information while keeping the overall meaning intact. Its goal is to reduce the size of long documents\, which would be difficult and expensive to process manually. With the current explosion of data circulating in digital space\, particularly unstructured textual data\, there is a need to build tools that allow people to extract insights from it. Taking notes is a popular practice for many people employed in situations where it is essential to keep track of what is said\, such as during an online lecture. The art of note-taking does not entail taking down every single word stated but rather broad summaries of what is covered. Making succinct yet informative summaries is the key to successful note-taking. In this seminar\, we will be discussing how we have used visualization and data storytelling techniques in our project to make informed decisions. This project aims to address the difficulties of note-taking by building an application that produces notes based on the transcripts generated by the Automatic Speech Recognition (ASR) technology of the meeting platforms. We relied on visualization concepts for three major decisions that would define our project as a whole. These decisions are the choice of online meeting platform\, preference of text summarization model and the messaging platform choice. \nSpeaker(s): Manoj Varma Alluri\, Navaneeth Jawahar\, Sharath Kumar Prabhu\, Jeel Jani \nRegister: https://events.vtools.ieee.org/m/313207
URL:https://www.ieeetoronto.ca/event/visualization-techniques-in-text-summarization-of-online-transcripts-students-research-in-ml-and-dl-at-durham-college/
LOCATION:Toronto\, Ontario\, Canada\, Virtual: https://events.vtools.ieee.org/m/313207
CATEGORIES:Magnetics,Women in Engineering
ORGANIZER;CN="Reza Dibaj":MAILTO:reza.dibaj@ieee.org
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220512T140000
DTEND;TZID=America/New_York:20220512T150000
DTSTAMP:20260415T191958
CREATED:20220518T191856Z
LAST-MODIFIED:20220524T100552Z
UID:10000531-1652364000-1652367600@www.ieeetoronto.ca
SUMMARY:Fraud Data Analysis & Exploration using Interactive Tableau Dashboard – Students’ Research in ML and DL at Durham College
DESCRIPTION:Credit card fraud detection is an ever-growing problem in today’s financial market with a rapid increase in plastic card usage worldwide. According to the Nelson report\, by 2027\, financial service providers are expected to take a $40 billion hit globally in credit card losses\, a significant increase compared to previous years. Hence\, data-driven decisions can largely help in mitigating that risk. We chose this topic to deep dive into different aspects of Fraud Data Analysis & Exploration using Interactive Tableau Dashboard. Tableau dashboards can be very powerful in driving data-driven decisions. We created an interactive dashboard to help stakeholders or less technical people to drive insights\, understand data better\, and help in business decisions making. The interactive feature helps users to add filters as per their needs and understand the data in a way they want to analyze. Our dashboard is dynamic and would be updated when several filters are applied together. Multiple filters can be added to multiple charts at the same time. These charts are intuitive which makes even new users easily interact and understand the data. \nSpeaker(s): Priyanka Singh & Devy Ratnasari \nRegister: https://events.vtools.ieee.org/m/313201
URL:https://www.ieeetoronto.ca/event/fraud-data-analysis-exploration-using-interactive-tableau-dashboard-students-research-in-ml-and-dl-at-durham-college/
LOCATION:Toronto\, Ontario\, Canada\, Virtual: https://events.vtools.ieee.org/m/313201
CATEGORIES:Magnetics,Women in Engineering
ORGANIZER;CN="Reza Dibaj":MAILTO:reza.dibaj@ieee.org
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220507T180000
DTEND;TZID=America/New_York:20220507T190000
DTSTAMP:20260415T191958
CREATED:20220505T200912Z
LAST-MODIFIED:20220507T074848Z
UID:10000361-1651946400-1651950000@www.ieeetoronto.ca
SUMMARY:Alert on Mask Detection System – Students Research in ML and DL at Durham College
DESCRIPTION:As a result of the fast development and spread of the COVID-19 pandemic throughout the world\, people’s everyday lives have been severely disrupted in recent times. One proposal for controlling the epidemic is to make individuals wear face masks in public. As a result\, we require face detection systems that are both automated and efficient for such enforcement. We propose a face mask identification model for static and real-time videos in this research\, and the pictures are classified as “with mask” or “without a mask.” The model uses a Kaggle dataset to train and test. The collected data set contains over 10\,000 images (considering 5\,000 with mask and similarly 5\,000 without) and has a 98 percent performance accuracy rate. The proposed model is computationally efficient and precise compared to Haar-Cascade & ANN. The application of this research are various\, including digitized scanning tool in schools\, hospitals\, banks\, airports\, and many other public or commercial locations. \nSpeaker(s): Henil Shah\, Neenu Markose \nRegister: https://events.vtools.ieee.org/m/312341
URL:https://www.ieeetoronto.ca/event/alert-on-mask-detection-system-students-research-in-ml-and-dl-at-durham-college/
LOCATION:Toronto\, Ontario\, Canada\, Virtual: https://events.vtools.ieee.org/m/312341
CATEGORIES:Magnetics,Women in Engineering
ORGANIZER;CN="Reza Dibaj":MAILTO:reza.dibaj@ieee.org
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220506T180000
DTEND;TZID=America/New_York:20220506T190000
DTSTAMP:20260415T191958
CREATED:20220502T172656Z
LAST-MODIFIED:20220507T074847Z
UID:10000357-1651860000-1651863600@www.ieeetoronto.ca
SUMMARY:Cancer Level Detection System – Students Research in ML and DL at Durham College
DESCRIPTION:Cancer ranks as a leading cause of death and an important barrier to increasing life expectancy everywhere. According to available data\, lung cancer contributes the most to cancer deaths. Also\, according to available data\, those diagnosed early have a 50 percent chance of survival over those diagnosed with late-stage cancer. It means that early detection is paramount to the survival of a lung cancer patient\, leading to a reduction in the number of cancer deaths. We\, therefore\, evaluated six different machine learning algorithms to see which one performed optimally in accurately predicting the level of lung cancer development in a patient. We considered various parameters when choosing the dataset for this evaluation as the pathogenesis of lung cancer involves a combination of intrinsic factors and exposure to environmental carcinogens. We also considered varying the features in our data\, categorizing them under diagnostic risk factors (age\, gender\, alcohol use\, air pollution\, balanced diet\, obesity\, smoking\, passive smoker) and symptoms (fatigue\, weight loss\, shortness of breath\, swallowing difficulty\, frequent cold\, dry cough) and the inferences we drew from this indicated that those that have the symptom features prior to diagnosis had the highest chance of being diagnosed with a high level of cancer. The final results of our evaluation showed that the best levels of predictions on new data were achieved by optimized Random Forest\, KNN\, and SVM models. \nSpeakers: Rakesh Pattanayak\, Chisom Nnabuisi\, Dhruv Mistry\, Kar Chun Kan\, Shanuka Rathnayake \nRegister: https://events.vtools.ieee.org/m/312340
URL:https://www.ieeetoronto.ca/event/cancer-level-detection-system-students-research-in-ml-and-dl-at-durham-college/
LOCATION:Toronto\, Ontario\, Canada\, Virtual: https://events.vtools.ieee.org/m/312340
CATEGORIES:Magnetics,Women in Engineering
ORGANIZER;CN="Reza Dibaj":MAILTO:reza.dibaj@ieee.org
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220503T180000
DTEND;TZID=America/New_York:20220503T190000
DTSTAMP:20260415T191958
CREATED:20220502T171646Z
LAST-MODIFIED:20220504T073258Z
UID:10000346-1651600800-1651604400@www.ieeetoronto.ca
SUMMARY:DDoS Detection System – Students Research in ML and DL at Durham College
DESCRIPTION:The research goal is to implement different machine learning algorithms to detect any DDoS (Distributed Denial of Service) attacks using the UNSW-NB15 dataset. We started by going through the data description and finding null values in our features. After that we dropped the ‘id’ column. \nWe have used the UNSW-15 dataset for AI-based DDOS detection systems. \nThe UNSW-15 dataset has a hybrid of the real modern normal and the contemporary synthesized attack activities of the network traffic. It contains different attacks\, including DoS\, worms\, Backdoors etc. The raw network packets of the UNSW-NB 15 datasets are created by the IXIA Perfect Storm tool in the Cyber Range Lab of the Australian Centre for Cyber Security (ACCS) for generating a hybrid of real modern normal activities and synthetic contemporary attack behaviours. We incorporated different feature selection methods for dropping insignificant features followed by the implementation of 6 classification algorithms\, namely Naive Bayes\, Random Forest\, Decision Tree\, KNN\, Logistic Regression and SVM. \nSpeaker(s): Minu Ahlawat\, Dwij Dua\, Megha Garg\, Taxil Savani \nRegister: https://events.vtools.ieee.org/m/312339
URL:https://www.ieeetoronto.ca/event/ddos-detection-system-students-research-in-ml-and-dl-at-durham-college/
LOCATION:toronto\, Ontario\, Canada\, Virtual: https://events.vtools.ieee.org/m/312339
CATEGORIES:Magnetics,Women in Engineering
ORGANIZER;CN="Reza Dibaj":MAILTO:reza.dibaj@ieee.org
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220501T180000
DTEND;TZID=America/New_York:20220501T190000
DTSTAMP:20260415T191958
CREATED:20220502T171452Z
LAST-MODIFIED:20220502T171549Z
UID:10000525-1651428000-1651431600@www.ieeetoronto.ca
SUMMARY:Sentiment Analysis on Twitter Data – Students Research in ML and DL at Durham College
DESCRIPTION:The rise of digitalization and the advent of social media and e-commerce have generated an abundance of data than before. Natural Language Processing (NLP) is a significant branch of artificial intelligence that helps the machine interpret human languages and perform the desired task by analyzing the semantics\, content\, and pattern. Sentiment analysis is the most common technique in Natural Language Processing used to determine the underlying sentiments of a text. This technique is currently in place for different Business Organizations to analyze their brand’s market value\, brand reputation\, and customer perception of new brand/new change. Businesses use social media channels to cater to their customer service\, and people use social media to express/share their wide range of opinions or experiences about a product/brand. These opinions and experiences reflect the real-time sentiments of a customer. Sentiment analysis will help businesses designing an effective marketing campaign\, better customer satisfaction\, boost sales\, help improve customer experience\, understand customer perception to change and the brand’s market reputation. The customer views expressed on Twitter\, Facebook\, and other online forums are forming the base of customer strategy for brands worldwide. Businesses are opting to shift their traditional customer feedback analysis method to text classification since people prefer to post the genuine reviews on the internet. Analyzing the underlying sentiments in the text will help the business to understand their customers’ voices and their brand reputation in the market in real-time. Sentiment analysis will help the businesses designing an effective marketing campaign\, better customer satisfaction\, boost sales\, help improve customer experience\, understand customer perception to change and the brand’s market reputation. Twitter sentiment analysis aims to classify text into positive/negative based on its underlying semantics. \nSpeaker(s): Akhil Mathew\, Anmol Wadera\, Deepan Ellenti Padmanabhan\, Saketh Vemula\, Sivaramakrishna Malakalapalli \nRegister: https://events.vtools.ieee.org/m/312338
URL:https://www.ieeetoronto.ca/event/sentiment-analysis-on-twitter-data-students-research-in-ml-and-dl-at-durham-college/
LOCATION:Toronto\, Ontario\, Canada\, Virtual: https://events.vtools.ieee.org/m/312338
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:20260415T191958
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220427T180000
DTEND;TZID=America/New_York:20220427T190000
DTSTAMP:20260415T191958
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:20220426T180000
DTEND;TZID=America/New_York:20220426T190000
DTSTAMP:20260415T191958
CREATED:20220425T202413Z
LAST-MODIFIED:20220426T234759Z
UID:10000521-1650996000-1650999600@www.ieeetoronto.ca
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
END:VEVENT
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