Women in Engineering

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. In 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. Fake 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. Speaker(s): Roshna Babu, Abraham Mathew, Neha Joseph Toronto, Ontario, Canada, Virtual: https://events.vtools.ieee.org/m/312334

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. Speaker(s): Priyanka Singh, Devy Ratnasari, Gopika Shaji, Oluwole Ayodele, Saurav Bisht, Toronto, Ontario, Canada, Virtual: https://events.vtools.ieee.org/m/312336

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. Speaker(s): Manoj Varma Alluri, Navaneeth Jawahar, Sharath Kumar Prabhu, Jeel Jani, Shravya Sandupata Toronto, Ontario, Canada, Virtual: https://events.vtools.ieee.org/m/312337

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. Speaker(s): Akhil Mathew, Anmol Wadera, Deepan Ellenti Padmanabhan, Saketh Vemula, Sivaramakrishna Malakalapalli Register: https://events.vtools.ieee.org/m/312338

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. We have used the UNSW-15 dataset for AI-based DDOS detection systems. The 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. Speaker(s): Minu Ahlawat, Dwij Dua, Megha Garg, Taxil Savani Register: https://events.vtools.ieee.org/m/312339

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. Speakers: Rakesh Pattanayak, Chisom Nnabuisi, Dhruv Mistry, Kar Chun Kan, Shanuka Rathnayake Register: https://events.vtools.ieee.org/m/312340

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. Speaker(s): Henil Shah, Neenu Markose Register: https://events.vtools.ieee.org/m/312341

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. Speaker(s): Priyanka Singh & Devy Ratnasari Register: https://events.vtools.ieee.org/m/313201

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. Speaker(s): Manoj Varma Alluri, Navaneeth Jawahar, Sharath Kumar Prabhu, Jeel Jani Register: https://events.vtools.ieee.org/m/313207

We will explain the significance of visualization charts in narratives and presentations with a brief explanation of chart appropriateness, noise reduction, and decluttering aspects. We will continue by shedding light on the necessity of good communication tactics, criteria and approaches for improving visuals and narrative techniques. Moreover, applying the above concepts, we will explain how to use tableau as a software application to produce visuals to perform the superstore sales data analysis. Furthermore, we will analyze supermarket sales data, using appropriate charts for six product lines, customer types, and payment methods. We will use six categories of products, i.e. Electronic accessories, Food & Beverages, Health & Beauty, Home & Lifestyle, and Sports & travelling products, to carry out the analysis. We will emphasize the research's target audience by providing pertinent insights and making recommendations. Speaker(s): Minu Ahlawat, Megha Garg, Dwij Dua & Taxil Savani Register: https://events.vtools.ieee.org/m/313209

We might always be confused about climate and weather, and what is the difference between each other? Weather refers to the day-to-day temperature and atmospheric conditions, whereas climate is the average weather in a specific region over a long period. The simplest way to describe climate is to analyze the average temperature and precipitation over time. Climate change relates to the shift in the average conditions such as average temperature and rainfall in a region over a period. Global climate change describes the average long-term changes over the entire Earth. Global warming, Rise in sea level, and Shrinking Mountain glaciers are a few of the adverse effects of climatic changes. Greenhouse gases are the prominent factors for the rising temperature, which is the main factor contributing to global warming. Among the greenhouse gases, carbon dioxide is the main factor that traps the heat in the atmosphere, which makes an increase in the overall temperature that can affect lives on Earth. Earth’s temperature has risen by 0.14° F (0.08° C) per decade since 1880, and the rate of warming over the past 40 years is more than twice that: 0.32° F (0.18° C) per decade since 1981. We will try to find the possible reasons for climatic changes and the factors that contributed to the current situation. Moreover, we will consider greenhouse gas emissions and their harmful effects on climatic changes, different countries’ contributions to this global problem, and measures taken by officials to reduce its impact. Speaker(s): Neenu Markose, Akhil Mathew Register: https://events.vtools.ieee.org/m/313211

Join the IEEE Toronto Magnetics Chapter and Women in Engineering for a C# Development workshop. Speaker(s): Reza Dibaj Register: https://events.vtools.ieee.org/m/314229

Cancer is one of the leading causes of death in the world. To tackle this menace, pathologists need a faster and better way to diagnose their patients. This led the team to work on evaluating different machine learning models to find out which model works best in accurately predicting the level of cancer development in a patient. In the course of the project, we explored different features of our datasets with the help of visualization tools like tableau and python data visualization libraries to enable us to see the relationship between each feature and the level of cancer in a patient. We also, in the end, evaluated the performance of each algorithm using python visualization tools to better understand which algorithms performed the best. Speaker(s): Rakesh Pattanayak, Chisom Nnabuisi, Dhruv Mistry, Kar Chun Kan, Shanuka Rathnayake Register: https://events.vtools.ieee.org/m/313212

IEEE Toronto's Magnetic Chapter and WIE C# Development 101 continues. Speaker(s): Reza Dibaj Register: https://events.vtools.ieee.org/m/314671

Join the IEEE Toronto Magnetics Chapter and Women In Engineering for this two-hour data visualization workshop! Visualization is an indispensable part of today's data science, and Tableau is one of the most common tools for visualization. In a two-hour workshop technical presentation, we will quickly go through the fundamentals of Tableau visualization. Speaker(s): Dr. Reza Dibaj Virtual: https://events.vtools.ieee.org/m/315202

Python Development 101 continues with the IEEE Toronto Magnetics chapter and WIE. Speaker(s): Reza Dibaj Register: https://events.vtools.ieee.org/m/314667

C# Development 101 continues with the Magnetics chapter and WIE. Speaker(s): Reza Dibaj Register: https://events.vtools.ieee.org/m/314672