Events

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