Women in Engineering

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

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

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