Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Cardiovascular Disease Prediction Using Machine Learning(Institute of Electrical and Electronics Engineers Inc., 2022) Prajwal, K.; Tharun, K.; Navaneeth, P.; Anand Kumar, M.As the human population increases, so is the chance of getting diseases. There are many illnesses globally, and one of the biggest problems faced by the hospital systems today is the lack of technology to know when the patients are ill. One such illness is Cardiovascular Disease or CVD. It refers to any heart disease, vascular disease, or blood vessel disease. According to WHO, more people die of CVD's worldwide than any other cause. It affects the low and middle-income countries more. It is very hard for people living alone to contact the hospital when they are sick. Therefore, we have developed a model that can detect when a patient is ill and report back to the hospital. The system currently only identifies patients with heart disease and reports back to the hospital. We decided to go with heart disease identification because it is one of the most deadly diseases, and the risk of patients dying because of heart disease is high. Predicting whether a patient has heart disease or not is very clearly a classification problem. Therefore, we have used five models to classify. We take several factors such as blood sugar level, age, cholesterol level, and many more and give the outcome based on the input. © 2022 IEEE.Item Modeling Uber Data for Predicting Features Responsible for Price Fluctuations(Institute of Electrical and Electronics Engineers Inc., 2022) Sindhu, P.; Gupta, D.; Meghana, S.; Anand Kumar, M.In the field of economics, the features and patterns of the transportation system, including classical modes of transportation such as subways and taxis, as well as innovative tools such as car pooling platforms(Uber, Lyft, etc), are key research topics. The study here demonstrates how an Uber dataset is, which comprises Uber's New York City data, works. Uber is an online service provider platform via internet or a mobile application that avails ride-hailing service. In essence, it matches passengers with drivers of vehicles to book a ride from one place to another. The service connects users with drivers who will drive them to their desired location. The dataset contains primary data about Uber pick-ups, including the date, time, longitude, and latitude coordinates. The paper attempts to examine data from different locations, weathers, hours, and dates (intraday and midweek) in New York City and apply time series data analysis, statistical regression on the dataset, and predict Uber ride prices. We arrive at conclusions by analyzing data using various graphs, calculating and estimating the influence of these elements on Uber riders' payment amounts, and emphasizing features that cause price fluctuation. © 2022 IEEE.Item Hate Speech Detection Using Audio in Portuguese Language(Springer Science and Business Media Deutschland GmbH, 2024) Tembe, L.A.; Anand Kumar, M.This study focuses on hate speech in Portuguese language using audio and introduces a novel methodology that integrates audio-to-text and self-image technologies to effectively tackle this problem. We utilize Machine Learning and Deep Learning models to differentiate between hate speech and normal speech. The research utilized a total of 200 datasets, which were categorized into hate speech and normal speech. These datasets were collected by me personally for this project. Four distinct models are presented in the analysis: LSTM, SVM, CNN, and Random Forest. The findings highlight the superior performance of the CNN model when applied to spectrogram data, achieving an accuracy rate of 90%. Conversely, the Random Forest model outperforms others when dealing with text data, achieving an impressive accuracy rate of 73.1%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.