M
Muhammad Umer
Researcher at University of Engineering and Technology, Lahore
Publications - 22
Citations - 805
Muhammad Umer is an academic researcher from University of Engineering and Technology, Lahore. The author has contributed to research in topics: Sentiment analysis & Convolutional neural network. The author has an hindex of 8, co-authored 22 publications receiving 152 citations. Previous affiliations of Muhammad Umer include Islamia University.
Papers
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Improving the Prediction of Heart Failure Patients’ Survival Using SMOTE and Effective Data Mining Techniques
Abid Ishaq,Saima Sadiq,Muhammad Umer,Saleem Ullah,Seyedali Mirjalili,Vaibhav Rupapara,Michele Nappi +6 more
TL;DR: In this paper, the authors analyzed the heart failure survivors from the dataset of 299 patients admitted in hospital and found significant features and effective data mining techniques that can boost the accuracy of cardiovascular patient's survivor prediction.
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Fake News Stance Detection Using Deep Learning Architecture (CNN-LSTM)
TL;DR: A hybrid Neural Network architecture, that combines the capabilities of CNN and LSTM, is used with two different dimensionality reduction approaches, Principle Component Analysis (PCA) and Chi-Square, to determine the relative stance of a news article towards its headline.
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COVINet: a convolutional neural network approach for predicting COVID-19 from chest X-ray images.
TL;DR: The proposed CNN model can predict COVID-19 patients with high accuracy and can help automate screening of the patients for CO VID-19 with minimal contact, especially areas where the influx of patients can not be treated by the available medical staff.
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COVID-19: Automatic Detection of the Novel Coronavirus Disease From CT Images Using an Optimized Convolutional Neural Network
TL;DR: An optimized convolutional neural network model (ADECO-CNN) is proposed to divide infected and not infected patients and is compared with pretrained convolutionAL neural network (CNN)-based VGG19, GoogleNet, and ResNet models.
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Emotion Recognition by Textual Tweets Classification Using Voting Classifier (LR-SGD)
Anam Yousaf,Muhammad Umer,Saima Sadiq,Saleem Ullah,Seyedali Mirjalili,Vaibhav Rupapara,Michele Nappi +6 more
TL;DR: In this article, seven Machine Learning models are implemented for emotion recognition by classifying tweets as happy or unhappy. And the proposed voting classifier(LR-SGD) with TF-IDF produces the most optimal result with 79% accuracy and 81% F1 score.