A multi-dilated convolution network for speech emotion recognition.

Speech emotion recognition (SER) is an important application in Affective Computing and Artificial Intelligence. Recently, there has been a significant interest in Deep Neural Networks using speech spectrograms. As the two-dimensional representation of the spectrogram includes more speech characteristics, research interest in convolution neural networks (CNNs) or advanced image recognition models is leveraged to learn deep patterns in a spectrogram to effectively perform SER.

Comparative analysis of machine learning approaches to analyze and predict the COVID-19 outbreak.

Background: Forecasting the time of forthcoming pandemic reduces the impact of diseases by taking precautionary steps such as public health messaging and raising the consciousness of doctors. With the continuous and rapid increase in the cumulative incidence of COVID-19, statistical and outbreak prediction models including various machine learning (ML) models are being used by the research community to track and predict the trend of the epidemic, and also in developing appropriate strategies to combat and manage its spread.

Methods: In this paper, we present a comparative analysis of various ML approaches including Support Vector Machine, Random Forest, K-Nearest Neighbor and Artificial Neural Network in predicting the COVID-19 outbreak in the epidemiological domain.