MD-CardioNet: A Multi-Dimensional Deep Neural Network for Cardiovascular Disease Diagnosis from Electrocardiogram.

Automated classification of cardiovascular diseases from electrocardiogram (ECG) signals using deep learning has gained significant interest due to its wide range of applications. However, existing deep learning approaches often overlook inter-channel shared information or lose time-sequence dependent information when considering 1D and 2D ECG representations, respectively. Moreover, besides considering spatial dimension, it is necessary to understand the context of the signals from a global feature space.

A ubiquitous method for predicting underground petroleum deposits based on satellite data.

The method of finding new petroleum deposits beneath the earth's surface is always challenging for having low accuracy while simultaneously being highly expensive. As a remedy, this paper presents a novel way to predict the locations of petroleum deposits. Here, we focus on a region of the Middle East, Iraq to be specific, and conduct a detailed study on predicting locations of petroleum deposits there based on our proposed method.

Escalating post-disaster rescue missions through ad-hoc victim localization exploiting Wi-Fi networks.

The number of disasters, accidents, and casualties in disasters is increasing, however, technological advancement has yet to ripe benefits to emergency rescue operations. This contrast is even more prominent in the Global South. The consequences are a huge loss of wealth and resources, but more importantly, the loss of lives.

Forecasting COVID-19 cases: A comparative analysis between Recurrent and Convolutional Neural Networks.

When the entire world is waiting restlessly for a safe and effective COVID-19 vaccine that could soon become a reality, numerous countries around the globe are grappling with unprecedented surges of new COVID-19 cases. As the number of new cases is skyrocketing, pandemic fatigue and public apathy towards different intervention strategies are posing new challenges to the government officials to combat the pandemic. Henceforth, it is indispensable for the government officials to understand the future dynamics of COVID-19 flawlessly in order to develop strategic preparedness and resilient response planning.

Forecasting COVID-19 cases: A comparative analysis between recurrent and convolutional neural networks.

Though many countries have already launched COVID-19 mass vaccination programs to control the disease outbreak quickly, numerous countries around worldwide are grappling with unprecedented surges of new COVID-19 cases due to a more contagious and deadly variant of coronavirus. As the number of new cases is skyrocketing, pandemic fatigue and public apathy towards different intervention strategies pose new challenges to government officials to combat the pandemic. Henceforth, it is indispensable for the government officials to understand the future dynamics of COVID-19 flawlessly to develop strategic preparedness and resilient response planning.