DeepInsight: Methodology to Handle Non-image Data, such as Genomics Data, with Deep Learning

Deep Bayesian Active Learning with Image Data

Even though active learning forms an important pillar of machine learning, deep learning tools are not prevalent within it. Deep learning poses several difficulties when used in an active learning setting. First, we have to handle small amounts of data. Recent advances in deep learning, on the other hand, are notorious for their dependence on large amounts of data. Second, many acquisition func...

Integration of remote sensing and meteorological data to predict flooding time using deep learning algorithm

Accurate flood forecasting is a vital need to reduce its risks. Due to the complicated structure of flood and river flow, it is somehow difficult to solve this problem. Artificial neural networks, such as frequent neural networks, offer good performance in time series data. In recent years, the use of Long Short Term Memory networks hase attracted much attention due to the faults of frequent ne...

Learning First-Order Rules to Handle Medical Data

Since information systems are used in large hospitals, a large amount of medical data is provided to physicians which often constitutes an information overload. Consequently, computers must extract useful information from such data. The most difficult issue in handling medical data is that included time-series data have irregularities. Here, we describe handling this type of data using a first ...

Revolutionary Methods to Handle Data

Cloud Data Scaling Total data produced per year surpassed one zettabyte in 2010 and continues to more than double every two years.3 To put this in perspective, about two zettabytes (ZB) of digital universe will be created (per IDC) in 2011 is two million terabytes, or over one million new 3.5" SATA disks with two terabyte (TB) capacities. At the same time, not only is total data stored growing ...

Image Reconstruction from Incomplete Data