Hospital Admission Rate Prediction, Strategy Supplies Forecasting, Arrhythmia Predictions, Brain Tumor Segmentation
Health care providers can leverage Ai and advanced analytics to personalize health care, while improving overall population health one patient at a time. There are numerous use cases. Computer vision helps radiologists improve the speed and accuracy of cancer diagnostics. Predictive analytics helps patients get placed in the right care setting and get seen by the right clinical staff.
Ai-MicroCloud® enables you to develop, deploy and manage bespoke real-time domain-specific Ai applications in hospital admission rate forecasting, strategic supplies forecasting, arrhythmia predictions and brain tumor segmentation.
Accurate energy forecasting is a crucial factor underlying utility company financial performance. They need accurate energy forecasts since extreme wholesale price volatility requires hedging against volume and price risk. It is important to determine which input variable have the highest relevance in calculating forecast. Examples of explanatory data variables can be historical load data in different levels of aggregation, as well as real-time measurement, weather data, calendar information, day/night etc.
Electricity distribution is impacted by line resistance, outside temperature and switching states in the grid, potentially resulting in energy grid losses. Transmission system operators (TSO) must compensate for losses and manage them as it influences balance on the grid. Examples of explanatory variables include historical actual values for losses and technical information such as relevant points on the power grid, load and weather data.
Keras model used to detect arrhythmias in ECG time series data
Application showcases classifying the probability of Pneumonia in X-Ray images
U-Net architecture used to segment brain tumors from raw MRI scans.
Semantic segmentation to identify the segments of robotic instruments within a video frame
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