Cross Selling and Customer Churn Detection using classical Machine ... also Deep Learning Stateful Stream Processing to combine different model execution steps into a more powerful workflow instead of ...

A deep-learning model called ... enabling it to make predictions specific to the context of each cell. This is crucial, given that genes’ functions vary in, for example, different cell types ...

Machine learning frameworks like Google’s TensorFlow ease the process of acquiring data, training models, serving predictions ... the TensorFlow Model Garden provides examples of best practices ...

We developed a new machine learning architecture (crisprHAL) that can be trained on existing datasets and that shows marked improvements in sgRNA activity prediction ... model with your own data, ...

Machine learning (ML) algorithms have the potential to surpass the prediction accuracy ... each institution trains a model locally using their own dataset, sharing the model trained weights with its ...

The model in the machine learning tool would then use an analytics tool called predictive analytics to make predictions on whether the mining industry will be profitable for a time period ...

Using a machine learning approach ... implementation into the clinical workflow particularly simple. A potential limitation of the present study is the number of patients that may affects the power of ...

Cemil Emre Yavas, reflecting on this achievement, remarked, "Our model ... of earthquake prediction but also sets the stage for future advancements in applying machine learning to other natural ...