- Version 4.3
- Sold by Mphasis
This solution assesses a corpus of text to predict the following emotion classes: Anger, Fear, Joy, and Sadness.
Mphasis applies next generation technology to help enterprises transform businesses globally. Customer centricity is foundational to Mphasis and is reflected in the Mphasis FrontBack™ Transformation approach. 'Front2Back' uses the exponential power of cloud and cognitive to provide hyper-personalized digital experience to clients and their customers. Mphasis Service Transformation approach helps 'shrink the core' through application of digital technologies across legacy environments within an enterprise, enabling businesses to stay ahead in a changing world.
This solution assesses a corpus of text to predict the following emotion classes: Anger, Fear, Joy, and Sadness.
This solution automatically identifies and trains the best performing deep learning model for tabular data.
Quantum simulated annealing based financial portfolio asset optimization aimed to maximize return and minimize risk.
Damaged Shipment Prediction analyzes images of shipment packages and predicts with whether they are damaged or not.
This solution automatically identifies and trains the best performing deep learning model for image classification.
The solution provides 30 weeks forecast of operating expenses using historical weekly operating expense data.
The solution analyses customer characteristics to predict which customers are more likely to leave the telecom company.
ML based solution which detects and shows the barcode position in the image data.
This solution is a deep learning-based trainable algorithm, capable of detecting anomalous behavior in IoT sensor data.
An NLP based approach to identify relationship among named entities in a corpus of text and present the same as a knowledge graph
showing 21 - 30