At umagine software, we believe in innovation and client satisfaction for the growth of the client and our people. Our objective is always to achieve the highest quality standards and Innovations, which are beneficial to the industry and society. Our knowledgeable and experienced team of engineers is capable to deliver robust and unique class of products and solutions.
- Deep Learning with hyper parameter tuning : We achieve significant and scalable changes in artificial neural network performance by applying unique hyper parameter tuning.
- Optimized Mathematics : Our specialists solve the problem using a collection of mathematical principles and methods to get durable and efficient product.
- Pre and Post processing of images and video data : Processing algorithm removes noise and small features from a video sequence while preserving salient information. Our combined class of algorithms defines a heuristic procedure (or set of filters) to process the image or video, relies on a rigorous model for the original image sequence and utilizes optimization techniques to obtain the best outcome.
- Text Processing with deep learning : Text preprocessing is often the first step in the pipeline of a Natural Language Processing (NLP) system but having potential impact in its final performance. Recurrent neural models based on our Deep learning algorithms able to capture the sequence information in a much better sense.
- Intensive Emotions & Sentiments Analysis : Human sentences are composed of words and phrases with a certain structure. We introduce emotions as a fine-grained alternative for sentiment evaluation. Our system predicts multiple desirable and exact emotions with their respective intensity scores and plots the histogram.
- Advance & details classification using YOLO : Using YOLO and our advanced algorithms, we classify distinctive object which seems too close. Our models can identify multiple objects and classify them.
- Time-Series algorithms : Team of experienced engineers works on time series algorithms to make a superior model for the current assignment. Our techniques and methods can be applied to any data that varies over time, including to the data points that are closed together or more similar than those which are apart.