AI & Machine Learning Development Services

Recognizing the shape of cells and detecting cells under mitosis is a challenging problem. Classical image processing was used for cell localization, image transformation and feature extraction. A trained convolutional neural network was used to classify the cell into different classes. However, to detect similar regions in a H&E stained tissue sample unsupervised approaches were used. Here a neural auto-encoder played the role of an image fingerprinting system.

Devices that capture multiple electro-mechanical features generate huge amounts of time-series data. We determined which set of features were significantly contributing to mechanical failure in different parts of the machine. Moving windows of time-series statistics were used to model conditions that represent the current state of the machine leading up to a future breakdown.

Processing text inputs can be challenging. NLP algorithms needed to work for multiple languages, cope with the use of slang and be able to process short-form text that has partial grammatical consistency. We used a combination of techniques involving parse tree traversals, a creation of overlapping n-gram sequences, language detection APIs and auto-encoder based pattern matching engine to extract concepts and questions from chat conversations or short reviews. These techniques can work for small data without the need for large trained models. Sequence2Sequence techniques were used to automatically convert a block of text into key-value pairs of attributes and their values. The final solution was an API-based service that was integrated into the main product.

Factors that influence rental or sale value of homes depends not only on the structural parameters of the home but also on geographical parameters and chronological factors. Multiple models were created after feature analysis while accounting for partial data and in some cases very little data for a region. Boosting based algorithms for regression were employed and the core implementation was adapted for a case where a price range needs to be predicted instead of a point estimate. Thousands of models were created and deployed so that models suitable for a region can be used to make the predictions. The final solution was an end-to-end flow adapted for the product architecture.

Higher prediction accuracy was achieved using an ensemble consisting of a trained deep neural network, a pre-existing application-specific predictor, and a support vector machine classifier. While the DNN acted as a new complementary predictor, the SVM served to determine which of the two predictors probabilistically gave the correct result at that moment. This is achieved by creating a probability surface that exhibits a bi-model distribution peaking at each of the predictors. Such techniques can be used in applications such as indoor locationing, object location predictions in images, voting based systems

In a multiparty contest involving rating by human evaluators, we algorithmically determined which of the many contest ideas should be presented to the human evaluator such that a likelihood estimate is maximized. In another situation, the objective function of a system of non-linear equations was minimized with constraints on the parameters. Here we solved the problem using Lagrangian relaxation with gradient descent to determine the optimal parameters.