Machine Learning for Intelligent Decision Support
Leveraging machine learning, you can optimize information-centric business processes, customize solutions per customer requirement, drive productivity, forecast demand, among a host of other possibilities.
Sample Machine Learning Problems We Solve
Supervised (Known Target)
Decision Tree, k-Nearest Neighbors, Naive Bayes, Support Vector Machines
Unsupervised (Unknown Target)
K-Means, Fuzzy Clustering, Heirarchical Clustering
The algorithms are chosen depending on the type of data, nature of questions to be answered, size of the dataset available for learning, and computational capabilities of the system.
Deep Learning: The Smarter AI
Recognition of speech, sounds, and images comes naturally to the human brain. In deep learning, machines simulate this functionality of the brain with the aid of massive computational power and advanced algorithms. Multi-layered artificial neural networks are exposed to millions of images and sound samples from which machines automatically learn to pick out patterns.
Deep learning algorithms make it possible for machines to understand spoken words in real time, recognize and describe images, play games, and even diagnose diseases more accurately.
Continuous monitoring of machines in geographically dispersed locations is crucial for smooth operations. Detection algorithms can identify the deteriorating condition of equipment by analyzing real-time machine parameters against historical data. Thus operators can initiate predictive maintenance, preventing irreversible damage to assets.
Medical Image Analysis
Energy Demand Forecasting
Machine learning forecasting systems can predict future energy demand using past energy consumption data and weather parameters. Hybrid prediction models combining time-tested SARIMA models and new machine learning techniques are also evolving. Power companies can now control power generation and optimize schedules and thereby reduce costs and energy wastage.
Models built on known cases of legitimate and fraudulent transactions can assign suspicion scores for new transactions and thus help identify credit card fraud. A host of algorithms including decision trees, neural networks, regression, k-means clustering, Support Vector Machines are applied for this. Decision trees and Bayesian network are used to predict and flag fraud in insurance claims.
Knowledge created by medical research is more than what practitioners can cope up with. An intelligent system that incorporates NLP with semantic knowledge processing and machine learning can help practitioners look up research literature on specific problems much faster.
Text to Speech
Although electronic health records are a rich source of patient data, they do not lend themselves to analysis as they are highly unstructured. Using Machine Learning in NLP, entities such as symptoms, diseases, and treatments can be parsed and tagged making them easily retrievable at the time of clinical decision-making.
Using real-time image recognition applications, retailers can segment customers based on their gender, age, and ethnicity. They can use this intelligence to display targeted advertisements on digital billboards to enhance brand awareness.
Content-based and collaborative filtering algorithms can be used to generate user-specific recommendations. These recommendations may include a set of similar items based on the common features of products chosen by users as well as items preferred by similar users.
Gauging sentiments of people from voters to customers has become vital for campaigns in fields as diverse as politics and retail. Deploying natural language processing, sentiments can be mined to help build more responsive campaigns and modify brand positioning.