Artificial Intelligence (AI) especially Machine Learning will be present across many industries, within a considerable number of software packages, and part of our daily lives by 2020. Gartner has also predicted that by 2020, AI will become one of the top five investment priorities for at least 30 percent of Chief Information Officers. Following some of the important use cases of Machine Learning.
Machine learning is used to forecast optimal connectivity for telecom networks, drive refined network analysis and simulations, intelligent network planning and drive optimizations. With Machine learning, using predictive maintenance, hit hardware problems (cell towers, power lines, etc.) before they break, detect signals and break points that typically lead to failures.
Telecom leaders are working to change that through machine learning models that will analyze video footage took by drones. The company can then passively sense potential risks, allowing human workers to fix structural matters before they disturb customers.
Fraud costs banks and customers billions of dollars each year. Banks can mature programs that can use deep learning and machine learning to well recognize what kind of a transaction is uncommon of a customer. Each transaction a customer makes is examined in real-time and specified a fraud-score that characterizes the probability of the transaction being fake. If the fraud score is above a particular threshold, a rejection will be caused spontaneously which would otherwise be difficult without the application of machine learning techniques as humans cannot verify 1000’s of data points in seconds and make a decision.
Using Machine learning, banking systems give a sound investing guidance as companies to make tries to automate the best practices of investors and deliver them to customers at insignificant costs than outdated fund managers.
Healthcare industry generate numerous amounts of data and use them for disease prediction. Nevertheless, the essential software to generate meaningful insights from this unstructured data is often not in place. So disease discovery end up taking time. Machine Learning solutions can determine signatures of diseases at rapid rates by letting systems to learn and make predictions based on the formerly managed data. With the firm discovery of diseases, the probabilities of detecting symptoms earlier and the likelihood of survival increases.
ML has stretched a point at which it is likely to systematize tasks that, until recently, could not be done without substantial human labor. Improved healthcare starts with improved record keeping. Patient document organization reinvented with Machine Learning-based technologies is advancing the process of planning, analyzing, collecting and organizing individual patient records.Nub8
is a Machine Learning Consulting firm experienced in applying AI and Machine Learning to business problems. We specialize in Machine learning solution development for Telecom, Banking and Healthcare industries. Our team of machine learning experts leverage ML technologies through strategic implementations.