28 February Machine Learning: Digital Transformation Trend 2 MIN READ February 28, 2019 By Gina Wurms general posts |infsoft tools|, |use case| 0 Machine learning has become a buzzword in the technology community and is an important pillar of the widely discussed digital change. For many organizations, big data is an untapped resource of intelligence that can support business decisions and improve operations. As data continues to diversify and change, more and more organizations are embracing machine learning and predictive analytics to access that resource and benefit from data at scale. What is Machine Learning? Machine learning is a technology used to enable computers to analyze data sets and learn from the insights gathered. By using complex algorithms, an artificial neural network is simulated that enables classifying, interpreting and understanding data (including real-time information) and then using the insights to solve problems (that previously seemed too complex) or make predictions. This often leads to improved efficiencies, cost savings, and increased competitiveness. Once programmed a machine learning algorithm improves and enriches itself based on the data fed to it. With infsoft Machine Learning, we offer a web-based tool that processes position and/or sensor data and uses self-optimizing algorithms that can learn from experience. The “models” created by the training process are used to provide for detection, classification and forecasting tasks. Predictive analytics is an application of machine learning that helps to understand possible future occurrences by analyzing the past. Predictive models can be trained over time to respond to new data or values, delivering the results organizations are looking for. infsoft tools generate data to continuously refine the machine learning functions. These can then be used to perform a more targeted, relevant and effective data analysis. The result is a multitude of new opportunities and higher business value thanks to shorter cycles, scalability, innovation and continuously increased productivity. Applications of Machine Learning and Predictive Analytics There are many use cases of machine learning in business and there are more to come. Machine learning can be used by just about every industry that can generate data. It can not only reveal trends about this information but can also give insight toward predicting things about future behavior. In healthcare, it is possible to alert when vulnerable patients go outside their normal movement pattern. In industrial environments, you might want to perform prognostics on sensor data (temperature, vibration, pressure etc.) to predict failure, minimize asset downtime, maximize productivity, and ensure workplace safety. In manufacturing, Machine learning can forecast the probability of delays and resulting production stoppages reliably. If a tugger train is likely to be delayed (taking into account factors that influence the driving time), the driver can receive an automated instruction to depart early to compensate for the additional driving time and thus prevent production stoppage. At the same time, a foreman will receive a warning so that further counteractions can be set in motion if necessary. To get the most out of predictive analytics and machine learning, organizations need to ensure they have the appropriate infrastructure to support these solutions, as well as high-quality data to help them learn. infsoft’s software solutions help organizations to turn their data into timely insights for better, faster decision making. Get the white paper for free Great introduction to the topic of indoor positioning Download Related Articles Digital Technologies Improve Travels by Air – what Passengers Wish for The travel chain is becoming more and more digital. A recent study by Bitkom focuses on the question, which digital services passengers desire. Read more about it and learn about the features which the Frankfurt Airport App already offers. Read More Corporate Events - Types and Trends You Have to Be Aware of Big companies organize different types of events in order to make new international standards, create strategies or celebrate success. 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