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Neuton Use Cases for Neural Network and Machine Learning

Employee satisfaction estimation
Challenge:
There is never much information on employee satisfaction in any company. But if a company wants to maintain a healthy morale and keep its employees, it’s very important to identify people who are not satisfied with current conditions.
Solution:
Because neural network machine learning can recognize patterns and analyze data at light speed, it can help HR directors make decisions with greater confidence. As a result, personnel outflow in the company can be reduced, making the corporate atmosphere more friendly and thereby improving the quality of labor productivity.
Why Neuton:
Unlike other solutions, Neuton allows accurate predictions to be made based on a small training dataset, which makes AI available even to small companies with a relatively small number of employees. Furthermore, Neuton is so easy to use that any HR manager can handle it.
Recruiting
Challenge:
Time is money, and saving recruiters time by using AI to make their tasks more effective and efficient can definitely improve the bottom line. In addition, AI can impact workforce productivity by successfully sourcing, screening and identifying top-tier candidates.
Solution:
Data processing capabilities of machines augment the role of HR employees in numerous aspects of hiring including finding qualified candidates, interviewing with bots to understand their fit, and evaluating their assessment results to decide if they should receive an offer.
Why Neuton:
Using Neuton, it is possible to address multiple time-consuming steps in the HR hiring process and subsequent talent acquisition. Neuton’s neural network can help companies evaluate the likelihood of a candidate being successful from a large volume of hiring applications based on data from the company’s past experiences. However, unlike other solutions, Neuton doesn’t require large sets of data to make accurate analyses. Furthermore, Neuton is so easy to use that you do not need to have a data science team on staff to solve this problem. Instead, domain experts and analysts can solve it.