Electronic device and control method thereof

A method of controlling an electronic device, the method comprising:

• providing measurement information of a home appliance as input to a first learning network model and a second learning network model trained to predict whether the home appliance will exhibit a potential defect;
• applying a first weight to first prediction information output from the first learning network model and a second weight to second prediction information output from the second learning network model;
• and
• identifying a probability that the home appliance will exhibit the potential defect based on weighted first prediction information of the first prediction information to which the first weight is applied and weighted second prediction information of the second prediction information to which the second weight is applied, and
• wherein the first learning network model is a supervised learning network model and the second learning network model is an unsupervised learning network model.

A non-transitory computer-readable medium storing a computer-readable instructions, which when executed by the processor of an electronic device control the electronic device to perform a method comprising:

• providing measurement information of a home appliance as input to a first learning network model and a second learning network model trained to predict whether the home appliance will exhibit a potential defect;
• applying a first weight to first prediction information output from the first learning network model and a second weight to second prediction information output from the second learning network model;
• and
• identifying a probability that the home appliance will exhibit the potential defect based on weighted first prediction information of the first prediction information to which the first weight is applied and weighted second prediction information of the second prediction information to which the second weight is applied, and
• wherein the first learning network model is a supervised learning network model and the second learning network model is an unsupervised learning network model.