Answer:
Neural networks are also ideally suited to help people solve complex problems in real-life situations. They can learn and model the relationships between inputs and outputs that are nonlinear and complex; make generalizations and inferences; reveal hidden relationships, patterns and predictions; and model highly volatile data (such as financial time series data) and variances needed to predict rare events (such as fraud detection). As a result, neural networks can improve decision processes in areas such as:
Credit card and Medicare fraud detection.
Optimization of logistics for transportation networks.
Character and voice recognition, also known as natural language processing.
Medical and disease diagnosis.
Targeted marketing.
Financial predictions for stock prices, currency, options, futures, bankruptcy and bond ratings.
Robotic control systems.
Electrical load and energy demand forecasting.
Process and quality control.
Chemical compound identification.
Ecosystem evaluation.
Computer vision to interpret raw photos and videos (for example, in medical imaging and robotics and facial recognition).
