Applications of Artificial Neural Networks
Artificial Neural Networks (ANNs) are widely used in various real-world domains due to their ability to handle complex, nonlinear, and noisy data.
1. Real-Time Applications:
- Face recognition
- Emotion detection
- Self-driving cars
- Navigation and routing systems
- Target tracking
- Vehicle scheduling
2. Business Applications:
- Stock market trading
- Sales forecasting
- Customer behavior modeling
- Market research and analysis
3. Banking & Finance:
- Credit and loan risk prediction
- Fraud detection
- Currency price prediction
- Real estate valuation
4. Education:
- Adaptive learning platforms
- Student performance modeling
5. Healthcare:
- Medical diagnosis
- Symptom mapping
- Image interpretation
- Drug discovery
6. Engineering & Other Domains:
- Robotics
- Aerospace and electronics
- Communication systems
- Chemical and food research
- Manufacturing systems
Advantages of Artificial Neural Networks
- Solves Non-linear Problems:
Can model and solve highly complex non-linear relationships in data. - Human-like Pattern Recognition:
Learns and adapts from experience to recognize patterns like humans. - Parallel Processing:
Fast predictions using parallelism. - Robust to Noise:
Works even with incomplete or noisy data. - Scalable:
Performs well with large datasets and can be used in large-scale systems.
Disadvantages of Artificial Neural Networks
- High Resource Requirement:
Requires powerful processors and multiple epochs to train. - Black Box Nature:
Difficult to interpret how the model makes decisions internally. - Complex Modelling:
Designing and developing ANN models is time-consuming. - Large Data Dependency:
Needs large training datasets; doesn’t perform well on small datasets. - Computational Cost:
High memory and processing requirements make it expensive.
Challenges in Using ANNs
- Training Difficulties:
Training a network is hard; prone to overfitting or underfitting if data is not suitable. - Generalization Issues:
Trained models may not generalize well to real-world data. - Weight & Bias Optimization:
Finding the right set of weights and biases is complex and time-consuming.