Artificial Neural Network is a well-organized computing system whose central theme is hired from the analogy of biological neural networks. Artificial Neural Network Thesis helps to explore new concepts by exchanging ideas. The other names of Artificial Neural Network are “Artificial Neural Systems,” or “Parallel Distributed Processing Systems,” or “Connectionist Systems.” Artificial Neural Network obtains a large collection of units that are interconnected and allows communication between the units. Here we have listed down the substantial features based on Artificial Neural networks.
What are the Main Features of an Artificial Neural Network?
Neural Network is emerging and they are the applications to complex problems that are being developed. Surely, today is a period of transition to Neural Network Technology.
- Size – 102 to 104 nodes
- Processing– Massively Parallel, Fast (Responding time is in milliseconds)
- Storage capacity – Stores Information in Constant Memory Locations
- Learning – Very Detailed, Organized and Arranged Data is Required to Endure Ambiguity
- Fault tolerance – Capable of Dynamic Performance, and Potential to be Fault Tolerant
Activation Functions in Neural Network are the critical elements. Activation functions regulate the output of any designed model in terms of its accuracy, delay tolerant and the computational efficiency of training a model which can make or break a large-scale neural network. The main characteristics and information about the activation functions of ANN are as follows,
- Ensures not linearity ensures gradients remain large through the hidden unit
- A new activation function discovered by Google researchers is Swish
- Special activation function used for output neurons is Softmax
- Parametric ReLu function permits the negative slope to be learned
- Leaky ReLu Function has a small positive slope in its negative area
- TanH Function is zero centered making it easier to model inputs
- Sigmoid Function has a smooth gradient and outputs values between zero & one
What kind of tasks can a Neural Network do?
Neural Networks are designed for spotting patterns in data. Notable tasks could include, classification (classifying datasets into predefined classes), clustering (classifying data into different defined and undefined categories), and prediction (using past events to estimate future ones, like supply chain forecasting).
If your field is Neural Network, then you can prefer any area. In any area, the idea must be novel to formulate best artificial neural network thesis. Find a brief list of new ideas which is highlighted below,
Artificial Neural Network Research Areas
- Cryptographic Applications
- Search Query Retrieval
- Computer Network
- Face Recognition
- Recognizing Scenes and Suggest Relevant Captions
- Powering Vision in Robots &
- Internet of Vehicles
- Identifying and Classifying Objects in Images
- Semantic Analyzing, Sentence Classification, and Prediction
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Here we have mentioned the detailed steps which help to design authentication based on an Artificial Neural network on your own. We have cited a concept based on “Secure Authentication System for Authentication Weight Computation by using ANN”. The steps are listed below,
- Specify weights to minimize the total errors
- Explain your optimization technique or theory
- Compare your proposed work with other weight-based security applications
- Determine the structure of neural networks for use, such as a mixture of experts or the radial basis function
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