Artificial Neural Network (ANN) is a very powerful area and it is used to perform any particular task as classification or clustering with any number of inputs. ANN is the focused learning scheme that is built upon a great number of simple mechanisms, called neurons or perceptrons. Each neuron can make modest decisions, and it will feed its decisions to the other neurons, schematized in interconnected layers.  This page is about the technical developments and exclusive research outlook of recent artificial neural network project topics.

What is an Artificial Neural Network?

ANN is inspired by the way the organic nervous system such as the statistics process occurs in the brain. In other words, it is a simple mathematical version of the brain which is used to process the nonlinear association between inputs and outputs.

What are the uses of Artificial Neural Networks?

ANN is used for various purposes that are particularly used for classification purposes. Neural Networks are used in the following, 

• Character recognition
• Image compression
• Autonomous car driving 
• Lots of other interesting applications

Actually, it is very powerful when it has massive datasets. This means that the neural network has enough amounts of data to create statistical samples of the data that has been entered.

What are the Neural Network Models?

• Generative Models
• Bayesian and Neural Networks
• Neural Network
• Reinforcement Learning
• Deep Learning
• Kernel Methods
• Information Theoretic Learning
• Convolutional Neural Networks
• Graphical Models
• Evolutionary Computing

Above, we have discussed the models in a neural network. Our research team is capable of selecting the appropriate models for the artificial neural network project topics. If you want to implement your own designed model, the development team assists you in the implementation process. Now, it’s time to converse about the massive data analysis in ANN.

How has the training data improved in ANN? 

ANN is an effective, powerful, and efficacious network to provide a sophisticated ability in managing multifaceted and noncomplex problems in every aspect of current real-world processes. For example, this method discourses the most important factors for improving the training and testing can be followed:  

• Choice of Data Sets
• Accuracy of Data 
• Data Instruments
• Data Standardization
• Type of Data Inputs 
• Data Division
• Data Preprocessing 
• Validations
• Processing 
• Output Techniques 

The neural network is used in the data mining process for time series. For additional information, they are enlightened in three steps

• Format of neural network 
  • Hidden layers 
  • Number of input entries 
  • Neuron in hidden 
• Sample data preparation for the cross-validation process 
• Time Series Statistical Data Analysis 

ANN can emulate almost any function, and practically it can answer any questions, given enough training samples and computing power. Thus, our research experts have explained how ANN learns the inputs.

How does ANN Learn the Inputs?

• Function of Activation
  • It describes the neuron outputs 
• Forward Propagation
  • Network gets exposed to the training data and then they cross the networks. Due to this, the forward propagation process takes place and the perdition is calculated.
• Loss Function
  • The result of forwarding propagation is the prediction. Here, the predictive model is used to check the quality. Finally, the loss is computed for training and testing
• Backpropagation Algorithm
  • This algorithm uses a chain rule for training the ANN. 
• Hyperparameter Optimization
  • Hyperparameter optimization is defined as the number of hyperparameters for tuning the optimization. 

Next, our research team shared the research areas for choosing interesting artificial neural network project topics. 

Research Areas in ANN

• Natural Learning Processing
• Image Processing and Understanding
• Intelligent Forecasting
• Industrial Applications
• Intelligent Agents
• Resource Allocation
• Big Data
• Optimization
• Speech Processing
• Computer Vision
• Scheduling
• Control and Robotics
• Cyber Security
• Signal Processing

These are the areas in ANN and we are currently doing our research to satisfy the researcher’s requests. ANN is used to test for any type of application. So, the technical experts have listed out some applications with their architecture/algorithm and activation function used.

Top 5 Artificial Neural Network Project Topics

• Fraud Detection
  • Architecture / Algorithm – Gradient – Descent Algorithm and Least Mean Square (LMS) algorithm
  • Activation Function – Logistic function
• Voice Recognition
  • Architecture / Algorithm – Multilayer Perceptron, Deep Neural Networks, or Convolutional Neural Networks
  • Activation Function – Logistic function
• Medical and Machine Diagnostics
  • Architecture / Algorithm – Multilayer Perceptron 
  • Activation Function – Tan- Sigmoid Function
• Intelligent Searching
  • Architecture / Algorithm – Deep Neural Network
  • Activation Function – Logistic function
• Target Recognition
  • Architecture / Algorithm – Modular Neural Network
  • Activation Function – Tan- Sigmoid Function
• Process Modelling and Control
  • Architecture / Algorithm – Radial Basis Network
  • Activation Function – Radial Basis

In sum, you can grasp any artificial neural network project topics from our technical experts. We guide you from choosing your topic, proposal writing help to publication process. In the same way, we make discussions with you at all stages of the work. So, contact us for your research projects in ANN.