What is Artificial Neural Network with Example? An Artificial Neural Network (ANN) is a data handling technique that is motivated by bio-nervous systems. They are referred to as an important part of machine learning. Here, the term “neural” proposes the brain-inspired systems which are envisioned to reproduce the artificial way of human learning.  This article is about the scientific advances and unique research perspective of current Artificial Neural Network Project Ideas!!!

These neural networks are composed of input and output layers. As well, it optionally includes a hidden layer in many cases. In this, units process the given data into something where the output layer can use. On the whole, it is an excellent approach for discovering patterns of a complex problem to make a machine recognize.

What is the purpose of neural networks?   

For making an effective decision in an undesired or unexpected situation, the neural network works with an intention to find the data transformation. Here, the transformation comes into sight from a hierarchical representation which is learned from the data in order. This hierarchical structure is continuously considered to be a critical issue while bringing up the network data from the individual distributions. Also, it is difficult to prevent the overlap of the distributions.How to change the traditional ANN structure?                            

How to change the traditional ANN structure?                            

ANNs are a biological-inspired nervous system that artificially pretends the human’s inference/perception through modern deep learning. It is intended to understand the complex linear and non-linear relations in the input data to solve real-world problems. For your knowledge, we have given you some of the Artificial Neural Network Project Ideas where scholars are working to make ANNs even more effectual and comprehensible.

• Learning Rate Tuning
• Data Representation Learning
• Hyper Parameter Tuning 
• Prevent Overfitting by Regularization 
• Appropriate Optimizer algorithms
• Optimal Number of Hidden Layers

For your add-on information, we have given you some most important research ideas that scholars are waiting to instigate their study. Moreover, if you want to know more about Artificial Neural Network Thesis Ideas then you can approach our team.

Research Topics in ANN 

• Neural Machine Translation
• Facial Recognition System
• LSTM Language modeling
• IoT Security 
• Object Detection
• Recognition

You can also upgrade your technical intelligence by learning and understanding the concepts in terms of qualitative and quantitative research. This will surely help you in the time of crafting novel artificial neural network project ideas, code development phase, dissertation ideas.

Significant Textbooks Available for Artificial Neural Network

• Artificial Neural Networks (ANNs)
  • Author(s) – Asoke K. Nandi , Hosameldin Ahmed
  • Pages – 239 – 258, Publisher: Wiley-IEEE Press, Copyright Year: 2019, Edition: 1
  • Description 
    • ANN algorithms for analyzing machine liability through vibration signals
• Fundamentals and Learning of Artificial Neural Networks 
  • Author(s) – Nan Zheng , Pinaki Mazumder 
  • Pages – 11 – 60, Publisher: Wiley-IEEE Press, Copyright Year: 2020, Edition: 1
  • Description 
    • Neural network working process in a reinforcement‐learning application
    • Three main kinds of network topologies
• Artificial Intelligence Applications in Renewable Energy Systems and Smart Grid – Some Novel Applications 
  • Author(s) – Bimal K. Bose 
  • Pages – 625 – 675, Publisher: Wiley-IEEE Press, Copyright Year: 2019, Edition: 1
  • Description 
    • Revolutionary background knowledge on computation on AI, ES, FL, ANN, NNW, and genetic algorithms 
    • Understanding the AI applications based on intelligent systems
• Artificial Neural Networks in Hardware 
  • Author(s) – Nan Zheng , Pinaki Mazumder 
  • Pages – 61 – 118, Publisher: Wiley-IEEE Press, Copyright Year: 2020, Edition: 1
  • Description 
    • Important hardware for neural network algorithms 
    • General Purpose Processors (GPP)
    • Application-Specific Integrated Circuits (ASICs)
    • Field Programmable Gate Arrays (FPGAs) 
• Neural Networks for Full‐Duplex Radios 
  • Author(s) – Fa-Long Luo 
  • Pages – 383 – 396, Publisher: Wiley-IEEE Press, Copyright Year: 2020, Edition: 1
  • Description
    • Several linear and nonlinear cancellation methods 
    • Comparison of computational difficulty in all approaches
• Performance Analysis of Dense Small Cell Networks with Line of Sight and Non‐Line of Sight Transmissions under Rician Fading 
  • Author(s) – Trung Q. Duong, Xiaoli Chu, Himal A. Suraweera 
  • Pages – 41 – 64, Publisher: Wiley Telecom, Copyright Year: 2019, Edition: 1
  • Description
    • Application Service Element (ASE) using a 3GPP path loss model 
    • Linear LoS probability function
• Operational Principles and Learning in Spiking Neural Networks 
  • Author(s) – Nan Zheng , Pinaki Mazumder 
  • Pages – 119 – 171, Publisher: Wiley-IEEE Press, Copyright Year: 2020, Edition: 1
  • Description 
    • Several methods for working out SNNs
    • Leaky Integrate And Fire Model for Simulation
• Energy Harvesting Ad Hoc Networks 
  • Author(s) – Chuang Huang, Sheng Zhou, Jie Xu, Zhisheng Niu, Rui Zhang, Shuguang Cui 
  • Pages – 167 – 202, Publisher: Wiley Telecom, Copyright Year: 2019, Edition: 1
  • Description 
    • Importance of energy diversity 
    • Study of scaling law for the expected throughput over the number of users 
• Machine Learning for Joint Channel Equalization and Signal Detection 
  • Author(s) – Fa-Long Luo 
  • Pages – 213 – 241, Publisher: Wiley-IEEE Press, Copyright Year: 2020, Edition: 1
  • Description 
    • OFDM systems performance along with NN‐based channel equalization
• Deep Learning for Autonomous Vehicle Control: Algorithms, State-of-the-Art, and Future Prospects 
  • Author(s) – Sampo Kuutti , Saber Fallah , Richard Bowden , Phil Barber , Amir Khajepour
  • Pages – 80, Publisher: Morgan & Claypool, Copyright Year: 2019, Edition: 1
  • Description 
    • Appropriate deep learning algorithms 
    • Detect merits and restrictions of approaches
    • Future research encounters and trends 

 Further, we are ready to give new information about technical content, latest artificial neural network project ideas, network tools and technologies used in implementing ANN.