Artificial Neural Networks (ANN) is the administered learning system built on a large number of simple components, called neurons or perceptron. In ANN, each neuron is an element that functions inputs and give the accurate decisions. Then the result of each neuron is forwarded to the other neurons and it’s processed in an interconnections. Artificial Neural Network Paper Publication Guidance deals with the novel research components and ideas design in ANN. On any number of training samples, ANN can functioned any questions for practical implementations with huge computation power. Every network has certain features, let’s discuss about the substantial features based on artificial neural networks.

Key Features in Artificial Neural Network

• Patter Extraction & Detection
• Usage of Adaptive Learning
• Supports Real Time Operations
• Semantic Meaning Extraction from Imprecise Data

What are the Advantages of Artificial Neural Network?

• Individual ANN is stored with number of inputs and the data loss is not affected.
• In ANN, ability of input data processing is high and also produces the accurate output for the given inputs.

Here we have listed down the algorithms based on ANN.

Artificial Neural Networks based Algorithms

• Feedforward Neural Networks
• Time Delay Neural Network
• Probabilistic Neural Network
• Radial Basis Function Networks
• Regulatory Feedback Network
• Modular Networks
• Associative Neural Network
• Recurrent Neural Networks (RNN)
• Stochastic Neural Networks
• Bi-Directional RNN
• Echo State Network
• Fully Recurrent Network
• Simple Recurrent Networks
• Long / Short Term Memory Networks
• Other Networks
• Dynamic Neural Networks
• Cascading Neural Networks
• Spiking Neural Networks
• Genetic Scale RNN
• Growing Neural Gas
• Neuro-Fuzzy Networks
• Oscillating Neural Network
• Hierarchical Temporal Memory
• One Shot Associative Memory
• Instantaneously Trained Networks
• Holographic Associative Memory
• Convolutional Neural Network
• Hybridization Neural Network
• Counter Propagation Neural Network

Research Problems in Artificial Neural Network

The present research in Artificial Neural Network is ongoing on all the below problems. Your field may belong to any network, but you have to aware of all these problems.

• How to avoid local optima issue?
• Use particular loss function for training and testing the input samples that avoids the data from the local optima issues.
• What is the best and adaptive learning rate for inputs?
• In general, small learning rate takes long for convergence and the higher learning rate leads to not convergence rate.
• What happen when the surface morphology changes?

When the global optima find as minimum, then there is no assurance for the data falls into the global minimum definitely. For instance, loss is different if the training is not representative for the new data. A dynamic functioning of training and testing samples change reflects in the loss surface.

To handle the growing demands, the current networks tend to integrate with other techs. To start with advanced techs, it is main thing to know about the base in ANN and towards to learn with advanced thoughts of ANN.

PhD Paper Publish Help in ANN

• For your ANN related research idea, we provide the mathematical proofs with theorems.
• Covers (algorithms procedure, and pseudocode) for any related research idea
• Writing with publication support for research articles, review articles, book reviews and magazines.
• Our each paper shows significant and new research ideas, applications and developments for the practical use.

In particular to ANN, various topics are covered in it that are computer networks processing, broadcast technologies, components, circuits, and devices and systems. Likewise, natural language processing, medical imaging and computer vision are used ANN techniques.

Reputed Journals for Artificial Neural Network

I. IEEE Transactions on Signal and Information Processing over Networks // SCI Indexed

Aims & Scope

• Filtering by Distributed ANN, Distributed Signal Processing Applications, Estimation, Model Selection, Data Fusion, and Diffusion or Evolution of Information over such Networks, and Adaptive Learning.

Impact Factor

• 3.76

Journal Calendar

• 2015-ongoing

II. Neurocomputing

Aims & Scope

• Sensorimotor Transformations and Interdisciplinary Topics with Artificial Intelligence, Genetic Algorithms, Self-organization, Pattern Recognition, Artificial Life, Cognitive Science, Biological Neural Network, Fuzzy Logic, Information Theory, Machine Learning, Neurobiology and Computational Learning Theory.

Impact Factor

• 4.438

Journal Calendar

• 2011-ongoing