Scienxt Journal of Neural Networks and Deep Learning (SJNNDL)

SJNNDL – Scienxt Journal of Neural Networks and Deep Learning, Double blind peer-reviewed Journal, disseminates the most recent findings and innovations in various fields of Neural Networks and Deep Learning in the form of Original Research, Reviews, Case Studies, Opinions, etc. Journal maintains a Quick Publication process with strict peer review to enhance the quality of content. Every submitted manuscript undergoes peer review by the subject matter experts under the supervision of the Editor-in-Chief or assigned Editorial Board member of the Journal.

The Journal also publishes conference proceedings.

Scope & Focus

SJNNDL  Journal has the following scope and focus but, not limited to these: Advanced Neural Networks, Kohonen Self-Organising Feature Maps architecture and training algorithm, Learning Vector Quantization architecture and training algorithm, Boltzmann Machine, Cognitron Network, Neocognitron Network, Optical Neural Networks Electro-optical Multipliers and Holographic Correlators, Basics of Machine Learning: Linear Regression, Basics of Machine Learning: Linear Classification, The Intuition of Neural Networks, Fully Connected Layer and Back Propagation, Image Classification, Loss Functions, and Optimization, Computational Graphs, Activation Functions in Neural Networks, Initializations in Neural Networks, Backpropagation and Neural Networks, Convolutional Neural Networks, Object Localization and Detection, Single Shot Multi Box Detector (SSD), Variational Autoencoder.

You can submit your manuscript(s) online at

computer@scienxt.com ; info@scienxt.com