Handbook of image processing operators
Handbook of image processing operators
Fast Iris Detection for Personal Verification Using Modular Neural Nets
Proceedings of the International Conference, 7th Fuzzy Days on Computational Intelligence, Theory and Applications
Human iris detection using fast cooperative modular neural nets and image decomposition
Machine Graphics & Vision International Journal
Speeding-up normalized neural networks for face/object detection
Machine Graphics & Vision International Journal
Learning Patterns and Pattern Sequences by Self-Organizing Nets of Threshold Elements
IEEE Transactions on Computers
Fast Code Detection Using High Speed Time Delay Neural Networks
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III
A new fast forecasting technique using high speed neural networks
WSEAS Transactions on Signal Processing
Fast Time Delay Neural Networks for Detecting DNA Coding Regions
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part I
A novel high-speed neural model for fast pattern recognition
Soft Computing - A Fusion of Foundations, Methodologies and Applications
A new approach for prediction by using integrated neural networks
AMERICAN-MATH'11/CEA'11 Proceedings of the 2011 American conference on applied mathematics and the 5th WSEAS international conference on Computer engineering and applications
A new expert system for pediatric respiratory diseases by using neural networks
AICT'11 Proceedings of the 2nd international conference on Applied informatics and computing theory
A new hybrid system for information security
ACA'12 Proceedings of the 11th international conference on Applications of Electrical and Computer Engineering
A new fast neural network model
ACACOS'12 Proceedings of the 11th WSEAS international conference on Applied Computer and Applied Computational Science
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Fast detection of biological viruses in DNA sequence is very important for investigation of patients and overcome diseases. First, an intelligent algorithm to completely retrieve DNA sequence is presented. DNA codes that may be missed during the splitting process are retrieved by using Hopfield neural networks. Then, a new approach for fast detection of biological viruses like H1N1 and H1N5 in DNA sequence is presented. Such algorithm uses fast time delay neural networks (FTDNNs). The operation of these networks relies on performing cross correlation in the frequency domain between the input DNA sequence and the input weights of neural networks. It is proved mathematically and practically that the number of computation steps required for the presented FTDNNs is less than that needed by conventional time delay neural networks (CTDNNs). Simulation results using MATLAB confirm the theoretical computations.