Handbook of image processing operators
Handbook of image processing operators
Human iris detection using fast cooperative modular neural nets and image decomposition
Machine Graphics & Vision International Journal
Complex-Valued Neural Networks: Theories and Applications (Series on Innovative Intelligence, 5)
Complex-Valued Neural Networks: Theories and Applications (Series on Innovative Intelligence, 5)
Learning to detect malicious executables in the wild
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Complex-valued multistate neural associative memory
IEEE Transactions on Neural Networks
Fast image matching on web pages
WSEAS Transactions on Signal Processing
Neural networks in face recognition for juridical communication process
AMERICAN-MATH'10 Proceedings of the 2010 American conference on Applied mathematics
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 fast neural network model
ACACOS'12 Proceedings of the 11th WSEAS international conference on Applied Computer and Applied Computational Science
| Hi-index | 0.00 |
This paper presents an intelligent approach to detect unknown malicious codes by using new high speed time delay neural networks. The entire data are collected together in a long vector and then tested as a one input pattern. The proposed fast time delay neural networks (FTDNNs) use cross correlation in the frequency domain between the tested data and the input weights of neural networks. It is proved mathematically and practically that the number of computation steps required for the presented time delay neural networks is less than that needed by conventional time delay neural networks (CTDNNs). Simulation results using MATLAB confirm the theoretical computations.