Highly regular, modular, and cascadable design of cellular automata-based pattern classifier
IEEE Transactions on Very Large Scale Integration (VLSI) Systems - Special issue on system-level interconnect prediction
Proceedings of the 7th Colloquium on Automata, Languages and Programming
Human iris detection using fast cooperative modular neural nets and image decomposition
Machine Graphics & Vision International Journal
EURASIP Journal on Applied Signal Processing
Fast Karnough map for simplification of complex Boolean functions
ACS'10 Proceedings of the 10th WSEAS international conference on Applied computer science
Fast human motion tracking by using high speed neural
SSIP '09/MIV'09 Proceedings of the 9th WSEAS international conference on signal, speech and image processing, and 9th WSEAS international conference on Multimedia, internet & video technologies
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
Surveillance of video signals over computer networks
ACC'11/MMACTEE'11 Proceedings of the 13th IASME/WSEAS international conference on Mathematical Methods and Computational Techniques in Electrical Engineering conference on Applied Computing
Real-time transmission of video streaming over computer networks
EHAC'12/ISPRA/NANOTECHNOLOGY'12 Proceedings of the 11th WSEAS international conference on Electronics, Hardware, Wireless and Optical Communications, and proceedings of the 11th WSEAS international conference on Signal Processing, Robotics and Automation, and proceedings of the 4th WSEAS international conference on Nanotechnology
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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In this paper, a fast tool for finding protein coding regions is presented. Such tool relies on performing cross correlation in the frequency domain and decision Tree. In addition, a modified trust region method is used to find the closet (optimized) DNA nucleotide. Moreover, a Sequential PRM-based protein folding algorithm for finding the point where these proteins add to the ladder is introduced. Furthermore, standard parallel scan algorithm is used to provide parallel processing of the strides and its transitions. This proposed tool produces more accurate results, than that have previously been obtained for a range of different sequence lengths. Experimental results confirm the scalability of the proposed classifying tool to handle large volume of datasets irrespective of the number of classes, tuples and attributes. High classification accuracy is achieved. The main achievement in this paper is the fast decision tree algorithm. Such algorithm relies on performing cross correlation in the frequency domain between the input data at each node and the input weights of neural networks. It is proved mathematically and practically that the number of computation steps required for the presented FNNs is less than that needed by conventional neural networks (CNNs). Simulation results using MATLAB confirm the theoretical computations.