Security term in mobile ad hoc networks has several aspects because of the special specification of these networks. In this paper a distributed architecture was proposed in which each node performed intrusion detection based on its own and its neighbors’ data. Fuzzy-neural interface was used that is the composition of learning ability of neural network and fuzzy Ratiocination of fuzzy system as...

Security term in mobile ad hoc networks has several aspects because of the special specification of these networks. In this paper a distributed architecture was proposed in which each node performed intrusion detection based on its own and its neighbors’ data. Fuzzy-neural interface was used that is the composition of learning ability of neural network and fuzzy Ratiocination of fuzzy system as...

A deeply-interactive hybrid neural-symbolic cognitive architecture is defined as one in which the neural-net and symbolic components interact frequently and dynamically, so that each intervenes significantly in the other's internal operations, and the two form a combined dynamical system at the time-scale of each component's individual cognitive operations. An example architecture of this natur...

Skin lesion segmentation plays a critical role in the precise and early detection of skin cancer via recent frameworks. The prerequisite for any computer-aided diagnosis system is accurate malignancy. To achieve this, specialized image analysis technique must be used separation cancerous parts from important healthy skin. This procedure called Dermatography. Researchers have often multiple tech...

Neural networks provide an alternative method of building models of human performance. They can learn behavior from examples, reducing the need for many identical repetitions and intensive analysis. A properly trained net can be very robust in its response to a novel stimulus. This opens the door to modeling performance in the presence of an interactive stimulus. Neural networks provide the pos...

The use of neural networks for recognition application is generally constrained by their inherent parameters inflexibility after the training phase. This means no adaptation is accommodated for input variations that have any influence on the network parameters. Attempts were made in this work to design a neural network that includes an additional mechanism that adjusts the threshold values acco...

The Back Propagation Model for neural network simulation is a very simple and very popular model for solving realworld problems. It does, however, suffer from one major problem, that is, the time taken for the network to adjust its weights in order to response to an input. This paper proposes a computer architecture, specifically optimised for neural network simulation, which is capable of unli...

A new associative memory neural network which can be constructed using optical matched filters is described. It has three layers, the centre one being iterative with its weights set prior to training. The other two layers are feedforward nets and the weights are set during training. The best choice of central layer weights, or in optical terms, of pairs of images associated in a hologram is con...

Artiicial Neural Networks (ANN) have often been used successfully in problems of classiication. Their main drawback is the long training times required particularly for problems of large dimensionality. This is in contrast to the human vision system which is capable of learning from a small number of examples. Another problem with ANNs is the lack of any theoretical guidance concerning the choi...