Mahboobeh Parsapoor

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— This paper presents a new architecture based on a brain emotional learning model that can be used in a wide varieties of AI applications such as prediction, identification and classification. The architecture is referred to as: Brain Emotional Learning Based Fuzzy Inference System (BELFIS) and it is developed from merging the idea of prior emotional(More)
In this paper, an architecture based on the anatomical structure of the emotional network in the brain of mammalians is applied as a prediction model for chaotic time series studies. The architecture is called Brain Emotional Learning-based Recurrent Fuzzy System (BELRFS), which stands for: Brain Emotional Learning-based Recurrent Fuzzy System. It adopts(More)
In this thesis the mammalian nervous system and mammalian brain have been used as inspiration to develop a computational intelligence model based on the neural structure of fear conditioning and to extend the structure of the previous proposed amygdala-orbitofrontal model. The proposed model can be seen as a framework for developing general computational(More)
paper introduces a new type of brain emotional learning inspired models (BELIMs). The suggested model is utilized as a suitable model for predicting geomagnetic storms. The model is known as BELPM which is an acronym for Brain Emotional Learning-based Prediction Model. The structure of the suggested model consists of four main parts and mimics the(More)
This paper presents an ant colony optimization (ACO) method as a method for channel assignment in a mobile ad hoc network (MANET), where achieving high spectral efficiency necessitates an efficient channel assignment. The suggested algorithm is intended for graph-coloring problems and it is specifically tweaked to the channel assignment problem in MANET(More)