This paper presents an adaptive neuro/fuzzy system which can be trained to detect the current human emotions from a set of measured responses. Six models are built using different types of input/output membership functions and trained by different kinds of input arrays. The models are compared based on their ability to train with lowest error values. Many factors impact the error values such as input/output membership functions, the training data arrays, and the number of epochs required to train the model. ANFIS editor in MATLAB is used to build the models.

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Recently, most of people have their own profiles in different social networks. Usually, their profiles have some brief description about their personnel picture, family members, home town, career, date of birth etc. which indicate other people know some general information about others. In social networks, usually friends recommendation is done by finding the most mutual friends and suggest them to be friends. In this paper, we will introduce an algorithm, with a linear time complexity, that helps people to get not only good friends but also  have same characteristics.Â

A novel optimization algorithm is proposed for detecting human emotions (responses) using artificial intelligence techniques such as exhaustive search, fuzzy logic and neural networks. Previous models for detecting human emotions have used fourteen measurable physical and physiological input factors to detect twenty two human emotions [1]. This paper presents an optimization method to reduce the number of input factors required to detect a set of emotions. The proposed method utilizes twelve optimization procedures (cases) each one has unique error values, and different input factors. Optimization is sought to reduce the cost and complexity of implementing human emotion detection systems. A performance measure index is used to evaluate the effectiveness of the proposed model. This study shows that using less than half of the factors (6-8 factors) is the most cost effective set of input parameters for the human emotions detection system.

This paper utilizes clustering tool in MATLAB to find an optimal set of input parameters for the detection of human emotions using a neuro-fuzzy logic system. Previous studies have relied on a total of 14 physiological factors to detect one or more of 22 different human emotions. In this paper, we use clustering techniques to rank the factors in terms of their significance and impact on the system, and thus find a smaller subset of the factors for the detection of emotions. The clustering method shows that the stroke volume factor (SV) has the lowest impact in the model and as such can be eliminated from the set of factors. The electroencephalography (EEG), heart rate (HR), systolic blood pressure (SBP) and diastolic blood pressure (DBP) are shown to have the highest impact on the model, and must be include in the input set of the model. We compare the clustering method with exhaustive methods for finding the optimal set of factors.