• OpenAccess
    • List of Articles Sara Motame

      • Open Access Article

        1 - Multimodal Biometric Recognition Using Particle Swarm Optimization-Based Selected Features
        Sara Motamed Ali Broumandnia Azam sadat  Nourbakhsh
        Feature selection is one of the best optimization problems in human recognition, which reduces the number of features, removes noise and redundant data in images, and results in high rate of recognition. This step affects on the performance of a human recognition system More
        Feature selection is one of the best optimization problems in human recognition, which reduces the number of features, removes noise and redundant data in images, and results in high rate of recognition. This step affects on the performance of a human recognition system. This paper presents a multimodal biometric verification system based on two features of palm and ear which has emerged as one of the most extensively studied research topics that spans multiple disciplines such as pattern recognition, signal processing and computer vision. Also, we present a novel Feature selection algorithm based on Particle Swarm Optimization (PSO). PSO is a computational paradigm based on the idea of collaborative behavior inspired by the social behavior of bird flocking or fish schooling. In this method, we used from two Feature selection techniques: the Discrete Cosine Transforms (DCT) and the Discrete Wavelet Transform (DWT). The identification process can be divided into the following phases: capturing the image; pre-processing; extracting and normalizing the palm and ear images; feature extraction; matching and fusion; and finally, a decision based on PSO and GA classifiers. The system was tested on a database of 60 people (240 palm and 180 ear images). Experimental results show that the PSO-based feature selection algorithm was found to generate excellent recognition results with the minimal set of selected features. Manuscript profile
      • Open Access Article

        2 - Fusion of Learning Automata to Optimize Multi-constraint Problem
        Sara Motamed Ali Ahmadi
        This paper aims to introduce an effective classification method of learning for partitioning the data in statistical spaces. The work is based on using multi-constraint partitioning on the stochastic learning automata. Stochastic learning automata with fixed or variable More
        This paper aims to introduce an effective classification method of learning for partitioning the data in statistical spaces. The work is based on using multi-constraint partitioning on the stochastic learning automata. Stochastic learning automata with fixed or variable structures are a reinforcement learning method. Having no information about optimized operation, such models try to find an answer to a problem. Converging speed in such algorithms in solving different problems and their route to the answer is so that they produce a proper condition if the answer is obtained. However, despite all tricks to prevent the algorithm involvement with local optimal, the algorithms do not perform well for problems with a lot of spread local optimal points and give no good answer. In this paper, the fusion of stochastic learning automata algorithms has been used to solve given problems and provide a centralized control mechanism. Looking at the results, is found that the recommended algorithm for partitioning constraints and finding optimization problems are suitable in terms of time and speed, and given a large number of samples, yield a learning rate of 97.92%. In addition, the test results clearly indicate increased accuracy and significant efficiency of recommended systems compared with single model systems based on different methods of learning automata. Manuscript profile
      • Open Access Article

        3 - Speech Emotion Recognition Based on Fusion Method
        Sara Motamed Saeed Setayeshi Azam Rabiee Arash  Sharifi
        Speech emotion signals are the quickest and most neutral method in individuals’ relationships, leading researchers to develop speech emotion signal as a quick and efficient technique to communicate between man and machine. This paper introduces a new classification meth More
        Speech emotion signals are the quickest and most neutral method in individuals’ relationships, leading researchers to develop speech emotion signal as a quick and efficient technique to communicate between man and machine. This paper introduces a new classification method using multi-constraints partitioning approach on emotional speech signals. To classify the rate of speech emotion signals, the features vectors are extracted using Mel frequency Cepstrum coefficient (MFCC) and auto correlation function coefficient (ACFC) and a combination of these two models. This study found the way that features’ number and fusion method can impress in the rate of emotional speech recognition. The proposed model has been compared with MLP model of recognition. Results revealed that the proposed algorithm has a powerful capability to identify and explore human emotion. Manuscript profile
      • Open Access Article

        4 - Recognition of Attention Deficit/Hyperactivity Disorder (ADHD) Based on Electroencephalographic Signals Using Convolutional Neural Networks (CNNs)
        Sara Motamed Elham Askari
        Impulsive / hyperactive disorder is a neuro-developmental disorder that usually occurs in childhood, and in most cases parents find that the child is more active than usual and have problems such as lack of attention and concentration control. Because this problem might More
        Impulsive / hyperactive disorder is a neuro-developmental disorder that usually occurs in childhood, and in most cases parents find that the child is more active than usual and have problems such as lack of attention and concentration control. Because this problem might interfere with your own learning, work, and communication with others, it could be controlled by early diagnosis and treatment. Because the automatic recognition and classification of electroencephalography (EEG) signals is challenging due to the large variation in time features and signal frequency, the present study attempts to provide an efficient method for diagnosing hyperactive patients. The proposed method is that first, the recorded brain signals of hyperactive subjects are read from the input and in order to the signals to be converted from time range to frequency range, Fast Fourier Transform (FFT) is used. Also, to select an effective feature to check hyperactive subjects from healthy ones, the peak frequency (PF) is applied. Then, to select the features, principal component analysis and without principal component analysis will be used. In the final step, convolutional neural networks (CNNs) will be utilized to calculate the recognition rate of individuals with hyperactivity. For model efficiency, this model is compared to the models of K- nearest neighbors (KNN), and multilayer perceptron (MLP). The results show that the best method is to use feature selection by principal component analysis and classification of CNNs and the recognition rate of individuals with ADHD from healthy ones is equal to 91%. Manuscript profile