Search results for: Shokoufeh Ghazanfari

Authors: Ali Kiani, Seyed Davod. Sharifi, Shokoufeh Ghazanfari

Abstract:

A study was conducted to evaluate the effects of diets containing different levels of lysine and canola oil on growth performance and fatty acid composition of meat of broilers chicks. 240-day old Ross broiler chicks were used in a 3×2 factorial arrangement with canola oil (1, 3, and 5%) and lysine (recommended, and 25% more than recommended by Ross broiler manual) in completely randomized design with four replicates and 10 birds per each. The experimental diets were iso-caloric and iso-nitrogenous. Feed intake and body weight gain were recorded at the end of starter (10 d), grower (24 d) and finisher (42 d) periods, and feed conversion ratio was calculated. The results showed that the weight gain of chickens fed diets containing 5% canola oil were greater than those of birds fed on other diets (P<0.05). The dietary lysine had significant effect on feed intake and diets with 25% more than recommended, increased feed intake significantly (P<0.05). The canola oil×lysine interaction effects on performance were not significant. Among all treatment birds, those fed diets containing 5% canola oil had the highest meristic acid and oleic acid content in their meat. Broilers fed diets containing 3 or 5% canola oil possessed the higher content of linolenic acid and lower content of arachidonic acid in their meat (P<0.05). The results of the present experiment indicated that the diets containing canola oil (5%) and lysine at 25% higher than requirement, improve the growth performance, carcass and breast yield of broiler, and increase the accumulation of Omega-3 fatty acids in breast meat.

Keywords: Broiler, canola oil, lysine, fatty acid.

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Authors: Somayeh Alizadeh, Mehdi Ghazanfari, Mohammad Fathian

Abstract:

Fuzzy Cognitive Maps (FCMs) have successfully been applied in numerous domains to show relations between essential components. In some FCM, there are more nodes, which related to each other and more nodes means more complex in system behaviors and analysis. In this paper, a novel learning method used to construct FCMs based on historical data and by using data mining and DEMATEL method, a new method defined to reduce nodes number. This method cluster nodes in FCM based on their cause and effect behaviors.

Keywords: Clustering, Data Mining, Fuzzy Cognitive Map(FCM), Learning.

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Authors: Somayeh Alizadeh, Mehdi Ghazanfari, Mostafa Jafari, Salman Hooshmand

Abstract:

Fuzzy Cognitive Maps (FCMs) is a causal graph, which shows the relations between essential components in complex systems. Experts who are familiar with the system components and their relations can generate a related FCM. There is a big gap when human experts cannot produce FCM or even there is no expert to produce the related FCM. Therefore, a new mechanism must be used to bridge this gap. In this paper, a novel learning method is proposed to construct causal graph based on historical data and by using metaheuristic such Tabu Search (TS). The efficiency of the proposed method is shown via comparison of its results of some numerical examples with those of some other methods.

Keywords: Fuzzy Cognitive Map (FCM), Learning, Meta heuristic, Genetic Algorithm, Tabu search.

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Authors: Samira Malekmohammadi Golsefid, Mehdi Ghazanfari, Somayeh Alizadeh

Abstract:

The goal of this paper is to segment the countries based on the value of export from Iran during 14 years ending at 2005. To measure the dissimilarity among export baskets of different countries, we define Dissimilarity Export Basket (DEB) function and use this distance function in K-means algorithm. The DEB function is defined based on the concepts of the association rules and the value of export group-commodities. In this paper, clustering quality function and clusters intraclass inertia are defined to, respectively, calculate the optimum number of clusters and to compare the functionality of DEB versus Euclidean distance. We have also study the effects of importance weight in DEB function to improve clustering quality. Lastly when segmentation is completed, a designated RFM model is used to analyze the relative profitability of each cluster.

Keywords: Customers segmentation, Customer relationship management, Clustering, Data Mining

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Authors: Atena Sajedin, Shokoufeh Zakernejad, Soheil Faridi, Mehrdad Javadi, Reza Ebrahimpour

Abstract:

This paper illustrates the use of a combined neural network model for classification of electrocardiogram (ECG) beats. We present a trainable neural network ensemble approach to develop customized electrocardiogram beat classifier in an effort to further improve the performance of ECG processing and to offer individualized health care. We process a three stage technique for detection of premature ventricular contraction (PVC) from normal beats and other heart diseases. This method includes a denoising, a feature extraction and a classification. At first we investigate the application of stationary wavelet transform (SWT) for noise reduction of the electrocardiogram (ECG) signals. Then feature extraction module extracts 10 ECG morphological features and one timing interval feature. Then a number of multilayer perceptrons (MLPs) neural networks with different topologies are designed. The performance of the different combination methods as well as the efficiency of the whole system is presented. Among them, Stacked Generalization as a proposed trainable combined neural network model possesses the highest recognition rate of around 95%. Therefore, this network proves to be a suitable candidate in ECG signal diagnosis systems. ECG samples attributing to the different ECG beat types were extracted from the MIT-BIH arrhythmia database for the study.

Keywords: ECG beat Classification; Combining Classifiers;Premature Ventricular Contraction (PVC); Multi Layer Perceptrons;Wavelet Transform

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