Amirkabir University of Technology

About: Amirkabir University of Technology is a education organization based out in Tehran, Iran. It is known for research contribution in the topics: Nonlinear system & Fuzzy logic. The organization has 15254 authors who have published 31165 publications receiving 487551 citations. The organization is also known as: Tehran Polytechnic & Tehran Polytechnic University.

Dye adsorption and desorption properties of Mentha pulegium in single and binary systems

Abstract: This article deals with the dye adsorption and desorption properties of Mentha pulegium (MP) from single and binary (mixture of dyes) systems. Direct Red 80 (DR80) and Acid Black 26 (AB26) were used as model dyes. The Fourier transform infrared (FTIR) was used to investigate the biosorbent characteristics. The effects of biosorbent dosage, contact time, dye concentration, salt, and pH on dye removal were studied. The biosorption isotherms, kinetics, and thermodynamic were studied. In addition, dye desorption was carried out to study adsorbent recovery. The results showed that the isotherm data of single and binary systems of dyes followed the Langmuir isotherm. The adsorption kinetic of the dyes was found to conform to a pseudosecond order kinetic model. Desorption tests showed maximum dye releasing of 97% for DR80 and 95% for AB26 in single system and 92% for DR80 and 94% for AB26 in binary system of dyes at pH 12. The thermodynamic data showed that the biosorption process is spontaneous, endothermic, and a physisorption reaction. It can be concluded that MP is an ecofriendly biosorbent to remove dyes from single and binary systems. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2011

Microfluidic assisted self-assembly of chitosan based nanoparticles as drug delivery agents

Abstract: We present a microfluidic platform for the synthesis of monodisperse chitosan based nanoparticles via self-assembly at physiological pH. The resultant nanoparticles are shown to encapsulate hydrophobic anticancer drugs while providing a sustainable release profile with high tunability.

A hybrid modeling approach for forecasting the volatility of S&P 500 index return

Abstract: Forecasting volatility is an essential step in many financial decision makings. GARCH family of models has been extensively used in finance and economics, particularly for estimating volatility. The motivation of this study is to enhance the ability of GARCH models in forecasting the return volatility. We propose two hybrid models based on EGARCH and Artificial Neural Networks to forecast the volatility of S&P 500 index. The estimates of volatility obtained by an EGARCH model are fed forward to a Neural Network. The input to the first hybrid model is complemented by historical values of other explanatory variables. The second hybrid model takes as inputs both series of the simulated data and explanatory variables. The forecasts obtained by each of those hybrid models have been compared with those of EGARCH model in terms of closeness to the realized volatility. The computational results demonstrate that the second hybrid model provides better volatility forecasts.

Salt Free Reactive Dyeing of Cationized Cotton

Abstract: In this study 3-chloro-2-hydroxypropyltrimethylammonium chloride was used as a cationic agent to cationize cotton fabric by a pad-batch process. The cationized cotton samples were dyed with different reactive dyes containing various reactive groups. The dyeability of the cationized cotton samples with reactive dyes without salt was significantly improved due to an increase in the ionic attraction between the dye and cationized cotton. The results showed that the wash and dry rubbing fastness of the cationized cotton dyed with different reactive dyes are similar to those of the untreated cotton. However, the light fastness of some of the cationized fabric samples was improved.

Brain Tumor Classification via Convolutional Neural Network and Extreme Learning Machines

Abstract: Tumor identification is one of the main and most influential factors in the identification of the type of treatment, the treatment process, the success rate of treatment and the follow-up of the disease. Convolution neural networks are one of the most important and practical classes in the field of deep learning and feed-forward neural networks that is highly applicable for analyzing visual imagery. CNNs learn the features extracted by the convolution and maxpooling layers. Extreme Learning Machines (ELM) are a kind of learning algorithm that consists of one or more layers of hidden nodes. These networks are used in various fields such as classification and regression. By using a CNN, this paper tries to extract hidden features from images. Then a kernel ELM (KELM) classifies the images based on these extracted features. In this work, we will use a dataset to evaluate the effectiveness of our proposed method, which consists of three types of brain tumors including meningioma, glioma and pituitary tumor in T1-weighted contrast-enhanced MRI (CE-MRI) images. The results of this ensemble of CNN and KELM (KE-CNN) are compared with different classifiers such as Support Vector Machine, Radial Base Function, and some other classifiers. These comparisons show that the KE-CNN has promising results for brain tumor classification.