Kharazmi University

About: Kharazmi University is a education organization based out in Tehran, Iran. It is known for research contribution in the topics: Membrane & Supply chain. The organization has 3395 authors who have published 5321 publications receiving 45645 citations. The organization is also known as: Tarbiat Moallem University of Tehran & Teacher Training University.

Predicting the Earthquake Magnitude Using the Multilayer Perceptron Neural Network with Two Hidden Layers

Abstract: Because of the major disadvantages of previous methods for calculating the magnitude of the earthquakes, the neural network as a new method is examined. In this paper a kind of neural network named Multilayer Perceptron (MLP) is used to predict magnitude of earthquakes. MLP neural network consist of three main layers; input layer, hidden layer and output layer. Since the best network configurations such as the best number of hidden nodes and the most appropriate training method cannot be determined in advance, and also, overtraining is possible, 128 models of network are evaluated to determine the best prediction model. By comparing the results of the current method with the real data, it can be concluded that MLP neural network has high ability in predicting the magnitude of earthquakes and it’s a very good choice for this purpose.

Do online communities support research collaboration

Abstract: Purpose – The purpose of this paper is to explore the extent to which academics are engaged with online communities for research purposes, and the research activities, platforms and tools associated with these communities. In addition, the paper aims to discover the benefits, disadvantages and barriers involved in the use of online communities, and especially in regard to the trust and authority issues, so important in scholarly communications. Design/methodology/approach – A layered, mixed-methods approach was used for this complex research topic. Interviews were undertaken with social science and humanities researchers, followed up with focus groups in both the USA and UK. This qualitative work was then followed up with an online questionnaire that generated over 1,000 responses. Findings – Over half the sample had experience of an online research community and a majority of researchers are making at least occasional use of one or more Web 2.0 services for communicating their research activity; for deve...

Cycloplatinated(II) Derivatives of Mercaptopurine Capable of Binding Interactions with HSA/DNA.

Abstract: In this study, two new bis-cyclometalated Pt(II) complexes, [Pt(C^N)(S^N)] [S^N = deprotonated 6-mercaptopurine (6-MP) and C^N = deprotonated 2-phenylpyridine (ppy), 2a; C^N = deprotonated benzo[h]...

A Hybrid Method Based on Fuzzy Clustering and Local Region-Based Level Set for Segmentation of Inhomogeneous Medical Images

Abstract: medical images are more affected by intensity inhomogeneity rather than noise and outliers This has a great impact on the efficiency of region-based image segmentation methods, because they rely on homogeneity of intensities in the regions of interest Meanwhile, initialization and configuration of controlling parameters affect the performance of level set segmentation To address these problems, this paper proposes a new hybrid method that integrates a local region-based level set method with a variation of fuzzy clustering Specifically it takes an information fusion approach based on a coarse-to-fine framework that seamlessly fuses local spatial information and gray level information with the information of the local region-based level set method Also, the controlling parameters of level set are directly computed from fuzzy clustering result This approach has valuable benefits such as automation, no need to prior knowledge about the region of interest (ROI), robustness on intensity inhomogeneity, automatic adjustment of controlling parameters, insensitivity to initialization, and satisfactory accuracy So, the contribution of this paper is to provide these advantages together which have not been proposed yet for inhomogeneous medical images Proposed method was tested on several medical images from different modalities for performance evaluation Experimental results approve its effectiveness in segmenting medical images in comparison with similar methods