International Journal of Mathematical Sciences and Computing

BRAINSEG – Brain Structures Segmentation Pipeline Using Open Source Tools

R. Neela, R. Kalaimagal

Статья научная

Structure segmentation is often the first step in the diagnosis and treatment of various diseases. Because of the variations in the various brain structures and overlapping structures, segmenting brain structures is a very crucial step. Though a lot of research had been done in this area, still it is a challenging field. Using prior knowledge about the spatial relationships among structures, called as atlases, the structures with dissimilarities can be segmented efficiently. Multiple atlases prove a better one when compared to single atlas, especially when there are dissimilarities in the structures. In this paper, we proposed a pipeline for segmenting brain structures using open source tools. We test our pipeline for segmenting brain structures in MRI using the publicly available data provided by MIDAS.

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Prediction of Rainfall Using Unsupervised Model based Approach Using K-Means Algorithm

G.Vamsi Krishna

Статья научная

Prediction of rainfall has gained a significant importance because of many associated factors like cultivating, aqua-culture and other indirect parameters allied with the rainfall like global heat. Therefore it is necessary to predict the rainfall from the satellite images effectively. In this article, a segmentation algorithm is developed based on Gaussian mixture models. The initial parameters are estimated using k-means algorithm. The process is presented by using an 2-fold architecture, where in the first stage database creation is considered and the second stage talks about the prediction. The performance analysis is carried out using metrics like PSNR, IF and MSE. The developed model analyzes the satellite images and predicts the Rainfall efficiently.

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Comparison of Four Interval ARIMA-base Time Series Methods for Exchange Rate Forecasting

Mehdi Khashei, Mohammad Ali Montazeri, Mehdi Bijari

Статья научная

In today's world, using quantitative methods are very important for financial markets forecast, improvement of decisions and investments. In recent years, various time series forecasting methods have been proposed for financial markets forecasting. In each case, the accuracy of time series methods fundamental to make decision and hence the research for improving the effectiveness of forecasting models have been curried on. In the literature, Many different time series methods have been frequency compared together in order to choose the most efficient once. In this paper, the performances of four different interval ARIMA-base time series methods are evaluated in financial markets forecasting. These methods are including Auto-Regressive Integrated Moving Average (ARIMA), Fuzzy Auto-Regressive Integrated Moving Average (FARIMA), Fuzzy Artificial Neural Network (FANN) and Hybrid Fuzzy Auto-Regressive Integrated Moving Average (FARIMAH). Empirical results of exchange rate forecasting indicate that the fuzzy artificial neural network model is more satisfactory than other models.

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An Efficient Impulse Noise Removal Image Denoising Technique for MRI Brain Images

Murugan, Balasubramanian

Статья научная

Image enhancement is an important challenge in medical field. There are various techniques for image enhancement during last two decades. The objective of this paper is to remove impulse noise for MRI brain image. This paper proposed an efficient filter for removing impulse noise. The shape of the filter is changed to diamond. Experiments are conducted for various noise levels. The proposed method is compared with the existing Denoising techniques. The experimental results proved that the proposed filter performed well than the other methods.

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Design Approaches for a Novel Reversible 4-bit Comparator

Harpreet Singh, Chakshu Goel

Статья научная

Reversible logic has shown considerable acceptance and growth in the research fields like quantum computing, Nano computing and optical computing promising lower power dissipation. This paper proposes an optimised design single-bit reversible comparator called SKAR gate with a purpose of reducing quantum cost. Besides, this novel SKAR gate is used as a single-bit reversible comparator to construct an optimised design for a four-bit reversible comparator. The paper discusses two designs, one with the use of SKAR gate and other one using a derivative gate constructed from SKAR gate. Since the reversible logic aims at reducing the value of its fundamental parameters viz. quantum cost, garbage outputs, ancillary inputs, delay and number of gates; Both the proposed designs for single-bit and four-bit reversible comparator are compared with other existing designs on the basis of elementary parameters of reversible logic.

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Efficient Optimization of Edge Server Selection Technique in Content Delivery Network

Debabrata Sarddar, Enakshmi Nandi

Статья научная

Cloud Computing provides the infrastructure as a "Cloud" from which businesses and users are permit to access applications from anywhere in the world on demand. Thus, the computing world is rapidly transforming towards developing software for millions to consume as a service, rather than to run on their individual computers. But many users could not satisfy on cloud services completely due to their uncovering security purpose for handling large numbers of data. Even the network becomes uncontrollable, when large numbers of user's request to the server create network congestion and data losses vigorously. Content Delivery Network OR CDN is an eminent solution of this problem. Our objective is to create optimized method for edge selection technique in Content Delivery Network to deliver and direct the user request to the nearest edge server and establish the connection between them and transfer the respective content

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