Prudent macroeconomic policy bear great importance on the economic development of any country, Bangladesh is not an exception. The objective this dissertation to identify influential variables, which have long run and short influence country. study employed most recent data economy from 2006-2017 fiscal year six variables such as inflation, money supply, exchange rate, DSE index, reserve money,...

Brain tumor is one of the most dreadful worldwide types cancer and affects people leading to death. Magnetic resonance imaging methods capture skull images that contain healthy affected tissue. Radiologists checked tissue in slice-by-slice manner, which was time-consuming hectic task. Therefore, auto segmentation part needed facilitate radiologists. we have considered a hybrid model inherits co...

Capacity utilization rate is one of the most important indicators of the efficiency of the manufacturing industry, and therefore of the return of the investments made. Estimation of these rates accurately renders it possible to make important economic decisions such as taking sectorial investment decisions, defining the optimal distribution of sectorial credits, determining noncompetitive secto...

Decision models under uncertainty need to be feeded with scenarios of the interest rate curve. Such scenarios have to comply, as close as possible, with the empirical distribution of each rate. Simulation models of the term structure usually assume that the conjugate distribution of the interest rates is lognormal. Dynamic models, like vector auto-regression, implicitly postulate that the logar...

We propose a new unsupervised sentence salience framework for Multi-Document Summarization (MDS), which can be divided into two components: latent semantic modeling and salience estimation. For latent semantic modeling, a neural generative model called Variational Auto-Encoders (VAEs) is employed to describe the observed sentences and the corresponding latent semantic representations. Neural va...

This study describes a classification methodology based on support vector machines (SVMs), which offer superior classification performance for fault diagnosis in chemical process engineering. The method incorporates an efficient parameter tuning procedure (based on minimization of radiudmargin bound for SVM's leave-one-out errors) into a multi-class classification strategy using a fuzzy decisio...

This paper examines the sectoral and intertemporal impacts of international emigrant remittances by using a vector auto-regression (VAR) estimation focusing on Cambodia, Lao PDR, Myanmar and Vietnam (CLMV countries). The reason for targeting the CLMV countries is that they have still depended largely on remittance-earnings from their emigrant workers in their economies, and that the macroeconom...

In this paper usefulness of time series based Auto Regressive (AR) modelling technique has been explored for identification of a person. For this purpose, time series is obtained from the contour coordinates of the ear. AR model is fitted to this time series. AR coefficients thus obtained serve as a feature vector. Recognition Rate (RR) has been found by a classifier that is based on Euclidian ...

We examine the Xeon Phi, which is based on Intel’s Many Integrated Cores architecture, for its suitability to run the FDK algorithm—the most commonly used algorithm to perform the 3D image reconstruction in cone-beam computed tomography. We study the challenges of efficiently parallelizing the application and means to enable sensible data sharing between threads despite the lack of a shared las...

We present a new approach of using Auto-Associative Neural Networks (AANNs) in the conventional GMM speaker verification framework with i-vector feature extraction and PLDA modeling. In this technique, an i-vector feature extractor is trained using adaptation parameters from a mixture of AANNs. In order to model parts of each speaker’s acoustic space, a training objective function based on post...