Mohammed Anwer - Academia.edu (original) (raw)

Papers by Mohammed Anwer

Prediction of exchange rate is very important for many international agents e.g. investors, money... more Prediction of exchange rate is very important for many international agents e.g. investors, money managers, investment banks, funds makers and others. We forecasted the daily Bangladeshi exchange rate series for the period of January 1992 to March 2009 using popular non-linear forecasting models, namely Markov switching autoregressive (MS_AR) model, fuzzy extension of artificial neural network model (ANFIS) and generalized autoregressive conditional heteroscedastic (GARCH) model. Our target is to investigate whether selected models can serve as useful forecasting models to find volatile and non-linear behaviours of the considered series. By most commonly used statistical measures: mean absolute percentage error, root mean square error and coefficient of determination, we found that ANFIS is a superior predictor than other two selected predictors. We believe findings of this paper will be helpful to make a wide range of policies for multinational companies who are involved with vario...

2009 12th International Conference on Computers and Information Technology, 2009

Page 1. Proceedings of2009 12th International Conference on Computer and Information Technology (... more Page 1. Proceedings of2009 12th International Conference on Computer and Information Technology (ICCIT 2009) 21-23 December, 2009, Dhaka, Bangladesh Challenges in Building Trust in B2C E-Commerce and Proposal to Mitigate Them: Developing Countries Perspective ...

Journal of Computers, 2013

The paper studies implementation of a Course Management System to explore teacher-student activit... more The paper studies implementation of a Course Management System to explore teacher-student activities, instructor's responsibilities, reactions of instructors, its pros and cons and user response rate-from a faculty perspective of a university in Bangladesh. Analyzed user response suggests universities in Bangladesh to use such easy to use, free-source; very low implementation, training and maintenance cost CMS as–“technology as an adjunct to traditional means of instruction”. Identifies that optimum use of CMS depends on types of ...

2007 10th International Conference on Computer and Information Technology, 2007

Digital libraries are fast expanding into the role of independent educational entities that aspir... more Digital libraries are fast expanding into the role of independent educational entities that aspire not only to complement traditional classroom teaching, but also allow remote access to Learning Objects. Multifaceted roles of Learning Objects can be realized only if the course content and the related content management system are versatile enough to be captured into any individual’s learning needs. This paper presents the design & implementation of such a Digital Archive, which will facilitate the use/re-use, store & bookmark of Learning Objects focusing to learning needs.

Computational Intelligence and Neuroscience, 2014

Forecasting stock market has been a difficult job for applied researchers owing to nature of fact... more Forecasting stock market has been a difficult job for applied researchers owing to nature of facts which is very noisy and time varying. However, this hypothesis has been featured by several empirical experiential studies and a number of researchers have efficiently applied machine learning techniques to forecast stock market. This paper studied stock prediction for the use of investors. It is always true that investors typically obtain loss because of uncertain investment purposes and unsighted assets. This paper proposes a rough set model, a neural network model, and a hybrid neural network and rough set model to find optimal buy and sell of a share on Dhaka stock exchange. Investigational findings demonstrate that our proposed hybrid model has higher precision than the single rough set model and the neural network model. We believe this paper findings will help stock investors to decide about optimal buy and/or sell time on Dhaka stock exchange.

Physics of Fluids A: Fluid Dynamics

International Journal of Innovation and Applied Studies, Oct 2, 2013

In present day business and consumer environment, a robust voice identification system is needed ... more In present day business and consumer environment, a robust voice identification system is needed to reduce false positives, and true negatives. In this work, a modified voice identification system is described using over sampled Haar wavelets followed by proper orthogonal decomposition. The audio signal is decomposed using over sampled Haar wavelets. This converts the audio signal into various non-correlating frequency bands. This allows us to calculate the linear predictive cepstral coefficient to capture the characteristics of individual speakers. Adaptive threshold was applied to reduce noise interference. This is followed by multi-layered vector quantization technique to eliminate the interference between multiband coefficients. Finally, proper orthogonal decomposition is used to evaluate unique characteristics for capturing more details of phoneme characters. The proposed algorithm was used on KING and MAT-400 databases. These databases were chosen as previous extraction results were available for them. In the present study, the KING database were trained with three sentences, and tested with two. On the other hand, the MAT-400 database were trained with two seconds of random voice signal, and tested with other two seconds. Results were compared with vector quantization and Gaussian mixture models. The present model gave consistently better performance on speech collected through mouthpieces, but gave comparatively poor performance on audio collected on telephones. The better performance is obtained at the cost of higher computational time.

ARCHIVE Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science 1989-1996 (vols 203-210)

The traditional audio de-noising technique using Daubechies wavelet is improved by using a prepro... more The traditional audio de-noising technique using Daubechies wavelet is improved by using a preprocessor using statistical grazing estimation. ‘Noisy’ signals were synthetically generated by introducing ‘noise’ to a ‘clean’ signal. The signal was sampled at 8 kHz and digitized at 16 bits per second. The noise from the audio signal was removed in the first step using statistical grazing estimation with specified threshold level. In the second step Daubechies-D4 wavelet transform was used to remove the noise further. The results were compared with results obtained with grazing estimation only, Daubechies-D4 wavelet transformation and Haar wavelet transformations separately. The results show that the composite denoising technique results in consistently better signal-to-noise ratio than any other technique

2012 15th International Conference on Computer and Information Technology (ICCIT), 2012

ABSTRACT Stock market prediction has been a challenging task due to the nature of the data which ... more ABSTRACT Stock market prediction has been a challenging task due to the nature of the data which is very noisy and time varying. However, this theory has been faced by many empirical studies and a number of researchers have successfully applied machine learning approaches to predict stock market. The problem studied here is about stock prediction for the use of investors. It is true investors usually get loss because of unclear investment objective and blind investment. This paper proposes to investigate the rough set model, the artificial neural network model and the hybrid artificial neural network model and the rough set model for determining the optimal buy and sell of a share on a Dhaka stock exchange. Confusion matrix is used to evaluate the performance of the observed and predicted classes for selected models. Our experimental result shows that the proposed hybrid model has higher accuracy than the single rough set model and the artificial neural network model. We believe this paper will be useful to stock investors to determine the optimal buy and sell time on Dhaka Stock Exchange.

The International Technology Management Review, 2009

1st National Fluid Dynamics Conference, 1988

Journal of Scientific Research and Reports, 2014

Aims: To ascertain the effectiveness of edge detection and contour detection algorithms to identi... more Aims: To ascertain the effectiveness of edge detection and contour detection algorithms to identify hard exudates and hemorrhage region in fundus images of diabetic patients. Study Design: Canny's algorithm was selected as the edge detection algorithm, and Snakes' algorithm was selected as the contour detection algorithm. Place and Duration of Study: A total of 212 fundus images were procured from the Department of Ophthalmology of Bangladesh Institute of Research and Rehabilitation for Diabetes, Endocrine and Metabolic Disorders for this study. The images were captured between 2010 and 2013. Methodology: Noise was removed from the images using successive Gaussian and median filtering. Green component of the image was used for detection of hard exudates, and red component was used for detection of hemorrhage. To apply Canny's algorithm, color gradient was calculated, and a threshold was applied to the gradient to select a candidate region. Snakes' algorithm was applied by scaling the color intensity from 0 to 1, and a threshold color value was chosen to draw the contours. Several filters were applied to the selected region to detect and discard the false-positives. A total of 32 images were