Dr. Shipra Banik - Academia.edu (original) (raw)

Papers by Dr. Shipra Banik

Lecture notes in networks and systems, Dec 31, 2022

Proceedings on Engineering Sciences, Mar 15, 2023

The process capability index (Cp) measures the amount of dispersion a process involves relative t... more The process capability index (Cp) measures the amount of dispersion a process involves relative to the limits of specification. This paper considers sixteen different available confidence intervals for estimating the population process capability index. A simulation study under different conditions has been conducted to compare the performance of the estimators. Our vast simulation records reveal that both augmented large sample (ALS) and modified augmented large sample (MALS) intervals have better coverage probability and shorter average width in all simulation conditions. We expect that the results of this paper will contribute to the literature on process capability and will guide the researchers to select an interval estimator when they are interested to estimate the population process capability index.

This paper considers fifteen different test statistics for testing the population process capabil... more This paper considers fifteen different test statistics for testing the population process capability index. To assess the performance of the test statistics, empirical sizes and powers are calculated at the 5% nominal level and compared with the classical statistic under both symmetric and skewed distributions. It is evident from the simulation study that some of our proposed tests have better size and power properties as compared to the existing approaches.

Production Engineering, Dec 3, 2019

This paper aims to compare the performances of modified confidence intervals based on robust scal... more This paper aims to compare the performances of modified confidence intervals based on robust scale estimators with classical confidence interval for process capability index (C p) when the process has a non-normal distribution. The estimated coverage probability and the average width of the confidence intervals were obtained by a Monte-Carlo simulation under different scenarios. Simulation results showed that the modified confidence intervals performed well in terms of coverage probability and average width for all cases. Two real-life numerical examples from industry are analyzed to illustrate the performance and the implementation of the classical and modified confidence intervals for the process capability index (C p) which also supported the results of the simulation study to some extent.

Capability to predict precise future stock values is the most important factor in financial marke... more Capability to predict precise future stock values is the most important factor in financial market to make profit. Because of virtual trading, now a day this market has turn into one of the hot targets where any person can earn profit. Thus, predicting the correct future value of a stock has become an area of hot interest. This paper attempt to forecast NASDAQ stock index values using novel hybrid forecasting models based on widely used soft computing models and time series models. The daily historical US NASDAQ closing stock index for the periods of 08 February 1971 to 24 July 2015 is used and is applied our proposed hybrid forecasting models to see whether considered forecasting models can closely forecast daily NASDAQ stock index values. Mean absolute error and root mean square error between observed and predicted NASDAQ stock index are considered as evaluation criterions. The result is compared on the basis of selected individual forecasting time series model and individual soft computing forecasting models and the proposed hybrid forecasting models. Our experimental evidences show that the proposed hybrid back-propagation artificial neural network and genetic algorithm forecasting model has outperformed as compare to other considered forecasting models for forecasting daily US NASDAQ stock index. We trust that daily US NASDAQ stock index forecasts will be notice for a number of spectators who wish to construct strategies about this index.

Stock market prediction has been a challenging task due to the nature of the data which is very n... more 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

Proceedings on Engineering Sciences

Internal medicine, Dec 30, 2022

Diabetes pedigree function (DPF) calculates diabetes likelihood depending on the subject's age an... more Diabetes pedigree function (DPF) calculates diabetes likelihood depending on the subject's age and his/her diabetic family history. Very little is known about the determinants of DPF for gestational diabetes mellitus (GDM) and normal women. The article focuses on the determinants of DPF for GDM and normal (non-diabetes) women. Results It has been derived that mean DPF is directly linked to age (p=0.0334), subject's type (p=0.0006), triceps skin-fold thickness (TSFT) (p=0.0083), insulin level (p=0.0032), the joint interaction effect of body mass index (BMI) and glucose level (BMI×Glucose) (p=0.0624), while it is inversely linked to pregnancy's number (p=0.0217), glucose level (p=0.0724) and BMI (p=0.1173). Moreover, the variance of DPF is partially inversely linked to pregnancy's number (p=0.1159) and directly to the joint interaction effect of diastolic blood pressure (DBP) and pregnancy's number (i.e., DBP×pregnancy's number) (p=0.1304). Conclusion It concludes that DPF is not only based on age and subject's diabetic family history, while it depends on many factors as stated above. So, for computing DPF, the above factors should be included in it.

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...

Advances in Intelligent Systems and Computing, 2020

Cumulative Grade Point Average (CGPA) prediction is an important area for understanding tertiary ... more Cumulative Grade Point Average (CGPA) prediction is an important area for understanding tertiary education performance trend of students and identifying the demographic attributes to devise effective educational strategies and infrastructure. This paper aims to analyze the accuracy of CGPA prediction of students resulted from predictive models, namely the ordinary least square model (OLS), the artificial neural network model (ANN) and the adaptive network based fuzzy inference model (ANFIS). We have used standardized examination (Secondary School Certificate and High School Certificate) results from secondary and high school boards and current CGPA in respective disciplines of 1187 students from Independent University, Bangladesh from the period of April 2013 to April 2015. Evaluation measures such as- Mean absolute error, root mean square error and coefficient of determination are used as to evaluate performances of above-mentioned models. Our findings suggest that the mentioned predictive models are unable to predict CGPA values of the students accurately with currently used parameters.

Copyright © 2014 Shipra Banik et al. This is an open access article distributed under the Creativ... more Copyright © 2014 Shipra Banik et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Forecasting stockmarket has been a difficult job for applied researchers owing to nature of factswhich 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 m...

International Journal of Computer Science & Applications, 2013

Many international agents (e.g. money managers, investment banks, investors, funds makers and oth... more Many international agents (e.g. money managers, investment banks, investors, funds makers and others) are very concerned about predicted values of exchange rates because it often moves dustically and generally affects the profits. This paper forecasted the daily Bangladeshi and Canadian exchange rates for the period of October 1996 to January 2013. With attention paid to recently developed econometric noises, the widely-used forecasting model the fuzzy extension of artificial neural network is considered and compared results with the Markov switching autoregressive forecasting model. Root mean square error and correlation coefficient are used as an evaluation measures. It has been found that the fuzzy extension of the artificial neural network model is a superior predictor compared to the other selected predictor for the Bangladeshi series and the reverse observed for the Canadian series. It is believed that the findings of this paper will be helpful for multinational organizations ...

Production Engineering, 2019

16th Int'l Conf. Computer and Information Technology, 2014

Right skewed data is available in the field of health science, biological science, epidemiologica... more Right skewed data is available in the field of health science, biological science, epidemiological science and others. This paper assessed some existing procedures for testing the difference of means of two right skewed populations. A Monte Carlo simulation study has been conducted to compare the performance of the test statistics in the sense of empirical size and power of the selected tests. Based on the simulation study results, some good test statistics are recommended for practitioners. A real life health related example has been considered to illustrate the application of the techniques.

2008 11th International Conference on Computer and Information Technology, 2008

Abstract-True information about rainfall is crucial for human activities such as the use and the ... more Abstract-True information about rainfall is crucial for human activities such as the use and the management of water resources, hydroelectric power projects, warning to impend droughts or floods, urban areas sewer systems and many others. This paper investigates the ...

2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), 2013

ABSTRACT Forecasting exchange rate is very important for many international agents e.g. investors... more ABSTRACT Forecasting 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 and Canadian exchange rate series for the period of October 1996 to January 2013. Paying attention with recently developed econometric noises, we considered widely-used non-linear forecasting model namely the fuzzy extension of artificial neural network model and compared results with the Markov switching autoregressive model. Our target is to investigate whether selected model can serve as a useful forecasting model to find volatile and non-linear behaviors of the considered exchange rate series. By most commonly used statistical measures: Root mean square error and correlation coefficient we found that fuzzy extension of the artificial neural network model is a superior predictor than the other selected predictor for the Bangladeshi series and the reverse observed for the Canadian series. The findings will have implications for many kinds of businessmen and multinational organizations. We believe findings of this paper will be helpful to make a wide range of policies for multinational companies who are involved with various international business activities.

Journal of Statistics: Advances in Theory and Applications, 2019

This paper considers some bootstrap version of the existing confidence intervals for estimating t... more This paper considers some bootstrap version of the existing confidence intervals for estimating the parameter of an autoregressive process of order one model. A simulation study has been conducted to compare the performance of the proposed intervals using two important measures: coverage probability and average width. It appears from our simulation study that all methods have confidence coefficient closest to the given confidence coefficient, however, our proposed bootstrap intervals have small average widths as compare to its counterpart. A real life data are analyzed, which supported the simulation results to some extent. We believe that the findings of this study will make important contribution to the time series literature.

International Journal of Computational and Applied Mathematics & Computer Science