Syeda Shabnam Hasan’s research while affiliated with North South University and other places

This article introduces Fuzzy Inspired Bat Algorithm (FIBA), which is an improved variant of the original Bat algorithm. The novelty of FIBA lies in the integration of a fuzzy controller with the basic Bat algorithm that tries to bring balance between the degree of explorations and exploitations during the mutation operation. Another novelty of FIBA is the introduction of a step size parameter, maintained separately for every candidate solution, to customize and control the mix of explorative and exploitative operations around each candidate solution. FIBA is tested on a standard benchmark set that includes 10 complex, scalable, high dimensional functions. The results on benchmark functions reveal that FIBA can perform sufficiently well, and often better than the original Bat algorithm and another recently proposed improved Bat variant. Such improvements on the experimental results imply that the fuzzy technique adopted by FIBA might be effective on other existing problems as well, and hence demand further research and investigation.

This paper employs a number of machine learning algorithms to predict the future stock price of Dhaka Stock Exchange. The outcomes of the different machine learning algorithms are combined to form an ensemble to improve the prediction accuracy. In addition, two popular and widely used technical indicators are combined with the machine learning algorithms to further improve the prediction performance. To evaluate the proposed techniques, historical price and volume data over the past 15 months of three prominent stocks enlisted in Dhaka Stock Exchange are collected, which are used as training and test data for the algorithms to predict the 1-day, 1-week and 1-month-ahead prices of these stocks. The predictions are made both on training and test data sets and results are compared with other existing machine learning algorithms. The results indicate that the proposed ensemble approach as well as the combination of technical indicators with the machine learning algorithms can often provide better results, with reduced overall prediction error compared to many other existing prediction algorithms.