Forecasting the United State Dollar(USD)/Bangladeshi Taka (BDT) exchange rate with deep learning models: Inclusion of macroeconomic factors influencing the currency exchange rates.

Forecasting a currency exchange rate is one of the most challenging tasks nowadays. Due to government monetary policy and some uncertain factors, such as political stability, it becomes difficult to correctly forecast the currency exchange rate. Previously, many investigations have been done to forecast the exchange rate of the United State Dollar(USD)/Bangladeshi Taka(BDT) using statistical time series models, machine learning models, and neural network models. But none of the previous methods considered the underlying macroeconomic factors of the two countries, such as GDP, import/export, government revenue, etc., for forecasting the USD/BDT exchange rate. We have included various time-sensitive macroeconomic features directly impacting the USD/BDT exchange rate to address this issue. These features will create a new dimension for researchers to predict and forecast the USD/BDT exchange rate. We have used various types of models for predicting and forecasting the USD/BDT exchange rate and found that Among all our models, Time Distributed MLP provides the best performance with an RMSE of 0.1984. Finally, we have proposed a pipeline for forecasting the USD/BDT exchange rate, which reduced the RMSE of Time Distributed MLP to 0.1900 and has proven effective in reducing the error of all our models.

Automatic segmentation of blood cells from microscopic slides: A comparative analysis.

With the recent developments in deep learning, automatic cell segmentation from images of microscopic examination slides seems to be a solved problem as recent methods have achieved comparable results on existing benchmark datasets. However, most of the existing cell segmentation benchmark datasets either contain a single cell type, few instances of the cells, not publicly available. Therefore, it is unclear whether the performance improvements can generalize on more diverse datasets. In this paper, we present a large and diverse cell segmentation dataset BBBC041Seg1, which consists both of uninfected cells (i.e., red blood cells/RBCs, leukocytes) and infected cells (i.e., gametocytes, rings, trophozoites, and schizonts). Additionally, all cell types do not have equal instances, which encourages researchers to develop algorithms for learning from imbalanced classes in a few shot learning paradigm. Furthermore, we conduct a comparative study using both classical rule-based and recent deep learning state-of-the-art (SOTA) methods for automatic cell segmentation and provide them as strong baselines. We believe the introduction of BBBC041Seg will promote future research towards clinically applicable cell segmentation methods from microscopic examinations, which can be later used for downstream tasks such as detecting hematological diseases (i.e., malaria).