In the field of numismatics, classifying ancient coins, especially those that have diverse information and cultural heritage is a difficult task. Machine learning algorithms have recently made remarkable advancements in these types of tasks. However, these algorithms largely rely on relevant datasets. This article presents a novel dataset of ancient Gupta archer-type coin images, collected from verified private collections and three popular auction houses with their permission. The images exclusively comprise authentic specimens of ancient Gupta archer-type coins. We aim to establish a reliable resource that adheres to the highest standards of numismatic research. These coins, characterized by their distinctive archer motifs, present a significant challenge in terms of identification due to their scarcity and the intricate nature of their design. To address this, we meticulously curated a dataset by annotating each coin through a combination of visual examination and leveraging insights from numismatic literatures. These coins inherit ancient Indian archaeological insights, and studying these coins could provide insights into ancient Indian archaeology.

Brain magnetic resonance imaging (MRI) offers intricate soft tissue contrasts that are essential for diagnosing diseases and conducting neuroscience research. At 7 Tesla (7T) magnetic field intensity, MRI enables increased resolution, enhanced tissue contrast, and improved SNR, compared to MRI collected from the commonly employed 3 Tesla (3T) MRI scanners. However, the exorbitant expenses associated with 7T MRI scanners hinder their broad use in research and clinical facilities. Efforts are underway to develop algorithms that can generate 7T MRI from 3T MRI to achieve better image quality without the need for 7T MRI machines. In this study, we have adopted a cycle consistent generative adversarial network (CycleGAN)-based approach for 3T MRI to 7T MRI translation, and vice versa, using a recently published dataset of paired T1-weighted MR images collected at 3T and 7T from a total of ten subjects. Various CycleGAN architectures were experimented with and compared on this dataset. The best performing CycleGAN architecture successfully produced the reconstructed images with a high level of accuracy based on different quantitative and qualitative evaluation criteria. Utilizing a post-processing technique, the best performing model generated 7T MRI from 3T MRI with a structural similarity index measure (SSIM) of 83.80%, peak SNR (PSNR) of 26.25, normalized mean squared error (NMSE) of 0.0088 and normalized mean absolute error (NMAE) of 0.0630. Utilizing CycleGAN to convert images from 3T to 7T MRI has shown a substantial improvement in MRI resolution, setting the stage for advancements in more informative and precise diagnostic imaging.

Different epidemiological compartmental models have been presented to predict the transmission dynamics of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this study, we have proposed a fuzzy rule-based Susceptible-Exposed-Infectious-Recovered-Death ([Formula: see text]) compartmental model considering a new dynamic transmission possibility variable as a function of time and three different fuzzy linguistic intervention variables to delineate the intervention and transmission heterogeneity on SARS-CoV-2 viral infection. We have analyzed the datasets of active cases and total death cases of China and Bangladesh. Using our model, we have predicted active cases and total death cases for China and Bangladesh. We further presented the correspondence of different intervention measures in relaxing the transmission possibility. The proposed model delineates the correspondence between the intervention measures as fuzzy subsets and the predicted active cases and total death cases. The prediction made by our system fitted the collected dataset very well while considering different fuzzy intervention measures. The integration of fuzzy logic in the classical compartmental model also produces more realistic results as it generates a dynamic transmission possibility variable. The proposed model could be used to control the transmission of SARS-CoV-2 as it deals with the intervention and transmission heterogeneity on SARS-CoV-2 transmission dynamics.

Bangladesh is in the floodplains of the Ganges, Brahmaputra, and Meghna River delta, crisscrossed by an intricate web of rivers. Although the country is highly prone to flooding, the use of state-of-the-art deep learning models in predicting river water levels to aid flood forecasting is underexplored. Deep learning and attention-based models have shown high potential for accurately forecasting floods over space and time. The present study aims to develop a long short-term memory (LSTM) network and its attention-based architectures to predict flood water levels in the rivers of Bangladesh. The models developed in this study incorporated gauge-based water level data over 7 days for flood prediction at Dhaka and Sylhet stations. This study developed five models: artificial neural network (ANN), LSTM, spatial attention LSTM (SALSTM), temporal attention LSTM (TALSTM), and spatiotemporal attention LSTM (STALSTM). The multiple imputation by chained equations (MICE) method was applied to address missing data in the time series analysis. The results showed that the use of both spatial and temporal attention together increases the predictive performance of the LSTM model, which outperforms other attention-based LSTM models. The STALSTM-based flood forecasting system, developed in this study, could inform flood management plans to accurately predict floods in Bangladesh and elsewhere.

Recently COVID-19 pandemic has affected the whole world quite seriously. The number of new infectious cases and death cases are rapidly increasing over time. In this study, a theoretical linguistic fuzzy rule-based Susceptible-Exposed-Infectious-Isolated-Recovered (SEIIsR) compartmental model has been proposed to predict the dynamics of the transmission of COVID-19 over time considering population immunity and infectiousness heterogeneity based on viral load in the model. The model’s equilibrium points have been calculated and stability analysis of the model’s equilibrium points has been conducted. Consequently, the fuzzy basic reproduction number, R0f of the fuzzy model has been formulated. Finally, the temporal dynamics of different compartmental populations with immunity and infectiousness heterogeneity using the fuzzy Mamdani model are delineated and some disease control policies have been suggested to get over the infection in no time.

Assessing flood risk is challenging due to complex interactions among flood susceptibility, hazard, exposure, and vulnerability parameters. This study presents a novel flood risk assessment framework by utilizing a hybridized deep neural network (DNN) and fuzzy analytic hierarchy process (AHP) models. Bangladesh was selected as a case study region, where limited studies examined flood risk at a national scale. The results exhibited that hybridized DNN and fuzzy AHP models can produce the most accurate flood risk map while comparing among 15 different models. About 20.45% of Bangladesh are at flood risk zones of moderate, high, and very high severity. The northeastern region, as well as areas adjacent to the Ganges–Brahmaputra–Meghna rivers, have high flood damage potential, where a significant number of people were affected during the 2020 flood event. The risk assessment framework developed in this study would help policymakers formulate a comprehensive flood risk management system.

Flooding has become an exceedingly complex problem in many developing countries of the world including Bangladesh. Currently, Bangladesh is using MIKE 11 hydrodynamic model for flood forecasting. Previous studies indicated that hybridized machine learning models, especially support vector regression (SVR) models outperform standalone machine learning and other numerical models in mapping flood susceptibility. However, no study has been conducted on the flood dataset of Bangladesh using hybridized SVR model to predict flood susceptibility. In the present study, we have collected and modeled the recent flood inundation dataset of Bangladesh in terms of nine flood factors and explored their relative importance rank using the random forest (RF) algorithm. Then, we employed a genetic algorithm (GA) optimized SVR with radial basis function (RBF) kernel (hybridized GA-RBF-SVR) model along with the stand-alone RBF-SVR and multilayer perceptron (MLP) models to predict the flood susceptibility map for the whole country. The result of the hybridized SVR model is very promising to be employed in decision making to deal with the flood forecasting problem in Bangladesh.

Spatial flood susceptibility mapping (FSM) is one of the key components of flood risk assessment. Recent studies showed the efficacy of hybridized machine learning (ML)-based models in predicting flood susceptibility. The quality of this prediction depends on the presence or absence of label noise in the training data collected from real world flood inundation maps. However, no study has been conducted to explore the effects of label noise in ML based spatial FSM. In the present study, we have explored the effects of four different random class label noises on the regression performances and model complexities of both standalone and hybridized ML based FSM. We also explored the optimal hyperparameter values of different hybridized ML and reported the robustness of each model in the presence and absence of label noise. The hybridized ML models showed greater efficacy in modeling spatial flood susceptibility in both cases.

A new approach to non-invasively uncover the pattern of myelinated Aα motor nerve fiber conduction velocity distribution (CVD) from the corresponding compound muscle action potential (CMAP), also known as the inverse problem of motor nerve conduction study, has been explored in this work. The previous works to solve this type of problem were mostly in the discrete manner. We leveraged a continuous approach to exploit the gradient optimization technique to solve this problem. A diphasic sinusoidal function was taken to model the motor unit action potential (MUAP) signal and a 5th order polynomial function was taken to model the assumed continuous CVD curve. The continuous CVD instead of a discrete CVD helps us to perform more computation. The inverse results derived using the proposed methodology closely matched (almost 100%) the predicted CVD with the original CVD of different shapes from the corresponding simulated CMAP data using the forward solution of nerve conduction.

Road crash is one of the major burning issues for Bangladesh. There are several factors that are responsible for occurring road crashes. If we can understand the causes and predict the severity level of a particular type of accident upfront, we can take necessary steps in the proper time to lessen the damages. In this study, we have built some predictive models of different homogeneous road crash groups of Bangladesh using machine learning methods that can predict that particular road crash severity level based on the environmental factors and road conditions. We have applied Agglomerative Hierarchical Clustering to find different clusters of road crashes and then applied Random Forest technique to extract the significant predictors of each cluster and then applied C5.0 to build predictive models of each cluster. Finally we have discussed the patterns of fatal and non-fatal accidents of Bangladesh through rule generation technique.

Abstract Different epidemiological compartmental models have been presented to predict the transmission dynamics of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which is the most burning issue all over the world right now. In this study, we have proposed a new fuzzy rule-based Susceptible-Exposed-Infected-Recovered-Death (SEIRD) compartmental model to delineate the intervention and transmission heterogeneity in China, New Zealand, United States and Bangladesh for SARS-CoV-2 viral infection. We have introduced a new dynamic fuzzy transmission possibility variable in the compartmental model. Through our model, we have presented the correspondence of the intervention measures in relaxing the transmission possibility. We estimated that the peak in the US might arrive during the last half of August and for Bangladesh, it might occur during the first half of August, 2020 if current intervention measures are not violated. We have modeled a prediction scenario for Bangladesh if current intervention measures are violated due to Eid-ul-Azha. We further investigated what might happen if Bangladesh government reopens everything from September, 2020. We suggested various effective epidemic control policies for the authority of Bangladesh to fight against the virus. We concluded analyzing the current scenario of Bangladesh suggesting that extensive tests must be carried out collecting more samples of the asymptomatic individuals along with the symptomatic cases and also proper isolation and quarantine measures should be maintained strictly to contain the epidemic sooner.

The authors have requested that this preprint be withdrawn due to a need to make corrections.