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Accidents due to defective railway lines and derailments are common disasters that are observed frequently in Southeast Asian countries. It is imperative to run proper diagnosis over the detection of such faults to prevent such accidents. However, manual detection of such faults periodically can be both time-consuming and costly. In this paper, we have proposed a Deep Learning (DL)-based algorithm for automatic fault detection in railway tracks, which we termed an Ensembled Convolutional Autoencoder ResNet-based Recurrent Neural Network (ECARRNet). We compared its output with existing DL techniques in the form of several pre-trained DL models to investigate railway tracks and determine whether they are defective or not while considering commonly prevalent faults such as—defects in rails and fasteners. Moreover, we manually collected the images from different railway tracks situated in Bangladesh and made our dataset. After comparing our proposed model with the existing models, we found that our proposed architecture has produced the highest accuracy among all the previously existing state-of-the-art (SOTA) architecture, with an accuracy of 93.28% on the full dataset. Additionally, we split our dataset into two parts having two different types of faults, which are fasteners and rails. We ran the models on those two separate datasets, obtaining accuracies of 98.59% and 92.06% on rail and fastener, respectively. Model explainability techniques like Grad-CAM and LIME were used to validate the result of the models, where our proposed model ECARRNet was seen to correctly classify and detect the regions of faulty railways effectively compared to the previously existing transfer learning models. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG) has emerged as the most favorable source for recognizing brain disorders like epileptic seizure (ES) using deep learning (DL) methods. This study investigated the well-performed EEG-based ES detection method by decomposing EEG signals. Specifically, empirical mode decomposition (EMD) decomposes EEG signals into six intrinsic mode functions (IMFs). Three distinct features, namely, fluctuation index, variance, and ellipse area of the second order difference plot (SODP), were extracted from each of the IMFs. The feature values from all EEG channels were arranged in two composite feature forms: a 1D (i.e., unidimensional) form and a 2D image-like form. For ES recognition, the convolutional neural network (CNN), the most prominent DL model for 2D input, was considered for the 2D feature form, and a 1D version of CNN was employed for the 1D feature form. The experiment was conducted on a benchmark CHB-MIT dataset as well as a dataset prepared from the EEG signals of ES patients from Prince Hospital Khulna (PHK), Bangladesh. The 2D feature-based CNN model outperformed the other 1D feature-based models, showing an accuracy of 99.78% for CHB-MIT and 95.26% for PHK. Furthermore, the cross-dataset evaluations also showed favorable outcomes. Therefore, the proposed method with 2D composite feature form can be a promising ES detection method. [ABSTRACT FROM AUTHOR]

Mapping spatial data is essential for the monitoring of flooded areas, prognosis of hazards and prevention of flood risks. The Ganges River Delta, Bangladesh, is the world's largest river delta and is prone to floods that impact social–natural systems through losses of lives and damage to infrastructure and landscapes. Millions of people living in this region are vulnerable to repetitive floods due to exposure, high susceptibility and low resilience. Cumulative effects of the monsoon climate, repetitive rainfall, tropical cyclones and the hydrogeologic setting of the Ganges River Delta increase probability of floods. While engineering methods of flood mitigation include practical solutions (technical construction of dams, bridges and hydraulic drains), regulation of traffic and land planning support systems, geoinformation methods rely on the modelling of remote sensing (RS) data to evaluate the dynamics of flood hazards. Geoinformation is indispensable for mapping catchments of flooded areas and visualization of affected regions in real-time flood monitoring, in addition to implementing and developing emergency plans and vulnerability assessment through warning systems supported by RS data. In this regard, this study used RS data to monitor the southern segment of the Ganges River Delta. Multispectral Landsat 8-9 OLI/TIRS satellite images were evaluated in flood (March) and post-flood (November) periods for analysis of flood extent and landscape changes. Deep Learning (DL) algorithms of GRASS GIS and modules of qualitative and quantitative analysis were used as advanced methods of satellite image processing. The results constitute a series of maps based on the classified images for the monitoring of floods in the Ganges River Delta. [ABSTRACT FROM AUTHOR]

Developing precise groundwater level (GWL) forecast models is essential for the optimal usage of limited groundwater resources and sustainable planning and management of water resources. In this study, an improved forecasting accuracy for up to 3 weeks ahead of GWLs in Bangladesh was achieved by employing a coupled Long Short Term Memory (LSTM) network-based deep learning algorithm and Maximal Overlap Discrete Wavelet Packet Transform (MODWPT) data preprocessing. The coupled LSTM-MODWPT model's performance was compared with that of the LSTM model. For both standalone LSTM and LSTM-MODWPT models, the Random Forest feature selection approach was employed to select the ideal inputs from the candidate GWL lags. In the LSTM-MODWPT model, input GWL time series were decomposed using MODWPT. The 'Fejér-Korovkin' mother wavelet with a filter length of 18 was used to obtain a collection of scaling coefficients and wavelets for every single input time series. Model performance was assessed using five performance indices: Root Mean Squared Error; Scatter Index; Maximum Absolute Error; Median Absolute Deviation; and an a-20 index. The LSTM-MODWPT model outperformed standalone LSTM models for all time horizons in GWL forecasting. The percentage improvements in the forecasting accuracies were 36.28%, 32.97%, and 30.77%, respectively, for 1-, 2-, and 3-weeks ahead forecasts at the observation well GT3330001. Accordingly, the coupled LSTM-MODWPT model could potentially be used to enhance multiscale GWL forecasts. This research demonstrates that the coupled LSTM-MODWPT model could generate more precise GWL forecasts at the Bangladesh study site, with potential applications in other geographic locations globally. Highlights: An improved forecasting accuracy for up to 3 weeks ahead of groundwater levels is proposed A coupled LSTM and Maximal Overlap Discrete Wavelet Packet Transform (MODWPT) data preprocessing are utilized The Random Forest feature selection approach is employed to select the most influential input variables The coupled LSTM-MODWPT model outperformed the standalone LSTM model. [ABSTRACT FROM AUTHOR]

The environmental physiognomy of an area can significantly diminish its aesthetic appeal, rendering it susceptible to visual pollution, the unbeaten scourge of modern urbanization. In this study, we propose using a deep learning network and a robotic vision system integrated with Google Street View to identify streets and textile-based visual pollution in Dhaka, the megacity of Bangladesh. The issue of visual pollution extends to the global apparel and textile industry, as well as to various common urban elements such as billboards, bricks, construction materials, street litter, communication towers, and entangled electric wires. Our data collection encompasses a wide array of visual pollution elements, including images of towers, cables, construction materials, street litter, cloth dumps, dyeing materials, and bricks. We employ two open-source tools to prepare and label our dataset: LabelImg and Roboflow. We develop multiple neural network models to swiftly and accurately identify and classify visual pollutants in this work, including Faster SegFormer, YOLOv5, YOLOv7, and EfficientDet. The tuna swarm optimization technique has been used to select the applied models' final layers and corresponding hyperparameters. In terms of hardware, our proposed system comprises a Xiaomi-CMSXJ22A web camera, a 3.5-inch touchscreen display, and a Raspberry Pi 4B microcontroller. Subsequently, we program the microcontroller with the YOLOv5 model. Rigorous testing and trials are conducted on these deep learning models to evaluate their performance against various metrics, including accuracy, recall, regularization and classification losses, mAP, precision, and more. The proposed system for detecting and categorizing visual pollution within the textile industry and urban environments has achieved notable results. Notably, the YOLOv5 and YOLOv7 models achieved 98% and 92% detection accuracies, respectively. Finally, the YOLOv5 technique has been deployed into the Raspberry Pi edge device for instantaneous visual pollution detection. The proposed visual pollutants detection device can be easily mounted on various platforms (like vehicles or drones) and deployed in different urban environments for on-site, real-time monitoring. This mobility is crucial for comprehensive street-level data collection, potentially engaging local communities, schools, and universities in understanding and participating in environmental monitoring efforts. The comprehensive dataset on visual pollution will be published in the journal following the acceptance of our manuscript. [ABSTRACT FROM AUTHOR]

Environmental sustainability achievement is an increasingly significant issue in current society. Globally, unimpeded greenhouse gas emissions threaten environmental sustainability. As a developing economy, Bangladesh is extremely reliant on energy consumption, which results in greenhouse gas emissions and raises a significant threat to environmental sustainability. In our comprehensive analysis to study the energy consumption patterns of Bangladesh and their environmental implications, we employed a suite of advanced tests, including the auto-regressive distributed lag model. The Augmented-Dicky Fuller test and the Bound test, respectively, confirm the unit root and the co-integration status of the study variables. These methodologies illuminated the intricate relationship between fossil fuel consumption, industrial growth, and the consequent CO 2 emissions in the region. Despite the evident challenges posed by non-renewable energy sources, there's a discernible shift towards renewable energy and sustainable practices, especially in industrial sectors. This transition is further evidenced by the Long Short-Term Memory forecasting model, which projects a promising decline in CO 2 emissions over the next six years, plummeting from 70 metric tons to a mere 15 metric tons annually. While these findings highlight the strides Bangladesh is making towards sustainability, they also underscore the importance of continued emphasis on green technology and eco-friendly policies to ensure a sustainable future. [ABSTRACT FROM AUTHOR]

This research presents a comprehensive study on developing and evaluating a deep learning-based forecasting model for hourly energy demand prediction in Bangladesh. Leveraging a novel dataset obtained from the Power Grid Company of Bangladesh (PGCB), the proposed model utilizes bi-directional long short-term memory networks (Bi-LSTMs), implemented through Tensor-Flow and Keras libraries. The study meticulously preprocesses the data, handling missing values and ensuring compatibility with the selected models. The models are trained and evaluated using Mean Absolute Error (MAE) and Mean Squared Error (MSE) metrics, revealing promising results of 376.72 of MAE. The experimental findings demonstrate the effectiveness of the developed forecasting model, showcasing its capability to predict energy demand accurately. The insights derived from this study pave the way for enhanced energy management strategies, fostering sustainable and efficient energy utilization practices. [ABSTRACT FROM AUTHOR]

Short-term load forecasting (STLF) plays a crucial role in the planning, management, and stability of a country's power system operation. In this study, we have developed a novel approach that can simultaneously predict the load demand of different regions in Bangladesh. When making predictions for loads from multiple locations simultaneously, the overall accuracy of the forecast can be improved by incorporating features from the various areas while reducing the complexity of using multiple models. Accurate and timely load predictions for specific regions with distinct demographics and economic characteristics can assist transmission and distribution companies in properly allocating their resources. Bangladesh, being a relatively small country, is divided into nine distinct power zones for electricity transmission across the nation. In this study, we have proposed a hybrid model, combining the Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU), designed to forecast load demand seven days ahead for each of the nine power zones simultaneously. For our study, nine years of data from a historical electricity demand dataset (from January 2014 to April 2023) are collected from the Power Grid Company of Bangladesh (PGCB) website. Considering the nonstationary characteristics of the dataset, the Interquartile Range (IQR) method and load averaging are employed to deal effectively with the outliers. Then, for more granularity, this data set has been augmented with interpolation at every 1 h interval. The proposed CNN-GRU model, trained on this augmented and refined dataset, is evaluated against established algorithms in the literature, including Long Short-Term Memory Networks (LSTM), GRU, CNN-LSTM, CNN-GRU, and Transformer-based algorithms. Compared to other approaches, the proposed technique demonstrated superior forecasting accuracy in terms of mean absolute performance error (MAPE) and root mean squared error (RMSE). The dataset and the source code are openly accessible to motivate further research. [ABSTRACT FROM AUTHOR]

Forecasting rainfall is crucial to the well-being of individuals and is significant everywhere in the world. It contributes to reducing the disastrous effects of floods on agriculture, human life, and socioeconomic systems. This study discusses the challenges of effectively forecasting rainfall and floods and the necessity of combining data with flood channel mathematical modelling to forecast floodwater levels and velocities. This research focuses on leveraging historical meteorological data to find trends using machine learning and deep learning approaches to estimate rainfall. The Bangladesh Meteorological Department provided the data for the study, which also uses eight machine learning algorithms. The performance of the machine learning models is examined using evaluation measures like the R2 score, root mean squared error and validation loss. According to this research's findings, polynomial regression, random forest regression, and long short-term memory (LSTM) had the highest performance levels. Random forest and polynomial regression have an R2 value of 0.76, while LSTM has a loss value of 0.09, respectively. [ABSTRACT FROM AUTHOR]

Mulberry leaves feed Bombyx mori silkworms to generate silk thread. Diseases that affect mulberry leaves have reduced crop and silk yields in sericulture, which produces 90% of the world's raw silk. Manual leaf disease identification is tedious and error-prone. Computer vision can categorize leaf diseases early and overcome the challenges of manual identification. No mulberry leaf deep learning (DL) models have been reported. Therefore, in this study, two types of leaf diseases: leaf rust and leaf spot, with disease-free leaves, were collected from two regions of Bangladesh. Sericulture experts annotated the leaf images. The images were pre-processed, and 6,000 synthetic images were generated using typical image augmentation methods from the original 764 training images. Additional 218 and 109 images were employed for testing and validation respectively. In addition, a unique lightweight parallel depth-wise separable CNN model, PDS-CNN was developed by applying depth-wise separable convolutional layers to reduce parameters, layers, and size while boosting classification performance. Finally, the explainable capability of PDS-CNN is obtained through the use of SHapley Additive exPlanations (SHAP) evaluated by a sericulture specialist. The proposed PDS-CNN outperforms well-known deep transfer learning models, achieving an optimistic accuracy of 95.05 ± 2.86% for threeclass classifications and 96.06 ± 3.01% for binary classifications with only 0.53 million parameters, 8 layers, and a size of 6.3 megabytes. Furthermore, when compared with other well-known transfer models, the proposed model identified mulberry leaf diseases with higher accuracy, fewer factors, fewer layers, and lower overall size. The visually expressive SHAP explanation images validate the models' findings aligning with the predictions made the sericulture specialist. Based on these findings, it is possible to conclude that the explainable AI (XAI)-based PDSCNN can provide sericulture specialists with an effective tool for accurately categorizing mulberry leaves. [ABSTRACT FROM AUTHOR]

Pitha, pithe, or peetha are all Bangla words referring to a native and traditional food of Bangladesh as well as some areas of India, especially the parts of India where Bangla is the primary language. Numerous types of pithas exist in the culture and heritage of the Bengali and Bangladeshi people. Pithas are traditionally prepared and offered on important occasions in Bangladesh, such as welcoming a bride grooms, or bride, entertaining guests, or planning a special gathering of family, relatives, or friends. The traditional pitha celebration and pitha culture are no longer widely practiced in modern civilization. Consequently, the younger generation is unfamiliar with our traditional pitha culture. In this study, an effective pitha image classification system is introduced. convolutional neural network (CNN) pre-trained models EfficientNetB6, ResNet50, and VGG16 are used to classify the images of pitha. The dataset of traditional popular pithas is collected from different parts of Bangladesh. In this experiment, EfficientNetB6 and ResNet50 show nearly 90% accuracy. The best classification result was obtained using VGG16 with 92% accuracy. The main motive of this study is to revive the Bengali pitha tradition among young people and people worldwide, which will encourage many other researchers to pursue research in this domain. [ABSTRACT FROM AUTHOR]

The guava plant is widely cultivated in various regions of the Sub-Continent and Asian countries, including Bangladesh, due to its adaptability to different soil conditions and climate environments. The fruit plays a crucial role in providing food security and nutrition for the human body. However, guava plants are susceptible to various infectious leaf diseases, leading to significant crop losses. To address this issue, several heavyweight deep learning models have been developed in precision agriculture. This research proposes a transfer learning-based model named GLD-Det, which is designed to be both lightweight and robust, enabling real-time detection of guava leaf disease using two benchmark datasets. GLD-Det is a modified version of MobileNet, featuring additional components with two pooling layers such as max and global average, three batch normalisation layers, three dropout layers, ReLU as an activation function with four dense layers, and SoftMax as a classification layer with the last lighter dense layer. The proposed GLD-Det model outperforms all existing models with impressive accuracy, precision, recall, and AUC score with values of 0.98, 0.98, 0.97, and 0.99 on one dataset, and with values of 0.97, 0.97, 0.96, and 0.99 for the other dataset, respectively. Furthermore, to enhance trust and transparency, the proposed model has been explained using the Grad-CAM technique, a class-discriminative localisation approach. [ABSTRACT FROM AUTHOR]

In the modern capital market, the price of a stock is often considered to be highly volatile and unpredictable because of various social, financial, political and other dynamic factors. With calculated and thoughtful investment, stock market can ensure a handsome profit with minimal capital investment, while incorrect prediction can easily bring catastrophic financial loss to the investors. This paper introduces the application of a recently introduced machine learning model — the transformer model, to predict the future price of stocks of Dhaka Stock Exchange (DSE), the leading stock exchange in Bangladesh. The transformer model has been widely leveraged for natural language processing and computer vision tasks, but, to the best of our knowledge, has never been used for stock price prediction task task using DSE data. Recently, the introduction of time2vec encoding to represent the time series features has made it possible to employ the transformer model for the stock price prediction. This paper aims to leverage these two effective techniques to discover forecasting ability on the volatile stock market of DSE. We deal with the historical daily and weekly data of eight specific stocks listed in DSE. Our experiments demonstrate promising results and acceptable root-mean-squared error on most of the stocks. We also compare the performance of our model with that of a well-known benchmark stock forecasting model called ARIMA and report satisfactory results. [ABSTRACT FROM AUTHOR]

In recent years, Bangladesh has seen significant development in the digitalization of various healthcare services. Although many mobile applications and social platforms have been developed to automate the services of the healthcare sector, there is still scope to make the process smooth and easily accessible for general people. This paper describes a system where the users can give their health-related problems or symptoms in the native Bengali language, and the system would recommend the medical specialist the user should visit based on their stated symptoms. The data is processed using various Natural Language Processing techniques. In this study, we have applied both Machine Learning and Deep Learning-based approaches. Three different models of Machine learning and four models of deep learning have been applied, analyzed and the accuracy of various models is evaluated to determine the best one that could provide superior performance on the given dataset. From the pool of traditional machine learning algorithms, the Random Forest (RF) classifier gives the highest accuracy of about 94.60% and Convolutional Neural Network performs the best among the deep-learning models, with an accuracy of 94.17%. [ABSTRACT FROM AUTHOR]

Pest attacks pose a substantial threat to jute production and other significant crop plants. Jute farmers in Bangladesh generally distinguish between different pests that appear to be the same using their eyes and expertise, which isn't always accurate. We developed an intelligent model for jute pests identification based on transfer learning (TL) and deep convolutional neural networks (DCNN) to solve this practical problem. The proposed DCNN model can realize fast and accurate automatic identification of jute pests based on photographs. Specifically, the VGG19 CNN model was trained by TL on the ImageNet database. A well-structured image dataset of four dominant jute pests is also established. Our model shows a final accuracy of 95.86% on the four most vital jute pest classes. The model's performance is further demonstrated by the precision, recall, F1-score, and confusion matrix results. The proposed model is integrated into Android and IOS applications for practical uses. [ABSTRACT FROM AUTHOR]

Modern development in deep learning and computer vision techniques, intelligent transportation system (ITS) has emerged as a useful tool for building a traffic infrastructure in smart cities. Previously, several computer vision techniques have been proposed for vehicle recognition, which were limited in handling undisciplined, dense and laneless traffic conditions. Moreover, these frameworks did not incorporate many of the local vehicle configurations common in South Asian countries such as Pakistan, Bangladesh, and India. Considering the limitations of previous frameworks, this paper presents efficient vehicle detection and counting model for undisciplined conditions including dense and laneless traffic, occulusion cases and diverse range of local vehicles. A dataset of more than 2400 images of vehicles has been collected comprising of six new categories of local vehicles, and considering undisciplined traffic conditions to ensure robustness in vehicle detection and counting system. Transfer learning based technique has been used, using faster R-CNN model with Inception V2 as underlying architecture. The experimental results show a precision of 86.14% in terms of mAP. The work finds its application in South Asian contexts as more smart cities are formed in this region. The proposed framework will enable traffic monitoring with higher reliability, accuracy and granularity, contributing in having next-generation ITS. [ABSTRACT FROM AUTHOR]

Cancer is a problematic global health issue with an extremely high fatality rate throughout the world. The application of various machine learning techniques that have appeared in the field of cancer diagnosis in recent years has provided meaningful insights into efficient and precise treatment decision-making. Due to rapid advancements in sequencing technologies, the detection of cancer based on gene expression data has improved over the years. Different types of cancer affect different parts of the body in different ways. Cancer that affects the mouth, lip, and upper throat is known as oral cancer, which is the sixth most prevalent form of cancer worldwide. India, Bangladesh, China, the United States, and Pakistan are the top five countries with the highest rates of oral cavity disease and lip cancer. The major causes of oral cancer are excessive use of tobacco and cigarette smoking. Many people's lives can be saved if oral cancer (OC) can be detected early. Early identification and diagnosis could assist doctors in providing better patient care and effective treatment. OC screening may advance with the implementation of artificial intelligence (AI) techniques. AI can provide assistance to the oncology sector by accurately analyzing a large dataset from several imaging modalities. This review deals with the implementation of AI during the early stages of cancer for the proper detection and treatment of OC. Furthermore, performance evaluations of several DL and ML models have been carried out to show that the DL model can overcome the difficult challenges associated with early cancerous lesions in the mouth. For this review, we have followed the rules recommended for the extension of scoping reviews and meta-analyses (PRISMA-ScR). Examining the reference lists for the chosen articles helped us gather more details on the subject. Additionally, we discussed AI's drawbacks and its potential use in research on oral cancer. There are methods for reducing risk factors, such as reducing the use of tobacco and alcohol, as well as immunization against HPV infection to avoid oral cancer, or to lessen the burden of the disease. Additionally, officious methods for preventing oral diseases include training programs for doctors and patients as well as facilitating early diagnosis via screening high-risk populations for the disease. [ABSTRACT FROM AUTHOR]

Job satisfaction among employees is a crucial factor in a burgeoning organization. Satisfied current employees mean more skilled employees will be interested in joining the organization in the future, which will reinforce the prosperity of the organization. Hence, employee job satisfaction is something that should be looked for both by the employees, and the employers. However, the job sector of a country often undergoes major changes, which affect the satisfaction of the employees. Analyzing anonymous employee job satisfaction may lead to an understanding of an organization's major pros and cons, which can help the employer determine the strategies they should adopt in the future. It can also be helpful for potential future employees to decide whether they would like to be a part of such an organization. Taking all of these into account, we attempted to analyze the job satisfaction of employees in this research. A deep hybrid learning-based architecture, BiLSTM-ANN is proposed to understand employee job satisfaction in a shorter period of time. To do so, we have accumulated employee reviews of 12 renowned IT and software companies in Bangladesh from Glassdoor.com. A sophisticated dataset is built to feed into the proposed architecture. Five polarities, namely super positive, positive, neutral, negative, and super negative, are assigned based on the rating given on Glassdoor.com. The evaluation shows that the proposed BiLSTM-ANN model outperformed the state-of-the-art architectures in different performance metrics. The model exhibits an F1 Score of 88.64% with a significantly less number of trainable parameters than other architectures. [ABSTRACT FROM AUTHOR]

Urban pollution is a massive global problem, especially in industrialized and developing nations. Visual pollution is an issue concerned with the external noticeable appearance of the modern urban areas causing human health disorders, emotional distress, driving distraction, environmental hazards, etc. Amidst the plethora of different forms of environmental pollution, visual pollution deteriorates the aesthetics of an urban environment, endorsing the importance of research and assessing it from different dimensions. The main objective of this study is to initialize a new concept of automatic identification and classification of visible contaminants related to textile industries using computer vision techniques. In this work, deep learning techniques have been applied for the automatic detection and classification of three categories of textile-based visual pollutants, i.e., cloth garbage, advertising billboards and signages, and textile dyeing waste materials. Initially, 1,709 visual pollutants images were obtained through web crawling of search engines. Additionally, 954 images were collected from two local garments factories, roadside vendors and shopping malls of Bangladesh. Next, the dataset was manually annotated by an open-source labeling tool. Finally, various deep learning techniques, Faster R-CNN, YOLOv5, and EfficientDet, have been used to classify the obtained dataset automatically. The EfficientDet framework achieved the best performance with 97 % and 93 % training and test accuracies, respectively. The YOLOv5 approach exhibits acceptable precision with a considerably lower number of epochs. The proposed automated classification system is expected to create future visual pollution ratings for the textile industries. Consequently, the corresponding stakeholders (industry owners, government authorities, factory workers, etc.) can introduce regulatory frameworks and control the proliferation of visual pollution. The open-source images obtained by web crawling, locally collected visual pollutants dataset and implementation code of this work are available at: https://github.com/SadiaAfrin163/Textile-Visual-Pollutants-Dataset. [ABSTRACT FROM AUTHOR]

Understanding the impact of input variables on black‐box machine learning and deep learning models is necessary. Therefore, this study proposed SHapley Additive exPlanations (SHAP) values to address the problem of the interpretability of the output of the support vector machine (SVM), random forest (RF), convolutional neural network (CNN), and long short‐term memory (LSTM) models to forecast climatic water balance (CWB) within 1–3 months lead‐time. The current study uses two Koppen–Geiger climate zones over Bangladesh: the humid subtropical climate with dry winter and hot summer (Cwa) and the tropical climate (Af‐Am). Monthly antecedent CWB, potential evapotranspiration (PET), convective available potential energy (CAPE), relative humidity (RH), air temperature (TEM), El Niño–Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), and North Atlantic oscillation (NAO) are used as the inputs to the proposed model. SHAP values show that CWB (current month) for both climate zones has the maximum impact on the prediction. The effects of atmospheric variables and ocean–atmospheric teleconnections on CWB forecasts over Bangladesh are low. Forecasting results show that the SVM model shows the best performance for CWB forecasts in the Cwa and Af‐Am climate zones in terms of antecedent CWB as input. And this model effectively decreases the negative effect of increasing forecast lead‐time. Since the proposed model can predict CWB in 3‐months lead‐time, it would help policy‐makers and practitioners to reduce the drought and flood impacts in the future by adopting better preparedness plans. [ABSTRACT FROM AUTHOR]

Freely available building maps of rapidly changing built and semi-built environments may contain label noise. When temporal correspondence between images and labels does not hold, the labels may be subject to incorrectly observed building instances. For example, in most growing semi-built environments, such as the Kutupalong mega-camp in Bangladesh, labels corresponding to a past date may not be updated or might not have been properly labelled, resulting in label noise. Tagging/labelling can be done either manually (by humans) or automatically (by a machine/model). We manually label images for our stricter evaluation regime, but a trained model can automatically label images without human supervision. Our best performing model generates labels which improve F1-score by 17.2% and improve Intersection-over-Union score by 23.2%, when compared to the fidelity of commonly used noisy labels. Our stricter evaluation regime reveals interesting insights about the paradoxical behaviour of deep neural networks in conjunction to label noise. [ABSTRACT FROM AUTHOR]

Floods are considered one of the most destructive natural hydrometeorological disasters across the world. The present study attempts to assess flood susceptibility of the Brahmaputra floodplain of Bangladesh using Deep Boost, Deep Learning Neural Network, and Artificial Neural Network. Primarily, flood inventory maps were prepared from fieldworks and satellite image classification. Consequently, the flood locations were segregated into 70% training and 30% validation samples randomly for running the models and validating the models, respectively. The complete procedure is designed to be considered 12 flood conditioning criteria under four relevant components. The efficiency assessment of DLNN, DB, and ANN models using validation data through the area under the curve (AUC) reveals that DB demonstrates higher accuracy (AUC = .917) than DLNN (AUC = .901) and ANN (AUC = .895) approaches. Therefore, proposed susceptibility mapping approaches are efficient in assessing flood susceptibility accurately and can be implemented in other flood-affected regions. [ABSTRACT FROM AUTHOR]

Lane detection plays a vital role in making the idea of the autonomous car a reality. Traditional lane detection methods need extensive hand-crafted features and post-processing techniques, which make the models specific feature-oriented, and susceptible to instability for the variations on road scenes. In recent years, Deep Learning (DL) models, especially Convolutional Neural Network (CNN) models have been proposed and utilized to perform pixel-level lane segmentation. However, most of the methods focus on achieving high accuracy while considering structured roads and good weather conditions and do not put emphasis on testing their models on defected roads, especially ones with blurry lane lines, no lane lines, and cracked pavements, which are predominant in the real world. Moreover, many of these CNN-based models have complex structures and require high-end systems to operate, which makes them quite unsuitable for being implemented in embedded devices. Considering these shortcomings, in this paper, we have introduced a novel CNN model named LLDNet based on an encoder–decoder architecture that is lightweight and has been tested in adverse weather as well as road conditions. A channel attention and spatial attention module are integrated into the designed architecture to refine the feature maps for achieving outstanding results with a lower number of parameters. We have used a hybrid dataset to train our model, which was created by combining two separate datasets, and have compared the model with a few state-of-the-art encoder–decoder architectures. Numerical results on the utilized dataset show that our model surpasses the compared methods in terms of dice coefficient, IoU, and the size of the models. Moreover, we carried out extensive experiments on the videos of different roads in Bangladesh. The visualization results exhibit that our model can detect the lanes accurately in both structured and defected roads and adverse weather conditions. Experimental results elicit that our designed method is capable of detecting lanes accurately and is ready for practical implementation. [ABSTRACT FROM AUTHOR]

Brain tumor, a leading cause of uncontrolled cell growth in the central nervous system, presents substantial challenges in medical diagnosis and treatment. Early and accurate detection is essential for effective intervention. This study aims to enhance the detection and classification of brain tumors in Magnetic Resonance Imaging (MRI) scans using an innovative framework combining Vision Transformer (ViT) and Gated Recurrent Unit (GRU) models. We utilized primary MRI data from Bangabandhu Sheikh Mujib Medical College Hospital (BSMMCH) in Faridpur, Bangladesh. Our hybrid ViT-GRU model extracts essential features via ViT and identifies relationships between these features using GRU, addressing class imbalance and outperforming existing diagnostic methods. We extensively processed the dataset, and then trained the model using various optimizers (SGD, Adam, AdamW) and evaluated through rigorous 10-fold cross-validation. Additionally, we incorporated Explainable Artificial Intelligence (XAI) techniques-Attention Map, SHAP, and LIME-to enhance the interpretability of the model's predictions. For the primary dataset BrTMHD-2023, the ViT-GRU model achieved precision, recall, and F1-score metrics of 97%. The highest accuracies obtained with SGD, Adam, and AdamW optimizers were 81.66%, 96.56%, and 98.97%, respectively. Our model outperformed existing Transfer Learning models by 1.26%, as validated through comparative analysis and cross-validation. The proposed model also shows excellent performances with another Brain Tumor Kaggle Dataset outperforming the existing research done on the same dataset with 96.08% accuracy. The proposed ViT-GRU framework significantly improves the detection and classification of brain tumors in MRI scans. The integration of XAI techniques enhances the model's transparency and reliability, fostering trust among clinicians and facilitating clinical application. Future work will expand the dataset and apply findings to real-time diagnostic devices, advancing the field., (© 2024. The Author(s).)

The encouraging trend of usage of human machine interfaces in diverse areas has driven the evolution of Automatic Speech Recognition (ASR) systems during last two decades. Lately, the inclination has been towards the use of machine learning techniques for under-resourced human languages primarily to focus on designing of voice activated digital tool for a sizable portion of computer illiterate speakers. A vast majority of the works in this field have employed shallow models like conventional Artificial Neural Network and Hidden Markov Model in combination with Mel Frequency Cepstral Coefficients and other relevant features for the applications of speech recognition systems. Although these shallow models are found effective, but to minimize human intervention from the approach and also to yield the better system performance, recent research has focused to incorporate deep learning models for ASR applications especially for under-resourced languages. Sylheti language, a member of Indo-Aryan language group, is an under resourced language which has more than 10 million Sylheti speakers living across the world mostly in India and Bangladesh. Focusing on the need of an ASR model for Sylheti, this work aims to design a robust ASR model for an under resourced language Sylheti by employing state-of-the-art deep learning technique Convolutional Neural Network (CNN). To find out the best and suitable ASR model for Sylheti, certain ASR approaches are formulated and trained by Sylheti isolated and connected words. The specially configured ASR model based on CNN is trained with clean, and noisy speech data which are necessary for training and making the system robust. Thereafter, a comparative analysis is presented by configuring the ASR model by some shallow models like Feed-forward neural network, Recurrent neural Network, Hidden Markov model and Time Delay neural Network. Experimental results indicate that the proposed CNN based ASR system works well for Sylheti language and the performance accuracy obtained by the system is found to be satisfactory despite the system demonstrating certain training latency. [ABSTRACT FROM AUTHOR]

Floods and landslides have cascading effects on coastal areas of Bangladesh. This study aims to develop multi-hazard maps (e.g., floods and landslides) for the coastal region, by integrating a genetic algorithm optimizer with Long Short-Term Memory deep learning algorithm (GA-LSTM-DLA), global climate model, and geospatial data. It assesses vulnerabilities to multiple hazards and projects future risks considering the Australian Community Climate and Earth System Simulator Earth System Model 1.5 (ACCESS-ESM1-5) under three climate scenarios, i.e., Shared Socioeconomic Pathways1-2.6, 2–4.5, and 5–8.5 (SSP1-2.6, SSP2-4.5, and SSP5-8.5). The Receiver Operating Characteristic (ROC) curve is used to assess the performance of GA-LSTM-DLA model. According to important feature obtained by random forest (RF) algorithm, most important features for flash, riverine, tidal floods, and landslides are drainage density, rainfall, and geology, respectively. The baseline susceptibility maps for flash, riverine, tidal floods, and landslides are initially recorded as 0.11%, 7.99%, 3.46%, and 0.03%. Projections for the year 2100, under different SSPs, show significant increase, i.e., SSP1-2.6—flash 1.31%, riverine 23.74%, tidal 26.97%, landslides 2.05%; SSP2-4.5—flash 0.12%, riverine 30.97%, tidal 10.23%, landslides 0.18%; SSP5-8.5—flash 0.02%, riverine 17.42%, tidal 6.19%, landslides 0.14%. These projections highlight urgent need for mitigation and adaptation measures against hazards susceptibility, particularly under more extreme socioeconomic scenarios. Overall, the findings of this work are critical for policymakers to develop informed strategies for climate resilience, sustainable development, and disaster risk reduction in the coastal region of Bangladesh. [ABSTRACT FROM AUTHOR]

This study aims to develop an android application to detect and identify plant diseases through deep convolutional neural network. Plant disease is a critical issue in agricultural countries like Bangladesh. Every year production of crops sustains heavy loss due to diseases. It is quite difficult to detect plant diseases with human eyes. So it is essential to build an automated system to detect the diseases. The proposed disease detection model takes an image of a plant leaf as input, processes it and uses deep convolutional neural network to detect and identify the disease. The developed mobile application is user-friendly and can be used by farmers without much technical knowledge. [ABSTRACT FROM AUTHOR]

COVID-19 disease is a major health calamity in twentieth century, in which the infection is spreading at the global level. Developing countries like Bangladesh, India, and others are still facing a delay in recognizing COVID-19 cases. Hence, there is a need for immediate recognition with perfect identification of infection. This clear visualization helps to save the life of suspected COVID-19 patients. With the help of traditional RT-PCR testing, the combination of medical images and deep learning classifiers delivers more hopeful results with high accuracy in the prediction and recognition of COVID-19 cases. COVID-19 disease is recently researched through sample chest X-ray images, which have already proven its efficiency in lung diseases. To emphasize corona virus testing methods and to control the community spreading, the automatic detection process of COVID-19 is processed through the detailed medication reports from medical images. Although there are numerous challenges in the manual understanding of traces in COVID-19 infection from X-ray, the subtle differences among normal and infected X-rays can be traced by the data patterns of Convolutional Neural Network (CNN). To improve the detection performance of CNN, this paper plans to develop an Ensemble Learning with CNN-based Deep Features (EL-CNN-DF). In the initial phase, image scaling and median filtering perform the pre-processing of the chest X-ray images gathered from the benchmark source. The second phase is lung segmentation, which is the significant step for COVID detection. It is accomplished by the Adaptive Activation Function-based U-Net (AAF-U-Net). Once the lungs are segmented, it is subjected to novel EL-CNN-DF, in which the deep features are extracted from the pooling layer of CNN, and the fully connected layer of CNN are replaced with the three classifiers termed "Support Vector Machine (SVM), Autoencoder, Naive Bayes (NB)". The final detection of COVID-19 is done by these classifiers, in which high ranking strategy is utilized. As a modification, a Self Adaptive-Tunicate Swarm Algorithm (SA-TSA) is adopted as a boosting algorithm to enhance the performance of segmentation and detection. The overall analysis has shown that the precision of the enhanced CNN by using SA-TSA was 1.02%, 4.63%, 3.38%, 1.62%, 1.51% and 1.04% better than SVM, autoencoder, NB, Ensemble, RNN and LSTM respectively. The comparative performance analysis on existing model proves that the proposed algorithm is better than other algorithms in terms of segmentation and classification of COVID-19 detection. [ABSTRACT FROM AUTHOR]

Globally, tea production and its quality fundamentally depend on tea leaves, which are susceptible to invasion by pathogenic organisms. Precise and early-stage identification of plant foliage diseases is a key element in preventing and controlling the spreading of diseases that hinder yield and quality. Image processing techniques are a sophisticated tool that is rapidly gaining traction in the agricultural sector for the detection of a wide range of diseases with excellent accuracy. This study focuses on a pragmatic approach for automatically detecting selected tea foliage diseases based on convolutional neural network (CNN). A large dataset of 3330 images has been created by collecting samples from different regions of Sylhet division, the tea capital of Bangladesh. The proposed CNN model is developed based on tea leaves affected by red rust, brown blight, grey blight, and healthy leaves. Afterward, the model's prediction was validated with laboratory tests that included microbial culture media and microscopic analysis. The accuracy of this model was found to be 96.65%. Chiefly, the proposed model was developed in the context of the Bangladesh tea industry., (© 2024. The Author(s).)

Computer-assisted algorithms have become a mainstay of biomedical applications to improve accuracy and reproducibility of repetitive tasks like manual segmentation and annotation. We propose a novel pipeline for red blood cell detection and counting in thin blood smear microscopy images, named RBCNet, using a dual deep learning architecture. RBCNet consists of a U-Net first stage for cell-cluster or superpixel segmentation, followed by a second refinement stage Faster R-CNN for detecting small cell objects within the connected component clusters. RBCNet uses cell clustering instead of region proposals, which is robust to cell fragmentation, is highly scalable for detecting small objects or fine scale morphological structures in very large images, can be trained using non-overlapping tiles, and during inference is adaptive to the scale of cell-clusters with a low memory footprint. We tested our method on an archived collection of human malaria smears with nearly 200,000 labeled cells across 965 images from 193 patients, acquired in Bangladesh, with each patient contributing five images. Cell detection accuracy using RBCNet was higher than 97 %. The novel dual cascade RBCNet architecture provides more accurate cell detections because the foreground cell-cluster masks from U-Net adaptively guide the detection stage, resulting in a notably higher true positive and lower false alarm rates, compared to traditional and other deep learning methods. The RBCNet pipeline implements a crucial step towards automated malaria diagnosis. [ABSTRACT FROM AUTHOR]

Improving compliance with environmental regulations is critical for promoting clean environments and healthy populations. In South Asia, brick manufacturing is a major source of pollution but is dominated by small-scale, informal producers who are difficult to monitor and regulate--a common challenge in low-income settings. We demonstrate a low-cost, scalable approach for locating brick kilns in high-resolution satellite imagery from Bangladesh. Our approach identifies kilns with 94.2% accuracy and 88.7% precision and extracts the precise GPS coordinates of every brick kiln across Bangladesh. Using these estimates, we show that at least 12% of the population of Bangladesh (>18 million people) live within 1 km of a kiln and that 77% and 9% of kilns are (illegally) within 1 km of schools and health facilities, respectively. Finally, we show how kilns contribute up to 20.4 μg/m3 of PM2:5 (particulate matter of a diameter less than 2.5 μm) in Dhaka when the wind blows from an unfavorable direction. We document inaccuracies and potential bias with respect to local regulations in the government data. Our approach demonstrates how machine learning and Earth observation can be combined to better understand the extent and implications of regulatory compliance in informal industry. [ABSTRACT FROM AUTHOR]

Building maps have a plethora of applications in government, industry and academia. In most cases, large scale maps can be retrieved from OpenStreetMap vector data. However, for certain rapidly changing built and semi-built environments, corresponding maps are not as accurate and contain label noise such as missing, incorrectly present, shifted labels, etc.; mainly because buildings in those regions are constantly being constructed, deconstructed, replaced and altered. One such case is extant in the Rohingya camps of southeastern border region of Bangladesh. Mass refugee influx in late 2017 and following population growth has necessitated the construction of buildings and expansion of camps. Consequently, reliable methods are necessary for detecting and documenting camp buildings. Ultra-high-resolution drone images of Rohingya camps are semantically segmented through fully convolutional U-Net deep learning systems for generating accurate building maps from noisy labels. A wide variety of noises are prevalent in the labels. Deep learning systems provide less noisy predictions compared to the classification tool in the most widely used Geographic Information System (GIS) software, ArcGIS. Data augmentation and regularization allows reliable learning, even in the presence of label noise. During testing, calculation of numeric performance metrics against noisy labels can grossly underestimate true skill and performance of the model. A subset of 22 million pixels of the testing data is relabelled by hand to obtain noise-free labels. Testing our generated maps against noisy and noise-free labels confirms that true performance is higher than otherwise indicated by freely available building maps. Empirical results reveal that utilized pipeline is able to learn from noisy data and produce labels which are more accurate and less noisy. Labels generated by our best performing system provide Intersection-over-Union (IoU) gain of 17.6% and Dice score gain of 13.6% over freely available labels from OpenStreetMap. Finally, spatio-temporal building maps are generated to portray the applicability of this research. [ABSTRACT FROM AUTHOR]

The task of author profiling aims to distinguish the author's profile traits from a given content. It has got potential applications in marketing, forensic analysis, fake profile detection, etc. In recent years, the usage of bi-lingual text has raised due to the global reach of social media tools as people prefer to use language that expresses their true feelings during online conversations and assessments. It has likewise impacted the use of bi-lingual (English and Roman-Urdu) text in the sub-continent (Pakistan, India, and Bangladesh) over social media. To develop and evaluate methods for bi-lingual author profiling, benchmark corpora are needed. The majority of previous efforts have focused on developing mono-lingual author profiling corpora for English and other languages. To fulfill this gap, this study aims to explore the problem of author profiling on bi-lingual data and presents a benchmark corpus of bi-lingual (English and Roman-Urdu) tweets. Our proposed corpus contains 339 author profiles and each profile is annotated with six different traits including age, gender, education level, province, language, and political party. As a secondary contribution, a range of deep learning methods, CNN, LSTM, Bi-LSTM, and GRU, are applied and compared on the three different bi-lingual corpora for age and gender identification, including our proposed corpus. Our extensive experimentation showed that the best results for both gender identification task (Accuracy = 0.882, F1-Measure = 0.839) and age identification (Accuracy = 0.735, F1-Measure = 0.739) are obtained using Bi-LSTM deep learning method. Our proposed bi-lingual tweets corpus is free and publicly available for research purposes. [ABSTRACT FROM AUTHOR]

A global pandemic on March 11th of 2020, which was initially renowned by the World Health Organization (WHO) revealed the coronavirus (the COVID-19 epidemic). Coronavirus was flown in -December 2019 in Wuhan, Hubei region in China. Currently, the situation is enlarged by the infection in more than 200 countries all over the world. In this situation it was rising into huge forms in Bangladesh too. Modulated with a public dataset delivered by the IEDCR health authority, we have produced a sustainable prognostic method of COVID-19 outbreak in Bangladesh using a deep learning model. Throughout the research, we forecasted up to 30 days in which per day actual prediction was confirmed, death and recoveries number of people. Furthermore, we illustrated that long short-term memory (LSTM) demands the actual output trends among time series data analysis with a controversial study that exceeds random forest (RF) regression and support vector regression (SVR), which both are machine learning (ML) models. The current COVID-19 outbreak in Bangladesh has been considered in this paper. Here, a well-known recurrent neural network (RNN) model in order to referred by the LSTM network that has forecasted COVID-19 cases on per day infected scenario of Bangladesh from May 15th of 2020 till June 15th of 2020. Added with a comparative study that drives into the LSTM, SVR, RF regression which is processed by the RMSE transmission rate. In all respects, in Bangladesh the gravity of COVID-19 has become excessive nowadays so that depending on this situation public health sectors and common people need to be aware of this situation and also be able to get knowledge of how long self-lockdown will be maintained. So far, to the best of our knowledge LSTM based time series analysis forecasting infectious diseases is a well-done formula [ABSTRACT FROM AUTHOR]

Multi-agent deep reinforcement learning (MADRL) approaches are at the forefront of contemporary research in optimum electric vehicle (EV) charging scheduling challenges. These techniques involve multiple agents that respond to a dynamic simulation environment to strategically integrate EV charging stations (EVCSs) on microgrids by incorporating the constraints posed by stochastic trip durations. In addition, recent research works have demonstrated that planning frameworks based on multi-objective optimization (MOO) techniques are suitable for the efficient functioning of microgrids comprising renewable energy sources (RESs) and battery energy storage systems (BESSs). Even though MADRL techniques have been used to solve the optimum EV charging scheduling challenges and MOO frameworks have been developed to determine the optimal RES-BESS allocation, the potential of merging MADRL and MOO is yet to be explored. Therefore, this research provides an opportunity to determine the effectiveness of combined MOO-MADRL dynamics and their computational efficacy. In this context, this work presents a novel Multi-objective Artificial Vultures Optimization Algorithm based on Multi-agent Deep Deterministic Policy Gradient (MOAVOA-MADDPG) planning framework for allocating RESs, BESSs, and EVCSs on microgrids. The objective function is formulated to optimize the network power losses, total installation and operational costs, greenhouse gas emissions, and system voltage stability. Moreover, the proposed framework incorporates the sporadic nature of RES systems and intends to improve the state of charge (SOC) of the EVs present in the network. The presented approach is validated using practical weather data and EV commuting behavior on the modified IEEE 33 bus network, two practical distribution feeders in Bangladesh, and the Turkish 141 bus network. According to the findings, the MOAVOA-MADDPG framework effectively accommodated the financial, technical, and environmental considerations with improved average SOC of the vehicles. Furthermore, statistical analysis, spacing, convergence, and hyper-volume metrics are employed to compare the suggested MOAVOA-MADDPG framework with five contemporary techniques. The findings indicate that, in every metric considered, the MOAVOA-MADDPG Pareto fronts provide superior solutions. • A novel MOO-based MADRL framework is proposed for microgrid resources planning. • The optimal configuration of RES, BESS, and EVCS facilities is determined. • Technical, economic, and environmental factors are assessed. • The sporadic nature of RES systems is considered. • Proposed framework provides superior Pareto fronts compared to other methods. [ABSTRACT FROM AUTHOR]

Stomatal traits of leaves are critical for regulating the exchange of gases between plant tissues and the atmosphere, and thus play a crucial role in the physiological activities of plants. The hypothesis of this study is that distinct stomatal features among different species grown in diverse habitats can serve as a potential marker for species identification. Leaf samples were collected from the mangrove forests of Sundarbans and the freshwater swamp forests of Ratargul in Bangladesh. In total, we examined 11 species from eight different families. We used deep convolutional neural network (DCNN) to automatically identify tree species from microscopic stomatal imprints, as there is currently no established protocol for this task. For model training, 80% (866 images) of the data was used for training the models. Our study observed significant variations in stomatal attributes such as length, width, and density among different species, families, and habitats. These variations could help in accurate species identification by machine learning approaches used in the present study. An empirical comparison was conducted among EfficientNetV2, Xception, VGG16, VGG19, MobileNetV2, ResNet50V2, Resnet152, DenseNet201, and NasNetLarge. We propose a novel approach called the "Normalized Leverage Factor" that utilizes accuracy, precision, recall, and f1-score to select the optimal model. This approach eliminates the non-uniformity of the scores. Although MobileNetV2 achieved an accuracy of 99.06%, our findings indicate that EfficientNetV2 is the optimal model for species identification. This is due to its higher normalized leverage factor (1.92) compared to MobileNetV2 (1.88). The findings demonstrate that plants of diverse habitats show a unique footprint of stomata that offers an innovative method of species identification using DCNN. The study would help to develop a stomatal image-based user interface to identify species even without expert taxonomic knowledge and could be particularly useful in fields such as pharmacology, conservation biology, forestry, and environmental science. [Display omitted] • Stomata bears a unique footprint for the species identifications. • Deep learning techniques are used for automated species identifications. • We studied 11 species under eight families from mangrove and freshwater swamp forest. • A new approach formulated to evaluate the performance weight of models. • EfficientNetV2 outperforms the other models while showing no overfitting or performance degradation. [ABSTRACT FROM AUTHOR]

The main purpose of this paper is to find the ICT sector strategy as an integral part of an optimal strategy for Bangladesh to become first a middle income country by 2030 and then a high income country by 2041. Formally, I argue for a Social Capabilities Enhancing National Innovation System (SCENIS) for Bangladesh to be built in the next few decades. The transition—indeed transformation— can be realized through a process of rapid inclusive growth leading to elimination of poverty while increasing the productive capacity and building an innovating learning economy. Since ICT globally is a dynamic sector, Bangladesh will need to focus—without being exhaustive— at least on the following areas of global ICT development: Cloud Orchestration; Distributed Storage; Alternatives to Java; Big Data Processmg; Deep Learning m AI (Artificial Intelligence); the Internet of Things. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A real-time Bangla Sign Language interpreter can enable more than 200 k hearing and speech-impaired people to the mainstream workforce in Bangladesh. Bangla Sign Language (BdSL) recognition and detection is a challenging topic in computer vision and deep learning research because sign language recognition accuracy may vary on the skin tone, hand orientation, and background. This research has used deep machine learning models for accurate and reliable BdSL Alphabets and Numerals using two well-suited and robust datasets. The dataset prepared in this study comprises of the largest image database for BdSL Alphabets and Numerals in order to reduce inter-class similarity while dealing with diverse image data, which comprises various backgrounds and skin tones. The papers compared classification with and without background images to determine the best working model for BdSL Alphabets and Numerals interpretation. The CNN model trained with the images that had a background was found to be more effective than without background. The hand detection portion in the segmentation approach must be more accurate in the hand detection process to boost the overall accuracy in the sign recognition. It was found that ResNet18 performed best with 99.99% accuracy, precision, F1 score, sensitivity, and 100% specificity, which outperforms the works in the literature for BdSL Alphabets and Numerals recognition. This dataset is made publicly available for researchers to support and encourage further research on Bangla Sign Language Interpretation so that the hearing and speech-impaired individuals can benefit from this research. [ABSTRACT FROM AUTHOR]

The rapid expansion of a country's economy is highly dependent on timely product distribution, which is hampered by terrible traffic congestion. Additional staff are also required to follow the delivery vehicle while it transports documents or records to another destination. This study proposes Delicar, a self-driving product delivery vehicle that can drive the vehicle on the road and report the current geographical location to the authority in real-time through a map. The equipped camera module captures the road image and transfers it to the computer via socket server programming. The raspberry pi sends the camera image and waits for the steering angle value. The image is fed to the pre-trained deep learning model that predicts the steering angle regarding that situation. Then the steering angle value is passed to the raspberry pi that directs the L298 motor driver which direction the wheel should follow. Based upon this direction, L298 decides either forward or left or right or backwards movement. The 3-cell 12V LiPo battery handles the power supply to the raspberry pi and L298 motor driver. A buck converter regulates a 5V 3A power supply to the raspberry pi to be working. Nvidia CNN architecture has been followed, containing nine layers including five convolution layers and three dense layers to develop the steering angle predictive model. Geoip2 (a python library) retrieves the longitude and latitude from the equipped system's IP address to report the live geographical position to the authorities. After that, Folium is used to depict the geographical location. Moreover, the system's infrastructure is far too low-cost and easy to install. [ABSTRACT FROM AUTHOR]

Deep learning is a promising method for image classification, including satellite images acquired by various sensors. However, the synergistic use of geospatial data for water body extraction from Sentinel-1 data using deep learning and the applicability of existing deep learning models have not been thoroughly tested for operational flood monitoring. Here, we present a novel water body extraction model based on a deep neural network that exploits Sentinel-1 data and flood-related geospatial datasets. For the model, the U-Net was customised and optimised to utilise Sentinel-1 data and other flood-related geospatial data, including digital elevation model (DEM), Slope, Aspect, Profile Curvature (PC), Topographic Wetness Index (TWI), Terrain Ruggedness Index (TRI), and Buffer for the Southeast Asia region. Testing and validation of the water body extraction model was applied to three Sentinel-1 images for Vietnam, Myanmar, and Bangladesh. By segmenting 384 Sentinel-1 images, model performance and segmentation accuracy for all of the 128 cases that the combination of stacked layers had determined were evaluated following the types of combined input layers. Of the 128 cases, 31 cases showed improvement in Overall Accuracy (OA), and 19 cases showed improvement in both averaged intersection over union (IOU) and F1 score for the three Sentinel-1 images segmented for water body extraction. The averaged OA, IOU, and F1 scores of the 'Sentinel-1 VV' band are 95.77, 80.35, and 88.85, respectively, whereas those of 'band combination VV, Slope, PC, and TRI' are 96.73, 85.42, and 92.08, showing improvement by exploiting geospatial data. Such improvement was further verified with water body extraction results for the Chindwin river basin, and quantitative analysis of 'band combination VV, Slope, PC, and TRI' showed an improvement of the F1 score by 7.68 percent compared to the segmentation output of the 'Sentinel-1 VV' band. Through this research, it was demonstrated that the accuracy of deep learning-based water body extraction from Sentinel-1 images can be improved up to 7.68 percent by employing geospatial data. To the best of our knowledge, this is the first work of research that demonstrates the synergistic use of geospatial data in deep learning-based water body extraction over wide areas. It is anticipated that the results of this research could be a valuable reference when deep neural networks are applied for satellite image segmentation for operational flood monitoring and when geospatial layers are employed to improve the accuracy of deep learning-based image segmentation. [ABSTRACT FROM AUTHOR]

Vehicle type classification plays an essential role in developing an intelligent transportation system (ITS). Based on the modern accomplishments of deep learning (DL) on image classification, we proposed a model based on transfer learning, incorporating data augmentation, for the recognition and classification of Bangladeshi native vehicle types. An extensive dataset of Bangladeshi native vehicles, encompassing 10,440 images, was developed. Here, the images are categorized into 13 common vehicle classes in Bangladesh. The method utilized was a residual network (ResNet-50)-based model, with extra classification blocks added to improve performance. Here, vehicle type features were automatically extracted and categorized. While conducting the analysis, a variety of metrics was used for the evaluation, including accuracy, precision, recall, and F 1   −   Score . In spite of the changing physical properties of the vehicles, the proposed model achieved progressive accuracy. Our proposed method surpasses the existing baseline method as well as two pre-trained DL approaches, AlexNet and VGG-16. Based on result comparisons, we have seen that, in the classification of Bangladeshi native vehicle types, our suggested ResNet-50 pre-trained model achieves an accuracy of 98.00%. [ABSTRACT FROM AUTHOR]

The rapid expansion of a country's economy is highly dependent on timely product distribution, which is hampered by terrible traffic congestion. Additional staff are also required to follow the delivery vehicle while it transports documents or records to another destination. This study proposes Delicar, a self-driving product delivery vehicle that can drive the vehicle on the road and report the current geographical location to the authority in real-time through a map. The equipped camera module captures the road image and transfers it to the computer via socket server programming. The raspberry pi sends the camera image and waits for the steering angle value. The image is fed to the pre-trained deep learning model that predicts the steering angle regarding that situation. Then the steering angle value is passed to the raspberry pi that directs the L298 motor driver which direction the wheel should follow. Based upon this direction, L298 decides either forward or left or right or backwards movement. The 3-cell 12V LiPo battery handles the power supply to the raspberry pi and L298 motor driver. A buck converter regulates a 5V 3A power supply to the raspberry pi to be working. Nvidia CNN architecture has been followed, containing nine layers including five convolution layers and three dense layers to develop the steering angle predictive model. Geoip2 (a python library) retrieves the longitude and latitude from the equipped system's IP address to report the live geographical position to the authorities. After that, Folium is used to depict the geographical location. Moreover, the system's infrastructure is far too low-cost and easy to install.