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Despite yielding considerable degrees of accuracy in landslide predictions, the outcomes of different landslide susceptibility models are prone to spatial disagreement; and therefore, uncertainties. Uncertainties in the results of various landslide susceptibility models create challenges in selecting the most suitable method to manage this complex natural phenomenon. This study aimed to propose an approach to reduce uncertainties in landslide prediction, diagnosing spatial agreement in machine learning-based landslide susceptibility maps. It first developed landslide susceptibility maps of Cox’s Bazar district of Bangladesh, applying four machine learning algorithms: K-Nearest Neighbor (KNN), Multi-Layer Perceptron (MLP), Random Forest (RF), and Support Vector Machine (SVM), featuring hyperparameter optimization of 12 landslide conditioning factors. The results of all the four models yielded very high prediction accuracy, with the area under the curve (AUC) values range between 0.93 to 0.96. The assessment of spatial agreement of landslide predictions showed that the pixel-wise correlation coefficients of landslide probability between various models range from 0.69 to 0.85, indicating the uncertainty in predicted landslides by various models, despite their considerable prediction accuracy. The uncertainty was addressed by establishing a Logistic Regression (LR) model, incorporating the binary landslide inventory data as the dependent variable and the results of the four landslide susceptibility models as independent variables. The outcomes indicated that the RF model had the highest influence in predicting the observed landslide locations, followed by the MLP, SVM, and KNN models. Finally, a combined landslide susceptibility map was developed by integrating the results of the four machine learning-based landslide predictions. The combined map resulted in better spatial agreement (correlation coefficients range between 0.88 and 0.92) and greater prediction accuracy (0.97) compared to the individual models. The modelling approach followed in this study would be useful in minimizing uncertainties of various methods and improving landslide predictions.

Recent advances in the field of natural language processing has improved state-of-the-art performances on many tasks including question answering for languages like English. Bengali language is ranked seventh and is spoken by about 300 million people all over the world. But due to lack of data and active research on QA similar progress has not been achieved for Bengali. Unlike English, there is no benchmark large scale QA dataset collected for Bengali, no pretrained language model that can be modified for Bengali question answering and no human baseline score for QA has been established either. In this work we use state-of-the-art transformer models to train QA system on a synthetic reading comprehension dataset translated from one of the most popular benchmark datasets in English called SQuAD 2.0. We collect a smaller human annotated QA dataset from Bengali Wikipedia with popular topics from Bangladeshi culture for evaluating our models. Finally, we compare our models with human children to set up a benchmark score using survey experiments.

Since the dawn of human civilization, forced migration scenarios have been witnessed in different regions and populations, and is still present in the twenty-first century. The current largest population of stateless refugees in the world, the Rohingya people, reside in the southeastern border region of Bangladesh. Due to rapid expansion of refugee camps and lack of suitable locations, a large proportion of the infrastructure are at risk of landslides. This study aims to use machine learning for predicting landslide risk of camp infrastructure using geospatial features. Four supervised classification algorithms have been employed viz., (i) Logistic Regression (LR), (ii) Multi-Layer Perceptron (MLP), (iii) Gradient Boosted Trees (GBT) and (iv) Random Forest (RF) and applied on preprocessing varied versions of features. Results show that RF achieves accuracy of 76.19% and AUC of 0.76 on un-scaled features which is higher than all other algorithms. The applications of the study reside in refugee management and landslide susceptibility mapping of Rohingya camps, which can both potentially save refugee lives and serve as a case study for global applications.

Neonatal sepsis that is a major threat for maternal and neonatal health worldwide. In this work we design non-invasive, deep learning classification models for predicting accurately and efficiently the early-onset sepsis in neonates in Neonatal Intensive Care Units. By non-invasive, it means that no external instrument or foreign body is introduced when taking data for the classifier. Moreover, the data collected for the purpose of predicting and classifying subjects with neonatal sepsis is in the form of tabular, structured data. The deep learning classification models we design and propose in this are known for working with time series, sequential or image data. Hence, the objective of the current research is to propose such a model that makes use of the powerful tools inherent in Neural Networks for pattern recognition, and use them to outperform traditional machine learning algorithms to detect early-onset neonatal sepsis. Real life neonatal sepsis data samples from two different hospitals are used (Crecer's Hospital Centre in Cartagena-Colombia and Children's Hospital of Philadelphia) to make the evaluation of the Neural Networks as authentic as possible.

Food delivery systems are gaining popularity recently due to the expansion of internet connectivity and for the increasing availability of devices. The growing popularity of such systems has raised concerns regarding (i) Information security, (ii) Business to business (B2B) deep discounting race, and (iii) Strict policy enforcement. Sensitive personal data and financial information of the users must be safeguarded. Additionally, in pursuit of gaining profit, the restaurants tend to offer deep discounts resulting in a higher volume of orders than usual. Therefore, the restaurants and the delivery persons fail to maintain the delivery time and often impair the food quality. In this paper, we have proposed a blockchain and smart contract-based food delivery system to address these issues. The main goal is to remove commission schemes and decrease service delays caused by a high volume of orders. The protocols have been deployed and tested on the Ethereum test network. The simulation manifests a successful implementation of our desired system; with the payment being controlled by our system. The actors (restaurant, delivery-person or consumer) are bound to be compliant with the policies or penalized otherwise.

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%.

Due to their high price, RFID tags are not yet widely used in applications for monitoring, tracing, and information inscribing, such as manufacturing lines, supply chains, and inventory management. The currently utilized barcode-like tracing techniques lack security, experience data limitation, are semi-automated, and are prone to external damage. Chipless RFID technology is a potential replacement, able to overcome shortcomings. However, due to the uncertainty created by the absence of a communication antenna and microchip, the detection of chipless RFID tags has a 2–5% detection error rate. The recent advancement in spatial chipless RFID technology has opened up a plethora of opportunities to use deep learning, which is becoming increasingly popular, to detect chipless RFID tags from a 2D virtual image created from the backscattered signal. In this study, we presented a comprehensive and exhaustive investigation of several deep learning techniques to improve the detection capability of deep learning-based chipless RFID tag detection. Our proposed fine-tuning methods yielded better mathematical metrics on the state-of-the-art on its original setup. A confusion matrix used to measure error rates on unseen data shows that the improved deep learning architecture we introduced works at roughly 99.99% accuracy.

In today's business world, providing reliable customer service is equally important as delivering better products for maintaining a sustainable business model. As providing customer service requires human resource and money, businesses are often shifting towards artificial intelligence system for necessary customer interaction. However, these traditional chatbot architectures depend heavily on natural language processing (NLP), it is not feasible to implement for the languages with little to no prior NLP backbone. In this work, we propose a semi-supervised artificially intelligent chatbot framework that can automate parts of primary interaction and customer service. The primary focus of this work is to build a chatbot which can generate contextualized responses in any language without depending much on rich NLP background and a vast number of a prior data set. This system is designed in such a way that with a dictionary of a language and regular customer interaction dataset, it can provide customer services for any business in any language. This architecture has been used to build a customer service bot for an electric shop, and different analysis has been done to evaluate the performance of individual components of the framework to show its competence to provide reliable response generation in comparison with other approaches.

Traditional voting procedures are non‐remote, time‐consuming, and less secure. While the voter believes their vote was submitted successfully, the authority does not provide evidence that the vote was counted and tallied. In most cases, the anonymity of a voter is also not sure, as the voter's details are included in the ballot papers. Many voters consider this voting system untrustworthy and manipulative, discouraging them from voting, and consequently, an election loses a significant number of participants. Although the inclusion of electronic voting systems (EVS) has increased efficiency; however, it has raised concerns over security, legitimacy, and transparency. To mitigate these problems, blockchain technology has been leveraged and smart contract facilities with a combination of artificial intelligence (AI) to propose a remote voting system that makes the overall voting procedure transparent, semi‐decentralized, and secure. In addition, a system that aids in boosting the number of turnouts in an election through an incentivization policy for the voters have also developed. Through the proposed virtual campaigning feature, the authority can generate a decent amount of revenue, which downsizes the overall cost of an election. To reduce the associated cost of transactions using smart contracts, this system implements a hybrid storage system where only a few cardinal data are stored in the blockchain network. A remote voting system is proposed with the blockchain and smart contract based technology with a combination of Artificial Intelligence technique. To reduce the associated cost of transactions using smart contracts, our system implements a hybrid storage system where only a few cardinal data is stored in the blockchain network.

This research introduces a learning model that estimates the cognitive perception of aesthetics. Taking psychology into account, this bridges the gap between human and machine. The goal is to build a machine-learning model that can estimate beauty in images perceived by human eyes. We have summand our research [Firoze, A., Osman, T., Psyche, S. S., & Rahman, R. M. (2018). Scoring photographic rule of thirds in a large MIRFLICKR dataset: A showdown between machine perception and human perception of image aesthetics. Asian Conference on Intelligent Information and Database Systems (pp. 466-475), Springer; Osman, T., Psyche, S. S., Deb, T., Firoze, A., & Rahman, R. M. (2018). Differential color harmony: A robust approach for extracting Harmonic Color features and perceive aesthetics in a large dataset. International Conference on Big Data and Cloud Computing, Springer] together with the idea of humans' personal preferences and achieved higher than state of the art performances. An extensive user study (374 participants) has been conducted to support claims. Several photographical compositional metrics have been used. Colour gradient, rule of thirds and human subject's psychology has been picked as features. The consideration of user's perspective or psychology is one of the key contributions of this research.