Bangla_MER: A unique dataset for Bangla mathematical entity recognition.

Mathematical entity recognition is essential for machines to define and illustrate mathematical substance faultlessly and to facilitate sufficient mathematical operations and reasoning. As mathematical entity recognition in the Bangla language is novel, to our best knowledge, there is no available dataset exists in any repository. In this paper, we present state of the art Bangla mathematical entity dataset containing 13,717 observations. Each record has a mathematical statement, mathematical type and mathematical entity. This dataset can be utilized to conduct research involving the recognition of mathematical operators, renowned mathematical terms (such as complex numbers, real numbers, prime numbers, etc.), and operands as numbers. The findings mentioned above, and their combination are also feasible with a modest tweak to the dataset. Furthermore, we have structured this dataset in raw format and made a CSV file, incorporating three columns: text, math entity, and label. As an outcome, researchers may easily handle the data, facilitating a variety of deep learning and machine learning explorations.

PharmaChain: Blockchain-based drug supply chain provenance verification system.

Over the last decade, pharmaceutical businesses have battled to standardize product traceability across the supply chain process, enabling counterfeiters to enter the market with counterfeit pharmaceuticals. As a result, an end-to-end product tracking system is crucial for ensuring product safety and eliminating counterfeit products across the pharmaceutical supply chain. In this paper, we introduce PharmaChain, a decentralized hyperledger fabric framework that leverages confidentiality, accountability, and interoperability. This system enables on-chain and off-chain storage for secured, rapid transactions, along with smart contracts establishing data provenance. To demonstrate security, we have provided double signing through the elliptic curve digital signature algorithm, hash data encryption, and 33% node attack. The purpose of this suggested framework is to engage particular governance disciplines to assess its effectiveness in improving drug traceability across the pharmaceutical supply chain to preserve public health by preventing counterfeit pharmaceuticals.

UDDIPOK: A reading comprehension based question answering dataset in Bangla language.

The popularity of reading comprehension (RC) is increasing day-to-day in Bangla Natural Language Processing (NLP) research area, both in machine learning and deep learning techniques. However, there is no original dataset from various sources in the Bangla language except translated from foreign RC datasets, which contain abnormalities and mismatched translated data. In his paper, we present UDDIPOK, a novel wide-ranging, open-domain Bangla reading comprehension dataset. This dataset contains 270 reading passages, 3636 questions, and answers from diverse origins, for instance, textbooks, exam questions from middle and high schools, newspapers, etc. Furthermore, this dataset is formated in CSV, which contains three columns: passages, questions, and answers. As a result, data can be handled expeditiously and easily for any machine learning research.

Reading comprehension based question answering system in Bangla language with transformer-based learning.

Question answering (QA) system in any language is an assortment of mechanisms for obtaining answers to user questions with various data compositions. Reading comprehension (RC) is one type of composition, and the popularity of this type is increasing day by day in Natural Language Processing (NLP) research area. Some works have been done in several languages, mainly in English. In the Bangla language, neither any dataset available for RC nor any work has been done in the past. In this research work, we develop a question-answering system from RC. For doing this, we construct a dataset containing 3636 reading comprehensions along with questions and answers. We apply a transformer-based deep neural network model to obtain convenient answers to questions based on reading comprehensions precisely and swiftly. We exploit some deep neural network architectures such as LSTM (Long Short-Term Memory), Bi-LSTM (Bidirectional LSTM) with attention, RNN (Recurrent Neural Network), ELECTRA, and BERT (Bidirectional Encoder Representations from Transformers) to our dataset for training. The transformer-based pre-training language architectures BERT and ELECTRA perform more prominently than others from those architectures. Finally, the trained model of BERT performs a satisfactory outcome with 87.78% of testing accuracy and 99% training accuracy, and ELECTRA provides training and testing accuracy of 82.5% and 93%, respectively.

COVID-19 analytics: Towards the effect of vaccine brands through analyzing public sentiment of tweets.

The COVID-19 outbreak has created effects on everyday life worldwide. Many research teams at major pharmaceutical companies and research institutes in various countries have been producing vaccines since the beginning of the outbreak. There is an impact of gender on vaccine responses, acceptance, and outcomes. Worldwide promotion of the COVID-19 vaccine additionally generates a huge amount of discussions on social media platforms about diverse factors of vaccines including protection and efficacy. Twitter is considered one of the most well-known social media platforms which have been widely used to share a public opinion on vaccine-related problems in the COVID-19 pandemic. However, there is a lack of research work to analyze the public perception of COVID-19 vaccines systematically from a gender perspective. In this paper, we perform an in-depth analysis of the coronavirus vaccine-related tweets to understand the people's sentiment towards various vaccine brands corresponding to the gender level. The proposed method focuses on the effect of COVID-19 vaccines on gender by taking into account descriptive, diagnostic, predictive, and prescriptive analytics on the Twitter dataset. We also conduct experiments with deep learning models to determine the sentiment polarities of tweets, which are positive, neutral, and negative. The results reveal that LSTM performs better compared to other models with an accuracy rate of 85.7%.

Detecting sentiment dynamics and clusters of Twitter users for trending topics in COVID-19 pandemic.

COVID-19 caused a significant public health crisis worldwide and triggered some other issues such as economic crisis, job cuts, mental anxiety, etc. This pandemic plies across the world and involves many people not only through the infection but also agitation, stress, fret, fear, repugnance, and poignancy. During this time, social media involvement and interaction increase dynamically and share one's viewpoint and aspects under those mentioned health crises. From user-generated content on social media, we can analyze the public's thoughts and sentiments on health status, concerns, panic, and awareness related to COVID-19, which can ultimately assist in developing health intervention strategies and design effective campaigns based on public perceptions. In this work, we scrutinize the users' sentiment in different time intervals to assist in trending topics in Twitter on the COVID-19 tweets dataset. We also find out the sentimental clusters from the sentiment categories. With the help of comprehensive sentiment dynamics, we investigate different experimental results that exhibit different multifariousness in social media engagement and communication in the pandemic period.

Discovering Topic-Oriented Highly Interactive Online Communities.

Community detection is an interesting field of online social networks. Most existing approaches either consider common attributes of social network users or rely on only social connections among the users. However, not enough attention is paid to the degree of interactions among the community members in the retrieved communities, resulting in less interactive community members. This inactivity will create problems for many businesses as they require highly interactive users to efficiently advertise their marketing information. In this paper, we propose a model to detect topic-oriented densely-connected communities in which community members have active interactions among each other. We conduct experiments on a real dataset to demonstrate the effectiveness of our proposed approach.