Co-F I N D: LSTM Based Adaptive Recurrent Neural Network for CoVID-19 Fraud Index Detection

On March 8, 2020, the IEDCR reported three cases of the first corona infection in Bangladesh, and there was a lot of fake news surrounding the virus, which the WHO Director-General called “infodemic”. Infodemic, additional information about any problem that is usually unbelievable, spreads quickly and makes that problem difficult to solve and it is even more dangerous than the Corona epidemic. The misinformation provided by the media, false information, religious discrimination, miraculous remedies, and vague instructions of the government have created panic among the people of Bangladesh. Many news portals are intentionally or accidentally publishing fake news about the covid vaccine, the rate of infection and survival, the situation in other countries, the symptoms, and what to do after being infected. The most widely reported controversy is China’s involvement in the creation and spread of the coronavirus. This article has been proposed in the context of identifying, sorting most of the fake news and misinformation about coronal infodemics in Bangladesh so that the people can take necessary steps accordingly. LSTM-Recurrent Neural Networks have been applied for classification and detection of fake news because RNN can easily detect complex sentences from textual data and LSTM is called a memory network that can easily perform detection work by remembering the sequence of the sentences. RNN has provided the most accuracy between LSTM and RNN models but LSTM has been able to perform the prediction work more accurately than RNN. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Exploring Public Attitude Towards Children by Leveraging Emoji to Track Out Sentiment Using Distil-BERT a Fine-Tuned Model

Sentiment analysis is a computational method that extracts emotional keywords from different texts through initial emotion analysis (e.g., Happy, Sad, Positive, Negative & Neutral). A recent study by a human rights organization found that 30% of children in Bangladesh are being abused on online in the COVID-19 epidemic by various obscene comments. The main goal of our research is to collect textual data from social media and classify the way children are harassed by various abusive comments online through the use of emoji in a text-mining method and to expose to society the risks that children face online. Another goal of this study is to set a precedent through a detailed study of child abuse and neglect in the big data age. To make the work effective, 3373 child abusive comments are collected manually from online (e.g. Facebook, Newspapers and various Blogs). At present, there is still a very limited number of Bengali child sentiment analysis studies. Fine-tuned general purpose language representation models, such as the BERT family model (BERT, Distil-BERT), and glove word embedding based CNN and Fast-Text models have been used to successfully complete the study. We show that Distil-BERT defeated BERT, Fast-Text, and CNN by 96.09% (relative) accuracy, while Bert, Fast-Text and CNN have 93.66%, 95.73%, and 95.05%, respectively. But observations show that the accuracy of the Distil-BERT does not differ much from the rest of the models. From our analysis, it can be said that the pre-trained models performed outstanding and in addition, child sentiment analysis can serve as a potential motivator for the government to formulate child protection policies and build child welfare systems. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Data Mining Technique for Prediction System of Heart Disease Using Associative Classifications

In this study, several aspects of the human body have been focused upon. This paper attempts to cast light on pre-and post-pathological conditions, man-machine interactions, human mindset, and ethics of AI. The paper emphasizes the cultural impacts of overeating, profuse drinking, and smoking habits. It uplifts the basic necessity of growing awareness schemes. Patients are seeking treatment in health care centers with the following serious pathological conditions and complications (We exclude the COVID-19 pandemic because it has been adequately publicized by media and press): Heart Attack, Stroke Cancer, Fatty liver & liver cirrhosis. Because of being the leading causes of sudden death prediction of heart attack is very important. Our main focus is to determine the best machine learning method. With optimal parameters, we evaluate the Dataset. Model Accuracy for the heart Attack Machine Learning Model was the highest for the Logistic Regression mode land it was 93.41%. On the contrary, the accuracy for Linear Regression Model was 60.10% which was the least. © 2021 IEEE.

Efficacy and Acceptance of Virtual Classrooms during COVID-19: Bangladesh Perspective

The sudden spread of COVID-19 shut down educational institutions worldwide, and Bangladesh was no exception. Educational institutions were forced to start their activities online;there was no alternative to keep the students in the study. Although online education has been seen as part of a futuristic approach, its effectiveness and acceptability still remains questionable when it comes to institutional education. It's yet to be investigated if online education can be as effective as contact teaching. We conducted an online survey to determine what students feel about online classes, how they accepted online classes, and how useful it was for them depending on their current situation. Our survey was open to everyone who has taken online classes during COVID-19, and the number of participants in our survey was 210. The survey provided with both qualitative and quantitative data which were then categorized into themes for analysis. Findings suggest, that most students feel that by rethinking the class style, if teachers can provide well-structured lecture content and have an equal focus on all students, it can be an alternative for them during emergency days. © 2021 IEEE.