Since December 2019, the novel coronavirus(COVID-19) has caused over 700,000 deaths with more than 10 million people being infected. Bangladesh, the most densely populated country in the world, is now under community trans-mission of the COVID-19 outbreak. This has created huge health, social, and economic burdens. Till the 10th of February 2020, Bangladesh has reported over 500,000 infected cases and 8000 deaths. To prevent further detriment in our scenario, predicting future consequences are very important. Studies have shown that machine learning(ML) models work extremely well in providing precise information regarding COVID-19 to the authorities thus enabling them to make decisions accordingly. However, to the best of our knowledge, no ML models have been applied that can help in determining the pandemic circumstance for Bangladesh demographics. In this study, we explore different machine learning algorithms that can provide more accurate estimations for predicting future cases which includes infections and deaths due to COVID-19 for Bangladesh. Based on this the government and policymakers can make a decision about the lockdown, resource mobilization, etc. Our study shows that in predicting the pandemic situations, amidst many predicting models the Facebook Prophet Model provided the best accuracy. We believe that using this information the authorities can take decisions that will lead to the saving of countless lives of the people. Additionally, this will also help to reduce the immeasurable economic burden our country is facing due to the present status quo. Furthermore, this study will help analysts to construct predicting models for future explorations.

/pdf/predicting-covid-19-infections-and-deaths-in-bangladesh-3f19w4uykq.pdf

Predicting COVID-19 infections and deaths in Bangladesh using Machine Learning Algorithms

In this study, we have introduced a method for utilizing the maximum parking space available for a metropolitan city. This will result in much lesser traffic congestion due to street-side parking. Furthermore, it will also decrease the hassle drivers face when they have to leave their vehicles on the side of the road to do other activities. The method introduces a Deep Learning based system where parking spaces are detected using Data Capturing Units (DCU). These DCUs feed data into our database which can be accessed by the users from our mobile application. The users can book parking spaces accordingly. All these data are saved in real-time and can be accessed through the mobile application. A vehicle classification system has also been designed that achieves an accuracy of 77% from multiple vehicle classes. Furthermore, a number plate recognition system has been used for the identification and safety protocols of the vehicles in parking sites. The number plate identification system is very precise and achieves an accuracy of over 90% for each digit. To the best of our knowledge, no other system of this kind has been implemented for the city of Dhaka before this. On top of that, successful implementation in a hectic city like Dhaka implies that it can be applied anywhere in the world. We believe this system can have a huge impact in reducing traffic congestions and can save an endless measure of time and money for citizens in a metropolitan area.

Deep Learning based Smart Parking for a Metropolitan Area

Analysis of COVID-19 Trends in Bangladesh: A Machine Learning Analysis

The objective of the article was to discuss the difficulties in diagnosing the novel Coronavirus on the ongoing COVID-19 pandemic

Novel coronavirus: a new challenge for medical scientist? (Special Issue: Newly emerging COVID19 infectious disease.)

Block-based environments are visual programming environments, which are becoming more and more popular because of their ease of use. The ease of use comes thanks to their intuitive graphical representation and structural metaphors (jigsaw-like puzzles) to display valid combinations of language constructs to the users. Part of the current popularity of block-based environments is thanks to Scratch. As a result they are often associated with tools for children or young learners. However, it is unclear how these types of programming environments are developed and used in general. So we conducted a systematic literature review on block-based environments by studying 152 papers published between 2014 and 2020, and a non-systematic tool review of 32 block-based environments. In particular, we provide a helpful inventory of block-based editors for end-users on different topics and domains. Likewise, we focused on identifying the main components of block-based environments, how they are engineered, and how they are used. This survey should be equally helpful for language engineering researchers and language engineers alike.

DRAFT-What you always wanted to know but could not find about block-based environments.

Since December 2019, the novel coronavirus (COVID-19) has been the cause of over 700,000 deaths with more than 10 million people being infected. Bangladesh, the most densely populated country in the world and now under community trans-mission of the COVID-19 outbreak. This has created huge health, social, and economic burdens. Till the 7th of August 2020, Bangladesh has reported over 250,000 infected cases and 3000 deaths. A total of 1.2 million tests has been conducted among which about 20\% of people were infected. To prevent further detriment in our scenario, predicting future consequences are very important. Previous studies have shown that machine learning(ML) models play a very significant role in forecasting the number of infections and deaths due to COVID-19. Some ML model works extremely well in providing precise information to the authorities thus enabling them to make decisions accordingly. However, Bangladesh is still lacking in this regard. To the best of our knowledge, no ML models have been applied or utilized at this point that can help in determining the pandemic circumstance for Bangladesh demographics. In this study, we explore different machine learning algorithms that can provide more accurate estimations for predicting future cases which includes infections and deaths due to COVID-19 for Bangladesh. Based on this the government, public health institutes, and policymakers can make a decision about the lockdown, management, resource mobilization, etc. Our study shows that in predicting the pandemic situations, amidst many forecasting models like Holt's Linear Regression(HLR), Support Vector Regression(SVR), Holt's Winter Additive Model(HWAM), etc. the Facebook Prophet Model(FPM) provided the best result in forecasting with remarkable precision. We believe that using this information the authorities can take decisions that will lead to the saving of countless lives of the people. Additionally, this will also help to reduce the immeasurable economic burden our country is facing due to the present status quo. On top of that our studies will help analysts to construct forecasting models for future explorations.

Analysing and Predicting Coronavirus Infections and Deaths in Bangladesh Using Machine Learning Algorithms

Block based programming is getting increasingly popular in a lot of countries. In this paper we have described a block based self-compiling education robot that uses Blockly to perform programming operations. The system provides a simple affordable platform to practice and learn programming and other STEM modules to enhance competency, efficiency and develop interest of students. STEM education is an interdisciplinary teaching concept that integrates the Arts with the traditional STEM subjects: Science, Technology, Engineering, and Mathematics. It emphasizes practice-based learning instead of, as traditional education does, theoretical knowledge. We have discussed the principles, hardware and other necessary modules incorporated to our system in this paper.

https://ieeexplore.ieee.org/iel7/8766340/8776675/08776756.pdf

Mazharul Islam Leon

Papers

Predicting COVID-19 infections and deaths in Bangladesh using Machine Learning Algorithms

Analysing and Predicting Coronavirus Infections and Deaths in Bangladesh Using Machine Learning Algorithms

Deep Learning based Smart Parking for a Metropolitan Area

EduBot: An Educational Robot for Underprivileged Children