LFR Waiter: Arduino Based Android Application Controlled Waiter Robot

One of the most pressing challenges facing restaurants since the COVID-19 outbreak began is personnel. A staffing scarcity across the business has resulted in a slew of issues, including significantly longer wait times and irritated clients. A robot waiter may make a huge impact in a restaurant in this situation. This research led to the formation of a low-cost Arduino-based Android application control Robot that can work as a restaurant waiter. The proposed model can follow a path, avoid obstacles, serve meals to a specific consumer, and return to the kitchen on its own. To precisely follow the line, the PID algorithm is utilized. To detect potential obstructions, a sonar sensor is used. On an LCD, messages and warnings are displayed. An Android app that allows the chief to select a particular table for serving meals. For convenience, the robot's current state is displayed in the application. Our testing results show that the robot performs satisfactorily over 90% of the time. It should be emphasized that the offered model is adaptable to any restaurant. © 2022 IEEE.

Comparison of Different Sentiment Analysis Techniques for Bangla Reviews

Internet adoption has increased rapidly during the worldwide COVID-19 pandemic. Nowadays people not only prefer to shop using various e-commerce platforms, but also like to provide feedback and express their opinions and experiences using the online platforms. Since new customers try to understand the products' utility and acceptability from other consumers' reviews, it has become crucial to analyze the customers' sentiments and opinions on each product. In this paper, we have presented a sentiment analysis technique on the basis of product reviews written in Bangla language to better understand the combined consumer perspective. Our work aims to compare existing classifiers' performance and find the best algorithm for our dataset. We collected reviews from the leading Bangla bookselling e-commerce site 'Rokomari.com' for this work. We implemented ML and DL classifier models and compared their overall performance on this dataset. The experimental studies show that the best accuracy is achieved from LSTM and SGD over the other implemented ML and DL based classifier models. © 2022 IEEE.

COVID-19 Prediction based on Infected Cases and Deaths of Bangladesh using Deep Transfer Learning

The severely infectious virus known as "COVID-19" has wreaked havoc on the planet, trapping to keep the disease from spreading, while billions of people are staying inside. Every experts and professionals in many disciplines are working tirelessly to create a vaccine and preventative techniques to help the globe overcome this difficult crisis. In Bangladesh, the number of persons infected with Coronavirus is particularly alarming. A accurate prognosis of the epidemic, on the other hand, may aid in the management of this contagious illness until a remedy is discovered. This study aims to forecast impending COVID-19 exposed instances and fatalities using a time series dataset utilizing proposed deep transfer learning model where encoder-decoder CNN-LSTM along with deep CNN pretrained models such as: ResNet-50, DenseNet-201, MobileNet-V2, and Inception-ResNet-V2 performed. We also predict the regular exposed instances and fatalities throughout the following 180 days in data visualization segment using AIC and BIC selection criteria. The suggested paradigms are also used to anticipate Bangladesh's daily confirmed cases and daily which is evaluated by error based on three performance criteria. We discovered that ResNet-50 performs better among others for predicting infected case and deaths owing to COVID-19 in Bangladesh in terms of MAPE, MAE and RMSE evaluations.