Computer Science

Car owners altering license plates using different typefaces and designs violate the law that strictly forbids such behaviour. Traffic police officers claim that changing the license plates makes it impossible to read the registration numbers due to an increase in fatal street collisions and car thefts. They worry that it may be difficult to track down vehicles used in hit-and-run incidents. It is challenging to impose further limitations on any algorithm used to identify and recognise license plates in a developing nation like Bangladesh. This work has the primary objective of designing a reliable detection and recognition system for transitional, standard car license plates, which are frequently seen in developing countries. Increase the effectiveness of reading license plates drawn or printed in various styles and typefaces employing cuttingedge technology, including machine learning (ML) models. For this study, You Only Look Once (YOLOv3) is used to utilising the most recent version of the object detection method. The raw image is pre-processed to increase its quality and then divided into appropriate-sized grid cells to determine where the license plate should be placed. After that, the data is post-processed, and the accuracy of the proposed model is evaluted using industry-recognised standards. A sizeable image dataset was used to be tested using this proposed methodology. The presented system is expected to be essential for vehicle monitoring, parking fee collection, lowering traffic accidents, and identifying unregistered vehicles. The results demonstrate that the suggested method achieves 97.1% mAP, 95.3% precision and 96.8% in plate detection.

Social media plays a vital role in our daily lives. To understand and interpret emotions and opinions expressed on social media platforms, analyzing sentiment is very important. Our study is based on Twitter sentiment analysis. Our aim is to classify tweets automatically as positive, negative, or neutral based on their content using natural language processing and machine learning algorithms. The dataset we used for our analysis is extracted from the website called mendeley data and also we have added some tweets manually which covers various topics. To remove noise, including URLs, hashtags, punctuations, and user mentions, and to retain essential textual content and emojis, we pre-processed the dataset. Additionally, for our research, we used VADER (Valence Aware Dictionary and sentiment Reasoner) and Transformers-RoBERTa to analyze the sentiment of various tweets. We evaluate the performance of these two models using evaluation metrics such as accuracy, precision, recall and F1-score, and also confusion metrics on the testing set. We also discuss the study's limitations and conclude that machine learning-based sentiment analysis models are a reliable tool for the sentiment analysis of the twitter dataset.