Breaking the Fake News Barrier: Deep Learning Approaches in Bangla Language

The rapid development of digital stages has greatly compounded the dispersal of untrue data, dissolving certainty and judgment in society, especially among the Bengali-speaking community. Our ponder addresses this critical issue by presenting an interesting strategy that utilizes a profound learning innovation, particularly the Gated Repetitive Unit (GRU), to recognize fake news within the Bangla dialect. The strategy of our proposed work incorporates intensive information preprocessing, which includes lemmatization, tokenization, and tending to course awkward nature by oversampling. This comes about in a dataset containing 58,478 passages. We appreciate the creation of a demonstration based on GRU (Gated Repetitive Unit) that illustrates remarkable execution with a noteworthy precision rate of 94%. This ponder gives an intensive clarification of the methods included in planning the information, selecting the show, preparing it, and assessing its execution. The performance of the model is investigated by reliable metrics like precision, recall, F1 score, and accuracy. The commitment of the work incorporates making a huge fake news dataset in Bangla and a demonstration that has outperformed other Bangla fake news location models.

Vehicle Speed Detection System Utilizing YOLOv8: Enhancing Road Safety and Traffic Management for Metropolitan Areas

In order to ensure traffic safety through a reduction in fatalities and accidents, vehicle speed detection is essential. Relentless driving practices are discouraged by the enforcement of speed restrictions, which are made possible by accurate monitoring of vehicle speeds. Road accidents remain one of the leading causes of death in Bangladesh. The Bangladesh Passenger Welfare Association stated in 2023 that 7,902 individuals lost their lives in traffic accidents during the course of the year. Efficient vehicle speed detection is essential to maintaining traffic safety. Reliable speed detection can also help gather important traffic data, which makes it easier to optimize traffic flow and provide safer road infrastructure. The YOLOv8 model can recognize and track cars in videos with greater speed and accuracy when trained under close supervision. By providing insights into the application of supervised learning in object identification for vehicle speed estimation and concentrating on the particular traffic conditions and safety concerns in Bangladesh, this work represents a noteworthy contribution to the area. The MAE was 3.5 and RMSE was 4.22 between the predicted speed of our model and the actual speed or the ground truth measured by the speedometer Promising increased efficiency and wider applicability in a variety of traffic conditions, the suggested solution offers a financially viable substitute for conventional approaches.