Smart city transportation has become an important part of improving road safety and traffic management. This project focuses on detecting traffic accidents using a deep learning ensemble approach that combines I3DConvLSTM2D with RGB and optical flow information. By analyzing both the appearance of vehicles and their movement, the system can identify accident events more accurately than traditional methods. It is designed to work in real time, making it suitable for surveillance cameras and smart city environments. The proposed model also addresses challenges such as limited training data and different road conditions, helping improve its reliability in practical situations. Early accident detection allows emergency services to respond quickly, reducing the impact of road accidents and improving public safety. Overall, this system demonstrates how artificial intelligence can support modern transportation by providing faster, more accurate, and efficient accident detection while contributing to safer roads and better traffic monitoring.
CHENCHETI INDHU, Dr.M.Ramesh· International Journal of Eng...· 0 citations
The rapid growth of the Internet of Things (IoT) has significantly improved connectivity and automation across various domains. However, it has also increased the risk of cyber threats, particularly botnet attacks that compromise connected devices and disrupt network operations. Traditional machine learning techniques often struggle to detect sophisticated and evolving botnet attacks due to the complexity and high-dimensional nature of network traffic. This project proposes a Hybrid Deep Learning Model for Efficient IoT Botnet Attack Detection by integrating Artificial Neural Networks , Convolutional Neural Networks , Long Short-Term Memory, and Recurrent Neural Networks into a stacked ACLR framework. The proposed model leverages the feature extraction capability of CNN, the temporal learning strength of LSTM and RNN, and the classification power of ANN to accurately identify malicious network traffic. The model is trained and evaluated using the UNSW-NB15 dataset after appropriate preprocessing, including data cleaning, normalization, and label encoding. Experimental results demonstrate that the proposed approach achieves high detection accuracy, precision, recall, F1-score, and ROCAUC, outperforming several existing machine learning and deep learning methods. The proposed hybrid framework provides a robust, scalable, and reliable solution for real-time botnet attack detection, enhancing the security and resilience of modern IoT networks against evolving cyber threats.
MUNAGALA KUSUMA, Dr.M.Ramesh· International Journal of Eng...· 0 citations