Scalar invariant transform based deep learning framework for detecting heart failures using ECG signals

Abstract Heart diseases are leading to death across the globe. Exact detection and treatment for heart disease in its early stages could potentially save lives. Electrocardiogram (ECG) is one of the tests that take measures of heartbeat fluctuations. The deviation in the signals from the normal sinus rhythm and different variations can help detect various heart conditions. This paper presents a novel approach to cardiac disease detection using an automated Convolutional Neural Network (CNN) system. Leveraging the Scale-Invariant Feature Transform (SIFT) for unique ECG signal image feature extraction, our model classifies signals into three categories: Arrhythmia (ARR), Congestive Heart Failure (CHF), and Normal Sinus Rhythm (NSR). The proposed model has been evaluated using 96 Arrhythmia, 30 CHF, and 36 NSR ECG signals, resulting in a total of 162 images for classification. Our proposed model achieved 99.78% accuracy and an F1 score of 99.78%, which is among one of the highest in the models which were recorded to date with this dataset. Along with the SIFT, we also used HOG and SURF techniques individually and applied the CNN model which achieved 99.45% and 78% accuracy respectively which proved that the SIFT–CNN model is a well-trained and performed model. Notably, our approach introduces significant novelty by combining SIFT with a custom CNN model, enhancing classification accuracy and offering a fresh perspective on cardiac arrhythmia detection. This SIFT–CNN model performed exceptionally well and better than all existing models which are used to classify heart diseases

Enhancing Road Safety and Cybersecurity in Traffic Management Systems: Leveraging the Potential of Reinforcement Learning

With the increasing reliance on technology in traffic management systems, ensuring road safety and protecting the integrity of these systems against cyber threats have become critical concerns. This research paper investigates the potential of reinforcement learning techniques in enhancing both road safety and cyber security of traffic management systems. The paper explores the theoretical foundations of reinforcement learning, discusses its applications in traffic management, and presents case studies and empirical evidence demonstrating its effectiveness in improving road safety and mitigating cyber security risks. The findings indicate that reinforcement learning can contribute to the development of intelligent and secure traffic management systems, thus minimizing accidents and protecting against cyber-attacks