Electrocardiogram (ECG) signals play a vital role in diagnosing and monitoring patients suffering from various cardiovascular diseases (CVDs). This research aims to develop a robust algorithm that can accurately classify the electrocardiogram signal even in the presence of environmental noise. A one-dimensional convolutional neural network (CNN) with two convolutional layers, two down-sampling layers, and a fully connected layer is proposed in this work. The same 1D data was transformed into two-dimensional (2D) images to improve the model’s classification accuracy. Then, we applied the 2D CNN model consisting of input and output layers, three 2D-convolutional layers, three down-sampling layers, and a fully connected layer. The classification accuracy of 97.38% and 99.02% is achieved with the proposed 1D and 2D model when tested on the publicly available Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database. Both proposed 1D and 2D CNN models outperformed the corresponding state-of-the-art classification algorithms for the same data, which validates the proposed models’ effectiveness.

Unmanned aerial vehicles (UAVs) have become popular in surveillance, security, and remote monitoring. However, they also pose serious security threats to public privacy. The timely detection of a malicious drone is currently an open research issue for security provisioning companies. Recently, the problem has been addressed by a plethora of schemes. However, each plan has a limitation, such as extreme weather conditions and huge dataset requirements. In this paper, we propose a novel framework consisting of the hybrid handcrafted and deep feature to detect and localize malicious drones from their sound and image information. The respective datasets include sounds and occluded images of birds, airplanes, and thunderstorms, with variations in resolution and illumination. Various kernels of the support vector machine (SVM) are applied to classify the features. Experimental results validate the improved performance of the proposed scheme compared to other related methods.