ECG Signal Classification of Cardiovascular Disorder using CWT and DCNN.

Background: Cardiovascular Diseases (CVD) requires precise and efficient diagnostic tools. The manual analysis of Electrocardiograms (ECGs) is labor-intensive, necessitating the development of automated methods to enhance diagnostic accuracy and efficiency.

Objective: This research aimed to develop an automated ECG classification using Continuous Wavelet Transform (CWT) and Deep Convolutional Neural Network (DCNN), and transform 1D ECG signals into 2D spectrograms using CWT and train a DCNN to accurately detect abnormalities associated with CVD.

Navigating the contemporary landscape of food waste management in developing countries: A comprehensive overview and prospective analysis.

This study employs a comparative analysis method to examine variations in food waste (FW) generation between developed and developing nations, focusing on income levels, population growth rates, and community engagement in waste management. Quantitative data from Taiwan, Malaysia, and Bangladesh are comprehensively analyzed using regression analysis and descriptive statistics. Results indicate that Taiwan, with its stringent regulatory frameworks and advanced recycling technologies, generates significantly less FW per capita compared to Malaysia and Bangladesh.