Abstract: Image processing and deep learning techniques have demonstrated their efficacy as valuable tools for classifying municipal solid waste. This study presents a comparative review of the recent ...
Abstract: It is essential to emphasise the importance of detecting dementia as early as possible, as this cognitive disorder exhibits signs that increasingly limit the patient. Consequently, early ...
A deep learning project for detecting Tuberculosis (TB) from chest X-ray images using convolutional neural networks (CNN). This project demonstrates the application of computer vision in medical ...
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Abstract: This study presents an integrated framework combining 1D-CNN-LSTM-Autoencoder-based anomaly detection with identity authentication using machine learning classifiers. The 1D-CNN-LSTM ...
Abstract: The strong stochasticity and volatility of wind power generation pose new challenges to the operational reliability of power systems. Traditional reliability assessment often struggles to ...
Abstract: Soft-tissue tumors (STTs) encompass a diverse group of tumors that arise in non-skeletal and non-epithelial tissues, presenting significant challenges in detection and classification. The ...
Abstract: Rice productivity is strongly affected by foliar diseases, yet field diagnosis in rural areas is often slow, subjective, and limited by internet access. This paper presents a real-time rice ...
This jupyter notebook tutorial is meant to be a general introduction to machine and deep learning. We use seismic time series data from i) real earthquakes and ii) nuisance signals to train a suite of ...
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