Emotion estimation is a field that has been studied for a long time, and several approaches using machine learning models exist. This article presents BlendFER-Lite, an LSTM model that uses ...

Time-series data in manufacturing (temperature, pressure, vibration, current...) is tricky. Data preprocessing, windowing, normalization, the format to pass to the model... "I'll visualize that data ...

If you're a beginner, diving into LSTM (Long Short-Term Memory) models might seem intimidating at first. However, I'm here to simplify this process for you and provide a template that you can use to ...

In today's data-driven world, the ability to predict text is invaluable. From autocomplete features to advanced natural language processing (NLP) applications, text prediction models are becoming ...

The original version of this Article contained errors throughout the article, where several non-standard terms were used. These are now replaced with established scientific terminology. As a result, ...

Please cite the paper if you use the code. If you have any questions, please post them in the Discussions Section. The code requires Python 3.11. TensorFlow currently ...

Bacteriophages are gaining increasing interest as antimicrobial tools, largely due to the emergence of multi-antibiotic–resistant bacteria. Although their huge diversity and virulence make them ...

LSTM networks are designed to overcome long-term dependency issues in sequential data. RNNs utilise memory from previous inputs to affect current outputs, unlike traditional neural networks. The main ...

Analysis and classification of clinical time-series data in physiology and disease processes are considered as a catalyst for biomedical research and education. Innovative computerized tools for ...

Forecasting the stock price of a particular has been a difficult task for many analysts and researchers. In fact, investors are highly interested in the research area of stock price prediction.