With the development of brain-computer interface (BCI) technology, the application of electroencephalography (EEG) signals in motion decoding has been expanding. Traditional BCI decoding is primarily ...
Long Short-Term Memory (LSTM) network with sequence-to-sequence architecture for building conversational chatbots with attention mechanism. lstm-chatbot/ ├── README.md ├── FEATURES.md # Additional ...
ABSTRACT: This work presents an innovative Intrusion Detection System (IDS) for Edge-IoT environments, based on an unsupervised architecture combining LSTM networks and Autoencoders. Deployed on ...
Katie Palmer covers telehealth, clinical artificial intelligence, and the health data economy — with an emphasis on the impacts of digital health care for patients, providers, and businesses. You can ...
I've been transcoding videos on handbrake using AV1 which I think is the latest encoder. AV1 on the Mac is often incredibly efficient. I'm talking 3gb -> 300mb efficient. Even tougher material with ...
Beyond tumor-shed markers: AI driven tumor-educated polymorphonuclear granulocytes monitoring for multi-cancer early detection. Clinical outcomes of a prospective multicenter study evaluating a ...
A team of scientists has unveiled how the hippocampus orchestrates multiple memory processes, including encoding new information, forming memories, and retrieving them. A team of scientists from the ...
Abstract: Accurate prediction of blood glucose levels is crucial for automated treatment in diabetic patients. This study proposes a blood glucose prediction model based on an improved attention ...
Large language models (LLMs) have changed the game for machine translation (MT). LLMs vary in architecture, ranging from decoder-only designs to encoder-decoder frameworks. Encoder-decoder models, ...
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