Health prediction is crucial for ensuring reliability, minimizing downtime, and optimizing maintenance in industrial systems. Remaining Useful Life (RUL) prediction is a key component of this process; ...
This research paper presents a proactive approach to congestion control in IoT networks using an encoder–decoder LSTM (ED-LSTM) model to predict packet loss ratios ahead of time. By forecasting ...
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 ...
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 ...
An improvement to an existing AI-based brain decoder can translate a person's thoughts into text without hours of training. When you purchase through links on our site, we may earn an affiliate ...
Today, virtually every cutting-edge AI product and model uses a transformer architecture. Large language models (LLMs) such as GPT-4o, LLaMA, Gemini and Claude are all transformer-based, and other AI ...
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, ...
Seq2SeqSharp is a tensor based fast & flexible deep neural network framework written by .NET (C#). It has many highlighted features, such as automatic differentiation, different network types ...
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