Swiss-based QoreChain Association releases production-grade testnet v2.19.0 featuring NIST-standard post-quantum signatures ...
The growing field of machine unlearning aims to make large language models forget harmful information without retraining them ...
Developed by Professor Sanjay Mehrotra, the Sliding Scale AdaptiVe Expedited (SAVE) algorithm could improve organ allocation ...
Machine learning can sound pretty complicated, right? Like something only super-smart tech people get. But honestly, it’s ...
The results show that the Decision Tree model emerged as the top-performing algorithm, achieving an accuracy rate of 99.36 percent. Random Forest followed closely with 99.27 percent accuracy, while ...
New energy-saving control strategy helps connected plug-in hybrids anticipate front-vehicle behavior
Researchers have developed an energy-saving control strategy for intelligent connected plug-in hybrid electric vehicles that ...
Opinion
9dOpinion
How do different data preprocessing techniques affect performance of machine learning models on prediction tasks?
Artificial intelligence (AI) and machine learning (ML) systems have become central to modern data-driven decision-making. They are now widely applied in fields as diverse as healthcare, finance, ...
Stock Price Prediction, Deep Learning, LSTM, GRU, Attention Mechanism, Financial Time Series Share and Cite: Kirui, D. (2026) ...
Abstract: Urban vegetation classification is challenging due to the heterogeneous nature of urban environments. Accurate mapping of urban vegetation, which plays a crucial role in regulating ...
Abstract: The purpose of this study is to estimate and predict onion wholesale price volatility using statistical and machine learning algorithms. Traditional models like ARIMA and GARCH were compared ...
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