When OpenAI's Sam Altman told reporters in San Francisco earlier this year that the AI sector is in a bubble, the American ...
Periodic maintenance is common too, but still inefficient and often based on time, not actual machine condition. That ...
The predominance of negative sentiment aligns with early public reactions to transformative technologies, where concerns about privacy, job loss, misinformation and ethical boundaries often amplify ...
Machine learning (ML), a subset of AI, has the capacity to address technical limitations that traditional diagnostic methods ...
AI-Driven Visual Intelligence forms a critical cornerstone of both computer vision and artificial intelligence, serving ...
Machine learning models can predict postoperative hyperopic shift in patients with primary angle-closure glaucoma.
A research team has mapped how machine learning is transforming the global tea industry, revealing that data-driven ...
Abstract: Process scheduling is crucial in optimizing resource utilization and reducing waiting times in computer systems. The traditional multilevel feedback queue scheduling algorithm (MLFQ) divides ...
Objective: This study aimed to develop and evaluate a machine learning (ML)–based algorithm to predict whether an initial vancomycin dose falls within the therapeutic range of the 24-hour area under ...
Abstract: In the era of large-scale machine learning, large-scale clusters are extensively used for data processing jobs. However, the state-of-the-art heuristic-based and Deep Reinforcement Learning ...
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