Introduction Application of artificial intelligence (AI) tools in the healthcare setting gains importance especially in the domain of disease diagnosis. Numerous studies have tried to explore AI in ...

Deep neural networks (DNNs), which power modern artificial intelligence (AI) models, are machine learning systems that learn hidden patterns from various types of data, be it images, audio or text, to ...

shenzhen, May 16, 2025 (GLOBE NEWSWIRE) -- Shenzhen, May. 16, 2025––MicroAlgo Inc. (the "Company" or "MicroAlgo") (NASDAQ: MLGO), today announced the development of a novel quantum entanglement-based ...

Software defect prediction and cost estimation are critical challenges in software engineering, directly influencing software quality and project management efficiency. This study presents a ...

The video game industry has evolved leaps and bounds over the last half century, from simple arcade-like gameplay to highly immersive, intelligent, and interactive gaming. With advancements in ...

The task of training deep neural networks, especially those with billions of parameters, is inherently resource-intensive. One persistent issue is the mismatch between computation and communication ...

A new data creation paradigm and algorithmic breakthrough from Georgia Tech has laid the groundwork for humanoid assistive robots to help with laundry, dishwashing, and other household chores. The ...

Objective: NIHSS for stroke is widely used in clinical, but it is complex and subjective. The purpose of the study is to present a quantitative evaluation method of stroke association based on ...

ABSTRACT: Burundi faces major agricultural constraints, including land fragmentation, soil erosion, limited access to inputs, inadequate infrastructure and demographic pressures that exacerbate food ...