NCAA is an annual international neural computing conference, which showcases state-of-the-art R&D activities in neural computing systems and their industrial and engineering applications. It provides a forum for technical presentations and discussions among neural computing researchers, developers and users from academia, business and industry.

The 2021 NCAA will be held in Guangzhou, China on July 2-5, 2021. China, a country which has a long history of 5000 years, is the perfect venue for addressing contemporary neural computing issues as new AI-related technical trends have been arising out of China’s top-notch IT infrastructure and cultural dynamicity. Guangzhou, also known as Canton and formerly romanized as Kwangchow, is the capital and most populous city of the province of Guangdong in southern China. On the Pearl River about 120 km (75 mi) north-northwest of Hong Kong and 145 km (90 mi) north of Macau, Guangzhou has a history of over 2,200 years and was a major terminus of the maritime Silk Road, and continues to serve as a major port and transportation hub, as well as one of China’s three largest cities. Due to a high urban population and large volumes of port traffic, Guangzhou is a Large-Port Megacity, the largest type of port-city in the world.

For up-to-date information on NCAA2021, visit its homepage: https://dl2link.com/ncaa2021

For more information about Guangzhou, see https://dl2link.com/ncaa2021/travel/aboutGuangzhou

TOPICS of interest are relevant to building practical systems are within its scope, including but not limited to:

    applicable neural networks theory
    applied statistics
    artificial intelligence
    computer vision
    control theory and systems
    data mining
    data security and privacy protection
    evolutionary computing
    fuzzy logic
    fault diagnosis
    hardware implementations
    hybrid intelligent systems
    image processing and understanding
    intelligent agents
    intelligent forecasting
    machine learning
    natural language processing
    neural networks
    neuro-fuzzy systems
    nn-based industrial applications
    pattern recognition
    self-learning systems
    software simulations
    supervised and unsupervised learning methods
    system engineering and integration