Industry

Fintech

Technological innovation:
Global big data collection; Analytical prediction; Quantitative algorithms; Machine learning and Large Language Models
Technological Innovation 1: Advanced AI Deep Neural Networks:
Applied a novel deep learning framework consisting of three stages:
1. Applied wavelet transform to the stock price time series to remove noises
2. Applied stacked autoencoder to generate deep hierarchical features for stock price prediction
3. The high-quality denoised features are selectively passed through a CNN, LSTM, or Bi-LSTM neural network to predict the closing price of the next day
GTechnological Innovation 2: lobal Supernode Computing Network/Web3:
Supernodes Have the Following Advantages:
1. Supernodes are autonomous and highly reliable in terms of security. It is built based on our self-developed federal learning framework, which meets the requirements of international standards for blockchain.
2. Flexible, convenient and easy-to-use. The "foolproof" operations make it easy to use supernodes and permanent nodes.
3. Greatly reduce the cost of flexible payments. The hourly rental cost of distributed GPU power is lower than 20% of that of Amazon and Ali cloud. The prediction results can be shared for multiple times and stocks can be sold synchronously. Prices are flexible based on user demand and volume.
4. Supernodes have strong credibility. Our system is composed of a globally distributed consortium of super nodes. We have introduced many strategic partners including authoritative organizations such as China Mobile and DiDi Cloud. We are also open for node access for commercial organizations and regulatory authorities.

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Convolutional Neural Networks (CNN)

CNN excels in identifying patterns in spatial or temporal data. It can be used to detect patterns in stock price movements.
Under the conditions with the same data size and complexity, usually CNN has lower computational costs compared with LSTM.

Long Short-term Memory (LSTM)

LSTM can study long-term dependencies, making it suitable for time series data such as stock prices.
The "memory cell" structure enables it to remember long-term dependencies and address the "vanishing gradient" problem that often encountered in traditional recurrent networks.

Bidirectional LSTM (Bi-LSTM)

Bi-LSTM is an extension of LSTM, formed by combining forward LSTM and backward LSTM.
Like LSTM, Bi-LSTM can remember long-term dependencies. Besides than that, Bi-LSTM can learn from future states.
By gathering information from both the past and future, Bi-LSTM makes better contextual judgments, making the spatial model more comprehensive.

Converting Supernodes into NFTs

Reputation of supernodes
Conversion of supernodes into NFTs
Ownership of NFTs
Aligning with the goals of Web3

DBC Distributed Computing Power

Sharing computing resources (GPU power)
Enhancing data security and privacy
Facilitating value exchange
Collaborative development of AI applications

Utility Token of AI industry: VNX

Global trading service for data, algorithms, and solutions in the AI industry
Investment asset that users can buy and hold
Cross-border remittance with faster transfer speed and lower fees facilitated by blockchain technology
Enjoying more freedom and transparency in financial services within the Web3 environment