BEIJING, Nov. 30,
2023 /PRNewswire/ -- WiMi Hologram Cloud Inc.
(NASDAQ: WIMI) ("WiMi" or the "Company"), a leading global Hologram
Augmented Reality ("AR") Technology provider, today announced that
the big data intelligent decision-making system based on machine
learning and situational awareness is of great significance and
value in solving complex decision-making problems in real life,
which can improve decision-making efficiency, optimize resource
allocation, enhance the accuracy of risk prediction, and promote
innovation and development.
Machine learning and situational awareness are areas that have
developed rapidly in recent years, enabling them to learn from
data, recognize it, and respond accordingly to the current context.
The development of these technologies supports intelligent
decision-making systems for big data.
In WiMi's big data intelligent decision-making system based on
machine learning and situational awareness, data acquisition and
pre-processing is a very important step, which involves collecting
data from various data sources and cleansing and transforming them
so that the subsequent analysis and decision-making process can
yield accurate and reliable results.
The design and training of machine learning models is also a
crucial step. With reasonable model design and sufficient training
data, we can construct efficient and accurate machine-learning
models to provide accurate decision support for the system.
In the big data intelligent decision-making system based on
machine learning and situational awareness, the design and
implementation of the situational awareness algorithm plays a
crucial role. The algorithm is able to acquire and understand the
current environment and situation in real time by analyzing and
mining big data, so as to provide accurate background information
and predictive results for decision-making. The design and
implementation of the situational awareness algorithm is a complex
process, which requires comprehensive consideration and
optimization of multiple aspects such as data collection, feature
extraction, model training, real-time monitoring and
decision-making, in order to achieve an accurate perception of the
current situation and decision support.
Data collection and pre-processing: First the system needs to
collect various decision-related data, including sensor data, web
data, social media data, etc. These data may come from different
sources with different formats and characteristics. Therefore,
before proceeding with the algorithm design, these data must be
pre-processed and cleaned to ensure the quality and consistency of
the data.
Feature extraction and selection: The system needs to extract
useful features from the raw data for subsequent machine learning
and analysis. The goal of feature extraction is to convert the data
into a numerical representation that can be used for model
training. Various feature extraction methods can be used in this
step, such as statistical features, frequency domain features, and
time domain features. Feature selection is also required to reduce
the dimensionality and redundancy of the features to improve the
efficiency and accuracy of the model.
Model training and optimization: After feature extraction and
selection are completed, the system needs to use machine learning
algorithms to train the data. Common machine learning algorithms
include decision trees, support vector machines, neural networks,
and so on. By learning and analyzing the historical data, the
system can construct a model that can accurately predict and
identify the posture. In order to improve the performance and
generalization ability of the model, the model can also be
optimized using techniques such as cross-validation and parameter
tuning.
Real-time monitoring and decision-making: Once the model
training is complete, the system can monitor and analyze new data
in real-time to achieve the perception of the current situation. By
comparing with the preset decision rules and strategies, the system
can generate corresponding decision suggestions or warning
information to help decision makers make accurate decisions. At the
same time, the system can also update and optimize the model based
on real-time feedback information to adapt to changing environments
and situations.
The system realizes the goal of intelligent decision-making by
analyzing and mining a large amount of data. WiMi employs a variety
of machine learning algorithms to train models and make predictions
on the data. At the same time, situational awareness algorithms are
introduced to monitor and analyze real-time data to detect and
identify potential risks and opportunities promptly.
In the future, WiMi will further promote the development and
application of the big data intelligent decision-making system in
terms of the improvement and optimization of deep learning
algorithms, the fusion and processing of multi-modal data, the
expansion of the application of augmented learning, as well as
privacy protection and security enhancement.
About WIMI Hologram Cloud
WIMI Hologram Cloud, Inc. (NASDAQ:WIMI) is a holographic cloud
comprehensive technical solution provider that focuses on
professional areas including holographic AR automotive HUD
software, 3D holographic pulse LiDAR, head-mounted light field
holographic equipment, holographic semiconductor, holographic cloud
software, holographic car navigation and others. Its services and
holographic AR technologies include holographic AR automotive
application, 3D holographic pulse LiDAR technology, holographic
vision semiconductor technology, holographic software development,
holographic AR advertising technology, holographic AR entertainment
technology, holographic ARSDK payment, interactive holographic
communication and other holographic AR technologies.
Safe Harbor Statements
This press release contains "forward-looking statements" within
the Private Securities Litigation Reform Act of 1995. These
forward-looking statements can be identified by terminology such as
"will," "expects," "anticipates," "future," "intends," "plans,"
"believes," "estimates," and similar statements. Statements that
are not historical facts, including statements about the Company's
beliefs and expectations, are forward-looking statements. Among
other things, the business outlook and quotations from management
in this press release and the Company's strategic and operational
plans contain forward−looking statements. The Company may also make
written or oral forward−looking statements in its periodic reports
to the US Securities and Exchange Commission ("SEC") on Forms 20−F
and 6−K, in its annual report to shareholders, in press releases,
and other written materials, and in oral statements made by its
officers, directors or employees to third parties. Forward-looking
statements involve inherent risks and uncertainties. Several
factors could cause actual results to differ materially from those
contained in any forward−looking statement, including but not
limited to the following: the Company's goals and strategies; the
Company's future business development, financial condition, and
results of operations; the expected growth of the AR holographic
industry; and the Company's expectations regarding demand for and
market acceptance of its products and services.
Further information regarding these and other risks is included
in the Company's annual report on Form 20-F and the current report
on Form 6-K and other documents filed with the SEC. All information
provided in this press release is as of the date of this press
release. The Company does not undertake any obligation to update
any forward-looking statement except as required under applicable
laws.
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SOURCE WiMi Hologram Cloud Inc.