EXPLORATION OF THE CONSTRUCTION
PATH OF ARTIFICIAL INTELLIGENCE BIG
DATA "INTEGRATED" INNOVATION AND
ENTREPRENEURSHIP ECOSYSTEM FROM
THE PERSPECTIVE OF LAND USE
ECOLOGICAL SUITABILITY
Wenchao Zhou*
Medical School, Jiangsu Vocational College of Medicine, Yancheng, Jiangsu,
224005, China
jsyyzwc@163.com
Ting Yang
Medical School, Jiangsu Vocational College of Medicine, Yancheng, Jiangsu,
224005, China
Reception: 03/03/2023 Acceptance: 25/04/2023 Publication: 17/05/2023
Suggested citation:
Zhou, W. and Yang, T. (2023). Exploration of the construction path of
artificial intelligence big data "integrated" innovation and
entrepreneurship ecosystem from the perspective of land use ecological
suitability. 3C TIC. Cuadernos de desarrollo aplicados a las TIC, 12(2), 210-225.
https://doi.org/10.17993/3ctic.2023.122.210-225
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ABSTRACT
Ecological environment has always been an important prerequisite while reflecting
people and nature. The construction reflects the degree of development and
civilization of a country as a whole, so it is related to the future of mankind. The deep
integration of land use is a major breakthrough in solving the complex problems in the
process of ecological civilization development and transformation. By establishing a
"fusion" innovation and entrepreneurship ecological civilization system, this paper
applies artificial intelligence and big data in the construction path of innovation and
entrepreneurship ecological system from the perspective of land use and ecological
suitability. Simulation studies were conducted in parasitic mode, biased symbiosis
mode, asymmetric symbiosis mode, and symmetric symbiosis mode respectively
through Matlab software. According to the results of the study, the subject size of the
relevant subjects in the parasitic mode is only 70.43% of the subject size of the
entrepreneurial enterprise. In the biased symbiosis model, the subject size of the
relevant subject is 87.82% of the subject size of the entrepreneurial enterprise.
KEYWORDS
Land use; artificial intelligence; big data; ecological civilization construction; innovation
and entrepreneurship.
INDEX
ABSTRACT
KEYWORDS
1. INTRODUCTION
2. THEORETICAL MODEL
2.1. BP neural network algorithm
2.2. Random Forest
2.3. Gaussian Process Regression Algorithm
2.4. Input variable selection method
2.5. Hyperparameter Optimization Methods
3. ANALYSIS AND DISCUSSION
3.1. Model Simplification
3.2. Simulation calculation
4. RESULTS AND ANALYSIS
REFERENCES
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1. INTRODUCTION
Innovation and entrepreneurship is the main theme of the current society and is an
important engine to promote China's economic development. In the process of
ecological system construction, all sectors should meet the needs of social and
ecological development. In addition, it is a new way to combine the "fusion" innovation
and entrepreneurship approach with system construction in the context of artificial
intelligence, combined with the rational use of land. To build an innovation and
entrepreneurship ecosystem that "continuously deepens the reform of the ecological
system and integrates entrepreneurship whole ecological civilization construction" is
the goal of current development.
Land use change is a core area of contemporary global environmental change
research, and scholars in China and abroad have been advancing their research work
in both depth and breadth over the past 30 years [1-3]. Driven by human economic
and social activities, land use change always has its "reasonable" and "unreasonable"
sides [4-6]. Reasonable land use change means that land users can obtain better
economic and ecological benefits by correctly choosing land use according to the
inherent suitability of the land [7,8]. An unreasonable land use change means that
people violate the law of land suitability, which will cause serious consequences in
terms of ecological degradation [9,10]. We need a reasonable calculation method to
explore the path of ecosystem construction from the perspective of land use and
ecological suitability.
Artificial intelligence. This describes the action form. Artificial intelligence is a
complex category that includes multiple disciplines [11-13]. Big data is also called a
huge amount of data, which refers to the collection of data that cannot be extracted,
summarized, and processed in a short period of time due to the huge and
cumbersome content of the data[14-16]. Later, other methods can be used to
integrate these disorganized data and transform them into our use [17-19].
In the long history of human development, the human and the natural environment
are interrelated and inseparable. Its value system profoundly affects the development
direction and degree of ecological civilization [20-23]. Ecosystem construction is a
non-independent systematic project, and the “integrated” innovation and
entrepreneurship ecosystem is to build a benign and intelligent ecosystem that
integrates ecologically suitable land use and artificial intelligence big data [24,25]. As
ecological civilization is closely related to territorial spatial planning, regional spatial
planning from the perspective of ecological civilization is becoming more and more
popular. Lu et al. [26] took Yongan, Chengdu as an example, and discussed the
development path of territorial space planning. Finally, the corresponding
development strategy and planning are put forward for Yongan Town. They practice
the concept of conservation in the planning of land and space. In addition, they have
formulated measures and specific planning schemes for the ecological compensation
system, hoping to provide certain theoretical guidance for the national land and space
planning of other cities and towns. Ma et al. [27] sorted out the relevant policies. The
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research shows that the protection policy is sufficiently perfect, but the correlation
between marine policy points and marine support policies is not high. These influence
the coupling of marine ecological civilization and ecological society to a certain extent.
Yang et al. [28] studied the civilization pilot city policies emissions based on the
pollution discharge of multiple cities in China. They found that this of cities, especially
for small cities. Xie et al. [29] studied the relationship between quality and economics.
They found that while economic growth has risen steadily in recent years,
environmental pollution, as measured by emissions of wastewater and air pollutants,
is still decreasing, and at a markedly faster rate. This can provide a practical and
effective path for the construction of ecological civilization in other countries. Meng et
al. [30] first combined the evaluation framework of the ecological civilization pilot area
with academic research to build a comprehensive framework and index system. Then,
they calculated the coupled coordination number (CCD) for each experimental plot
based on the entropy weights. Finally, they used relative development coefficients to
measure ecological and economic development and studied different development
patterns of cities. The results show that the regional economy and CCD are closely
related, which shows that the relationship between economy and ecology is
complementary.
To sum up, the advanced ecological ethics concept is the value orientation, the
developed ecological economy is the material basis, and the perfect ecological
civilization system is the incentive and restraint mechanism. At present, from the
ecological perspective of land use, there are still some vacancies "integrated" system.
Combined with the topic of " drives the construction of ecosystems in our province and
countermeasures - based on the perspective of innovation reefs", this paper takes
artificial intelligence and big data as the background, combines intelligent algorithms
with the construction of ecological civilization system, and proposes. This kind of
"integrated" innovation and entrepreneurship ecological civilization system
construction path. By creating an immersive and integrated intelligent environment,
improving the intelligent literacy of human-human synergy, achieving the intelligent
integration required by the whole ecology, and exploring the practical path of building
an innovative ecological system.
2. THEORETICAL MODEL
A neural network is an information processing system formed by studying the
structure and function of the human brain through physical and mathematical
methods. A neural network has many nodes called neurons, each node is connected
with each other, and each node is connected by a connecting line. When data is fed
into a neural network, it spreads among the nodes, each of which then processes the
data. In this case, the nodes in the ANN will find an optimal state, and this process is
called training. From its basic working mechanism, if you find suitable training data to
train the neural network model, you can easily deal with some problems that cannot
be solved at present. Due to the particularity of neural network structure and
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processing methods, it has been widely used in many aspects such as image
processing, robotics, and data mining.
2.1. BP NEURAL NETWORK ALGORITHM
BP has good adaptive ability, regularity, and high parallel processing information
ability. It is a multi-layer feed-forward backward transfer that has excellent high-
dimensional function mapping ability and can handle complex classification problems
and overcomes the problems of exclusive or (XOR) that cannot be handled by simple
perceptrons and the learning of hidden layer connections in multi-layer neural
networks. Through long-term research and exploration, the BP neural network can
solve problems such as prediction, classification, and evaluation.
Taking a simple BP neural network as an example, set the number of node
neurons, single-layer hidden layer, and to be [2, 3, 1], respectively, and the activation
function is the function, that calculates the output value of the output layer.
The mathematical expression is as follows:
(1)
Where, are variables.
The activation function is as follows:
(2)
The weight between nodes and is set to , is the threshold of node , is
the each node, and the specific calculation method value of each node is as follows:
(3)
(4)
Where, is an activation function, usually the function is chosen.
Assume that the full result of the output layer is , and the error function is as
follows:
tan sig
sim
y=w(2,3)
11 ×tansig
(
w(1,2)
11 ×x1+w(1,2)
21 ×x2+b(2)
1
)
+w(2,3)
21 ×tansig(w(1,2)
12 ×x1+w(1,2)
22 ×x2+b(2)
2)
+w(2,3)
31
×tansig
(
w(1,2)
13
×x1+w(1,2)
23
×x2+b(2)
3)
+b(3
)
1
x,w,b
tan sig
f(u) =
2
1+e−2
u−
1
i
j
wij
bj
j
xj
S
j=
m−1
∑
i=0
wij xi+b
j
xj=f
(
Sj
)
f
sigmoid
w
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(5)
Using the gradient descent method, the gradient at the current position is
proportional to the correction of the weight vector, so the output node is:
(6)
Assume that the chosen activation function is as follows:
(7)
Self-feedback network, as a widely used network model in BP neural network,
transmits the error signal of its output layer to the connection weights between its
other layers, so that the error tends to the minimum value. The expression is as
follows:
(8)
Where, is the expected output, is the output layer output, and is the error
signal.
In view of the characteristics of the transfer function and in order to meet the
training requirements, the samples need to be normalized between , and the
algorithm is used:
(9)
Where, is the original data, and is the normalized data.
2.2. RANDOM FOREST
Random forest is one of the most widely used machine learning models, and it is
also an ensemble learning method. Random forest improves the output without
increasing the amount of computation and is not sensitive to multivariate collinearity.
This algorithm is robust to missing data and unbalanced data, and can effectively
predict thousands of different explanatory variables.
Random forest contains several decision trees, and there is no correlation between
these decision trees. Random forest uses the different characteristics of multiple
subsamples to construct multiple decision trees to make similar predictions for the
E
(w,b) =
1
2
n−1
∑
j=0
(
dj−yj
)2
j
Δ
w(i,j)=−η
δE(w,b)
δw(i,j)
f(x) =
A
1+e
−x
B
E
=∑1
(
T1−y1
)2
2
T1
y1
E
[−1,1]
min −max
y
=(ymax −ymin)×
(x−x
min
)
(xmax −xmin)
+y
min
x
y
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same phenomenon. A random forest is a forest composed of multiple decision trees
using the Bagging idea. Each decision tree is a weak classifier. In the classification
problem, the result of each decision tree is voted to obtain the final result, thus
forming a strong classifier.
Random forest uses random samples and random features, that is, random rows
and columns, which reduces the correlation of the base model, that is, each tree, and
can directly deal with categorical and numerical features, avoiding the occurrence of
overfitting to a certain extent. The anti-overfitting and stability characteristics of
Bagging allow random forests to trade-off between bias and variance by adjusting
parameters. These characteristics of random forests make random forests
unnecessary for feature selection. It is suitable for high-dimensional data and can
perform parallel computing, which also makes the selection of effective factors more
simple, efficient, and high-precision in this paper.
2.3. GAUSSIAN PROCESS REGRESSION ALGORITHM
It is suitable for dimensionality and nonlinearity and has good generalization ability.
(10)
(11)
Where, , are arbitrary random variables, is noise, obey a Gaussian
distribution with mean 0 and variance . is the mean function and is
the covariance function. Since follows a Gaussian distribution that is independent
of , follows a Gaussian distribution:
(12)
where is an dimensional unit matrix and given an input variable , the
corresponding output is , the joint distribution of the algorithm's prediction set
and training set is, according to Bayes' principle:
(13)
So the expression for the prediction set is as follows:
(14)
Where,
(15)
(16)
f(x)∝GP (u(x), k(x,x′
))
y=f(x)+ε
x
x′
ε
σ2
n
u(x)
k(x,x′
)
f(x)
ε
y
y∝N(0,K(X,X)+σ2
nIn)
In
n
x*
y*
y*
y
[
y
y∗
]
=N0, [K
(
X,X+σ2
nIn
)
K
(
X,x∗
)
K(x∗,X)k(x∗,x∗)]
y*
y*∣X,y,x*∝N(μ,Σ)
μ=K(x*, X)[K(X,X)+σ2
nIn]
∑=
k
(
x
*,
x
*)−
K
(
x
*,
X
)[
K
(
X
,
X
) +
σ
2
n
I
n]−1
K
(
X
,
x
*)
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For the covariance function, this paper uses the most commonly used square
exponential covariance kernel function:
(17)
2.4. INPUT VARIABLE SELECTION METHOD
The Pearson correlation coefficient describes the degree of correlation between
two spaced variables, generally represented by r, and its calculation formula is:
(18)
Where, is the number of samples, are the current variables, . The
larger the absolute value of r, the stronger the correlation between the two variables,
and the closer the correlation coefficient is to 1 or -1. The weaker the correlation, the
closer the correlation coefficient is to 0.
2.5. HYPERPARAMETER OPTIMIZATION METHODS
The choice of the number directly determines the quality of the model: if there are
too many, the model will be over-fitted and the generalization will be poor. If the
number is too small, it is difficult to complete the fitting of the samples.
In this paper, root mean square error (RMSE), mean error (MAE), mean absolute
error (MAPE) and coefficient of determination ( ) are used as model evaluation
indexes to evaluate the effectiveness and generalization of the model.
Root Mean Square Error:
(19)
Average error:
(20)
Mean absolute error:
K
(xi,xj)=σ2
fexp −
xi−xj
2
2l2
r
=
N∑x
i
y
i
−∑x
i
∑y
i
N∑x2
i−
(
∑xi
)
2N∑y2
i−
(
∑yi
)
2
N
xi,yi
i∈N
R2
R
MSE =
1
n
n
∑
i=1
(
yi−yi)2
M
AE =
1
n
n
∑
i=1
yi−yi
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(21)
Decisive factor:
(22)
Where, represents the actual predicted model, is the number of test samples,
and is the average value of the sample .
This section describes how to process the original dataset, how to choose input
and output variables, and how to optimize hyperparameters for best predictions when
using machine learning methods. Provide theoretical guidance for follow-up work.
3. ANALYSIS AND DISCUSSION
3.1. MODEL SIMPLIFICATION
The growth function model can well describe the growth process of
ecological populations in the ecosystem. The growth of population size is subject to
external environmental factors such as resources, technology, policies, and
institutions. In the entrepreneurial ecosystem, resources are limited, and the growth of
entrepreneurial enterprises, large enterprises, investment institutions, intermediary
service institutions, universities, and research institutes will be constrained by
resources. With the increase in the population density of the subject, the growth of the
subject will slow down, and the growth process of the subject conforms to the
evolution process of the ecological population.
1.
Participants in the entrepreneurial ecosystem include entrepreneurial
enterprises, large enterprises, investment institutions, intermediaries, and
universities and research institutes. Any type of subject other than start-up
enterprises can be a relevant subject.
2. The scale changes of entrepreneurial enterprises and related entities represent
the growth process of the entities. As the entrepreneurial ecosystem evolves,
the size of each entity represents its growth process. The larger scale of the
subject, the greater the number and types of resources in the entrepreneurial
ecosystem, and the better the growth. Conversely, the smaller the scale of the
main body, the less the number and types of resources in the entrepreneurial
ecosystem, and the worse the growth.
M
APE =
1
n
n
∑
i=1
y
i
−y
i
yi
×100 %
R2=∑
n
i=1
(
yi−¯yi
)2
∑
n
i=1 (
y
i−¯
y
i)
2
y,
y
n
¯yi
yi
logistic
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3.
The scale changes of various entities affect each other, and their growth
processes all serve the growth law of
. Due to the limited number of
resources, the growth of subjects is constrained by resources, so in the model
of this paper, the growth of one type of subject will be affected by the density of
another type of subject. The increase in the density of another type of main
body will bring about a decrease in the growth rate of this type of main body.
4. the subject is growing and enters a stable state.
3.2. SIMULATION CALCULATION
Through Matlab software, under the same parameter background, when setting the
relevant subjects, it is found that the of entrepreneurial enterprises, entrepreneurial
enterprises, entrepreneurial enterprises and intermediaries, entrepreneurial
enterprises and universities and scientific research institutions is consistent. Due to
space reasons, this paper takes the symbiotic evolution path of entrepreneurial
enterprises and investment institutions as an example to simulate. The maximum
scale between start-ups and related entities under specific resource constraints is
1000. The initial size of both types of entities is 100. The evolution cycle is 800
simulation times. By exploring the relationship between different A and B, we can
obtain the evolution process, and path.
1.
Parasitism. Taking A as -0.15 and B as 0.15, respectively, the parasitic
evolution results, when A takes a negative value, the entrepreneurial enterprise
belongs to the party with increased interest in the parasitic relationship, and the
related enterprises play a positive the growth of the entrepreneurial enterprise,
and the steady state value exceeds its maximum capacity for independent
growth. B takes a positive value, the relevant subject is on the side of the
parasitic relationship with impaired interests. The startup plays a negative role
in weakening the growth of the size of the relevant subject, and the steady
state value is less than its maximum capacity for independent growth. After
stabilisation, the subject size of the entrepreneurial enterprise reaches 1150
and the subject size of the related subject is only 810, which is only 70.43% of
the subject size of the entrepreneurial enterprise.
logistic
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Figure 1. Symbiotic evolution results in a parasitic mode
2.
Symbiosis of partial benefit. Taking A as -0.15 and B as 0, respectively, the
result of partial benefit, when A takes a negative value, the entrepreneurial
enterprise belongs to the party with increased interests in the symbiotic
relationship of partial interests. The steady-state value exceeds the maximum
capacity of independent growth. When B is 0, the relevant subject belongs to
the party whose interests are not affected by the symbiosis of partial interests.
Entrepreneurial enterprises have no effect on the growth of the scale of related
entities, and the steady state value is equal to the maximum capacity of
independent growth. In the stable simulation stage, the main body scale of
entrepreneurial enterprises has reached 1150, and the main body scale of
related entities is 1010, which is only 87.82% of the main body scale of
entrepreneurial enterprises.
Figure 2. Symbiotic evolution results under the partial benefit symbiosis model
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3.
Asymmetric mutualism. Taking A as -0.35 and B as -0.15, respectively, the
results of asymmetric reciprocal A and B have negative values, and the scale
growth of start-ups and related entities benefited from the other entity. The
scale growth of start-ups and related entities is positively promoted by each
other, and the steady state values of the two types of entities both exceed the
maximum capacity of their independent growth. However, when |A|>|B|, it
means that the relevant subject has a greater influence on the entrepreneurial
enterprise. In the stable simulation stage, the main body scale of
entrepreneurial enterprises has reached 1400, and the main body scale of
related entities is 1250, which is only 89.28% of the main body scale of
entrepreneurial enterprises. Therefore, the steady-state scale of
entrepreneurial enterprises is larger than the steady-state scale of related
entities.
Figure 3. The results of symbiotic evolution under the asymmetric symbiotic model
4.
Symmetrical mutualism. Take -0.35 for A and -0.35 for B, respectively, to obtain
the symmetrical reciprocal. Both A and B take negative values, and |A|=|B|, the
scale growth of entrepreneurial enterprises and related entities both benefit
from the other entity and are affected to the same extent. The steady-state
scale of entrepreneurial enterprises is equal to the steady-state scale of related
entities, and both are larger than the maximum scale of their independent
growth. In the late stage of simulation, the scale of the main body of
entrepreneurial enterprises has reached 1570, and the scale of the main body
of related entities has also reached 1570, but the simulation time is slightly
longer.
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Figure 4. Symbiotic evolution results under the symmetrical symbiosis model
Through the above simulation results, it can be found that of A and B represent
different symbiosis modes. Different symbiosis modes affect the stable equilibrium
point, ultimately leading to different evolution paths of the subjects. It can be
concluded that the path is affected by the symbiotic mode among multiple types of
subjects. Observing Figures 1-4, it is found that different symbiosis coefficients
represent different symbiosis modes. Under different symbiosis modes, the evolution
equilibrium point of the main body is different, and the final stable scale of the main
body is different.
4. RESULTS AND ANALYSIS
In the context of dual innovation, the entrepreneurial ecology of major cities in
China is developing well and is becoming a world-leading entrepreneurial ecosystem.
In this paper, we establish a "convergent" innovation and entrepreneurship eco-
civilization system and apply it in the construction of the innovation and
entrepreneurship eco-system from the perspective of land use and ecological
suitability. Simulation studies were conducted in parasitic mode, biased symbiosis
mode, asymmetric symbiosis mode, and symmetric symbiosis mode by Matlab
software, respectively, and based on the results, the following conclusions can be
drawn:
1.
Different values are taken to represent different symbiosis patterns, and
different symbiosis patterns affect the stability of symbiotics, which eventually
leads to different evolutionary paths of the subjects. In the parasitic mode, the
subject size of the relevant subject is only 70.43% of the subject size
enterprise.
2.
The steady state scale is related to its symbiosis coefficient and maximum
scale and has nothing to do with initial population size and natural growth rate.
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Under the partial benefit symbiosis model, the scale of the main body of
entrepreneurial enterprises has reached 1150, and the scale of the main body
of related entities is 1010, which is only 87.82% of the scale of the main body
of entrepreneurial enterprises.
3.
Under the symmetrical symbiosis model, the main body scale of the relevant
entities also reaches the optimum, reaching 1570, which is consistent with the
main body scale of entrepreneurial enterprises. From this, it can be concluded
that the equilibrium point is related to the symbiotic coefficient.
REFERENCES
(1) Ali, N., Nakayama, S., & Yamaguchi, H. (2021). Analysis of Bus Fare Structure to
Observe Modal Shift, Operator Profit, and Land-Use Choices through Combined
Unified Transport Model. Sustainability, 14.
(2)
Jiang, C., Yang, Z., Wen, M., et al. (2021). Identifying the spatial disparities and
determinants of ecosystem service balance and their implications on land use
optimization. Science of The Total Environment, 793(1), 148472.
(3) A, S. M., B, A. B., C, M. H., D, J. S., & Ulrich Müller e. (2021). Palaeo-ecological
development and interpretation of the macrofauna inventory (bivalvia and
gastropoda) in marine Eemian deposits at Warnow Bay (NE Germany).
Quaternary International.
(4)
An, Y., Liu, S., Sun, Y., Shi, F., Zhao, S., & Liu, Y., et al. (2022). A partitioning
approach for regional sustainability based on economic development indicators
and ecological values for China. Journal for Nature Conservation, 67.
(5) Li, D., & Zhang, J. (2022). Measurement and analysis of ecological pressure due
to industrial development in the Yangtze River Economic Belt from 2010 to 2018.
Journal of Cleaner Production, 353.
(6)
Sun, B. (2022). Comprehensive evaluation and analysis of ecological language
development in consideration of Q-learning algorithm. Mathematical Problems in
Engineering, 2022.
(7)
Zhang, X., Sun, D., Zhang, X., & Yan, H. (2021). Regional ecological efficiency
and future sustainable development of marine ranch in China: an empirical
research using DEA and system dynamics. Aquaculture, 534, 736339.
(8)
Xu, X. (2021). Mechanism and test of the impact of lack of human capital on
ecological development in the innovation sector. Mathematical Problems in
Engineering, 2021(8), 1-8.
(9) Bloem, S. J. V., & Martin, P. H. (2021). Socio-
ecological lessons from the
multiple landfalls of Hurricane Georges. Ecosphere, 12(2).
(10)
Chen, C. (2022). Multipoint ecological environment monitoring system with the
Internet of Things. Mathematical Problems in Engineering, 2022.
(11) Hu, G., & Yu, B. (2022). Artificial Intelligence and Applications. Journal of Artificial
Intelligence Technology, 2(2), 3.
https://doi.org/10.17993/3ctic.2023.122.210-225
3C TIC. Cuadernos de desarrollo aplicados a las TIC. ISSN: 2254-6529
Ed.43 | Iss.12 | N.2 April - June 2023
223
(12)
Yang, Y. (2021). Linear fractional differential equations in bank resource
allocation and financial risk management model. Applied Mathematics and
Nonlinear Sciences. doi:10.2478/AMNS.2021.2.00148.
(13)
Börner, K., Scrivner, O., Cross, L. E., et al. (2020). Mapping the co-evolution of
artificial intelligence, robotics, and the internet of things over 20 years
(1998-2017).
(14)
Górriz, J. M., Ramírez, J., Ortíz, A., et al. (2020). Artificial intelligence within the
interplay between natural and artificial Computation: advances in data science,
trends, and applications. Neurocomputing, 410(2).
(15)
Trujillo, J., Davis, K. C., Du, X., et al. (2021). Conceptual modeling in the era of
Big Data and Artificial Intelligence: Research topics and introduction to the
special issue. Data & Knowledge Engineering, 135, 101911.
(16)
Saha, S., Gan, Z., Cheng, L., et al. (2020). Hierarchical Deep Learning Neural
Network (HiDeNN): an Artificial Intelligence (AI) Framework for Computational
Science and Engineering. Computer Methods in Applied Mechanics and
Engineering, 373(113452).
(17)
Bouzidi, Z., Boudries, A., Amad, M. (2021). Enhancing Crisis Management
because of Deep Learning, Big Data and Parallel Computing Environment:
Survey. In 2021 International Conference on Electrical, Communication, and
Computer Engineering (ICECCE).
(18)
Chen, S., Sanweng, I. (2021). Economic Information Analysis and Its Application
in Decision-Making under the Background of Big Data. Mobile Information
Systems.
(19)
Dozier, J. (2022). Revisiting Topographic Horizons in the Era of Big Data and
Parallel Computing. IEEE Geoscience and Remote Sensing Letters, 19.
(20)
Ma, K., Wei, F. (2021). Ecological civilization: a revived perspective on the
relationship between humanity and nature.
(21)
Wei, F., Cui, S., Liu, N., et al. (2020). Ecological civilization: China's effort to build
a shared future for all life on earth. National Science Review.
(22)
Sahel, R. (2020). China’s approach to the environmental civilization. Human
Affairs.
(23)
Zhang, J. (2022). Thoughts on the Modernization of Governance Systems of
Ecological Environment under the Background of the New Era. Nombre de la
revista, 13(1), 5.
(24)
(2022) Entrepreneurship and Innovation Policy and the Economy. Editorial:
University of Chicago Press Economics Books, 2022.
(25) Meglio, O., & Paola, N. D. (2021). Innovation and Entrepreneurship for Well-
Being and Sustainability. Sustainability, 13.
(26)
Lu, H. (2021). Exploration and Research on Territorial Space Planning from the
Perspective of Ecological Civilization—Taking Yong'an Town, Chengdu as an
Example. Open Access Library Journal, 8(8), 8.
(27)
Ma, J. (2020). Research on Marine Ecological Civilization Policies in Shandong
Province Based on Content Analysis. Nombre de la revista, 10(5), 25.
https://doi.org/10.17993/3ctic.2023.122.210-225
3C TIC. Cuadernos de desarrollo aplicados a las TIC. ISSN: 2254-6529
Ed.43 | Iss.12 | N.2 April - June 2023
224
(28)
Yang, Q., Gao, D., Song, D., et al. (2021). Environmental regulation, pollution
reduction and green innovation: The case of the Chinese Water Ecological
Civilization City Pilot policy. Economic Systems, 100911.
(29)
Xie, M., Duan, H., Kang, P., et al. (2021). Toward an Ecological Civilization:
China's Progress as Documented by the Second National General Survey of
Pollution Sources. Engineering, 2021.
(30)
Meng, F., Guo, J., Guo, Z., et al. (2021). Urban ecological transition: The practice
of ecological civilization construction in China. Science of The Total Environment,
755(Pt 2), 142633.
https://doi.org/10.17993/3ctic.2023.122.210-225
3C TIC. Cuadernos de desarrollo aplicados a las TIC. ISSN: 2254-6529
Ed.43 | Iss.12 | N.2 April - June 2023
225
