Content uploaded by Munir Ahmad
Author content
All content in this area was uploaded by Munir Ahmad on Aug 12, 2021
Content may be subject to copyright.
Intelligent Model Of Ecosystem For Smart Cities Using Artificial Neural
Networks
Tooba Batool
1
, Sagheer Abbas
1
, Yousef Alhwaiti
2
, Muhammad Saleem
1
, Munir Ahmad
1
,
Muhammad Asif
1
,*
and Nouh Sabri Elmitwally
2,3
1
School of Computer Science, National College of Business administration and Economics, Lahore, 54000, Pakistan
2
College of Computer and Information Sciences, Jouf University, Sakaka, 72341, Saudi Arabia
3
Department of Computer Science, Faculty of Computers and Artificial Intelligence, Cairo University, 12613, Egypt
Corresponding Author: Muhammad Asif. Email: muhammadasif@ncbae.edu.pk
Received: 20 March 2021; Accepted: 21 April 2021
Abstract: A Smart City understands the infrastructure, facilities, and schemes
open to its citizens. According to the UN report, at the end of 2050, more than
half of the rural population will be moved to urban areas. With such an increase,
urban areas will face new health, education, Transport, and ecological issues. To
overcome such kinds of issues, the world is moving towards smart cities. Cities
cannot be smart without using Cloud computing platforms, the Internet of Things
(IoT). The world has seen such incredible and brilliant ideas for rural areas and
smart cities. While considering the Ecosystem in Smart Cities, there is a consider-
able requirement to improve the model to make life better. This proposed research
integrates a city into a smart city using the Internet of Things (IoT) which focuses
on the smart ecosystem. In this research work, a model is proposed to overcome
an ecosystem’s IoT and Machine Learning techniques issues. The Levenberg-
Marquardt (LM), Bayesian Regularization (BR), and the Scaled Conjugate Gradi-
ent (SCG) algorithms are implemented with an ANN-based approach named to
empower the ecosystem of the smart city while developing an efficient and smart
ecosystem model. The proposed method’s evaluation indicates that the BR algo-
rithm achieves promising results concerning accuracy and miss rates. The pre-
dicted accuracy of the proposed model shows 91.55% performance of the
ecosystem on the given factors.
Keywords: Ecosystem; machine learning; artificial neural network; smart city
1 Introduction
Most of the world’s rural population is migrating to urban areas. One of the UN reports stated that, by the
end of 2050, more than 60% of the world population will migrate to urban areas. This seems to be a large
number and facilitates inhabitants, and cities need to be more facilitated, equipped with innovative,
intelligent, and modern technologies. In these particular situations, Information Technologies can bind
with the city’s local governments. It can be the breakthrough for implementing innovative, intelligent,
and smart applications with privately-owned local businesses. The Internet of Things is one of the major
This work is licensed under a Creative Commons Attribution 4.0 International License, which
permits unrestricted use, distribution, and reproduction in any medium, provided the original
work is properly cited.
Intelligent Automation & Soft Computing
DOI:10.32604/iasc.2021.018770
Article
ech
T
PressScience
domains that play an important role in creating such applications for all major industries and humans. With
the Number of human in-creases in urban areas, IoT is rapidly growing with advancements in intelligent
devices like sensors, gadgets, house equipment, etc. On the other hand, IoT devices were not possible to
be intelligent without the advancements of hardware devices, sensors networks, and IoT devices and
networks interconnected. With their interconnectivity, the world is exploring a new way of life [1,2].
On the other hand, Cloud services boosted the IoT, sensing devices, and actuation resources to build
great innovative solutions to the world. But as known that, still there is a way long to make everything to
the expectations and so that, there are still a few more challenges in the current system, such as:
According to ICT patterns, sensing and actuation tools can be included in the Cloud, and solutions for
the integration and evolution of IoT and cloud computing infrastructures exist. Nonetheless, there are certain
obstacles to overcome, such as:
The ability of different devices among different systems of ICT. The devices should process the large
real-time scale of data provided, and devices can deploy those large amounts of data into smart systems.
Etymologizing fragmentation of the multiple smart devices and their architectures are associated with the
middleware involved in them. Heterogeneous resources are sometimes mixed up and need a smart system
to treat those mixed up at various Clouds levels [3].
Regarding the last point, when talked about smart systems, the talk will be about the cloud services
concept. Cloud services and IoT are interconnected with each other. Cloud Services play the role of
connectivity with the IoT, with the people on the Internet and the internet services provider. With this
theory, a new term is discovered, named Cloud of Things (CoT).
Regarding the last point, the Internet of Things (IoT) definition, with underlying physical structures
abstracted according to thing-like semantics, appears to be a good starting point for orchestrating the
various tools. In this sense, the Cloud paradigm may help link the Internet of Things with the Internet of
People through the Internet of Services, allowing for horizontal convergence of disparate silos. This
horizontal integration and Cloud computing will be refereed associated with the IoT as the Cloud of
Things (CoT). Advancement of the assembly of differing IoT stages and Clouds experiences
appropriately planned and executed reflection, virtualization, and the board of things. An exact structure
of these instruments will allow the advancement of mechanical rationalist architecture. The joining and
sending of various gadgets and items can be considered by disregarding their fundamental engineering [4].
Urban areas are transitioning from sophisticated to smart urban communities, computerized or astute
urban communities that are more creatively arranged reciprocals of smart city ideas. When a city is
instrumented, in-reconnected, versatile, self-ruling, learning, self-fixing, and vigorous, it moves closer to
being “smart”. As illustrated in Fig. 1, sections of its base and offices unite people and update ICT to
deliver their citizens and other partners’administrations.
My explanation’s main purpose is to talk about the overall reference structure for an urban IoT plan.
It will define the major factors, features, and properties involved around the urban city at the IoT level. It
will explain the importance of major services like energy, water, and an urban city ecosystem for all
government sections.
2 Literature Review
Urbanization is the process which is related to the Economic Development, Social-Economic the
administration of urbanization, and occupants’prerequisites, and the world has presented the diver’s
administration policies for future smart cities like technology, connectivity, sustainability, comfort, safety,
social and cultural protection, and pleasing quality shape of the effective milestones to attain the better
way of life, which led to the implication of “Smart Cities”.
514 IASC, 2021, vol.30, no.2
In the 20th century, the Smart city was like a fiction concept for most of the population. In any case, on
account of the telematics concept and the gadget’s knowledge, the “Smart City”becomes the achievable
reality [5]. Moreover, the utilization of Information and Communication Technologies (ICTs) is a
significant factor that empowers and makes it easy for urban areas to end up in Smart Cities.
Accordingly, Smart City is established in clever frameworks formation and ICTs-Human association. The
city development must regard these three tomahawks [6]: Sustainability improves the city/condition
relationship and utilizes a green economy. Smartness; setting mindful economy and administration.
Comprehensiveness; by cultivating a high-work economy conveying social and regional attachment.
The Internet of Things (IoT) is the interconnection of numerous devices like portable, vehicles, gadgets,
and various things that are embedded with equipment and can work alongside the product, programming,
sensors, actuators, and accessibility which involves these things to interface and exchange data (BROWN,
2016). IoT and Smart City have a hard bond relation; without IoT, no city becomes Smart City.
Fig. 2 shows IoT features fluctuate starting with one area then onto the next space. Some of the core
features of IoT explore during the case studies are as follows:
2.1 Intelligence
IoT combines hardware and software, algorithms, and combinations of different associations between
various gadgets.
2.2 Connectivity
Different IoT devices interact with each other through proper connectivity, which empowers them to
communicate daily. Availability of these articles is urgent because basic item level collaborations
contribute towards aggregate insight in IoT arrange, which empowers organize openness and similarity in
the various gadgets. With this availability, different new open doors are made in the market with
equipment and programming assistance.
2.3 Sensing
IoT sensors bring offline networks to active networks; without an active network, IoT devices cannot
bring real-life IoT devices. The IoT devices are used to detect and measure real-time changes in the IoT
Figure 1: ICT with different Natives
IASC, 2021, vol.30, no.2 515
devices and reports based on results they detect. Without Sensors, there could not be an active and effective
IoT environment.
2.4 Data
Data play a role as a communicator between devices and the network; without effective and useful data,
the IoT devices will not communicate as per the expectations. Suppose all the properties of the related data
are described in good manners. It will be easy for IoT devices to communicate with each other, so in this case,
data plays a vital role between networks and IoT devices.
2.5 Artificial Intelligence
IoT makes things reasonable and upgrades life through the use of knowledge the IoT devices have.
Artificial Intelligence (AI) is a very important factor in making devices more intelligent and effective in
doing jobs. If we have an AI base Ice Cream maker machine, and if the flavor ends, then with the AI
integrated, it will generate an alarm to the merchant to refill the flavor.
2.6 Interoperability
IoT devices mostly aim to incorporate the physical world using the virtual world with the Internet’s help
as the medium of communication. As for the IoT devices are a concern, future net-works will continue to be
heterogeneous, multi-vendor, multi-tasking. Most importantly, they will be largely disrupted on a large scale.
Still, consequently, the non-interoperability will increase too. So, Interoperability between IoT devices is a
key challenge for the coming years.
2.7 Dynamic Changes
When we talk about smart IoT devices, we alternatively talk about the devices that can adapt to the
situation and learn easily with past experiences. Without the nature of devices to be intelligent, no device
can be smart.
Figure 2: Features of IoT
516 IASC, 2021, vol.30, no.2
2.8 Ecosystem
IoT ecosystems are interconnected with cloud services, AI devices, and different applications for
consumers and businesses. On the other hand, most world users think that the ecosystem is small and not
worth it. As most of the world’s rural population is moving to urban areas, it is very important to make
our environment eco-friendly. To do so, we need eco-friendly devices. To keep in mind this, companies
are moving towards Eco-friendly devices; one of the eco-friendly examples is the Tesla car.
2.9 Smart City
The Smart city is an intricate biological system of individuals, firms, strategies, innovation, and different
empowering influences cooperating to convey many results. The smart city isn’t“possessed”exclusively by
the city. Other worth makers are additionally included, once in a while working in a joint effort and now and
then independent from anyone else. Fruitful and economically smart urban areas adopt an automatic strategy
to connect with their partners over the ecosystem system.
2.10 Role of Planet in Smart City
To date, many cities have not approached smart city strategies with an ecosystem perspective. This is
partly because smart city initiatives are overseen by Information Technology (IT) agency, whose mission
is to design and implement systems. Smart city projects are managed by internal cross-functional
“Transformation”or “Innovation”organizations in more seasoned smart cities [7].
Notwithstanding where urban communities are in their shrewd city venture, they should stretch out
beyond the “bend”with savvy city ventures. They start by speculation as far as structure the more
extensive ecosystem system to make a feasible and versatile savvy city. The key subsequent stages are to
Comprehend the smart city biological system structure and adapt it to their particular city’s substances.
Fuse this model to improve their brilliant city vision, procedure, and implementation plans [8].
Concerning the smart city environment system, distinguish current abilities and holes over the different
layers. Comprehend what is expected to help the four sorts of significant worth creators.
Assess existing and new savvy city ventures and activities against the environment outline work. Utilize
this system to distinguish what is absent from the undertaking plans and what is expected to make the ventures
completely fruitful. Determine which competencies should be prioritized and developed across the various
ecosystem layers. A smart city necessitates the acquisition of new skills and competencies. As required,
complement existing capabilities by forming strategic alliances and contracting with service providers.
Smart City can be classified into six aspects:
Environment
Economy
Governance
Living
Mobility
People
These are key focuses for answers for the real divergences of urban advancement, and the board of these
subjects will prompt a smarter city [9].
Numerous nations and urban communities are trying to create smart urban communities, and some of them
have developed a brilliant situation, savvy portability, and shrewd vitality, among others. One of the enormous
issues for smart urban communities, be that as it may, is realizing how to deal with the immense amounts of data
produced by associations, frameworks, and individuals consistently [10]. However, the proper research and
analysis of this data can lead to the information, helping build a smart city [11].
IASC, 2021, vol.30, no.2 517
According to [12], the mix of vital approaches and procedures are essential for smart urban areas,
advancing manageable advancement, financial development, and better conditions for its residents. In this
sense, Data Mining (DM) and Machine Learning (ML) procedures are pivotal for applications including
smart urban communities since they aid issues including urban advancement, for example, recognizing
areas that need checking by cops, handling of traffic, etc. [13].
Although enthusiasm for advancing smart urban communities, there is still an absence of agreement in
current writing about the real impacts of these methods on urban communities. What’s more, most research
investigates explicit issues, not concentrating on DM and ML’s job in savvy urban areas. Applications of IoT
are shown below in Fig. 3.
2.11 Factors of Planet
Tab. 1. shown the planet factors of a smart city. The smart city contains five subcomponents like Energy
and mitigation, Materials, Water and Land, Pollution & Wastage, Ecosystem, and Climate Changes.
Figure 3: IoT applications
Table 1: Planet factors of Smart City [14]
Energy and
mitigation
Denigrate less energy utilization, waste energy and produce more useful power
resources
Materials, Water, and
Land
Water supply utilization observing to acquire counsel on the most proficient method
to spare expense and assets.
Pollution & Wastage Controlling of CO2 emissions of production lines, contamination transmitted via
vehicles, and poisonous gases created in ranches
Ecosystem Use of IoT devices to make environment eco-friendlier and use nature conservations
Climate Changes Climate conditions and unexpected events
518 IASC, 2021, vol.30, no.2
2.11.1 Energy and Mitigation
According to a report, at the end of 2050, more than 70% of the world populace will migrate from rural
to urban areas population. That equates to another 2.5bn people live in cities, which was estimated at
4.2 billion in 2018 [15]. In such a rapid case of growing urban population, there is much need for
sustainable energy, Transport, and Infrastructure sectors, to make a better, smarter and better way of
living. Energy and mitigation play an important role in making a city Smarter [16].
2.11.2 Material, Water, and Land
One of the major issues of any city is the leakage of water and material. To make the water supply by
24 7, leakage removal is essential. For better water and material management, various smart tools or
equipment are used to eliminate or detect leakage at various levels. “World can live without love, but no
one can live without water. –W.H. Auden”.
2.11.3 Pollution and Wastage
According to World Health Organization (WHO), the death rate will increase enormously due to air
pollution and can be in thousands rather than in hundreds between 2030 to 2050 [17]. The global air
pollution problems extend to the majority of the world countries. This is due to a large number of
migrations of rural population to the urban areas. Pakistan’s major cities like Karachi, Lahore, and
Peshawar face many pollution and wastage issues due to many migrants. In this case, pollution and
wastage is an important factor for cities to make them smarter.
2.11.4 Ecosystem
An essential and reasonable city is an environment involving single clients to the network level,
organization and business, laws, and processes integrated to create the most vibrant and adaptive smart
city. One of a smart city’s major agendas is to make the city digital and innovative for peoples, with
digital and smart tools of Transport, education, healthcare, and better options for better living standards.
2.11.5 Climate Changes
Countries all over the world have set an ambitious target for reducing greenhouse gas emissions.
Cities will need to use considerably fewer resources if certain goals are met. The revolution of Urban
pollution and the industrial revolution directly impact the environment. With these revolutions, a new
debate started that “how is responsible,”to what extent these problems are “man-made,”and what can be
done to get rid of these crises.
3 Research Methodology
The proposed model based on Machine Learning and AI, which describes how the hardware and
software, physical gadgets communicate with each other and will help in building a smarter ecosystem
for the smart city. The proposed model present in more detail and in advanced form, which gives more
accurate results, and on that basis, the system can measure and evaluate the outcomes. The proposed
system also helps us make better policies for any perspective of life, especially for Ecosystems.
Over the past couple of decades, AI and Machine Learning have turned out to be data innovation
qualities. With that, a fairly major, despite typically concealed, some portion of our life portrayed in.
Over time, the data increases rapidly, and it was time’s need that we have a system which handles a
complex and large amount of data and process quickly and gives us more accurate results. In this aspect,
ML and AI plays an important role and becomes the top-level mechanism to handle such large
and crucial kind of information on such a large scale. On the other hand, we can say that Machine
Learning is the broader and advanced form of fuzzy logic, automata, neural networks, adaptive agents,
and genetic algorithms.
IASC, 2021, vol.30, no.2 519
3.1 Artificial Neural Network
Artificial Neural Network (ANN) is the advanced term used to transform and implement human minds’
functional and structural information on a computer. The most overall ANN structure comprises an info layer,
hidden layers, and data output result layer and experiences a learning method that depends on the back-
proliferation calculation. When sustained with estimated information for input and output, the neural
system decides weighted interrelation among neurons’input and output values. Neurons are the one,
those are interconnected elements and are located in the hidden layer of ANN. They typically worked as
a middle man between the input and output layers and transferred the information among input and
output layers. The information is typically a non-linear function. After passing all the non-linear
information, the resulting neuron is then multiplied with weighted factors, which gives each neuron a
separate weightage and the ability to communicate individually among input and output layers.
3.2 Proposed Model
In this proposed research, a Model of Intelligent Ecosystem using Machine Learning Technique (IEML)
using various ecosystem parameters is developed to predict the ecosystem performance smartly and
efficiently using machine learning techniques. An ecosystem is one of the important Smart city factors,
which are shown below in Fig. 4.
Fig. 4 is demonstrating the important factors of a smart city. A smart city is further divided into five
factors: People, Planet, Prosperity, Governance, and Propagation. The planet factor is further divided into
five factors: energy and mitigation, materials, water and land, climate resilience, population and waste,
and ecosystem. Improving the ecosystem’s performance is the main concern in this research, which has
five factors: public services, technology companies, city councils, university and entrepreneurship
companies, and individuals.
Figure 4: Smart city factors
520 IASC, 2021, vol.30, no.2
Fig. 5 demonstrates the proposed model, which consists of two phases, the first one is the training phase,
and the second one is the validation phase. Both phases are linked through a cloud. The training phase
involves three Layers (Sensory, Preprocessing, and Application). The Sensory layer senses the values
from input parameters like Public Services, Technology Companies, City Councils, University and
Entrepreneur Companies, and Individuals. It saves these values in the database via IoT. The data stored in
the database can be noisy because of wireless communication. The next and very important is the
preprocessing layer, which mitigates noisy data using moving average, handling missing values and
normalization. After preprocessing, the output will be sent to the application layer as input. The
application layer is further divided into two layers (Prediction and Performance). In the prediction layer,
an ML-based approach is used to predict the ecosystem.
The proposed method consisted of three layers: input, output, and a hidden layer. Backpropagation
was achieved using extreme machine learning, which involves various techniques for measuring error,
such as weight initialization, feedforward, and backpropagation, and then updating their weight
and bias. The hidden layer comprises neurons, each of which has an activation function, such as f
(x) = Sigmoid (x). this function for the proposed model’s input layer and hidden layer could be
read as follows:
Figure 5: Proposed modelling intelligent ecosystem of smart cities using artificial neural networks
IASC, 2021, vol.30, no.2 521
In Eqs. (1) and (2), the input from the output layer is taken
Eq. (3) demonstrates backpropagation error where, pў&outўdemonstrates the desired output and
estimated output. The activation function for the output layer is found in Eqs. (4) and (5)
The layer is written as Eq. (6) rates of change in weight for the output.
Eq. (6) put on the chain rule method
Eq. (7) substitute the values obtained in Eq. (8) for both the values of weight change
where,
Use the chain rule to update the weights between the input and hidden layers
The above equation, erepresents the constant,
After simplification above equation can be written as
522 IASC, 2021, vol.30, no.2
The weights between the output and hidden layers were modified using Eqs. (9) and (10). The weights
between the hidden and input layers are updated using Eq. (11).
This proposed method consists of one hidden layer and 20 neurons, each with six inputs and one output.
A two-layer feedforward approach is used in this method. The data trained the ANN into sets such as training,
validation, and testing to explain the technique. Regression analysis was used to predict the method’s
implementation. We test the device as a regression fit and Mean Square Error (MSE) to determine the
results. If the desired outcome is not achieved, the process is retrained using a new dataset.
After the prediction layer, the output will be sent to the performance layer to predict the ecosystem’s
performance in terms of Accuracy and Miss-rate and check whether the learning criteria are to meet or
not. In the case of No, the prediction layer will be updated and so on, while in the case of Yes, the output
value will be stored on the cloud database.
Then there the Validation phase will be activated. The input will be sensed from the Input Layer and sent
to the ML approach to predict the Ecosystem performance and will be checked whether Ecosystem
performance is predicted or Not. In the case of No, the process will be discarded, and in the case of Yes,
the Ecosystem performance message will be displayed.
4 Simulation Results
MATLAB 2019a tool is used for predicting the smartness rank of the SV. BR, SCG, and LM were used
to train and fit 1385 sets of datasets randomly divide into 70% of training, 15% of validation, and 15% of
testing with a different number of hidden layer neurons of the proposed model.
Three different algorithms, namely LM, BR, and SCG, have been applied to the dataset, and obtained
results are shown in Tab. 2. It is clearly shown in Tab. 2; the BR algorithm has the highest accuracy. It is
shown that using multiple hidden layer neurons 3(10-15 - 20), the proposed system is improving as
increasing the Number of neurons, and the results of our proposed BR algorithm are better as compared
to LM and SCG algorithms.
Tab. 2 also shows the performance of the proposed system with different number of Hidden Layer
neurons in terms of MSE and Regression with Training and Validation. In training the MSE and
Regression of LM approach by means of 10, 15 & 20 neurons are 33.1165e-6, 31.4725e-6 & 31.7974e-6
and 91.5756, 91.6008 & 92.1043 respectively. The MSE and Regression of BR approach with 10,15 &
20 neurons are 34.6154e-6, 35.3284e-6 & 35.9025e-6 and 90.6500, 90.7832 & 91.3086 respectively.
The MSE and Regression of SCG approach with 10, 15 & 20 neurons are 35.6204e-6, 404771e-6 &
36.9306e-6 in addition 90.3416, 89.6994 & 89.9392 respectively.
In Validation the MSE and Regression of LM approach with 10, 15 & 20 neurons are 38.6907e-6,
42.4828e-6 & 50.2186e-6 and 87.0432, 87.7941 & 89.95008 respectively. The MSE and Regression of
BR approach by means of 10, 15 & 20 neurons are 36.6907e-6, 40.4828e-6 & 48.2186e-6 and 89.6832,
90.2086 & 91.5500 respectively. The MSE in addition Regression of SCG approach with 10, 15 &
20 neurons are 31.6570e-6, 461944e-6 & 55.3208e-6 and 92.9389, 88.5523 & 89.4772 respectively.
IASC, 2021, vol.30, no.2 523
In the context of hidden layer neurons, the proposed solution was varied with 10, 15, and 20 neurons,
and it was discovered that growing neurons improved system efficiency. It also means that increasing the
Number of neurons improves the system’sefficiency. The proposed BR approach has produced promising
results compared to LM and SCG approaches with 20 hidden layer neurons. If in hidden layers the
Number of neurons is 20, LM gives 50.2186e-6MSE and 89.95008regression, BR gives 48.2186e-6MSE
and 91.5500 regression, and the SCG gives 55.3208e-6 MSE and 89.4772 Regression in Validation, with
this. It is observed that Bayesian Regularization gives stunning results in terms of MSE and Regression
when hidden layer neurons are 15 as associated to Levenberg Marquardt and Scale Conjugate. It was
seen that the BR algorithm has the highest accuracy rate, with 91.55%.
5 Conclusion
A smart city is the dream of innovation, creativity, better way of life for the future, where people can live
with smart and intelligent gadgets and can communicate with the world in the more advanced and fastest
way. A smart city is a way to change the social and economical way of life. This research incorporates an
intelligent ecosystem using the IoT and designed for a smart city. This research defines a smart city to
predict the ecosystem immediately by implementing ANN approaches. The framework is developed using
Cloud and IoT to facilitate storage, index, and data visualization generated through a smart city’s input
parameters. The SCG, LM, and BR algorithms are implemented with an ANN-based proposed approach
to develop a predictive and smart ecosystem model. The proposed system’s evaluation shows that the BR
algorithm gives promising results in accuracy and Miss rate. The predicted accuracy of the proposed
model shows 91.55% performance in the ecosystem on the given factors.
Acknowledgement: Thanks to our families & colleagues who supported us morally.
Funding Statement: The authors received no specific funding for this study.
Conflicts of Interest: The authors declare that they have no conflicts of interest to report regarding the
present study.
References
[1] H. Arasteh, V. Hosseinnezhad, V. Loia, A. Tommasetti, O. Troisi et al., “Iot-based smart cities: A survey,”in
2016 IEEE 16th Int. Conf. on Environment and Electrical Engineering, Florence, Italy, pp. 1–6, 2016.
Table 2: Training and validation of proposed system
Proposed
Algorithm
Training Validation
10
Neurons
15
Neurons
20
Neurons
10
Neurons
15
Neurons
20
Neurons
LM MSE 33.1165e
-6
31.4725e
-6
31.7974e
-6
38.6907e
-6
42.4828e
-6
50.2186e
-6
Regression% 91.5756 91.6008 92.1043 87.0432 87.7941 89.95008
BR MSE 34.6154e
-6
35.3284e
-6
35.9025e
-6
36.6907e
-6
40.4828e
-6
48.2186e
-6
Regression% 90.6500 90.7832 91.3086 89.6832 90.2086 91.5500
SCG MSE 35.6204e
-6
404771e
-6
36.9306e
-6
31.6570e
-6
461944e
-6
55.3208e
-6
Regression% 90.3416 89.6994 89.9392 92.9389 88.5523 89.4772
524 IASC, 2021, vol.30, no.2
[2] M. Batty, K. W. Axhausen, F. Giannotti, A. Pozdnoukhov, A. Bazzani et al., “Smart cities of the future,”European
Physical Journal Special Topics, vol. 214, no. 1, pp. 481–518, 2012.
[3] R. B. Uriarteac, R. D. Nicolaa, V. Scocaa and F. Tiezzib, “Defining and guaranteeing dynamic service levels in
clouds,”Future Generation Computer Systems, vol. 99, no. 1, pp. 27–40, 2019.
[4] M. Aazam, I. Khan, A. A. Alsaffar and E. N. Huh, “Cloud of things: integrating internet of things and cloud
computing and the issues involved,”in Proc. of 2014 11th Int. Bhurban Conf. on Applied Sciences &
Technology (IBCAST) Islamabad, Pakistan, Islamabad, Pakistan, pp. 414–419, 2014.
[5] E. G. Carayannis and R. Rakhmatullin, “The quadruple/quintuple innovation helixes and smart specialization
strategies for sustainable and inclusive growth in Europe and beyond,”Journal of the Knowledge Economy,
vol. 5, no. 2, pp. 212–239, 2014.
[6] A. Akande, P. Cabral, P. Gomes and S. Casteleyn, “The Lisbon ranking for smart sustainable cities in Europe,”
Sustainable Cities and Society, vol. 44, no. 4, pp. 475–487, 2019.
[7] Z. Iker, S. Alessandro and A. Saioa, “Smart city concept: What it is and what it should be,”Journal of Urban
Planning and Development, vol. 142, no. 1, pp. 04015005–0415007, 2016.
[8] Y. Wu, W. Zhang, J. Shen, Z. Mo and Y. Peng, “Smart city with Chinese characteristics against the background of
big data: Idea, action and risk,”Journal of Cleaner Production, vol. 173, no. 4, pp. 60–66, 2018.
[9] C. Lim, K. Kim and P. P. Maglio, “Smart cities with big data: Reference models, challenges, and considerations,”
Cities, vol. 82, no. 1, pp. 86–99, 2018.
[10] A. R. Honarvar and A. Sami, “Towards sustainable smart city by particulate matter prediction using urban big
data,”Excluding Expensive Air Pollution Infrastructures, vol. 17, pp. 56–65, 2018.
[11] R. Wenge, X. Zhang, C. Dave, L. Chao and S. Hao, “Smart city architecture: A technology guide for
implementation and design challenges,”China Communications, vol. 11, no. 3, pp. 56–69, 2014.
[12] R. Kitchin, “The real-time city? Big data and smart urbanism,”GeoJournal, vol. 79, no. 1, pp. 1–14, 2014.
[13] M. Batty, J. Shen, F. Giannotti, A. Pozdnoukhov, A. Bazzani et al., “Smart cities of the future,”European Physical
Journal Special Topics, vol. 214, no. 1, pp. 481–518, 2012.
[14] N. Osada, R. Miyagi and A. Takahashi, “Cis-and trans-regulatory effects on gene expression in a natural
population of Drosophila melanogaster,”Genetics, vol. 206, no. 4, pp. 2139–2148, 2017.
[15] P. M. Sousa, A. M. Ramos, C. C. Raible, M. Messmer, R. Tomé et al., “North atlantic integrated water vapor
transport—from 850 to 2100 ce: impacts on western european rainfall,”Journal of Climate, vol. 33, no. 1, pp.
263–279, 2020.
[16] T. D. Ge, D. D. Wang, Z. K. Zhu, L. Wei, X. M. Wei et al., “Tracing technology of carbon isotope and its
applications to studies of carbon cycling in terrestrial ecosystem,”Chinese Journal of Plant Ecology, vol. 44,
no. 4, pp. 360–372, 2020.
[17] A. Fatima, S. Abbas, M. Asif and M. S. Khan, “Optimization of governance factors for smart city through
hierarchical mamdani type-1 fuzzy expert system empowered with intelligent data ingestion techniques,”EAI
Endorsed Transactions on Scalable Information Systems, vol. 6, no. 23, pp. 1–6, 2019.
IASC, 2021, vol.30, no.2 525
