Technological Solution for Crime Prevention in Los Olivos

Juan-Pablo Mansilla, Mat

ıas Beteta and David Casta

neda

Private University of Applied Sciences, Lima, Peru

Keywords:

Citizen Security, Crime, Machine Learning, Naive Bayes.

Abstract:

This research proposes a technological solution for citizen security and crime prevention based on machine

learning in the district of Los Olivos, which alerts if the area in which a citizen is located is unsafe, showing

a probability of the level of insecurity in each area, making more visible the areas with the highest level

of insecurity; this was achieved using a machine Learning model, with the Naive Bayes algorithm exactly.

A sample of 108 users was used for validation, with whom the technological solution was tested using a

test scenario. In this sense, a questionnaire was elaborated to evaluate the perception of the users with an

acceptance level of 93.5%. On the other hand, when using the Naive Bayes algorithm is ensured to obtain

a better “Accuracy” and distribution by category in comparison with the following algorithms: classiﬁcation

forest, carboost classiﬁer and KNN respectively. Therefore, it was with the use of one the Naive Bayes

algorithm that the technological solution was carried out. The technological solution proposed is innovative

for Peru because it uses machine learning as a technology. In addition, this solution could be replicated in any

other district of Metropolitan Lima.

1 INTRODUCTION

There is no country in Latin America where the per-

ception of insecurity is as high as in Peru, to the extent

that 9 out of 10 people think they will be victims of

crime in the next 12 months. Likewise, within Peru,

Lima is considered one of the cities with the highest

perception of insecurity and has become a national

problem. For the development of this research, we

have focused on the district of Los Olivos, since, ac-

cording to the Citizen Security Technical Report N°4,

it indicates that this district is the second most inse-

cure in all of Lima (INEI, 2021). Also, only 15.5%

of the victims of a criminal act formalize the com-

plaint (Peruano, 2022). The purpose of the research

proposal is to implement a technological solution for

citizen security, which is capable of sending an alert

signal in real time to the users of the district of Los

Olivos indicating the probability of the occurrence of

a criminal act. For example, robbery, aggravated rob-

bery, theft, aggravated theft, homicide, murder and

micro-commercialization of drugs, depending on the

area where the user is located. It is proposed to de-

velop a model based on machine learning using the

Naive Bayes algorithm for crime prevention in the

district of Los Olivos. In addition, the application

will be like a social network, in the sense that it will

have publications with photos, data, news, among oth-

ers. Users will also be able to access communities by

zones, in which they will be able to report assaults,

robberies, among others, and thus send this informa-

tion to the corresponding authorities through interac-

tive reports, so that they can take the corresponding

measures.

2 RELATED WORK

In (Hongning Wang a., 2022) Wang and Ma state

that in predicting crimes against public health it is

largely use the data analysis technology, and the data

classiﬁcation and prediction capabilities of the ran-

dom forest algorithm. This system can effectively

predict the relevant data of crimes that endanger so-

ciety. Also, in (Md Amiruzzaman, 2021) the au-

thors indicates that there is a classiﬁcation of crime

hotspots based on neighborhood visual appearance

and police geonarratives using Machine Learning to

study whether street level built environment images

can be used to classify locations with high and low

crime activities. In addition, it´s stated as a fact in

(John R. Hipp, 2022) that crime can be detected using

Google Street View images with a Machine Learning

technique to extract various features of the built envi-

Mansilla, J., Beteta, M. and Castañeda, D.

Technological Solution for Crime Prevention in Los Olivos.

DOI: 10.5220/0012154000003543

In Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2023) - Volume 1, pages 115-122

ISBN: 978-989-758-670-5; ISSN: 2184-2809

115

ronment, and use this information to assess their rela-

tionship with crime in street segments. To avoid that,

Forradellas and others propose in (Reier Forradel-

las and Rodriguez, 2021) a crime prediction model

through a neural network called multilayer percep-

tron in order to obtain future information not only

regarding possible crimes, but with a level of de-

tail adequate for their deﬁnition. In addition, in

(Ana Amante, 2021) it is indicated that conclusions

are drawn based on the experiences of municipalities,

police and administration, which contribute to the de-

bate on community crime prevention and highlight

the need for multidisciplinary, multilevel and place-

speciﬁc approaches. Likewise, Janakiramaiah and

others (B. Janakiramaiah, 2021) describes and pro-

poses an automated method for detecting abnormal

human behavior in intelligent surveillance systems.

On the other hand, in (Kimihiro Hino, 2021) Hino

and Chronopoulos reviews crime prevention policies

in the Adachi district where the Beautiful Windows

Movement and Action Plan is discussed. In another

case, as stated in (William E, 2020) by William and

others, it was developed a detection algorithm that

incorporated facets of teacher-reported outsourcing

problems and other known risk factors. We exam-

ined detection approaches based on logistic regres-

sion and machine learning algorithms. While it is

true that, Van Steden (Steden, 2021) based his re-

search on categorizing the following items: effect,

mechanisms, moderators, implementations, and eco-

nomics. It was concluded that these groups can gen-

erate a greater problem for citizens, since they try to

confront crime directly (without the presence of au-

thorities), which can generate the exposure of more

people and lead to new crimes. Communication and

technology can be a good way to support against the

crime rate they are facing in the Netherlands. In addi-

tion, in (Hongjie Yu and Lan, 2020) it is demonstrated

the complexity of the spatial and temporal distribu-

tion of criminal activities and stressed that the con-

struction of covariates based on classical crime the-

ory and ﬁne-scale data are effective for crime predic-

tion. Another research by Niu and others (Niu, 2019),

is based on being able to create, test and compare

crime prediction algorithms based on the patterns of

criminal activity and why they are inﬂuenced in the

community areas of the city of Chicago. In addition,

K-means (KNN), decision tree (DT), Naive Bayes

(NB) and Support Vector Machine (SVM) algorithms

were used. Moreover, in (Wajiha Safat, 2021) is de-

scribed improved efﬁciency for accurate crime pre-

diction compared to what was previously achieved

with additional analysis based on different machine

learning algorithms. In addition, Albahli and others

(Albahli, 2021) propose a prediction method using

Machine Learning technology (Naive Bayes, Random

Forest, KNN, Decision Tree, Deep Learning) and se-

lection methods such as: FAMD (Mixed Data Factor

Analysis and PCA (Principal Component Analysis).

Also, the proposed method has as its main objective to

predict the factors that most affected crimes in Saudi

Arabia. In addition, in (Myung-Sun Baek and Lee,

2021) MYUNG-SUN and others reports that differ-

ent prediction models were developed to detect the

type of crime, of which respective tests were made

to verify their performance and authentication at the

time of analysis of criminal cases. It was veriﬁed

that their differences are minimal, ranging between

7% and 8% difference in results, and that they can be

viable for the use of case analysis. On the other hand,

in (Obagbuwa and Abidoye, 2021) is indicated that

crime data analysis can extract vital unknown infor-

mation from raw data and thus help the government

speed up procedures to solve crimes. It would en-

able the relevant government authorities to gain a bet-

ter understanding of crime trends and mitigate them.

When crime is prevented it can boost different eco-

nomic areas and attract more people to invest in the

locality. Along with, Kim and others (Kim, 2021) in-

dicate that using predictive technology in geographic

areas where they suffer from burglary will reduce the

triggering of potential burglaries in areas surround-

ing the burglarized areas. Likewise, Verma and others

(Verma, 2021) perform model training, validation and

testing using the Random Forest and Gradient Boost

Machine (GBM) ensemble approach with a hyper-

parameter optimizer using the “CSE-CIC-IDS2018-

V2” dataset and demonstrating performance testing

with attack categories such as inﬁltration, SQL In-

jection, etc. In (Aziz and Kumar, 2022) Aziz, Hus-

sain and others detail a Machine Learning based soft

computing regression analysis approach for analyzing

crime data occurred in India. Different regression al-

gorithms will be used, which are simple linear regres-

sion, multiple linear regression, decision tree regres-

sion, support vector regression, and random forest re-

gression. Also, in (Machin, 2021) is indicated that

privacy and security of shared information in cogni-

tive cities become critical issues that need to be ad-

dressed to ensure the proper deployment of cogni-

tive cities and the fundamental rights of individuals.

Dahlstedt and Foultier (Dahlstedt and Foultier, 2021)

point out as a point of improvement the promotion of

peer safety and the feeling of support among citizens,

and as a speciﬁc approach, schools and municipali-

ties are mentioned as key points where important cit-

izen information can be imparted to reduce the crime

rate. Likewise, in (Chaparro L., 2021) Chaparro and

ICINCO 2023 - 20th International Conference on Informatics in Control, Automation and Robotics

116

others provide a general approach to security percep-

tion metrics, an innovative way to measure people’s

security index, involving not only the number of pub-

lications in social networks but also the tone of these,

under the premise that the polarity of the tone real-

istically expresses the fear of crime that the popula-

tion could have or perceive. On the other hand, Al-

Taleb and Saqib (Al-Taleb, 2022) indicates that the

quality of life could be improved through continuous

data analysis to improve services provided by govern-

ments and other organizations. Although the presence

of many devices and the ﬂow of data on networks

could mean an increased likelihood of cyberattacks

and intrusion detection. Monitoring this huge amount

of data trafﬁc can be handled by a Machine Learning

algorithm that has enormous potential to support this

task. Likewise, in (Cozzubo A. and J, 2021) Cozzubo

and others indicate that the analysis focuses on crime

victimization expressed in robberies or attempted rob-

beries, for two main reasons. First, robbery is the

most prevalent crime in the country. Almost 39% of

the population suffered at least one robbery in the last

thirty-six months before being surveyed.

3 MACHINE LEARNING

Technology that enables prediction from learning

data, rather than using explicit programming; by us-

ing the algorithm to import training data, it is possible

to generate more accurate models. An autonomous

learning model is the information output that is pro-

duced when you train your data-driven algorithm. In

addition, you have different forms of learning: super-

vised learning, unsupervised learning, reinforcement

learning and deep learning respectively (IBM, 2021).

3.1 Components of Autonomous

Learning

3.1.1 Dataset

It is deﬁned as consolidated data of a similar genre,

which is captured from different environments. Once

the dataset is ready, we proceed to train, validate and

test the machine learning model, it should be noted

that the larger the dataset, the better the learning op-

portunities for the model and the greater the chances

of achieving accuracy in the results (Daffodil, 2020).

When building a dataset, it must have the following

characteristics:

- Volume: Data scalability is important, as the

larger the dataset the better it is for the machine learn-

ing model (Daffodil, 2020).

- Variety: The dataset may be in different forms,

such as images or videos, the variety of which is im-

portant to ensure the accuracy of the results (Daffodil,

2020).

- Speed: It matters how fast the data accumulates

in the dataset (Daffodil, 2020).

- Value: The dataset should have valuable and

meaningful information (Daffodil, 2020).

- Truthfulness: Data accuracy is important to en-

sure accurate results (Daffodil, 2020).

3.1.2 Algorithm

It is deﬁned as a mathematical or logical program

that converts a set of data into a model, different

types of algorithms can be chosen depending on

the type of problem the model is trying to solve.

Autonomous learning algorithms use computational

models to “learn” information directly from the data

without relying on a predetermined equation as a

model (Daffodil, 2020). Some examples are as fol-

lows:

- Regression Algorithm: Estimates the presence

of relationships between variables that are part of the

object of study, this focuses on setting a variable as

dependent and see their respective behavior with an-

other set of independent variables (Grapheverywhere,

2021).

- Naive Bayes Algorithm: They are based on

the famous Bayes Theorem, within the operation of

the algorithm, classiﬁcations of each value are made

as independent of another, this allows us to predict

a class or category within a given set of characteris-

tics through probabilistic models (Grapheverywhere,

2021).

- Clustering Algorithm: These allow us to es-

tablish categories within unlabeled data, i.e., data be-

longing to undeﬁned groups can be sorted (Graphev-

erywhere, 2021).

3.1.3 Model

Computational representation of processes, a machine

learning model recognizes patterns when trained on a

data set using relevant algorithms, once a model is

trained, it can be used to make predictions (Daffodil,

2020).

3.1.4 Feature Extraction

Feature extraction aims to reduce the number of vari-

ables in a new data set with features from existing

ones (Daffodil, 2020).

Technological Solution for Crime Prevention in Los Olivos

117

3.1.5 Training

The process by which the model learns autonomously

by detecting patterns and making decisions. There

are different ways of doing this, including supervised

learning, unsupervised learning, reinforcement learn-

ing and deep learning (Daffodil, 2020).

3.2 Neural Network Based on Naive

Bayes Algorithm

For the development of the training model, use has

been made of the Naive Bayes algorithm, which has

methods based on the application of Bayes’ Theorem

with the “Naive” assumption of conditional indepen-

dence between each pair of features given the value

of the class variable. Bayes’ Theorem establishes the

following relationship, given the class variable and

the dependent feature vector through x

, x

P(y|x

, ..., x

) =

(P(y)P(x

, ..., x

|y))

P(x

, ..., x

)

(1)

Using the naive conditional independence as-

sumption that

P(x

|y, x

, ..., x

) = P(x

|y) (2)

for all i, this relationship simpliﬁes to

P(y|x

, ..., x

) =

P(y)

∏

i=1

P(x

|y)

P(x

, ..., x

)

(3)

Given P(x

, ..., x

) which is constant given the in-

put, we can use the following classiﬁcation rule:

P(y|x

, ..., x

) ∝ P(y)

∏

i=1

P(x

|y) (4)

ˆy = argmaxP(y)

∏

i=1

P(x

|y) (5)

Naive Bayes models are a special class of ma-

chine learning classiﬁcation algorithms that assume

that predictor variables are independent of each other,

i.e., that the presence of some feature within a dataset

is unrelated to the presence of another feature. In

addition, they provide a simple way to build models

with optimal behavior, and they achieve this by pro-

viding a way to calculate the “posterior” probability

of a certain event occurring, given some probabilities

of “prior” events.

Figure 1: Example of Naive Bayes model probabilities.

4 PROPOSED SOLUTION

4.1 Physical Architecture

Figure 2: Physical Architecture.

The physical architecture of the solution has a com-

ponent that starts when the client, administrator or

authority accesses either the application itself, on the

part of the client and administrator, or the dashboard,

on the part of the authority, after which the login will

be validated. On the API side is where they will ac-

cess the application or reporting itself. However, the

load balancer is the one that will distribute the traf-

ﬁc, whether you want to access the application or the

reports. From the dataset training model, a labeled

dataset containing prerecorded classiﬁcation codes is

extracted from Amazon Redshift, which is reserved

in an Amazon Simple Storage Service (Amazon S3)

repository. The data is encrypted at rest with server-

side encryption using an AWS Key Management Ser-

vice (AWS KMS) key. This is known as server-side

encryption with AWS KMS (SSE-KMS). The extract

query uses the AWS KMS key to encrypt the data

when it is stored in the S3 repository. Each time the

required dataset is loaded into the S3 repository, a

message is sent to an Amazon SQS queue. This gen-

erates a Lambda function. Amazon SQS is used to en-

sure resiliency. If the Lambda function fails, the mes-

sage is automatically retried. In general, the message

is either processed successfully or ends up in a queue

of failed messages that are monitored. If the message

is processed successfully, the Lambda function gen-

erates the necessary input parameters. It then initiates

a Step Functions workﬂow execution for the training

process. The training process involves orchestrating

Amazon SageMaker processing jobs to prepare the

data. Once the data is prepared, a hyperparameter op-

timization job invokes multiple training jobs. These

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118

are run in parallel with different values of a range

of hyperparameters. The model that performs best is

chosen to proceed. Once the model is successfully

trained, an EventBridge event is requested, which will

be used to invoke the performance comparison pro-

cess. The functionalities of the components used in

the physical architecture will be explained next:

• Redshift: Database that will allow storing the

values of the data set to be recorded, i.e. it would ful-

ﬁll the role of a transactional database (AWS, 2022).

• KMS: It works for the encryption of the data to

be passed to the next component, the S3 (AWS, 2022).

• S3: Stores the data required for training, so that

Sagemaker can consume it at the time of the training

process (AWS, 2022).

• SQS QUEUE: Messaging queue manager,

which allows balancing the load and delivery of the

required operations, in this case an event (message)

is executed in order to launch the “start” of execution

that would be the whole training process, it should be

noted that a lambda is needed for the SQS to be exe-

cuted (AWS, 2022).

• Lambda: Serverless execution environment

in which code of each language can be executed,

through this component communication with Sage-

maker is performed (AWS, 2022).

• Sagemaker: ML training tool, a Step Function

will be used to mash up the whole training process

(AWS, 2022).

• Training Step Function: The cycle starts as

follows: Create data set group, create data set, im-

port data, train predictor, evaluating a predictor, host

model and generate forecasts, consult forecast and ﬁ-

nally, export forecast (AWS, 2022).

• Model: Result of the data training, which will

be used to make the corresponding predictions (AWS,

2022).

• Flutter App: Application through which the

client and administrator will access the system, this

is developed in Flutter, as it will have a standard for

both Android and IOS with a single code base (AWS,

2022).

• Dashboard: Iterative report developed for the

authorities, which will be developed with the React

framework for its development (AWS, 2022).

• Cognito: Enables you to incorporate registra-

tion, login and user access control into your web and

mobile applications (AWS, 2022).

• API Gateway: Service for creating, publishing,

maintaining, monitoring, and securing REST, HTTP,

and WebSocket APIs at any scale (AWS, 2022).

• Network Load Balancer: Automatically dis-

tributes incoming trafﬁc among multiple destinations,

e.g. EC2 instances, containers and IP addresses in one

or more availability zones (AWS, 2022).

• Node JS: Asynchronous event-driven JavaScript

runtime environment, Node.js is designed to create

scalable network applications (AWS, 2022).

• Fargate: Serverless computing engine that al-

lows us to focus on building applications without hav-

ing to manage the servers (AWS, 2022).

• Internet Gateway: A horizontally scalable, re-

dundant and highly available virtual private cloud

component that allows us to communicate between

the cloud and the internet (AWS, 2022).

• AWS Firewall Manager: Security manage-

ment service that enables centralized conﬁguration

and management of ﬁrewalls rules across and appli-

cations (AWS, 2022).

4.2 Logical Architecture

Figure 3: Motivation Layer.

In the Motivation Layer we will describe who will be

part of this project, directly and indirectly, such as the

PO, developers, among others, and the roles they will

play, in addition to the validations they must perform,

as can be seen in Figure 3, it is an overview of the pro-

posed technological solution. In addition, scopes, ad-

justments of objectives, compliances and a ﬁnal vali-

dation by the user are established.

The Business Layer reﬂects the ﬂow of the pro-

posed solution, i.e., the different processes and func-

tions it has, in addition, it details the step by step of

each process assigned to its respective role (client, au-

thority and administrator). As can be seen in Figure

4, this layer maps a more detailed view of the solution

itself, since it shows the development of each process

and the interaction of the processes with their respec-

tive role.

Technological Solution for Crime Prevention in Los Olivos

119

Figure 4: Business Layer.

Figure 5: Application Layer.

The Application Layer shows the necessary com-

ponents for the solution software, which support the

Business Layer with the services it offers. In sum-

mary, this layer shows the technological solution at

a more technical level, since it touches technologi-

cal components in charge of supporting the Business

Layer.

Figure 6: Technology Layer.

Finally, the technology layer contains the services

and components that will support the Application

Layer, in short, this layer is similar to the physical ar-

chitecture shown in the solution, in this layer the dif-

ferent components and services used in the presented

solution are evidenced.

5 VALIDATION

The dataset obtained from the “Datacrime” platform

was used for this research, in which the data was ex-

posed from 2017 to 2022. Using the platform’s delim-

itation tools, the amount of 300 thousand to 100 thou-

sand data was reduced, considering groupings and ﬁl-

ters that represent the incidents that occurred within

the district of Los Olivos, 70% of the ﬁnal dataset was

used for the training of the machine learning model

proposed, 20% for experimentation with users, and

the ﬁnal 10% for prediction.

5.1 Validation with Users

In order to validate the technological solution, a ques-

tionnaire was used as a measurement instrument, con-

sidering the following variables: functionality, usabil-

ity and level of satisfaction, and the following ques-

tions were asked: Which of the functionalities did you

like the most, this question refers to the functionality

of the solution, since the corresponding query is made

about the key functions of the application; Would you

recommend the application? this question refers to

the usability of the solution, since the user explains

his experience with the application; ﬁnally, Do you

think this application helps to prevent and deter pos-

sible incidents that occur in Los Olivos? this question

refers to the level of satisfaction, since it shows the ac-

ceptance of the application. Taking into account that

the population of Los Olivos is approximately 380

thousand inhabitants, it is taken into account that 250

thousand inhabitants are within our target audience,

people between 15 and 55 years old with knowledge

of technology, once the size of the population is de-

ﬁned, a 99% conﬁdence level is taken with a 12.5%

margin of error, in order to obtain the ﬁnal size of the

sample, which in this case is approximately 108 peo-

ple.

After completing the questionnaire of validation,

58% of users accepted both functionalities (Commu-

nity and Report Incidents), being both favorites. Re-

garding usability and satisfaction level, 99% of users

expressed their satisfaction with the application and

93.5% of users thought that the technological solu-

tion will help deter and prevent criminal incidents in

Los Olivos. Among the recommendations, users indi-

cated that alert notiﬁcations should be added and that

an emergency button should be implemented to allow

direct communication with police authorities.

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120

5.2 Algorithm Validation

To validate the Bayesian algorithm, a comparison was

made with different algorithms, these are: Classiﬁca-

tion Forest Algorithm, Catboost Classiﬁer and KNN,

using the confusion matrix in SageMaker which

gives us the percentages of each category that has

the dataset, this matrix works with numerical val-

ues looking for the “TruePositive (TP)”, “FalseNeg-

ative (FN)”, “TrueNegative (TN)” and “FalsePositive

(FP)”, these values are shown as percentages (Ter-

ence, 2020). For the present project, priority is given

to the “Accuracy” value of the algorithms and the dis-

tribution by category, respectively.

The following formulas will be taken into account:

• Accuracy: Accuracy is the same as the correct

proportion of models that are correctly classiﬁed (Ter-

ence, 2020).

Accuracy =

T P + T N

T P + T N + FP + FN

(6)

• Precision: Known as the predictive value which

shows the proportions of relevant instances among the

retrieved instances (Terence, 2020).

Precision =

T P

T P + FP

(7)

• Recall: Are the total number of relevant in-

stances actually recovered (Terence, 2020).

Recall =

T P

T P + FN

(8)

• FI-Score: It is the measure of the accuracy of a

test; it is the harmonic mean of precision and recall

(Terence, 2020).

FIScore =

2T P

2T P + FP + FN

(9)

A comparison was made, with the confusion ma-

trix, of the Naive Bayes, Classiﬁcation Forest, Cat-

boost Classiﬁer and KNN algorithms respectively,

with a dataset of 10 000 data, from this analysis it was

obtained that the Naive Bayes algorithm has the best

variable distribution per category. Therefore, this al-

gorithm was chosen because it will provide us with a

more accurate percentage by category in order to eval-

uate different areas of Los Olivos with small ranges

delimited by the location of the users and thus have

more realistic values according to the reports and in-

cidents that occur in these places. Although it is true

that the Classiﬁcation Forest algorithm presents a dis-

tribution similar to that of Na

ıve Bayes with a similar

value in the “Accuracy”, the Naive Bayes algorithm

is chosen because of the speed of prediction of the

model.

6 CONCLUSIONS

The objective of this project was to develop a tech-

nological solution for citizen security and crime pre-

vention based on machine learning in the district of

Los Olivos, which allows sending an alert signal in

real time to users in the district, notifying them if

the area where they are located is unsafe. In addi-

tion, a probability about the insecurity of each area

can be evidenced, so that the user can be aware of the

exact information. This objective could be achieved

through the development of the technological solu-

tion presented in section 4. To demonstrate the results

of the project, a test was carried out on the basis of

the categories handled in the technological solution.

To this end, the dataset was fed with data from the

robbery category, and then the training of the model

was updated to obtain greater visibility in this cate-

gory per zone, demonstrating the optimal functioning

of the technological solution presented. A sample of

108 users was used to test the proposed technological

solution. A questionnaire was prepared to evaluate

the perception of the users, 93.5% of whom indicated

that the proposed technological solution helps prevent

criminal incidents occurring in Los Olivos. The tech-

nology used in this project can be applied to different

problems, for example, it is proposed as a continua-

tion of the project to apply the same technology and

structure to monitor trafﬁc accidents by zones, that

is, users will create precedents by zones where differ-

ent trafﬁc accidents occur, and thus, the probability in

those zones can be reﬂected.

ACKNOWLEDGMENTS

We would like to express our gratitude to the Uni-

versidad Peruana de Ciencias Aplicadas (UPC) for

providing us with the necessary resources and qual-

ity for our higher academic education. In addition,

we are especially grateful to our professors

Alvaro

Chavarri and Juan-Pablo Mansilla for their constant

support and dedication throughout this process; their

experiences and knowledge were a key factor in the

successful completion of our research project.

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