Arias Flores, Hugo Patricio
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Arias Flores, Hugo Patricio
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Quito
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hugoarias@uti.edu.ec
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57210802646

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Control of an Arm-Hand Prosthesis by Mental Commands and Blinking

2020 , Varela Aldas, José , Castillo Salazar, David Ricardo , Borja Galeas, Carlos , Guevara Maldonado, César Byron , Arias Flores, Hugo Patricio , Fierro-Saltos, W. , Rivera, R. , Hidalgo-Guijarro, J. , Yandún-Velasteguí, M.

Patients who lack upper and lower extremities have difficulties in carrying out their daily activities. The new technological advances have allowed the development of robotic applications to support people with disabilities, also, portable electroencephalographic (EEG) sensors are increasingly accessible and allow the development of new proposals which involve the mental control of electronic systems. This work presents the control by mental orders of an arm-hand prosthesis using low-cost devices, the objective is to command the arm using the user’s attention and blinking, where the components are a brain signal sensor, a prosthesis, an Arduino board, six servomotors, and a computer. The developed program in Matlab allows controlling the arm by means of an attention level y blinking. The results show the functioning of the system through experimental tests and a usability test is applied, finally, the conclusions establish adequate coordination in the movements of the prosthesis and the patient indicate satisfaction with the proposal. © Springer Nature Switzerland AG 2020.

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Detection of Student Behavior Profiles Applying Neural Networks and Decision Trees

2020 , Guevara Maldonado, César Byron , Sanchez-Gordon S. , Arias Flores, Hugo Patricio , Varela Aldas, José , Castillo Salazar, David Ricardo , Borja M. , Fierro-Saltos W. , Rivera R. , Hidalgo-Guijarro J. , Yandún-Velasteguí M.

Education worldwide is a significant aspect for the development of the peoples and much more in developing countries such as those in Latin America, where less than 22% of its inhabitants have higher education. Research in this field is a matter of interest for each of the governments to improve education policies. Therefore, the analysis of data on the behavior of a student in an educational institution is of utmost importance, because multiple aspects of progress or student dropout rates during their professional training period can be identified. The most important variables to identify the student’s behavior are the socio-economic ones, since the psychological state and the economic deficiencies that the student faces while is studying can be detected. This data provides grades, scholarships, attendance and information on student progress. During the first phase of the study, all the information is analyzed and it is determined which provides relevant data to develop a profile of a student behavior, as well as the pre-processing of the data obtained. In this phase, voracious algorithms are applied for the selection of attributes, such as greedy stepwise, Chi-squared test, Anova, RefiefF, Gain Radio, among others. In this work, we apply the artificial intelligence techniques, the results obtained are compared to generate a normal and unusual behavior of each student according to their professional career. In addition, the most optimal model that has had a higher accuracy percentage, false positive rate, false negative rate and mean squared error in the tests results are determined. © Springer Nature Switzerland AG 2020.

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Preprocessing Information from a Data Network for the Detection of User Behavior Patterns

2020 , Hidalgo-Guijarro J. , Yandún-Velasteguí M. , Bolaños-Tobar D. , Borja Galeas, Carlos , Guevara Maldonado, César Byron , Varela Aldas, José , Castillo Salazar, David Ricardo , Arias Flores, Hugo Patricio , Fierro-Saltos W. , Rivera R.

This study focuses on the preprocessing of information for the selection of the most significant characteristics of a network traffic database, recovered from an Ecuadorian institution, using a method of classifying optimal entities and attributes, with the In order to achieve a complete understanding of its real composition to be able to generate patterns and identification of trends of behavior in the network, both of patterns that deviate from normal traffic behavior (intrusive), as well as normal, to detect with high precision possible attacks. Network management tools were used as a multifunctional security server software, as well as pre-processing of data tools for the selection of attributes, as well as the elimination of noise from the instances of the database, It allowed to identify which ins- tances and attributes are correct and contribute with effective information in the study. Among them we have: Greedy Stepwise Algorithm (Algoritmo Voráz), K-Means Algorithm, Discrete Chi-square Attributes and the use of computational models as Evolutionary Neural Networks and Gene Algorithms. © Springer Nature Switzerland AG 2020.

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Detection and Classification of Facial Features Through the Use of Convolutional Neural Networks (CNN) in Alzheimer Patients

2020 , Castillo Salazar, David Ricardo , Varela Aldas, José , Borja M. , Guevara Maldonado, César Byron , Arias Flores, Hugo Patricio , Fierro-Saltos W. , Rivera R. , Hidalgo-Guijarro J. , Yandún-Velasteguí M. , Lanzarini L. , Alvarado H.G.

In recent years, the widespread use of artificial neural networks in the field of image processing has been of vital relevance to research. The main objective of this research work is to present an effective and efficient method for the detection of eyes, nose and lips in images that include faces of Alzheimer’s patients. The methods to be used are based on the extraction of deep features from a well-designed convolutional neural network (CNN). The result focuses on the processing and detection of facial features of people with and without Alzheimer’s disease. © Springer Nature Switzerland AG 2020.

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Autonomous Learning Mediated by Digital Technology Processes in Higher Education: A Systematic Review

2020 , Fierro-Saltos W. , Sanz C. , Zangara A. , Guevara Maldonado, César Byron , Arias Flores, Hugo Patricio , Castillo Salazar, David Ricardo , Varela Aldas, José , Borja Galeas, Carlos , Rivera R. , Hidalgo-Guijarro J. , Yandún-Velasteguí M.

The concept of autonomous learning has been resignified in recent years as a result of the expansion of the different types of study. Online education in higher education institutions has become an effective option to increase and diversify opportunities for access and learning, however, high rates of dropout, reprisal and low averages still persist. academic performance. Recent research shows that the problem is accentuated because most students have difficulty self-regulating their own learning process autonomously. From this perspective, the purpose of the study was to examine and analyze, through a systematic review of the literature, on autonomous/self-regulated learning, theoretical models and determine which variables influence a learning process mediated by technology processes in the higher education. The findings indicate that: (1) autonomous learning is a synonym of self-regulation; (2) Pintrich’s self-regulatory model is the most used in digital contexts; and (3) the self-regulatory variables identified are wide and varied. © Springer Nature Switzerland AG 2020.

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Twitter Mining for Multiclass Classification Events of Traffic and Pollution

2020 , Chamorro V. , Rivera R. , Varela Aldas, José , Castillo Salazar, David Ricardo , Borja Galeas, Carlos , Guevara Maldonado, César Byron , Arias Flores, Hugo Patricio , Fierro-Saltos W. , Hidalgo-Guijarro J. , Yandún-Velasteguí M.

During the last decade social media have generated tons of data, that is the primal information resource for multiple applications. Analyzing this information let us to discover almost immediately unusual situations, such as traffic jumps, traffic accidents, state of the roads, etc. This research proposes an approach for classifying pollution and traffic tweets automatically. Taking advantage of the information in tweets, it evaluates several machine learning supervised algorithms for text classification, where it determines that the support vector machine (SVM) algorithm achieves the highest accuracy value of 85,8% classifying events of traffic and not traffic. Furthermore, to determine the events that correspond to traffic or pollution we perform a multiclass classification. Where we obtain an accuracy of 78.9%. © Springer Nature Switzerland AG 2020.

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Editorial Design Based on User Experience Design

2020 , Borja Galeas, Carlos , Guevara Maldonado, César Byron , Castillo Salazar, David Ricardo , Arias Flores, Hugo Patricio , Fierro-Saltos, W. , Rivera, R. , Yandún-Velasteguí, M. , Varela Aldas, José , Hidalgo-Guijarro J.

This research deals with editorial design based on user experience design. The traditional editorial design has had to adapt to the new digital media composition, where multimedia audiovisual elements unthinkable a few years ago need to be integrated. The participation of the reader, as an external observer who only receives information through texts and images, now has new scenarios in which he can actively participate and decide what will come to his hands. In this study, a work methodology based on UxD User Experience Design is presented, in which will generate the editorial design of an educational book on environmental issues, which includes augmented reality for children from 6 to 8 years of age. The aim of this study is to know if an editorial product with augmented reality and developed from the user experience design can improve meaningful learning in a playful and active way. For its development, a composition model based on the Fibonacci sequence and the golden ratio will be used. Additionally, its graphic composition will be guided by the Massimo Vignelli canon and will be complemented by the reticular model of Beth Tondreau. The augmented reality markers position will also be based on the composition model previously mentioned, which will allow keeping the attention of the reader in the printed document and in the augmented reality animations. The user experience design will be applied with teachers, parents and students from 4 schools in Quito and Ambato. Once the production is completed, the impact on teaching-learning process will be evaluated with a control and a test group, and the methodology with which they will work in the classroom with the educational material developed will be defined. At the end of the study, copies of the book will be delivered to the participating schools of this research for its implementation. © Springer Nature Switzerland AG 2020.

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Mathematical model of intrusion detection based on sequential execution of commands applying pagerank

2020 , Guevara Maldonado, César Byron , Hidalgo, J. , Yandún, M. , Arias Flores, Hugo Patricio , Zapata-Saavedra, L. , Ramirez-Morales, I. , Aguilar-Galvez, F. , Chalco-Torres, L. , Ortiz, D.P.

Cybersecurity in networks and computer systems is a very important research area for companies and institutions around the world. Therefore, safeguarding information is a fundamental objective, because data is the most valuable asset of a person or company. Users interacting with multiple systems generate a unique behavioral pattern for each person (called digital fingerprint). This behavior is compiled with the interactions between the user and the applications, websites, communication equipment (PCs, mobile phones, tablets, etc.). In this paper the analysis of eight users with computers with a UNIX operating system, who have performed their tasks in a period of 2 years, is detailed. This data is the history of use in Shell sessions, which are sorted by date and token. With this information a mathematical model of intrusion detection based on time series behaviors is generated. To generate this model a data pre-processing is necessary, which it generates user sessions (Equation presented), where u identifies the user and m the number of sessions the user u has made. Each session (Equation presented) contains a sequence of execution of commands (Equation presented), that is (Equation presented), where n is the position in wich the C command was executed. Only 17 commands have been selected, which are the most used by each user u. In the creation of the mathematical model we apply the page Rank algorithm [1], the same that within a command execution session (Equation presented), determines which command (Equation presented) calls another command (Equation presented), and determines which command is the most executed. For this study we will perform a model with sb subsequences of two commands, (Equation presented), where the algorithm is applied and we obtain a probability of execution per command defined by (Equation presented). Finally, a profile is generated for each of the users as a signal in time series, where maximum and minimum normal behavior is obtained. If any behavior is outside those ranges, it is determined as intrusive behavior, with a detection probability value. Otherwise, it is determined that the behavior is normal and can continue executing commands in a normal way. The results obtained in this model have shown that the proposal is quite effective in the testing phase, with an accuracy rate greater than 90% and a false positive rate of less than 4%. This shows that our model is effective and adaptable to the dynamic behavior of the user. On the other hand, a variability in the execution of user commands has been found to be quite high in periods of short time, but the proposed algorithm tends to adapt quite optimally. © Springer Nature Switzerland AG 2020.

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Development of Behavior Profile of Users with Visual Impairment

2020 , Guevara Maldonado, César Byron , Arias Flores, Hugo Patricio , Varela Aldas, José , Castillo Salazar, David Ricardo , Borja M. , Fierro-Saltos W. , Rivera R. , Hidalgo-Guijarro J. , Yandún-Velasteguí M.

The interaction of the user with visual impairment with assistive technologies, and in particular with screen readers, generates a group of actions and events during their navigation. These interactions are defined as behavioral patterns, which have a sequence that occurs at specific time slot. Understanding user behavior by analyzing their interaction with applications, in addition, details the characteristics, relationships, structures and functions of the sequence of actions in a specific application domain. The objective of this document is to find activity patterns from a set of commands used by the user, combining data mining and a Bayesian model. This model calculates the probability of the functions used with the screen reader and generates a behavior profile to improve the user experience. For this study, the screen reader JAWS version 2018, the Open Journal Systems platform version 3.0.1 and a computer with Windows 10 operating system were used. During the first phase, command history used by the user by interacting with the Open Journal Systems platform were collected. The result is that the accessibility of users with visual impairment to interact with the computer and its applications has been improved by applying this model. © Springer Nature Switzerland AG 2020.

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Detection of Cutaneous Tumors in Dogs Using Deep Learning Techniques

2020 , Zapata, L. , Chalco, L. , Aguilar, L. , Ramírez-Morales, I. , Hidalgo, J. , Yandún, M. , Arias Flores, Hugo Patricio , Guevara Maldonado, César Byron

Cytological diagnosis is useful in the practical context compared to the histopathology, since it can classify pathologies among the cutaneous masses, the samples can be collected easily without anesthetizing the patient, at very low cost. However, an experimented veterinarian performs the cytological diagnosis in approximately 25 min. Artificial intelligence is being used for the diagnosis of many pathologies in human medicine, the experience gained by years of work in the area of work allow to issue correct diagnoses, this experience can be trained in an intelligent system. In this work, we collected a total of 1500 original cytologic images, performed some preliminary tests and also propose a deep learning based approach for image analysis and classification using convolutional neural networks (CNN). To adjust the parameters of the classification model, we recommend to perform a random and grid search will be applied, modifying the batch size of images for training, the number of layers, the learning speed and the selection of three optimizers: Adadelta, RMSProp and SGD. The performance of the classifiers will be evaluated by measuring the accuracy and two loss functions: cross-categorical entropy and mean square error. These metrics will be evaluated in a set of images different from those with which the model was trained (test set). By applying this model, an image classifier can be generated that efficiently identifies a cytology diagnostic in a short time and with an optimal detection rate. This is the first approach for the development of a more complex model of skin mass detection in all its types. © 2020, Springer Nature Switzerland AG.

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