Castillo Salazar, David Ricardo
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Castillo Salazar, David Ricardo
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Ambato
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davidcastillo@uti.edu.ec
Scopus Author ID
57210800584

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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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Chemical profiling and cholinesterase inhibitory activity of five phaedranassa herb. (amaryllidaceae) species from ecuador

2020 , Moreno R. , Tallini L.R. , Castillo Salazar, David Ricardo , Osorio E.H. , Montero E. , Bastida J. , Oleas N.H. , León K.A.

It is estimated that 50 million people in the world live with dementia, 60-70% of whom suffer from Alzheimer's disease (AD). Different factors are involved in the development of AD, including a reduction in the cholinergic neurotransmission level. The Amaryllidaceae plant family contains an exclusive, large, and still understudied alkaloid group characterized by a singular skeleton arrangement and a broad spectrum of biological activities. The chemistry and biodiversity of Ecuadorian representatives of the Phaedranassa genus (Amaryllidaceae) have not been widely studied. In this work, five Ecuadorian Phaedranassa species were examined in vitro for their activity towards the enzymes acetyl- (AChE) and butyrylcholinesterase (BuChE), and the alkaloid profile of bulb extracts was analyzed by GC-MS. The species Phaedranassa cuencana and Phaedranassa dubia showed the most AChE and BuChE inhibitory activity, respectively. To obtain insight into the potential role of the identified alkaloids in these inhibitory effects, docking experiments were carried out, and cantabricine showed in silico inhibitory activity against both cholinesterase structures. Our results show that Amaryllidaceae species from Ecuador are a potential source of new drugs for the palliative treatment of AD. © 2020 by the authors.

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Using Kinect to Detect Gait Movement in Alzheimer Patients

2021 , Castillo Salazar, David Ricardo , Lanzarini L. , Guevara C. , Alvarado H.G.

During the aging process, the elderly can experience a progressive and definitive deterioration in their gait, especially when they have neurological disorders such as Alzheimer’s disease. Effective treatment requires accurately assessing these issues in mechanical stability, the muscular-skeletal system, and postural reflexes. For Alzheimer patients in particular, gait analysis represents an important method for determining stability and treatment, which is the key objective of this investigation. Thus, this article describes the creation of a dataset on the walking gait, focusing on the distance covered by the patients and the angle of their legs as registered by a Kinect device. All patients were examined at a recognized center for elderly care in the canton of Ambato, Ecuador. We worked with a population of 30 Alzheimer patients whose ages ranged between 75 and 89 years old. The retrieved numerical data were processed with Diffused Logic, which, when based on a series of rules, can determine the instability and stability of a patient with a neurological illness. As a result, it was possible to create a dataset that included numerical values of the walking distance for each patient. This information will be important to future health care research, especially for physiotherapists and pose estimation. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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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 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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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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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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A Small Robotic Step for the Therapeutic Treatment of Mental Illnesses: First Round

2020 , Martinez C. , Castillo Salazar, David Ricardo , Rivera R.M. , Gomez A H.F.

The advances in psychological therapies to treat mental illnesses have been of vital importance in recent times, especially by the combination with technology. Appealing to new mechanisms gives some hope of improvement in treatments and patients. In this work we show the ease in the development of routines that can help the quality of life of people suffering from Alzheimer’s and autism, diseases that lend themselves to the programming of routines related to the daily life of patients. The routines developed by students of psycho-pedagogy and documented by our work team are waiting for being applied in case studies in future works. The general conclusion of the assignment is the concentration that arose having to use a robot as part of therapy routines and the quest for information to combine new technologies with psychology. The assignment can be considered as impressive because working with a robot awakens positive emotions in those who are developing robotic therapies. © 2020, Springer Nature Switzerland AG.

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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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Ontological Model in the Identification of Emotional Aspects in Alzheimer Patients

2023 , Castillo Salazar, David Ricardo , Lanzarini L. , Gómez H. , Thirumuruganandham, S , Castillo Salazar D.X.

The present work describes the development of a conceptual representation model of the domain of the theory of formal grammars and abstract machines through ontological modeling. The main goal is to develop an ontology capable of deriving new knowledge about the mood of an Alzheimer’s patient in the categories of wandering, nervous, depressed, disoriented or bored. The patients are from elderly care centers in Ambato Canton-Ecuador. The population consists of 147 individuals of both sexes, diagnosed with Alzheimer’s disease, with ages ranging from 75 to 89 years. The methods used are the taxonomic levels, the semantic categories and the ontological primitives. All these aspects allow the computational generation of an ontological structure, in addition to the use of the proprietary tool Pellet Reasoner as well as Apache NetBeans from Java for process completion. As a result, an ontological model is generated using its instances and Pellet Reasoner to identify the expected effect. It is noted that the ontologies come from the artificial intelligence domain. In this case, they are represented by aspects of real-world context that relate to common vocabularies for humans and applications working in a domain or area of interest. © 2023 by the authors.

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