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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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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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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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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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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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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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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Active Methodologies: An Approach to Virtual Teaching in Natural Sciences

2023 , Becerra-García E. , Castillo Salazar, David Ricardo , Viera Muñoz F.

During the training process on Natural Science subject in elementary school, it is important for the student to know, understand, respect, conserve, care for and value nature, the benefits it receives, interact with the environment, recognize the characteristics of each element of nature, its cycles, risks and care. The main objective of this work was to implement active methodologies in the science teaching-learning process. The methodology is based on quantitative methods, non-experimental designs, cross-sectional studies, theoretical, empirical and statistical methods. The technique used was a survey as an instrument and a questionnaire for data collection. A survey was applied to 40 people (students, teachers) of eighth grade, to obtain information on the interventions implemented by teachers. As a result of the survey analysis, the need to renew the essential methodological strategies to improve the teaching-learning process was identified, for which three active methodological strategies were developed for Natural Sciences’ teaching associated with the environment (physical, chemical and environmental). The strategies were designed with creativity, the use of environmental and recycling materials together with technology, each with the following elements: title, materials to be used, procedure, graphic representations and application examples. These active methodological strategies allow students to learn through the development of processes and skills that can be used in a variety of emerging situations and also allow teachers to provide a comprehensive and quality education. Based on the experience of the application of strategies in the area of Natural Sciences as future work in the educational context, it is intended to identify a set of abilities and skills in students in various subjects in order to strengthen teaching methodologies in teachers with the application of new technologies and digital resources that generate a new vision in the field of research. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

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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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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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