Ayala-Chauvin, Manuel Ignacio

Preferred name

Main Affiliation

Centro de Investigación de Ciencias Humanas y de la Educación

Ambato

mayala@uti.edu.ec

ORCID

0000-0002-3911-377X

Scopus Author ID

57215428914

Now showing 1 - 10 of 42

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Predicting Academic Performance in Mathematics Using Machine Learning Algorithms
(2022)
Espinosa Pinos, Carlos Alberto
;
Ayala-Chauvin, Manuel Ignacio
;
Buele, Jorge
Several factors, directly and indirectly, influence students’ performance in their various activities. Children and adolescents in the education process generate enormous data that could be analyzed to promote changes in current educational models. Therefore, this study proposes using machine learning algorithms to evaluate the variables influencing mathematics achievement. Three models were developed to identify behavioral patterns such as passing or failing achievement. On the one hand, numerical variables such as grades in exams of other subjects or entrance to higher education and categorical variables such as institution financing, student’s ethnicity, and gender, among others, are analyzed. The methodology applied was based on CRISP-DM, starting with the debugging of the database with the support of the Python library, Sklearn. The algorithms used are Decision Tree (DT), Naive Bayes (NB), and Random Forest (RF), the last one being the best, with 92% accuracy, 98% recall, and 97% recovery. As mentioned above, the attributes that best contribute to the model are the entrance exam score for higher education, grade exam, and achievement scores in linguistic, scientific, and social studies domains. This confirms the existence of data that help to develop models that can be used to improve curricula and regional education regulations. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Data Analysis for Performance Improvement of University Students Using IoT
(2024)
Ayala-Chauvin, Manuel Ignacio
;
Lara-Álvarez P.
;
Castro R.
Using Internet of Things (IoT) devices and data analysis techniques can potentially transform how universities approach improving student achievement. In this sense, the project is based on implementing a remotely operated pneumatic bank applying IoT for university education. With this technology, it is possible to obtain information about the factors that impact student achievement and design targeted interventions to help students improve their performance. The control system with low-cost technology was developed with Raspberry Pi, AnyDesk, and Canvas LMS for the remote connection. The experiment was carried out with two groups of 7 people, and it was identified that there are correlations of 0.87 and 0.62 between the performance of the students and the time they dedicate to studying and the hours they spend on the platform; this suggests a positive correlation between these variables. Therefore, as students spend more time studying and spending more hours on the platform, they are more likely to achieve better academic results. On the other hand, the study time of the group of students who used the bank remotely increased by 32% compared to those who used the bank in person; therefore, we can infer that with the implementation of the IoT, the use of the system is encouraged. Finally, based on the insights gained from the analysis, targeted interventions can be designed to help students improve their academic performance. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Power Flow Optimization in Electrical Networks using Gekko
(2025)
Ayala-Chauvin, Manuel Ignacio
;
Avilés-Castillo, Fátima
;
Riba-Romeva, Carles
Power flow optimization in the electrical grid is critical to improve the stability and performance of power systems. The main challenge lies in finding an optimal distribution of power generation that meets the constraints imposed by the grid, such as voltage limits and power system stability conditions. The objective of this research was to evaluate the performance of Gekko in power flow optimization in electrical grids. To do so, a comparison was made with SciPy, a widely used benchmark framework in numerical optimization in order to assess their relative efficiency in problems with complex constraints. The comparison is based on metrics such as solution accuracy, convergence speed, and number of objective function evaluations. The results showed that both methods achieved the same objective value: SciPy (19.7) and Gekko (19.7). However, SciPy was slightly faster (0.01496 seconds vs. 0.0191 seconds), but required 60 objective function evaluations. In contrast, Gekko demonstrated greater computational efficiency, reducing the number of evaluations required for convergence. While SciPy is more efficient on small problems with explicit constraints, Gekko offers greater flexibility on problems with more complex constraints, making it more suitable for larger power systems.
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Performance and Real-World Variability of Predictive Maintenance Models for Vehicle Fleets
(2024)
Avilés-Castillo, Fátima
;
Dayanara Yánez-Arcos
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Ayala-Chauvin, Manuel Ignacio
;
Elena Blanco-Romero
This study presents a comprehensive evaluation of predictive maintenance models for vehicle fleets, detailing a sequence of systematic steps to ensure model performance and address real-world variability. The process begins with database creation and data preprocessing, where relevant maintenance records are filtered, and datetime columns are converted to facilitate time-based calculations. Grouping and aggregation techniques are then applied to count occurrences of specific maintenance activities and identify common failure types. For model training, we define a neural network architecture comprising dense and dropout layers to mitigate overfitting, compile the model with suitable loss functions and optimizers, and train it using the prepared data. The trained model, along with the scaler and encoder, is saved for future use. To augment the dataset, synthetic data is generated using the Faker library and random distributions, with added noise to mimic real-world variability. Preprocessing steps are reapplied to this synthetic data to ensure consistency. By implementing this neural network, we achieved a sensitivity of 0.93 and an ROC of 0.71. Following these detailed steps, we develop a robust predictive maintenance model that effectively identifies failures and non-failures, ultimately enhancing the reliability and efficiency of vehicle fleet management.
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Evaluation of Accessibility on the PAR Platform from the Perspective of Physicians
(2024)
Patricia Acosta-Vargas
;
Gloria Acosta-Vargas
;
Marco Santórum
;
Mayra Carrión-Toro
;
Ayala-Chauvin, Manuel Ignacio
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Prevention of Failures in the Footwear Production Process by Applying Machine Learning
(2022)
Tierra-Arévalo M.
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Ayala-Chauvin, Manuel Ignacio
;
Nacevilla C.
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de la Fuente-Morato A.
At present, the handcrafted footwear sector is affected by the high competitiveness due to the increasing automation of companies. In this sense, in order to improve its competitiveness, a system was proposed to predict the failures of a production system and to carry out preventive maintenance actions. Samples were taken from 25 productions and 7 activities were established: cutting, stitching, pre fabrication, final preparation, gluing, assembly and finishing. The company produces batches of 90 pairs per day, with a standard time of 274.53 min and a promised productivity of 1.8. A support vector machine model was developed to predict the possible failures of the process taking as a reference the standard time of each stage. Finally, the results allow predicting the faults to optimise the production process by applying Support Vector Machine (SVM). © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Sustainable Development in Higher Education Curricula for Software Engineering Chairs
(2023)
León Toro, Jenny Marcela
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Buele, Jorge
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Camino-Morejón V.M.
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Ayala-Chauvin, Manuel Ignacio
Nowadays, society demands that high quality teaching practices must be part of the curriculum in higher education institutions. The interdisciplinarity view of the contents taught has made the technical aspects of engineering merge with social, cultural, and economic nuances. In this sense, the new generations of students show their interest in learning and carrying out activities that contribute to sustainability, for this reason, the inclusion of ecological themes in the subjects of computer science and software career is required. A bibliographical analysis was carried out that allowed recognition of main concepts and methodologies applied to the subject. As a result of them, an adjustment of chairs is presented allowing integrating conventional teaching with the new trends of green technology. Reforms were implemented from introductory courses to theoretical knowledge of green software, to the development of web applications with the same approach. In the same way, it involves the management of computer projects, modeling, monitoring, and optimization of resources, and green evaluation. According to the socio-educational model, the articulation between technology and sustainability will allow managing software projects that provide real solutions to problems in context. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Automation of an Electro-Hydraulic Test Bench Using a Weitek CMT3092 HMI- PLC
(2022)
Altamirano-Haro D.
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Sánchez-Díaz P.E.
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Buele, Jorge
;
Ayala-Chauvin, Manuel Ignacio
The industrial environment demands mean that future professionals must acquire more and more technical skills. However, this represents a high investment that many higher education institutions cannot afford. Therefore, the laboratory equipment is updated, and this study begins with the automation of an electro-hydraulic test bench that was manual. For this, a PLC - Weintek was selected, whose programming was carried out in ladder language using CODESYS as a development platform, using an open Modbus programming code through the SFD block language. A human-machine interface (HMI) on a touch screen in the system allows user input (with various hierarchies) and operations control. The operator can also store data for later analysis. Finally, the validation of this proposal is carried out with the respective experimental tests, obtaining a significant reduction in the execution time of the three proposed tasks and improvement of learning conditions. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Scopus© Citations 1 33
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Vortex Optimization of a Low-Head Gravity Hydroelectric Power Plant
(2022)
Ayala-Chauvin, Manuel Ignacio
;
Rojas-Asuero H.
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Riba-Sanmartí G.
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Ramón-Campoverde J.
Gravitational Water Vortex Power Plant (GWVPP) is a Small-Scale Hydropower System which converts energy in a moving fluid to rotational energy. The main advantage of this technology is the low head hydraulic requirements. The aim of this work is to optimize the hydraulic geometry of the vortex, to achieve this, two prototypes (A and B) were designed and built to validate the proposed design process. The prototype A has a flat-bottom chamber and prototype B has a conical chamber outlet; both induce spiraling fluid streamlines. Prototypes were studied numerically and experimentally. The numerical study was developed in ANSYS CFX R19.0 software and the experimental phase was carried out in the fluid’s laboratory of the Technical University of Loja in Ecuador. The results show that the conical chamber improves strong free-surface vortex formation and increases water velocity in the center of the vortex flow. Finally, the proposed design method was validated and allows to reproduce the hydraulic structures of the gravity water vortex power plant. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Towards Smart Agriculture: An Overview of Big Data in the Agricultural Industry
(2025)
Ayala-Chauvin, Manuel Ignacio
;
Avilés-Castillo, Fátima
Agriculture is currently undergoing progressive diversification and expansion, as demonstrated by the wide range of research topics and methodologies being employed. The growing need for technology to enhance agricultural processes is increasingly evident and prominently highlighted in recent studies. To assess the influence of Big Data on agriculture and its potential to advance Smart Agriculture, this bibliometric study was conducted. The research consolidates bibliometric data from the Scopus database, using the Bibliometrix package to identify trends in this field. The findings show a growing annual publication rate, indicating increasing interest in the integration of data analysis methodologies within agriculture. The collaborative nature of the research, combined with a high citation rate per document and diversity of key terms, underscores the importance of this field and its potential contribution to achieving Smart Agriculture. The convergence of Big Data, the Internet of Things, and agriculture is particularly noteworthy, as these technologies are improving decision-making and efficiency in the agricultural sector. Despite certain limitations, this study highlights the transformative potential of these advancements and suggests areas for future research, thus laying the groundwork for a more sustainable, productive, and intelligent agricultural future.
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Ayala-Chauvin, Manuel Ignacio

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