Ayala-Chauvin, Manuel Ignacio
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Ayala-Chauvin, Manuel Ignacio
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Ambato
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mayala@uti.edu.ec
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57215428914

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U-Net Models for Breast Cancer Detection: Improving Diagnostic Accuracy and Specificity

2024 , Dayanara Yánez-Arcos , Ayala-Chauvin, Manuel Ignacio , Elena Blanco-Romero

Breast cancer remains a critical global health issue, necessitating continuous research and innovative approaches for diagnosis, treatment, and prevention. This study evaluates the effectiveness of U -Net models in enhancing diagnostic precision and efficiency using real hospital samples. We aim to improve key diagnostic metrics such as accuracy, sensitivity, and specificity through the application of U-Net models. Our image classification model, tailored for 256 × 256 × 3 input images, excels in detecting and categorizing tumor cells. The architecture begins with initial convolutional layers featuring 64 filters, progresses to layers with 128 filters, and includes a Dropout layer to prevent overfitting. The deep network for object detection utilizes both region proposal and regression/classification approaches, achieving 92.27% confidence and 100% accuracy. Additionally, our deep learning algorithms accurately segment nuclei in histopathological images, employing a clustering strategy that delivers 88.81% confidence and 100% accuracy. Visual results demonstrate precise tumor cell localization and prediction confidence. Performance metrics from ten experimental runs indicate average confidence levels between 74.19% and 92.31%, with 90.0% accuracy and specificity in benign analysis. The model's ability to classify non-carcinomas versus carcinomas achieved an AUC of 0.78, illustrating its effective differentiation between classes.

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Segmentation of Energy Consumption Using K-Means: Applications in Tariffing, Outlier Detection, and Demand Prediction in Non-Smart Metering Systems

2025 , Darío Muyulema-Masaquiza , Ayala-Chauvin, Manuel Ignacio

The management of energy demand in systems lacking smart metering presents a significant challenge for electric distributors, primarily due to the absence of real-time data. This research assesses the efficacy of the K-Means algorithm when applied to the monthly billing records of 221,401 residential customers from Empresa Eléctrica Ambato Regional Centro Norte S.A. (EEASA) (Ecuador) over the period 2023–2024. The methodology encompassed data cleaning, Z-score normalization, and validation employing the Silhouette (0.55) and Davies–Bouldin (0.51) indices. Additionally, linear regression (LR) and Random Forest (RF) models were utilized to forecast demand, with the latter yielding an R2 of 0.67. The findings delineated eight distinct clusters, facilitating the formulation of more representative rates, the identification of outliers through the interquartile range (IQR) method, and the enhancement of consumption estimation. It is concluded that this unsupervised segmentation approach constitutes a robust and cost-effective tool for energy planning in network environments devoid of smart infrastructure.

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Performance and Real-World Variability of Predictive Maintenance Models for Vehicle Fleets

2024 , Avilés-Castillo, Fátima , Dayanara Yánez-Arcos , 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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Predictive Maintenance in Industrial Robotics Using Big Data: Techniques, Challenges, and Opportunities

2024 , Ayala-Chauvin, Manuel Ignacio , Avilés-Castillo, Fátima , Dayanara Yánez-Arcos , Buele, Jorge

In industrial robotics, predictive maintenance is important to improve efficiency and reduce costs, addressing early detection and diagnosis of failures. The use of Big Data allows us to identify patterns and trends that at first glance are complex. This review examines research on the application of big data in predictive maintenance of industrial robots, which use advanced techniques such as cloud-based architectures, filtering algorithms, and machine learning. The review methodology included an analysis of the big data techniques used, the challenges identified, and the opportunities presented. The results show significant improvements in the accuracy of predictions and fault diagnoses. Key anomaly drivers were identified that improved production performance and enabled accurate fault identification and reduced downtime in industrial robots. Despite the benefits, challenges remain in data security and communications latency, underscoring the need to develop innovative algorithms and techniques to balance computing load and minimize delays. The continuous evolution of these techniques promises to improve the failure management capacity in industrial robotics, thus optimizing the operability and efficiency of robotic systems.

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Sustainable Development in Higher Education Curricula for Software Engineering Chairs

2023 , León Toro, Jenny Marcela , Buele, Jorge , Camino-Morejón V.M. , 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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Preface

2022 , Ayala-Chauvin, Manuel Ignacio , Botto-Tobar M. , Cadena Á.D. , León S.M.

[No abstract available]

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Big Data as a Tool for Analyzing Academic Performance in Education

2024 , Ayala-Chauvin, Manuel Ignacio , Chucuri-Real B. , ESCUDERO VILLA, PEDRO FERNANDO , Buele, Jorge

Educational processes are constantly evolving and need upgrading according to the needs of the students. Every day an immense amount of data is generated that could be used to understand children’s behavior. This research proposes using three machine learning algorithms to evaluate academic performance. After debugging and organizing the information, the respective analysis is carried out. Data from eight academic cycles (2014–2021) of an elementary school are used to train the models. The algorithms used were Random Trees, Logistic Regression, and Support Vector Machines, with an accuracy of 93.48%, 96.86%, and 97.1%, respectively. This last algorithm was used to predict the grades of a new group of students, highlighting that most students will have acceptable grades and none with a grade lower than 7/10. Thus, it can be corroborated that the daily stored data of an elementary school is sufficient to predict the academic performance of its students using computational algorithms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

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Vortex Optimization of a Low-Head Gravity Hydroelectric Power Plant

2022 , Ayala-Chauvin, Manuel Ignacio , Rojas-Asuero H. , Riba-Sanmartí G. , 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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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 , Verónica Maldonado-Garcés , Mario Gonzalez-Rodriguez

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Electric Monitoring System for Residential Customers Using Wireless Technology

2022 , Buele, Jorge , Morales-Sánchez J.C. , Varela Aldas, José , Palacios-Navarro G. , Ayala-Chauvin, Manuel Ignacio

Power grids continue to develop and it is increasingly difficult to guarantee the quality of service offered to the user. In several developing countries, consumption is calculated on the basis of visual inspection, which is prone to errors. Consequently, this document outlines the construction of electrical consumption telemetering equipment. This is designed to reduce human error through manual measures and have a web backup that can be accessed from anywhere. To develop the prototype voltage and current sensors are used, and the signal is conditioned for the control stage. The processing unit is the Arduino Mega embedded board, which incorporates a GPRS Shield (General Packet Radio Services) that handles communication with a LAMP server (Linux, Apache, MySQL, PHP) connected to the Internet. It also incorporates a block of connection and disconnection of the electrical service that would leave the whole house without service. Two functionalities are used to present the data, one is local on the LCD display of the equipment installed in the home (user) and the second is remote access to a website (server). The results show that in comparison with a standard voltage device it presents an error of 0.28% and 4.12% in current. In this way, the use of this prototype for real-time monitoring of electricity consumption is validated, since it works similarly to a conventional one. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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