Varela Aldas, José
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Varela Aldas, José
Main Affiliation
Ambato
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josevarela@uti.edu.ec
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Scopus Author ID
57192546163

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Modeling of Bayesian machine learning with sparrow search algorithm for cyberattack detection in IIoT environment

2024 , Faten Khalid Karim , Varela Aldas, José , Mohamad Khairi Ishak , Ayman Aljarbouh , Samih M. Mostafa

With the fast-growing interconnection of smart technologies, the Industrial Internet of Things (IIoT) has revolutionized how industries work by connecting devices and sensors and automating regular operations via the Internet of Things (IoTs). IoT devices provide seamless diversity and connectivity in different application domains. This system and its transmission channels are subjected to targeted cyberattacks due to their round-the-clock connectivity. Accordingly, a multilevel security solution is needed to safeguard the industrial system. By analyzing the data packet, the Intrusion Detection System (IDS) counteracts the cyberattack for the targeted attack in the IIoT platform. Various research has been undertaken to address the concerns of cyberattacks on IIoT networks using machine learning (ML) and deep learning (DL) approaches. This study introduces a new Bayesian Machine Learning with the Sparrow Search Algorithm for Cyberattack Detection (BMLSSA-CAD) technique in the IIoT networks. The proposed BMLSSA-CAD technique aims to enhance security in IIoT networks by detecting cyberattacks. In the BMLSSA-CAD technique, the min-max scaler normalizes the input dataset. Additionally, the method utilizes the Chameleon Optimization Algorithm (COA)-based feature selection (FS) approach to identify the optimal feature set. The BMLSSA-CAD technique uses the Bayesian Belief Network (BBN) model for cyberattack detection. The hyperparameter tuning process employs the sparrow search algorithm (SSA) model to enhance the BBN model performance. The performance of the BMLSSA-CAD method is examined using UNSWNB51 and UCI SECOM datasets. The experimental validation of the BMLSSA-CAD method highlighted superior accuracy outcomes of 97.84% and 98.93% compared to recent techniques on the IIoT platform

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Prototype System of Geolocation Educational Public Transport Through Google Maps API

2020 , Salazar F.W. , Naranjo-Ávalos H. , Buele, Jorge , Pintag M.J. , Buenaño É.R. , Reinoso C. , Urrutia-Urrutia P. , Varela Aldas, José

Urban traffic complications in most underdeveloped countries and congestion in all metropolitan areas has become a daily problem with a difficult solution. Disorganized mobility of drivers and pedestrians along with the increase in travel time, non-compliance with schedules, air pollution and intolerable sound levels, have harmful effects on human health. Therefore, this research describes a geolocation system of urban transport through a mobile application developed on the Xamarin platform. Drivers send the latitude and longitude points when starting a route, this data will be sent to the SQL SERVER online database server, using the SmarterASP.NET platform. By developing the geolocation system in ASP.NET, the coordinates are available to users in an interval of 5 s. The developed interface shows a location map, where the route in real time is presented. It also shows the administration of users, drivers, buses, assignment of routes, assignment of buses and registration of static routes. Being a prototype system, the university transport system has been taken as an object of study to corroborate its correct operation with the respective experimental tests. Satisfaction surveys have also been carried out on a group of 300 people, among students and university teachers and their validation is carried out through the Technological Acceptance Model (TAM). To interpret the results, Kendall Tau-b correlation analysis was used, obtaining positive correlation values with a high significance value. © 2020, Springer Nature Switzerland AG.

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Optimal position control of a mobile manipulator for minimum energy

2022 , Varela Aldas, José

This work presents the position control with energy optimization for a mobile manipulator and the proposal is based on the Pontryagin's Minimum Principle. The objective function is subject to the kinematic model of the robot with the non-holonomic motion constraint of the mobile platform and the boundary conditions as the desired parameters of the controller. The obtained differential equations are solved using the Shooting method to find the optimal co-states. The simulation performed shows that the position errors tend to zero and the control actions are optimized. Furthermore, these results are evaluated with respect to a control law based on the inverse kinematics of the robot. Finding that the minimum energy objective function is affected by the final time. © 2022 IEEE.

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Sizing and Characterization of Load Curves of Distribution Transformers Using Clustering and Predictive Machine Learning Models

2025 , Pedro Torres-Bermeo , Kevin López-Eugenio , Carolina Del-Valle-Soto , Guillermo Palacios-Navarro , Varela Aldas, José

The efficient sizing and characterization of the load curves of distribution transformers are crucial challenges for electric utilities, especially given the increasing variability of demand, driven by emerging loads such as electric vehicles. This study applies clustering techniques and predictive models to analyze and predict the behavior of transformer demand, optimize utilization factors, and improve infrastructure planning. Three clustering algorithms were evaluated, K-shape, DBSCAN, and DTW with K-means, to determine which one best characterizes the load curves of transformers. The results show that DTW with K-means provides the best segmentation, with a cross-correlation similarity of 0.9552 and a temporal consistency index of 0.9642. For predictive modeling, supervised algorithms were tested, where Random Forest achieved the highest accuracy in predicting the corresponding load curve type for each transformer (0.78), and the SVR model provided the best performance in predicting the maximum load, explaining 90% of the load variability (R2 = 0.90). The models were applied to 16,696 transformers in the Ecuadorian electrical sector, validating the load prediction with an accuracy of 98.55%. Additionally, the optimized assignment of the transformers’ nominal power reduced installed capacity by 39.27%, increasing the transformers’ utilization factor from 31.79% to 52.35%. These findings highlight the value of data-driven approaches for optimizing electrical distribution systems.

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3D Object Reconstruction Using Concatenated Matrices with MS Kinect: A Contribution to Interiors Architecture

2020 , Buele, Jorge , Varela Aldas, José , Castellanos E.X. , Jadán Guerrero, Janio , Barberán J.

Interior architecture is part of the individual, social and business life of the human being; it allows structuring the spaces to inhabit, study or work. This document presents the design and implementation of a system that allows the three-dimensional reconstruction of objects with a reduced economic investment. The image acquisition process and treatment of the information with mathematical support that it entails are described. The system involves an MS Kinect as a tool to create a radar that operates with the structured light principle to capture objects at a distance of less than 2 meters. The development of the scripts is done in the MATLAB software and in the same way the graphical interface that is presented to the user. As part of the initial tests of this prototype, the digitization of geometric shape structures has been performed with an accuracy of over 98%. This validates its efficient operation, which serves as the basis for the development of modeling in interior architecture for future work. © 2020, Springer Nature Switzerland AG.

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Static reactive power compensator design, based on three-phase voltage converter

2021 , Ayala-Chauvin, Manuel Ignacio , Kavrakov B.S. , Buele, Jorge , Varela Aldas, José

At present, electrical network stability is of the utmost importance because of the increase in electric demand and the integration of distributed generation deriving from renewable energy. In this paper, we proposed a static reactive power compensator model with common direct current voltage sources. Converter parameters were calculated and designed to fulfill specifications. In order to ascertain the device response for different operating modes as reactive power consumer and generator, we developed the model’s power and control circuits in Matlab Simulink. Simulations were performed for different conditions, and as a result, the current and voltage waveforms and the circular power chart were obtained. This paper has theoretically proven it is possible to achieve the consumption or generation of purely active or reactive power by implementing a static reactive power compensator with common DC voltage sources. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

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Movement Monitoring in Commercial Areas Using Internet of Things

2023 , Varela Aldas, José , Ruales, María Belén , Bastidas G.

The Internet of Things has been a trend in the last decade and is now found in countless applications, solving problems in almost any field. Specifically, in the commercial area, there is an effort to apply this technology to facilitate the control of services or processes. This work presents the movement monitoring of commercial areas. The proposal uses the ESP32 board that, using passive infrared sensors located perpendicular to each other, covers two areas of interest for commercial premises with multiple products. The collected data travel to the ThingSpeak Platform, where there are graphs with the states of the sensors, allowing identify the active area. The results present the motion state readings in the two areas, indicating the correct functioning of the system. Finally, we use a technology acceptance model to analyze this proposal, determining an acceptance of 69.6% that is not favorable for this proposal. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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Educational Quality in Teaching Magnetism Using Dedicated Mobile Devices

2024 , Junta, Christian , Varela Aldas, José , Washington Collay , David Rivas-Lalaleo , Guillermo Palacios-Navarro

New technologies offer unprecedented opportunities to enhance classroom education, especially in practical subjects like physics, where experiments can be controlled via mobile devices. This facilitates both theoretical and practical learning. This study assesses the impact of using dedicated mobile devices in educational settings compared to traditional teaching methods. The application allows students to view educational information on an M5Stack Core2 device that also includes an electromagnet. We use the Student Evaluation of Educational Quality (SEEQ) to gather feedback and evaluate various aspects of the learning experience. Results indicate significant improvements in learning, enthusiasm, organization, group interaction, individual relationships, and breadth of device usage. Evaluations show that device-assisted classes outperform traditional classes in most categories, highlighting the benefits of integrating technology into education. The findings emphasize the importance of adapting teaching methods to enhance the overall quality of education and student engagement using dedicated mobile devices.

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A Literature Review on Enterprise Credit Assessment Using Random Forest

2024 , Henry Guamán-Lloacana , Alex Muzo-Bombón , Christopher Sánchez-Briceño , Varela Aldas, José

This article presents a literature review on enterprise credit assessment using the Random Forest model, distinguishing it from general credit assessment, which includes a broader range of entities. The study highlights the limitations of traditional methods in credit risk evaluation. The primary objective of this research is to assess the technical configurations, predictive capabilities, and ethical considerations of applying Random Forest in credit assessment. Methodologically, a literature review approach guided by PRISMA principles was adopted, focusing on relevant studies published between 2018 and 2024. The findings indicate that Random Forest models enhance predictive accuracy and effectively manage high-dimensional data, outperforming traditional statistical methods. Furthermore, the study emphasizes the need for transparency and bias mitigation in automated credit scoring systems.

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Depth-Enhanced Tumor Detection Framework for Breast Histopathology Images by Integrating Adaptive Multi-Scale Fusion, Semantic Depth Calibration, and Boundary-Guided Detection

2025 , A. Robert Singh , Suganya Athisayamani , Hariharasitaraman S , Faten Khalid Karim , Varela Aldas, José , Samih M. Mostafa

Multiple modalities offer the advantage of providing complementary visual information that enhances overall understanding. This capability is particularly valuable in the domain of autonomous tumor detection. However, the challenge of occlusions in autonomous tumor perception—especially in cases of tumor-to-tumor occlusion—hinders the effective utilization of occlusion-related features. As a result, these limitations lead to a decline in the accuracy of object detection. We propose a novel framework for tumor detection in histopathological images by integrating multi-scale RGB features and depth-enhanced semantic information. The method comprises three primary modules: the Adaptive Multi-Scale Fusion Module (AMSF), Semantic Depth Integration and Calibration Module (SDICM), and Depth-Guided Tumor Detection Module (DG-TDM). AMSF combines RGB histopathology image channels, binary masks, and multi-scale convolution outputs using attention mechanisms to generate a fused feature map that captures occlusion information and boundary relevance. SDICM constructs dense depth maps by fusing RGB, semantic, and sparse depth data through bidirectional feature aggregation, enhancing spatial continuity and edge clarity. DG-TDM refines tumor boundary detection by combining depth and RGB features using spatial and channel-wise attention mechanisms, which highlight boundary differences and reduce redundancy from background noise. The proposed loss function optimizes RGB-depth feature alignment, balancing contributions from each modality for robust tumor detection. This approach addresses challenges such as overlapping boundaries, occlusions, and poor contrast in histopathology images, enabling precise localization of tumor regions. Experimental results demonstrate that the proposed framework significantly improves detection accuracy by 98% and boundary delineation compared to existing methods. This robust and efficient methodology provides a promising solution for advancing tumor detection in medical imaging.

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