Sullon Macalupu, Abel Angel

Cargando...
Miniatura
Nombre preferido
Sullon Macalupu, Abel Angel
Nombre oficial
Sullon Macalupu, Abel Angel
Nombre alternativo
A. Angel Sullon
Afiliación principal
Universidad Peruana Unión
ORCID
ID de autor de Scopus

Ajustes

Mostrando 1 - 9 de 9
  • Publicacion
    Algunas métricas están bloqueadas por su 
    configuración de consentimiento
    Item type:Publicación,
    Intelligent automated control of abiotic water and thermal stress in greenhouses for the optimization of pre-basic potato production
    (2025-08-05)
    Medina, Rudy
    ;
    Canaza, Bill
    ;
    Sullon Macalupu, Abel Angel  
    ;
    Ivanovich, Muss Evgeny
    ;
    Maquera Sosa, Nelida Gladys  
    The projected 50% increase in global food demand by 2050, coupled with declining freshwater availability and climate change, highlights the urgent need for more efficient agricultural practices. This study presents the development and implementation of a low-cost automated irrigation and thermal control system for pre-basic potato ( Solanum tuberosum L.) production in greenhouses in Siberia, Russia. The system is part of the DARY project and focuses on optimizing water and energy use under controlled water and thermal stress conditions. This experiment was conducted during two consecutive seasons in 2024 and employed environmental sensors and advanced algorithms to dynamically regulate irrigation and temperature according to the crop's physiological needs. It was compared with a conventional system under similar conditions, evaluating operational cost and control panel functionality. The results showed a 22% increase in pre-basic potato production, 27% water savings, and 12% reduction in electrical consumption. Additionally, the control panel cost was 35% lower than that of the commercial system, highlighting its scalability and adaptability. The system architecture includes an intuitive web application, MQTT communication, NoSQL database, and microcontrollers. The results validate DARY as a viable and sustainable technological solution for precision agriculture, especially in resource-constrained contexts.
    Citas (OpenAlex): 6metric-badges.view 8
  • Publicacion
    Algunas métricas están bloqueadas por su 
    configuración de consentimiento
    Item type:Publicación,
    Machine Learning Models for Predicting Professional Disqualification in Peruvian Association Members
    (2026-04-30)
    Pretel Pretel, Manuel
    ;
    Chávez Llempén, Yeny
    ;
    Sullon Macalupu, Abel Angel  
    ;
    Rodrigues, Paulo Canas
    ;
    López Gonzales, Javier Linkolk  
    The disqualification of licensed professionals for non-payment of their monthly fees constitutes a significant operational risk to the financial sustainability of professional associations. This problem highlights the need for predictive tools that can anticipate the risk of disqualification and protect institutional stability. The main objective of this study was to develop a supervised machine learning model for estimating the risk of disqualification among registered professionals based on historical and contextual variables. An empirical, applied, and quantitative study was conducted by analyzing more than 5.7 million financial records corresponding to 27,964 registered professionals. Multiple supervised classification algorithms, including ensemble models such as CatBoost and XGBoost, were evaluated using stratified cross-validation and class-balancing techniques to address the substantial imbalance in the data. The results indicated that CatBoost performed best (F1-score = 57.96%; AUC = 0.72), whereas XGBoost showed greater stability across cross-validation folds. In conclusion, the model developed supports the timely identification of members at high-risk of disqualification, enabling the implementation of early warning systems and proactive institutional financial management strategies.
    Citas (OpenAlex): 0metric-badges.view 6
  • Publicacion
    Algunas métricas están bloqueadas por su 
    configuración de consentimiento
    Item type:Publicación,
    Reference Method for the Care of Health Consultations of Older Adult Users Based on Chatbot, in a Context of Pandemic
    (Springer Science and Business Media Deutschland GmbH, 2023-01-01)
    Quispe Poma, Eleuterio  
    ;
    Labra Chino, Elbio  
    ;
    Huanca Torres, Fredy Abel  
    ;
    Sullon Macalupu, Abel Angel  
    The COVID-19 pandemic has marked a considerable event in the history of all countries, causing a high degree of mortality rate in older adults, in Peru health care for this group of users has become relevant. In this sense, the EsSalud Center for the Elderly (Social...
    Citas (OpenAlex): 0metric-badges.view 10
  • Publicacion
    Algunas métricas están bloqueadas por su 
    configuración de consentimiento
    Item type:Publicación,
    Prediction Model of the Burkholderia glumae Pest in Rice Crops Using Machine Learning and Spatial Interpolation
    (2022-01-01)
    Perez-Suarez, Joel
    ;
    Saboya Rios, Nemias  
    ;
    Sullon Macalupu, Abel Angel  
    In recent years, agriculture, especially rice crops, has been affected by constant climatic changes. Climatic changes cause some pathogens to be favored by altering their products and generating economic losses. The study aimed to develop a machine learning model to...
    Citas (OpenAlex): 3metric-badges.view 8
  • Publicacion
    Algunas métricas están bloqueadas por su 
    configuración de consentimiento
    Item type:Publicación,
    Reference Method for Load Balancing in Web Services with REST Topology Using Edge Route Tools
    (Springer Science and Business Media Deutschland GmbH, 2022)
    Calsin Quinto, Edwin Rene  
    ;
    Sullon Macalupu, Abel Angel  
    ;
    Huanca Torres, Fredy Abel  
    Due to the great demand for information technologies, every organization currently offers products and services through web services, thus it is unquestionable that these must work uninterruptedly and stably. The strong implementation of web services with REST...
    Citas (OpenAlex): 1metric-badges.view 3
  • Publicacion
    Algunas métricas están bloqueadas por su 
    configuración de consentimiento
    Item type:Publicación,
    Determination of the best license plate detection algorithm in controlled and uncontrolled environments
    (Associacao Iberica de Sistemas e Tecnologias de Informacao, 2023-05-31)
    Escuela Profesional de Ingeniería de Sistemas, Universidad Peruana Unión
    ;
    Ccoto Huallpa, Elias  
    ;
    Sullon Macalupu, Abel Angel  
    ;
    Escuela Profesional de Ingeniería de Sistemas, Universidad Peruana Unión
    ;
    Otazu Luque, Jorge Eddy  
    La seguridad es una prioridad en la gestión y parte de esto es el control y monitoreo para detectar situaciones que agredan el bien público o privado. En este sentido el reconocimiento de matrículas de auto se suma a estos sistemas de monitoreo y control. Este trabajo de investigación aplicó una serie de algoritmos de inteligencia artificial para automatizar dicha detección. En este sentido se utilizó funciones de procesamiento de imágenes con el framework OpenCV considerando que las fuentes de información pudieron tener distitnos escenarios, siendo que el ambiente de detección es abierto y expuesto a las condiciones meteorológicas de la zona. Las tomas de fotos se realizaron en un ambiente ubicado al sur peruano, cuyas condiciones fueron de lluvia, día soleado, día que cayó el granizo. La base de datos de las imágenes entonces se dividió en dos categorías; ambientes controlados donde se consideró una misma distancia, un solo ángulo, pero no necesariamente un mismo clima; y los ambientes no controlados con diferentes ángulos, diferentes distancias y climas. Al procesamiento de imágenes también se utilizó la transformación morfológica, suavizado gaussiano y umbral gaussiano. Con las imágenes segmentadas y extraídos los dígitos de la matrícula; se comparó 3 algoritmos K-NN, SVM y Tesseract. en cada algoritmo se utilizó sus hiperparámetros para el respectivo reconocimiento de caracteres en las imágenes, se obtuvo los siguientes resultados con imágenes tomadas con distintos ángulos y en distintas luminosidades (ambiente no controlado) donde el mejor Overall accuracy con 86 % y el segundo grupo fueron imágenes tomadas con un ángulo similar y distancias similares (ambiente controlado), obtuvo un Overall accuracy de 95.5 %.
    Citas (OpenAlex): 1metric-badges.view 5
  • Publicacion
    Algunas métricas están bloqueadas por su 
    configuración de consentimiento
    Item type:Publicación,
    Machine Learning-based predictive model for the prognosis of human papillomavirus (HPV) vaccination attrition
    (2021-04-09)
    Marroquin, Urlish
    ;
    Saboya Rios, Nemias  
    ;
    Sullon Macalupu, Abel Angel  
    research-article Share on Machine Learning-based predictive model for the prognosis of human papillomavirus (HPV) vaccination attrition Authors: Urlish Marroquin Universidad Peruana Union, Peru Universidad Peruana Union, PeruView Profile , Nemias Saboya Universidad Peruana Union, Peru Universidad Peruana Union, PeruView Profile , A. Angel Sullon Universidad Peruana Union, Peru Universidad Peruana Union, PeruView Profile Authors Info & Claims ICRSA 2021: 2021 4th International Conference on Robot Systems and ApplicationsApril 2021 Pages 44–49https://doi.org/10.1145/3467691.3467695Online:09 September 2021Publication History 0citation33DownloadsMetricsTotal Citations0Total Downloads33Last 12 Months33Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
    Citas (OpenAlex): 2metric-badges.view 3
  • Publicacion
    Algunas métricas están bloqueadas por su 
    configuración de consentimiento
    Item type:Publicación,
    Automatic recognition and license plate detection model based on OpenCV and Machine Learning
    (Institute of Electrical and Electronics Engineers Inc., 2022-10-19)
    Ccoto Huallpa, Elias  
    ;
    Sullon Macalupu, Abel Angel  
    ;
    Otazu Luque, Jorge Eddy  
    ;
    Sanchez Garces, Jorge Alejandro  
    Automatic number plate recognition (ALPR) is an important task with many applications in intelligent transportation and surveillance systems. This research takes into consideration the functions of image processing for the detection and recognition of number plates; which can come from noisy sources, low illumination, different angles and distances taken from the images (uncontrolled environments); most of the existing automated number plate recognition systems only work in a controlled environment where images are captured from a right angle with good illumination and clarity [25]. According to [11] in uncontrolled environments the probability of recognising the characters on the plates decreases and this has been observed in the research. To achieve image processing, morphological transformation, Gaussian smoothing and Gaussian thresholding were used, then 3 different algorithms K-NN, SVM and Tesseract were used for character recognition, each algorithm with their respective hyperparameters for optimisation. The images were separated into two groups, the first with 80 images taken from different angles and distance (uncontrolled environment) where the best Overall accuracy was obtained with 86 % and the second group were images taken at a right angle and similar distance (controlled environment), this group obtained an Overall accuracy of 95.5 %.
    Citas (OpenAlex): 3metric-badges.view 3
  • Publicacion
    Algunas métricas están bloqueadas por su 
    configuración de consentimiento
    Item type:Publicación,
    Predictive Model Based on Machine Learning for the Detection of Physically Mistreated Women in the Peruvian Scope
    (2019-10-26)
    Saboya Rios, Nemias  
    ;
    Sullon Macalupu, Abel Angel  
    ;
    Loaiza Jara, Omar Leonel  
    Violence against women in recent years in Peru showed alarming results, where 65.0 % of them were victims of some type of violence at some time in their lives. The study consisted of the elaboration of a predictive model that allows the recognition of the future physically mistreated woman. The data used was from a public survey of national scope. The methodology specified the data obtained; three supervised learning models Random Forest Classifier, Decision Tree Classifier, and Extra Trees Classifier were developed with the intention of buying the results and selecting that of the best performance. Regarding the results, Random Forest Classifier was the best model to obtain a precision of 51.0 % and a recall of 40.0 %, above those obtained by the other algorithms used. Subsequently, the best model passed to a calibration process where the average score was 0.7182, the precision was 0.76, and the recall was 0.31; concluding that the model classifies 76.0% of cases as physically mistreated women of which 31.0 % are effectively mistreated.
    Citas (OpenAlex): 4metric-badges.view 8