Sanchez Garces, Jorge Alejandro

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Sanchez Garces, Jorge Alejandro
Nombre oficial
Sanchez Garces, Jorge Alejandro
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Sánchez-Garcés, Jorge
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Universidad Peruana Unión
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Mostrando 1 - 10 de 11
  • Publicación
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    Implementing the RECONAC Marketing Strategy for the Interaction and Brand Adoption of Peruvian University Students
    (2021-02-28)
    Sanchez Garces, Jorge Alejandro  
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    Soria Quijaite, Juan Jesus  
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    Turpo Chaparro, Josue Edison  
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    Avila-George, Himer
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    López Gonzales, Javier Linkolk  
    In marketing, there is a notable lack of models, which leads to the loss of potential customers since there is no feedback for improvement in the marketing strategy. In the face of the Internet revolution, this study used digital channels and interaction to achieve potential customer retention through a Funnel; this allowed a potential client to go through the acquisition, conversion, and retention stages. In addition, activities were developed that allow the potential user to become a retained client. Thus, considering the stages of a marketing strategy, a non-probabilistic sample was used, composed of a pre-test period with 1088 users and a post-test period with 1450 users. Overall, there was a significant improvement in the time a client remained observing the website per session, with a 20.6% efficiency; furthermore, there was a 36.25% efficiency increase of the user conversion process per session in the digital channel. Likewise, a 46.43% efficiency increase was measured in visits to the digital channel, which confirms that the proposed marketing strategy significantly improves previous marketing strategies. Therefore, it is concluded that the proposal based on the elements of insight aligned with the measurement proposed by Google Analytics Key Performance Indicators, which was used to test and observe the improvements to continue the feedback, taking advantage of the characteristic of the digital channel with its two-way participation.
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    Proposal of a guide for the interpretation, simplification of the regulatory process and good tax compliance, case of digital taxpayers, influencers
    (2023-06-16)
    Mamani Monrroy, Karen Yosio  
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    Moreno Leyva, Nelly Rosario  
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    Santander, Kodi
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    Salinas, Shirley Eliza
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    Sanchez Garces, Jorge Alejandro  
    Influencers generate opinions in individuals through multiple virtual platforms, this phenomenon implies social influence that induces consumers to buy and direct these activities to the sponsorship of brands, which means monetary income for the influencer. Many of these incomes are not reported to the tax system, which causes evasion due to misinformation or lack of knowledge. Therefore, the need for a correct adaptation and interpretation of the Peruvian tax regulations for the payment of taxes on income received by this segment of taxpayers was observed. The purpose of this research was a guide that interprets, simplifies the processes and provides a regulatory framework for tax compliance for domiciled and non-domiciled influencers. The tax guide was designed thanks to the adaptation of the Scribber methodology and consisted of 4 steps: Familiarization, coding, theme generation, defining themes. The guide was organized in level 01, describing how to achieve the tax obligation in the sector of digital taxpayers influencers, level 02, where the activities described by the regulations are mentioned and level 3, tax procedures carried out by the tax administration to influencers. This guide is an aid to define the category that attributes the taxpayer's tax payment method. By identifying the tax categorization code according to the type of activity. It identifies the key factors to be able to interpret and adapt the law to the influencer's activities.
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    Análisis cualitativo femenológico de las finanzas en las empresas de transporte durante la pandemia del Covid-19
    (2023-12-28)
    Flores-Quispe, Gina Lisseth
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    Quispe-Chambi, Milagros Jhempy
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    Quispe-Chambi, Irma Candy
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    Cusilayme-Barrantes, Helfer Rudel
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    Sanchez Garces, Jorge Alejandro  
    Investigación cuya finalidad fue demostrar si el Covid-19 influyó en las finanzas de las empresas de transporte interprovincial de una región del sur del Perú. De metodología cualitativa, de técnica fenomenológica y como instrumento aplicado es, la entrevista estructurada y no estructurada. A través del intercambio de opiniones se logró identificar las categorías y subcategorías: el compromiso financiero, problemas y soluciones de liquidez. Los gerentes de las empresas y dueños de las unidades móviles identificaron estrategias como el ahorro de los saldos del dinero recaudado; contar con otras fuentes de ingreso, adquirir un negocio dedicado a otro rubro. Soluciones con respecto a la liquidez de la empresa, de esa manera de forma progresiva puedan cubrir sus gastos sin dificultad.
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    Digital Competencies and Transformational Leadership as Predictors of Job Performance in University Teachers
    (2024-05-31)
    Madrid, Carina
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    Chimborazo, J Luis
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    Morales Garcia, Wilter Charming  
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    Quispe Sanca, David  
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    Huancahuire Vega, Salomon  
    COVID-19 has adversely impacted the entire university community worldwide, including teachers, administrative staff, and students.This study analyzed the relationship between digital competencies, transformational leadership, and job performance of Peruvian university teachers.A predictive crosssectional study was conducted with 201 teachers from a private university in Peru in three regions of the country (coast, highlands, and jungle).The Digital Competencies in Teaching (CDD), Multifactor Leadership Questionnaire (MLQ 5X Short), and the Job Performance scale were used for data collection.The samples were analyzed with structural equation modeling and an adequate fit to the data was obtained (χ 2 = 194.342,p < 0; χ 2 /df = 2.23, CFI = .952,TLI=0.942,RMSEA = .078,SRMR = .061).In addition, both digital competencies (β = .28,p < 0) and transformational leadership (β = .76,p < 0) were found to be predictive factors of job performance.These findings provide evidence that digital competencies and transformational leadership were directly related to the job performance of university teachers during the COVID-19 pandemic.Consequently, it would be appropriate for educational leaders to consider these findings to enhance the digital competencies of teachers, promote a positive work environment, and support professional growth by stimulating motivation and job satisfaction during crisis situations.
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    Exploratory Analysis of Fundamental Spiritual Support Factors to a Positive Attitude in Patients with COVID-19 Using Natural-Language Processing Algorithms
    (2021-10-13)
    Sanchez Garces, Jorge Alejandro  
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    López Gonzales, Javier Linkolk  
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    Palacio-Farfán, Miguel
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    Coronel-Sacón, Víctor
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    Ferney-Teheran, Yonny
    The SARS-CoV-2 virus that causes COVID-19 affects the respiratory tract and is highly infectious. Those patients who knew that the disease could cause death or that their healing process is quite painful because of the symptoms and conditions developed extreme stress, anxiety, and depression, which aggravated the effects of the disease. Therefore, it is vital to conduct research to analyze these effects and generate self-help and support mechanisms during the disease process. This paper presents exploratory analysis related to stress, coping attitudes, emotional responses, and sources of support that were vital in patients affected by COVID-19; the focus of this study is the consideration of the spiritual factor, which may influence religious resilience that allows for a positive attitude and tenacity. To carry out this research, interviews were conducted with patients who had suffered from COVID-19 disease, and the collected information was processed using text-mining techniques using a two-phase methodology. The first phase is based on the Colaizzi method. Interview responses were coded through the search for patterns in the key phrases, and these codes were grouped, forming semantic relationships. In the second phase, natural-language processing algorithms (WordCloud, WordEmbedding, sentiment analysis of opinions) were used, summarizing the interviews in relevant factors of the patient’s experience during the disease. Spiritual resilience stood out the most of all key phrases of the code group tables. Likewise, words such as security, confidence, tranquility, and peace indicated that the patients took a positive attitude towards the symptoms and complications of the disease. Therefore, it is important to be the resilience to face a crisis process, and one of the factors that generated such resilience in COVID-19 patients was religious faith, which was expressed in the interviews using the factors of security, trust, promises of healing, tranquility, and the impossibility of discouragement. All this contributed to the positive attitude of the interviewees during the process of recovery from the disease.
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    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
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    Ccoto Huallpa, Elias  
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    Sullon Macalupu, Abel Angel  
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    Escuela Profesional de Ingeniería de Sistemas, Universidad Peruana Unión
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    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 %.
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    Discrimination of foreign bodies in quinoa (Chenopodium quinoa Willd.) grains using convolutional neural networks with a transfer learning approach
    (2023-01-30)
    Avila-George, Himer
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    De‐la‐Torre, Miguel
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    Sanchez Garces, Jorge Alejandro  
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    Coaquira Quispe, Joel Jerson  
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    Prieto, Jose Manuel  
    Willd.) is due to its high protein content and gluten-free condition; nonetheless, the presence of foreign bodies in quinoa processing facilities is an issue that must be addressed. As a result, convolutional neural networks have been adopted, mostly because of their data extraction capabilities, which had not been utilized before for this purpose. Consequently, the main objective of this work is to evaluate convolutional neural networks with a learning transfer for foreign bodies identification in quinoa samples. For experimentation, quinoa samples were collected and manually split into 17 classes: quinoa grains and 16 foreign bodies. Then, one thousand images were obtained from each class in RGB space and transformed into four different color spaces (L*a*b*, HSV, YCbCr, and Gray). Three convolutional neural networks (AlexNet, MobileNetv2, and DenseNet-201) were trained using the five color spaces, and the evaluation results were expressed in terms of accuracy and F-score. All the CNN approaches compared showed an F-score ranging from 98% to 99%; both color space and CNN structure were found to have significant effects on the F-score. Also, DenseNet-201 was the most robust architecture and, at the same time, the most time-consuming. These results evidence the capacity of CNN architectures to be used for the discrimination of foreign bodies in quinoa processing facilities.
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    Mortality from all causes before and during the COVID-19 pandemic in Peru: The role of sociodemographic factors and accessibility
    (2022-10-19)
    Quispe, Antonio M.
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    Vargas, Lucía
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    Morales Garcia, Wilter Charming  
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    López Gonzales, Javier Linkolk  
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    Sanchez Garces, Jorge Alejandro  
    Abstract Peru suffered the highest mortality rates worldwide during the COVID-19 pandemic. In this study, we assessed the Peruvian districts' all causes of mortality-associated sociodemographic factors before and during the COVID-19 pandemic using mixed-effects Poisson regression models. During the pre-pandemic and the first four COVID-19 waves, the Peruvian districts reported mean weekly mortality of 22.3 (standard deviation 40.4), 29.2 (38.7), 32.5 (47.2), 26.8 (38.9), and 24.4 (38.0), respectively. We observed that before the COVID-19 pandemic, the districts' weekly deaths were associated with the human development index ((HDI) adjusted incidence rate ratio (aIRR) 0.11 (95% confidence interval 0.11–0.12)), accessibility (aIRR 0.99 (0.99–0.99)), poverty (aIRR 0.99 (0.99–0.99)), and anemia (aIRR 0.99 (0.99–0.99)). However, during each of the first four COVID-19 waves, the magnitude of association between the districts' weekly deaths and HDI decreased (first, aIRR 0.61 (0.58−0.64); second, aIRR 0.54 (0.52−0.57); third, aIRR 0.20 (0.19−0.22); fourth, aIRR 0.17 (0.15−0.19)), but the association with accessibility (aIRR 0.99 (0.99–0.99)), poverty (aIRR 0.99 (0.99–0.99)), and anemia (aIRR 0.99(0.99–0.99)) remain constant. Before and during the COVID-19 pandemic, a solid association existed between all-cause mortality and the district's sociodemographics, increasing with lower HDI, accessibility, poverty, and anemia rates.
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    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  
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    Sullon Macalupu, Abel Angel  
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    Otazu Luque, Jorge Eddy  
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    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 %.
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    A hybrid intelligent approach to predict discharge diagnosis in pediatric surgical patients
    (MDPI AG, 2021-04-15)
    Avila-George, Himer
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    De-La-Torre, Miguel
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    Castro, Wilson Manuel
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    Dominguez, Danny
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    Turpo Chaparro, Joel Ricardo  
    Computer-aided diagnosis is a research area of increasing interest in third-level pediatric hospital care. The effectiveness of surgical treatments improves with accurate and timely information, and machine learning techniques have been employed to assist practitioners in making decisions. In this context, the prediction of the discharge diagnosis of new incoming patients could make a difference for successful treatments and optimal resource use. In this paper, a computer-aided diagnosis system is proposed to provide statistical information on the discharge diagnosis of a new incoming patient, based on the historical records from previously treated patients. The proposed system was trained and tested using a dataset of 1196 records; the dataset was coded according to the International Classification of Diseases, version 10 (ICD10). Among the processing steps, relevant features for classification were selected using the sequential forward selection wrapper, and outliers were removed using the density-based spatial clustering of applications with noise. Ensembles of decision trees were trained with different strategies, and the highest classification accuracy was obtained with the extreme Gradient boosting algorithm. A 10-fold cross-validation strategy was employed for system evaluation, and performance comparison was performed in terms of accuracy and F-measure. Experimental results showed an average accuracy of 84.62%, and the resulting decision tree learned from the experience in samples allowed it to visualize suitable treatments related to the historical record of patients. According to computer simulations, the proposed classification approach using XGBoost provided higher classification performance than other ensemble approaches; the resulting decision tree can be employed to inform possible paths and risks according to previous experience learned by the system. Finally, the adaptive system may learn from new cases to increase decisions’ accuracy through incremental learning.
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