López Gonzales, Javier Linkolk

Cargando...
Miniatura
Nombre preferido
López Gonzales, Javier Linkolk
Afiliación principal
Correo electrónico
linkolklg@upeu.edu.pe
ORCID
ID de autor de Scopus

Ajustes

Mostrando 1 - 10 de 67
  • Publicacion
    Algunas métricas están bloqueadas por su 
    configuración de consentimiento
    Item type:Publicación,
    Bayesian Spatio-Temporal Modeling of the Dynamics of COVID-19 Deaths in Peru
    (2024-05-30)
    César Raúl Castro Galarza
    ;
    Omar Nolberto Díaz Sánchez
    ;
    Jonatha Sousa Pimentel
    ;
    Rodrigo de Souza Bulhões
    ;
    Javier Linkolk López‐Gonzales  
    Amid the COVID-19 pandemic, understanding the spatial and temporal dynamics of the disease is crucial for effective public health interventions. This study aims to analyze COVID-19 data in Peru using a Bayesian spatio-temporal generalized linear model to elucidate mortality patterns and assess the impact of vaccination efforts. Leveraging data from 194 provinces over 651 days, our analysis reveals heterogeneous spatial and temporal patterns in COVID-19 mortality rates. Higher vaccination coverage is associated with reduced mortality rates, emphasizing the importance of vaccination in mitigating the pandemic's impact. The findings underscore the value of spatio-temporal data analysis in understanding disease dynamics and guiding targeted public health interventions.
    Citas (OpenAlex): 4
  • Publicacion
    Algunas métricas están bloqueadas por su 
    configuración de consentimiento
    Item type:Publicación,
    A Spatio-Temporal Visualization Approach of PM10 Concentration Data in Metropolitan Lima
    (2021-05-07)
    Alexandra Abigail Encalada-Malca
    ;
    Javier David Cochachi-Bustamante
    ;
    Paulo Canas Rodrigues
    ;
    Rodrigo Salas
    ;
    Javier Linkolk López‐Gonzales  
    Lima is considered one of the cities with the highest air pollution in Latin America. Institutions such as DIGESA, PROTRANSPORTE and SENAMHI are in charge of permanently monitoring air quality; therefore, the air quality visualization system must manage large amounts of data of different concentrations. In this study, a spatio-temporal visualization approach was developed for the exploration of data of the PM10 concentration in Metropolitan Lima, where the spatial behavior, at different time scales, of hourly concentrations of PM10 are analyzed using basic and specialized charts. The results show that the stations located to the east side of the metropolitan area had the highest concentrations, in contrast to the stations located in the center and north that reported better air quality. According to the temporal variation, the station with the highest average of biannual and annual PM10 was the HCH station. The highest PM10 concentrations were registered in 2018, during the summer, highlighting the month of March with daily averages that reached 435 μμg/m3. During the study period, the CRB was the station that recorded the lowest concentrations and the only one that met the Environmental Quality Standard for air quality. The proposed approach exposes a sequence of steps for the elaboration of charts with increasingly specific time periods according to their relevance, and a statistical analysis, such as the dynamic temporal correlation, that allows to obtain a detailed visualization of the spatio-temporal variations of PM10 concentrations. Furthermore, it was concluded that the meteorological variables do not indicate a causal relationship with respect to PM10 levels, but rather that the concentrations of particulate material are related to the urban characteristics of each district.
    Citas (OpenAlex): 18
  • Publicacion
    Algunas métricas están bloqueadas por su 
    configuración de consentimiento
    Item type:Publicación,
    A Flat-Hierarchical Approach Based on Machine Learning Model for e-Commerce Product Classification
    (2024-01-01)
    Harold Enrique Cotacallapa Mamani
    ;
    Nemías Saboya  
    ;
    Paulo Canas Rodrigues
    ;
    Rodrigo Salas
    ;
    Javier Linkolk López‐Gonzales  
    Within the e-commerce sphere, optimizing the product classification process assumes pivotal importance, owing to its direct influence on operational efficiency and profitability. In this context, employing machine learning algorithms stands out as a premier solution for effectively automating this process. The design of these models commonly adopts either a flat or local (hierarchical) approach. However, each of them exhibits significant limitations. The regional approach introduces taxonomic inconsistencies in predictions, whereas the flat approach becomes inefficient when dealing with extensive datasets featuring high granularity. Therefore, our research introduces a solution for hierarchical product classification based on a Machine Learning model that integrates flat and local (hierarchical) classification approaches using a 4-level electronic product dataset obtained from a renowned e-commerce platform in Latin America. In pursuit of this goal, a comparative analysis of seven machine learning algorithms, including Multinomial Naive Bayes, Linear Support Vector Classifier, Multinomial Logistic Regression, Random Forest, XGBoost, FastText, and Voting Ensemble, was conducted. This hybrid approach model performs better than models using a single approach. It surpassed the top-performing flat approach model by 0.15% and outperformed the leading local approach (Local Classifier per Level) model by 4.88%, as measured by the weighted F1-score. Additionally, this paper contributes to the academic community by presenting a significant Spanish-language dataset comprising over one million products and discussing the preprocessing techniques tailored for the dataset. It also addresses the study’s inherent limitations and potential avenues for future exploration in this field.
    Citas (OpenAlex): 5
  • Publicacion
    Algunas métricas están bloqueadas por su 
    configuración de consentimiento
    Item type:Publicación,
    Solid Waste Management in Peru’s Cities: A Clustering Approach for an Andean District
    (2023-01-27)
    Katherine Quispe
    ;
    Mayra Martínez
    ;
    Kleyton da Costa
    ;
    Hilario Romero Girón
    ;
    José Francisco Via y Rada Vittes
    There is a great deficiency in the collection and disposal of solid waste, with a considerable amount disposed of in dumps instead of in landfills. In this sense, the objective of this research is to propose a solid waste mitigation plan through recovery in the District of Santa Rosa, Ayacucho. For this, a solid waste characterization plan was executed in eight days, and through ANOVA it was shown that there is a significant difference in means between business pairs except between a bakery and a hotel. Through clustering, zones A and B are highly correlated, reflecting that the amount of organic waste was greater than inorganic waste. In the organic waste valorization plan, the results through ANOVA indicate a significant difference for monthly and daily averages, and the clustering shows the different behavior of each month, drawing attention to August, concluding that the valorization pilot plan is viable due to the contribution of a large amount of organic solid waste to the valorization plant.
    Citas (OpenAlex): 10
  • Publicacion
    Algunas métricas están bloqueadas por su 
    configuración de consentimiento
    Item type:Publicación,
    Air quality biomonitoring of trace elements in the metropolitan area of Huancayo, Peru using transplanted Tillandsia capillaris as a biomonitor
    (2020-01-01)
    Alex Rubén Huamán De La Cruz
    ;
    RODOLFO FRANKLIN O. AYUQUE
    ;
    RONY WILLIAM H. DE LA CRUZ
    ;
    Javier Linkolk López‐Gonzales  
    ;
    Adriana Gioda
    The air quality and distribution of trace elements in a metropolitan area of the Peruvian Andes were evaluated using transplanted epiphytic Tillandsia capillaris as biomonitors. Biomonitors were collected from the non-contaminated area and exposed to five sites with different types of contamination for three months in 2017. After exposure, the content of twenty-one elements were determined by ICP-MS analysis. Datasets were evaluated by one-way ANOVA, exposed-to-baseline (EB), hierarchical cluster analysis (HCA) and principal component analysis (PCA). Results showed significant differences among sampling sites for several elements. According to EF ratios for Ba, Cr, Cu, Pb, Sb, and Zn EB ratios value greater than 1.75 were found around urban areas, indicating anthropogenic influence, which can be attributed to vehicular sources. The highest values of As and Cd were found in areas of agricultural practices, therefore their presence could be related to the employment of agrochemicals (pesticides, herbicides, and phosphate fertilizers). HCA shows that most elements come from vehicular sources and lower from agricultural and natural sources.
    Citas (OpenAlex): 25
  • Publicacion
    Algunas métricas están bloqueadas por su 
    configuración de consentimiento
    Item type:Publicación,
    Electricity Demand Forecasting Using a Novel Time Series Ensemble Technique
    (2024-01-01)
    Hasnain Iftikhar
    ;
    Salvatore Mancha Gonzales
    ;
    Justyna Żywiołek
    ;
    Javier Linkolk López‐Gonzales  
    Accurate and efficient demand forecasting is essential to grid stability, supply, and management in today’s electricity markets. Due to the complex pattern of electric power demand time series, it is challenging to model them directly. Therefore, this research proposes a novel time series ensemble approach to forecast electric power demand in the Peruvian electricity market one month ahead. This approach treats the first preprocessed electricity demand time series for missing values, variance stabilization, normalization, stationarity, and seasonality issues. Secondly, six single time series and three of their proposed ensemble models forecast the clean demand time series. The results indicate that the proposed time ensemble approach is an efficient and precise one-month-ahead forecast for electricity demand in the Peruvian electricity market. Additionally, the final best ensemble forecasting model within the proposed ensemble time series forecasting approach obtained the smallest average accuracy errors, performing statistically significantly better than those mentioned in the best-proposed models in the literature. Lastly, while numerous global studies have been conducted from various perspectives, no analysis has been undertaken using an ensemble learning approach to forecast electric power demand in the Peruvian electricity market.
    Citas (OpenAlex): 44
  • Publicacion
    Algunas métricas están bloqueadas por su 
    configuración de consentimiento
    Item type:Publicación,
    An Intelligent Hybrid Ensemble Model for Early Detection of Breast Cancer in Multidisciplinary Healthcare Systems
    (2026-01-23)
    Hasnain Iftikhar
    ;
    Atef F. Hashem
    ;
    Moiz Qureshi
    ;
    Paulo Canas Rodrigues
    ;
    S. O. Ali
    Background/Objectives: In the modern healthcare landscape, breast cancer remains one of the most prevalent malignancies and a leading cause of mortality among women worldwide. Early and accurate prediction of breast cancer plays a pivotal role in effective diagnosis, treatment planning, and improving survival outcomes. However, due to the complexity and heterogeneity of medical data, achieving high predictive accuracy remains a significant challenge. This study proposes an intelligent hybrid system that integrates traditional machine learning (ML), deep learning (DL), and ensemble learning approaches for enhanced breast cancer prediction using the Wisconsin Breast Cancer Dataset. Methods: The proposed system employs a multistage framework comprising three main phases: (1) data preprocessing and balancing, which involves normalization using the min–max technique and application of the Synthetic Minority Over-sampling Technique (SMOTE) to mitigate class imbalance; (2) model development, where multiple ML algorithms, DL architectures, and a novel ensemble model are applied to the preprocessed data; and (3) model evaluation and validation, performed under three distinct training–testing scenarios to ensure robustness and generalizability. Model performance was assessed using six statistical evaluation metrics—accuracy, precision, recall, F1-score, specificity, and AUC—alongside graphical analyses and rigorous statistical tests to evaluate predictive consistency. Results: The findings demonstrate that the proposed ensemble model significantly outperforms individual machine learning and deep learning models in terms of predictive accuracy, stability, and reliability. A comparative analysis also reveals that the ensemble system surpasses several state-of-the-art methods reported in the literature. Conclusions: The proposed intelligent hybrid system offers a promising, multidisciplinary approach for improving diagnostic decision support in breast cancer prediction. By integrating advanced data preprocessing, machine learning, and deep learning paradigms within a unified ensemble framework, this study contributes to the broader goals of precision oncology and AI-driven healthcare, aligning with global efforts to enhance early cancer detection and personalized medical care.
    Citas (OpenAlex): 3metric-badges.view 1
  • Publicacion
    Algunas métricas están bloqueadas por su 
    configuración de consentimiento
    Item type:Publicación,
    Short-term PM2.5 forecasting using a unique ensemble technique for proactive environmental management initiatives
    (2024-09-10)
    Hasnain Iftikhar
    ;
    Moiz Qureshi
    ;
    Justyna Żywiołek
    ;
    Javier Linkolk López‐Gonzales  
    ;
    Olayan Albalawi
    Particulate matter with a diameter of 2.5 microns or less ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m2"><mml:msub><mml:mrow><mml:mtext>PM</mml:mtext></mml:mrow><mml:mrow><mml:mn>2.5</mml:mn></mml:mrow></mml:msub></mml:math> ) is a significant type of air pollution that affects human health due to its ability to persist in the atmosphere and penetrate the respiratory system. Accurate forecasting of particulate matter is crucial for the healthcare sector of any country. To achieve this, in the current work, a new time series ensemble approach is proposed based on various linear (autoregressive, simple exponential smoothing, autoregressive moving average, and theta) and nonlinear (nonparametric autoregressive and neural network autoregressive) models. Three ensemble models are also developed, each employing distinct weighting strategies: equal distribution of weight among all single models (ESME), weight assignment based on training average accuracy errors (ESMT), and weight assignment based on validation mean accuracy measures (ESMV). This technique was applied to daily <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m3"><mml:msub><mml:mrow><mml:mtext>PM</mml:mtext></mml:mrow><mml:mrow><mml:mn>2.5</mml:mn></mml:mrow></mml:msub></mml:math> concentration data from 1 January 2019, to 31 May 2023, in Pakistan’s main cities, including Lahore, Karachi, Peshawar, and Islamabad, to forecast short-term <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m4"><mml:msub><mml:mrow><mml:mtext>PM</mml:mtext></mml:mrow><mml:mrow><mml:mn>2.5</mml:mn></mml:mrow></mml:msub></mml:math> concentrations. When compared to other models, the best ensemble model (ESMV) demonstrated mean errors ranging from 3.60% to 25.79% in Islamabad, 0.81%–13.52% in Lahore, 1.08%–7.06% in Karachi, and 1.09%–12.11% in Peshawar. These results indicate that the proposed ensemble approach is more efficient and accurate for short-term <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m5"><mml:msub><mml:mrow><mml:mtext>PM</mml:mtext></mml:mrow><mml:mrow><mml:mn>2.5</mml:mn></mml:mrow></mml:msub></mml:math> forecasting than existing models. Furthermore, using the best ensemble model, a forecast was made for the next 15 days (June 1 to 15 June 2023). The forecast showed that in Lahore, the highest <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m6"><mml:msub><mml:mrow><mml:mtext>PM</mml:mtext></mml:mrow><mml:mrow><mml:mn>2.5</mml:mn></mml:mrow></mml:msub></mml:math> value (236.00 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m7"><mml:mi>μ</mml:mi><mml:mi>g</mml:mi><mml:mo>/</mml:mo><mml:msup><mml:mrow><mml:mi>m</mml:mi></mml:mrow><mml:mrow><mml:mn>3</mml:mn></mml:mrow></mml:msup></mml:math> ) was observed on 8 June 2023. Other days also displayed higher and poor air quality throughout the 15 days. Conversely, Karachi experienced moderate <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m8"><mml:msub><mml:mrow><mml:mtext>PM</mml:mtext></mml:mrow><mml:mrow><mml:mn>2.5</mml:mn></mml:mrow></mml:msub></mml:math> concentration levels between 50 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m9"><mml:mi>μ</mml:mi><mml:mi>g</mml:mi><mml:mo>/</mml:mo><mml:msup><mml:mrow><mml:mi>m</mml:mi></mml:mrow><mml:mrow><mml:mn>3</mml:mn></mml:mrow></mml:msup></mml:math> and 80 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m10"><mml:mi>μ</mml:mi><mml:mi>g</mml:mi><mml:mo>/</mml:mo><mml:msup><mml:mrow><mml:mi>m</mml:mi></mml:mrow><mml:mrow><mml:mn>3</mml:mn></mml:mrow></mml:msup></mml:math> . In Peshawar, the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m11"><mml:msub><mml:mrow><mml:mtext>PM</mml:mtext></mml:mrow><mml:mrow><mml:mn>2.5</mml:mn></mml:mrow></mml:msub></mml:math> concentration levels were consistently unhealthy, with the highest peak (153.00 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="m12"><mml:mi>μ</mml:mi><mml:mi>g</mml:mi><mml:mo>/</mml:mo><mml:msup><mml:mrow><mml:mi>m</mml:mi></mml:mrow><mml:mrow><mml:mn>3</mml:mn></mml:mrow></mml:msup></mml:math> ) observed on 9 June 2023. This forecasting experience can assist environmental monitoring organizations in implementing cost-effective planning to minimize air pollution.
    Citas (OpenAlex): 19
  • Publicacion
    Algunas métricas están bloqueadas por su 
    configuración de consentimiento
    Item type:Publicación,
    ¿Qué tan válidas y confiables son las interpretaciones derivadas del Florida Patient Acceptance Survey en español?
    (2019-03-27)
    Renzo Felipe Carranza Esteban
    ;
    Javier Linkolk López‐Gonzales  
    ;
    Tomás Caycho‐Rodríguez
    que define la validez como el grado en que, tanto la evidencia como la teora, apoyan las interpretaciones
    Citas (OpenAlex): 0
  • Publicacion
    Algunas métricas están bloqueadas por su 
    configuración de consentimiento
    Item type:Publicación,
    Forecasting Day-Ahead Electricity Prices for the Italian Electricity Market Using a New Decomposition—Combination Technique
    (2023-09-17)
    Hasnain Iftikhar
    ;
    Josué Edison Turpo Chaparro  
    ;
    Paulo Canas Rodrigues
    ;
    Javier Linkolk López‐Gonzales  
    Over the last 30 years, day-ahead electricity price forecasts have been critical to public and private decision-making. This importance has increased since the global wave of deregulation and liberalization in the energy sector at the end of the 1990s. Given these facts, this work presents a new decomposition–combination technique that employs several nonparametric regression methods and various time-series models to enhance the accuracy and efficiency of day-ahead electricity price forecasting. For this purpose, first, the time-series of the original electricity prices deals with the treatment of extreme values. Second, the filtered series of the electricity prices is decomposed into three new subseries, namely the long-term trend, a seasonal series, and a residual series, using two new proposed decomposition methods. Third, we forecast each subseries using different univariate and multivariate time-series models and all possible combinations. Finally, the individual forecasting models are combined directly to obtain the final one-day-ahead price forecast. The proposed decomposition–combination forecasting technique is applied to hourly spot electricity prices from the Italian electricity-market data from 1 January 2014 to 31 December 2019. Hence, four different accuracy mean errors—mean absolute error, mean squared absolute percent error, root mean squared error, and mean absolute percent error; a statistical test, the Diebold–Marino test; and graphical analysis—are determined to check the performance of the proposed decomposition–combination forecasting method. The experimental findings (mean errors, statistical test, and graphical analysis) show that the proposed forecasting method is effective and accurate in day-ahead electricity price forecasting. Additionally, our forecasting outcomes are comparable to those described in the literature and are regarded as standard benchmark models. Finally, the authors recommended that the proposed decomposition–combination forecasting technique in this research work be applied to other complicated energy market forecasting challenges.
    Citas (OpenAlex): 36metric-badges.view 1