López Gonzales, Javier Linkolk

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López Gonzales, Javier Linkolk
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linkolklg@upeu.edu.pe
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  • Publicacion
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    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.
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    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.
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    Multiple Novel Decomposition Techniques for Time Series Forecasting: Application to Monthly Forecasting of Electricity Consumption in Pakistan
    (2023-03-09)
    Hasnain Iftikhar
    ;
    Nadeela Bibi
    ;
    Paulo Canas Rodrigues
    ;
    Javier Linkolk López‐Gonzales  
    In today’s modern world, monthly forecasts of electricity consumption are vital in planning the generation and distribution of energy utilities. However, the properties of these time series are so complex that they are difficult to model directly. Thus, this study provides a comprehensive analysis of forecasting monthly electricity consumption by comparing several decomposition techniques followed by various time series models. To this end, first, we decompose the electricity consumption time series into three new subseries: the long-term trend series, the seasonal series, and the stochastic series, using the three different proposed decomposition methods. Second, to forecast each subseries with various popular time series models, all their possible combinations are considered. Finally, the forecast results of each subseries are summed up to obtain the final forecast results. The proposed modeling and forecasting framework is applied to data on Pakistan’s monthly electricity consumption from January 1990 to June 2020. The one-month-ahead out-of-sample forecast results (descriptive, statistical test, and graphical analysis) for the considered data suggest that the proposed methodology gives a highly accurate and efficient gain. It is also shown that the proposed decomposition methods outperform the benchmark ones and increase the performance of final model forecasts. In addition, the final forecasting models produce the lowest mean error, performing significantly better than those reported in the literature. Finally, we believe that the framework proposed for modeling and forecasting can also be used to solve other forecasting problems in the real world that have similar features.
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    Time series forecasting via integrating a filtering method: an application to electricity consumption
    (2025-01-13)
    Felipe Leite Coelho da Silva
    ;
    Josiane da Silva Cordeiro
    ;
    Kleyton da Costa
    ;
    Nemías Saboya  
    ;
    Paulo Canas Rodrigues
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    An intelligent ensemble machine learning model for early detection of chronic kidney disease in aging populations
    (2026-01-13)
    Hasnain Iftikhar
    ;
    Atef F. Hashem
    ;
    Liban Ali Mohamud
    ;
    A. S. Al-Moisheer
    ;
    Ronny Ivan Gonzales Medina
    Chronic Kidney Disease (CKD) remains a pressing global public health concern, accounting for approximately 1.7 million deaths annually and disproportionately affecting aging and underserved populations. The increasing burden of CKD, particularly in low-resource settings, underscores the urgent need for early, accurate, and scalable diagnostic tools. This study proposes a hybrid mathematical and artificial intelligence (AI) framework for the early prediction of CKD, with a focus on supporting healthcare strategies in aging and resource-limited communities. Utilizing clinical data from a case-control study conducted in District Buner, Khyber Pakhtunkhwa, Pakistan, the framework incorporates a structured modeling pipeline that involves data preprocessing (feature extraction, missing data imputation, and categorical encoding) and class balancing via the Synthetic Minority Over-Sampling Technique (SMOTE). The proposed system integrates multiple machine learning algorithms, including logistic regression, feedforward neural networks, decision trees, support vector machines, and random forests, within a novel ensemble learning strategy designed to enhance diagnostic precision. Model robustness was assessed using three distinct train–test scenarios: (90%, 10%), (75%, 25%), and (50%, 50%). Performance evaluation employed six metrics: accuracy, specificity, sensitivity, Youden index, Brier score, and F1 score, supported by comprehensive graphical and statistical analysis. The ensemble model consistently outperformed individual classifiers, achieving a mean accuracy of 97.71%, specificity of 97.19%, sensitivity of 99.84%, Youden index of 86.55, Brier score of 1.43%, and F1 score of 98.19%. Support vector machines and random forests ranked second and third, respectively, while decision trees exhibited the lowest performance. To the best of our knowledge, this is the first ensemble-based predictive framework for CKD developed using clinical data from Pakistan. The system holds strong potential for integration into real-world biomedical decision support systems, particularly in aging and underserved populations, thereby contributing to early detection, enhanced care delivery, and optimized resource utilization in the management of chronic diseases.
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    Data-Driven Socioeconomic Segmentation for Residential Energy Planning: A Machine Learning Approach
    (2026-05-05)
    Lucas Camaz Ferreira
    ;
    Felipe Leite Coelho da Silva
    ;
    Josiane da Silva Cordeiro
    ;
    Javier Linkolk López‐Gonzales  
    ;
    Esteban Tocto Cano  
    The Brazilian residential sector is one of the largest consumers of electricity, making residential energy consumption a critical component of national energy systems. Electricity consumption patterns in this sector are closely associated with household appliance ownership and, consequently, with socioeconomic status. For residential energy planning to operate more equitably and efficiently, it is essential that consumption analyses be aligned with the socioeconomic conditions of the population. This study examines the role of socioeconomic variables in residential energy planning through the application of supervised machine learning algorithms within a data-driven socioeconomic segmentation framework. Decision trees, support vector machines, and artificial neural networks were implemented using data from the Brazilian residential sector to evaluate model performance and to determine the extent to which household socioeconomic status can be inferred from variables related to appliance ownership and electricity consumption characteristics. The results showed that household appliances, such as refrigerators, microwave ovens, and air conditioners, exhibited substantial predictive power in relation to socioeconomic status, thus improving the interpretation and understanding of residential energy consumption from a multidimensional perspective. The neural network model achieved the highest predictive performance. By enabling data-driven socioeconomic segmentation based on observable electricity consumption patterns, this approach provides relevant insights for residential energy planning and contributes to more targeted and equitable energy policy design, supporting Sustainable Development Goal 7 on Affordable and Clean Energy and Sustainable Development Goal 10 on Reduced Inequalities.
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    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.
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    An exploratory analysis of PM$$_{2.5}$$/PM$$_{10}$$ ratio during spring 2016–2018 in Metropolitan Lima
    (2024-04-23)
    Rita Cabello Torres  
    ;
    Natalí Carbo‐Bustinza
    ;
    Edison Alessandro Romero-Cabello
    ;
    Jeremias Macias Ureta Tolentino
    ;
    Elías A Torres Armas
    Abstract Aerosols (PM $$_{2.5}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow/> <mml:mrow> <mml:mn>2.5</mml:mn> </mml:mrow> </mml:msub> </mml:math> and PM $$_{10}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow/> <mml:mn>10</mml:mn> </mml:msub> </mml:math> ) represent one of the most critical pollutants due to their negative effects on human health. This research analyzed the relationship of PM and its PM $$_{2.5}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow/> <mml:mrow> <mml:mn>2.5</mml:mn> </mml:mrow> </mml:msub> </mml:math> /PM $$_{10}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow/> <mml:mn>10</mml:mn> </mml:msub> </mml:math> ratios with climatic variables in the austral spring (2016–2018) in Metropolitan Lima. Overall, there was an average PM $$_{2.5}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow/> <mml:mrow> <mml:mn>2.5</mml:mn> </mml:mrow> </mml:msub> </mml:math> /PM $$_{10}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow/> <mml:mn>10</mml:mn> </mml:msub> </mml:math> ratio of 0.33 with fluctuations from 0.30 to 0.35. However, there have also been high point values that reached ratios greater than one. This situation indicates a moderate condition of contamination by particulate matter with a predominance of coarse aerosols in spring, with an increasing trend over the years. The locations Ate and Villa Maria del Triunfo , especially Ate , presented poor quality conditions. Thursdays showed outstanding pollution peaks by PM $$_{10}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow/> <mml:mn>10</mml:mn> </mml:msub> </mml:math> , and a decrease is visible on Sundays. On the other hand, the PM $$_{2.5}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow/> <mml:mrow> <mml:mn>2.5</mml:mn> </mml:mrow> </mml:msub> </mml:math> showed a similar pattern every day, including Sundays. The maximum peaks occurred in the morning and night hours. The increase in anthropogenic emissions associated with the formation of secondary aerosols has been evident, being the case of the location Campo de Marte , the one that had a significant increase in ratios PM $$_{2.5}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow/> <mml:mrow> <mml:mn>2.5</mml:mn> </mml:mrow> </mml:msub> </mml:math> /PM $$_{10}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow/> <mml:mn>10</mml:mn> </mml:msub> </mml:math> , which confirms a greater intensity of secondary formations of carbonaceous particles from industrial oil sources, vehicle exhaust, as well as aerosols from metal smelting and biomass burning. There were negative correlations of the ratios with PM $$_{10}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow/> <mml:mn>10</mml:mn> </mml:msub> </mml:math> , temperature, wind speed, and direction, and positive correlations with PM $$_{2.5}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow/> <mml:mrow> <mml:mn>2.5</mml:mn> </mml:mrow> </mml:msub> </mml:math> and relative humidity. Contour lines were successfully developed that demonstrated the interaction of climate with PM $$_{2.5}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow/> <mml:mrow> <mml:mn>2.5</mml:mn> </mml:mrow> </mml:msub> </mml:math> /PM $$_{10}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow/> <mml:mn>10</mml:mn> </mml:msub> </mml:math> ratios. This will deepen the exploration of emission sources and modeling, which allows for optimizing air quality indices to control emissions and adequately manage air quality in Metropolitan Lima.
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    Application of artificial intelligence techniques in the aquaculture sector: Systematic review of the American context
    (2026-06-01)
    Danny Lévano-Rodriguez  
    ;
    Lloy Pinedo
    ;
    Gorky Vizalote
    ;
    Manuel Enrique Navas-Vásquez
    ;
    Javier Linkolk López‐Gonzales  
    Aquaculture is an essential productive activity in food security, economy, and the sustainability of water resources globally. The study analyzes the application of artificial intelligence techniques in aquaculture on the American continent through a systematic review of 31 articles published between 2020 and 2024 in the Scopus and SciELO databases. Five key areas of application were identified: monitoring and control, organism identification and counting, biomass and mortality rate prediction, behavioral analysis, and production optimization. The most commonly used techniques include machine learning, deep learning, artificial vision, and genetic algorithms, with models such as Convolutional Neural Networks, Random Forest, and YOLO standing out, demonstrating high accuracy in aquaculture processes. However, research has focused mainly on fish, while other organisms, such as shellfish and shrimp, have received less attention. In addition, adopting these technologies faces challenges related to infrastructure, data availability, and staff training. It is concluded that the integration of AI in aquaculture has a high potential to improve the efficiency and sustainability of the sector. However, it is necessary to expand the study to other species and strengthen technological accessibility for small and medium-sized producers.
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    Statistical and Artificial Neural Networks Models for Electricity Consumption Forecasting in the Brazilian Industrial Sector
    (2022-01-14)
    Felipe Leite Coelho da Silva
    ;
    Kleyton da Costa
    ;
    Paulo Canas Rodrigues
    ;
    Rodrigo Salas
    ;
    Javier Linkolk López‐Gonzales  
    Forecasting the industry’s electricity consumption is essential for energy planning in a given country or region. Thus, this study aims to apply time-series forecasting models (statistical approach and artificial neural network approach) to the industrial electricity consumption in the Brazilian system. For the statistical approach, the Holt–Winters, SARIMA, Dynamic Linear Model, and TBATS (Trigonometric Box–Cox transform, ARMA errors, Trend, and Seasonal components) models were considered. For the approach of artificial neural networks, the NNAR (neural network autoregression) and MLP (multilayer perceptron) models were considered. The results indicate that the MLP model was the one that obtained the best forecasting performance for the electricity consumption of the Brazilian industry under analysis.
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