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1
artículo
Publicado 2024
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Vector-borne diseases (VBDs) are major threats to human health. They are estimated to cause more than 700,000 deaths each year. This presents serious health problems for CBD. In recent years, the incidence of VBDs has increased globally, affecting one billion people approximately and accounting for 17% of all infectious diseases. Globally, disease rates have risen at an alarming rate, with more than 3.9 billion people at risk of infection. Therefore, it is essential to find approaches to detect these diseases; this is where machine learning (ML) models come into play. The purpose of this study was to predict VBDs using tabular epidemiological data. For this purpose, a set of ML models was used, such as support vector classifier (SVC), extreme gradient boosting (XGBoost), LightGBM, CatBoost, random forest (RF), and balanced random forest (BRF). A dataset consisting of 65 features and 1262...
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objeto de conferencia
This material is based upon work supported in part by the U.S. Department of Energy, Ofce of Science, Ofce of Advanced Scientifc Computing Research, under contract number DE-AC05-00OR22725. Research sponsored in part by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory, managed by UT-Battelle, LLC, for the U. S. Department of Energy. This research used resources of the Oak Ridge Leadership Computing Facility, which is a DOE Ofce of Science User Facility supported under Contract DE-AC05-00OR22725. We would like to thank the MINERvA collaboration for the use of their simulated data and for many useful and stimulating conversations. MINERvA is supported by the Fermi National Accelerator Laboratory under US Department of Energy contract No. DE-AC02-07CH11359 which included the MINERvA construction project. MINERvA construction support was also granted ...
3
artículo
Publicado 2024
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Author contribution: All authors made an equal contribution to the development and planning of the study. Conflict of Interest: The authors have no potential conflicts of interest, or such divergences linked with this research study. Data Availability Statement: Data are available from the authors upon request.
4
tesis de maestría
Publicado 2020
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Descargue el texto completo en el repositorio institucional de la Universidade Estadual de Campinas: https://hdl.handle.net/20.500.12733/1641108
5
artículo
Publicado 2022
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so, machine learning techniques are being developed to improve performance and maintenance prediction. Increasing our knowledge of the relationship between humans and algorithms, Because data is so valuable, improving strategies for intelligently having to manage the now-ubiquitous content infrastructures is a necessary part of the process toward completely autonomous agents. Numerous researchers recently developed numerous computer-aided diagnostic algorithms employing various supervised learning approaches. Early identification of sickness may help to reduce the number of people who die as a result of these illnesses. Using machine learning techniques, this research creates an efficient automated illness diagnostic algorithm. We chose three key disorders in this paper: coronavirus, cardiovascular diseases, and diabetes. The data are inputted into a mobile application in the suggested m...
6
artículo
Geometallurgy is defined as the study of the genesis of minerals with respect to the performance of their metallurgical processing. The construction of geometallurgical models is of the utmost importance for the technical-economic evaluation of the deposit. The robustness of the model depends on the number of resources invested to generate potential information that will serve in decision making. The relevance of the geometallurgical model has a high value in mining management, which serves as an instrument for the planning, exploitation and design of metallurgical processes according to the type of deposit. Using the information to maximize economic performance in concentration processes has enormous potential and challenge for plant operators. This article will discuss the use of data analysis and its applications in several successful cases for porphyry-type deposits, taking...
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artículo
Publicado 2023
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Introduction: The COVID-19 pandemic has had a significant impact worldwide, especially in health, where it is crucial to identify patients at high risk of clinical deterioration early. Objective: This study aimed to design a model based on the support vector machine (SVM) algorithm, optimizing its parameters to classify patients with suspected COVID-19. Methodology: One thousand patient records from two health establishments in Peru were used. After applying data preprocessing and variable engineering, the sample was reduced to 700 records. The construction of the model followed a machine learning methodology, using the linear, polynomial, sigmoid, and radial kernel functions, along with their estimated optimal parameters, to ensure the best performance. Results: The results revealed that the SVM model with the linear and sigmoid kernels presented an accuracy of 95%, surpassing the polyn...
8
artículo
Publicado 2020
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This study describes a model of explanations in natural language for classification decision trees. The explanations include global aspects of the classifier and local aspects of the classification of a particular instance. The proposal is implemented in the ExpliClas open source Web service [1], which in its current version operates on trees built with Weka and data sets with numerical attributes. The feasibility of the proposal is illustrated with two example cases, where the detailed explanation of the respective classification trees is shown.
9
artículo
Publicado 2023
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This research aims to enhance the classification of the rock mass in underground mining, a common problem due to geological alterations that do not fit existing methods. Artificial neural networks are proposed as a solution, which use input/output data to learn and solve problems. The process involves gathering data on rock properties and training the neural networks to identify and classify various types of rock. Once trained, the neural networks can classify the rock mass in real-time during mine design and progression, adapting to different rock types with a low margin of error of 0.279% in determining the RMR index. This research overcomes the limitations of current classification methods, providing a more accurate and reliable solution for the classification of the rock mass in underground mining. In summary, artificial neural networks are utilized to improve the classification of r...
10
artículo
Publicado 2017
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This article presents the application of the non-parametric Random Forest method through supervised learning, as an extension of classification trees. The Random Forest algorithm arises as the grouping of several classification trees. Basically it randomly selects a number of variables with which each individual tree is constructed and predictions are made with these variables that will later be weighted through the calculation of the most voted class of these trees that were generated, to finally do the prediction by Random Forest. For the application, we worked with 3168 recorded voices, for which the results of an acoustic analysis are presented, registering variables such as frequency, spectrum, modulation, among others, seeking to obtain a pattern of identification and classification according to gender through a voice identifier. The data record used is in open access and can be do...
11
artículo
Publicado 2017
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This article presents the application of the non-parametric Random Forest method through supervised learning, as an extension of classification trees. The Random Forest algorithm arises as the grouping of several classification trees. Basically it randomly selects a number of variables with which each individual tree is constructed and predictions are made with these variables that will later be weighted through the calculation of the most voted class of these trees that were generated, to finally do the prediction by Random Forest. For the application, we worked with 3168 recorded voices, for which the results of an acoustic analysis are presented, registering variables such as frequency, spectrum, modulation, among others, seeking to obtain a pattern of identification and classification according to gender through a voice identifier. The data record used is in open access and can be do...
12
objeto de conferencia
Publicado 2024
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The global tire marketing sector faced disruptions due to the pandemic, affecting production and demand.The recovery, driven by economic revival and increased vehicle demand, especially in strategic countries like China, the US, and the EU, is evident.In Peru, the tire trade sector mirrors global trends, with sustained growth linked to increased car sales.The investigated company, operating for over two decades, engages in stores, distribution, fleet, and web businesses.85% of purchases come from foreign suppliers, mainly Brazil and Germany.Despite pandemic challenges, the company maintained and exceeded pre-pandemic sales since August 2021.However, inventory management remains a challenge, impacting customer satisfaction and sales.This research project aims to demonstrate that implementing an inventory management improvement model enhances company productivity and efficiency.Specific ob...
13
tesis de grado
Publicado 2025
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The global tire marketing sector faced disruptions due to the pandemic, affecting production and demand. The recovery, driven by economic revival and increased vehicle demand, especially in strategic countries like China, the US, and the EU, is evident. In Peru, the tire trade sector mirrors global trends, with sustained growth linked to increased car sales. The investigated company, operating for over two decades, engages in stores, distribution, fleet, and web businesses. 85% of purchases come from foreign suppliers, mainly Brazil and Germany. Despite pandemic challenges, the company maintained and exceeded pre-pandemic sales since August 2021. However, inventory management remains a challenge, impacting customer satisfaction and sales. This research project aims to demonstrate that implementing an inventory management improvement model enhances company productivity and efficiency. Spe...
14
artículo
Support Vector Machines are extensively used to solve classification problems in Pattern Recognition. They deal with small errors in the training data using the concept of soft margin, that allowfor imperfect classification. However, if the training data have systematic errors or outliers such strategy is not robust resulting in bad generalization. In this paper we present a model for robust Support Vector Machine classification that can automatically ignore spurius data. We show then that the model can be solved using a high performance Mixed Integer Quadratic Programming solver and present preliminary numerical experiments using real world data that looks promissing.
15
artículo
Publicado 2024
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In the context of IT incident management, the prioritization and automation of tickets can be a challenge for companies that lack advanced technologies. However, these difficulties can be overcome today by applying machine learning algorithms and techniques that use historical data to train predictive models, which allows for more efficient and effective IT incident management. The article proposes the implementation of a predictive model that uses machine learning to prioritize IT incidents in these companies. The goal of this proposal is to allow small and medium-sized enterprises to prioritize their incidents automatically, using a model that has been previously trained with a supervised multi-label classification algorithm technique to achieve high accuracy. Experimental results show that the Mean Absolute Error (MAE) is 2.79 and a Mean Squared Error (MSE) of 8.21, using the metrics ...
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objeto de conferencia
Presentación que se llevó a cabo durante el I Congreso Internacional de Computación y Telecomunicaciones COMTEL 2009 del 18 al 20 de noviembre de 2009 en Lima, Perú. COMTEL, es un certamen organizado por la Facultad de Ingeniería de Sistemas, Cómputo y Telecomunicaciones de la Universidad Inca Garcilaso de la Vega, que congrega a profesionales, investigadores y estudiantes de diversos países con el fin de difundir e intercambiar conocimientos, mostrar experiencias académicas-científicas y soluciones para empresas en las áreas de Computación, Telecomunicaciones y disciplinas afines.
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artículo
Publicado 2024
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Quinoa is an Andean crop that stands out as a high-quality protein-rich and gluten-free food. However, its increasing popularity exposes quinoa products to the potential risk of adulteration with cheaper cereals. Consequently, there is a need for novel methodologies to accurately characterize the composition of quinoa, which is influenced not only by the variety type but also by the farming and processing conditions. In this study, we present a rapid and straightforward method based on matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS) to generate global fingerprints of quinoa proteins from white quinoa varieties, which were cultivated under conventional and organic farming and processed through boiling and extrusion. The mass spectra of the different protein extracts were processed using the MALDIquant software (version 1.19.3), detecting 49 prot...
18
artículo
Publicado 2024
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This project focuses on developing an NLP-based text analysis tool to evaluate Android app user feedback, specifically collected from F-Droid. The lack of an automated solution to analyze and understand these opinions, classifying them into specific topics, motivates research. The goal is to provide developers, users, and data analysts with a detailed view of user preferences and perceptions. Using data sets in English between 2014 and 2017, the proposal is implemented in Python with the Pandas library. The BERT model is used for classification, with a specific focus on the comparison of different models. The graphical interface is built in Visual Studio, allowing users to enter comments and obtain topic rankings, along with word cloud visualizations.
19
artículo
Publicado 2023
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The present project consists of developing a Natural Language Processing model to classify news using a set of data or DataSets already evaluated. The main objective is to create a system that can automatically identify and assign news to one of the predefined categories: business, entertainment, politics, sports or technology. This involves data preprocessing, feature extraction, training a machinelearning model and then evaluating its performance using metrics such as "accuracy", "recall 2" F1 - score". This will allow to determine how well the model can predict the correct category for a new or unlabeled news item. If the performance of the model is satisfactory, it can be used to classify unlabeled news in real time. In summary, it seeks to provide an efficient and accurate solution for organizing and labeling the informative content of a news item with the help of Artificial Intelli...
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artículo
“The identification, classification and treatment of crop plant diseases are essential for agricultural production. Some of the most common diseases include root rot, powdery mildew, mosaic, leaf spot and fruit rot. Machine learning (ML) technology and convolutional neural networks (CNN) have proven to be very useful in this field. This work aims to identify and classify diseases in crop plants, from the data set obtained from Plant Village, with images of diseased plant leaves and their corresponding Tags, using CNN with transfer learning. For processing, the dataset composing of more than 87 thousand images, divided into 38 classes and 26 disease types, was used. Three CNN models (DenseNet-201, ResNet-50 and Inception-v3) were used to identify and classify the images. The results showed that the DenseNet-201 and Inception-v3 models achieved an accuracy of 98% in plant disease identif...
