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1
tesis de grado
Publicado 2019
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RESUMEN El presente informe de tesis tiene como enfoque principal, poder analizar de qué manera influye la implementación de una red social en el desarrollo profesional de los egresados de las carreras relacionadas a la Association for Computing Machinery en el período 2015 - 2017. Para esta investigación se utilizó un diseño experimental, de tipo pre- experimental, ya que se ha manipulado una variable experimental, aplicando un pre test y post test, que ha permitido realizar una comparación cuantitativa en un momento previo y posterior. Se utilizó como instrumento tres encuestas, dos a los egresados en diferentes momentos de la investigación y una encuesta a un especialista que mida la calidad de la red social; así como un análisis documental y bibliográfico para la revisión de información. Los resultados obtenidos son trascendentales, pues permitieron recopilar el nivel d...
2
artículo
Attention deficit hyperactivity disorder (ADHD) represents a medical condition characterized by the presence of inattention, hyperactivity, and impulsivity, which affects the academic development of students globally. In Peru, it affects a proportion of the pediatric population ranging from 2% to 12%, with a prevalence of 12.1% in South Lima, particularly in public schools. This research presents an online application with machine learning to improve the detection of ADHD in elementary school children. Several machine learning algorithms were reviewed and Random Forest was selected as the best-performing model with an accuracy of 96.08%. The model uses 27 selected variables, optimizing data collection and training. The child answers the questionnaire within the app and psychologists can access the app to visualize the results, aiding in the early detection of ADHD. The experiment involve...
3
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 ...
4
artículo
In this research, we posit the importance of including Computer Science topics in undergraduate Information Systems courses. A review of the existing literature has mentioned the importance of learning some Computer Science topics for an Information Systems career. Unfortunately, we have not found a consensus that could push this initiative. There is a set of concepts that Information Systems students should acquire from Computer Science or at least have a solid background in these areas. Therefore, we propose a set of courses from Computer Science that, we believe, should be considered in an Information Systems education.
5
objeto de conferencia
Publicado 2018
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Classification green coffee beans is one of the main tasks during the quality grading process. This evaluation is normally carried out by specialist doing a visual inspection or using traditional instruments which have some limitations. This work is focused on the implementation of a computer vision system combining a hardware prototype and a software module. The hardware was made to guarantee the controlled conditions to capture the images of green coffee beans, the software is based on computer vision algorithms in order to detect defects of the coffee beans. The novelty of our proposal is the combination of algorithms to enhance the accuracy and the high number of defects detected. We applied a White Patch algorithm as an image enhancement procedure, color histograms as feature extractor and Support Vector Machine (SVM) for the classification task. It was constituted an image beans da...
6
artículo
Publicado 2023
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Identifying and classifying text extracted from social networks, following the traditional method, is very complex. In recent years, computer science has advanced exponentially, helping significantly to identify and classify text extracted from social networks, specifically Twitter. This work aims to identify, classify and analyze tweets related to real natural disasters through tweets with the hashtag #NaturalDisasters, using Machine learning (ML) algorithms, such as Bernoulli Naive Bayes (BNB), Multinomial Naive Bayes (MNB), Logistic Regression (LR), K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF). First, tweets related to natural disasters were identified, creating a dataset of 122k geolocated tweets for training. Secondly, the data-cleaning process was carried out by applying stemming and lemmatization techniques. Third, exploratory data analysis (EDA) was performed...
7
artículo
Publicado 2024
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The trade of horticultural products is a crucial sector in the local economy of Lima, Peru. Microenterprises dedicated to this activity face various challenges, including demand volatility. This volatility can decrease the likelihood of generating profits and impact the stability of the business, primarily due to the challenges associated with adjusting selling prices. To address this issue, our proposal is based on implementing the XGBoost algorithm, which has the capability to handle heterogeneous data and variables of different types. This algorithm leverages historical data to provide accurate and up-to-date price recommendations for horticultural products. This, in turn, enables micro-entrepreneurs to make informed decisions when setting prices, thereby achieving expected benefits and enhancing their competitiveness. The integration of our project with microenterprises in Lima has t...
8
artículo
Publicado 2024
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The persistent issue of student dropout negatively impacts the educational sector and society at large. This study presents a machine learning model that leverages data from the National Household Survey to predict student dropout in Peru, integrating a wide range of socio-demographic variables. The research fills a gap in existing literature by providing a model that incorporates socio-demographic variables, an area not fully explored in previous studies. The predictive model aims to identify factors associated with student dropout, aiding educational stakeholders in implementing effective interventions. The findings underscore the model's potential to enhance educational outcomes by enabling early identification of at-risk students, thereby facilitating targeted support. This work contributes to refining predictive models of university dropout rates and sug- gests the use of ensemble m...
9
artículo
Publicado 2024
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Dry forests are ecosystems of great importance worldwide, but in recent decades they have been affected by climate change and changes in land use. In this study, we evaluated land use and land cover changes (LULC) in dry forests in Peru between 2017 and 2021 using Sentinel-2 images, and cloud processing with Machine Learning (ML) models. The results reported a mapping with accuracies above 85% with an increase in bare soil, urban areas and open dry forest, and reduction in the area of crops and dense dry forest. Protected natural areas lost 2.47% of their conserved surface area and the areas with the greatest degree of land use impact are located in the center and north of the study area. The study provides information that can help in the management of dry forests in northern Peru.
10
tesis de grado
Publicado 2024
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Machine learning is becoming increasingly important and pervasive in people's lives, yet when its conclusions reflect biases that support ingrained prejudices in society, many vulnerable groups' psychological wellbeing may be impacted. To investigate if gender biases exist in image search engine algorithms that use machine learning, the study focuses on occupations. To do this, searches for various professions were run on Google, DuckDuckGo, and Yandex. Using web scraping techniques, a sample of images was retrieved for each selected profession and search engine. The images were then manually classified by gender, and statistical indicators and analyses were computed to detect potential biases in the representation of each gender. This analysis included a comparison between search engines, the calculation of mean, standard deviation, and coefficient of variation, a confidence interval an...
11
tesis de grado
Publicado 2023
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Market basket analysis provides an insight into customer consumption patterns and trends in the industry. These will be achieved by analyzing and studying the performance of the large datasets of transactions made by consumers held in retail stores. These commercial transactions will be analyzed using the Machine Learning technique called the A priori algorithm by establishing association rules and determining those groups of items in a market basket whose association could represent better economic benefits for companies. This study will analyze the historical sales data of the product groups, in order to identify relationships that al-low companies in the sector to generate patterns to propose the increase of their portfolio based on the products with the greatest purchasing trends. At the end of this investigation, commercial strategies will be proposed to improve sales, take advantag...
12
artículo
Publicado 2024
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Making raw material purchase forecasts for companies is very difficult and, if inadequately controlled, can affect the company's decision making and profitability. Currently, there are optimized systems or mathematical models to try to predict the demands and solve this problem. In this study, a raw material purchase prediction model is proposed that uses the Elastic Net algorithm to analyze historical sales and inventory data. The model is used to improve prediction accuracy, allowing SMEs to optimize inventories, reduce costs and improve efficiency. Experimental results indicate that the proposed model obtains better results in the MAE, RMSE and R2 indicators.
13
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 ...
14
tesis de grado
Publicado 2023
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En las edificaciones, el movimiento de tierras es una de las partidas más importantes dentro de los procesos constructivos, es primordial comprobar que el tiempo de ejecución de esta partida esté acorde al cronograma de actividades, y esto se relaciona directamente con el rendimiento de las maquinarias para el movimiento de tierras. Por ello, la presente investigación de tipo cuantitativa se centra en estudiar diferentes metodologías para determinar los rendimientos de las maquinarias para el movimiento de tierras, las metodologías estudiadas son: Inteligencia artificial, automatización de conceptos convencionales mediante hojas de cálculo, y automatización usando un programa denominado RENDEXCA. A través de esta investigación se estudiaron conceptos asociados a la inteligencia artificial, potente herramienta para la computación evolutiva, investigaciones han demostrado que h...
15
artículo
Publicado 2024
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Smartphone addiction has emerged as a growing concern in society, particularly among teenagers, due to its potential negative impact on physical, emotional social well-being. The excessive use of smartphones has consistently shown associations with negative outcomes, highlighting a strong dependence on these devices, which often leads to detrimental effects on mental health, including heightened levels of anxiety, distress, stress depression. This psychological burden can further result in the neglect of daily activities as individuals become increasingly engrossed in seeking pleasure through their smartphones. The aim of this study is to develop a predictive model utilizing machine learning techniques to identify smartphone addiction based on the "Big Five Personality Traits (BFPT)". The model was developed by following five out of the six phases of the "Cross Industry Standard Process ...
16
objeto de conferencia
Publicado 2017
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In this paper, we present the first attempts to develop a machine translation (MT) system between Spanish and Shipibo-konibo (es-shp).
17
objeto de conferencia
Publicado 2019
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WordNet-like resources are lexical databases with highly relevance information and data which could be exploited in more complex computational linguistics research and applications. The building process requires manual and automatic tasks, that could be more arduous if the language is a minority one with fewer digital resources. This study focuses in the construction of an initial WordNetdatabase for a low-resourced and indigenous language in Peru: Shipibo-Konibo (shp). First, the stages of development from a scarce scenario (a bilingual dictionary shp-es) are described. Then, it is proposed a synset alignment method by comparing the definition glosses in the dictionary (written in Spanish) with the content of a Spanish WordNet. In this sense, word2vec similarity was the chosen metric for the proximity measure. Finally, an evaluation process is performed for the synsets, using a manually...
18
artículo
Publicado 2019
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El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado.
19
artículo
Publicado 2018
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Nowadays people use mobile devices in different ways to make a profit. In this paper we present a literature review to know the use of the mobile devices as a learning tool, the factors that influence their use, and the advantages and disadvantages of the use of mobile applications that strengthen learning. Researchers seek to dispel doubts about the possibility of choosing mobile devices as tools for learning. As a result of this study it was found that the factors that influence the adoption of these tools are relevant, the advantages are really beneficial, and that students' academic performance can increase relatively.
20
tesis de grado
Publicado 2021
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The present research thesis has the purpose of serving as contribution to promote the development of the processing of alpaca fiber in Peru when raising as proposal the design of a semi-industrial machine for the process of alpaca fiber opening, which has part within the preoperational stage in the production of yarn from alpaca fiber. This machine has an expected capacity of 2 kg/h of open fiber with a loss of 5%, giving a capacity 10 times higher compared to the 200g/h produced manually at the artisan level. As fundamental objective of the present research, the design of a semi-industrial machine for alpaca fiber opening process with motor of 1/2 HP must be completed. To achieve this, the diagnosis and evaluation of the opening process was carried out at an artisan level as well as at an industrial level to obtain characteristics that allow to complete the gap present between both leve...
