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https://purl.org/pe-repo/ocde/ford#5.02.04
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https://purl.org/pe-repo/ocde/ford#2.02.04
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https://purl.org/pe-repo/ocde/ford#2.11.04
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https://purl.org/pe-repo/ocde/ford#3.03.03
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http://purl.org/pe-repo/ocde/ford#5.02.04
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https://purl.org/pe-repo/ocde/ford#2.01.00
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https://purl.org/pe-repo/ocde/ford#2.01.01
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classification processes » pacification processes (Expander búsqueda), classification trees (Expander búsqueda), identification processes (Expander búsqueda)
data classification » abc classification (Expander búsqueda), a classification (Expander búsqueda), image classification (Expander búsqueda)
classification processes » pacification processes (Expander búsqueda), classification trees (Expander búsqueda), identification processes (Expander búsqueda)
data classification » abc classification (Expander búsqueda), a classification (Expander búsqueda), image classification (Expander búsqueda)
1
artículo
Publicado 2019
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This article presents a methodology that applies natural language processing and classification algorithms by using data mining techniques, and incorporating procedures for validation and verification of significance. This is conducted according to the analysis and selection of data and results based on quality statistical analysis, which guarantees the effectiveness percentage in knowledge construction. The analysis of computer incidents within an educational institution and a standardized database of historical computer incidents collected by the Service Desk area is used as case study. Such area is linked to all information technology processes and focuses on the support requirements for the performance of employee activities. As long as users’ requirements are not fulfilled in a timely manner, the impact of incidents may give rise to work problems at different levels, making it d...
2
artículo
Publicado 2019
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This article presents a methodology that applies natural language processing and classification algorithms by using data mining techniques, and incorporating procedures for validation and verification of significance. This is conducted according to the analysis and selection of data and results based on quality statistical analysis, which guarantees the effectiveness percentage in knowledge construction. The analysis of computer incidents within an educational institution and a standardized database of historical computer incidents collected by the Service Desk area is used as case study. Such area is linked to all information technology processes and focuses on the support requirements for the performance of employee activities. As long as users’ requirements are not fulfilled in a timely manner, the impact of incidents may give rise to work problems at different levels, making it d...
3
artículo
Publicado 2023
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A dissatisfied customer with a product and/or service is motivated to express a complaint. Classifying complaints manually is a process that represents high costs in human and material resources. Artificial Intelligence (AI) allows the use of various algorithms to perform tasks that can simulate human intelligence, a branch of this is Natural Language Processing (NLP), its objective is that machines have the capacity to understand human language, allowing, for example, to classify and categorize data automatically. This article provides a systematic review of the literature addressing challenges in the classification of complaint texts, such as the lack of class balance, the presence of unlabeled data, and the interpretation of model results. Preprocessing techniques are explored, such as tokenization, stopword removal, and lemmatization, which influence model performance. Additionally, ...
4
capítulo de libro
Publicado 2019
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This work proposes a semi-automated analysis and modeling package for Machine Learning related problems. The library goal is to reduce the steps involved in a traditional data science roadmap. To do so, Sparkmach takes advantage of Machine Learning techniques to build base models for both classification and regression problems. These models include exploratory data analysis, data preprocessing, feature engineering and modeling. The project has its basis in Pymach, a similar library that faces those steps for small and medium-sized datasets (about ten millions of rows and a few columns). Sparkmach central labor is to scale Pymach to overcome big datasets by using Apache Spark distributed computing, a distributed engine for large-scale data processing, that tackle several data science related problems in a cluster environment. Despite the software nature, Sparkmach can be of use for local ...
5
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.
6
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...
7
tesis doctoral
Publicado 2024
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This dissertation investigates the potential improvement of volcanic eruption understanding and forecasting methods by using advanced data processing techniques to analyze large datasets at three target volcanoes (Piton de la Fournaise (PdlF) (France), Sabancaya, and Ubinas (Peru)). The central objective of this study is to search for possible empirical relationships between the pre-eruptive behavior of the accelerated increase in seismic activity using the Failure Forecast Method (FFM) and velocity variations measured by Coda Wave Interferometry (CWI), since both observations are reported to be independently associated with medium damage. The FFM is a deterministic method used to forecast volcanic eruptions using an empirical relationship of increased and accelerated evolution of an observable (e.g., volcano-seismic event rates). The event rates used with FFM in this study were generate...
8
tesis doctoral
Publicado 2024
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This dissertation investigates the potential improvement of volcanic eruption understanding and forecasting methods by using advanced data processing techniques to analyze large datasets at three target volcanoes (Piton de la Fournaise (PdlF) (France), Sabancaya, and Ubinas (Peru)). The central objective of this study is to search for possible empirical relationships between the pre-eruptive behavior of the accelerated increase in seismic activity using the Failure Forecast Method (FFM) and velocity variations measured by Coda Wave Interferometry (CWI), since both observations are reported to be independently associated with medium damage. The FFM is a deterministic method used to forecast volcanic eruptions using an empirical relationship of increased and accelerated evolution of an observable (e.g., volcano-seismic event rates). The event rates used with FFM in this study were generate...
9
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...
10
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...
11
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...
12
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...
13
artículo
Publicado 2019
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The increased volume of data in this digital age is enormous, the task of analyzing, processing, identifying and classify them for to have a good data mining system where we can index the information contained regardless the amount and data type, it is no easy task. That is the reason for it is becoming more necessary to develop more effective methods to facilitate these tasks automatically. This paper presents an overview of different works performed throughout the world that use data compression techniques as a basis for developing a classification method, these techniques are based on Kolmogorov Complexity and use this complexity for implement a similarity metrics between data. The main contribution of these methods is, no need a feature extraction process for classification, which makes it a parameter-free method, so it can be applied to any type of data, whether text, images, audio,...
14
artículo
Publicado 2020
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The wind off the Peru coast has influence on marine physical, chemical and biological processes, both in surface and sub-surface layers and impacts coastal sea currents, sea surface temperature, vertical mixing layer, transport and retention of larvae in the sea. Furthermore it influences the approach of oceanic waters towards the coast and in other cases it favors the extension of Cold Coastal Waters and Mixing Waters off the Peruvian coast through the coastal upwelling. Therefore, the intensification and weakening of coastal wind have a strong impact on environmental conditions and the ecosystem. In this work, satellite wind data obtained through the ASCAT (Advanced Scatterometer) scaterometer from a coastal strip up to 100 km (approximately 62 nautical miles) off Peru was used in five areas: Paita, Chicama, Callao, San Juan de Marcona and Ilo, during the period from March 21, 2007 to ...
15
artículo
This article describes a general vision about the use of the Cyclosizer used mainly for the realization o experimental tests of metallic and non metallic minerals in the processing of minerals such as: gold bearing minerals and dolomite minerals, respectively. The cyclosizer is an elutriator that separates a specimen in specifically sized fractions using a technique that depends on the forces produced by the relative speed of the particles and the elutriation fluid. It is different from a conventional elutriation where the elutriation action takes place in a hydraulic cyclone where the fluid is spinning and the centrifuge forces are acting on the particles due to the gravity. The fluid models in the cyclone are stable and the changes in the condition of the atmosphere are not as critical as in the case of the conventional processing for elutriation. Also, the high cutting forces that are...
16
artículo
This article describes a general vision about the use of the Cyclosizer used mainly for the realization o experimental tests of metallic and non metallic minerals in the processing of minerals such as: gold bearing minerals and dolomite minerals, respectively. The cyclosizer is an elutriator that separates a specimen in specifically sized fractions using a technique that depends on the forces produced by the relative speed of the particles and the elutriation fluid. It is different from a conventional elutriation where the elutriation action takes place in a hydraulic cyclone where the fluid is spinning and the centrifuge forces are acting on the particles due to the gravity. The fluid models in the cyclone are stable and the changes in the condition of the atmosphere are not as critical as in the case of the conventional processing for elutriation. Also, the high cutting forces that are...
17
artículo
This study aims to use machine learning classifiers to predict the kingdom to which an organism belongs by the frequency of use of DNA codons. The study used 13,028 data from GenBank organisms distributed in eleven kingdoms and reduced them to six kingdoms (archaea, bacteria, invertebrates, plants, viruses, and vertebrates) with 9,027 regrouped data. The process required cleaning irrelevant attributes, using measurement metrics of accuracy, precision, sensitivity, and score classifiers, and the adjustment of hyperparameters of the models. The classification algorithms were voting, bagging, boosting, and stacking, using KNN, AD, MLP, SVC, and RF. Random forest was used in selecting the attributes. The stacking ensemble, with its models, better predicts the classification of organisms in the present study.
18
artículo
This study aims to use machine learning classifiers to predict the kingdom to which an organism belongs by the frequency of use of DNA codons. The study used 13,028 data from GenBank organisms distributed in eleven kingdoms and reduced them to six kingdoms (archaea, bacteria, invertebrates, plants, viruses, and vertebrates) with 9,027 regrouped data. The process required cleaning irrelevant attributes, using measurement metrics of accuracy, precision, sensitivity, and score classifiers, and the adjustment of hyperparameters of the models. The classification algorithms were voting, bagging, boosting, and stacking, using KNN, AD, MLP, SVC, and RF. Random forest was used in selecting the attributes. The stacking ensemble, with its models, better predicts the classification of organisms in the present study.
19
artículo
Publicado 2022
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Research shows that data analysis and artificial intelligence applied to agriculture in Peru can help manage crop production and mitigate monetary losses. This work presents SmartAgro, a system based on pattern mining and classification techniques that takes information from multiple sources related to the agricultural process to extract knowledge and produce recommendations about the crop growth process. The problem we seek to mitigate with our system is the economic losses generated in Peruvian agriculture caused by poor crop planning. Our results show a high accuracy in regards to type of crop recommendation, and a knowledge base useful for agricultural planning.
20
objeto de conferencia
Publicado 2017
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The aim of this work is to design, implement and validate a primary communication system based on a brain-computer interface. There are people who-for various reasons-are affected in their ability to externalize their communication, however, they receive and process information from different sources. This system would allow basic communication-allowing the user to answer closed questions-through thought. The system was implemented by analyzing and interpreting electrical signals from brain activity, collected through electrodes attached to the scalp. The analog electrical signals were received by a data acquisition system and digitized for computer analysis. We implemented different signal processing techniques, pattern analysis, and classification and discrimination methods. By analyzing these signals and interpreting the electrical patterns, was achieved understand answers to simple q...
