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1
artículo
Publicado 2021
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Time association data has been critical to the exploration field of paddy yield forecast. At durations the path of recent many years, countless flossy legitimate time arrangement. For this reason, this paper canters round searching forward to statistics esteems on a huge variety of flossy precept calculations. To clarify the approach in the course of gauging, the verifiable statistics of paddy yield. The method for acknowledgment used at some point of this exam can also be an extreme information grouping. The technique joins the coaching capacities of fake neural device with the human like data portrayal and clarification capacities of flossy precept frameworks and furthermore a trendy primarily based in maximum instances hold close framework. It's miles for the most half of used in Brobdingnagian expertise getting equipped applications. As we have a tendency to in all opportunity am awa...
2
tesis de grado
Publicado 2021
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Las enfermedades parasitarias gastrointestinales representan un problema latente en los países en desarrollo; es necesario crear herramientas de apoyo para el diagnóstico médico de estas enfermedades, se requiere automatizar tareas como la clasificación de muestras de los parásitos causantes obtenidas a través del microscopio utilizando métodos como el aprendizaje profundo. Sin embargo, estos métodos requieren grandes cantidades de datos. Actualmente, la recolección de estas imágenes representa un procedimiento complejo, importante consumo de recursos y largos períodos. Por tanto, es necesario proponer una solución computacional a este problema. En este trabajo se presenta un enfoque para generar conjuntos de imágenes sintéticas de 8 especies de parásitos, utilizando Redes Generativas Adversarias Convolucionales Profundas (DCGAN). Además, buscando mejores resultados, se a...
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
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.
5
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...
6
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.
7
artículo
This study investigates the application of the Xception architecture for accurate classification of skin lesions, focusing on the early detection of melanoma and other malignant skin conditions. Utilizing deep learning techniques, the research aims to enhance the precision and efficiency of skin lesions diagnosis. The study utilizes the TensorFlow framework and the HAM10000 dataset, comprising a vast collection of benign and malignant skin lesion images, for training and evaluating the Xception model. Preprocessing steps, including data splitting, augmentation, and image resizing, are applied to the dataset. The Xception architecture, a deep convolutional neural network, serves as the foundational model, supplemented with customized classification layers for specialized features and predictions. The model’s performance is evaluated using diverse metrics. The experimental outcomes revea...
8
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.
9
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.
10
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...
11
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...
12
artículo
Publicado 2021
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We want to thank the Image Processing Research Laboratory. (INTI-Lab) and the Universidad de Ciencias y Humanidades. (UCH) for their support in this research, the National Fund for. Scientific, Technological and Technological Innovation (FONDECYT), according to the research: ?SAMAYCOV: ?Desarrollo de un dispositivo electr?nico port?til a bajo costo para evaluar riesgo de neumon?a basado en sonido pulmonar anormal en pacientes con sospecha de COVID-19 en zonas vulnerables?. CONVENIO 054-2020-FONDECYT?; for the financing of this research and the Electronics Laboratory of the UCH for assigning us their facilities and being able to carry out the respective tests.
13
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
14
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...
15
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...
16
artículo
Publicado 2023
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Abstract—In recent years, computer science has advanced exponentially, helping significantly to identify and classify text extracted from social networks, specifically Twitter. This work identifies, classifies, and analyzes tweets related to real natural disasters through tweets with the hashtag #Nat-uralDisasters, 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 geo-located tweets for training. Secondly, the data-cleaning process was carried out by applying stemming and lemmatization techniques. Third, exploratory data analysis (EDA) was performed to gain an initial understanding of the data. Fourth, the training and testing process of the BNB, MNB, ...
17
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...
18
artículo
Publicado 2023
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Portable electronic systems allow the analysis and monitoring of continuous time signals, such as human activity, integrating deep learning techniques with cloud computing, causing network traffic and high energy consumption. In addition, the use of algorithms based on neural networks are a very widespread solution in these applications, but they have a high computational cost, not suitable for edge devices. In this context, solutions are created that bring data analysis closer to the edge of the network, so in this paper models adapted to an edge device for the recognition of human activity are evaluated, considering characteristics such as inference time, memory, and precision. Two categories of models based on deep and convolutional neural networks are developed by implementing them in C language and comparing with the TensorFlow Lite platform. The results show that the implementation...
19
artículo
Publicado 2005
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This study has the aim to find a pattern in delayed payments from the information obtained at the moment of requesting credit in a specific creditable product: At the same time, we show a very useful new statistical technique for this area, that is the classification tree (CART) that is applied in situations where we have independent predictor variables of classification or criterion that define the group to which every individual belongs. The paper also tries to find a set of decision rules that allow an explanation of the actual classification and the use of these rules to classify any new individual.
20
artículo
Publicado 2005
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This study has the aim to find a pattern in delayed payments from the information obtained at the moment of requesting credit in a specific creditable product: At the same time, we show a very useful new statistical technique for this area, that is the classification tree (CART) that is applied in situations where we have independent predictor variables of classification or criterion that define the group to which every individual belongs. The paper also tries to find a set of decision rules that allow an explanation of the actual classification and the use of these rules to classify any new individual.
