Tópicos Sugeridos dentro de su búsqueda.
Tópicos Sugeridos dentro de su búsqueda.
1
artículo
Publicado 2023
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In the field of education and technology companies, online judges play an important role in the development of programming skills because on these platforms students must solve challenges using specific programming languages. However, the sheer number of coding challenges available can be overwhelming for students, leading to frustration and loss of interest. To resolve this situation, recommender systems can be an effective solution. However, programming judges have not delved far enough into this area. Therefore, this research focused on evaluating six artificial intelligence techniques through a cloud-based architecture for the prediction of the level of difficulty from the statements of the problems to be coupled to a recommendation system. To validate the experiments, a real CodeChef programming judge was used and the experiments were evaluated through statistical tests. The results...
2
artículo
Currently, population growth in cities results in an increase in urban vehicle traffic. That is why it is necessary to improve the quality of life of citizens based on the improvement of transport control services. To solve this problem, there are solutions, related to the improvement of the road infrastructure by increasing the roads or paths. One of the solutions is using traffic lights that allow traffic regulation automatically with machine learning techniques. That is why the implementation of an intelligent traffic light system with automatic learning by reinforcement is proposed to reduce vehicular and pedestrian traffic. As a result, the use of the YOLOv4 tool allowed us to adequately count cars and people, differentiating them based on size and other characteristics. On the other hand, the position of the camera and its resolution is a key point for counting vehicles by detectin...
3
objeto de conferencia
The present work was supported by grant 234-2015-FONDECYT (Master Program) from Cienciactiva of the National Council for Science, Technology and Technological Innovation (CONCYTEC-PERU) and the Office Research of Universidad Nacional de Ingeniería (VRI - UNI).
4
artículo
Publicado 2024
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Denoising diffusion probabilistic models (DDPMs) have demonstrated significant potential in addressing complex image processing challenges. This paper explores the application of DDPMs in three different areas: reconstruction of remote sensing imagery affected by cloud cover, reconstruction of facial images with occluded areas, and segmentation of bodies of water from remote sensing imagery. Inpainting involves filling in missing regions in images, while DDPMs act as data generators capable of synthesizing information that alings coherently with the context of the original data. Inspired by the inpainting technique, the RePaint approach was adapted and applied to reconstruction tasks. The WaterSegDiff approach, which uses a diffusion model as a backbone, was employed for the segmentation task. To illustrate the model’s behavior and provide examples of the tasks, experiments were carrie...
5
artículo
Publicado 2024
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Denoising diffusion probabilistic models (DDPMs) have demonstrated significant potential in addressing complex image processing challenges. This paper explores the application of DDPMs in three different areas: reconstruction of remote sensing imagery affected by cloud cover, reconstruction of facial images with occluded areas, and segmentation of bodies of water from remote sensing imagery. Inpainting involves filling in missing regions in images, while DDPMs act as data generators capable of synthesizing information that alings coherently with the context of the original data. Inspired by the inpainting technique, the RePaint approach was adapted and applied to reconstruction tasks. The WaterSegDiff approach, which uses a diffusion model as a backbone, was employed for the segmentation task. To illustrate the model’s behavior and provide examples of the tasks, experiments were carrie...
6
tesis de grado
Publicado 2019
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En este artículo proponemos la creación de un chatbot que sea capaz de poder realizar una conversación, basándose este Bot en un diccionario de aprendizaje y este sea capaz de aprender con cada entrenamiento. Este trabajo tuvo como objetivo crear un bot que aprenda a tener una conversación con un usuario utilizando tecnologías de lenguaje de proceso natural, el lenguaje de las maquinas. Tomando en consideración que este bot pueda ser implementado en cualquier tipo de empresa ayudaría a que esta pueda reducir costos operativos al interactuar con los clientes, y pueda responder de manera adecuada a alguna pregunta que realicemos, se tomó en cuenta que para el aprendizaje de este bot se necesitó de texto en este caso guardado como diccionario del cual el bot tomara referencia para su aprendizaje cabe mencionar que el texto en el cual se trabajó está escrito en inglés y el bot e...
7
artículo
Potato is economically important in Peru, which is the first potato producer in Latin America, however, the quality of native potatoes need to be improved to increment their consumption. An automatic classification process to detect potato defects is important within the entire production chain to guarantee the high quality of the product. In the present research, a Convolutional Neural Network is used to detect defects in the Huayro potato surface. This is an Andean potato originally from Peru and is special because it has very marked eyes that can complicate the differentiation from pests that leaves holes in the potato. An adaptive learning was proposed in the work, where the principal idea is to evaluate continuously the learning of the neural network to adapt the training process (in this case the training data) to increment the learning performance. The detection results were aroun...
8
tesis de maestría
Publicado 2018
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I would like to thank in a special way the National Council of Science, Technology and Technological Innovation (CONCYTEC) and the National Fund for Scientific, Technological development and Technological Innovation (FONDECYT-CIENCIACTIVA), which through the Management Agreement N 234-2015-FONDECYT, they have allowed the grant and financing of my studies of Master in Computer Science at the Universidad Cat´olica San Pablo (UCSP).
9
tesis de maestría
Studies indicate that air pollutant concentrations affect human health. Especially, Fine Particulate Matter (PM2.5) is the most dangerous pollutant because this is related to cardiovascular and respiratory diseases, among others. Therefore, governments must monitor and control pollutant concentrations. To this end, many of them have implemented Air quality monitoring (AQM) networks. However, AQM stations are usually spatially sparse due to their high costs in implementation and maintenance, leaving large áreas without a measure of pollution. Numerical models based on the simulation of diffusion and reaction process of air pollutants have been proposed to infer their spatial distribution. However, these models often require an extensive inventory of data and variables, as well as high-end computing hardware. In this research, we propose two deep learning models. The first is a generative...
10
artículo
Publicado 2024
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Este artículo presenta un análisis bibliométrico de la producción científica sobre aprendizaje profundo y big data a nivel mundial. Usando la R paquete y la biblioshiny asociada, el estudio analizó 456 artículos de investigación publicados en Scopus entre 2003 y 2023. El estudio análisis de rendimiento aplicado, análisis de palabras clave y análisis temático. China es el país con mayor producción (536 publicaciones) seguido de India (260 publicaciones), asimismo, la mayoría de estas colaboraciones se dan desde China hasta Estados Unidos, Hong Kong, Suecia, Australia, Pakistán, Arabia Saudita y otros países. El rápido crecimiento de las palabras clave Aprendizaje profundo, big data, sistemas de aprendizaje y datos analítica; Demostró el interés de investigadores, profesionales de la industria, gobiernos, inversores y todos los demás actores clave en la necesidad. par...
11
artículo
Publicado 2020
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Los valores literarios forman la mente y el corazón de los estudiantes, impactando a unos más que otros, según nuestra sensibilidad y el hábito en la lectura de los libros. De esta forma aprendemos el sentir del alma de los hombres, mediante la literatura: “La lectura, aunque sea fragmentaria, de Homero, Shakespeare, Balzac, Dostoievski y Martí, puede enseñarnos, más sobre la condición humana que el resto de nuestro saber”. (Espinoza, 1992). En cambio, para Max Scheler los valores son aprendidos en una manera: emotiva, psicológica, lógica y del pensamiento. Siendo una de las características de los valores la jerarquía (éticos y estéticos), la cual gradúa los valores concebidos en forma a priori: “Valores sensibles: agradable- desagradable, gozar-sufrir. Valores vitales: salud, vejez, muerte. Valores espirituales: estéticos, jurídicos, gnoseológicos, etc. Valores r...
12
objeto de conferencia
The diagnostic process of respiratory diseases requires experience and skills to assess the different pathologies that patients may develop. Unfortunately, the lack of qualified radiologists is a global problem that limits respiratory diseases diagnosis. Therefore, it will be useful to have a tool that minimizes errors and workload, improves efficiency, and speeds up the diagnostic process in order to provide a better healthcare service to the community. This research proposes a methodology to detect pathologies by using deep learning architectures. The present proposal is divided into three types of experiments. The first one evaluates the performance of feature descriptors such as SIFT, SURF, and ORB in medical images with machine learning models as an introduction to the last experiment. The second one evaluates the performance of deep learning architectures such as ResNet50, Alexnet,...
13
tesis de maestría
Publicado 2018
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Nowadays, Question Answering is being addressed from a reading comprehension approach. Usually, Machine Comprehension models are poweredby Deep Learning algorithms. Most related work faces the challenge by improving the Interaction Encoder, proposing several architectures strongly based on attention. In Contrast, few related work has focused on improving the Context Encoder. Thus, our work has explored in depth the Context Encoder. We propose a gating mechanism that controls the ow of information, from the Context Encoder towards Interaction Encoder. This gating mechanism is based on additional information computed previously. Our experiments has shown that our proposed model improved the performance of a competitive baseline model. Our single model reached 78.36% on F1 score and 69.1% on exact match metric, on the Stanford Question Answering benchmark.
14
tesis de maestría
In this study, we address a fundamental and still relatively less explored aspect in the field of neural networks for image dehazing: the unsupervised dehazing of an image. By conceiving a hazy image as the superposition of several “simpler“ layers, such as a haze-free image layer, a transmission map layer, and an atmospheric light layer, inspired by the atmospheric scattering model, we propose an approach based on the concept of layer disentangling. Our method, called XYZ, represents a substantial improvement in image quality metrics, such as SSIM and PSNR as well as BRISQUE, PIQE and NIQE. This advancement is achieved through the strategic combination of the XHOT, YOLY and ZID methods, capitalizing on the individual strengths of each. A distinctive and valuable aspect of the XYZ approach is its unsupervised nature, which implies that it does not rely on data sets containing pairs o...
15
artículo
Publicado 2022
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The Internet of Things (IoT) connects schemes, programs, data management, and operations, and as they continuously assist in the corporation, they may be a fresh entryway for cyber-attacks. Presently, illegal downloading and virus attacks pose significant threats to IoT security. These risks may acquire confidential material, causing reputational and financial harm. In this paper hybrid optimization mechanism and deep learning,a frame is used to detect the attack prevention in IoT. To develop a cybersecurity warning system in a huge data set, the cybersecurity warning systems index system is first constructed, then the index factors are picked and measured, and finally, the situation evaluation is done.Numerous bio-inspired techniques were used to enhance the productivity of an IDS by lowering the data dimensionality and deleting unnecessary and noisy input. The Grey Wolf Optimization al...
16
tesis de maestría
Publicado 2023
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Multibiometric systems rely on the idea of combining multiple biometric methods into one single process that leads to a more reliable and accurate system. The combination of two different biometric traits such as face and ear results in an advantageous and complementary process when using 2D images taken under uncontrolled conditions. In this work, we investigate several approaches to fuse information from the face and ear images to recognize people in a more accurate manner than using each method separately. We leverage the research maturity level of the face recognition field to build, first a truly multimodal database of ear and face images called VGGFace-Ear dataset, second a model that can describe ear images with high generalization called VGGEar model, and finally explore fusion strategies at two different levels in a common recognition pipeline, feature and score levels. Experime...
17
artículo
Publicado 2014
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The study of Neuroscience and The seven Learnings introduce Neuroscience as the new paradigm that enables to analyze and explain the intelligent human behavior. The discoveries found in this field have been shown not only in the theory but in the pedagogical practice as well. In the first case, one of the most recent theories about intelligent behavior has been conceived from the perspective of neuroscience (science that studies HUMAN BRAIN). (Beauport and Diaz, 1994) This study has given more relevance to the relation between the function of the brain and human behavior as well as the conditions that lead to an efficient teaching – learning process. In this context, we introduce The seven Learnings, described by Edgar Morin that state central problems which have been ignored and that are necessary in order to reach a successful teachinglearning process in this century. These seven Lea...
18
artículo
Publicado 2022
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Deep learning methods can be applied to generate predictive models. We worked with the gross domestic product (GDP) of six Latin American countries: Argentina, Brazil, Chile, Colombia, Mexico, and Peru, using annual and quarterly macroeconomic indicators from the World Bank and the Economic Commission for Latin America and the Caribbean (ECLAC), respectively. For the pre-processing of the data, we decomposed the quarterly series into trend, seasonality, and residual and used them as additional characteristics to provide more information to the models. In addition, outliers resulting from the impact of the COVID-19 pandemic on the world economy were replaced. Multilayer perceptron, convolutional neural networks, LSTM, GRU, and SeqToSeq models were built for each country and their series’ frequency, then evaluated by continuous cross-validation and MAE, RMSE, and MAPE metrics. The optima...
19
artículo
Publicado 2022
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Deep learning methods can be applied to generate predictive models. We worked with the gross domestic product (GDP) of six Latin American countries: Argentina, Brazil, Chile, Colombia, Mexico, and Peru, using annual and quarterly macroeconomic indicators from the World Bank and the Economic Commission for Latin America and the Caribbean (ECLAC), respectively. For the pre-processing of the data, we decomposed the quarterly series into trend, seasonality, and residual and used them as additional characteristics to provide more information to the models. In addition, outliers resulting from the impact of the COVID-19 pandemic on the world economy were replaced. Multilayer perceptron, convolutional neural networks, LSTM, GRU, and SeqToSeq models were built for each country and their series’ frequency, then evaluated by continuous cross-validation and MAE, RMSE, and MAPE metrics. The optima...
20
tesis de grado
Publicado 2025
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La neumonía y la tuberculosis (TB) son las enfermedades respiratorias más prevalentes en niños de uno a cinco años en Perú. Un diagnóstico rápido y eficaz es esencial para hospitales, clínicas e instituciones de salud, ya que mejora los resultados del paciente y optimiza la eficiencia operativa. El deep learning (DL) aplicado al análisis de imágenes médicas ha demostrado ser valioso para el diagnóstico, ayudando a los médicos a incrementar la exactitud y reducir el error humano. Este estudio desarrolló un modelo basado en la arquitectura ResNet para detectar neumonía y TB en radiografías de tórax pediátricas, utilizando una base de datos de 5856 imágenes del Centro Médico de Mujeres y Niños de Guangzhou (4100 de entrenamiento, 878 de validación y 878 de prueba) y del Instituto Nacional de Alergias y Enfermedades Infecciosas (988 de entrenamiento, 211 de validación ...
