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1
artículo
Publicado 2022
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“The most devastating disease to humanity is commonly known as elephantiasis. Infection usually acquired in childhood but visible indication like pain, disfiguring occur later in life. Severely affected people will have a permanent disability. Not only had it generated physical challenges it also cost for social, psychological, and economical losses. The impact of the disease is so painful and devastating among young men and women as they live with the lifelong disfiguring condition. As the parasite attacks directly to the lymphatic system of the body whose primary function is to drain all the harmful components and impurities from tissues and cells also make a strong immune system of the body to fight against infection and diseases. Since the parasite attack damages the lymphatic vessels and capillaries. Hence the effect is on the flow of lymph resulting in lymphoedema. 90% of the cas...
2
artículo
Publicado 2022
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“The most devastating disease to humanity is commonly known as elephantiasis. Infection usually acquired in childhood but visible indication like pain, disfiguring occur later in life. Severely affected people will have a permanent disability. Not only had it generated physical challenges it also cost for social, psychological, and economical losses. The impact of the disease is so painful and devastating among young men and women as they live with the lifelong disfiguring condition. As the parasite attacks directly to the lymphatic system of the body whose primary function is to drain all the harmful components and impurities from tissues and cells also make a strong immune system of the body to fight against infection and diseases. Since the parasite attack damages the lymphatic vessels and capillaries. Hence the effect is on the flow of lymph resulting in lymphoedema. 90% of the cas...
3
artículo
Publicado 2022
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In this research satellite image classification for environmental change prediction using image processing and machine learning methods is used. As we know satellite images is one of the important sources of collecting information for all area and region of interest which is suitable for any difficult situation around the world. The satellite image helps in collecting information on areas which is unpredictable and unreachable through digital cameras. In this research work, an advanced study on environmental change perdition has been examined using three classes’ ice land area, cropland area, and forest area. This research help in characterizing the type of satellite image classification for the particular three classes. The following stages have been considered are preprocessing, segmentation, and classification methods using K- Nearest Neighbor classifier. The present investigation r...
4
tesis de grado
Publicado 2024
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Las tecnologías emergentes posibilitan reenfocar las estrategias de comunicación e interacción con los clientes y usuarios con innovaciones importantes, tales como el uso de vídeos de 360 y la realidad virtual inmersiva (VR) en la promoción turística y hotelera. El objetivo de este trabajo es aprovechar estas tecnologías para optimizar la creación de experiencias en 360, a partir de una automatización de procesos enfocada en la clasificación de imágenes que compondrán dicha experiencia. En nuestra propuesta diseñamos una red neuronal convolucional (CNN), cuyas funciones esenciales son el proceso de extracción de características y el proceso de clasificación y salida de imágenes, debido a que serán utilizados para la composición de tours virtuales. La etapa de extracción de características, está compuesta por varias capas ocultas, como la capa de convolución, la fun...
5
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...
6
objeto de conferencia
Publicado 2018
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The authors would like to CONCYTEC (Consejo Nacional de Ciencia, Tecnolog ıa e Innovacion Tecnoloogica ), FONDE- ´ CYT (Fondo Nacional de Desarrollo Cient´ıfico y Tecnologico) ´and ANA (Autoridad Nacional del Agua) for satellite imagesand supporting this project
7
objeto de conferencia
The presence of outliers, noise, corrupt pieces of data and great quantity of samples in a multispectral image, makes the segmentation analysis work tedious. The fuzzy clustering approach, specially, is susceptible to inhomogeneity of characteristics. Furthermore, many algorithms such us FCM, PFCM, FCC, FWCM and modification aim to solve these problems by integrating spacial information. This process is carried through the analysis of the sample's neighborhood. This paper proposes the integration of the sample presence probability into a ”term” like form inside the existent model NFCC. This algorithm presents the basic steps for fuzzy clustering. With a middle variant that integrates the measure between each sample to all the centroids, this replaces the existent term by a new term. This new term integrates the spatial relationship between each sample of the multispectral image into ...
8
objeto de conferencia
Acknowledgments. P. Uceda and H. Yoshida acknowledge the financial support from Project Concytec – The World Bank “Mejoramiento y Ampliación de los Servicios del Sistema Nacional de Ciencia Tecnología e Innovación Tecnológica” 8682-PE through Fondecyt [contract no 006–2018].
9
objeto de conferencia
El texto completo de este trabajo no está disponible en el Repositorio Académico UPN por restricciones de la casa editorial donde ha sido publicado.
10
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.
11
artículo
Forest census allows getting precise data for logging planning and elaboration of the forest management plan. Species identification blunders carry inadequate forest management plans and high risks inside forest concessions. Hence, an identification protocol prevents the exploitation of non-commercial or endangered timber species. The current Peruvian legislation allows the incorporation of non-technical experts, called “materos”, during the identification. Materos use common names given by the folklore and traditions of their communities instead of formal ones, which generally lead to misclassifications. In the real world, logging companies hire materos instead of botanists due to cost/time limitations. Given such a motivation, we explore an end-to-end software solution to automatize the species identification. This paper introduces the Peruvian Amazon Forestry Dataset, which includ...
12
artículo
Publicado 2025
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The emergence of Machine Learning (ML) technologies and their integration into agriculture has demonstrated a significant impact on disease detection in crops, enabling continuous monitoring and enhancing risk planning and management. This study applied image processing techniques such as thresholding, gamma correction, and the Stretched Neighborhood Effect Color to Grayscale (SNECG) method, alongside ML, to develop a predictive model for identifying five types of rice diseases. The ML techniques used included Logistic Regression, Multilayer Perceptron, Support Vector Machines, Decision Trees, and Random Forests (RF). Hyperparameters were optimized and evaluated through 5-fold cross-validation. In the results, the SNECG method successfully converted images to grayscale, capturing essential features of lesions on rice leaves. The ML models developed with these techniques showed evaluation...
13
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...
14
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.
15
artículo
Publicado 2018
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Pneumonia is one of the major causes of child mortality, yet with a timely diagnosis, it is usually curable with antibiotic therapy. In many developing regions, diagnosing pneumonia remains a challenge, due to shortages of medical resources. Lung ultrasound has proved to be a useful tool to detect lung consolidation as evidence of pneumonia. However, diagnosis of pneumonia by ultrasound has limitations: it is operator-dependent, and it needs to be carried out and interpreted by trained personnel. Pattern recognition and image analysis is a potential tool to enable automatic diagnosis of pneumonia consolidation without requiring an expert analyst. This paper presents a method for automatic classification of pneumonia using ultrasound imaging of the lungs and pattern recognition.
16
artículo
Publicado 2025
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Dysplastic nevi are skin lesions that have distinctive clinical features and are considered risk markers for the development of melanoma, the deadliest type of skin cancer. A specific deep learning technique to identify diseases is convolutional neural networks (CNNs) because of their great capacity to extract features and classify objects. Therefore, the research aims to develop a model to diagnose dysplastic nevi using a deep learning network whose classification is based on the pre-trained architecture EfficientNet-B7, which was selected for its high classification accuracy and low computational complexity. As for the results obtained, an accuracy of 78.33% was achieved in the classification model. Also, the degree of similarity between the detection by a dermatology expert and the proposed model reached an accuracy of 79.69%.
17
artículo
Publicado 2020
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ABSTRACT Diabetic retinopathy is a leading cause of vision loss in developed countries. Regular diabetic retinal eye screenings are needed to detect early signs of retinopathy, so that appropriate treatments can be rendered to prevent blindness. Digital imaging is becoming available as a means of screening for diabetic retinopathy. However, with the large number of patients undergoing screenings, medical professionals require a tremendous amount of time and effort in order to analyse and diagnose the fundus photo-graphs, the treatment is done on a digital image, to obtain results as complex as recognizing patterns or as simple as enhancing con-tours, may involve filtering, transformations of gray levels, based on histogram processing, describing, between others. The design of a teaching tool to facilitate image processing, allowing access to each of the steps involved in the system, ensu...
18
artículo
Publicado 2023
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The introduction of artificial intelligence methods and techniques in the construction industry has fostered innovation and constant improvement in the automation of monitoring and control processes at construction sites, although there are areas where more studies still need to be conducted. This paper proposes a method to determine the criticality of cracks in concrete samples. The proposed method uses a previously trained YOLOv4 neural network to identify concrete cracks. Then, the region of interest, determined by the bounding box resulting from the neural network model classification, is extracted. Finally, the extracted image is converted to negative grayscale to quantify the number of white pixels above a certain threshold, automatically allowing the system to characterize the fracture’s extent and criticality. The classification module reached a veracity between 98.36% and 99.7...
19
artículo
Tomographic evaluation of lower second molars with type C configuration in an imaging center in Lima
Publicado 2024
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The lower second molar mostly has a C configuration and it is important to know the internal anatomy and classification to have a better clinical approach, so the objective was to evaluate the prevalence and types of C-shaped canals in permanent mandibular second molars by using cone beam computed tomography (CBCT).. Materials and Methods: 150 CTHC images of mandibular second molars were used. The following was recorded: sex, presence of C-shaped canal, location, morphology and type of C configuration according to Fan’s classification, which is divided into C1, C2, C3 (c and d), C4 and C5. The chi- square test was used to analyze the differences between groups (p˂0.05, considered a statistically significant difference). Results: Of the 300 teeth evaluated, 12.6% had C-shaped canals. There were no statistically significant differences between C-shaped canals and sex. This configuration...
20
artículo
Publicado 2021
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ABSTRACT Althoughknowledgeofthemicrostructureoffoodofvegetaloriginhelpsustounderstand the behavior of food materials, the variability in the microstructural elements complicates this analysis. In this regard, the construction of learning models that represent the actual microstructures of the tissue is important to extract relevant information and advance in the comprehension of such behavior. Consequently, the objective of this research is to compare two machine learning techniques—Convolutional Neural Networks (CNN) and Radial Basis Neural Networks (RBNN)— when used to enhance its microstructural analysis. Two main contributions can be highlighted from this research. First, a method is proposed to automatically analyze the microstructural elements of vegetal tissue; and second, a comparison was conducted to select a classifier to discriminate between tissue structures. For the com...
