Initial Optimal Parameters of Artificial Neural Network and Support Vector Regression

This paper presents architecture of backpropagation Artificial Neural Network (ANN) and Support Vector Regression (SVR) models in supervised learning process for cement demand dataset. This study aims to identify the effectiveness of each parameter of mean square error (MSE) indicators for time series dataset. The study varies different random sample in each demand parameter in the network of ANN and support vector function as well. The variations of percent datasets from activation function, learning rate of sigmoid and purelin, hidden layer, neurons, and training function should be applied for ANN. Furthermore, SVR is varied in kernel function, lost function and insensitivity to obtain the best result from its simulation. The best results of this study for ANN activation function is Sigmoid. The amount of data input is 100% or 96 of data, 150 learning rates, one hidden layer, trinlm training function, 15 neurons and 3 total layers. The best results for SVR are six variables that run in optimal condition, kernel function is linear, loss function is ౬-insensitive, and insensitivity was 1. The better results for both methods are six variables. The contribution of this study is to obtain the optimal parameters for specific variables of ANN and SVR

Clustering Techniques Selection for a Hybrid Regression Model: A Case Study Based on a Solar Thermal System

[EN] This work addresses the performance comparison between four clustering techniques with the objective of achieving strong hybrid models in supervised learning tasks. A real dataset from a bio-climatic house named Sotavento placed on experimental wind farm and located in Xermade (Lugo) in Galicia (Spain) has been collected. Authors have chosen the thermal solar generation system in order to study how works applying several cluster methods followed by a regression technique to predict the output temperature of the system. With the objective of defining the quality of each clustering method two possible solutions have been implemented. The first one is based on three unsupervised learning metrics (Silhouette, Calinski-Harabasz and Davies-Bouldin) while the second one, employs the most common error measurements for a regression algorithm such as Multi Layer Perceptron.S

Polyp Detection using Convolutional Neural Networks : An Exploratory Study

This project presents an exploratory study on the potential of using convolutional neural networks (CNNs) to aid in polyp detection tasks. Early polyp detection is crucial for patient survival and it could be very useful for clinicians to have a tool that could aid clinicians in real time to identify polyps in the image. The main challenge we faced was to work in a domain where publicly available datasets are scarce and, moreover, they are not balanced, we aim to assess the potential of using pre-trained networks and fine-tuning to take profit of the capa-bilities of CNNs. We propose two different strategies: classification using the whole image and patch-based classification. As the databases that we use to validate our methodology are clearly not balanced, we also explore the use of early image augmentation techniques to increase dataset size. Preliminary results indicate the potential of our proposal and assess quantitatively the impact of each of the modifications we propose to the baseline. More precisely, the use of patch-based classification outperforms image-based one, and there is a proven impact of supressing image borders and performing data augmentation. Finally, our results shows the importance of having balanced datasets for both training and testing of the network.Este proyecto presenta un estudio exploratorio sobre el potencial de usar redes neuronales convolutivas (CNNs) para ayudar en tareas de detección de pólipos. La detección temprana de pólipos es crucial para la supervivencia del paciente, y podría ser muy útil para los médicos disponer de una herramienta que les ayude a identificar pólipos en tiempo real en las imágenes. El principal reto al que nos enfrentamos es trabajar en un dominio en el que los sets de datos disponibles públicamente son escasos y no están balanceados. Nuestro objetivo es comprobar el potencial de usar redes preentrenadas y fine-tuning para aprovechar las capacidades de las CNNs. Proponemos dos estrategias diferentes: clasificación usando las imágenes enteras y clasificación basada en trozos de imágenes o patches. Como las bases de datos que usamos para validar nuestra metodología no están balanceadas, también exploraremos el uso de técnicas de early data augmentation para incrementar el tamaño del dataset. Resultados preliminares indican el potencial de nuestra propuesta y comprueban cuantitativa-ente el impacto de cada una de las modificaciones que proponemos para el baseline. Más específicamente, el uso de clasificación basada en patches supera a la basada en imágenes, y hay un impacto demostrado en la eliminación del borde de las imágenes y en la aplicación de data augmentation. Finalmente, nuestros resultados muestran la importancia de tener datasets balanceados tanto en el entrenamiento como en el test de la red.Aquest treball presenta un estudi exploratori sobre el potencial d'usar xarxes neuronals convolutives (CNNs) per ajudar en tasques de detecció de pòlips. La detecció primerenca de pòlips és crucial per a la supervivencia del pacient, i podría ser molt útil per als mèdics disposar d'una eina que els ajudi a identificar pòlips en temps real a les imatges. El principal repte al que ens enfrentem és treballar en un domini en el que els sets de dades disponibles públicament són molt escasos i no estan balançejats . El nostre objectiu és comprobar el potencial d'utilitzar xarxes preentrenades i fine-tuning per tal d'aprofitar les capacitats de les CNNs. Proposem dues estratègies diferents: classificació usant les imatges senceres i classificació basada en trossos d'imatges o patches. Com les bases de dades que fem servir per validar la nostra metodologia no estan balancejades, també explorarem l'ús de tècniques de early data Augmentation per incrementar la mida del dataset. Resultats preliminars indiquen el potencial de la nostra proposta i comproven quantitativament l'impacte de cadascuna de les modificacions que proposem per al baseline. Més específicament, l'ús de classificació basada en patches supera la basada en imatges, i hi ha un impacte demostrat en l'eliminació de la vora de les imatges i en l'aplicació de data Augmentation. Finalment, els nostres resultats mostren la importància de tenir datasets balancejats tant en l'entrenament com en el test de la xarxa

Classification of glomerular hypercellularity using convolutional features and support vector machine

Glomeruli are histological structures of the kidney cortex formed by interwoven blood capillaries, and are responsible for blood filtration. Glomerular lesions impair kidney filtration capability, leading to protein loss and metabolic waste retention. An example of lesion is the glomerular hypercellularity, which is characterized by an increase in the number of cell nuclei in different areas of the glomeruli. Glomerular hypercellularity is a frequent lesion present in different kidney diseases. Automatic detection of glomerular hypercellularity would accelerate the screening of scanned histological slides for the lesion, enhancing clinical diagnosis. Having this in mind, we propose a new approach for classification of hypercellularity in human kidney images. Our proposed method introduces a novel architecture of a convolutional neural network (CNN) along with a support vector machine, achieving near perfect average results with the FIOCRUZ data set in a binary classification (lesion or normal). Our deep-based classifier outperformed the state-of-the-art results on the same data set. Additionally, classification of hypercellularity sub-lesions was also performed, considering mesangial, endocapilar and both lesions; in this multi-classification task, our proposed method just failed in 4\% of the cases. To the best of our knowledge, this is the first study on deep learning over a data set of glomerular hypercellularity images of human kidney.Comment: 26 page