Search results for: M. Cabrerizo

Authors: Marcos Bosques-Perez, Walter Izquierdo, Harold Martin, Liangdon Deng, Josue Rodriguez, Thony Yan, Mercedes Cabrerizo, Armando Barreto, Naphtali Rishe, Malek Adjouadi

Abstract:

Aerial imagery is a powerful tool when it comes to analyzing temporal changes in ecosystems and extracting valuable information from the observed scene. It allows us to identify and assess various elements such as objects, structures, textures, waterways, and shadows. To extract meaningful information, multispectral cameras capture data across different wavelength bands of the electromagnetic spectrum. In this study, the collected multispectral aerial images were subjected to principal component analysis (PCA) to identify independent and uncorrelated components or features that extend beyond the visible spectrum captured in standard RGB images. The results demonstrate that these principal components contain unique characteristics specific to certain wavebands, enabling effective object identification and image segmentation.

Keywords: big data, image processing, multispectral, principal component analysis

Procedia PDF Downloads 126

Authors: M. Aghili, S. Tabarestani, C. Freytes, M. Shojaie, M. Cabrerizo, A. Barreto, N. Rishe, R. E. Curiel, D. Loewenstein, R. Duara, M. Adjouadi

Abstract:

A major challenge in medical studies, especially those that are longitudinal, is the problem of missing measurements which hinders the effective application of many machine learning algorithms. Furthermore, recent Alzheimer's Disease studies have focused on the delineation of Early Mild Cognitive Impairment (EMCI) and Late Mild Cognitive Impairment (LMCI) from cognitively normal controls (CN) which is essential for developing effective and early treatment methods. To address the aforementioned challenges, this paper explores the potential of using the eXtreme Gradient Boosting (XGBoost) algorithm in handling missing values in multiclass classification. We seek a generalized classification scheme where all prodromal stages of the disease are considered simultaneously in the classification and decision-making processes. Given the large number of subjects (1631) included in this study and in the presence of almost 28% missing values, we investigated the performance of XGBoost on the classification of the four classes of AD, NC, EMCI, and LMCI. Using 10-fold cross validation technique, XGBoost is shown to outperform other state-of-the-art classification algorithms by 3% in terms of accuracy and F-score. Our model achieved an accuracy of 80.52%, a precision of 80.62% and recall of 80.51%, supporting the more natural and promising multiclass classification.

Keywords: eXtreme gradient boosting, missing data, Alzheimer disease, early mild cognitive impairment, late mild cognitive impair, multiclass classification, ADNI, support vector machine, random forest

Procedia PDF Downloads 156

Authors: Francisco Javier Montes Ruiz-Cabello, Guillermo Guerrero-Vacas, Sara Bermudez-Romero, Miguel Cabrerizo Vilchez, Miguel Angel Rodriguez-Valverde

Abstract:

Galvanized steel is one of the widespread metallic materials used in industry. It consists on a iron-based alloy (steel) coated with a layer of zinc with variable thickness. The zinc is aimed to prevent the inner steel from corrosion and staining. Its production is cheaper than the stainless steel and this is the reason why it is employed in the construction of materials with large dimensions in aeronautics, urban/ industrial edification or ski-resorts. In all these applications, turning the natural hydrophilicity of the metal surface into superhydrophobicity is particularly interesting and would open a wide variety of additional functionalities. However, producing a superhydrophobic surface on galvanized steel may be a very difficult task. Superhydrophobic surfaces are characterized by a specific surface texture which is reached either by coating the surface with a material that incorporates such texture, or by conducting several roughening methods. Since galvanized steel is already a coated material, the incorporation of a second coating may be undesired. On the other hand, the methods that are recurrently used to incorporate the surface texture leading to superhydrophobicity in metals are aggressive and may damage their surface. In this work, we used a novel strategy which goal is to produce superhydrophobic galvanized steel by a two-step non-aggressive process. The first process is aimed to create a hierarchical structure by incorporating zinc nanoparticles sintered on the surface at a temperature slightly lower than the zinc’s melting point. The second one is a hydrophobization by a thick fluoropolymer layer deposition. The wettability of the samples is characterized in terms of tilting plate and bouncing drop experiments, while the roughness is analyzed by confocal microscopy. The durability of the produced surfaces was also explored.

Keywords: galvanaized steel, superhydrophobic surfaces, sintering nanoparticles, zinc nanopowder

Procedia PDF Downloads 118