Knowledge about the spatiotemporal tree growth variability and its associations with climate provides key insights into forest dynamics under future scenarios of climate change. We synthesized 17 tree-ring width chronologies from four tree species at the high-elevation sites in the southeast Tibetan Plateau (SETP) to study the regional tree growth variability and climate-growth relationships. D...

Diabetic Retinopathy (DR) is a vascular disorder where the retina is damaged because fluid leaks from blood vessels into the retina. One of the primary lesions of diabetic retinopathy is exudates, which appear on retinal images as bright patches with various borders. In this work an image processing framework is presented to automatically detect and classify the presence of hard exudates in the...

Most Gene Regulatory Network (GRN) studies ignore the impact of the noisy nature of gene expression data despite its significant influence upon inferred results. This paper presents an innovative Collateral-Fuzzy Gene Regulatory Network Reconstruction (CF-GeNe) framework for Gene Regulatory Network (GRN) inference. The approach uses the Collateral Missing Value Estimation (CMVE) algorithm as it...

Problem statement: Segmentation plays an important role in medical imaging. Segmentation of an image is the division or separation of the image into dissimilar regions of similar attribute. In this study we proposed a methodology that integrates clustering algorithm and marker controlled watershed segmentation algorithm for medical image segmentation. The use of the conservative watershed algor...

The classification of the electrocardiogram registration into different pathologies diseases devises is a complex pattern recognition task. The traditional methods of diagnosis and classification present some inconveniences; seen that the precision of credit note one diagnosis exact depends on the cardiologist experience and the rate concentration. Due to the high mortality rate of heart diseas...

Data mining is a collection of techniques used to extract useful information from large data bases. Data clustering is a popular data mining technique. It is the task of grouping a set of objects into classes such that similar objects are placed in the same cluster while dissimilar objects are in separate clusters. Fuzzy cmeans (FCM) is one of the most popular clustering algorithms. However, it...

In this section, the performance of the proposed roughfuzzy clustering algorithm1 is compared with that of hard c-means (HCM)2, fuzzy c-means (FCM)3, rough-fuzzy cmeans (RFCM)4, cluster identification via connectivity kernels (CLICK)5, and self organizing map (SOM)6 with respect to gene ontology. The performance of the normalized rangenormalized city block distance (NRNCBD) over Pearson distanc...

Fuzzy clustering has been used widely in education, statistics, engineering, communication...etc. The fuzzy partition clustering algorithms are most based on Euclidean distance function, which can only be used to detect spherical structural clusters. Extending Euclidean distance to Mahalanobis distance, GustafsonKessel (GK) clustering algorithm and Gath-Geva (GG) clustering algorithm were devel...

Remotely sensed data are an attractive source of land cover data over a wide range of spatial and temporal scales. The realisation of the full potential of remote sensing as a source of land cover data is, however, restricted by numerous factors. One commonly encountered problem is the presence of mixed pixels, which cannot be appropriately accommodated in conventional image classi®cation techn...

Computational intelligence provides a variety of means that can perform complex image processing in a rather effective way. Among them, self-learning systems, especially selflearning artificial neural networks (self-organizing maps, ART neural networks, ‘BrainState-in-a-Box’ neuromodels, etc.) (Haykin, 1999) and fuzzy clustering systems (fuzzy cmeans, algorithms of Gustafson-Kessel, Yager-Filev...