scholarly journals High-Silica Lava Morphology at Ocean Spreading Ridges: Machine-Learning Seafloor Classification at Alarcon Rise

Geosciences ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. 245 ◽  
Author(s):  
Christina H. Maschmeyer ◽  
Scott M. White ◽  
Brian M. Dreyer ◽  
David A. Clague
Keyword(s):  
Fuzzy Inference ◽  
Full Range ◽  
Automated Classification ◽  
Submarine Lava ◽  
Seafloor Classification ◽  
High Silica ◽  
Lava Morphology ◽  
Ocean Ridge ◽  
The Relationship

The oceanic crust consists mostly of basalt, but more evolved compositions may be far more common than previously thought. To aid in distinguishing rhyolite from basaltic lava and help guide sampling and understand spatial distribution, we constructed a classifier using neural networks and fuzzy inference to recognize rhyolite from its lava morphology in sonar data. The Alarcon Rise is ideal to study the relationship between lava flow morphology and composition, because it exhibits a full range of lava compositions in a well-mapped ocean ridge segment. This study shows that the most dramatic geomorphic threshold in submarine lava separates rhyolitic lava from lower-silica compositions. Extremely viscous rhyolite erupts as jagged lobes and lava branches in submarine environments. An automated classification of sonar data is a useful first-order tool to differentiate submarine rhyolite flows from widespread basalts, yielding insights into eruption, emplacement, and architecture of the ocean crust.

scholarly journals COMPARISON OF AUTOMATIC AND SEMI-AUTOMATIC METHODS FOR CLASSIFICATION OF SIDE SCAN SONAR IMAGERY

2019 ◽  
Vol 37 (1) ◽  
pp. 1
Author(s):  
Tiago Rafael de Barros Pereira ◽  
Helenice Vital ◽  
André Giskard Aquino da Silva ◽  
Cecília Alves de Oliveira
Keyword(s):  
Maximum Likelihood ◽  
Remote Sensing Data ◽  
Automated Classification ◽  
Side Scan Sonar ◽  
Swath Bathymetry ◽  
Seafloor Classification ◽  
Automatic Methods ◽  
Acoustic Remote Sensing ◽  
Occurrence Matrix

ABSTRACT. The main scope of this paper is the analysis of seafloor classification using acoustic remote sensing data. These data were acquired in a hydroacoustic survey of bathymetry and sonography using an interferometric swath bathymetry system. The study area is a sector of the internal northeast Brazilian shelf adjacent to the Ponta Negra beach - Natal (RN). The objective of the work is to identify and draw the different textural patterns, which characterize the seafloor of the study area. In addition, two approaches for textural classification of sonograms were compared and evaluated, which were: Automatic Gray Level Co-occurrence Matrix (GLCM) classification available in SonarWiz software; and the semi-automatic Maximum Likelihood, available in ArcGIS software. The comparison tested the capacity for identifying and drawing the textural patterns distribution. The automated classification identified 4 patterns while on the semi-automated 5 patterns were identified. It was made the correlation between the textural patterns found in each classification, besides the correlation between textural patterns and the levels of intensity of reflectance presents on the sonogram.Keywords: sonography, textural classification, textural patterns, hydroacoustic. RESUMO. Este trabalho foi realizado a partir da análise de dados geofísicos adquiridos em levantamento hidroacústico de batimetria e sonografia utilizando um sonar interferométrico EdgeTech 4600. A área de estudo é uma porção da plataforma interna do nordeste brasileiro adjacente Natal (RN). O objetivo deste trabalho é identificar e delimitar os diferentes padrões texturais que caracterizam o substrato marinho da área de estudo. Adicionalmente, são avaliadas e comparadas duas abordagens distintas de classificação textural de sonogramas, sendo elas: a classificação automática GLCM disponível no software SonarWiz, e a classificação semi-automática máxima verossimilhança (Maximum Likelihood) disponível no software ArcGIS. A comparação foi realizada com base na capacidade de identificação e delimitação da distribuição dos padrões texturais. A utilização da classificação automática identificou 4 padrões, enquanto que, na classificação semi-automática 6 padrões foram identificados. Foi feita a correlação entre os padrões texturais encontrados em cada classificação, além da correlação entre os padrões texturais e os níveis de intensidade de reflectância presente no sonograma.Palavras-chave: sonografia, classificação textural, padrões de textura, hidroacústica.

scholarly journals Automated classification of cells in electron microscopic images of lymphoreticular tissue.

1977 ◽  
Vol 25 (7) ◽  
pp. 655-661 ◽  
Author(s):  
M Pfoch ◽  
W Kade
Keyword(s):  
Automated Classification ◽  
Cell Nuclei ◽  
Electron Microscopic ◽  
Two Dimensional Image ◽  
Tissue Sections ◽  
Lymphoreticular Tissue ◽  
The Relationship ◽  
Geometric Measurement ◽  
Different Levels

In classifying cells in tissue sections, one must consider the fact that only random projections of cells and of subcellular structures are available in the two-dimensional image. Therefore, measurement values that solely reflect the size of such projections cannot be taken on their own as a basis for cell classification. More complex morphologic features such as shape, texture and distribution pattern of cells and their components should be analyzed. Using cell nuclei as an example, the relationship between such features and geometric measurement values is evaluated. It can be shown that a well balanced combination of geometric parameters provides a suitable basis for reproducing the visual preclassification of lymphocytes in tissue sections. Moreover, using a cluster algorithm, which allows different levels of similarity to be defined, a hierarchical sequence of subclusters turns out, indicating the heterogeneity of the visually determined cell classes. Whether or not these subclusters can be correlated to functionally defined subpopulations of lymphocytes remains a matter for further investigation.

K-MEANS CLUSTERING ALGORITHM BASED CLASSIFICATION OF SOIL FERTILITY IN NORTH WEST NIGERIA

2020 ◽  
Vol 4 (2) ◽  
pp. 780-787
Author(s):  
Ibrahim Hassan Hayatu ◽  
Abdullahi Mohammed ◽  
Barroon Ahmad Isma’eel ◽  
Sahabi Yusuf Ali
Keyword(s):  
Soil Fertility ◽  
Crop Yield ◽  
Clustering Algorithm ◽  
Soil Samples ◽  
North West ◽  
R Programming ◽  
Available Information ◽  
Northwest Region ◽  
The Relationship

Soil fertility determines a plant's development process that guarantees food sufficiency and the security of lives and properties through bumper harvests. The fertility of soil varies according to regions, thereby determining the type of crops to be planted. However, there is no repository or any source of information about the fertility of the soil in any region in Nigeria especially the Northwest of the country. The only available information is soil samples with their attributes which gives little or no information to the average farmer. This has affected crop yield in all the regions, more particularly the Northwest region, thus resulting in lower food production.  Therefore, this study is aimed at classifying soil data based on their fertility in the Northwest region of Nigeria using R programming. Data were obtained from the department of soil science from Ahmadu Bello University, Zaria. The data contain 400 soil samples containing 13 attributes. The relationship between soil attributes was observed based on the data. K-means clustering algorithm was employed in analyzing soil fertility clusters. Four clusters were identified with cluster 1 having the highest fertility, followed by 2 and the fertility decreases with an increasing number of clusters. The identification of the most fertile clusters will guide farmers on where best to concentrate on when planting their crops in order to improve productivity and crop yield.

DONG SON BRONZE DRUM - THE WORK OF THE LAC VIET PEOPLE

2018 ◽  
Author(s):  
Nguyen Van Hao
Keyword(s):  
Classification Criteria ◽  
Way Of Life ◽  
Tribal Identity ◽  
The Relationship

Bronze drums are widely distributed, broader than the range of a nation. Therefore, the identification of each kind of drum is a basic subject, should be concerned. In determining the tribal identity of the drum, the classification of drum is the key stage, the relationship between the objective of the classification and classification criteria is the relation as shape and shadow, if there is no right criteria then the result of division will be difficult to reach the desired goal. Likewise, the criterion of the pattern on the bronze drum brought to the affirmation is the Dong Son bronze drum of the Lac Viet people. And the parallel is the affirmation of the culture, way of life, residence of the nation created the drum.

Optimization Fuzzy Inference System based Particle Swarm Optimization for Onset Prediction of the Rainy Season

2020 ◽  
pp. 61-70
Author(s):  
Noviandi Noviandi ◽  
Ahmad Ilham
Keyword(s):  
Particle Swarm Optimization ◽  
Fuzzy Inference System ◽  
Rainy Season ◽  
Fuzzy Inference ◽  
Sea Surface ◽  
Good Work ◽  
Swarm Optimization ◽  
Inference System ◽  
Coefficient Corrélation ◽  
The Relationship

Rainfall which is occurred in an area explain the Onset Rainy Season (ORS). ORS is a characteristic of the rainy season which is important to know, but the characteristics of the rain itself is very difficult to predict. We use the method of Fuzzy Inference System (FIS) to predict ORS. Unfortunately, FIS is weak to determine parameters so that influences the working FIS method. In this study, we use PSO to optimize parameter of the FIS method to increase perform of the FIS method for onset prediction of the rainy season with the predictor Sea Surface Temperature Nino 3.4 and Index Ocean Dipole. We used coefficient correlation to determine the relationship between two variables as predictors and RMSE as evaluate to all methods. The experiment result has shown that the work of FIS-PSO after optimizing produced the good work with the coefficient correlation = 0.57 and RMSE = 2.96 that is the smallest value that is better performance if compared with other methods. It can be concluded that the method proposed can increase the onset prediction of the rainy season.

scholarly journals Pituitary MRI characteristics in 297 acromegaly patients based on T2-weighted sequences

2015 ◽  
Vol 22 (2) ◽  
pp. 169-177 ◽  
Author(s):  
Iulia Potorac ◽  
Patrick Petrossians ◽  
Adrian F Daly ◽  
Franck Schillo ◽  
Claude Ben Slama ◽  
...  
Keyword(s):  
Large Population ◽  
Optic Chiasm ◽  
Research Priority ◽  
Maximal Diameter ◽  
Magnetic Resonance Imaging Mri ◽  
Multimodal Management ◽  
Mri Characteristics ◽  
Weighted Sequences ◽  
The Relationship

Responses of GH-secreting adenomas to multimodal management of acromegaly vary widely between patients. Understanding the behavioral patterns of GH-secreting adenomas by identifying factors predictive of their evolution is a research priority. The aim of this study was to clarify the relationship between the T2-weighted adenoma signal on diagnostic magnetic resonance imaging (MRI) in acromegaly and clinical and biological features at diagnosis. An international, multicenter, retrospective analysis was performed using a large population of 297 acromegalic patients recently diagnosed with available diagnostic MRI evaluations. The study was conducted at ten endocrine tertiary referral centers. Clinical and biochemical characteristics, and MRI signal findings were evaluated. T2-hypointense adenomas represented 52.9% of the series, were smaller than their T2-hyperintense and isointense counterparts (P<0.0001), were associated with higher IGF1 levels (P=0.0001), invaded the cavernous sinus less frequently (P=0.0002), and rarely caused optic chiasm compression (P<0.0001). Acromegalic men tended to be younger at diagnosis than women (P=0.067) and presented higher IGF1 values (P=0.01). Although in total, adenomas had a predominantly inferior extension in 45.8% of cases, in men this was more frequent (P<0.0001), whereas in women optic chiasm compression of macroadenomas occurred more often (P=0.0067). Most adenomas (45.1%) measured between 11 and 20 mm in maximal diameter and bigger adenomas were diagnosed at younger ages (P=0.0001). The T2-weighted signal differentiates GH-secreting adenomas into subgroups with particular behaviors. This raises the question of whether the T2-weighted signal could represent a factor in the classification of acromegalic patients in future studies.

Sign in / Sign up

Export Citation Format

Share Document