Image classification accuracy from LANDSAT data for tropical rain forest in Sumatra: An evaluation of ERDAS imagine software

Abstract not available

An SVM-Fuzzy system design for diabetes risk classification

Diagnóstico ecográfico de tumores anexiais: modelos do grupo international ovary tumor analysis (IOTA) versus classificação gynecologic imaging report and data system (GI-RADS)

A ecografia é o exame de primeira linha na identificação e caraterização de tumores anexiais. Foram descritos diversos métodos de diagnóstico diferencial incluindo a avaliação subjetiva do observador, índices descritivos simples e índices matematicamente desenvolvidos como modelos de regressão logística, continuando a avaliação subjectiva por examinador diferenciado a ser o melhor método de discriminação entre tumores malignos e benignos. No entanto, devido à subjectividade inerente a esta avaliação tornouse necessário estabelecer uma nomenclatura padronizada e uma classificação que facilitasse a comunicação de resultados e respectivas recomendações de vigilância. O objetivo deste artigo é resumir e comparar diferentes métodos de avaliação e classificação de tumores anexiais, nomeadamente os modelos do grupo International Ovary Tumor Analysis (IOTA) e a classificação Gynecologic Imaging Report and Data System (GI-RADS), em termos de desempenho diagnóstico e utilidade na prática clínica.

A Phylogenetic Classification of the Video-Game Industry’s Business Model Ecosystem

Part 8: Business Strategies Alignment

Classification of plant communities, structure and diversity of forests in Acre, Brazil.

2016

Adverse drug reaction classification with deep neural networks

We study the problem of detecting sentences describing adverse drug reactions (ADRs) and frame the problem as binary classification. We investigate different neural network (NN) architectures for ADR classification. In particular, we propose two new neural network models, Convolutional Recurrent Neural Network (CRNN) by concatenating convolutional neural networks with recurrent neural networks, and Convolutional Neural Network with Attention (CNNA) by adding attention weights into convolutional neural networks. We evaluate various NN architectures on a Twitter dataset containing informal language and an Adverse Drug Effects (ADE) dataset constructed by sampling from MEDLINE case reports. Experimental results show that all the NN architectures outperform the traditional maximum entropy classifiers trained from n-grams with different weighting strategies considerably on both datasets. On the Twitter dataset, all the NN architectures perform similarly. But on the ADE dataset, CNN performs better than other more complex CNN variants. Nevertheless, CNNA allows the visualisation of attention weights of words when making classification decisions and hence is more appropriate for the extraction of word subsequences describing ADRs.

Subject Ontogeny: Subject Access through Time and the Dimensionality of Classification

Classification schemes undergo revision. However, in a networked environment revisions can be used to add dimensionality to classification. This dimensionality can be used to help explain conceptual warrant, explain the shift from disciplinary to multidisciplinary knowledge production, and as a component method of domain analysis. Further, subject ontogeny might be used in cooperative networked projects like digital preservation, online access tools, and interoperability frameworks.

International human resource management in the introductory HRM course

This paper explores the extent to which students in the introductory HRM course in US institutions are likely to be exposed to information on international and cross-cultural aspects of HRM. Two methods are used: (1) an analysis of international content in fifteen popular introductory HRM textbooks and (2) a survey of professors teaching introductory HRM. The vast majority of responding instructors said their classes got some exposure to international issues in HRM, and most introductory texts included some relevant content. Critiques of international boxed features and dedicated IHRM chapters are provided, and suggestions for improving the quality and depth of IHRM content in introductory textbooks are made.