The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Catastrophes have caused tremendous damages in human history and triggered record high post-disaster relief from the governments. The research of catastrophic modeling can help estimate the effects of natural disasters like hurricanes, floods, surges, and earthquakes. In every Atlantic hurricane season, the state of Florida in the United States has the potential to suffer economic and human losses...
The Florida Public Hurricane Loss Model (FPHLM) is a public catastrophe model that integrates and regulates all key components, such as meteorology, engineering, and actuarial components, by following a certain workflow in the execution phase. The validation phase governed by an Automatic Data Validation (ADV) program simulates each modeled execution component with a large number of historical insurance...
In this paper, we propose an extended deep learning approach that incorporates instance selection and bootstrapping techniques for imbalanced data classification. In supervised learning, classification performance often deteriorates when the training set is imbalanced where at least one of the classes has a substantially fewer number of instances than the others. We propose to use adaptive synthetic...
Nowadays, in such a high-tech living lifestyle, profusion of multimedia data are produced and propagated around the world. To identify meaningful semantic concepts from the large amount of data, one of the major challenges is called the data imbalance problem. Data imbalance occurs when the number of positive instances (i.e., instances which contain the target concept) is greatly less than the number...
Technological developments have lead to the propagation of massive amounts of data in the form of text, image, audio, and video. The unstoppable trend draws researchers' attention to develop approaches to efficiently retrieve and manage multimedia data. The inadequacy of keyword-based search in multimedia data retrieval due to non-existent or incomplete text annotations has called for the development...
Feature selection is an actively researched topic in varies domains, mainly owing to its ability in greatly reducing feature space and associated computational time. Given the explosive growth of high-dimensional multimedia data, a well-designed feature selection method can be leveraged in classifying multimedia contents into high-level semantic concepts. In this paper we present a multi-phase feature...
The Florida Public Hurricane Loss Model (FPHLM) is a large scale, multidisciplinary project developed to assist the state of Florida with the regulation of residential insurance premiums as they relate to insured losses caused by hurricane winds. The modeling services provided to clients using the FPHLM involve physically distributed personnel with different levels of technical expertise. Bringing...
Semantic concept detection is among the most important and challenging topics in multimedia research. Its objective is to effectively identify high-level semantic concepts from low-level features for multimedia data analysis and management. In this paper, a novel re-ranking method is proposed based on correlation among concepts to automatically refine detection results and improve detection accuracy...
A multimedia semantic retrieval system based on hidden coherent feature groups (HCFGs) can support multimedia semantic retrieval on mobile applications. The system can capture the correlation between features and partition the original feature set into HCFGs, which have strong intragroup correlation while maintaining low intercorrelation. The authors present a novel, multimodel fusion scheme to effectively...
In this paper, we propose a Correlation based Feature Analysis (CFA) and Multi-Modality Fusion (CFA-MMF) framework for multimedia semantic concept retrieval. The CFA method is able to reduce the feature space and capture the correlation between features, separating the feature set into different feature groups, called Hidden Coherent Feature Groups (HCFGs), based on Maximum Spanning Tree (MaxST) algorithm...
The booming multimedia technology is incurring a thriving multi-media data propagation. As multimedia data have become more essential, taking over a major potion of the content processed by many applications, it is important to leverage data mining methods to associate the low-level features extracted from multimedia data to high-level semantic concepts. In order to bridge the semantic gap, researchers...
Sustainable building has emerged as an important topic due to the fact that it can significantly reduce the impact of buildings and their operation on the natural environment and more efficiently utilize resources throughout a building's life-cycle. When compared with a traditional buildingdesign process, integrated project delivery has proven to be more efficient, and is thus gaining wider acceptance...
We present a novel visual analytics system and multimedia enabled mobile application that allows emergency management (EM) personnel access to timely and relevant disaster situation information. The system is able to semantically integrate text-based emergency management disaster situation reports with related disaster imagery taken in the field by EM responders and community residents. In addition,...
In this paper, a hierarchical disaster image classification (HDIC) framework based on multi-source data fusion (MSDF) and multiple correspondence analysis (MCA) is proposed to aid emergency managers in disaster response situations. The HDIC framework classifies images into different disaster categories and sub-categories using a pre-defined semantic hierarchy. In order to effectively fuse different...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.