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Various computer-assisted technologies have been developed to assist radiologists in detecting cancer; however, the algorithms still lack high degrees of sensitivity and specificity, and must undergo machine learning against a training set with known pathologies in order to further refine the algorithms with higher validity of truth. This work describes an approach to learning cue phrase patterns...
The Particle Swarm Optimization (PSO) clustering algorithm can generate more compact clustering results than the traditional K-means clustering algorithm. However, when clustering high dimensional datasets, the PSO clustering algorithm is notoriously slow because its computation cost increases exponentially with the size of the dataset dimension. Dimensionality reduction techniques offer solutions...
Assessing the potential property and social impacts of an event, such as tornado or wildfire, continues to be a challenging research area. From financial markets to disaster management to epidemiology, the importance of understanding the impacts that events create cannot be understated. Our work describes an approach to fuse information from multiple sources, then to analyze the information cycles...
Assessing and monitoring events and their impacts continues to present multiple challenges. From financial markets to disaster management to epidemiology, the importance of understanding the impacts that events create cannot be understated. This work describes an approach that incorporates information from multiple sources and then analyzes the overall information flow to identify temporal patterns...
The use of mobile agents to support the development of practical applications is limited primarily by the risks to which hosts in the system are subject to. This article introduces a distributed and adaptive security-monitoring framework to decrease such potential threats. The proposed framework is based on a modified version of the popular Boosting algorithm to classify malicious agents based on...
The requirements to access and manipulate data across multiple heterogeneous existing databases and the proliferation of mobile technologies have propelled the development of mobile multidatabase system (MDBS). In that environment, transaction management is not a trivial task due to the technological constraints. This paper proposes an agent-based transaction management for mobile multidatabase (AT3M)...
To better understand insurgent activities and asymmetric warfare, a social adaptive model for modeling multiple insurgent groups attacking multiple military and civilian targets is proposed and investigated. This report presents a pilot study using the particle swarm modeling, a widely used non-linear optimal tool, to model the emergence of insurgency campaign. The objective of this research is to...
Particle swarm optimization (PSO) is a population-based stochastic optimization technique, which can be used to find an optimal, or near optimal, solution to a numerical and qualitative problem. In PSO algorithm, the problem solution emerges from the interactions among many simple individual agents called particles. In the real world, we have to frequently deal with searching and tracking an optimal...
Intelligence analysts are currently overwhelmed with the amount of information streams generated everyday. There is a lack of comprehensive tool that can real-time analyze the information streams. Document clustering analysis plays an important role in improving the accuracy of information retrieval. However, most clustering technologies can only be applied for analyzing the static document collection...
Recent years saw the rapid development of peer-to-peer (P2P) networks in a great variety of applications. However, similarity-based k-nearest-neighbor retrieval (k-NN) is still a challenging task in P2P networks due to the multiple constraints such as the dynamic topologies and the unpredictable data updates. Caching is an attractive solution that reduces network traffic and hence could remedy the...
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