25,708 research outputs found
An Unsupervised Feature Learning Approach to Improve Automatic Incident Detection
Sophisticated automatic incident detection (AID) technology plays a key role
in contemporary transportation systems. Though many papers were devoted to
study incident classification algorithms, few study investigated how to enhance
feature representation of incidents to improve AID performance. In this paper,
we propose to use an unsupervised feature learning algorithm to generate higher
level features to represent incidents. We used real incident data in the
experiments and found that effective feature mapping function can be learnt
from the data crosses the test sites. With the enhanced features, detection
rate (DR), false alarm rate (FAR) and mean time to detect (MTTD) are
significantly improved in all of the three representative cases. This approach
also provides an alternative way to reduce the amount of labeled data, which is
expensive to obtain, required in training better incident classifiers since the
feature learning is unsupervised.Comment: The 15th IEEE International Conference on Intelligent Transportation
Systems (ITSC 2012
Intelligent Financial Fraud Detection Practices: An Investigation
Financial fraud is an issue with far reaching consequences in the finance
industry, government, corporate sectors, and for ordinary consumers. Increasing
dependence on new technologies such as cloud and mobile computing in recent
years has compounded the problem. Traditional methods of detection involve
extensive use of auditing, where a trained individual manually observes reports
or transactions in an attempt to discover fraudulent behaviour. This method is
not only time consuming, expensive and inaccurate, but in the age of big data
it is also impractical. Not surprisingly, financial institutions have turned to
automated processes using statistical and computational methods. This paper
presents a comprehensive investigation on financial fraud detection practices
using such data mining methods, with a particular focus on computational
intelligence-based techniques. Classification of the practices based on key
aspects such as detection algorithm used, fraud type investigated, and success
rate have been covered. Issues and challenges associated with the current
practices and potential future direction of research have also been identified.Comment: Proceedings of the 10th International Conference on Security and
Privacy in Communication Networks (SecureComm 2014
A Deep Network Model for Paraphrase Detection in Short Text Messages
This paper is concerned with paraphrase detection. The ability to detect
similar sentences written in natural language is crucial for several
applications, such as text mining, text summarization, plagiarism detection,
authorship authentication and question answering. Given two sentences, the
objective is to detect whether they are semantically identical. An important
insight from this work is that existing paraphrase systems perform well when
applied on clean texts, but they do not necessarily deliver good performance
against noisy texts. Challenges with paraphrase detection on user generated
short texts, such as Twitter, include language irregularity and noise. To cope
with these challenges, we propose a novel deep neural network-based approach
that relies on coarse-grained sentence modeling using a convolutional neural
network and a long short-term memory model, combined with a specific
fine-grained word-level similarity matching model. Our experimental results
show that the proposed approach outperforms existing state-of-the-art
approaches on user-generated noisy social media data, such as Twitter texts,
and achieves highly competitive performance on a cleaner corpus
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