International Conference on Networking, Sensing and Control 

About: International Conference on Networking, Sensing and Control is an academic conference. The conference publishes majorly in the area(s): Wireless sensor network & Control theory. Over the lifetime, 2511 publications have been published by the conference receiving 20110 citations.

Survey of fraud detection techniques

Abstract: Due to the dramatic increase of fraud which results in loss of billions of dollars worldwide each year, several modern techniques in detecting fraud are continually developed and applied to many business fields Fraud detection involves monitoring the behavior of populations of users in order to estimate, detect, or avoid undesirable behavior Undesirable behavior is a broad term including delinquency, fraud, intrusion, and account defaulting This paper presents a survey of current techniques used in credit card fraud detection, telecommunication fraud detection, and computer intrusion detection The goal of this paper is to provide a comprehensive review of different techniques to detect frauds

Particle swarm optimization algorithm and its application to clustering analysis

Abstract: Clustering analysis is applied generally to pattern recognition, color quantization and image classification. It can help the user to distinguish the structure of data and simplify the complexity of data from mass information. The user can understand the implied information behind extracting these data. In real case, the distribution of information can be any size and shape. A particle swarm optimization algorithm-based technique, called PSO-clustering, is proposed in this article. We adopt the particle swarm optimization to search the cluster center in the arbitrary data set automatically. PSO can search the best solution from the probability option of the social-only model and cognition-only model. This method is quite simple and valid, and it can avoid the minimum local value. Finally, the effectiveness of the PSO-clustering is demonstrated on four artificial data sets.

Driver fatigue detection based on eye tracking and dynamk, template matching

Abstract: A vision-based real-time driver fatigue detection system is proposed for driving safely. The driver's face is located, from color images captured in a car, by using the characteristic of skin colors. Then, edge detection is used to locate the regions of eyes. In addition to being used as the dynamic templates for eye tracking in the next frame, the obtained eyes' images are also used for fatigue detection in order to generate some warning alarms for driving safety. The system is tested on a Pentium III 550 CPU with 128 MB RAM. The experiment results seem quite encouraging andpromising. The system can reach 20 frames per second for eye tracking, and the average correct rate for eye location and tracking can achieve 99.1% on four test videos. The correct rate for fatigue detection is l00%, but the average precision rate is 88.9% on the test videos.

Random forest for credit card fraud detection

Abstract: Credit card fraud events take place frequently and then result in huge financial losses. Criminals can use some technologies such as Trojan or Phishing to steal the information of other people's credit cards. Therefore, an effictive fraud detection method is important since it can identify a fraud in time when a criminal uses a stolen card to consume. One method is to make full use of the historical transaction data including normal transactions and fraud ones to obtain normal/fraud behavior features based on machine learning techniques, and then utilize these features to check if a transaction is fraud or not. In this paper, two kinds of random forests are used to train the behavior features of normal and abnormal transactions. We make a comparison of the two random forests which are different in their base classifiers, and analyze their performance on credit fraud detection. The data used in our experiments come from an e-commerce company in China.

High-Order Consensus Algorithms in Cooperative Vehicle Systems

Abstract: In this paper we study lthorder (l >= 3) consensus algorithms, which generalize the existing first-order and second-order consensus algorithms in the literature. We will show sufficient conditions under which each information variable and their higher-order derivatives converge to common values. We will present the idea of higher-order consensus with a leader and introduce the concept of an lthorder model-reference consensus problem, where each information variable and their high-order derivatives not only reach consensus but also converge to the solution of a prescribed dynamic model. The effectiveness of these algorithms are demonstrated through simulations and a multi-vehicle cooperative control application which mimics flocking behavior in birds.