This paper provides a review and discussion of the incorporation of social influence into discrete choice models with specific application in travel behavior analysis.
Community detection is a fundamental component of network analysis for sensor systems and is an enabling technology for higher level analytical applications such as behavior analysis, prediction, and identity and pattern-of-life analysis [2].
In this paper, we describe a visualization system for the analysis of application behavior on superscalar processors.
The performance of person re-identification in sensor networks is closely related to many other applications, such as person retrieval, behavior analysis, long-term person tracking, and so on [5].
The continuing improvements in background modeling techniques have led to many new and fascinating applications like event detection, object behavior analysis, suspicious object detection, traffic monitoring, and so forth.
Data from multiple sources (e.g., site properties, line network topology, and train schedules) and technological applications (e.g., statistical analysis, traffic behavior analysis, data mining, and cloud computing), were used to determine the key elements that forecast rail transit network passenger flow.
It has applications in many fields, including visual surveillance, human behavior analysis, maneuvering target tracking, and traffic monitoring.
Then we propose fuzzy port behavior analysis to quantitatively map all earlier discussed network applications or scenarios into the corresponding MPCP zones.
Tracking of facial points has many applications in pattern recognition, such as FE analysis, face recognition, speech recognition, behavior analysis, etc. AUs' detection enables us to analyze FE for emotion, mood, deception, and attitude recognition.
Current approaches for detecting suspicious application activity on mobile platforms rely on static analysis: reverse-engineering and examining sequences of program code to infer application behavior.
