Detection Of Behavioral Patterns Research Articles

Behavioral and Temporal Pattern Detection within Financial Data with Hidden Information

Abstract : This paper describes a technique for behavioral and temporal pattern detection within financial data, such as credit card and bank account data, where the required information is only partially visible. Typically, transaction amount, transaction date, merchant name and type, and location of transaction are all visible data items, i.e., they are readily available in the financial institutions database. In contrast, the transaction status as a business transaction (using a personal card), a personal transaction, an investment related transaction, or perhaps a suspicious transaction, is information not explicitly available in the database. Our behavioral pattern detection technique combines well-known Hidden Markov Model (HMM) techniques for learning and subsequent identification of hidden artifacts, with run-time pattern detection of probabilistic UML-based formal specifications. The proposed approach entails a process in which the end-user first develops his or her deterministic patterns, s/he then identifies hidden artifacts in those patterns. Those artifacts induce the state set of the identifying HMM, whose remaining parameters are learned using standard frequency analysis techniques. In the run-time pattern detection phase, the system emits visible information, used by the HMM to deduce invisible information, and sequences thereof; both types of information are then used by a probabilistic pattern detector to monitor the pattern.

Tactical analysis of the Barcelona counter-attack

The purpose of this study was to observe and to characterise the tactical performance in the counter-attack (CA) of the Football Club Barcelona (FCB) in the 2009/2010 sporting season, using...

Exigences système d'une nouvelle fonctionnalité dans l'habitat individuel : cas de la surveillance des personnes âgées et handicapées

Automatic systems based on electronics and computers can be used to develop products, systems and services intended to improve the quality of life. A new system for monitoring elderly persons in the home uses a microcomputer and a system sensors to detect a predefined set of normal daily activities. The system also contains modules for communications, control and safety functions that can summon assistance if needed. The work described focuses on the simultaneous acquisition of data, detection of abnormal behaviour patterns and a self-adaptive learning function to be implemented in the system prior to field trials.

Data Smoothing: Prediction of Human Behavior, Detection of Behavioral Patterns, and Monitoring Treatment Effectiveness in Single-Subject Behavioral Studies

Data-smoothing can be particularly useful in predicting human behavior, detecting behavioral patterns, and monitoring treatment effectiveness in highly variable single-subject behavioral experiments that cannot be determined by only visual inspection of their graphs. Using an example from the applied behavior analytic literature, the use of moving-averag e and exponential data-smoothing aided the detection of the unique behavioral patterns of a child with autism across different treatments. Furthermore, the utility of the data-smoothing procedures to monitor and control the effectiveness of an intervention is discussed. The ease of their calculations suggest use of data-smoothing by behavior analysts whenever the effects of particular interventions are questionable.

Detection of temporal behaviour patterns of free-ranging cattle by means of diversity spectra

The aim of this paper is to detect temporal patterns of cattle behaviour. The method, diversity spectra, provides, on the one hand, the number of parts into which a temporary transect should be divided in order to understand the maximum segregation of cattle activities and, on the other, the clarity with which each segregation is defined. In the case under study (a 'dehesa' pasture-land in central Spain) the maximum segregation of fundamental activities in cattle behaviour is reached by considering the year as divided into two periods: spring-summer and autumn-winter. Cattle behaviour shows an annual coarse grain pattern, which is associated with management activities and with the meteorological seasonality of the Mediterranean climate. However, within each of the two annual periods, maximum segregation is reached considering separately the days of observation. This fine grain pattern indicates within each season, a certain capacity for response to a fluctuating environment and determines very different behaviour on close days. During autumn-winter period cattle show seasonal and daily activity segregations which are clearer than during spring-summer. In the former period, the lack of grass, more severe climatic conditions and management would seem to be determining factors of this temporal behaviour pattern.