The Transition Probabilities from Captive Animal’s Behavior by Non-invasive Sensing Method Using Stochastic Multilevel State Model

• Main Authors: Phudinan Singkahmfu, Pruet Boonma, Wijak Srisujjalertwaja, Anurak Panyanuwat, Natapot Warrit
• Format: Book Series
• Published: 2018
• Subjects: Computer Science; Mathematics
• Online Access: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85049053043&origin=inward; http://cmuir.cmu.ac.th/jspui/handle/6653943832/58596
• Institution: Chiang Mai University

© 2018, Springer Nature Singapore Pte Ltd. This article proposes a stochastic multilevel-state framework to model the animal’s behavior. The motivation of this article is the variety behavior influenced by several factors, which can lead to the state explosion. The proposed framework processes data from an automated sensing system and constructs the model. The data gathered from captive Antelope goral (Naemorhedusgriseus) in Chiang Mai Night Safari, Thailand. The data is gathered from the activity and the environmental factors in the cage by none invasive method. The model separated observed data into two main classes: the upper-level data and the lower-level data. The upper-level data represents the environment data such as temperature, humidity, and light density. Moreover, the landscape of the captivity area also takes into consideration. On the other hand, the lower-level represents the location of the animal of interested in the captivity area. The working strategy of this work is to cluster the each type of data and link them together by a stochastic approach. Both layers of data will be handled independently in clustering algorithm and determine probabilistic of state transaction. From the data observed from the sensor, the Probabilistic Automaton (PA) function is constructed. It is a function of producing the next stage based on the previous behavior states. The initial state of the framework is in the lower-level data (the current location of the animal). Then, the PA using the current lower stage and the current upper stage generates the next stage, location of the animal. Both the lower stage and the upper stage are traverse along the constructed automaton. The result can suggest computing methods, which can utilize to zoo research, which performs behavior monitoring, and in the other studies area, or subject of study, such as, air pollution dispersion that tracks the movement of pollutant according to the environment. The benefits of the proposed methods also can be used to create the application to attract the tourist to the area, which animally is likely to display themselves.