Loading...
Search for:
dynamic-bayesian-networks
0.007 seconds
Inferring Gene Regulatory Networks, Using Machine Learning Approaches
, M.Sc. Thesis Sharif University of Technology ; Manzuri, Mohammad Taghi (Supervisor)
Abstract
Gene regulatory network consists of a set of genes; interacting with each other via their protein products. Such interations lead to the regulation of the genes’ production rate. A breakdown in the regulatory process, may lead to some kinds of diseases. Therefore, understanding the gene regulatory process, is beneficial for both diagnosis and treatment. In this thesis, gene regulatory networks are modeled by the means of dynamic Bayesian networks. We have used sampling based methods, in order to learn the network structure. As these methos have a very high computational cost; we have used a correlation test to prune the search space. This way, an undirected network skeleton is obtained; for...
Tracking of Human Sperm Cell using a Dynamic Bayesian Network Based Framework
, Ph.D. Dissertation Sharif University of Technology ; Vosoughi Vahdat, Bijan (Supervisor) ; Salman Yazdi, Reza (Co-Supervisor)
Abstract
Infertility is an important problem to deal in medicine. In every four couples, on average, one couple is affected by infertility in developing countries. In the majority of cases, the infertility of men has a relationship with spermatozoa and semen, and can be measured by semen and spermatozoa analysis for more advanced diagnosis and treatments. Analysis of the movement patterns of spermatozoa were performed by expert screeners earlier, but nowadays, many of these analyzes are performed using computer-based systems called computer assisted sperm analysis (CASA). The benefits of using CASA instead of expert screeners are achieving system-independency and numerical results at the end of the...
Applications of Hidden Markov Models in Activity Recognition in an Ambient Intelligent Environment
, M.Sc. Thesis Sharif University of Technology ; Rabiei, Hamid Reza (Supervisor)
Abstract
Ambient Intelligence (AmI) is an environment in which devices are embedded and connected to each other with a communication network, working in concert to predict users’ wishes according to the context of the environment (devices and people) to help them with their everyday activities. An ambient intelligent environment should be context-aware. One of the most complicated problems in context-aware computations is recognition of the activities in which users of the environment are engaged. These activities could be recognized by means of the information hidden in communication networks of the devices, especially different sensors embedded in the environment to ease up the process. Most of...
Dynamic risk assessment of decommissioning offshore jacket structures
, Article Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE, 17 June 2018 through 22 June 2018 ; Volume 3 , 2018 ; 9780791851227 (ISBN) ; Khorasanchi, M ; Kurt, R. E ; Ocean, Offshore and Arctic Engineering Division ; Sharif University of Technology
American Society of Mechanical Engineers (ASME)
2018
Abstract
The need to develop an integrated dynamic safety and risk analysis model for decommissioning offshore jacket structures is driven by the risky, expensive and complex nature of the operation. Many of the existing risk analysis techniques applicable to offshore assets failed to recognise and capture evolving risks during different stages of the decommissioning operation. This paper describes risk-based safety model to conduct quantitative risk analysis for offshore jacket decommissioning failure. First, a bow-tie technique is developed to model the accident cause-consequence relationship. Subsequently, a Bayesian belief network is used to update the failure probabilities of the contributing...
An asynchronous dynamic Bayesian network for activity recognition in an ambient intelligent environment
, Article ICPCA10 - 5th International Conference on Pervasive Computing and Applications, 1 December 2010 through 3 December 2010 ; December , 2010 , Pages 20-25 ; 9781424491421 (ISBN) ; Rabiee, H. R ; Sharif University of Technology
2010
Abstract
Ambient Intelligence is the future of computing where devices predict what users need and help them carry out their everyday life activities easier. To make this prediction possible these environments should be aware of the context. Activity recognition is one of the most complex problems in context-aware environments. In this paper we propose a layered Dynamic Bayesian Network (DBN) to recognize activities in an oral presentation. The layered architecture gives us the opportunity to recognize complex activities using the classification results of sensory data in the first layer regardless of the physical environment. Our model is event-driven meaning the classification takes place only when...