Weber here is instated as per the

Weber and Lionel
et al. 5 discusses a technique for developing Dynamic Bayesian Networks (DBN)
to formalize the need of solving complex dynamic models. Also to analyze the
dependability estimations obtained by the proposed DBN model and the classical
Markov Chain model, a small valve system is used. This paper introduces the DBNs
as an equivalent model to Markov Chain. The issues considered in Weber and
Lionel et al. 5 are those involving systems whose flow can be demonstrated as
stochastic procedures and where the decision maker’s activities impact the
system behavior. Here the probability distribution over the next states are
determined by the current system state and the action applied to the system.
Due to the uncertainty, the paper considers the process state to be a random
variable that takes its value from a finite state space comparing to the
possible process state. The Markov Chain permits demonstrating the elements of
successions taken by these states. To compute the probability distribution over
the states at a certain time, it uses a compact network form, which is based on
iterative inference. Here, the notion of time is introduced through inference.
The paper states that the proposed method, based on the DBN theory, allows
building DBN structures for demonstrating of the transient development of
complex systems and this proposal can be the solution to model the reliability
of the complex systems. It also states that the acquired model is significantly
more compact and readable than the processes modelled using the Markov Chain.
The major aspect that Weber and Lionel et al. 
5, failed to show/talk about is that the proposed methodology fails to
define how the learning calculations of Bayesian Network can add to show the
progression of the system dependability and how the parameters conduct can be
then demonstrated.

 

Carmona et al.
6 presents a student model to address the Learning system based on the model
proposed by Felder and Sylverman, and implementing the system using Dynamic
Bayesian Networks. The model proposed here is instated as per the outcomes from
the student in the Index of learning Style Questionnaire, and afterward tweaked
amid the course of the interaction using Bayesian model. Finally the model is
used to group objects in repository as suitable or not for a particular
student. The paper tries to resolve the issue from the Learning Object Metadata
(LOM). LOM is a standard to indicate the language structure and semantics of
learning objects utilizing a set of that completely/sufficiently portray a
learning question. The issue with LOM is the retrieval and searching facilities
of learning objects 6. To resolve this issue, Carmona et al. 6, proposes to
“filter” and “sort” the learning objects according to the current student’s
learning and preferences. This paper uses a hybrid approach i.e. combining the
Index of Learning Style Questionnaire (ILSQ) proposed by Felser and Soloman,
and then adding the dynamic Bayesian network (DNB) to further process the
outcome from the ILSQ. The DNB is initialized as the student’s model and the
scores from ILSQ are used as the initial beliefs. Therefore, at whatever point
a new evidences about the preferences of the student arrives, a new time slice
of the Dynamic Bayesian Network is instantiated 6, consequently setting off
the propagation mechanism and updating the beliefs for the Learning System. By
doing this, Carmona et al. 6 claims that it refines the initial values for
each student in the learning system that was acquired from the ILSQ as the
student interacts with it.

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Pavlovic et al.
7 presents a model for figure motion analysis based on a novel Dynamic
Bayesian Network (DBN) using Switching Linear Dynamic System (SLDS). The key
component presented in this paper is an inexact Viterbi inference strategy for
defeating the immovability of correct inference in blended state DBNs. The
approach used here for dynamic modelling is an approach that is taken from the
field of biomechanics, which has also been utilized effectively to create
computer designs liveliness and to track abdominal area movement in a user-interface
(UI) setting. The drawback of this model is that it is difficult to track large
objects which is hard to measure using only visual data and may not reduce the
complexity of the model to exploit this confined core interest. To overcome
this difficulty, Pavlovic et al. 7 proposes an alternative strategy for
learning dynamic models from a preparation corpus of watched state space
directions. Finally, this paper illustrate the use of the SLDS structure to
demonstrating figure elements, where it demonstrates the learning of exchanging
models of fronto-parallel walking and running movement from video information.
Also it derives a mixed state version of the Viterbi estimation calculation for
induction in DBNs.

 

The context-aware
recommender systems (CARS), Adomavicius and Alexander et al. 9 create more
pertinent suggestions by adjusting them to the particular logical circumstance
of the user. The work presented in Adomavicius and Alexander et al. 9
investigates on how relevant data can be utilized to make keen and helpful
recommender systems. It gives a review of the multifaceted thought of setting,
talks about a few methodologies for consolidating logical data in the proposal
process, and outlines the use of such methodologies in a few application regions
where different types of contexts are exploited. The context in the recommender
systems is based on assumption of certain contextual factors such as time,
location, etc. that distinguish the context in which the recommendations are
given. It also assumes that each of these relevant components can have a
structure; the Time factor, for instance, can be characterized in wording of
seconds, minutes, hours, days, months, and years. “What a recommender system
may know about contextual factors”, and “How contextual factors change over
time” are the two aspects of contextual factors that Adomavicius and Alexander
et al.  9, is based on for the
classification of context in recommender systems.

 

Park, Ji-Oh and
Sung-Bae el al.  10, uses fuzzy
Bayesian Network to propose a context-aware music recommendation system. In
Context-aware music recommendation system, Park, Ji-Oh and Sung-Bae el al.
10, they are using fuzzy Bayesian networks and utility theory. The
context-aware music recommendation system (CA-MARS) exploits fuzzy system to
incorporate diverse source of information. While providing recommendation to
user, context also plays vital role for any decision system (example:
Temperature, Humidity, Noise, Season etc.). The problem with Bayesian Network
is it cannot deal with the diverse information. There is a possibility of loss
of information and it may not reflect the context appropriately. To overcome
this limitation, Park, Ji-Oh and Sung-Bae el al. 10 are proposing fuzzy
system.