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Fig. 2. The online active learning algorithm and steps for developing the framework.
regardless of whether it is
correct or incorrect? If you
do the input and output
analysis, the interpretability
of the learning model
can be greatly improved.
Understanding of black
box decision-making can
be enhanced by both visual
representations of the data
and mathematical analysis
of the data.
Fig. 2b illustrates that
the training data for the machine
learning system must
be efficient, rich in patterns,
and diverse. Compared to
traditional offline training,
which is mostly performed
by human annotators, online
training is computed
based on feedback and actions.
In their respective
stages of growth, however,
offline and Internet games
encounter several obstacles.
A new problem has
emerged in collecting, laremains
incomplete. An essential phase of the process is the
analysis of the model. Both the training of the model and the
actual testing are performed on the computer. In addition, the
model is tested against a local data set to evaluate it. When deploying
a model, it is important to choose the configuration
that works best, and one should also follow the prescribed
launch protocol. This should include quality assurance testing,
A/B testing, as well as performance testing of the model.
The following suggestions are given below that can be used to
optimize and develop the network-enabled learning system
for better achievement of the best performance: identify the
problem; instrument the data; simulate the prototype; train the
model; evaluate the model; and deploy the model.
Feasibility Analysis
Another obstacle to overcome is the practicality of the learning
methods. The black box behavior of machine learning
and deep learning models leads to a lack of interpretations and
generalization. Models with high accuracy must have strong
interpretability and the ability to generalize to solve any problem
that may arise with the structure of the network. It is
necessary to perform the communication analysis to determine
whether the model can effectively handle the network
requirement or not. Interoperability is defined as how well
a person understands the workflow and theory underlying
the black arc decision process, regardless of whether the
particular conclusion is correct or not, as can be seen in the discussion
of Fig. 2a. Why does the model make such a judgment
36
beling, and organizing data to maximize optimization and
network efficiency. Network traffic patterns provide rich
examples of training data and support the generalization capabilities,
but this leads to an increase in transmission load and
recording overhead. Another important aspect for efficient operation
is the data format, i.e., whether or not to provide the
packet arrival rate and buffer. Both the learning system and the
load balancing application can be affected by the data recording
interval and traffic patterns. Another problem that needs
to be explored is scheduling training data protocols, as well as
intervals to capture training instances while minimizing the
load on the network.
Conclusion and Future Work
In this research work, we have developed a unique load
balancing technique that uses network and application information
to distribute SDN loads across many sensors. The
proposed architecture can balance the stack of applications
distributed among the sensors connected via SDN. We also
provide a concise summary of the design and optimization
strategy for the learning environment and wireless network
issues. The number of different training instances available to
the learning model is greatly increased when a feedback-based
learning strategy is used. On the other hand, it brings particular
difficulties for development and deployment that need
to be managed in terms of architectural design, optimization,
and feasibility analysis. In the not-too-distant future, an active
learning mechanism will be integrated into the network when
IEEE Instrumentation & Measurement Magazine
October 2022
Instrumentation & Measurement Magazine 25-7

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