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This proposed technique has two levels of implementation methodology.
Additionally, the efficacy of this proposed technique is compared with a conventional sliding mode controller (CSMC).
This proposed technique utilizes a simple source follower without the need of complex circuits.
This proposed technique is the coupling of Adomian decomposition method (ADM) and Laplace transform method (LTM).
This proposed technique is capable of identifying potential churners at the contract level for a specific prediction time-period.
The greatest advantage of this proposed technique is that no intermediate parameters need to be acquired from other methods.
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In addition to this, the proposed technique generates a ranked list of network configurations, from the best to the worst.
In this paper, the proposed technique has been utilized for binary classification of EEG signals.
This way the proposed technique proves its robustness for the industrial environment.
A quantitative measure is proposed to objectify performance; under this measure, the proposed technique outperforms most of the recent techniques in most cases.
This way the proposed technique improves the classification performance by enhancing the feature representation and reducing the size of the training and testing data vectors.
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