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- PublicationDeploying model obfuscation: towards the privacy of decision-making models on shared platforms(2024)The automation of the industrial paradigms characterizes the era of Industry 4.0. The implementation nuances involve data and model sharing among allies and partners working on the same domain. Privacy and security of data and models are fundamental necessities that must be satisfied for this protocol's proper functioning. To this end, we propose a conceptual and algorithmic framework of a model obfuscation scheme. It is built upon the extant data obfuscation paradigm. The future work lies with the implementation and establishment of its viability. This research is expected to develop into deployable model obfuscation technique which practitioners from the industrial domain can adopt.
1 5 - PublicationKnowing the class distinguishing abilities of the features, to build better decision-making models(2024)Explainability allows end-users to have a transparent and humane reckoning of an ML scheme's capability and utility. ML model's modus opernadi can be explained via the features which trained it. To this end, we found no work explaining the features' importance based on their class-distinguishing abilities. In a given dataset, a feature is not equally good at distinguishing between the data points' possible categorizations (or classes). This work explains the features based on their class or category-distinguishing capabilities. We estimate the variables' class-distinguishing capabilities (scores) for pair-wise class combinations, utilize them in a missing feature context, and propose a novel decision-making protocol. A key novelty of this work lies in the refusal to render a decision option when the missing feature (of the test point) has a high class-distinguishing potential for the likely classes. Two real-world datasets are used empirically to validate the explainability of our scheme.
9 1 - PublicationOptimum Track to Track Fusion Using CMA-ES and LSTM Techniques(2024)This paper presents two different methods for track-to-track fusion of drone tracks. The sensors are unbiased radars with fixed locations. The first method uses an offline technique based on a global optimizer called the CMA-ES algorithm and the second one uses LSTM in its different forms to learn the online adjustment of the fusion weights between the two tracks. An objective function utilizing the covariance of the fused tracks is used by the first algorithm while a cost function based on the Kullback-Leibler (KL) divergence measure is used in the second case for training the LSTM. The two methods are compared with other baseline methods using performance metrics such as SIAP and OSPA. Simulations are done for a single object (drone) and repeated for multiple objects in the presence of two radars to demonstrate the validity of the two proposed techniques. The JPDA (Joint Probability Data Association) with fixed gating and moderate clutter is used in the case of multiple objects. Stone Soup was chosen as the radar simulation environment.
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