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Indian Institute of Technology Guwahati

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Selected work

Representative Papers

Characterization of Split Comparability Graphs

Apr 27, 2025

This paper characterizes the structure of split comparability graphs and establishes an upper bound on their permutation representation number. We first provide an exact combinatorial characterization—via a necessary and sufficient condition on vertex labelings—yielding the first precise structural description of this graph class. Building on this, we prove that the permutation representation number of any split comparability graph is at most three. As a corollary, the dimension of any split poset is at most three, and we supply a purely combinatorial proof independent of the Dushnik–Miller theorem. Our approach integrates split graph decomposition, transitive orientations, poset dimension theory, and permutation graph representation techniques. The key innovation lies in establishing a direct correspondence between vertex labelings and comparability structure, thereby unifying the interpretation of permutation representation number and poset dimension. This resolves a fundamental gap in the representation theory of split graphs.

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An Explainable Vision Transformer with Transfer Learning Combined with Support Vector Machine Based Efficient Drought Stress Identification

Jul 31, 2024arXiv.org

Early detection of drought stress is critical for minimizing crop losses, yet subtle phenotypic changes necessitate non-invasive aerial imaging and advanced modeling. This paper proposes an interpretable Vision Transformer (ViT)-driven framework tailored for potato crops. We introduce two novel architectures: a ViT-SVM hybrid model and an end-to-end ViT classifier—the first integration of ViT with SVM for agricultural stress recognition. Leveraging transfer learning and multispectral/RGB drone imagery, our method localizes key stress indicators—including leaf wilting and canopy texture degradation—via attention maps. Experimental results demonstrate significant improvements in detection accuracy and provide full interpretability of model decisions through visualized attention mechanisms. The framework enables real-time, trustworthy drought monitoring and management in field conditions. (136 words)

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