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University of Hyderabad

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Research library13linked papers
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Selected work

Representative Papers

A Multi-tiered Solution for Personalized Baggage Item Recommendations using FastText and Association Rule Mining

Jan 16, 2025

Existing luggage packing recommendation systems for air travelers suffer from insufficient personalization and struggle to jointly satisfy spatial and weight constraints. Method: This paper proposes a multi-layer recommendation framework integrating FastText semantic embeddings with Association Rule Mining (ARM). It jointly models user search behavior, destination-related textual semantics, and item co-occurrence patterns through multi-stage data fusion to achieve fine-grained demand understanding. Crucially, it pioneers the synergistic use of FastText word vectors and ARM metrics (support, confidence, lift) for luggage item recommendation—overcoming cold-start and long-tail limitations inherent in conventional collaborative filtering. Results: Experiments demonstrate significant improvements: +28.6% in recommendation coverage, +19.3% in NDCG@5, and a 37% increase in user packing efficiency. The framework delivers interpretable, deployable technical support for precision marketing and travel service optimization.

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Model-Based Systems Engineering Framework for SysML-Driven Design of Autonomous UAVs

Aug 10, 2026

This work addresses the common challenges in autonomous drone development—such as fragmented requirements, architectural inconsistencies, and poor traceability—stemming from disjointed design processes. To bridge these gaps, the authors propose a SysML-based model-driven systems engineering framework that integrates a unified four-layer model encompassing requirements, functions, logical components, and physical/software elements. Crucially, this approach establishes, for the first time, a deep alignment between multiple SysML diagrams—including requirement, activity, and block definition diagrams—and the ROS 2 architecture, specifically its nodes, topics, services, and actions. The framework enables end-to-end traceable design, allowing early-stage allocation of requirements, precise interface specification, clear subsystem responsibility assignment, and verification planning—all prior to simulation or deployment—thereby effectively supporting typical mission scenarios such as obstacle avoidance and return-to-home operations.

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Fog Computing and Large Language Models: A vision for the mutual beneficiaries

Jun 28, 2026

This work addresses the challenges of high latency, network overhead, and privacy concerns associated with deploying large language models (LLMs) in cloud-centric architectures for Internet of Things (IoT) applications. To overcome these limitations, the authors propose an integrated fog computing framework that brings LLMs closer to end devices through lightweighting techniques—including parameter quantization, pruning, and low-rank adaptation—enabling near-edge intelligence. The approach further incorporates resource-aware scheduling and LLM-driven automated code generation to facilitate dynamic deployment of fog applications. This study presents the first systematic architecture establishing a symbiotic relationship between fog computing and LLMs, demonstrating not only the feasibility of deploying LLMs in resource-constrained fog environments but also significantly enhancing fog nodes’ autonomous programming and service orchestration capabilities, thereby advancing the convergence of edge intelligence and generative AI.

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Recent publications

Latest Papers

Model-Based Systems Engineering Framework for SysML-Driven Design of Autonomous UAVs

Aug 10, 2026

This work addresses the common challenges in autonomous drone development—such as fragmented requirements, architectural inconsistencies, and poor traceability—stemming from disjointed design processes. To bridge these gaps, the authors propose a SysML-based model-driven systems engineering framework that integrates a unified four-layer model encompassing requirements, functions, logical components, and physical/software elements. Crucially, this approach establishes, for the first time, a deep alignment between multiple SysML diagrams—including requirement, activity, and block definition diagrams—and the ROS 2 architecture, specifically its nodes, topics, services, and actions. The framework enables end-to-end traceable design, allowing early-stage allocation of requirements, precise interface specification, clear subsystem responsibility assignment, and verification planning—all prior to simulation or deployment—thereby effectively supporting typical mission scenarios such as obstacle avoidance and return-to-home operations.

0 citationsRead paper

Fog Computing and Large Language Models: A vision for the mutual beneficiaries

Jun 28, 2026

This work addresses the challenges of high latency, network overhead, and privacy concerns associated with deploying large language models (LLMs) in cloud-centric architectures for Internet of Things (IoT) applications. To overcome these limitations, the authors propose an integrated fog computing framework that brings LLMs closer to end devices through lightweighting techniques—including parameter quantization, pruning, and low-rank adaptation—enabling near-edge intelligence. The approach further incorporates resource-aware scheduling and LLM-driven automated code generation to facilitate dynamic deployment of fog applications. This study presents the first systematic architecture establishing a symbiotic relationship between fog computing and LLMs, demonstrating not only the feasibility of deploying LLMs in resource-constrained fog environments but also significantly enhancing fog nodes’ autonomous programming and service orchestration capabilities, thereby advancing the convergence of edge intelligence and generative AI.

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On the Complexity of Signed Domination

Jun 09, 2026

This study addresses the problem of computing the minimum weight of signed domination functions on graphs. Although this problem is known to be NP-complete on bipartite graphs, chordal graphs, and planar graphs, the paper establishes its NP-completeness even on split graphs. Through a parameterized complexity analysis, the authors show that the problem is W[1]-hard when parameterized by the feedback vertex set number. In contrast, they present the first fixed-parameter tractable (FPT) algorithms using two structural parameters: neighborhood diversity and twin-cover number. These results refine the complexity landscape of the problem across fine-grained graph classes and offer efficient solutions for specific sparse or highly structured graphs.

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