arXiv subjects

Inavamsi Enaganti

Publications and source records attributed to Inavamsi Enaganti.

4 recordsLinked to original sources

arXiv2026

Interaction Density as a Behavioural Signature of Exhibit Type: A Minimal-Log Study from a Two-Venue Science Experience Centre

Understanding how visitors engage with interactive exhibits usually calls for either labour-intensive manual observation or invasive multimodal sensing -- eye-tracking, cameras, wearables -- that few science centres can deploy at scale. We ask how much can be learned instead from the handful of fields that most touch-enabled exhibits already log by default: a session's start time, end time, and press count. Analysing 2,816 visitor sessions across eight exhibits at two venues of a science experience centre in Bengaluru, India, we derive interaction density -- presses per second -- as a simple behavioural signature, and use it to distinguish fast-paced games from slower, deliberate quizzes. Density does so cleanly (Mann-Whitney r=0.556) and predicts exhibit type on its own with a cross-validated AUC=0.778. But the data complicates the obvious story: games are not just more intense, visitors also dwell on them longer (r=0.172), reversing the intuitive trade-off between intensity and duration -- traced to exhibits whose escalating difficulty creates open-ended re-engagement loops rather than fixed endpoints. Density is not a universal replacement for existing metrics either: raw press count alone explains far more variance in dwell time (R^2=0.527) than density does (R^2=0.081), though combining both improves on either alone (R^2=0.667). Exhibit-level anomalies, a cross-venue replication check, and a session-length censoring artefact further stress-test rather than simply confirm these results. The broader case we make is methodological: minimal, privacy-preserving interaction logs -- not additional sensors -- can already support rigorous, falsifiable behavioural research at any science centre with touch-enabled exhibits.

cs.HC↗

arXiv2023

An innovative Deep Learning Based Approach for Accurate Agricultural Crop Price Prediction

Accurate prediction of agricultural crop prices is a crucial input for decision-making by various stakeholders in agriculture: farmers, consumers, retailers, wholesalers, and the Government. These decisions have significant implications including, most importantly, the economic well-being of the farmers. In this paper, our objective is to accurately predict crop prices using historical price information, climate conditions, soil type, location, and other key determinants of crop prices. This is a technically challenging problem, which has been attempted before. In this paper, we propose an innovative deep learning based approach to achieve increased accuracy in price prediction. The proposed approach uses graph neural networks (GNNs) in conjunction with a standard convolutional neural network (CNN) model to exploit geospatial dependencies in prices. Our approach works well with noisy legacy data and produces a performance that is at least 20% better than the results available in the literature. We are able to predict prices up to 30 days ahead. We choose two vegetables, potato (stable price behavior) and tomato (volatile price behavior) and work with noisy public data available from Indian agricultural markets.

cs.LG↗

arXiv2021

Testing the efficacy of epidemic testing

The cataclysmic contagion based calamity -- Covid-19 has shown us a clear need for a comprehensive community based strategy that overcomes the sheer complexity of controlling it and the caveats of current methods. In this regard, as seen in earlier epidemics, testing has always been an integral part of containment policy. But one has to consider the optimality of a testing scheme based on the resultant disease spread in the community and not based on purely increasing testing efficiency. Therefore, taking a decision is no easy feat and must consider the community utility constrained by its priorities, budget, risks, collateral and abilities which can be encoded into the optimization of the strategy. We thus propose a simple pooling strategy that is easy to customize and practical to implement, unlike other complex and computationally intensive methods.

physics.soc-ph↗

arXiv2021

To mock a Mocking bird : Studies in Biomimicry

This paper dwells on certain novel game-theoretic investigations in bio-mimicry, discussed from the perspectives of information asymmetry, individual utility and its optimization via strategic interactions involving co-evolving preys (e.g., insects) and predators (e.g., reptiles) who learn. Formally, we consider a panmictic ecosystem, occupied by species of prey with relatively short lifespan, which evolve mimicry signals over generations as they interact with predators with relatively longer lifespans, thus endowing predators with the ability to learn prey signals. Every prey sends a signal and provides utility to the predator. The prey can be either nutritious or toxic to the predator, but the prey may signal (possibly) deceptively without revealing its true "type." The model is used to study the situation where multi-armed bandit predators with zero prior information are introduced into the ecosystem. As a result of exploration and exploitation the predators naturally select the prey that result in the evolution of those signals. This co-evolution of strategies produces a variety of interesting phenomena which are subjects of this paper.

q-bio.PE↗