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Tithi Paul

Publications and source records attributed to Tithi Paul.

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Sight, smell and more: What cues do free-ranging dogs use for decision-making while scavenging?

Finding food is a fundamental activity for survival of all living organisms. Free-ranging dogs have been known to use their olfaction to assess the quality and type of available food but their use of visual ability in foraging is not well-documented. In the current study, we seek to remedy that by testing free-ranging dogs in a food-based choice test. We tested whether the dogs implemented hierarchical or synergistic usage of cues while finding food. We found limited prioritization of olfactory cues over visual cues in dichromatic choice tests but in phases with similar perceptual elements, the sensory choice was not clear. Furthermore, free-ranging dogs display a dynamic decision-making in unpredictable urban environments adopting a good-enough strategy during foraging. They prefer speed over accuracy, settling for intermediate quality food if their preferred food item is not available. These dogs also displayed left-bias during food choice. In multi-sensorial, natural setting multiple modulators like environmental noise, risk, and internal perceptual elements apart from food cues seem to be affecting the decision-making in dogs.

q-bio.NC

A Comparative Analysis of CNN-Based Pretrained Models for the Detection and Prediction of Monkeypox

Monkeypox is a rare disease that raised concern among medical specialists following the convi-19 pandemic. It's concerning since monkeypox is difficult to diagnose early on because of symptoms that are similar to chickenpox and measles. Furthermore, because this is a rare condition, there is a knowledge gap among healthcare professionals. As a result, there is an urgent need for a novel technique to combat and anticipate the disease in the early phases of individual virus infection. Multiple CNN-based pre-trained models, including VGG-16, VGG-19, Restnet50, Inception-V3, Densnet, Xception, MobileNetV2, Alexnet, Lenet, and majority Voting, were employed in classification in this study. For this study, multiple data sets were combined, such as monkeypox vs chickenpox, monkeypox versus measles, monkeypox versus normal, and monkeypox versus all diseases. Majority voting performed 97% in monkeypox vs chickenpox, Xception achieved 79% in monkeypox against measles, MobileNetV2 scored 96% in monkeypox vs normal, and Lenet performed 80% in monkeypox versus all.

cs.CV