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Kathiravan Srinivasan

Researcher at VIT University

Publications -  112
Citations -  1370

Kathiravan Srinivasan is an academic researcher from VIT University. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 10, co-authored 80 publications receiving 451 citations. Previous affiliations of Kathiravan Srinivasan include National Ilan University.

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Sensors Driven AI-Based Agriculture Recommendation Model for Assessing Land Suitability

TL;DR: This research proposes an expert system by integrating sensor networks with Artificial Intelligence systems such as neural networks and Multi-Layer Perceptron (MLP) for the assessment of agriculture land suitability.
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Hybrid context enriched deep learning model for fine-grained sentiment analysis in textual and visual semiotic modality social data

TL;DR: A hybrid deep learning model for fine-grained sentiment prediction in real-time multimodal data that reinforces the strengths of deep learning nets in combination to machine learning to deal with two specific semiotic systems, namely the textual and visual systems.
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Virtual reality among the elderly: a usefulness and acceptance study from Taiwan

TL;DR: Older people have positive perceptions towards accepting and using VR to support active aging, implying positive attitudes toward adopting this new technology.
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Performance Comparison of Deep CNN Models for Detecting Driver’s Distraction

TL;DR: This research develops a deep convolutional neural network (deep CNN) models for predicting the reason behind the driver’s distraction and the ResNet model outperformed all other models as the best detection model for predicting and accurately determining the drivers’ activities.
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Recent Advances on IoT-Assisted Wearable Sensor Systems for Healthcare Monitoring

TL;DR: In this article, a review rigorously discusses the various IoT architectures, different methods of data processing, transfer, and computing paradigms, and problems commonly faced in IoT-assisted wearable sensor systems and the specific issues that need to be tackled to optimize these systems in healthcare.