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Logesh Ravi

Researcher at Techno India

Publications -  57
Citations -  949

Logesh Ravi is an academic researcher from Techno India. The author has contributed to research in topics: Computer science & Recommender system. The author has an hindex of 11, co-authored 42 publications receiving 494 citations. Previous affiliations of Logesh Ravi include Birla Institute of Technology and Science & Shanmugha Arts, Science, Technology & Research Academy.

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Unmanned Aerial Vehicle (UAV) based Forest Fire Detection and monitoring for reducing false alarms in forest-fires

TL;DR: This work focuses on the improvement of trustworthy and exact FF recognition algorithms which apply to UAVs, which include Color Code Identification, Smoke Motion Recognition, and Fire Classification algorithms.
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A Collaborative Location Based Travel Recommendation System through Enhanced Rating Prediction for the Group of Users

TL;DR: Views on social network data based recommender systems are expressed by considering usage of various recommendation algorithms, functionalities of systems, different types of interfaces, filtering techniques, and artificial intelligence techniques.
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Detection of Cracks and damage in wind turbine blades using artificial intelligence-based image analytics

TL;DR: The significance of the proposed method is that it is the first of its kind for WTB damage classification and detection for different classes of damage without using transfer learning but by training the model from the image dataset prepared by image augmentation methods and manual editing.
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Fog-assisted personalized healthcare-support system for remote patients with diabetes

TL;DR: An energy efficient fog-assisted healthcare system to maintain the blood glucose level is proposed and the J48Graft decision tree is used to predict the risk level of diabetes with higher classification accuracy by deploying fog computing.
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Data mining-based tag recommendation system: an overview

TL;DR: A review of the tagging recommendation systems and the constraints that affects the available tag recommendation systems is presented, and the use of spreading activation algorithm to study the role of constructed topic ontology for efficient tag recommendations is proposed.