Faculty Publications

Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736

Publications by NITK Faculty

Browse

Author: search.filters.author.Reddy, G.

Search Results

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    Role of intensity of emotions for effective personalized video recommendation: A reinforcement learning approach
    (Springer Verlag service@springer.de, 2018) Tripathi, A.; Manasa, D.G.; Rakshitha, K.; Ashwin, T.S.; Reddy, G.
    Development of artificially intelligent agents in video recommendation systems over past decade has been an active research area. In this paper, we have presented a novel hybrid approach (combining collaborative as well as content-based filtering) to create an agent which targets the intensity of emotional content present in a video for recommendation. Since cognitive preferences of a user in real world are always in a dynamic state, tracking user behavior in real time as well as the general cognitive preferences of the users toward different emotions is a key parameter for recommendation. The proposed system monitors the user interactions with the recommended video from its user interface and web camera to learn the criterion of decision-making in real time through reinforcement learning. To evaluate the proposed system, we have created our own UI, collected videos from YouTube, and applied Q-learning to train our system to effectively adapt user preferences. © Springer Nature Singapore Pte Ltd. 2018
  • No Thumbnail Available
    Item
    Critical Review on Heart Disease Prediction: A Machine Learning Approach
    (Institute of Electrical and Electronics Engineers Inc., 2023) Mahapatro, S.R.; Mahapatra, R.K.; Shet, N.S.V.; Prusty, S.B.; Satapathi, G.S.; Manjukiran, B.; Reddy, G.; Chandana, O.; Divya, N.; DImri, P.
    The heart is the second-most significant organ in the human body after the brain, which is the most significant organ. All of the body's organs are nourished and the blood is circulated. In the medical field, it might be difficult to anticipate the development of heart diseases. Data analytics is crucial for developing predictions based on new information, and it helps hospitals predict diseases. Every year, cardiovascular diseases account for more than 31 % of all fatalities globally. Different Machine learning algorithms are in this paper to predict heart disease. It presents a general overview of the previous work and offers insight into the current algorithm. © 2023 IEEE.