Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Chandrasekaran, K.Subject: search.filters.subject.Optimization

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    A reinforcement learning approach to optimize downloads over mobile networks
    (Institute of Electrical and Electronics Engineers Inc., 2017) Mohan, J.; Vittal, A.; Chandrasekaran, K.; Krishnamachari, B.
    Dedicated Short Range Communication is attracting a lot of interest these days due to its utility in vehicular safety applications, intelligent transportation system and infotainment applications. Such vehicular networks are characterized by the highly dynamic changes in topology, no significant power constraints and ephemeral links. Considering an interaction between the client and server nodes that last for a random duration of time, an important question is to maximize the amount of useful content downloaded by the client, either in a single request phase, or iteratively in multiple phases. The aim of this work is to propose and investigate a multiphase request model using Markov Decision Process and compare its efficiency against a single phase version. We show that a multiphase request protocol performs better than single phase protocol. © 2017 IEEE.
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    Optimized diet plan using unbounded knapsack Algorithm
    (Institute of Electrical and Electronics Engineers Inc., 2020) Bobade, P.; Kumar, P.; Chandrasekaran, K.; Divakarla, D.
    Cholesterol, hypertension and diabetes are the three major chronic diseases from which most of the people suffers and these peoples often use search engines to acquire related information about these problems. But, almost every information related to diet on the internet isn't suitable for people to gather information about the diet suggestions. A system for diet suggestion which can advocate a prudent diet for such peoples is suggested in this paper. We designed a system that recommends a proper diet which has the adequate knowledge of three above mentioned highly chronic diseases. We propose a solution to the menu recommending problem using the optimization algorithm known as unbounded knapsack. We designed a model which satisfies the nutritional requirements of individuals while imposing the 'Laws of Nutrition', a set of hypothesis used by almost all Latin America's nutrition scientists. This prototype corresponds to a numerical optimization problem with constraints. We design a menu items generator application model to set up a convenient menu for a user with different properties. © 2020 IEEE.