Conference Papers

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

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Author: search.filters.author.Chandavarkar, B.R.Subject: search.filters.subject.LSTM

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    Intelligent Code Completion
    (Springer Science and Business Media Deutschland GmbH, 2021) Waseem, D.; Pintu; Chandavarkar, B.R.
    Auto complete suggestions for IDEs are widely used and often extremely helpful for inexperienced and expert developers alike. This paper proposes and illustrates an intelligent code completion system using an LSTM based Seq2Seq model that can be used in concert with traditional methods (Such as static analysis, prefix filtering, and tries) to increase the effectiveness of auto complete suggestions and help accelerate coding. © 2021, Springer Nature Switzerland AG.
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    Predictive Selective Repeat - an Optimized Selective Repeat for Noisy Channels
    (Institute of Electrical and Electronics Engineers Inc., 2023) Rati Preethi, S.; Praveen Kumar, P.; Anil, S.; Chandavarkar, B.R.
    Automatic Repeat reQuest (ARQ) is a technique used in two-way communication systems to make sure that the transmitted data is received properly without any errors. The underlying mechanism on which ARQ operates is the acknowledgment (ACK) sent by the receiver to the sender on the orderly arrival of data frames. Some protocols which have been employed include Stop and Wait ARQ, Go-Back-N ARQ, and Selective Repeat ARQ. There have been many attempts to optimize these protocols to decrease the number of Round-Trips and increase the throughput. Predictive Selective Repeat (PSR) ARQ is the technique introduced in this paper to send multiple copies of frames without waiting for ACK. The main goal of PSR ARQ model is to optimally predict the number of required copies for a particular frame so as to reduce the number of Round-Trips by using a Time series Forecasting Long Short-Term Memory (LSTM) model. © 2023 IEEE.