G
Goutam Saha
Researcher at North Eastern Hill University
Publications - 151
Citations - 860
Goutam Saha is an academic researcher from North Eastern Hill University. The author has contributed to research in topics: Speaker recognition & Gene regulatory network. The author has an hindex of 13, co-authored 143 publications receiving 583 citations. Previous affiliations of Goutam Saha include Indian Institute of Technology Kharagpur & West Bengal University of Technology.
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Journal ArticleDOI
Recurrent neural network-based modeling of gene regulatory network using elephant swarm water search algorithm
TL;DR: A new metaheuristic namely Elephant Swarm Water Search Algorithm (ESWSA) to infer Gene Regulatory Network (GRN) is proposed, mainly based on the water search strategy of intelligent and social elephants during drought, utilizing the different types of communication techniques.
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6LE-SDN: An Edge-Based Software-Defined Network for Internet of Things
TL;DR: A new protocol—edge-based 6LoWPAN-SDN protocol (6LE-SDNP) is proposed, which is capable of ensuring optimal routing of the packet for efficient communication among the devices and uses the SDN-based edge controller for reducing the latency of the network apart from improving the interoperability feature.
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Large-Scale Recurrent Neural Network Based Modelling of Gene Regulatory Network Using Cuckoo Search-Flower Pollination Algorithm
TL;DR: The results obtained show that the proposed methodology is capable of increasing the inference of correct regulations and decreasing false regulations to a high degree and the proposed method sacrifices computational time complexity in both cases due to the hybrid optimization process.
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Runoff estimation using modified adaptive neuro-fuzzy inference system
TL;DR: This paper incorporated one of the evolutionary algorithms known as Particle Swarm Optimization (PSO) which was used in estimating the parameters pertaining to ANFIS and found that proposed PSO-ANFIS performed better than ARIMA and conventional ANfIS with respect to the prediction accuracy of runoff.
Book ChapterDOI
A Comparative Study of Feature Extraction Algorithms on ANN Based Speaker Model for Speaker Recognition Applications
TL;DR: A comparative study of usefulness of four of the most popular feature extraction algorithm in Artificial Neural Network based Text dependent speaker recognition system shows normalized Mel Frequency Cepstral Coefficients performing better in false acceptance rate as well as in size of the network for an admissible error rate.