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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

Blockchain‐based security aspects in heterogeneous Internet‐of‐Things networks: A survey

TL;DR: This paper first discussed the evolution of conventional IoT to the SDN‐based IoT, which can resolve many drawbacks of a conventional IoT system and focused on how the concept of blockchain can be converged with SDN-based IoT system to further improve its security aspects.
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Multichannel lung sound analysis for asthma detection.

TL;DR: The proposed multichannel asthma detection method, where the presence of wheeze in lung sound is not a necessary requirement, outperforms commonly used lung sound classification methods in this field and provides significant relative improvement.
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Classification of coronary artery diseased and normal subjects using multi-channel phonocardiogram signal

TL;DR: A new multi-channel PCG-based system to classify CAD-affected and normal subjects is proposed, and it does not require any additional reference signal, such as an electrocardiogram (ECG) signal.
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Construction of Gene Regulatory Networks Using Recurrent Neural Networks and Swarm Intelligence.

TL;DR: The results indicate that a reduction of 50% in the number of time points does not have an effect on the accuracy of the proposed methodology significantly, with a maximum of just over 15% deterioration in the worst case.
Proceedings ArticleDOI

Performance comparison of speaker recognition systems in presence of duration variability

TL;DR: This study reveals that the relative improvement of total variability based system gradually drops with the reduction in test utterance length, and if the speakers are enrolled with sufficient amount of training data, GMM-UBM system outperforms i-vector system for very short test utterances.