N
Nilanjan Sinhababu
Researcher at Indian Institute of Technology Kharagpur
Publications - 10
Citations - 118
Nilanjan Sinhababu is an academic researcher from Indian Institute of Technology Kharagpur. The author has contributed to research in topics: Computer science & Support vector machine. The author has an hindex of 2, co-authored 9 publications receiving 50 citations. Previous affiliations of Nilanjan Sinhababu include Indian Institutes of Technology.
Papers
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Journal ArticleDOI
Power Consumption Analysis, Measurement, Management, and Issues: A State-of-the-Art Review of Smartphone Battery and Energy Usage
Pijush Kanti Dutta Pramanik,Nilanjan Sinhababu,Bulbul Mukherjee,Sanjeevikumar Padmanaban,Aranyak Maity,Bijoy Kumar Upadhyaya,Jens Bo Holm-Nielsen,Prasenjit Choudhury +7 more
TL;DR: This paper provides a generalized, but detailed analysis of the power consumption causes (internal and external) of a smartphone and also offers suggestive measures to minimize the consumption for each factor.
Journal ArticleDOI
Deep Learning Based Resource Availability Prediction for Local Mobile Crowd Computing
TL;DR: In this paper, the authors proposed an effective model to predict the availability of the users (i.e., their smart mobile devices) in such an MCC environment, which is applied to LSTM and GRU-based time-series prediction models for predicting SMD availability.
Journal ArticleDOI
Mining multilingual and multiscript Twitter data: unleashing the language and script barrier
TL;DR: This work has developed a system that automatically identifies and classifies native tweets, irrespective of the script used, and found that the proposed framework gives better precision than the prevailing approaches.
Journal ArticleDOI
Hidden features identification for designing an efficient research article recommendation system
TL;DR: In this paper, the authors proposed four indirect features: keyword diversification, text complexity, citation analysis over time, and scientific quality measurement to represent a research article, which are matchable with user's profile features, thus satisfying an important criterion in collaborative filtering.
Proceedings ArticleDOI
Predicting Device Availability in Mobile Crowd Computing using ConvLSTM
TL;DR: In this paper, the authors proposed a model to predict the availability of smart mobile devices (SMDs) in a campus-based MCC, where, generally, a set of users are available for a certain period regularly.