Faculty Publications
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Publications by NITK Faculty
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Item Long Short Term Memory Networks for Lexical Normalization of Tweets(Institute of Electrical and Electronics Engineers Inc., 2021) Nayak, P.; Praueeth, G.; Kulkarni, R.; Anand Kumar, M.Lexical normalization is converting a non-standard text into a standard text that is more readable and universal. Data obtained from social media sites and tweets often contain much noise and use non-canonical sentence structures such as non-standard abbrevlatlons, skipping of words, spelling errors, etc. Hence such data needs to be appropriately processed before it can be used. The processing can be done by lexical normalization, which reduces randomness and converts the sentence structure to a predefined standard. Hence. lexical normalization can help in improving the performance of systems that use user-generated text as inputs. There are several ways to perform lexical normalization, such as dictionary lookups, most frequent replacements, etc. However, VVe aim to explore the domain of deep learning to find approaches that can be used to normalize texts lexically. © 2021 IEEE.Item Cadmium (II) and nickel (II) biosorption by Bacillus laterosporus (MTCC 1628)(Taiwan Institute of Chemical Engineers, 2014) Kulkarni, R.; Shetty K, V.; Srinikethan, G.Biosorption of heavy metals is a promising technology that involves removal of toxic metals from industrial waste streams and natural waters. The study describes the sorption of cadmium (II) [Cd (II)] and nickel (II) [Ni (II)] by dead biomass of Bacillus laterosporus, MTCC 1628. The biosorption conditions for the removal of Cd (II) and Ni (II) were examined by studying the effect of pH, contact time, biosorbent dosage and initial metal ion concentration. Shake flask studies yielded adsorption equilibrium in almost 120. min, for both the metals. It was found from Langmuir model that the maximum adsorption capacity for Cd (II) and Ni (II) ions was 85.47. mg/g and 44.44. mg/g respectively. Kinetic evaluation of the experimental data showed that the biosorption process followed pseudo-second order kinetics. Thermodynamic analysis showed that biosorption is an endothermic process with ?. H° of 5.45. kJ/mol for Cd (II) biosorption and 24.33. kJ/mol for Ni (II) biosorption. The surface characteristics of B. laterosporus biomass before and after metal biosorption were analyzed by using scanning electron microscope (SEM) with energy dispersive X-ray spectroscopy (EDAX) to study the changes in surface morphology and elemental constitution of the adsorbent. B. laterosporus exhibited a higher and better potential biosorbent for the removal of Cd (II) as compared to Ni (II) from aqueous solution. © 2013 Taiwan Institute of Chemical Engineers.Item OntoPred: An Efficient Attention-Based Approach for Protein Function Prediction Using Skip-Gram Features(Springer, 2023) Chintawar, S.; Kulkarni, R.; Patil, N.Proteins play an essential role in performing many cellular functions in organisms and are responsible for various biochemical activities. The main objective of this task of protein function prediction is to annotate protein sequences with their correct functions, which are represented by Gene Ontology (GO) terms. Recently, the number of new proteins released has been increasing. As the experimental approach of annotating these proteins is very time-consuming, the need for faster annotation techniques has arisen. Approaches using deep learning and machine learning have been shown to be beneficial in this regard. In this research, we propose a novel approach, OntoPred, for the task of function prediction which makes use of the standalone protein sequences and annotates them with their corresponding functions (GO terms). The core idea is to use an attention mechanism to identify which parts of a sequence influence the presence of a function. The model uses a combination of n-grams and skip-gram features extracted from the sequences. The proposed model was evaluated on multiple datasets including the CAFA3 evaluation benchmark. The maximal F1 scores obtained on molecular function (MF), biological process (BP), and cellular component (CC) aspect on the CAFA3 evaluation benchmark are 0.494, 0.480, and 0.637 respectively. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.