Dr. Rashmi S

@iisc.ac.in

Postdoctoral Fellow, funded by SERB-DBT, Govt of India
Indian Institute of Science

Dr. Rashmi S


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https://researchid.co/rashmikarthik

EDUCATION

B.Sc
M.Sc (Computer Science)
Ph.D ( NLP)

RESEARCH INTERESTS

NLP
ML
DL
Neural Networks
Data Mining
Artificial Intelligence
12

Scopus Publications

331

Scholar Citations

10

Scholar h-index

10

Scholar i10-index

Scopus Publications

  • Offline Handwritten Signature Analysis for Age Classification using Deep Features
    Sathish Kumar, Shivanand Gornale, Abhijit Patil, Rashmi S
    2023 4th International Conference for Emerging Technology Incet 2023, 2023
    Handwritten Signatures are prominent and convincing behavioral biometric data, used in many authentication application areas such as commercial, financial data, document analysis, health care, and forensic science etc. Different writing styles, cursive characters, and inconsistency in the shapes make it tougher to identify the writer from a handwritten signature. This problem remains still a challenging task, because of the considerable intra-class variations in handwriting. The proposed work focuses on the classification of the age using the different handwritten signatures collected from male and female writers of different age groups (adolescents (18-60)). In-house of total 6010 signatures were collected from 610 individuals. Experiments were carried out using well known deep neural architecture namely VGG16 model. Classification task carried out in end-to-end framework. The proposed work achieves comparatively better results than the existing methods respectively.
  • Bi-directional long short term memory using recurrent neural network for biological entity recognition
    Rashmi Siddalingappa, Kanagaraj Sekar
    Iaes International Journal of Artificial Intelligence, 2022
    <p>Biomedical named entity recognition (NER) aims at identifying medical entities from unstructured data. A quintessential task in the supervision of biological databases is handling biomedical terms such as cancer type, DeoxyriboNucleic and RiboNucleic Acid, gene and protein name, and others. However, due to the massive size of online medical repositories, data processing becomes a challenge for a gazetteer without proper annotation. The traditional NER systems depend on feature engineering that is tedious and time-consuming. The research study presents a new model for Bio-NER using recurrent neural network. Unlike existing approaches, the proposed method uses bidirectional traversing with GloVe vector modelling performed at character and word levels. Bio-NER is performed in three stages; firstly, the relevant medical entities in electronic medical records from PubMed were extracted using the skip-gram model. Secondly, a vector representation for each word is created through the 1-hot method. Thirdly, the weights of the recurrent neural network (RNN) layers are adjusted using backward propagation. Finally, the long-short-term memory cells store the previously encountered medical entity to tackle context-dependency. The accuracy and F-score are calculated for each medical entity type. The MacroR, MacroP, and MacroF are equal to 0.86, 0.88, and 0.87. The overall accuracy achieved was 94%.</p>
  • Methods and applications of machine learning in structure-based drug discovery
    Madhumathi Sanjeevi, Prajna N. Hebbar, Natarajan Aiswarya, S. Rashmi, Chandrashekar Narayanan Rahul, Ajitha Mohan, Jeyaraman Jeyakanthan, Kanagaraj Sekar
    Advances in Protein Molecular and Structural Biology Methods, 2022
  • Anomaly Detection on Medical Images using Autoencoder and Convolutional Neural Network
    Rashmi Siddalingappa, Sekar Kanagaraj
    International Journal of Advanced Computer Science and Applications, 2021
    Detection of anomalies from the medical image dataset improves prognosis by discovering new facts hidden in the data. The present study aims to discuss anomaly detection using autoencoders and convolutional neural networks. The autoencoder identifies the imbalance between normal and abnormal samples. They create learning models flexible and accurate on training data. The problem is addressed in four stages: 1) training: an autoencoder is initialized with the hyperparameters and trained on the lung cancer CT scan images, 2) test: the autoencoder reconstructs the input from the latent space representation with a slight variation from the original data, indicated by a reconstruction error as Mean Squared Error (MSE), 3) evaluate: the MSE value of the training and test dataset are compared. The MSE values of anomalous data are higher than a base threshold, detecting those as anomalies, 4) validate: the efficiency metrics such as accuracy and MSE scores are used at both training and validation phases. The dataset was further classified as benign and malignant. The accuracy reported for outlier detection and the classification task is 98% and 97.2%. Thus, the proposed autoencoder-based anomaly detection could positively isolate anomalies from the CT scan images of lung cancer. Keywords—Anomalies; autoencoder; convolutional neural networks (CNN) (ConvNets); deep neural network architecture; regularization
  • An Invasion to Human - Computer Interaction: Stages of Speech Recognition Process using Speech Processing Techniques
    S. Rashmi, M. Hanumanthappa, Vasantha Kavitha
    Icsns 2018 Proceedings of IEEE International Conference on Soft Computing and Network Security, 2018
    Language is the primary form of communication in today's world. Though learning any natural language is easy, it's comforting is always measured with the exactness achieved through the freedom of speech and written text. Imparting natural language and its coherence with computer is cumbersome due to the language ambiguities and free form nature of linguistic theories. Hence the main focus of a speech recognition process is to bridge the outfielders between the conceptual and practical aspects of linguistic abstraction in the view of computer thereby solving the difficult impedance. In this direction, it is important to concentrate on one of the major fields of linguistics under Natural Language Processing (NLP) - Phonetics. Phonetics condenses the spoken form of language and its structure achieved in the thought experiment process. The current research paper encompasses various stages of speech recognition process.
  • Training Based Noise Removal Technique for a Speech-to-Text Representation Model
    S Rashmi, M Hanumanthappa, B Gopala
    Journal of Physics Conference Series, 2018
    The accomplishments of a Speech Recognition Process is significantly deteriorated by the presence of an unwanted speech signal called noise entity. This entity is present in the primary audio source. During the Speech – Recognition process, the presence of noise in the original audio signal adversely impact the output generated. Therefore, noise must be removed before performing any functions on the speech signal. With such observation of noise, it becomes essential to apply a unique procedure that perforce the noise without causing any distortion to the original audio. This research paper presents a novel approach to de-noise the given input audio signal based on the training method. Further, the paper explains the architecture adopted for Training Based Noise Removal Technique (TBNRT), steps of noise removal process, and the evaluation of the results obtained by the proposed procedure. The SNR values of the input are compared with the SNR values of the audio signal after applying the proposed TBNRT. Improvements in the SNR values were observed after the application of the proposed method. The obtained results were compared with the existing techniques and the proposed TBNRT gave promising results.
  • Hidden Markov Model for speech recognition system—a pilot study and a Naive approach for speech-to-text model
    S. Rashmi, M. Hanumanthappa, Mallamma V. Reddy
    Advances in Intelligent Systems and Computing, 2018
    Today’s advancement in the research field has brought a new horizon to design the state-of-the-art systems that produce sound utterance. In order to attain a higher level of speech understanding potentiality, it is of utmost importance to achieve good efficiency. Speech-to-Text (STT) or voice recognition system is an efficacious approach that aims at recognizing speech and allows the conversion of the human voice into the text. By this, an interface between the human and the computer is created. In this direction, this paper introduces a novel approach to convert STT by using Hidden Markov Model (HMM). HMM along with other techniques such as Mel-Frequency Cepstral Coefficients (MFCCs), Decision trees, Support Vector Machine (SVM) is used to ascertain the speakers’ utterances and catalyse these utterances into quantization features by evaluating the likelihood extremity of the spoken word. The accuracy of the proposed architecture is studied, which is found to be better than the existing methodologies.
  • Qualitative and quantitative study of syntactic structure: a grammar checker using part of speech tags
    S. Rashmi, M. Hanumanthappa
    International Journal of Information Technology Singapore, 2017
    One of the fascinating features of English language is its robust grammar construction and syntactic structure. Learning grammar is not difficult in the present era as there are many online tools available for grammar teaching. In spite of its abundance presence and relevance, when one takes a deep dive into finding the syntactic structure used for grammar checker, it is perhaps a complex paradigm. It is hence important to study the logic of defining the grammar rules. Therefore the objective of this paper is to describe the prototype of an efficient grammar checker and to design an interface to perform grammar check. The efficiency of the proposed algorithm is improved as compared to the existing methods.
  • Text-to-Speech translation using Support Vector Machine, an approach to find a potential path for human-computer speech synthesizer
    Rashmi S, Hanumanthappa M, Jyothi N M
    Proceedings of the 2016 IEEE International Conference on Wireless Communications Signal Processing and Networking Wispnet 2016, 2016
    Text-to-Speech (TTS), an astounding feature to assemble computer with intelligence and to induce sound is seemingly a challenging task as it is related to the propagation of uncertainty with the input. This is because TTS evolutes the input based on the probabilities and not with certainty ratios. TTS is accomplished by generating the sound structure/phoneme and then classifying these phonemes in the phonetic dictionary. The Wards' algorithms, BIRCH, Support Vector Machine (SVM) are used to figure out the appropriate sound representation for the given context. To distinguish correct elocution, the SVM procedures are equipped with the principles of pruning. The output was analyzed using divergent stages of uncertainty. In order to study the effect of the output 10 listeners were considered for determining Signal-to-Noise (SNR) ratio. SNR shows that the errors of both type phase and uncertainty were approximately 6% resulting 94% of accuracy. These results manifested that SVM stratagem can be used to obtain better results for TTS synthesizer.
  • “Part of speech tagging – A corpus based approach”
    S. Rashmi, M. Hanumanthappa
    Communications in Computer and Information Science, 2016
    POS tagging, an ideal way to augment a corpus is an imperative abstraction for text mining. However with an increase in the amount of linguistic errors and distinctive fashion of language ambiguities, the data filtered by POS tagging is noisier. In this paper, probabilistic tagging and tagging based on Markov models are combined to estimate the association probabilities. Based on this combined approach, error estimation model is defined. Comparison study is made on different corpus available in NLTK such as Crubadan, Brown and INSPEC. The results obtained by the proposed methodologies show a drastic increase in the accuracy rate of about 98% when compared to the existing algorithms which shows an average of 96% accurate. The performance measure is plotted to calculate the error ratio across the maximum-likelihood estimation.
  • Dimensionality reduction for text preprocessing in text mining using NLTK
    International Journal of Applied Engineering Research, 2015
  • Metrics for evaluating phonetics machine translation in Natural Language Processing through modified Edit Distance algorithm-A naïve approach
    M Hanumanthappa, Rashmi S, Mallamma V Reddy
    2015 International Conference on Computer Communication and Informatics Iccci 2015, 2015

RECENT SCHOLAR PUBLICATIONS

  • Automated Maternal–Fetal Health Analysis through Deep Neural Network Integration in Ultrasound Imaging
    SS Gornale, R Siddalingappa, K Li, KW Goh
    2026
  • Adaptive Phoneme State Learning Architecture for Enhanced Speech Recognition Using Backpropagation Neural Network and Hidden Markov Model
    R Siddalingappa, M Savitha, P Stella Mary I, S Gornale, L BA, K Li, ...
    F1000Research 15, 338 , 2026
    2026
  • Enhanced radiation exposure of airline crew and passengers during the May 2024 geomagnetic storm
    H Aryan, J Bortnik, WK Tobiska, P Mehta, B Hogan, R Siddalingappa, ...
    Journal of Geophysical Research: Space Physics 131 (1), e2025JA034217 , 2026
    2026
  • FEDGE: FEDerated learning at the EDGE on space platforms using deep neural network architectures
    R Siddalingappa, L BA
    International Journal of Information Technology, 1-12 , 2025
    2025
    Citations: 1
  • Enhancing image compression through a novel Structural Fidelity Weighted Ensemble (SFWE) model
    PS Mary, R Siddalingappa, M Savitha
    MethodsX 15, 103695 , 2025
    2025
  • A Comparative Analysis of L1, L2, and L1L2 Regularization Techniques in Neural Networks for Image Classification
    A Deepa, R Siddalingappa, P Kalpana, J Loveline Zeema, M Vinay, ...
    International Journal of Engineering Trends and Technology 73 (10), 107-116 , 2025
    2025
  • Artificial Intelligence and Human-AI Driven Accreditation System for Higher Education Quality Assurance
    P Prakash, SS Gornale, R Siddalingappa
    2025 2nd International Conference on Artificial Intelligence and Knowledge … , 2025
    2025
  • A hybrid ensemble of denoising autoencoders and deep learning models for fetal image analysis
    S Gornale, P Kamat, P Hiremath, R Siddalingappa
    Cureus Journal Of Computer Science , 2025
    2025
    Citations: 2
  • Enhancing Image Classification Performance through Hybrid Self-Supervised Learning Strategies
    R Siddalingappa
    International Journal of Electronics and Communication Engineering 12 (7 … , 2025
    2025
  • AI-Driven Lead Scoring: Enhancing Real Estate Decisions with Predictive Analytics
    RS Maansi Tomer
    IEEE International Conference on Electronics, Computing and Communication … , 2025
    2025
  • Comparative Analysis of Machine Learning Models and Interpolation Techniques for Seasonal Rainfall Prediction in Tamil Nadu
    K Evangilin, R Powell, J Suganthi, S Deepa, R Siddalingappa
    2025 International Conference on Emerging Trends in Industry 4.0 … , 2025
    2025
    Citations: 1
  • A Fine-tuned ProtGPT2 (transformer model) for predicting more virulent SARS-CoV-2 variants and understanding its virulence by biophysical methods
    DS Paul, AR Jeyaraj, R Siddalingappa, R Venkatachalam, G Kothandan
    Authorea Preprints , 2025
    2025
  • Cross correlation between plasmaspheric hiss waves and enhanced radiation levels at aviation altitudes
    H Aryan, J Bortnik, WK Tobiska, P Mehta, R Siddalingappa, B Hogan
    Space weather 23 (2), e2024SW004184 , 2025
    2025
    Citations: 6
  • A Fine-tuned ProtGPT2 (transformer model) for Predicting more Virulent SARS-CoV-2 variants and understanding its virulence by biophysical methods
    SP Dhamodharan, AR Jeyaraj, R Siddalingappa, R Venkatachalam, ...
    bioRxiv, 2025.01. 13.632691 , 2025
    2025
    Citations: 1
  • Digital workflow integration in All-on-4 implant rehabilitation: A case report of complete mandibular restoration
    MB Ravi, A Aradya, N Vinodkumar, MR Dhakshaini, S Rashmi
    International Journal of Health & Allied Sciences 14 (1), 121-125 , 2025
    2025
  • A Knowledge Based Grade Prediction System using Machine Learning for Higher Education Institutions
    R Siddalingappa, SS Gornale, S Kumar
    Nanotechnology Perceptions 20 (S14) , 2024
    2024
  • Reduced‐order probabilistic emulation of physics‐based ring current models: Application to RAM‐SCB particle flux
    AA Cruz, R Siddalingappa, PM Mehta, SK Morley, HC Godinez, ...
    Space weather 22 (6), e2023SW003706 , 2024
    2024
    Citations: 2
  • Deep learning techniques for a comprehensive analysis of fetal biometric parameters across trimesters
    S Gornale, P Kamat, R Siddalingappa, S Kumar
    Transactions on Machine Learning and Artificial Intelligence 12 (3), 18-45 , 2024
    2024
    Citations: 3
  • Finding identical sequence repeats in multiple protein sequences: An algorithm: VK Maurya et al
    VK Maurya, M Sanjeevi, CN Rahul, A Mohan, D Ramachandran, ...
    Journal of Biosciences 49 (1), 41 , 2024
    2024
  • A KNOWLEDGE BASED GRADE PREDICTION SYSTEM USING MACHINE LEARNING FOR HIGHER EDUCATION INSTITUTIONS
    P PRAKASH, SG SHIVANAND, S RASHMI, K SATISH
    NANOTECHNOLOGY, 1683-1697 , 2024
    2024

MOST CITED SCHOLAR PUBLICATIONS

  • International journal of advanced research in computer science and software engineering
    S Roy, S Nag, IK Maitra, SK Bandyopadhyay
    International Journal 3 (6) , 2013
    2013.0
    Citations: 61
  • K-nearest-neighbor algorithm to predict the survival time and classification of various stages of oral cancer: a machine learning approach
    R Siddalingappa, S Kanagaraj
    F1000Research 11, 70 , 2023
    2023.0
    Citations: 53
  • Anomaly detection on medical images using autoencoder and convolutional neural network
    R Siddalingappa, S Kanagaraj
    International Journal of Advanced Computer Science and Applications , 2021
    2021.0
    Citations: 33
  • Gender classification based on online signature features using machine learning techniques
    R Siddalingappa, S Gornale, S Kumar, A Mane
    International Journal of Intelligent Systems and Applications in Engineering … , 2022
    2022.0
    Citations: 20
  • Survey on handwritten signature biometric data analysis for assessment of neurological disorder using machine learning techniques
    S Gornale, S Kumar, R Siddalingappa, PS Hiremath
    Transactions on Machine Learning and Artificial Intelligence 10 (2), 27-60 , 2022
    2022.0
    Citations: 20
  • Hidden Markov Model for speech recognition system—a pilot study and a naive approach for speech-to-text model
    S Rashmi, M Hanumanthappa, MV Reddy
    Speech and Language Processing for Human-Machine Communications: Proceedings … , 2017
    2017.0
    Citations: 15
  • Bi-directional long short term memory using recurrent neural network for biological entity recognition
    R Siddalingappa, K Sekar
    IAES International Journal of Artificial Intelligence 11 (1), 89-101 , 2022
    2022.0
    Citations: 13
  • Methods and applications of machine learning in structure-based drug discovery
    M Sanjeevi, PN Hebbar, N Aiswarya, S Rashmi, CN Rahul, A Mohan, ...
    Advances in Protein Molecular and Structural Biology Methods, 405-437 , 2022
    2022.0
    Citations: 11
  • Qualitative and quantitative study of syntactic structure: a grammar checker using part of speech tags
    S Rashmi, M Hanumanthappa
    International Journal of Information Technology 9 (2), 159-166 , 2017
    2017.0
    Citations: 11
  • Enhanced radiation levels at aviation altitudes and their relationship to plasma waves in the inner magnetosphere
    H Aryan, J Bortnik, WK Tobiska, P Mehta, R Siddalingappa
    Space weather 21 (10), e2023SW003477 , 2023
    2023.0
    Citations: 10
  • Venkat and Ravikiran S. 2014
    S Rashmi, S Addamani
    Spectral Angle Mapper Algorithm for Remote Sensing Image Classification , 0
    Citations: 8
  • Text-to-Speech translation using Support Vector Machine, an approach to find a potential path for human-computer speech synthesizer
    S Rashmi, M Hanumanthappa, NM Jyothi
    2016 International Conference on Wireless Communications, Signal Processing … , 2016
    2016.0
    Citations: 7
  • Phonetic dictionary for natural language processing: Kannada
    MV Reddy, M Hanumanthappa, NM Jyothi, S Rashmi
    International Journal of Engineering Research and Applications 4 (7), 1-4 , 2014
    2014.0
    Citations: 7
  • Cross correlation between plasmaspheric hiss waves and enhanced radiation levels at aviation altitudes
    H Aryan, J Bortnik, WK Tobiska, P Mehta, R Siddalingappa, B Hogan
    Space weather 23 (2), e2024SW004184 , 2025
    2025.0
    Citations: 6
  • The role of the national assessment and accreditation council in ensuring quality education in the Indian education system: an analysis of its accreditation standards and …
    P Prakash, S Gornale, MS Shymasundar, R Siddalingappa
    British Journal of Multidisciplinary and Advanced Studies 4 (6), 1-18 , 2023
    2023.0
    Citations: 6
  • Impact of Phonetics in Natural Language Processing: A Literature Survey
    M Hanumanthappa, S Rashmi, NM Jyothi
    IIJISET–International Journal of Innovative Science, Engineering … , 2014
    2014.0
    Citations: 6
  • Training based noise removal technique for a speech-to-text representation model
    S Rashmi, M Hanumanthappa, B Gopala
    Journal of Physics: Conference Series 1142 (1), 012019 , 2018
    2018.0
    Citations: 5
  • Dimensionality Reduction for Text Pre-processing in Text Mining Using NLTK
    S Rashmi
    International Journal of Applied Engineering Research 10 (ISSN 0973-4562 … , 2015
    2015.0
    Citations: 5
  • Metrics for evaluating phonetics machine translation in Natural Language Processing through modified Edit Distance algorithm-A naive approach
    M Hanumanthappa, S Rashmi, MV Reddy
    2015 International Conference on Computer Communication and Informatics … , 2015
    2015.0
    Citations: 4
  • Deep learning techniques for a comprehensive analysis of fetal biometric parameters across trimesters
    S Gornale, P Kamat, R Siddalingappa, S Kumar
    Transactions on Machine Learning and Artificial Intelligence 12 (3), 18-45 , 2024
    2024.0
    Citations: 3