Kishore Babu Nampalle

@iitr.ac.in

Research Scholar, Computer Science and Engineering Department
Indian Institute of Technology Roorkee

Kishore Babu Nampalle


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https://researchid.co/kishorenkb

EDUCATION

Ph.D., Department of Computer Science and Engineering, Indian Institute of Technology Roorkee.

M.Tech., Department of Computer Science and Engineering, Indian Institute of Technology Roorkee.

RESEARCH, TEACHING, or OTHER INTERESTS

Artificial Intelligence, Computer Science Applications, Cancer Research, Computer Vision and Pattern Recognition
9

Scopus Publications

96

Scholar Citations

5

Scholar h-index

5

Scholar i10-index

Scopus Publications

  • Privacy-Preserving Deep Learning for Medical Imaging: An Optimized Federated Classification Framework
    Kishore Babu Nampalle, Ankita Kumari
    2025 3rd International Conference on Computational Intelligence and Network Systems Cins 2025, 2025
  • Differential Privacy in Federated Learning for Medical Image Classification
    Kishore Babu Nampalle, Ankita Kumari, Gaurav Sundaram
    2025 3rd International Conference on Computational Intelligence and Network Systems Cins 2025, 2025
  • Towards improved U-Net for efficient skin lesion segmentation
    Kishore Babu Nampalle, Anshul Pundhir, Pushpamanjari Ramesh Jupudi, Balasubramanian Raman
    Multimedia Tools and Applications, 2024
  • Integrating Physiological Signals with Dynamical Attention Networks for Personality Trait Analysis
    Deepak Kumar, Pradeep Singh, Richa, Kishore Babu Nampalle, Balasubramanian Raman
    Proceedings of the International Joint Conference on Neural Networks, 2024
    In the expanding field of personality psychology, the assessment and analysis of personality traits are of paramount importance. This paper introduces a novel deep learning network designed to predict the OCEAN personality traits (openness, conscientiousness, extraversion, agreeableness, and neuroticism), leveraging physiological signals such as electroencephalogram (EEG), electrocardiogram (ECG), and galvanic skin response (GSR). The model incorporates convolutional layers and is augmented with specifically engineered attention modules that meticulously process EEG, ECG, and GSR signals. By integrating a novel blend of component-wise attention alongside gating mechanisms, the model significantly enhances feature selection and representation. This is further complemented by the inclusion of a temporal attention module that emphasizes significant timesteps in the data, ensuring a more dynamic and responsive analysis. The model’s architecture culminates in a robust and integrated feature representation, encapsulating the nuanced interplay of the processed physiological signals. This composite representation is subsequently flattened and fed into a dense module, designed for the accurate prediction of personality traits. Our approach not only marks a significant stride in understanding the intricate relationship between physiological signals and personality traits but also lays the groundwork for diverse applications. These range from advancements in psychological research and personalized health monitoring to refining human-computer interaction paradigms. By bridging the gap between physiological data and psychological traits, this research contributes to the burgeoning field of affective computing and offers a new vista in the personalized assessment of individual traits.
  • Medical image security and authenticity via dual encryption
    Kishore Babu Nampalle, Shriansh Manhas, Balasubramanian Raman
    Applied Intelligence, 2023
  • An efficient multi-functional deep learning model for effective medical image classification using skin lesion database
    Kishore Babu Nampalle, Balasubramanian Raman
    Proceedings 5th International Conference on Multimedia Information Processing and Retrieval Mipr 2022, 2022
    The automatic process of classifying a medical image plays a vital role in Computer-Aided Diagnosis (CADx). Due to the advent of Convolutional Neural Networks (CNNs) and wide usage, there has been a substantial improvement in the performance of the classification process combined with the process of implicit feature extraction. CNN requires a large amount of data, but building an extensive data set is challenging. Hence, Transfer learning appeared to resolve the same issue. Predefined models like MobileNet, VGG19, Inception-V3, and ResNet50, based on datasets with more sizes such as ImageNet, playa vital role in training and improving the performance. Extracting such unique features from medical images is a challenging task due to the different properties of images. Training a Deep Neural Network is an intensive task because it requires high configured computing machines and may require more time. Hence, this paper proposed a multi-functional deep learning architecture, including an ensemble of Logistic Regression classifiers and a MobileNet pre-trained model. Here, the input data of skin lesion images from the ISI C challenge dataset for binary and multi-class classification. Obtained results are compared with other models with the help of performance metrics.
  • Automatic Evaluation of Machine Generated Feedback For Text and Image Data
    Pratham Goyal, Anjali Raj, Puneet Kumar, Kishore Babu Nampalle
    Proceedings 5th International Conference on Multimedia Information Processing and Retrieval Mipr 2022, 2022
    In this paper, a novel system, ‘AutoEvaINet,’ has been developed for evaluating machine-generated feedback in response to multimodal input containing text and images. A new metric, ‘Automatically Evaluated Relevance Score’ (AER Score), has also been defined to automatically compute the similarity between human-generated comments and machine-generatedfeedback. The AutoEvalNet's architecture comprises a pre-trained feedback synthesis model and the proposed feedback evaluation model. It uses an ensemble of Bidirectional Encoder Representations from Transformers (BERT) and Global Vectors for Word Representation (GloVe) models to generate the embeddings of the ground-truth comment and machine-synthesized feedback using which the similarity score is calculated. The experiments have been performed on the MMFeed dataset. The generated feedback has been evaluated automatically using the AER score and manually by having the human users evaluate the feedbackfor relevance to the input and ground-truth comments. The values of the AER score and human evaluation scores are in line, affirming the AER score's applicability as an automatic evaluation measure for machine-generated text instead of human evaluation.
  • An Efficient Approach for Skin Lesion Segmentation Using Dermoscopic Images: A Deep Learning Approach
    Kishore Babu Nampalle, Balasubramanian Raman
    Communications in Computer and Information Science, 2021
  • Semantic segmentation of multispectral images using res-seg-net model
    Nidhi Saxena, Kishore Babu N., Balasubramanian Raman
    Proceedings 14th IEEE International Conference on Semantic Computing ICSC 2020, 2020
    Semantic segmentation is pixel-wise labeling of the image. Recently deep convolutional neural network (DCNN) providing progressive results in semantic segmentation. However, in remote sensing multispectral imagery very limited work has been done due to lack of training dataset. In this paper, a Res-Seg-net model is proposed for the semantic segmentation which is motivated by the existing Resnet and Segnet models. This model consists of encoder-decoder parts in which residual mapping is followed. For validation and testing of the proposed model, the RIT-18 dataset of multispectral imagery is used. The comparison results of the experiment on a multispectral imagery dataset have demonstrated the effectiveness of the proposed model.

RECENT SCHOLAR PUBLICATIONS

  • AMS-ETL: An Adaptive Multi-Source Ensemble Transfer Learning Framework for Robust Multi-Disease Diagnostic Classification
    KB Nampalle, DK Mahto
    Medical Imaging with Deep Learning , 2026
    2026.0
  • Differential Privacy in Federated Learning for Medical Image Classification
    KB Nampalle, A Kumari, G Sundaram
    2025 3rd International Conference on Computational Intelligence and Network … , 2025
    2025.0
  • Privacy-Preserving Deep Learning for Medical Imaging: An Optimized Federated Classification Framework
    KB Nampalle, A Kumari
    2025 3rd International Conference on Computational Intelligence and Network … , 2025
    2025.0
  • Towards improved U-Net for efficient skin lesion segmentation
    KB Nampalle, A Pundhir, PR Jupudi, B Raman
    Multimedia Tools and Applications 83 (28), 71665-71682 , 2024
    2024.0
    Citations: 12
  • Integrating physiological signals with dynamical attention networks for personality trait analysis
    D Kumar, P Singh, KB Nampalle, B Raman
    2024 International Joint Conference on Neural Networks (IJCNN), 1-8 , 2024
    2024.0
    Citations: 4
  • Medical image security and authenticity via dual encryption
    KB Nampalle, S Manhas, B Raman
    Applied Intelligence 53 (17), 20647-20659 , 2023
    2023.0
    Citations: 10
  • DeepMediX: a deep learning-driven resource-efficient medical diagnosis across the spectrum
    KB Nampalle, P Singh, UV Narayan, B Raman
    arXiv preprint arXiv:2307.00324 , 2023
    2023.0
    Citations: 4
  • Vision through the veil: Differential privacy in federated learning for medical image classification
    KB Nampalle, P Singh, UV Narayan, B Raman
    arXiv preprint arXiv:2306.17794 , 2023
    2023.0
    Citations: 20
  • See through the fog: curriculum learning with progressive occlusion in medical imaging
    P Singh, KB Nampalle, UV Narayan, B Raman
    arXiv preprint arXiv:2306.15574 , 2023
    2023.0
    Citations: 12
  • Transcending Grids: Point Clouds and Surface Representations Powering Neurological Processing
    KB Nampalle, P Singh, VN Uppala, S Gangwar, RS Negi, B Raman
    arXiv preprint arXiv:2305.15426 , 2023
    2023.0
    Citations: 1
  • Transfer learning based framework for image segmentation using medical images and Tversky similarity
    KB Nampalle, VN Uppala, B Raman
    2023.0
    Citations: 4
  • An efficient multi-functional deep learning model for effective medical image classification using skin lesion database
    KB Nampalle, B Raman
    2022 IEEE 5th International Conference on Multimedia Information Processing … , 2022
    2022.0
    Citations: 2
  • Automatic Evaluation of Machine Generated Feedback for Text and Image Data
    P Goyal, A Raj, P Kumar, KB Nampalle
    2022 IEEE 5th International Conference on Multimedia Information Processing … , 2022
    2022.0
    Citations: 2
  • An efficient approach for skin lesion segmentation using dermoscopic images: A deep learning approach
    KB Nampalle, B Raman
    International Conference on Computer Vision and Image Processing, 430-439 , 2020
    2020.0
    Citations: 3
  • Semantic segmentation of multispectral images using res-seg-net model
    N Saxena, B Raman
    2020 IEEE 14th international conference on semantic computing (ICSC), 154-157 , 2020
    2020.0
    Citations: 22
  • U-Net Based Efficient Deep Learning Architecture for Effective Skin Lesion Segmentation
    KB Nampalle, P Ramesh, B Raman
    Available at SSRN 4185484 , 0

MOST CITED SCHOLAR PUBLICATIONS

  • Semantic segmentation of multispectral images using res-seg-net model
    N Saxena, B Raman
    2020 IEEE 14th international conference on semantic computing (ICSC), 154-157 , 2020
    2020.0
    Citations: 22
  • Vision through the veil: Differential privacy in federated learning for medical image classification
    KB Nampalle, P Singh, UV Narayan, B Raman
    arXiv preprint arXiv:2306.17794 , 2023
    2023.0
    Citations: 20
  • Towards improved U-Net for efficient skin lesion segmentation
    KB Nampalle, A Pundhir, PR Jupudi, B Raman
    Multimedia Tools and Applications 83 (28), 71665-71682 , 2024
    2024.0
    Citations: 12
  • See through the fog: curriculum learning with progressive occlusion in medical imaging
    P Singh, KB Nampalle, UV Narayan, B Raman
    arXiv preprint arXiv:2306.15574 , 2023
    2023.0
    Citations: 12
  • Medical image security and authenticity via dual encryption
    KB Nampalle, S Manhas, B Raman
    Applied Intelligence 53 (17), 20647-20659 , 2023
    2023.0
    Citations: 10
  • Integrating physiological signals with dynamical attention networks for personality trait analysis
    D Kumar, P Singh, KB Nampalle, B Raman
    2024 International Joint Conference on Neural Networks (IJCNN), 1-8 , 2024
    2024.0
    Citations: 4
  • DeepMediX: a deep learning-driven resource-efficient medical diagnosis across the spectrum
    KB Nampalle, P Singh, UV Narayan, B Raman
    arXiv preprint arXiv:2307.00324 , 2023
    2023.0
    Citations: 4
  • Transfer learning based framework for image segmentation using medical images and Tversky similarity
    KB Nampalle, VN Uppala, B Raman
    2023.0
    Citations: 4
  • An efficient approach for skin lesion segmentation using dermoscopic images: A deep learning approach
    KB Nampalle, B Raman
    International Conference on Computer Vision and Image Processing, 430-439 , 2020
    2020.0
    Citations: 3
  • An efficient multi-functional deep learning model for effective medical image classification using skin lesion database
    KB Nampalle, B Raman
    2022 IEEE 5th International Conference on Multimedia Information Processing … , 2022
    2022.0
    Citations: 2
  • Automatic Evaluation of Machine Generated Feedback for Text and Image Data
    P Goyal, A Raj, P Kumar, KB Nampalle
    2022 IEEE 5th International Conference on Multimedia Information Processing … , 2022
    2022.0
    Citations: 2
  • Transcending Grids: Point Clouds and Surface Representations Powering Neurological Processing
    KB Nampalle, P Singh, VN Uppala, S Gangwar, RS Negi, B Raman
    arXiv preprint arXiv:2305.15426 , 2023
    2023.0
    Citations: 1
  • AMS-ETL: An Adaptive Multi-Source Ensemble Transfer Learning Framework for Robust Multi-Disease Diagnostic Classification
    KB Nampalle, DK Mahto
    Medical Imaging with Deep Learning , 2026
    2026.0
  • Differential Privacy in Federated Learning for Medical Image Classification
    KB Nampalle, A Kumari, G Sundaram
    2025 3rd International Conference on Computational Intelligence and Network … , 2025
    2025.0
  • Privacy-Preserving Deep Learning for Medical Imaging: An Optimized Federated Classification Framework
    KB Nampalle, A Kumari
    2025 3rd International Conference on Computational Intelligence and Network … , 2025
    2025.0
  • U-Net Based Efficient Deep Learning Architecture for Effective Skin Lesion Segmentation
    KB Nampalle, P Ramesh, B Raman
    Available at SSRN 4185484 , 0