PANGAMBAM SENDASH SINGH

@dhsgsu.edu.in

Assistant Professor (Computer Science & Applications)
Doctor Harisingh Gour Vishwavidyalaya, Sagar

PANGAMBAM SENDASH SINGH


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https://researchid.co/drpangambam

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Vision and Pattern Recognition, Artificial Intelligence, Modeling and Simulation, Computer Science
13

Scopus Publications

137

Scholar Citations

7

Scholar h-index

6

Scholar i10-index

Scopus Publications

  • Unsupervised deep autoencoder-based reconstruction for ink mismatch detection in hyperspectral document images
    Pangambam Sendash Singh, Subbiah Karthikeyan, Govind Murari Upadhayay, Sounak Sadhukhan, Pramod Kumar Soni, et al.
    Discover Computing, 2025
  • A Hybrid Mutual Information-Based Feature Selection Framework with Redundancy Reduction through Clustering and Voting
    Anubha Prajapati, Sounak Sadhukhan, Pangambam Sendash Singh
    Indiscon 2025 IEEE 6th India Council International Subsections Conference Proceedings, 2025
  • Analysis of Urbanization Impact on Land Surface Temperature Variability by Using Landsat Imagery
    Vikash Kumar Mishra, Kamlesh Kumar Verma, Triloki Pant, Govind Murari Upadhyay, Pangambam Sendash Singh, et al.
    SN Computer Science, 2024
  • Exploiting ensemble learning and negative sample space for predicting extracellular matrix receptor interactions
    Abhigyan Nath, Sudama Rathore, Pangambam Sendash Singh
    Mathematical Biology and Bioinformatics, 2023
    The extracellular matrix (ECM) is best described as a dynamic three-dimensional mesh of various macromolecules. These include proteoglycans (e.g., perlecan andagrin), non-proteoglycan polysaccharides (e.g., hyaluronan), and fibrous proteins (e.g., collagen, elastin, fibronectin, and laminin). ECM proteins are involved in various biological functions and their functionality is largely governed by interaction with other ECM proteins as well as trans-membrane receptors including integrins, proteoglycans such assyndecan, other glycoproteins, and members of the immunoglobulin superfamily. In the present work, a machine learning approach is developed using sequence and evolutionary features for predicting ECM protein-receptor interactions. Two different feature vector representations, namely fusion of feature vectors and average of feature vectors are used within corporation of the best representation employing feature selection. The current results show that the feature vector representation is an important aspect of ECM protein interaction prediction, and that the average of feature vectors performed better than the fusion of feature vectors. The best prediction model with boosted random forest resulted in 72.6 % overall accuracy, 74.4 % sensitivity and 70.7 % specificity with the 200 best features obtained using the ReliefF feature selection algorithm. Further, a comparative analysis was performed for negative sample subset selection using three sampling methods, namely random sampling, k-Means sampling, and Uniform sampling. k-Means based representative sampling resulted in enhanced accuracy (75.5 % accuracy with 80.8 % sensitivity, 68.1 % specificity and 0.801 AUC) for the prediction of ECM protein-receptor interactions in comparison to the other sampling methods. On comparison with other three state of the art protein-protein interaction predictors, it is observed that the latter displayed low sensitivity but higher specificity. The current work presents the first machine learning based prediction model specifically developed for ECM protein-receptor interactions.
  • Enhanced classification of remotely sensed hyperspectral images through efficient band selection using autoencoders and genetic algorithm
    Pangambam Sendash Singh, Subbiah Karthikeyan
    Neural Computing and Applications, 2022
  • Enhanced classification of hyperspectral images using improvised oversampling and undersampling techniques
    Pangambam Sendash Singh, Vijendra Pratap Singh, Manish Kumar Pandey, Subbiah Karthikeyan
    International Journal of Information Technology Singapore, 2022
  • Salient object detection in hyperspectral images using deep background reconstruction based anomaly detection
    Pangambam Sendash Singh, Subbiah Karthikeyan
    Remote Sensing Letters, 2022
    Salient object detection is a significant task that forms the basis for many image processing and computer vision applications. In recent years, the research in this area is being extended beyond RGB images to applications involving multispectral and hyperspectral images. But most of the existing algorithms for salient object detection often yield an incomplete representation of the object and often produce saliency maps with blurred edges. In this paper, we propose an efficient hyperspectral image salient object detection method through anomaly detection by combining deep learning autoencoders with one-class support vector machines. Here, the saliency detection problem in hyperspectral images is formulated as an unsupervised deep background spectral reconstruction-based anomaly detection. Our proposed method first employs deep autoencoders to model the background of an input hyperspectral image in terms of spectral reconstruction residuals of the autoencoders and then detect the salient objects from the image through a one-class support vector machine-based anomaly detection. The proposed method was evaluated on a publicly available hyperspectral image dataset for salient object detection. The experimental results show that our proposed method is found to be more efficient and superior over other previous methods in terms of various performance measures.
  • One-class Classifier Ensemble based Enhanced Semisupervised Classification of Hyperspectral Remote Sensing Images
    Pangambam Sendash Singh, Vijendra Pratap Singh, Manish Kumar Pandey, Subbiah Karthikeyan
    2020 International Conference on Emerging Smart Computing and Informatics Esci 2020, 2020
    The scarcity of labelled training data as well as uneven class distribution among the limitedly available labelled data have posed a critical issue in supervised hyperspectral remote sensing image classification. Semisupervised methods can be an easy solution to this critical problem. However, traditional self-training based semi-supervised approaches often give poor classification results in high dimensional multiclass classification problems. This paper proposes a novel efficient one-class classifier ensemble based self-training approach for semisupervised classification of hyperspectral remote sensing images with limited labelled data. The proposed method initially trains an ensemble of locally specialized one-class classifiers independently by using the dimensionally reduced spectral feature vectors of the available labelled samples. The trained one-class classifiers are then used to extend the labelled set by iterative addition of high quality unlabelled samples to it through the exploitation of both spectral and spatial information. The classifiers are then retrained with the extended dataset in a batchwise fashion. The procedure is repeated until an adequate quantity of labelled samples are generated. Finally, a supervised multiclass classifier is trained on the extended dataset for the final image classification purpose. Experimental results on two benchmark hyperspectral images verify the effectiveness of the proposed method over supervised and traditional self-training based semisupervised pixelwise classification in terms of different classification measures.
  • An LSTM Based Time Series Forecasting Framework for Web Services Recommendation
    Vijendra Pratap Singh, Manish Kumar Pandey, Pangambam Sendash Singh, Subbiah Karthikeyan
    Computacion Y Sistemas, 2020
    The convergence of Social Mobility Analytics and Cloud (SMAC) technologies gives rise to an unforeseen aggrandization of the web services on the internet. The resilience and payment-based approach of the cloud makes it an obvious choice for the deployment of web services-based applications. Out of available web services, to gratify the similar functionalities, the choice of the web service based on the personalized quality of service (QoS) parameters plays an importantrole in determining the selection of the web service. The role of time is rarely being discussed in deciding the QoS of web services. The delivery of QoS is not made as declared due to the non-functional performance of web services correlated behavior with the invocation time. This happens because service status usually changes over time. Hence, the design of the time a ware web service recommendation system based on the personalized QoS parameters is very crucial and becomes a challenging research issue. In this study, LSTM based deep learning models were used for the prediction of these time aware QoS parameters and the results are compared with the previous approaches. The experimental results show that the LSTM based Time Series Forecasting Framework is performing better. The RMSE, MAE, and MAPE are used as an evaluation metric and their value for the prediction of Response time (RT) is found to be 0.030269, 0.02382 and 0.59773 respectively with adaptive moment estimation as the training option and is found to be 0.66988, 0.66465 and 27.9934 respectively with root mean square propagation as the training option. The RMSE, MAE, and MAPE value for the prediction of through put (TP) is found to be 0.77787, 0.4792 and 1.61 respectively with adaptive moment estimation as the training option and is found to be 0.2.7087, 1.4076 and 7.1559 respectively with root mean square propagation as the training option respectively. Thus, the experimental results show that the LSTM model of Time Series Fore casting for Web Services Recommendation Framework is performing better as compared to previous methods.
  • Local Binary Ensemble based Self-training for Semi-supervised Classification of Hyperspectral Remote Sensing Images
    Pangambam Sendash Singh, Vijendra Pratap Singh, Manish Kumar Pandey, Subbiah Karthikeyan
    Computacion Y Sistemas, 2020
    Supervised classification of hyperspectral remote sensing images is still challenging due to the scarcity of enough labelled samples. Semi-supervised methods have been adopted to handle this issue. Self-training is a popular semi-supervised technique which is widely used for training a classifier with limited labelled data and a large quantity of unlabeled data. However, traditional self-training approaches often give poor classification results in high dimensional data. In the current work, a novel efficient self-training approach for handling the deficiency of labelled samples for semi-supervised classification of hyperspectral remote sensing images is proposed. The proposed method first trains an ensemble of locally specialized supervised binary classifiers independently by using the dimensionally reduced spectral feature vectors of few available labelled samples. The trained local binary classifiers are then used to extend the labelled set by iterative addition of highly informative unlabeled samples to it by exploiting both the spectral and spatial information of the hyperspectral image. The classifiers are then retrained with the extended dataset in a batchwise manner and the procedure is repeated until adequate quantity of labelled samples are generated. Finally, a supervised multiclass classifier is trained on the extended dataset to produce the final classification map. Experimental results on two benchmark hyperspectral image datasets prove the effectiveness of the proposed method over supervised and traditional self-training based semi-supervised pixelwise classification approach in terms of different classification measures.
  • An Econometric Time Series Forecasting Framework for Web Services Recommendation
    Vijendra Pratap Singh, Manish Kumar Pandey, Pangambam Sendash Singh, Subbiah Karthikeyan
    Procedia Computer Science, 2020
  • Neural Net Time Series Forecasting Framework for Time-Aware Web Services Recommendation
    Vijendra Pratap Singh, Manish Kumar Pandey, Pangambam Sendash Singh, Subbiah Karthikeyan
    Procedia Computer Science, 2020
  • An empirical mode decomposition (EMD) enabled long sort term memory (LSTM) based time series forecasting framework for web services recommendation
    V. Singh, M. Pandey, Pangambam Sendash Singh, Karthikeyan Subbiah
    Frontiers in Artificial Intelligence and Applications, 2019

RECENT SCHOLAR PUBLICATIONS

  • Unsupervised deep autoencoder-based reconstruction for ink mismatch detection in hyperspectral document images
    PS Singh, S Karthikeyan, GM Upadhayay, S Sadhukhan, PK Soni, ...
    Discover Computing 28 (1), 333 , 2025
    2025
  • Comparative analysis of machine learning-based specific capacitance prediction and experimental validation of supercapacitive performance in cerium-based electrodes
    M Singh, N Sharma, PS Singh, P Singh
    Emerging Trends and Future Directions in Artificial Intelligence, Machine … , 2025
    2025
  • An extended SMOTE-based class balancing technique to improve classification accuracy on imbalanced datasets
    PS Singh, KN Singh, B Th, MS Singh, J N., KR Singh
    Emerging Trends and Future Directions in Artificial Intelligence, Machine … , 2025
    2025
  • A Hybrid Mutual Information-Based Feature Selection Framework with Redundancy Reduction through Clustering and Voting
    A Prajapati, S Sadhukhan, PS Singh
    2025 IEEE 6th India Council International Subsections Conference (INDISCON), 1-6 , 2025
    2025
  • Analysis of Urbanization Impact on Land Surface Temperature Variability by Using Landsat Imagery
    VK Mishra, KK Verma, T Pant, GM Upadhyay, PS Singh, PK Soni
    SN Computer Science 5 (7), 863 , 2024
    2024
    Citations: 4
  • Exploiting ensemble learning and negative sample space for predicting extracellular matrix receptor interactions
    A Nath, S Rathore, PS Singh
    Маthematical biology and bioinformatics 18 (1), 113-127 , 2023
    2023
  • Enhanced classification of remotely sensed hyperspectral images through efficient band selection using autoencoders and genetic algorithm
    PS Singh, S Karthikeyan
    Neural Computing and Applications 34 (24), 21539-21550 , 2022
    2022
    Citations: 34
  • Salient object detection in hyperspectral images using deep background reconstruction based anomaly detection
    PS Singh, S Karthikeyan
    Remote Sensing Letters 13 (2), 184-195 , 2022
    2022
    Citations: 15
  • Enhanced classification of hyperspectral images using improvised oversampling and undersampling techniques
    PS Singh, VP Singh, MK Pandey, S Karthikeyan
    International Journal of Information Technology 14 (1), 389-396 , 2022
    2022
    Citations: 28
  • Machine learning based efficient approaches for improving the performance of multi faceted hyperspectral imaging applications
    PS Singh
    http://hdl.handle.net/10603/448962 , 2022
    2022
  • Local binary ensemble based self-training for semi-supervised classification of hyperspectral remote sensing images
    PS Singh, VP Singh, MK Pandey, S Karthikeyan
    Computación y Sistemas 24 (2), 497-509 , 2020
    2020
    Citations: 2
  • An LSTM based time series forecasting framework for web services recommendation
    VP Singh, MK Pandey, PS Singh, S Karthikeyan
    Computación y Sistemas 24 (2), 687-702 , 2020
    2020
    Citations: 5
  • One-class classifier ensemble based enhanced semisupervised classification of hyperspectral remote sensing images
    PS Singh, VP Singh, MK Pandey, S Karthikeyan
    2020 International Conference on Emerging Smart Computing and Informatics … , 2020
    2020
    Citations: 7
  • Neural net time series forecasting framework for time-aware web services recommendation
    VP Singh, MK Pandey, PS Singh, S Karthikeyan
    Procedia Computer Science 171, 1313-1322 , 2020
    2020
    Citations: 18
  • An econometric time series forecasting framework for web services recommendation
    VP Singh, MK Pandey, PS Singh, S Karthikeyan
    Procedia computer science 167, 1615-1625 , 2020
    2020
    Citations: 12
  • An empirical mode decomposition (EMD) enabled long sort term memory (LSTM) based time series forecasting framework for web services recommendation
    VP Singh, MK Pandey, PS Singh, S Karthikeyan
    Fuzzy systems and data mining V, 715-723 , 2019
    2019
    Citations: 12

MOST CITED SCHOLAR PUBLICATIONS

  • Enhanced classification of remotely sensed hyperspectral images through efficient band selection using autoencoders and genetic algorithm
    PS Singh, S Karthikeyan
    Neural Computing and Applications 34 (24), 21539-21550 , 2022
    2022
    Citations: 34
  • Enhanced classification of hyperspectral images using improvised oversampling and undersampling techniques
    PS Singh, VP Singh, MK Pandey, S Karthikeyan
    International Journal of Information Technology 14 (1), 389-396 , 2022
    2022
    Citations: 28
  • Neural net time series forecasting framework for time-aware web services recommendation
    VP Singh, MK Pandey, PS Singh, S Karthikeyan
    Procedia Computer Science 171, 1313-1322 , 2020
    2020
    Citations: 18
  • Salient object detection in hyperspectral images using deep background reconstruction based anomaly detection
    PS Singh, S Karthikeyan
    Remote Sensing Letters 13 (2), 184-195 , 2022
    2022
    Citations: 15
  • An econometric time series forecasting framework for web services recommendation
    VP Singh, MK Pandey, PS Singh, S Karthikeyan
    Procedia computer science 167, 1615-1625 , 2020
    2020
    Citations: 12
  • An empirical mode decomposition (EMD) enabled long sort term memory (LSTM) based time series forecasting framework for web services recommendation
    VP Singh, MK Pandey, PS Singh, S Karthikeyan
    Fuzzy systems and data mining V, 715-723 , 2019
    2019
    Citations: 12
  • One-class classifier ensemble based enhanced semisupervised classification of hyperspectral remote sensing images
    PS Singh, VP Singh, MK Pandey, S Karthikeyan
    2020 International Conference on Emerging Smart Computing and Informatics … , 2020
    2020
    Citations: 7
  • An LSTM based time series forecasting framework for web services recommendation
    VP Singh, MK Pandey, PS Singh, S Karthikeyan
    Computación y Sistemas 24 (2), 687-702 , 2020
    2020
    Citations: 5
  • Analysis of Urbanization Impact on Land Surface Temperature Variability by Using Landsat Imagery
    VK Mishra, KK Verma, T Pant, GM Upadhyay, PS Singh, PK Soni
    SN Computer Science 5 (7), 863 , 2024
    2024
    Citations: 4
  • Local binary ensemble based self-training for semi-supervised classification of hyperspectral remote sensing images
    PS Singh, VP Singh, MK Pandey, S Karthikeyan
    Computación y Sistemas 24 (2), 497-509 , 2020
    2020
    Citations: 2
  • Unsupervised deep autoencoder-based reconstruction for ink mismatch detection in hyperspectral document images
    PS Singh, S Karthikeyan, GM Upadhayay, S Sadhukhan, PK Soni, ...
    Discover Computing 28 (1), 333 , 2025
    2025
  • Comparative analysis of machine learning-based specific capacitance prediction and experimental validation of supercapacitive performance in cerium-based electrodes
    M Singh, N Sharma, PS Singh, P Singh
    Emerging Trends and Future Directions in Artificial Intelligence, Machine … , 2025
    2025
  • An extended SMOTE-based class balancing technique to improve classification accuracy on imbalanced datasets
    PS Singh, KN Singh, B Th, MS Singh, J N., KR Singh
    Emerging Trends and Future Directions in Artificial Intelligence, Machine … , 2025
    2025
  • A Hybrid Mutual Information-Based Feature Selection Framework with Redundancy Reduction through Clustering and Voting
    A Prajapati, S Sadhukhan, PS Singh
    2025 IEEE 6th India Council International Subsections Conference (INDISCON), 1-6 , 2025
    2025
  • Exploiting ensemble learning and negative sample space for predicting extracellular matrix receptor interactions
    A Nath, S Rathore, PS Singh
    Маthematical biology and bioinformatics 18 (1), 113-127 , 2023
    2023
  • Machine learning based efficient approaches for improving the performance of multi faceted hyperspectral imaging applications
    PS Singh
    http://hdl.handle.net/10603/448962 , 2022
    2022