KRISHNA SINGH

@dseu.ac.in

ASSOCIATE PROFESSOR ELECTRONICS AND COMMMUNICATION ENGINEERING
Delhi Skill and Entrepreneurship University

KRISHNA SINGH


                  Download Compact PDF  
https://researchid.co/singhkrishna5

RESEARCH, TEACHING, or OTHER INTERESTS

Signal Processing, Electrical and Electronic Engineering, Computer Networks and Communications, Information Systems and Management
23

Scopus Publications

Scopus Publications

  • A hybrid learning network with progressive resizing and PCA for diagnosis of cervical cancer on WSI slides
    Nitin Kumar Chauhan, Krishna Singh, Amit Kumar, Ashutosh Mishra, Sachin Kumar Gupta, Shubham Mahajan, Seifedine Kadry, Jungeun Kim
    Scientific Reports, 2025
    Current artificial intelligence (AI) trends are revolutionizing medical image processing, greatly improving cervical cancer diagnosis. Machine learning (ML) algorithms can discover patterns and anomalies in medical images, whereas deep learning (DL) methods, specifically convolutional neural networks (CNNs), are extremely accurate at identifying malignant lesions. Deep models that have been pre-trained and tailored through transfer learning and fine-tuning become faster and more effective, even when data is scarce. This paper implements a state-of-the-art Hybrid Learning Network that combines the Progressive Resizing approach and Principal Component Analysis (PCA) for enhanced cervical cancer diagnostics of whole slide images (WSI) slides. ResNet-152 and VGG-16, two fine-tuned DL models, are employed together with transfer learning to train on augmented and progressively resized training data with dimensions of 224 × 224, 512 × 512, and 1024 × 1024 pixels for enhanced feature extraction. Principal component analysis (PCA) is subsequently employed to process the combined features extracted from two DL models and reduce the dimensional space of the feature set. Furthermore, two ML methods, Support Vector Machine (SVM) and Random Forest (RF) models, are trained on this reduced feature set, and their predictions are integrated using a majority voting approach for evaluating the final classification results, thereby enhancing overall accuracy and reliability. The accuracy of the suggested framework on SIPaKMeD data is 99.29% for two-class classification and 98.47% for five-class classification. Furthermore, it achieves 100% accuracy for four-class categorization on the LBC dataset.
  • Tackling third-order intermodulation distortion: modeling and analysis of linearized RoF link for future perspective networks
    Balram Tamrakar, Krishna Singh, Varun Gupta
    Journal of Optics India, 2024
  • Performance analysis of DD-DPMZM based RoF link for emerging wireless networks
    Balram Tamrakar, Krishna Singh, Parvin Kumar, Varun Gupta
    Analog Integrated Circuits and Signal Processing, 2024
  • A comparative analysis between different optical modulators of analog and digital radio over fiber (RoF) link for the next-generation networks
    Balram Tamrakar, Krishna Singh, Parvin Kumar
    Journal of Optics India, 2023
  • Performance analysis of DP-MZM radio over fiber links against fiber impairments
    Balram Tamrakar, Krishna Singh, Parvin Kumar
    Journal of Optical Communications, 2023
    In this research article, the radio over fiber (RoF) Architecture using dual-parallel Mach–Zehnder modulator (DP-MZM) is analyzed and simulated. The nonlinearity is the main issues which degrade the performance of the RoF links. Using DP-MZM modulators, the nonlinearity can be reducing in the significant amount by controlling the bias voltages. The DP-MZM consists of two MZM modulators in parallel form with the variations of extinction ratio (ER). The proposed RoF link operated with single tone frequency signal at 10 GHz and the optical signal is transmitted further on Single mode optical fiber. The electrical spectrum at the output of photodetector confirms the quality of received signal while having desired and undesired terms. The proposed structure is designed using Optical Simulator simulation Software to confirm and validate the proposed results. The simulation output shows that the RF power of DP-MZM based RoF link has been found a decrement of 6.95 dB corresponding to undesired components with respect to desired RF signal for the increment in fiber length from 10 to 30 km. The digital transmission RoF link model is proposed and optimized for fiber impairments for 2–30 km, using 10 Gbps digital data string. electrical spectrum analyzer shows that very good eye openings for different fiber impairments, its shoes that proposed RoF model is optimized and having good SNR.
  • Performance optimization of conventional RoF link by considering the effect of RF source linewidth and photonic source linewidth for the next-generation networks
    Balram Tamrakar, Krishna Singh, Parvin Kumar, Shubham Shukla
    Journal of Optics India, 2023
  • Machine Learning Algorithms for Binary Classification of Breast Cancer
    Preeti Katiyar, Krishna Singh
    Lecture Notes in Electrical Engineering, 2023
  • HDFCN: A Robust Hybrid Deep Network Based on Feature Concatenation for Cervical Cancer Diagnosis on WSI Pap Smear Slides
    Nitin Kumar Chauhan, Krishna Singh, Amit Kumar, Swapnil Baburav Kolambakar
    Biomed Research International, 2023
    Cervical cancer is a critical imperilment to a female’s health due to its malignancy and fatality rate. The disease can be thoroughly cured by locating and treating the infected tissues in the preliminary phase. The traditional practice for screening cervical cancer is the examination of cervix tissues using the Papanicolaou (Pap) test. Manual inspection of pap smears involves false‐negative outcomes due to human error even in the presence of the infected sample. Automated computer vision diagnosis revamps this obstacle and plays a substantial role in screening abnormal tissues affected due to cervical cancer. Here, in this paper, we propose a hybrid deep feature concatenated network (HDFCN) following two‐step data augmentation to detect cervical cancer for binary and multiclass classification on the Pap smear images. This network carries out the classification of malignant samples for whole slide images (WSI) of the openly accessible SIPaKMeD database by utilizing the concatenation of features extracted from the fine‐tuning of the deep learning (DL) models, namely, VGG‐16, ResNet‐152, and DenseNet‐169, pretrained on the ImageNet dataset. The performance outcomes of the proposed model are compared with the individual performances of the aforementioned DL networks using transfer learning (TL). Our proposed model achieved an accuracy of 97.45% and 99.29% for 5‐class and 2‐class classifications, respectively. Additionally, the experiment is performed to classify liquid‐based cytology (LBC) WSI data containing pap smear images.
  • An Evaluative Investigation of Deep Learning Models by Utilizing Transfer Learning and Fine-Tuning for Cervical Cancer Screening of Whole Slide Pap-Smear Images
    Nitin Kumar Chauhan, Krishna Singh, Shravan Namdeo, Ankit Muley
    2023 7th International Conference on Computer Applications in Electrical Engineering Recent Advances Sustainable Transportation Systems Cera 2023, 2023
    Deep learning (DL) is a prominent tool utilized today in many applications across many industries, including the healthcare realm. DL methods can manage several problems that traditional artificial intelligence (AI) methods find challenging. In this paper, we analyzed the performance of nine prevalent DL models i.e. VGG-16, DenseNet-121, ResNet50, VGG-19, DenseNet-169, Xception, EfficientNetB0, InceptionV3, and ResNet-152 pre-trained on ImageNet dataset for cervical cancer screening. These previously trained models are fine-tuned by utilizing transfer learning (TL) for 5-class and 2-class classification of whole slide pap-smear images (WSI). Two-step data augmentation is being used for the preprocessing of data to enhance the efficacy and robustness of classifiers by increasing the amount of the training data and reducing overfitting. Among the aforementioned DL methods, VGG-16 performs best among all with an accuracy of 94.89% for 5-class and 97.16% for 2-class classification.
  • A Comparison of 1G to 6G Network in Association with Radio over Fiber Systems
    Balram Tamrakar, Krishna Singh, Gauri Brijaria, Manjari Singh, Mayank Kashyap, Manu Gupta, Shweta Sharma
    2023 IEEE Renewable Energy and Sustainable E Mobility Conference Resem 2023, 2023
    The Spectrum generation technology provides the basic platform to perform research-oriented work. As the number of internet user increase, the bandwidth required to provide access to everyone increases. This problem is solved by introducing newer generations of network communication technologies. In the presented article, we reviewed the spectrum technologies that were required to implement the first to the fifth generation of network technology and what to expect from the coming successor i.e., the sixth generation. We discussed the real-world applications of each generation and in addition to that, we also discussed the various challenges and difficulties faced by them. We also presented an exhaustive comparison between each generation and its technologies. We discussed the Radio Over Fiber technology and how it is implemented. We have analyzed the RF spectrum and Optical Spectrum using a Radio Over Fiber System. It is said that the potential of the current generation, the fifth generation, is yet to be discovered completely. This generation brought along huge changes in AI/ML technologies. The sixth generation is said to even amplify the performance and provide greater speed, better bandwidth, and overall greater experience.
  • Performance Investigation of Bit Error Rate using mostly utilized Modulation Schemes in RoF system for the Next Generation Networks
    Balram Tamrakar, Krishna Singh, Govind Jha, Tanisha Garg, Shubhangi Goel, Anuja Sharma, Shubham Shukla, Yashika Verma
    2023 International Conference on Power Instrumentation Energy and Control Piecon 2023, 2023
  • Performance Assessment of Machine Learning Classifiers Using Selective Feature Approaches for Cervical Cancer Detection
    Nitin Kumar Chauhan, Krishna Singh
    Wireless Personal Communications, 2022
  • Diagnosis of Cervical Cancer with Oversampled Unscaled and Scaled Data Using Machine Learning Classifiers
    Nitin Kumar Chauhan, Krishna Singh
    2022 IEEE Delhi Section Conference Delcon 2022, 2022
  • Impact of Variation in Number of Channels in CNN Classification model for Cervical Cancer Detection
    Nitin Kumar Chauhan, Krishna Singh
    2021 9th International Conference on Reliability Infocom Technologies and Optimization Trends and Future Directions Icrito 2021, 2021
  • A comparative study of lung cancer detection and classification approaches in CT images
    Preeti Katiyar, Krishna Singh
    2020 7th International Conference on Signal Processing and Integrated Networks Spin 2020, 2020
  • A review on conventional machine learning vs deep learning
    Nitin Kumar Chauhan, Krishna Singh
    2018 International Conference on Computing Power and Communication Technologies Gucon 2018, 2019
  • Performance Measurement of Radio over Fiber System at 20 GHz and 30 GHz by Employing with and without Optical Carrier Suppression
    Proceedings of the 2019 6th International Conference on Computing for Sustainable Global Development Indiacom 2019, 2019
  • Graphical Structure for Block Codes and Complexity Comparison of Dual Codes
    12th Indiacom 5th International Conference on Computing for Sustainable Global Development Indiacom 2018, 2018
  • Comparisons of three classifier for classification of bamboo plant
    Krishna Singh, Surendra Singh
    Advances in Intelligent and Soft Computing, 2012
  • A comparison of 2D moment based description techniques for classification of bamboo plant
    Krishna Singh, Indra Gupta, Sangeeta Gupta
    Proceedings 2011 International Conference on Computational Intelligence and Communication Systems Cicn 2011, 2011
  • Classification of bamboo plant based on digital image processing by central moment
    Krishna Singh, Indra Gupta, Sangeeta Gupta
    Iciip 2011 Proceedings 2011 International Conference on Image Information Processing, 2011
  • Retrieval and classification of leaf shape by Support Vector Machine using binary Decision Tree, probabilistic neural network and generic Fourier Moment technique: A comparative study
    Proc of the Iadis Int Conf Computer Graphics Visualization Computer Vision and Image Processing Cgvcvip 2010 Visual Commun Vc 2010 Web3dw 2010 Part of the Mccsis 2010, 2010
  • Comparison of PNN-PCA with SVM-BDT and moment based technique for leaf shape recognition and classification
    Proceedings of the 2010 International Conference on Image Processing Computer Vision and Pattern Recognition Ipcv 2010, 2010