MUZAMMIL KHAN

@utwente.nl

Researcher in the Robotics and Mechatronics (RAM) Group at the Faculty of EEMCS, University of Twente, Enschede
University of Twente

MUZAMMIL KHAN


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https://researchid.co/muzammil_khan
Muzammil Khan is a researcher at the Faculty of EEMCS, University of Twente. His current research is focused on implementing simultaneous localization and mapping (SLAM) algorithms for improving laparoscopic liver resection. Prior to this, he was a Ph.D. scholar in the Department of Mathematics, Bioinformatics, and Computer Applications at the National Institute of Technology Bhopal, India. His broad research area encompasses modern computer vision and machine learning applications in the field of healthcare. He explored the topic of Optical Flow in his thesis titled “Novel Algorithms for Optical Flow Estimation and Its Application”. In his work, he developed mathematical models for accurate optical flow estimation and further utilized them with deep learning techniques to perform anomaly detection in image sequences. During his Ph.D., he wrote 12 journal articles, 15 international conferences, and 5 book chapter. He presented his research at 10 international conferences.

EDUCATION

Postdoc: Since Oct 2023 as a Computer Vision and Artificial Intelligence researcher in the Robotics and Mechatronics Group of University of Twente
Awarded on April 2024 and Submitted on Sep 2023 from Maulana Azad National Institute of Technology
CSIR-NET exam: Qualified on Aug 2019 in Mathematical Sciences
GATE (Graduate Aptitude Test in Engineering- 2019) exam: Qaulified on Mar 2019 in Mathematics
Master of Science: Awarded on Sep 2018 from Bundelkhand University Jhansi
Bachelor of Science: Awarded on May 2016 from Bundelkhand University Jhansi

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Vision and Pattern Recognition, Medical Laboratory Technology, Applied Mathematics, Arts and Humanities
24

Scopus Publications

100

Scholar Citations

6

Scholar h-index

3

Scholar i10-index

Scopus Publications

  • Ensemble learning and skip connection-based CNN framework for COVID-19 identification using CXR and CT images
    Pushpendra Kumar, Bhavana Singh, Muzammil Khan
    International Journal of Computational Vision and Robotics, 2026
    Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.
  • Unifying Scale-Aware Depth Prediction and Perceptual Priors for Monocular Endoscope Pose Estimation and Tissue Reconstruction
    Muzammil Khan, Enzo Kerkhof, Matteo Fusaglia, Koert Kuhlmann, Theo Ruers, Françoise J. Siepel
    IEEE Access, 2026
    Accurate endoscope pose estimation and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3D</i> tissue reconstruction are essential for enhancing navigation and spatial awareness in monocular minimally invasive surgery. However, these tasks remain challenging due to depth ambiguity, physiological tissue deformation, inconsistent endoscope motion, limited texture fidelity, and the restricted field of view. To address these limitations, a unified monocular reconstruction framework is proposed that integrates scale-aware depth prediction with temporally constrained perceptual refinement. The proposed MAPIS-Depth module combines Depth Pro for robust scale initialisation with Depth Anything for efficient per-frame prediction, followed by L-BFGS-B optimisation to obtain pseudo-metric depth. Temporal consistency is further improved using RAFT-based pixel correspondences and LPIPS-guided adaptive blending, reducing artefacts caused by motion and deformation. For reliable registration of the synthesised pseudo-RGBD frames, the WEMA-RTDL module is introduced, which jointly optimises rotation and translation. Finally, truncated signed distance fusion and marching cubes are used to extract coherent <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3D</i> tissue surfaces. Experiments on the HEVD and SCARED datasets, supported by ablation studies and comparisons with state-of-the-art methods, demonstrate the robustness and superior accuracy of the proposed approach.
  • Explainable and likelihood aware AI framework for MRI-based pixel-level bladder tumour prediction
    Muzammil Khan, Antonius G. de Groot, Erik B. Cornel, Antoine G. van der Heijden, Françoise J. Siepel
    Scientific Reports, 2025
    Bladder tumours (BTs) pose significant clinical challenges due to their high recurrence rates and risk of progression to invasive malignancies, which emphasises the need for early and accurate detection. Magnetic resonance imaging (MRI), with its superior soft tissue contrast, is a potential modality for BT detection. To analyse the MRI scans, artificial intelligence (AI) models are increasingly being leveraged. However, these models are often limited by a scarcity of annotated datasets, challenges in pixel-level tumour prediction, and insufficient transparency in predictions. This study introduces the Explainable and Likelihood-Aware AI (ELAAI) framework, designed to address these limitations. Trained solely on annotated normal bladder MRI scans, ELAAI integrates three novel modules: MFA-Net, a robust multi-scale feature aggregation network for bladder segmentation; a refinement step employing adaptive tolerance technique to enhance segmentation of irregularities; and a single-step likelihood prediction network (SLIP-Net), which is a vision transformer with a novel multi-scale deterministic uncertainty (MSDU) head for tumour likelihood prediction. Rigorous evaluation against state-of-the-art (SOTA) models highlights ELAAI's superior performance, enhancing transparency, and reliability in clinical settings by fostering trust in AI-assisted decision-making.
  • An Ensemble Learning Model for Smoke Classification and Localization Based on Fractional Order Optical Flow
    Nitish Kumar Mahala, Pushpendra Kumar, Muzammil Khan
    Machine Vision Analysis in Industry 5 0 Fundamentals Applications and Challenges, 2025
    Fire is considered one of the most likely calamities in terms of both natural activity and social impacts. Therefore, it poses a significant hazard to humans, forests, and animals. To mitigate fire losses, the early detection of smoke is particularly important. Smoke, which has a variety of shapes, colors, and textures, occurs in the initial stage of fire and can aid in the early prediction of fire. Therefore, this chapter introduces a novel approach for smoke detection by integrating an ensemble learning technique with transfer learning from pretrained models. The classification of smoke is done by utilizing its dynamic characteristics. The dynamic characteristics of smoke are determined through optical flow. The motivation for deploying optical flow color maps instead of images is to accurately determine the growth rate and its accuracy. The estimated color maps are input to the proposed attention-based ensemble learning model for classification purposes. The architecture of the proposed model utilizes the attention layer, which aids the network in achieving improved model performance. The presented framework also employed the Grad-CAM and Guided Grad-CAM techniques for the localization and visualization of smoke regions in the images. In addition, this chapter also discusses the counterfactual explanations of the model. Different evaluation metrics are used for a comparative analysis.
  • Caputo derivative based nonlinear fractional order variational model for motion estimation in various application oriented spectrum
    Muzammil Khan, Nitish Kumar Mahala, Pushpendra Kumar
    Sadhana Academy Proceedings in Engineering Sciences, 2024
  • Early Detection of Diabetic Foot Ulcers Using Optical Flow–Based Ensemble Learning CNN Framework
    Bhavana Singh, Pushpendra Kumar, Muzammil Khan
    Artificial Intelligence in Healthcare Emphasis on Diabetes Hypertension and Depression Management, 2024
    Diabetic foot ulcers (DFUs) are a significant complication of diabetes, leading to over one million amputations annually. Hence, there is an urgent need for more effective and accessible techniques to assist the healthcare system in detecting DFUs. The deep learning models are playing a crucial role in improving the early detection of DFUs; however, there is always a scope of betterment. This work employs computer vision techniques to classify the images of DFUs for early and swift diagnosis. The initial localization of the infected DFUs in the image datasets is performed using optical flow color maps and segmented images, which are further classified with the help of ensemble- and transfer learning–based convolutional neural networks (CNN). Thus, the proposed framework employs the color maps as well as segmented image datasets for feature extraction and classification. Also, optical flow helps in the continuous monitoring of the infection. The validity of the proposed algorithm is established by a comparative analysis with several existing CNN algorithms and also with the ablation of the ensemble model. The experimental findings are elucidated using various evaluation metrics, confusion matrices, and learning curves.
  • A Nonlinear Fractional Order Optical Flow and CNN Based Framework for Fire Prediction Using Smoke Features
    Nitish Kumar Mahala, Muzammil Khan, Pushpendra Kumar
    2024 Opju International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4 0 Otcon 2024, 2024
    Thousands of fires break out daily worldwide, leading to numerous casualties and posing a significant threat to property safety and forest vegetation. Thus, detecting a fire in its early stages is crucial, since it can quickly escalate into a catastrophic and uncontrollable situation. The early detection of fire could be assisted with the help of smoke, which seems small at the beginning and exhibits various colors, shapes, and textures. This can be easily observed by security cameras placed in numerous public areas. The paper introduced a convolutional neural network (CNN) framework for predicting fires by analyzing the dynamic characteristics of smoke. The dynamic characteristics are analyzed using an optical flow color map. The aim of this work is to utilize fractional order optical flow instead of images to accurately determine the position and growth rate. Optical flow estimation is performed using a nonlinear Charbonnier norm-based fractional order variational model, which effectively preserves dynamic discontinuities in the optical flow. Optical flow assists in identifying the dynamic areas inside images (video). The color map is divided into its RGB channels, and the channel most sensitive to smoke motion is isolated using a binary mask. Finally, the segmented optical flow color maps along with the corresponding image sequences are classified using a novel CNN architecture. Various accuracy metrics are used to evaluate the performance and compare with other techniques. Experiments are conducted using a diverse set of datasets comprising 16 smoke and 19 non-smoke videos, respectively.
  • Integration of Visual SLAM in Robot-Assisted Minimally Invasive Surgery: Advances, Challenges, and Solutions
    Muzammil Khan, Françoise Siepel, Theo Ruers
    Springer Proceedings in Advanced Robotics, 2024
  • A Relative Analysis of Different CNN Based Models for COVID-19 Detection using CXR and CT Images
    Pushpendra Kumar, Dipshi Jayaswal, Muzammil Khan, Bhavana Singh
    Procedia Computer Science, 2024
    COVID-19 is an extremely contagious disease that transmits from person to person by contaminated droplets or virus containing airborne particles. It causes severe damage to a patient’s lungs by forming patchy pulmonary lesions and consolidations, which are apparent in chest radiographs such as CXR (X-ray) and CT (computed tomography) images. Therefore, CXR and CT are considered crucial sources of information for early detection of COVID-19 infection. Manual inspection of this information requires expertise, high manpower and substantial amount of time. In order to tackle these issues, deep learning techniques can be utilized in the field of COVID-19 detection. This paper aims at analyzing the performance of different convolutional neural network models in COVID-19 detection using CXR and CT images. These models employ transfer learning and are formed by combining four well-known convolution bases with five distinct machine learning classifiers. All the models are comprehensively trained and tested on CXR and CT datasets each and are thoroughly compared with one another in terms of various evaluation metrics. Amongst these models, the best classification accuracy of 91.18% is provided by the Inception V3 with a neural network classifier on CXR images. Moreover, to assess the improvement of a COVID-19 detection method due to using different techniques, a comparative study of these transfer learning based models with other existing frameworks is also provided.
  • Identification of Potential Biomarkers for Diabetes Mellitus Using Gene Expression Datasets, Machine Learning, and R Packages to Predict the Risk of Diabetes
    Sambedika Jena, Pushpendra Kumar, Dheerendra Mishra, Muzammil Khan
    Artificial Intelligence in Healthcare Emphasis on Diabetes Hypertension and Depression Management, 2024
    Diabetes is a chronic disease affecting millions of people worldwide. The condition occurs when the glucose level in the blood becomes imbalanced or abnormal due to the insulin-glucose modulation mechanism. In humans, glucose is the most important source of energy for cellular functions, which should be maintained all the time in the blood. Among four types, type 2 diabetes, commonly known as diabetes mellitus 2, is heritable and a global concern. Generally, this disorder occurs from the mid-thirties onwards. Several techniques are available for manual analysis of signature-based biomarkers, but few employ machine learning approaches. The objective of this work is to predict the risk of diabetes by identifying the potential biomarkers using the gene expression datasets based on machine learning models and the R package. A genetic biomarker is a sequence of nucleotides or a gene associated with a particular disease. Also, the gene expression profile is vital to an organism’s genetic makeup as well as a major indicator for many diseases and their susceptibility. The gene expression data is the information relevant to the traits encoded by any gene at different levels in different conditions through transcription and translation, which is also referred to as differential expression of genes. Differentially expressed genes signify the expression of genes concerning different diseases, such as diabetes mellitus. Hence, using this gene expression data, the risk assessment of type 2 diabetes mellitus will help in the disease diagnosis.
  • A nonlinear fractional order variational model for the robust estimation of optical flow
    Ankit Kumar, Bhavana Singh, Muzammil Khan, Pushpendra Kumar
    2024 Opju International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4 0 Otcon 2024, 2024
  • A level set based fractional order variational model for motion estimation in application oriented spectrum
    Muzammil Khan, Pushpendra Kumar
    Expert Systems with Applications, 2023
  • Fire Detection Using Level Set Segmentation Based Fractional Order Optical Flow and 4D Fire Features with Mixed Data CNN-LSTM Model
    Muzammil Khan, Pushpendra Kumar
    IEEE Region 10 Annual International Conference Proceedings TENCON, 2023
  • Study of Various Deep Learning Models for COVID-19 Detection Based on Fractional Order Optical Flow
    Bhavana Singh, Muzammil Khan, Pushpendra Kumar
    2023 14th International Conference on Computing Communication and Networking Technologies Icccnt 2023, 2023
  • Prediction of Fire Signatures Based on Fractional Order Optical Flow and Convolution Neural Network
    Shreya Gupta, Muzammil Khan, Pushpendra Kumar
    Communications in Computer and Information Science, 2023
  • A Segmentation Based Robust Fractional Variational Model for Motion Estimation
    Pushpendra Kumar, Muzammil Khan, Nitish Kumar Mahala
    Communications in Computer and Information Science, 2023
  • A Non-Local Weighted Fractional Order Variational Model for Smoke Detection Using Deep Learning Models
    Nitish Kumar Mahala, Muzammil Khan, Pushpendra Kumar
    IEEE Region 10 Annual International Conference Proceedings TENCON, 2023
  • Smoke Detection Using its Static and Dynamic Features Based on Fractional Order Optical Flow and Deep Learning Models for Fire Prediction
    Muzammil Khan, Nitish Kumar Mahala, Pushpendra Kumar
    2023 14th International Conference on Computing Communication and Networking Technologies Icccnt 2023, 2023
  • A nonlinear modeling of fractional order based variational model in optical flow estimation
    Muzammil Khan, Pushpendra Kumar
    Optik, 2022
  • Charbonnier-Marchaud Based Fractional Variational Model for Motion Estimation in Multispectral Vision System
    Pushpendra Kumar, Muzammil Khan
    Journal of Physics Conference Series, 2022
  • Discontinuity Preserving Optical Flow Based on Anisotropic Operator
    Muzammil Khan, Pushpendra Kumar
    Proceedings of 2021 13th International Conference on Information and Communication Technology and System Icts 2021, 2021
  • Early Prediction of COVID-19 Suspects Based on Fractional Order Optical Flow
    Pushpendra Kumar, Muzammil Khan
    2021 5th International Conference on Information Systems and Computer Networks Iscon 2021, 2021
  • A Vision Based Fractional Order TV-Model for Underwater Motion Estimation
    Muzammil Khan, Pushpendra Kumar
    2021 IEEE Bombay Section Signature Conference Ibssc 2021, 2021
  • Development of an IR Video Surveillance System Based on Fractional Order TV-Model
    Pushpendra Kumar, Muzammil Khan, Shreya Gupta
    Proceedings 2021 International Conference on Control Automation Power and Signal Processing Caps 2021, 2021

RECENT SCHOLAR PUBLICATIONS

  • Unifying Scale-Aware Depth Prediction and Perceptual Priors for Monocular Endoscope Pose Estimation and Tissue Reconstruction
    M Khan, E Kerkhof, M Fusaglia, K Kuhlmann, T Ruers, FJ Siepel
    IEEE Access , 2026
    2026
    Citations: 1
  • Explainable and likelihood aware AI framework for MRI-based pixel-level bladder tumour prediction
    M Khan, AG de Groot, EB Cornel, AG van der Heijden, FJ Siepel
    Scientific Reports 15 (1), 40760 , 2025
    2025
  • Reliable Smoke Detection via Optical Flow-Guided Feature Fusion and Transformer-Based Uncertainty Modeling
    NK Mahala, M Khan, P Kumar
    arXiv preprint arXiv:2508.14597 , 2025
    2025
  • Unifying Scale-Aware Depth Prediction and Perceptual Priors for Monocular Endoscope Pose Estimation and Tissue Reconstruction
    M Khan, E Kerkhof, M Fusaglia, K Kuhlmann, T Ruers, F J. Siepel
    arXiv , 2025
    2025
  • Identification of Potential Biomarkers for Diabetes Mellitus Using Gene Expression Datasets, Machine Learning, and R Packages to Predict the Risk of Diabetes
    S Jena, P Kumar, D Mishra, M Khan
    Artificial Intelligence in Healthcare, 47-76 , 2024
    2024
  • Early Detection of Diabetic Foot Ulcers Using Optical Flow–Based Ensemble Learning CNN Framework
    B Singh, P Kumar, M Khan
    Artificial Intelligence in Healthcare, 26-46 , 2024
    2024
    Citations: 1
  • A Nonlinear Fractional Order Optical Flow and CNN Based Framework for Fire Prediction Using Smoke Features
    NK Mahala, M Khan, P Kumar
    2024 OPJU International Technology Conference (OTCON) on Smart Computing for … , 2024
    2024
    Citations: 1
  • A nonlinear fractional order variational model for the robust estimation of optical flow
    A Kumar, B Singh, M Khan, P Kumar
    2024 OPJU International Technology Conference (OTCON) on Smart Computing for … , 2024
    2024
  • Integration of Visual SLAM in Robot-Assisted Minimally Invasive Surgery: Advances, Challenges, and Solutions
    M Khan, F Siepel, T Ruers
    European Robotics Forum, 399-404 , 2024
    2024
    Citations: 1
  • Caputo derivative based nonlinear fractional order variational model for motion estimation in various application oriented spectrum
    M Khan, NK Mahala, P Kumar
    Sādhanā 49 (1), 1-28 , 2024
    2024
    Citations: 12
  • A Relative Analysis of Different CNN Based Models for COVID-19 Detection using CXR and CT Images
    P Kumar, D Jayaswal, M Khan, B Singh
    Procedia Computer Science 235, 3163-3173 , 2024
    2024
    Citations: 2
  • Ensemble learning and skip connection-based CNN framework for COVID-19 identification using CXR and CT images
    PK Muzammil Khan, Bhavana Singh
    International Journal of Computational Vision and Robotics , 2024
    2024
  • CNN-Based Fire Prediction Using Fractional Order Optical Flow and Smoke Features
    M Khan, P Kumar, NK Mahala
    Applications of Optimization and Machine Learning in Image Processing and IoT , 2023
    2023
    Citations: 6
  • A Non-Local Weighted Fractional Order Variational Model for Smoke Detection Using Deep Learning Models
    NK Mahala, M Khan, P Kumar
    TENCON 2023-2023 IEEE Region 10 Conference (TENCON), 146-151 , 2023
    2023
    Citations: 2
  • Fire Detection Using Level Set Segmentation Based Fractional Order Optical Flow and 4D Fire Features with Mixed Data CNN-LSTM Model
    M Khan, P Kumar
    TENCON 2023-2023 IEEE Region 10 Conference (TENCON), 152-157 , 2023
    2023
    Citations: 1
  • Smoke Detection Using its Static and Dynamic Features Based on Fractional Order Optical Flow and Deep Learning Models for Fire Prediction
    M Khan, NK Mahala, P Kumar
    2023 14th International Conference on Computing Communication and Networking … , 2023
    2023
    Citations: 1
  • Study of Various Deep Learning Models for COVID-19 Detection Based on Fractional Order Optical Flow
    B Singh, M Khan, P Kumar
    2023 14th International Conference on Computing Communication and Networking … , 2023
    2023
    Citations: 1
  • Prediction of Fire Signatures Based on Fractional Order Optical Flow and Convolution Neural Network
    S Gupta, M Khan, P Kumar
    Computer Vision and Image Processing: 7th International Conference, CVIP … , 2023
    2023
    Citations: 3
  • A Segmentation Based Robust Fractional Variational Model for Motion Estimation
    P Kumar, M Khan, NK Mahala
    Computer Vision and Image Processing: 7th International Conference, CVIP … , 2023
    2023
    Citations: 2
  • A level set based fractional order variational model for motion estimation in application oriented spectrum
    M Khan, P Kumar
    Expert Systems with Applications, 119628 , 2023
    2023
    Citations: 16

MOST CITED SCHOLAR PUBLICATIONS

  • A nonlinear modeling of fractional order based variational model in optical flow estimation
    M Khan, P Kumar
    Optik 261, 169136 , 2022
    2022.0
    Citations: 27
  • A level set based fractional order variational model for motion estimation in application oriented spectrum
    M Khan, P Kumar
    Expert Systems with Applications, 119628 , 2023
    2023.0
    Citations: 16
  • Caputo derivative based nonlinear fractional order variational model for motion estimation in various application oriented spectrum
    M Khan, NK Mahala, P Kumar
    Sādhanā 49 (1), 1-28 , 2024
    2024.0
    Citations: 12
  • Development of an IR Video Surveillance System Based on Fractional Order TV-Model
    P Kumar, M Khan, S Gupta
    2021 International Conference on Control, Automation, Power and Signal … , 2021
    2021.0
    Citations: 9
  • CNN-Based Fire Prediction Using Fractional Order Optical Flow and Smoke Features
    M Khan, P Kumar, NK Mahala
    Applications of Optimization and Machine Learning in Image Processing and IoT , 2023
    2023.0
    Citations: 6
  • Charbonnier-Marchaud Based Fractional Variational Model for Motion Estimation in Multispectral Vision System
    P Kumar, M Khan
    Journal of Physics: Conference Series 2327 (1), 012031 , 2022
    2022.0
    Citations: 6
  • Early Prediction of COVID-19 Suspects Based on Fractional Order Optical Flow
    P Kumar, M Khan
    2021 5th International Conference on Information Systems and Computer … , 2021
    2021.0
    Citations: 5
  • Prediction of Fire Signatures Based on Fractional Order Optical Flow and Convolution Neural Network
    S Gupta, M Khan, P Kumar
    Computer Vision and Image Processing: 7th International Conference, CVIP … , 2023
    2023.0
    Citations: 3
  • A Relative Analysis of Different CNN Based Models for COVID-19 Detection using CXR and CT Images
    P Kumar, D Jayaswal, M Khan, B Singh
    Procedia Computer Science 235, 3163-3173 , 2024
    2024.0
    Citations: 2
  • A Non-Local Weighted Fractional Order Variational Model for Smoke Detection Using Deep Learning Models
    NK Mahala, M Khan, P Kumar
    TENCON 2023-2023 IEEE Region 10 Conference (TENCON), 146-151 , 2023
    2023.0
    Citations: 2
  • A Segmentation Based Robust Fractional Variational Model for Motion Estimation
    P Kumar, M Khan, NK Mahala
    Computer Vision and Image Processing: 7th International Conference, CVIP … , 2023
    2023.0
    Citations: 2
  • A Vision Based Fractional Order TV-Model for Underwater Motion Estimation
    M Khan, P Kumar
    2021 IEEE Bombay Section Signature Conference (IBSSC), 1-6 , 2021
    2021.0
    Citations: 2
  • Unifying Scale-Aware Depth Prediction and Perceptual Priors for Monocular Endoscope Pose Estimation and Tissue Reconstruction
    M Khan, E Kerkhof, M Fusaglia, K Kuhlmann, T Ruers, FJ Siepel
    IEEE Access , 2026
    2026.0
    Citations: 1
  • Early Detection of Diabetic Foot Ulcers Using Optical Flow–Based Ensemble Learning CNN Framework
    B Singh, P Kumar, M Khan
    Artificial Intelligence in Healthcare, 26-46 , 2024
    2024.0
    Citations: 1
  • A Nonlinear Fractional Order Optical Flow and CNN Based Framework for Fire Prediction Using Smoke Features
    NK Mahala, M Khan, P Kumar
    2024 OPJU International Technology Conference (OTCON) on Smart Computing for … , 2024
    2024.0
    Citations: 1
  • Integration of Visual SLAM in Robot-Assisted Minimally Invasive Surgery: Advances, Challenges, and Solutions
    M Khan, F Siepel, T Ruers
    European Robotics Forum, 399-404 , 2024
    2024.0
    Citations: 1
  • Fire Detection Using Level Set Segmentation Based Fractional Order Optical Flow and 4D Fire Features with Mixed Data CNN-LSTM Model
    M Khan, P Kumar
    TENCON 2023-2023 IEEE Region 10 Conference (TENCON), 152-157 , 2023
    2023.0
    Citations: 1
  • Smoke Detection Using its Static and Dynamic Features Based on Fractional Order Optical Flow and Deep Learning Models for Fire Prediction
    M Khan, NK Mahala, P Kumar
    2023 14th International Conference on Computing Communication and Networking … , 2023
    2023.0
    Citations: 1
  • Study of Various Deep Learning Models for COVID-19 Detection Based on Fractional Order Optical Flow
    B Singh, M Khan, P Kumar
    2023 14th International Conference on Computing Communication and Networking … , 2023
    2023.0
    Citations: 1
  • Leveraging Mixed Data Cnn-Lstm with Fractional Order Optical Flow for Early Fire Detection and Xai-Guided Segmentation
    M Khan, NK Mahala, P Kumar
    Available at SSRN 5372325 , 0
    Citations: 1