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Dr Surbhi Khan's Outputs (78)

Cutting-Edge Amalgamation of Web 3.0 and Hybrid Chaotic Blockchain Authentication for Healthcare 4.0 (2024)
Journal Article
Kumar, A., Abhishek, K., Khan, S. B., Alzahrani, S., & Alojail, M. (in press). Cutting-Edge Amalgamation of Web 3.0 and Hybrid Chaotic Blockchain Authentication for Healthcare 4.0. Mathematics, 12(19), 3067. https://doi.org/10.3390/math12193067

Healthcare 4.0 is considered the most promising technology for gathering data from humans and strongly couples with a communication system for precise clinical and diagnosis performance. Though sensor-driven devices have largely made our everyday liv... Read More about Cutting-Edge Amalgamation of Web 3.0 and Hybrid Chaotic Blockchain Authentication for Healthcare 4.0.

The BCPM method: decoding breast cancer with machine learning (2024)
Journal Article
Almarri, B., Gupta, G., Kumar, R., Vandana, V., Asiri, F., & Khan, S. B. (in press). The BCPM method: decoding breast cancer with machine learning. BMC Medical Imaging, 24, Article 248. https://doi.org/10.1186/s12880-024-01402-5

Breast cancer prediction and diagnosis are critical for timely and effective treatment, significantly impacting patient outcomes. Machine learning algorithms have become powerful tools for improving the prediction and diagnosis of breast cancer. The... Read More about The BCPM method: decoding breast cancer with machine learning.

6GTelMED: Resources Recommendation Framework on 6G Enabled Distributed Telemedicine Using Edge-AI (2024)
Journal Article
Ahmed, S. T., Patil, K. K., S, S. K., Shanraj, R. K., Khan, S. B., Alzahrani, S., & Rani, S. (2024). 6GTelMED: Resources Recommendation Framework on 6G Enabled Distributed Telemedicine Using Edge-AI. IEEE Transactions on Consumer Electronics, 70(3), 5524 - 5532. https://doi.org/10.1109/tce.2024.3473291

Telemedicine infrastructure is enhanced in recent times and applications developed have adopted base-line networking standards according to 4G/5G and LTE. The major challenge in exiting infrastructural setups is higher-latency and exposed privacy of... Read More about 6GTelMED: Resources Recommendation Framework on 6G Enabled Distributed Telemedicine Using Edge-AI.

TinyDeepUAV: A Tiny Deep Reinforcement Learning Framework for UAV Task Offloading in Edge-Based Consumer Electronics (2024)
Journal Article
Bebortta, S., Tripathy, S. S., Khan, S. B., Dabel, M. M. A., Almusharraf, A., & Bashir, A. K. (2024). TinyDeepUAV: A Tiny Deep Reinforcement Learning Framework for UAV Task Offloading in Edge-Based Consumer Electronics. IEEE Transactions on Consumer Electronics, 70(4), https://doi.org/10.1109/tce.2024.3445290

Recently, there has been a rise in the use of Unmanned Areal Vehicles (UAVs) in consumer electronics, particularly for the critical situations. Internet of Things (IoT) technology and the accessibility of inexpensive edge computing devices present no... Read More about TinyDeepUAV: A Tiny Deep Reinforcement Learning Framework for UAV Task Offloading in Edge-Based Consumer Electronics.

Enhancing Software Fault Prediction Through Feature Selection With Spider Wasp Optimization Algorithm (2024)
Journal Article
Das, H., Das, S., Kumar Gourisaria, M., Bhatia Khan, S., Almusharraf, A., Alharbi, A. I., & Mahesh, T. R. (2024). Enhancing Software Fault Prediction Through Feature Selection With Spider Wasp Optimization Algorithm. IEEE Access, 12, 105309-105325. https://doi.org/10.1109/access.2024.3435333

Software fault prediction (SFP) is a critical focus in software engineering, aiming to enhance productivity and minimize costs by detecting faults early. Feature selection (FS) is pivotal in SFP, enabling the identification of pertinent features for... Read More about Enhancing Software Fault Prediction Through Feature Selection With Spider Wasp Optimization Algorithm.

Multi-class Breast Cancer Classification Using CNN Features Hybridization (2024)
Journal Article
Chakravarthy, S., Bharanidharan, N., Khan, S. B., Kumar, V. V., Mahesh, T. R., Almusharraf, A., & Albalawi, E. (2024). Multi-class Breast Cancer Classification Using CNN Features Hybridization. International Journal of Computational Intelligence Systems, 17, Article 191. https://doi.org/10.1007/s44196-024-00593-7

Breast cancer has become the leading cause of cancer mortality among women worldwide. The timely diagnosis of such cancer is always in demand among researchers. This research pours light on improving the design of computer-aided detection (CAD) for e... Read More about Multi-class Breast Cancer Classification Using CNN Features Hybridization.

Explainable lung cancer classification with ensemble transfer learning of VGG16, Resnet50 and InceptionV3 using grad-cam (2024)
Journal Article
Kumaran S, Y., Jeya, J. J., R, M. T., Khan, S. B., Alzahrani, S., & Alojail, M. (2024). Explainable lung cancer classification with ensemble transfer learning of VGG16, Resnet50 and InceptionV3 using grad-cam. BMC Medical Imaging, 24(1), 176. https://doi.org/10.1186/s12880-024-01345-x

Medical imaging stands as a critical component in diagnosing various diseases, where traditional methods often rely on manual interpretation and conventional machine learning techniques. These approaches, while effective, come with inherent limitatio... Read More about Explainable lung cancer classification with ensemble transfer learning of VGG16, Resnet50 and InceptionV3 using grad-cam.

Hybrid healthcare unit recommendation system using computational techniques with lung cancer segmentation (2024)
Journal Article

Introduction: Our research addresses the critical need for accurate segmentation in medical healthcare applications, particularly in lung nodule detection using Computed Tomography (CT). Our investigation focuses on determining the particle compositi... Read More about Hybrid healthcare unit recommendation system using computational techniques with lung cancer segmentation.

Water quality level estimation using IoT sensors and probabilistic machine learning model (2024)
Journal Article
TR, M., Bhatia Khan, S., Balajee, A., Almusharraf, A., Gadekallu, T. R., Albalawi, E., & Kumar, V. (in press). Water quality level estimation using IoT sensors and probabilistic machine learning model. Hydrology Research, 55(7), 775–789. https://doi.org/10.2166/nh.2024.048

Drinking water purity analysis is an essential framework that demands several real-world parameters to ensure the quality of water. So far, sensor-based analysis of water quality in specific environments is done concerning certain parameters includin... Read More about Water quality level estimation using IoT sensors and probabilistic machine learning model.

Intelligent Recognition of Multimodal Human Activities for Personal Healthcare (2024)
Journal Article
Sannasi Chakravarthy, S. R., Bharanidharan, N., Vinoth Kumar, V., Mahesh, T. R., Khan, S. B., Almusharraf, A., & Albalawi, E. (2024). Intelligent Recognition of Multimodal Human Activities for Personal Healthcare. IEEE Access, 1-1. https://doi.org/10.1109/access.2024.3405471

Nowadays, the advancements of wearable consumer devices have become a predominant role in
healthcare gadgets. There is always a demand to obtain robust recognition of heterogeneous human activities
in complicated IoT environments. The knowledge att... Read More about Intelligent Recognition of Multimodal Human Activities for Personal Healthcare.

Enhancing brain tumor classification in MRI scans with a multi-layer customized convolutional neural network approach (2024)
Journal Article

Background: The necessity of prompt and accurate brain tumor diagnosis is unquestionable for optimizing treatment strategies and patient prognoses. Traditional reliance on Magnetic Resonance Imaging (MRI) analysis, contingent upon expert interpretati... Read More about Enhancing brain tumor classification in MRI scans with a multi-layer customized convolutional neural network approach.

Few-Shot Learning for Medical Image Segmentation Using 3D U-Net and Model-Agnostic Meta-Learning (MAML) (2024)
Journal Article
Alsaleh, A. M., Albalawi, E., Algosaibi, A., Albakheet, S. S., & Khan, S. B. (in press). Few-Shot Learning for Medical Image Segmentation Using 3D U-Net and Model-Agnostic Meta-Learning (MAML). Diagnostics, 14(12), 1213. https://doi.org/10.3390/diagnostics14121213

Deep learning has attained state-of-the-art results in general image segmentation problems; however, it requires a substantial number of annotated images to achieve the desired outcomes. In the medical field, the availability of annotated images is o... Read More about Few-Shot Learning for Medical Image Segmentation Using 3D U-Net and Model-Agnostic Meta-Learning (MAML).

Refining neural network algorithms for accurate brain tumor classification in MRI imagery (2024)
Journal Article
Alshuhail, A., Thakur, A., Chandramma, R., Mahesh, T. R., Almusharraf, A., Vinoth Kumar, V., & Khan, S. B. (in press). Refining neural network algorithms for accurate brain tumor classification in MRI imagery. BMC Medical Imaging, 24(1), 118. https://doi.org/10.1186/s12880-024-01285-6

Brain tumor diagnosis using MRI scans poses significant challenges due to the complex nature of tumor appearances and variations. Traditional methods often require extensive manual intervention and are prone to human error, leading to misdiagnosis an... Read More about Refining neural network algorithms for accurate brain tumor classification in MRI imagery.

Integrated approach of federated learning with transfer learning for classification and diagnosis of brain tumor (2024)
Journal Article
Albalawi, E., T.R., M., Thakur, A., Kumar, V. V., Gupta, M., Khan, S. B., & Almusharraf, A. (in press). Integrated approach of federated learning with transfer learning for classification and diagnosis of brain tumor. BMC Medical Imaging, 24(1), Article 110. https://doi.org/10.1186/s12880-024-01261-0

Brain tumor classification using MRI images is a crucial yet challenging task in medical imaging. Accurate diagnosis is vital for effective treatment planning but is often hindered by the complex nature of tumor morphology and variations in imaging.... Read More about Integrated approach of federated learning with transfer learning for classification and diagnosis of brain tumor.

Artificial Intelligence in Next-Generation Networking: Energy Efficiency Optimization in IoT Networks Using Hybrid LEACH Protocol (2024)
Journal Article
Khan, S. B., Kumar, A., Mashat, A., Pruthviraja, D., Imam Rahmani, M. K., & Mathew, J. (in press). Artificial Intelligence in Next-Generation Networking: Energy Efficiency Optimization in IoT Networks Using Hybrid LEACH Protocol. SN Computer Science, 5(5), 546. https://doi.org/10.1007/s42979-024-02778-5

The convergence of the Internet of Things (IoT) and Artificial Intelligence (AI) is significantly transforming the landscape of future networking. The Internet of Things (IoT) is a technological paradigm that encompasses embedded systems, wireless se... Read More about Artificial Intelligence in Next-Generation Networking: Energy Efficiency Optimization in IoT Networks Using Hybrid LEACH Protocol.

Towards blockchain based federated learning in categorizing healthcare monitoring devices on artificial intelligence of medical things investigative framework (2024)
Journal Article
Ahmed, S. T., Mahesh, T. R., Srividhya, E., Vinoth Kumar, V., Khan, S. B., Albuali, A., & Almusharraf, A. (in press). Towards blockchain based federated learning in categorizing healthcare monitoring devices on artificial intelligence of medical things investigative framework. BMC Medical Imaging, 24(1), 105. https://doi.org/10.1186/s12880-024-01279-4

Categorizing Artificial Intelligence of Medical Things (AIoMT) devices within the realm of standard Internet of Things (IoT) and Internet of Medical Things (IoMT) devices, particularly at the server and computational layers, poses a formidable challe... Read More about Towards blockchain based federated learning in categorizing healthcare monitoring devices on artificial intelligence of medical things investigative framework.

Advancing solar energy integration: Unveiling XAI insights for enhanced power system management and sustainable future (2024)
Journal Article
Nallakaruppan, M., Shankar, N., Bhuvanagiri, P. B., Padmanaban, S., & Bhatia Khan, S. (2024). Advancing solar energy integration: Unveiling XAI insights for enhanced power system management and sustainable future. Ain Shams Engineering Journal ASEJ / Ain Shams University, 15(6), 102740. https://doi.org/10.1016/j.asej.2024.102740

Solar energy has emerged as a vital renewable alternative to fossil fuels, enhancing environmental sustainability in response to the pressing need to reduce carbon emissions. However, the integration of solar power into the electric... Read More about Advancing solar energy integration: Unveiling XAI insights for enhanced power system management and sustainable future.

Decoupled SculptorGAN Framework for 3D Reconstruction and Enhanced Segmentation of Kidney Tumors in CT Images (2024)
Journal Article
Suman Prakash, P., Kiran Rao, P., Suresh Babu, E., Khan, S. B., Almusharraf, A., & Quasim, M. T. (2024). Decoupled SculptorGAN Framework for 3D Reconstruction and Enhanced Segmentation of Kidney Tumors in CT Images. IEEE Access, 1-1. https://doi.org/10.1109/access.2024.3389504

Our proposed work, SculptorGAN, represents a novel advancement in the domain of medical imaging, for the accurate and automatic diagnosis of renal tumors, using the techniques and principles of Generative Adversarial Network (GAN). This dichotomous f... Read More about Decoupled SculptorGAN Framework for 3D Reconstruction and Enhanced Segmentation of Kidney Tumors in CT Images.

PrEGAN: Privacy Enhanced Clinical EMR Generation: Leveraging GAN Model for Customer De-Identification (2024)
Journal Article
Ahmed, S. T., Sivakami, R., V, V. K., R, M. T., Khan, S. B., Mashat, A., & Almusharraf, A. (2024). PrEGAN: Privacy Enhanced Clinical EMR Generation: Leveraging GAN Model for Customer De-Identification. IEEE Transactions on Consumer Electronics, 1-1. https://doi.org/10.1109/tce.2024.3386222

Privacy in medical records while data sharing is a major concern for distributed learning models. The dataset generated and shared via Electronic Medical Records (EMR) consist of sensitive medical information such as patient identify and experts reco... Read More about PrEGAN: Privacy Enhanced Clinical EMR Generation: Leveraging GAN Model for Customer De-Identification.