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Dr Surbhi Khan's Outputs (37)

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.

Investigating attention mechanisms for plant disease identification in challenging environments. (2024)
Journal Article
Duhan, S., Gulia, P., Gill, N. S., Shukla, P. K., Khan, S. B., Almusharraf, A., & Alkhaldi, N. (2024). Investigating attention mechanisms for plant disease identification in challenging environments. Heliyon, 10(9), e29802. https://doi.org/10.1016/j.heliyon.2024.e29802

There is an increasing demand for efficient and precise plant disease detection methods that can quickly identify disease outbreaks. For this, researchers have developed various machine learning and image processing techniques. However, real-field im... Read More about Investigating attention mechanisms for plant disease identification in challenging environments..

User-centric secured smart virtual assistants framework for disables (2024)
Journal Article
Alfayez, F., & Khan, S. B. (in press). User-centric secured smart virtual assistants framework for disables. Alexandria engineering journal : AEJ, 95, 59-71. https://doi.org/10.1016/j.aej.2024.03.033

Research on intelligent secured virtual assistant (ISVA) systems for disabled people is essential in order to meet the special requirements and overcome the difficulties they confront. The delicate nature of user interactions makes... Read More about User-centric secured smart virtual assistants framework for disables.

Hybrid optimization algorithm for enhanced performance and security of counter-flow shell and tube heat exchangers (2024)
Journal Article

A shell and tube heat exchanger (STHE) for heat recovery applications was studied to discover the intricacies of its optimization. To optimize performance, a hybrid optimization methodology was developed by combining the Neural Fitting Tool (NFTool),... Read More about Hybrid optimization algorithm for enhanced performance and security of counter-flow shell and tube heat exchangers.

Enhancing accessibility for improved diagnosis with modified EfficientNetV2-S and cyclic learning rate strategy in women with disabilities and breast cancer (2024)
Journal Article
Al Moteri, M., Mahesh, T. R., Thakur, A., Vinoth Kumar, V., Khan, S. B., & Alojail, M. (in press). Enhancing accessibility for improved diagnosis with modified EfficientNetV2-S and cyclic learning rate strategy in women with disabilities and breast cancer. Frontiers in Medicine, 11, 1373244. https://doi.org/10.3389/fmed.2024.1373244

Breast cancer, a prevalent cancer among women worldwide, necessitates precise and prompt detection for successful treatment. While conventional histopathological examination is the benchmark, it is a lengthy process and prone to variations among diff... Read More about Enhancing accessibility for improved diagnosis with modified EfficientNetV2-S and cyclic learning rate strategy in women with disabilities and breast cancer.

A New Approach for Speech Emotion Recognition using Single Layered Convolutional Neural Network (2024)
Journal Article
Vinoth Kumar, V., Palaiahnakote, S., Khan, S. B., & Almusharraf, A. (2024). A New Approach for Speech Emotion Recognition using Single Layered Convolutional Neural Network. Malaysian journal of computer science, 37(1), 89–106. https://doi.org/10.22452/mjcs.vol37no1.5

Creating a computational device to identify human emotions via voice analysis represents a notable achievement in the sector of human-computer interaction, especially within the healthcare domain. We propose a new lightweight model for addressing cha... Read More about A New Approach for Speech Emotion Recognition using Single Layered Convolutional Neural Network.

Oral squamous cell carcinoma detection using EfficientNet on histopathological images (2024)
Journal Article
Albalawi, E., Thakur, A., Ramakrishna, M. T., Bhatia Khan, S., SankaraNarayanan, S., Almarri, B., & Hadi, T. H. (in press). Oral squamous cell carcinoma detection using EfficientNet on histopathological images. Frontiers in Medicine, 10, 1349336. https://doi.org/10.3389/fmed.2023.1349336

Introduction: Oral Squamous Cell Carcinoma (OSCC) poses a significant challenge in oncology due to the absence of precise diagnostic tools, leading to delays in identifying the condition. Current diagnostic methods for OSCC have limitations in accura... Read More about Oral squamous cell carcinoma detection using EfficientNet on histopathological images.

An artificial intelligence-based decision support system for early and accurate diagnosis of Parkinson’s Disease (2024)
Journal Article

People with Parkinson’s Disease (PD) might struggle with sadness, restlessness, or difficulty speaking, chewing, or swallowing. A diagnosis can be challenging because there is no specific PD test. It is diagnosed by doctors using a... Read More about An artificial intelligence-based decision support system for early and accurate diagnosis of Parkinson’s Disease.