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All Outputs (65)

Machine learning-driven intelligent water quality assessment for enhanced drinking safety and real-time consumer awareness (2025)
Journal Article

As to the sphere of smart water management and managing water Internet of Things (IoT) systems, water condition safety for drinking is very important. The proposed methodology, known as the Smart Water Consumption Monitoring System (SWCMS), is based... Read More about Machine learning-driven intelligent water quality assessment for enhanced drinking safety and real-time consumer awareness.

Enhancing Drug Discovery and Patient Care through Advanced Analytics with The Power of NLP and Machine Learning in Pharmaceutical Data Interpretation (2024)
Journal Article

This study delves into the transformative potential of Machine Learning (ML) and Natural Language Processing (NLP) within the pharmaceutical industry, spotlighting their significant impact on enhancing medical research methodologies... Read More about Enhancing Drug Discovery and Patient Care through Advanced Analytics with The Power of NLP and Machine Learning in Pharmaceutical Data Interpretation.

Adaptive federated learning for resource-constrained IoT devices through edge intelligence and multi-edge clustering (2024)
Journal Article
Mughal, F. R., He, J., Das, B., Dharejo, F. A., Zhu, N., Khan, S. B., & Alzahrani, S. (2024). Adaptive federated learning for resource-constrained IoT devices through edge intelligence and multi-edge clustering. Scientific Reports, 14, Article 28746. https://doi.org/10.1038/s41598-024-78239-z

In the rapidly growing Internet of Things (IoT) landscape, federated learning (FL) plays a crucial role in enhancing the performance of heterogeneous edge computing environments due to its scalability, robustness, and low energy consumption. However,... Read More about Adaptive federated learning for resource-constrained IoT devices through edge intelligence and multi-edge clustering.

Employing Xception convolutional neural network through high-precision MRI analysis for brain tumor diagnosis. (2024)
Journal Article
Sathya, R., Mahesh, T. R., Bhatia Khan, S., Malibari, A. A., Asiri, F., Rehman, A. U., & Malwi, W. A. (in press). Employing Xception convolutional neural network through high-precision MRI analysis for brain tumor diagnosis. Frontiers in Medicine, 11, 1487713. https://doi.org/10.3389/fmed.2024.1487713

The classification of brain tumors from medical imaging is pivotal for accurate medical diagnosis but remains challenging due to the intricate morphologies of tumors and the precision required. Existing methodologies, including manual MRI evaluations... Read More about Employing Xception convolutional neural network through high-precision MRI analysis for brain tumor diagnosis..

Exploring Topic Coherence with PCC-LDA and BERT for Contextual Word Generation (2024)
Journal Article
Rachamadugu, S. K., Pushphavathi, T., Khan, S. B., & Alojail, M. (2024). Exploring Topic Coherence with PCC-LDA and BERT for Contextual Word Generation. IEEE Access, 12, 175252 - 175267. https://doi.org/10.1109/access.2024.3477992

In the field of natural language processing (NLP), topic modeling and word generation are crucial for comprehending and producing texts that resemble human languages. Extracting key phrases is an essential task that aids document summarization, infor... Read More about Exploring Topic Coherence with PCC-LDA and BERT for Contextual Word Generation.

Redefining retinal vessel segmentation: empowering advanced fundus image analysis with the potential of GANs (2024)
Journal Article

Retinal vessel segmentation is a critical task in fundus image analysis, providing essential insights for diagnosing various retinal diseases. In recent years, deep learning (DL) techniques, particularly Generative Adversarial Networks (GANs), have g... Read More about Redefining retinal vessel segmentation: empowering advanced fundus image analysis with the potential of GANs.

Enhancing Image Security via Block Cyclic Construction and DNA Based LFSR (2024)
Journal Article
Deb, S., Das, A., Biswas, B., Sarkar, J. L., Khan, S. B., Alzahrani, S., & Rani, S. (2024). Enhancing Image Security via Block Cyclic Construction and DNA Based LFSR. IEEE Transactions on Consumer Electronics, 70(3), https://doi.org/10.1109/tce.2024.3481260

The rapidly growing multimedia image data driven by real-time messaging technologies is particularly evident in applications such as autonomous vehicle tracking, smart cities, surveillance systems and many more. Considering images, data privacy and s... Read More about Enhancing Image Security via Block Cyclic Construction and DNA Based LFSR.

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).