Kateb Faris, Ragab Mahmoud, Abukhodair Felwa, Abdulkader Omar Ahmed, Maghrabi Louai A, Binyamin Sami Saeed, Al-Hanawi Mohammed Khaled
Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.
Center of Excellence in Smart Environment Research, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.
Sci Rep. 2025 Apr 17;15(1):13223. doi: 10.1038/s41598-025-97065-5.
With an increased chronic disease and an ageing population, remote health monitoring is a substantial method to enhance the care of patients and decrease healthcare expenses. The Internet of Things (IoT) presents a promising solution for remote health monitoring by collecting and analyzing vital data like body temperature, ECG, and heart rate, giving real-time insights to medical professionals. However, maintaining effectual monitoring in environments with bandwidth or energy constraints presents crucial threats. While machine analysis and human insight performance must be content, conveying extra data to gratify both would be evaded for efficient resource application. Therefore, this article proposes an Enhanced Security Mechanism for Human-Centered Systems using Deep Learning with Jellyfish Search Optimizer (ESHCS-DLJSO) approach for IoT healthcare applications. The projected ESHCS-DLJSO approach allows IoT devices in the healthcare field to securely convey medical data and early recognition of health problems in the human-machine interface. To achieve this, the ESHCS-DLJSO approach utilizes a min-max normalization technique to transform the input data into a more suitable format. The bacterial foraging optimization algorithm (BFOA) method is used for feature extraction. Moreover, a convolutional neural network with long short-term memory (CNN-LSTM-Attention) technique is used for disease detection and classification. Finally, the ESHCS-DLJSO technique employs the jellyfish search optimizer (JSO) technique for hyperparameter tuning. The simulation of the ESHCS-DLJSO technique is examined on an IoT healthcare security dataset. The performance validation of the ESHCS-DLJSO technique portrayed a superior accuracy value of 99.43% over existing approaches.
随着慢性病的增加和人口老龄化，远程健康监测是加强患者护理和降低医疗费用的重要方法。物联网（IoT）通过收集和分析体温、心电图和心率等重要数据，为远程健康监测提供了一个有前景的解决方案，为医疗专业人员提供实时洞察。然而，在带宽或能源受限的环境中维持有效的监测存在重大挑战。虽然机器分析和人类洞察性能必须满足要求，但为了有效利用资源，将避免传输额外数据以同时满足两者。因此，本文提出了一种用于物联网医疗应用的基于深度学习与水母搜索优化器的以人为中心系统的增强安全机制（ESHCS-DLJSO）方法。预计的ESHCS-DLJSO方法允许医疗领域的物联网设备安全地传输医疗数据，并在人机界面中早期识别健康问题。为了实现这一点，ESHCS-DLJSO方法利用最小-最大归一化技术将输入数据转换为更合适的格式。细菌觅食优化算法（BFOA）方法用于特征提取。此外，采用具有长短期记忆的卷积神经网络（CNN-LSTM-Attention）技术进行疾病检测和分类。最后，ESHCS-DLJSO技术采用水母搜索优化器（JSO）技术进行超参数调整。在物联网医疗安全数据集上对ESHCS-DLJSO技术进行了仿真。ESHCS-DLJSO技术的性能验证表明，其准确率高达99.43%，优于现有方法。
