IoT enables continuous patient monitoring and immediate response to healthcare needs by creating a connected environment where healthcare services can interact seamlessly and continuously with one another. However, IoT-based healthcare systems represent an attractive target for cybercriminals, as they hold sensitive medical information on individuals. An IoT-based healthcare platform can be compromised through data manipulation, unauthorized access, and denial-of-service (DoS) attacks. To keep this issue from occurring, this paper describes a secure solution for the continuous monitoring of patient health through the use of IoT technology. The Secure IoT-based Biomedical Monitoring Framework (SIBMF) will utilize lightweight encryption techniques and the Machine Learning-based Intrusion Detection System (MLIDS) to enhance security for IoT users. Physiological signals from patients’ bodies (ECG, heart rate, and temperature) will be captured by IoT sensors and transmitted in an encrypted format to a cloud-based server. IoT sensors will utilize a cryptographic algorithm to encrypt their biomedical data prior to sending it to a cloud-based server, ensuring the confidentiality of patient data. MLIDS will detect unauthorized suspect access to the IoT network by analyzing the features of the traffic created by the IoT devices. A prototype of the SIBMF was built in MATLAB, and various performance metrics (i.e., accuracy, detection rate, false positive rate, data encryption time, and latency) were assessed. The experimental results demonstrate that this approach significantly improves data security and will provide superior accuracy for monitoring patients’ health and has minimal computational requirements.
Dr. N. Nalini, N. S. Gunapriya, Almousa et al.· International Conference on...· 0 citations
Delay of emergency services and lack of instantaneous reporting of accidents is the main contributor to the serious injuries or death in two-wheeler accidents. A Smart Helmet system has been created to solve this acute problem through intelligent detection of accidents during the accident and automatic notification of emergency services. The helmet has an Inertial Measurement Unit (IMU), an accelerator and a gyroscopic device that constantly reads the movement of the riders and matches any abrupt impact or unusual movement patterns that are signs of a collision. When an accident happens, the system sends the live position of the rider through SMS to a GSM/GPS receiver that triggers an automatic emergency call to predefined contacts or closest police department. The system has a cancel window to avoid false alarms when the bike is suddenly braking or making a minor slip. The rider is free to remain on alert. In addition, it has machine learning (TinyML) that improves the precision of the detection of various motion patterns, including actual collisions, potholes, or regular riding to improve false positives. This use of AI will make sure that there are real accidents that cause the alarm. The Smart Helmet is an IoT-enabled safety solution that was designed as a low-cost solution to safety, guaranteeing not only the speed of medical help but also making roads smarter.
ASHWINI A, N. Nalini, A. Rosi et al.· International Conference on...· 0 citations
The current study describes an advanced hybrid multi-modal approach that will be used in predicting soil health and crop productivity through the application of several types of deep learning models and machine learning approaches to enhance predictions. In the current work, a hybrid model is considered, and three kinds of pre-trained CNN, known as ResNet50, VGG16, and Mobile Net, are used to extract the features of soil. The pre-fetched features are subsequently processed through multiple fully connected layers with SoftMax activation functions for multi-class types of soil classification, namely clay, sandy, and loam. To enhance the generalization and robustness of the models, data augmentation and normalization techniques are applied to the data. The models are optimized with Adam and SGD optimizers using categorical cross-entropy parameters for the loss function. The results of the experimentation show high accuracy and robust performance for the precision-recall and F1-score metrics. The model would highly benefit precision agriculture applications to facilitate autonomous soil type detection, and to enable farmers and growers to better inform crop selection and nutrient management for sustainable yield optimization models in smart farming.
D. Stephen, D. Ferlin, D. Shahila· International Conference on...· 0 citations
Images of medical patients that were taken in low light or low contrast areas frequently have issues with noise, visibility, and important information for physicians to use in deciding on patient care. An Adaptive Hybrid Convolutional Neural Network Discrete Wavelet Transform Enhancement Technique for Low Light Medical Images is presented for use in helping to detect latent disease in X-ray and Magnetic Resonance Images. The method works as follows: The input image first goes through a Discrete Wavelet Transform to break it down into high frequency and low frequency components. Separating out the high frequency (noise) and low frequency parts of the image allows for a more efficient way to reduce noise while still preserving the critical structure of the image. Once the first step has been completed, multi-scale wavelet features are used to create a Convolutional Neural Network (CNN) enhancement module that learns how to adaptively learn how to make illumination corrections and improve contrast. The final part of the process is an Adaptive Histogram Equalization postprocessing step that improves visual clarity, therefore enhancing the final image to allow for good clinical interpretation. There are experimental results that demonstrate the new proposed framework is significantly better than existing methods on several common image quality metrics such as PERMANENT CRYSTAL, PSNR, and SSIM, and that it preserves the critical diagnostic features of the medical image. This method is extremely beneficial to radiologists because it allows for the accurate and reliable analysis of MRI images using Computer Assisted Diagnosis (CAD) systems and can be integrated with existing CAD systems to enhance and improve the radiologist’s ability to interpret the medical images of their patients.
D. Ferlin, D. Shahila, D. Stephen· International Conference on...· 0 citations
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