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The rapid development of Internet of Things (IoT) technology has enabled the emergence of the Internet of Medical Things (IoMT), especially in body area network applications. To protect sensitive medical data, it is essential to ensure privacy preservation and detect intrusions in this context. This study proposes a novel intrusion detection system that protects the privacy of IoMT networks, specifically in the context of body area networks. For feature extraction, the system employs a recurrent U-Net autoencoder algorithm, which effectively captures temporal dependencies in IoMT data. In addition, privacy is protected through the combination of data anonymization techniques and data classification using Principal Component Analysis (PCA). Combining the recurrent U-Net autoencoder algorithm, privacy preservation mechanisms, and PCA-based data classification, the proposed system architecture comprises the U-Net autoencoder algorithm. The proposed method is superior to existing approaches in terms of accuracy, precision, recall, F-measure, and classification loss, as demonstrated by experimental evaluations. This research contributes to the field of privacy protection and intrusion detection in IoMT, specifically in body area network applications. [ABSTRACT FROM AUTHOR]

Self-driving vehicles have emerged as a revolutionary breakthrough in modern transportation, promising unparalleled safety, efficiency, and convenience. However, navigating through unpredictable road conditions, especially in the presence of potholes, remains a significant challenge that poses potential safety risks. This study introduces an innovative cloud-powered next-generation self-driving safety system that harnesses the power of AI, specifically YOLOv8 (You Only Look Once version 8), in conjunction with the wandb (Weights & Biases) deep learning platform. This integration enables pothole detection and advanced navigation, elevating the safety standards of autonomous driving. The selection of YOLOv8, a cutting-edge object detection model, is by its exceptional accuracy and speed. YOLOv8 employs a singular neural network to predict object bounding boxes and class probabilities directly, allowing for rapid and precise object detection. This cloud-based architecture also supports continuous model updates and refinements, ensuring the system's adaptability to evolving road conditions and pothole variations. With potential applications extending beyond potholes, this system paves the way for safer and more reliable autonomous transportation, revolutionizing the landscape of self-driving technology. [ABSTRACT FROM AUTHOR]

In older people, mild cognitive impairment (MCI) is a precursor to more severe forms of dementia like AD (AD). In diagnosing patients with primary AD and amnestic MCI, modern neuroimaging techniques, especially MRI, play a key role. To efficiently categorize MRI images as normal or abnormal, the research presents a machine learning-based automatic labelling system, with a focus on boosting performance via texture feature analysis. To this end, the research implements a preprocessing phase employing Log Gabor filters, which are particularly well-suited for spatial frequency analysis. In addition, the research uses Gray Wolf Optimization (GWO) to acquire useful information from the images. For classification tasks using the MRI images, the research also make use of DenseNets, a form of deep neural network. The proposed method leverages Log Gabor filters for preprocessing, Gray Wolf Optimization (GWO) for feature extraction, and DenseNets for classification, resulting in a robust approach for categorizing MRI images as normal or abnormal. When compared to earlier trials performed without optimization, the proposed systematic technique shows a significant increase in classification accuracy of 15%. For neuroimaging applications, our research emphasizes the use of Log Gabor filters for preprocessing, GWO for feature extraction, and DenseNets for classification, which can help with the early detection and diagnosis of MCI and AD. [ABSTRACT FROM AUTHOR]

DUS measurements for popliteal artery aneurysms (PAAs) specifically can be time-consuming, error-prone, and operator-dependent. To eliminate this subjectivity and provide efficient segmentation, we applied artificial intelligence (AI) to accurately delineate inner and outer lumen on DUS. DUS images were selected from a cohort of patients with PAAs from a multi-institutional platform. Encord is an easy-to-use, readily available online AI platform that was used to segment both the inner lumen and outer lumen of the PAA on DUS images. A model trained on 20 images and tested on 80 images had a mean Average Precision of 0.85 for the outer polygon and 0.23 for the inner polygon. The outer polygon had a higher recall score than precision score at 0.90 and 0.85, respectively. The inner polygon had a score of 0.25 for both precision and recall. The outer polygon false-negative rate was the lowest in images with the least amount of blur. This study demonstrates the feasibility of using the widely available Encord AI platform to identify standard features of PAAs that are critical for operative decision making. [ABSTRACT FROM AUTHOR]

The creation of sensor-based software for health monitoring using Internet of Things (IoT) technology is the main goal of this project. The program's objective is to continuously monitor human physiological data, including ECG, SPO2, heart rate, and respiration, by employing biomedical sensor networks. These sensors collect data, which is then processed by a processor and transmitted to an edge server through a transceiver. A node of corner facilitates for real transmission has processed each data will be patient's phone and the clinicians' LED display. To address the optimization challenge, the program utilizes a Double Deep-Q-Network approach, with parameters optimized using a hybrid genetic algorithm-based simulated annealing technique. However, healthcare records obtained from the sensors are susceptible to change due to environmental factors, leading to potential performance issues. In order to overcome this challenge, an optimization approach is employed to refine the proposed technique, ensuring accurate prediction of readings. The study conducted experiments to evaluate the program's performance, utilizing various metrics and different parameters. The results to provide light on how well the program that was created for leveraging IoT technologies for health monitoring is working. This study presents an innovative sensor-based program for IoT technology-based health monitoring, which continuously monitors human physiological data. The program incorporates a hybrid optimization approach to ensure accurate prediction of readings, accounting for environmental factors. The proposed Double Deep-Q-Network and the evaluation metrics employed demonstrate the originality and contributions of this research in advancing health monitoring systems. [ABSTRACT FROM AUTHOR]

The autonomous driving aims at ensuring the vehicle to effectively sense the environment and use proper strategies to navigate the vehicle without the interventions of humans. Hence, there exist a prediction of the background scenes and that leads to discontinuity between the predicted and planned outputs. An optimal prediction engine is required that suitably reads the background objects and make optimal decisions. In this paper, the author(s) develop an autonomous model for vehicle driving using ensemble model for large Sport Utility Vehicles (SUVs) that uses three different modules involving (a) recognition model, (b) planning model and (c) prediction model. The study develops a direct realization method for an autonomous vehicle driving. The direct realization method is designed as a behavioral model that incorporates three different modules to ensure optimal autonomous driving. The behavioral model includes recognition, planning and prediction modules that regulates the input trajectory processing of input video datasets. A deep learning algorithm is used in the proposed approach that helps in the classification of known or unknown objects along the line of sight. This model is compared with conventional deep learning classifiers in terms of recall rate and root mean square error (RMSE) to estimate its efficacy. Simulation results on different traffic environment shows that the Ensemble Convolutional Network Reinforcement Learning (E-CNN-RL) offers increased accuracy of 95.45%, reduced RMSE and increased recall rate than existing Ensemble Convolutional Neural Networks (CNN) and Ensemble Stacked CNN. [ABSTRACT FROM AUTHOR]