The modern wireless technology exploiting the full potential of 5G IoT is the future for healthcare sector. In the healthcare sector, the 5G technology will maximize the performance and will reduce the chances of damage to the patient by providing careful and advance activity monitoring scenarios. We have proposed the idea of monitoring different body posture in Huntington disease by exploiting the low cost wireless devices operating at 4.8 GHz frequency. The system captures the wireless channel information for three body motions and classification of these motions was performed by using support vector machine. The SVM used 10 time-domain features for the classification process by using three main kernel functions, such as, Linear, Polynomial and Radial basis function. The system minimizes all the external noise by using the microwave absorbing materials. This increases the performance and feasibility of sensing body motions.
As criminal activity increasingly relies on digital devices, the field of digital forensics plays a vital role in identifying and investigating criminals. In this paper, we addressed the problem of anomaly detection in digital forensics data. Our objective was to propose an effective approach for identifying suspicious patterns and activities that could indicate criminal behavior. To achieve this, we introduce a novel method called the Novel Support Vector Neural Network (NSVNN). We evaluated the performance of the NSVNN by conducting experiments on a real-world dataset of digital forensics data. The dataset consisted of various features related to network activity, system logs, and file metadata. Through our experiments, we compared the NSVNN with several existing anomaly detection algorithms, including Support Vector Machines (SVM) and neural networks. We measured and analyzed the performance of each algorithm in terms of the accuracy, precision, recall, and F1-score. Furthermore, we provide insights into the specific features that contribute significantly to the detection of anomalies. Our results demonstrated that the NSVNN method outperformed the existing algorithms in terms of anomaly detection accuracy. We also highlight the interpretability of the NSVNN model by analyzing the feature importance and providing insights into the decision-making process. Overall, our research contributes to the field of digital forensics by proposing a novel approach, the NSVNN, for anomaly detection. We emphasize the importance of both performance evaluation and model interpretability in this context, providing practical insights for identifying criminal behavior in digital forensics investigations.
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