RESUMO
BACKGROUND: Brain tumours represent a diagnostic challenge, especially in the imaging area, where the differentiation of normal and pathologic tissues should be precise. The use of up-to-date machine learning techniques would be of great help in terms of brain tumor identification accuracy from MRI data. Objective This research paper aims to check the efficiency of a federated learning method that joins two classifiers, such as convolutional neural networks (CNNs) and random forests (R.F.F.), with dual U-Net segmentation for federated learning. This procedure benefits the image identification task on preprocessed MRI scan pictures that have already been categorized. METHODS: In addition to using a variety of datasets, federated learning was utilized to train the CNN-RF model while taking data privacy into account. The processed MRI images with Median, Gaussian, and Wiener filters are used to filter out the noise level and make the feature extraction process easy and efficient. The surgical part used a dual U-Net layout, and the performance assessment was based on precision, recall, F1-score, and accuracy. RESULTS: The model achieved excellent classification performance on local datasets as CRPs were high, from 91.28% to 95.52% for macro, micro, and weighted averages. Throughout the process of federated averaging, the collective model outperformed by reaching 97% accuracy compared to those of 99%, which were subjected to different clients. The correctness of how data is used helps the federated averaging method convert individual model insights into a consistent global model while keeping all personal data private. CONCLUSION: The combined structure of the federated learning framework, CNN-RF hybrid model, and dual U-Net segmentation is a robust and privacypreserving approach for identifying MRI images from brain tumors. The results of the present study exhibited that the technique is promising in improving the quality of brain tumor categorization and provides a pathway for practical utilization in clinical settings.
RESUMO
Wireless Sensor Network (WSN) is built with the wireless interconnection of Sensor Nodes (SNs) generally deployed to monitor the changes within the environment of hostile, rugged, and unreachable target regions. The optimal placement of SNs is very important for the efficient and effective operation of any WSN. Unlike small and reachable regions, the deployment of the SNs in large-scale regions (e.g., forest regions, nuclear radiation affected regions, international border regions, natural calamity affected regions, etc.) is substantially challenging. Present paper deals with an autonomous air-bone scheme for the precise placement of SNs in such large-scale regions. It uses an Omni-directional Circular Glider (OCG) per SN. After being aerially dropped, SN pilots the OCG to glide itself to the predetermined locations (PL) within a target region. The major advantage of using OCG is its capability to quickly update the direction, during the flight (with turning radius = 0) toward its PL. The proposed uses a recursive path correction model to maintain the orientation of the gliding SN towards the PL. The simulation results, and the hardware implementation, indicate that the proposed model is effectively operational in the environmental winds. It is time-efficient and more accurate in the deployment of the SNs in comparison to existing state of art SN deployment models.