An IoT system is a smart network that connects all items to the Internet and exchanges data using Internet Engineering Task Force established protocols. As a consequence, everything is instantly accessible from any place and at any time. The Internet of Things (IoT) network is built on the backbone of tiny sensors embedded in common objects. There is no need for human intervention in the interactions of IoT devices. The Internet of Things (IoT) security risk cannot be ignored. Untrusted networks, such as the Internet, are utilized to provide remote access to IoT devices. As a result, IoT systems are susceptible to a broad range of harmful activities, including cyberattacks. If security problems are not addressed, critical information may be hacked at any time. This article describes a feature selection and machine learning-based paradigm for improving security in the Internet of Things. Because network data are inherently abundant, it must be reduced in size before processing. Dimension reduction is the process of constructing a subset of an original data collection that removes superfluous content from the essential data set. Dimension reduction is a data mining approach. To minimize the number of dimensions in a dataset, linear discriminant analysis (LDA) is used. Following that, the data set with fewer dimensions is put into machine learning predictors as a training set. The effectiveness of machine learning approaches has been assessed using a range of criteria.
Automated segmentation and classification of biomedical images act as a vital part of the diagnosis of brain tumors (BT). A primary tumor brain analysis suggests a quicker response from treatment that utilizes for improving patient survival rate. The location and classification of BTs from huge medicinal images database, obtained from routine medical tasks with manual processes are a higher cost together in effort and time. An automatic recognition, place, and classifier process was desired and useful. This study introduces an Automated Deep Residual U-Net Segmentation with Classification model (ADRU-SCM) for Brain Tumor Diagnosis. The presented ADRU-SCM model majorly focuses on the segmentation and classification of BT. To accomplish this, the presented ADRU-SCM model involves wiener filtering (WF) based preprocessing to eradicate the noise that exists in it. In addition, the ADRU-SCM model follows deep residual U-Net segmentation model to determine the affected brain regions. Moreover, VGG-19 model is exploited as a feature extractor. Finally, tunicate swarm optimization (TSO) with gated recurrent unit (GRU) model is applied as a classification model and the TSO algorithm effectually tunes the GRU hyperparameters. The performance validation of the ADRU-SCM model was tested utilizing FigShare dataset and the outcomes pointed out the better performance of the ADRU-SCM approach on recent approaches.
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