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In the healthcare research community, Internet of Medical Things (IoMT) is transforming the healthcare system into the world of the future internet. In IoMT enabled Computer aided diagnosis (CAD) system, the Health-related information is stored via the internet, and supportive data is provided to the patients. The development of various smart devices is interconnected via the internet, which helps the patient to communicate with a medical expert using IoMT based remote healthcare system for various life threatening diseases, e.g., brain tumors. Often, the tumors are predecessors to cancers, and the survival rates are very low. So, early detection and classification of tumors can save a lot of lives. IoMT enabled CAD system plays a vital role in solving these problems. Deep learning, a new domain in Machine Learning, has attracted a lot of attention in the last few years. The concept of Convolutional Neural Networks (CNNs) has been widely used in this field. In this paper, we have classified brain tumors into three classes, namely glioma, meningioma and pituitary, using transfer learning model. The features of the brain MRI images are extracted using a pre-trained CNN, i.e. GoogLeNet. The features are then classified using classifiers such as softmax, Support Vector Machine (SVM), and K-Nearest Neighbor (K-NN). The proposed model is trained and tested on CE-MRI Figshare and Harvard medical repository datasets. The experimental results are superior to the other existing models. Performance measures such as accuracy, specificity, and F1 score are examined to evaluate the performances of the proposed model. [ABSTRACT FROM AUTHOR]

The Industrial 5.0 Model integrates enabling technologies such as deep learning, digital twins, and the meta-universe with new development concepts. However, model and data security may pose challenges for developing zero-defect production and other industrial manufacturing industries. To address this issue, we generate adversarial examples using a one-pixel attack in adversarial machine learning, which can fool the defect detection classification model. The traditional one-pixel attack based on the Differential Evolution (DE) algorithm has limited global search ability. Therefore, we use a novel algorithm called T eaching and L earning-based M oth- F lame O ptimization (TLMFO), which enhances the global search performance and improves the attack effectiveness. We evaluate TLMFO on benchmark functions and attacks on Cifar10 and ImageNet datasets, and compare it with MFO and DE. The results show that TLMFO outperforms both MFO and DE in terms of accuracy and speed of convergence. Moreover, TLMFO achieves notably better attack effectiveness than DE under targeted and untargeted attacks on the Cifar10 dataset and under-targeted attacks on the ImageNet dataset. Our research confirms that safety prevention is a link worth considering in developing Industry 5.0. [ABSTRACT FROM AUTHOR]