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Deep learning is one of the most promising machine learning techniques that revolutionalized the artificial intelligence field. The known traditional and convolutional neural networks (CNNs) have been utilized in medical pattern recognition applications that depend on deep learning concepts. This is attributed to the importance of anomaly detection (AD) in automatic diagnosis systems.In this paper, the AD is performed on medical electroencephalography (EEG) signal spectrograms and medical corneal images for Internet of medical things (IoMT) systems. Deep learning based on the CNN models is employed for this task with training and testing phases. Each input image passes through a series of convolution layers with different kernel filters. For the classification task, pooling and fully-connected layers are utilized. Computer simulation experiments reveal the success and superiority of the proposed models for automated medical diagnosis in IoMT systems.

In this paper, Isoparametric finite element formulations are derived for special elements for representing the steel-concrete interface. Curved multi-noded Isoparametric element for reinforcing steel idealization is proposed. In addition, special thin Isoparametric element in a form of a sheath is suggested in order to model the bond-slip characteristics. Special provisions are taken into account to avoid numerical difficulties. The proposed elements are incorporated in non-linear finite element program DMGPLSTS and applied to the problem of tension stiffening of reinforced concrete members. The results are noted to reflect a softer overall response attributable to the slip effect.