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.
In the field of Artificial Intelligence (AI), deep learning is a method falls in the wider family of machine learning algorithms that works on the principle of learning. Convolutional Neural Networks (CNNs) can be used for pattern recognition from different images based on deep learning. Anomaly detection is a very vital area in medical signal and image processing due to its importance in automatic diagnosis. Anomaly detection from medical EEG signals based on spectrogram and medical corneal images are tested and evaluated in this paper. Technically, deep learning CNN models are used in the train and test processes, each input image will pass through a series of convolution layers with filters (Kernels), pooling, and fully connected layers (FC) for the classification purposes. The presented simulation results reveal the success of the proposed techniques towards automated medical diagnosis.
This paper presents a new technique for electroencephalography (EEG) seizure detection from multi-channel EEG signals based on image processing concepts in Internet-of-Health-Things (IoHT) systems. The multi-channel EEG segments are treated as two-dimensional matrices as if they were images. Scale-space analysis with Scale Invariant Feature Transform (SIFT) is used to extract the feature points in the up-mentioned two-dimensional matrices. The number of points is used as a discriminating factor between seizure segments and normal segments. An exhaustive study of the 24 patients of the Children's Hospital Boston (CHB-MIT) database is presented in this paper. The EEG signals are transmitted via WiFi/Bluetooth, then all their signals are segmented into one-second segments, the numbers of features points are extracted from these segments, the Probability Density Function (PDF) of the number of feature points for normal and seizure segments are estimated. The Equal Error Rate (EER) is estimated between PDFs of the numbers of feature points in seizure and normal segments. Simulation results on all patients reveal the ability of the proposed technique to set a patient-specific discrimination threshold of 70% of Max spectral power for seizure detection with an accuracy of 95.6%.
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