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About 50 million people around the world are affected by epilepsy disorders of different kinds. Any person, of any age, gender, race, or class, may be affected by epilepsy. In addition, epilepsy seizures can also vary in frequency of occurrence. Such seizures sometimes cause cognitive disorders, which may lead to physical injury of the patients. 1 Epilepsy is recognized by the World Health Organization (WHO) as a public health concern because of its physical and psychological consequences. Moreover, epilepsy may lead to premature death, loss of work productivity, and increased healthcare needs and expenditure. 2 For diagnosing epileptic seizures, distinct screening techniques have been developed; including Electroencephalography (EEG), positron emission tomography, magneto encephalography, and magnetic resonance imaging. EEG signals are characterized by being easily acquired with portable devices. 3 EEG can be defined as an electrophysiological exploration method by which electrical activities of the brain are measured using electrodes fixed on the scalp. 4 These electrodes may be bulky for patients. Utilization of EEG signals for diagnosing epilepsy is time-and effort-consuming; as epileptologists have to screen EEG signals minute by minute. Furthermore, human error is inevitable. Hence, a computer-based diagnosis, by which epileptic seizures can be early detected, is expected to help the patients. [5][6][7][8][9] Artificial intelligence covers several areas and includes several branches such as Machine Learning (ML) and Deep Learning (DL).Conventional ML algorithms, including feature extraction and classification, were formerly used before the appearance of DL. Hand-crafted features limit the performance of the classification algorithms, but deep features are preferred due to their better representation of signals and images. Such techniques have achieved great progress, when used in many aspects of medicine, especially in the diagnosis of epileptic seizures. In many fields, such as anomaly detection from medical signals and images, feature learning, target monitoring, and recognition; DL has achieved great advances. [10][11][12] In this paper, we propose an efficient strategy for both seizure detection and prediction from medical EEG signals. Three models are presented for the classification task. Two of them are patient-specific, while the third one is patient non-specific. EEG signals for epilepsy patients can be divided into three states: normal (inter-ictal), ictal (seizure), and pre-ictal which represents the period of 30-60 min before the ictal state. 13 We assumed in this paper that the pre-ictal state occurs 30 min before the ictal state. The two-class classification is implemented between normal and pre-ictal activities for seizure prediction and between normal and ictal activities for seizure detection. A more generalized threeclass classification framework is considered to identify all EEG signal activities. For the first two proposed models, the spectrogram estimation process is performed on EEG si...

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.

Deoxyribo Nucleic Acid (DNA) computing is a new method of simulating the bimolecular structure of DNA and computing by means of molecular biology. DNA cryptography is a new field which has been explored worldwide. The concept of using DNA computing in the fields of cryptography and steganography has been identified as a possible technology, which may bring forward a new hope for unbreakable algorithms. This paper proposed a new DNA cryptographic algorithm which used the key features of DNA and amino acid coding to overcome limitations of the classical One Time Pad (OTP) cipher. A significant feature of the proposed algorithm is that; it is considered an encryption and hiding algorithm at the same time. The proposed algorithm also enhances the security level of OTP cipher. An evaluation for the proposed algorithm is performed according to randomness testing by using the National Institute of Standards and Technology (NIST) test. The study showed that the proposed algorithm had better performance with respect to time, capacity and robustness compared to previous studies. General TermsSecurity, Information Hiding.