Seizure is an abnormal electrical activity of the brain. Neurologists can diagnose the seizure using several methods such as neurological examination, blood tests, computerized tomography (CT), magnetic resonance imaging (MRI) and electroencephalogram (EEG). Medical data, such as the EEG signal, usually includes a number of features and attributes that do not contains important information. This paper proposes an automatic seizure classification system based on extracting the most significant EEG features for seizure diagnosis. The proposed algorithm consists of five steps. The first step is the channel selection to minimize dimensionality by selecting the most affected channels using the variance parameter. The second step is the feature extraction to extract the most relevant features, 11 features, from the selected channels. The third step is to average the 11 features extracted from each channel. Next, the fourth step is the classification of the average features using the classification step. Finally, cross-validation and testing the proposed algorithm by dividing the dataset into training and testing sets. This paper presents a comparative study of seven classifiers. These classifiers were tested using two different methods: random case testing and continuous case testing. In the random case process, the KNN classifier had greater precision, specificity, positive predictability than the other classifiers. Still, the ensemble classifier had a higher sensitivity and a lower miss-rate (2.3%) than the other classifiers. For the continuous case test method, the ensemble classifier had higher metric parameters than the other classifiers. In addition, the ensemble classifier was able to detect all seizure cases without any mistake.

Nowadays, there is a strong demand for increasing the protection of resource-constrained devices such as Radio-frequency identification (RFID) systems. Current cryptographic algorithms are sufficient for high-resource desktop computers. RFID systems are commonly used in high-security applications such as access control systems, transaction banking systems, and payment systems. The attacker attempts to mislead RFIDs for unauthorized access to services without payment or to circumvent security mechanisms by detecting a secret password. The biggest challenge in RFID systems is how to ensure successful protection against such infringements. Lightweight cryptography can provide security assurance for protecting RFID systems. This paper presents a new ultra-lightweight cryptography algorithm for RFID systems called Agile. Agile is a 32-bit block cipher based on the Feistel structure since block ciphers are the most commonly used cryptographic and provide very tight protection for IoT devices. The key challenge in designing a lightweight block cipher is to cope with performance, cost, and security. Agile, like all symmetric block cipher, uses the same key for encryption and decryption. The proposed algorithm has an excellent performance in both hardware and software environments, with a limited implementation area, an acceptable cost/security for RFID systems, and an energy-efficient behaviour. Agile has demonstrated high immunity against the most effective linear and differential cryptanalysis attacks and has a sufficient margin of defence against these attacks.

Classification is one of the main applications of machine learning, which can group and classify the cases based on learning and development using the available data and experience knowledge. Classification is used widely in biological and medical aspects. This paper presents the problem of electroencephalogram (EEG) signal classification. Classification is the step of identifying groups or classes based on similarities between them. This step is essential to differentiate between seizure and normal periods. EEG is a monitoring tool to determine the electrical activity of the brain. The nature of EEG is quite long, so it consumes time and very difficult in processing. Epilepsy is an illness that affects people of all ages, both cases males and females. Epilepsy is a neurological disorder that makes the activities of the brain abnormal and generates seizures. Seizure symptoms vary from one people to another; it depends on the location of epileptic discharge in the cortex. To speed up the classification process and make it efficient, EEG signal needs to be preprocessed. This paper reviews the epilepsy mentality disorder and the types of seizure, preprocessing operations that performed on EEG data, a common extracted feature from the signal, and detailed view on classification techniques that can be used in this problem.