Brain signals required for the brain-computer interface are obtained through the electroencephalography (EEG) method. EEG data is used in the analysis of many problems such as epileptic seizure detection, bipolar mood disorder, attention deficit, and detection of the sleep state of the vehicle driver. It is very important to determine whether the eye is open or closed, which is a substantial organ for the determination of the cognitive state of the person. The aim of this paper is to present a stable and successful model for detecting the eye states that are opened or closed. In this context, the performances of several ensemble classifiers were examined on the Emotiv EEG Neuroheadset dataset, which has 14 features excluding the target variable, 14980 records that have 8225 eye states opened and 6755 eye states closed. In the experiments, firstly the min-max normalization process was applied to the dataset, and then the classification performances of these classifiers were evaluated via a 5-fold cross-validation technique. The performance of each model was measured using accuracy, sensitivity, and specificity metrics. The obtained results show that the Random Forest algorithm is an acceptable level with 92.61% value of accuracy, 94.31% value of sensitivity and 91.36% value of specificity for detecting the eye state.

Child abuse is a social and medical problem that has negative effects on the individual development of the child and can lead to mental disorders such as depression and post-traumatic stress disorder in both short and long-term mental health. Therefore, any abuse that the child may encounter should be immediately intervened. This paper presents the design of an integrated embedded warning system that includes an embedded system module, a server-based module, and a mobile-based module as a solution to concerns of ensuring the safety of students in places where there are fewer safety measures. Our solution aims to ensure that the school management team is quickly informed about the adverse situation that primary school students may encounter and able to respond to them. In this context, this system activates the warning status when it correctly detects the phrases 'help me' and 'give it up'. Thus, any negativity that may be encountered in a closed environment is prevented. The embedded warning system detected correctly the phrase "help me" with 80%, and the phrase "give it up" with 75%.