Teachers' knowledge levels about virtual information security

In this study, it was aimed to determine the level of knowledge about the threats of teachers' virtual information security and the precautions to be taken against these elements. The sample of the study carried out in the survey model was composed of 135 teachers who were selected by the method of unstructured element sampling among the teachers working in the high school in Elaz province center. The Information Security Achievement Scale was used as a data collection tool. Mean, percentage and frequency techniques and t test were used to analyze the data. It has emerged that most teachers in the study are not aware of the threats to the information in the virtual environment and do not know the precautions to be taken against to the attacks or threats to information security. Teachers' knowledge levels regarding threats and precautions related to information security differed significantly according to gender variables. At the end of the study, some suggestions were made about the security of virtual information

Teachers' knowledge levels about virtual information security

In this study, it was aimed to determine the level of knowledge about the threats of teachers' virtual information security and the precautions to be taken against these elements. The sample of the study carried out in the survey model was composed of 135 teachers who were selected by the method of unstructured element sampling among the teachers working in the high school in Elaz province center. The Information Security Achievement Scale was used as a data collection tool. Mean, percentage and frequency techniques and t test were used to analyze the data. It has emerged that most teachers in the study are not aware of the threats to the information in the virtual environment and do not know the precautions to be taken against to the attacks or threats to information security. Teachers' knowledge levels regarding threats and precautions related to information security differed significantly according to gender variables. At the end of the study, some suggestions were made about the security of virtual information

Okullarda Örgütsel Öğrenme Engellerinin Vignette Tekniği İle İncelenmesi

Bu araştırmanın amacı, Senge’nin (1997) tanımladığı örgütsel öğrenme engellerini (pozisyonum neyse ben oyum, düşman dışarıda, sorumluluk üstlenme kuruntusu, olaylara takılıp kalma, haşlanmış kurbağa meselesi, deneyimden öğrenme avunması ve yönetici ekip miti) okullar açısından incelemektir. Araştırmanın çalışma grubu; Elazığ ve Muş illerindeki ilk ve ortaokullarda görev yapmakta olan 25 öğretmenden oluşmaktadır. Verilerin toplanmasında nitel veri toplama yöntemleri içerisinde yer alan vignette tekniği kullanılmıştır. Araştırmadan elde edilen bulgulara göre; öğretmenlerin sorumluluklarını pozisyonları ile sınırlandırma eğiliminde oldukları, başarısızlık durumunda sorunu başkalarında aradıkları ve sorumluluk üstlenme konusunda isteksiz davrandıkları belirlenmiştir. Ayrıca tecrübeye önem verdikleri ve karşılaştıkları sorunların çözümünü yöneticilerden bekledikleri sonucuna ulaşılmıştır. Bu sonuçlar doğrultusunda, okulların örgütsel öğrenme kapasitelerini geliştirebilmeleri adına birtakım öneriler ortaya konulmuştur

The investigation of the organizational learning barriers in Schools using vignette technique

Bu araştırmanın amacı, Sengenin (1997) tanımladığı örgütsel öğrenme engellerini (pozisyonum neyse ben oyum, düşman dışarıda, sorumluluk üstlenme kuruntusu, olaylara takılıp kalma, haşlanmış kurbağa meselesi, deneyimden öğrenme avunması ve yönetici ekip miti) okullar açısından incelemektir. Araştırmanın çalışma grubu; Elazığ ve Muş illerindeki ilk ve ortaokullarda görev yapmakta olan 25 öğretmenden oluşmaktadır. Verilerin toplanmasında nitel veri toplama yöntemleri içerisinde yer alan Vignette tekniği kullanılmıştır. Araştırmadan elde edilen bulgulara göre; öğretmenlerin sorumluluklarını pozisyonları ile sınırlandırma eğiliminde oldukları, başarısızlık durumunda sorunu başkalarında aradıkları ve sorumluluk üstlenme konusunda isteksiz davrandıkları belirlenmiştir. Ayrıca tecrübeye önem verdikleri ve karşılaştıkları sorunların çözümünü yöneticilerden bekledikleri sonucuna ulaşılmıştır. Bu sonuçlar doğrultusunda, okulların örgütsel öğrenme kapasitelerini geliştirebilmeleri adına öneriler ortaya konulmuştur.The aim of this research is to examine organizational learning disabilities (whats my position thats me, the outside enemies, the delusion of responsibility, stuck to events, the matter of boiled frog, consolation of learning from the experience and myth of the executive team) that defined by Senge (1997) in terms of schools. The group of research; consists of 25 teachers who work in primary and secondary schools in Elazığ and Muş. The Vignette that takes place in qualitative data collection techniques is used in the data collection methods. According to the findings obtained from the research; it is determined that the teachers are tend to limit their responsibility with their position and in the event of failure, they are looking for the problem in others and they are reluctant to take responsibility . And also, it was concluded that they give importance to experience and they expect the solution of their problems from the managers. In line with these results, some suggestions were put forward in order to improve the capacity of the organizational learning of schools

Quantum Machine‑Based Decision Support System for the Detection of Schizophrenia from EEG Records

Schizophrenia is a serious chronic mental disorder that signifcantly afects daily life. Electroencephalography (EEG), a method used to measure mental activities in the brain, is among the techniques employed in the diagnosis of schizophrenia. The symptoms of the disease typically begin in childhood and become more pronounced as one grows older. However, it can be managed with specifc treatments. Computer-aided methods can be used to achieve an early diagnosis of this illness. In this study, various machine learning algorithms and the emerging technology of quantum-based machine learning algorithm were used to detect schizophrenia using EEG signals. The principal component analysis (PCA) method was applied to process the obtained data in quantum systems. The data, which were reduced in dimensionality, were transformed into qubit form using various feature maps and provided as input to the Quantum Support Vector Machine (QSVM) algorithm. Thus, the QSVM algorithm was applied using diferent qubit numbers and diferent circuits in addition to classical machine learning algorithms. All analyses were conducted in the simulator environment of the IBM Quantum Platform. In the classifcation of this EEG dataset, it is evident that the QSVM algorithm demonstrated superior performance with a 100% success rate when using Pauli X and Pauli Z feature maps. This study serves as proof that quantum machine learning algorithms can be efectively utilized in the feld of healthcare

Efficient Airport Detection Using Line Segment Detector and Fisher Vector Representation

In this letter, a two-stage method for airport detection on remote sensing images is proposed. In the first stage, a new algorithm composed of several line-based processing steps is used for extraction of candidate airport regions. In the second stage, the scale-invariant feature transformation and Fisher vector coding are used for efficient representation of the airport and nonairport regions and support vector machines employed for classification. In order to evaluate the performance of the proposed method, extensive experiments are conducted on airports around the world with different layouts. The measures used in the evaluation are accuracy, sensitivity, and specificity. The proposed method achieved an accuracy of 94.6%, which was benchmarked with two previous methods to prove its superiority

An Automated Wavelet-Based Sleep Scoring Model Using EEG, EMG, and EOG Signals with More Than 8000 Subjects

Human life necessitates high-quality sleep. However, humans suffer from a lower quality of life because of sleep disorders. The identification of sleep stages is necessary to predict the quality of sleep. Manual sleep-stage scoring is frequently conducted through sleep experts’ visually evaluations of a patient’s neurophysiological data, gathered in sleep laboratories. Manually scoring sleep is a tough, time-intensive, tiresome, and highly subjective activity. Hence, the need of creating automatic sleep-stage classification has risen due to the limitations imposed by manual sleep-stage scoring methods. In this study, a novel machine learning model is developed using dual-channel unipolar electroencephalogram (EEG), chin electromyogram (EMG), and dual-channel electrooculgram (EOG) signals. Using an optimum orthogonal filter bank, sub-bands are obtained by decomposing 30 s epochs of signals. Tsallis entropies are then calculated from the coefficients of these sub-bands. Then, these features are fed an ensemble bagged tree (EBT) classifier for automated sleep classification. We developed our automated sleep classification model using the Sleep Heart Health Study (SHHS) database, which contains two parts, SHHS-1 and SHHS-2, containing more than 8455 subjects with more than 75,000 h of recordings. The proposed model separated three classes if sleep: rapid eye movement (REM), non-REM, and wake, with a classification accuracy of 90.70% and 91.80% using the SHHS-1 and SHHS-2 datasets, respectively. For the five-class problem, the model produces a classification accuracy of 84.3% and 86.3%, corresponding to the SHHS-1 and SHHS-2 databases, respectively, to classify wake, N1, N2, N3, and REM sleep stages. The model acquired Cohen’s kappa (κ) coefficients as 0.838 with SHHS-1 and 0.86 with SHHS-2 for the three-class classification problem. Similarly, the model achieved Cohen’s κ of 0.7746 for SHHS-1 and 0.8007 for SHHS-2 in five-class classification tasks. The model proposed in this study has achieved better performance than the best existing methods. Moreover, the model that has been proposed has been developed to classify sleep stages for both good sleepers as well as patients suffering from sleep disorders. Thus, the proposed wavelet Tsallis entropy-based model is robust and accurate and may help clinicians to comprehend and interpret sleep stages efficiently