The number of Major Depressive Disorder (MDD) patients is rising rapidly these days following the incidence of COVID‐19 pandemic. It is challenging to detect MDD through personal interviews and by observing electroencephalogram (EEG) signals. Hence, an automated MDD detection system developed using deep learning techniques can help reduce the workload of clinicians by diagnosing MDD accurately. In this study, we have proposed a novel deep learning model based on Convolutional Neural Network (CNN) and spectrogram images. In this work, Short‐Time Fourier Transform (STFT) is first applied to the EEG signals to obtain spectrogram images of MDD patients and healthy subjects. These spectrogram images are then fed to the CNN model for automated detection of MDD patients and healthy subjects. The EEG signals used in this study were obtained from public database with 34 MDD patients and 30 healthy subjects. The highest classification accuracy, precision, sensitivity, specificity, and F1‐score of 99.58%, 99.40%, 99.70%, 99.48%, and 99.55% respectively were obtained with hold‐out validation. Our MDD detection model is highly accurate and needs to be validated with more diverse MDD database before it can be used in clinical settings. Also, we plan to use our developed prototype to detect depression using other physiological signals like electrocardiogram (ECG) and speech signals for accurate and faster diagnosis.

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Major depressive disorder (MDD) is one of the most common modern ailments affected huge population throughout the world. The electroencephalogram (EEG) signal is widely used to screen the MDD. The manual diagnosis of MDD using EEG is time consuming, subjective and may cause human errors. Therefore, nowadays various automated systems have been developed to diagnose MDD accurately and rapidly. In this work, we have proposed a novel automated MDD detection system using EEG signals. Our proposed model has three steps: (i) Melamine pattern and discrete wavelet transform (DWT)-based multileveled feature generation, (ii) selection of most relevant features using neighborhood component analysis (NCA) and (iii) classification using support vector machine (SVM) and k nearest neighbor (kNN) classifiers. The novelty of this work is the application of melamine pattern. The molecular structure of melamine (also named chemistry spider-ChemSpider) is used to generate 1536 features. Also, various statistical features are extracted from DWT coefficients. The NCA is used to select the most relevant features and these selected features are classified using SVM and kNN classifiers. The presented model attained greater than 95% accuracies using all channels with quadratic SVM classifier. Our results obtained highest classification accuracy of 99.11% and 99.05% using Weighted kNN and Quadratic SVM respectively using A2A1 EEG channel. We have developed the automated depression model using a big dataset and yielded high classification accuracies. These results indicate that our presented model can be used in mental health clinics to confirm the manual diagnosis of psychiatrists.

Mask usage is one of the most important precautions to limit the spread of COVID-19. Therefore, hygiene rules enforce the correct use of face coverings. Automated mask usage classification might be used to improve compliance monitoring. This study deals with the problem of inappropriate mask use. To address that problem, 2075 face mask usage images were collected. The individual images were labeled as either mask, no masked, or improper mask. Based on these labels, the following three cases were created: Case 1: mask versus no mask versus improper mask, Case 2: mask versus no mask + improper mask, and Case 3: mask versus no mask. This data was used to train and test a hybrid deep feature-based masked face classification model. The presented method comprises of three primary stages: (i) pre-trained ResNet101 and DenseNet201 were used as feature generators; each of these generators extracted 1000 features from an image; (ii) the most discriminative features were selected using an improved RelieF selector; and (iii) the chosen features were used to train and test a support vector machine classifier. That resulting model attained 95.95%, 97.49%, and 100.0% classification accuracy rates on Case 1, Case 2, and Case 3, respectively. Having achieved these high accuracy values indicates that the proposed model is fit for a practical trial to detect appropriate face mask use in real time.

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Öz Teknolojinin gelişimi ile veritabanlarının boyutları doğru orantılı olarak ilerlemektedir. Bu kadar çok verinin tutulması sonucu çeşitli analizler ile bu veriler arasındaki gizli bağlantıların araştırılması kaçınılmaz olmuştur. Buradaki çalışmada öğrencilerin Yabancı Dil-II dersindeki geçme notları veri madenciliği yöntemleriyle tahmin edilmiştir. Araştırmada Türkiye'deki bir üniversitede Yabancı Dil-II dersini alan 3794 öğrenci verileri kullanılmıştır. Çalışmada 12'si girdi ve biri çıktı olmak üzere toplam 13 adet değişkenin yer aldığı Yapay Sinir Ağları, M5P, DecisionStump, M5Rules, DecisionTable, Bagging yöntemleri ile geliştirilen tahmin modelleri oluşturulmuş ve birbirleriyle karşılaştırılmıştır. Verilerin eğitim ve test olarak ayrıştırılmasında 10-katlı çapraz doğrulama yöntemi kullanılmıştır. Modellerde öğrencinin ders geçme notunu etkileyecek öğrenim tipi, fakülte, bölüm, program, program tipi, öğretim elemanı ve unvanı, öğrenci programa giriş türü, giriş puanı ve giriş sıralaması ile bir önceki dönemin not ortalaması dikkate alınmıştır. Modeller arasında Bagging yöntemi ile kurulan modelin en iyi sonuç olan 1.22 ortalama mutlak hata ve 0.80 korelasyon katsayısı ile tahminler ürettiği görülmüştür. Çalışma sonucunda öğrencilerin ders geçme notunu önceden öğrenip önlemler alacağı düşünülmektedir.

If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -The purpose of this study is to determine whether the use of online puzzles in the instructional process has an effect on student achievement and learning retention. This study examined students' perception and experiences on use of puzzle as an alternative evaluation tool. To achieve this aim, the following hypotheses were tested: using puzzle activities in lessons increases student achievement, using puzzle activities in lessons increases retention of information learned by the students and students have positive attitudes toward using puzzle activities in lessons. This study uses an online puzzle system (OPS) by which instructors can prepare puzzle activities for students to solve online. The technical and functional properties of the OPS developed and used are beyond the scope of this study. Design/methodology/approach -A pre-and post-test with control group experimental research design was implemented. Study participants were tenth-grade students in the Information