Cognitive prediction in the complicated and active environments is of great importance role in artificial learning. Classification accuracy of sound events has a robust relation with the feature extraction. In this paper, deep features are used in the environmental sound classification (ESC) problem. The deep features are extracted by using the fully connected layers of a newly developed Convolutional Neural Networks (CNN) model, which is trained in the end-to-end fashion with the spectrogram images. The feature vector is constituted with concatenating of the fully connected layers of the proposed CNN model. For testing the performance of the proposed method, the feature set is conveyed as input to the random subspaces K Nearest Neighbor (KNN) ensembles classifier. The experimental studies, which are carried out on the DCASE-2017 ASC and the UrbanSound8K datasets, show that the proposed CNN model achieves classification accuracies 96.23% and 86.70%, respectively. INDEX TERMS Environmental sound classification, spectrogram images, CNN model, deep features. DABAN ABDULSALAM ABDULLAH received the B.Sc. degree in computer science from the University of Human and Development, Iraq, in 2012, and the M.Sc. degree in applied mathematics and computer science from Eastern Mediterranean University, Cyprus, in 2015. He became a Lecturer with the Technical College of Informatics, Sulaimani Polytechnic University, in 2018. His research interests include data mining, association rules mining, and pattern recognition. ABDULKADIR SENGUR received the B.Sc. degree in electronics and computer education, the M.Sc. degree in electronics education, and the Ph.D. degree in electrical and electronics engineering from Firat University, Turkey, in 1999, 2003, and 2006, respectively. He became a Research Assistant with the Faculty of Technical Education, Firat University, in February 2001, where he is currently a Professor with the Faculty of Technical Education. His research interests include signal processing, image segmentation, pattern recognition, medical image processing, and computer vision.

ECG beat type analysis is important in the detection of various heart diseases. The ECG beats give useful information about the status of the monitored heart condition. Up to now, various artificial intelligence-based methods have been proposed for ECG based heart failure detection. These methods were generally based on either time or frequency domain signal processing routines. In this study, we propose a different approach for ECG beat classification. The proposed approach is based on image processing. Thus, the initial step of the proposed work is converting the ECG beat signals to the ECG beat images. To do that, the ECG beat snapshots are initially saved as ECG beat images and then local feature descriptors are considered for feature extraction from ECG beat images. Eight local feature descriptors namely Local Binary Patterns, Frequency Decoded LBP, Quaternionic Local Ranking Binary Pattern, Binary Gabor Pattern, Local Phase Quantization, Binarized Statistical Image Features, CENsus TRansform hISTogram and Pyramid Histogram of Oriented Gradients are considered for feature extraction. The Support Vector Machines (SVM) classifier is used in the classification stage of the study. Linear, Quadratic, Cubic and Gaussian kernel functions are used in the SVM classifier. Five types of ECG beats from the MIT-BIH arrhythmia dataset are considered in experiments and the classification accuracy is used for performance measure. To construct a balanced training and test sets, 5000 and 10,000 ECG beat samples are randomly selected and are used in experiments in tenfold cross-validation fashion. The obtained resultsshow that the proposed method is quite efficient where the calculated accuracy score is 99.9% and the comparisons with the state-of-the-art method show that the proposed method outperforms other methods.

Determining the rate of student attendance is an important task in determining the completion of the courses. Despite the success of the technology, it is unfortunate that in many academic institutions, the current systems used to detect student absences. Furthermore, one of the crucial problems in the attendance system does not count student background for continuing in the courses. In this study, we propose an intelligent approach for calculating student attendance based on their Grade Point Average (GPA) and their activities, this approach uses Artificial Neural Network (ANN) for proposing an intelligent attendance system to calculate the attendance rating accurately, meaning the system provide a new rating for each student based on their background. The aim of this research is developing an attendance system for motivation students taking attendance or taking high grade in the class. The result of this approach helps the instructor to allow students who have more activities with more absents to continue in the courses, if not the students have low activity should taking high attendance. This system will more efficient for monitoring students in the classes and replacing absent to activity.

Determining the rate of student attendance is an important task in determining the completion of the courses. Despite the success of the technology, it is unfortunate that in many academic institutions, the current systems used to detect student absences. Furthermore, one of the crucial problems in the attendance system does not count student background for continuing in the courses. In this study, we propose an intelligent approach for calculating student attendance based on their Grade Point Average (GPA) and their activities, this approach uses Artificial Neural Network (ANN) for proposing an intelligent attendance system to calculate the attendance rating accurately, meaning the system provide a new rating for each student based on their background. The aim of this research is developing an attendance system for motivation students taking attendance or taking high grade in the class. The result of this approach helps the instructor to allow students who have more activities with more absents to continue in the courses, if not the students have low activity should taking high attendance. This system will more efficient for monitoring students in the classes and replacing absent to activity.