Epilepsy is one of the chronic neurological disorders that is characterized by a sudden burst of excess electricity in the brain. This abnormality appears as a seizure, the detection of which is an important research topic. An important tool used to study brain activity features, neurological disorders and particularly epileptic seizures, is known as electroencephalography (EEG). The visual inspection of epileptic abnormalities in EEG signals by neurologists is time-consuming. Different scientific approaches have been used to accurately detect epileptic seizures from EEG signals, and most of those approaches have obtained good performance. In this study, deep learning based on convolutional neural networks (CNN) was considered to increase the performance of the identification system of epileptic seizures. We applied a cross-validation technique in the design phase of the system. For efficiency, comparative results between other machine-learning approaches and deep CNNs have been obtained. The experiments were performed using standard datasets. The results obtained indicate the efficiency of using CNN in the detection of epilepsy.

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Improving the quality, developing and implementing systems that can provide advantages to students, and predicting students’ success during the term, at the end of the term, or in the future are some of the primary aims of education. Due to its unique ability to create relationships and obtain accurate results, artificial intelligence and machine learning are tools used in this field to achieve the expected goals. However, the diversity of studies and the differences in their content create confusion and reduce their ability to pioneer future studies. In this study, we performed a systematic literature review of student performance prediction studies in three different databases between 2010 and 2020. The results are presented as percentages by categorizing them as either model, dataset, validation, evaluation, or aims. The common points and differences in the studies are determined, and critical gaps and possible remedies are presented. The results and identified gaps could be eliminated with standardized evaluation and validation strategies. It is determined that student performance prediction studies should be more frequently focused on deep learning models in the future. Finally, the problems that can be solved using a global dataset created by a global education information consortium, as well as its advantages, are presented.

is the scientific, peer reviewed, open access international publication organ of Cyprus Turkish Medical Association. The journal is published three times a year, in April, August, and December. The journal's publication language is English.

This study presents a learning mode-base Fuzzy Neural Networks (FNN) to detect chronic kidney disease (CKD). Combining the fuzzy set theory with the NN structure helps the proposed system to learn sensor data and adjust network parameters. The structure and algorithms of multi-input multi-output FNN are presented. The FNN algorithms implement the TSK type fuzzy rules. The learning of the system is executed by utilizing a gradient descent algorithm and c-means clustering. The presented system is trained using kidney datasets. The performance of the system is evaluated using mean accuracy, sensitivity, specificity and precision which were obtained as 99.75%, 100%, 99.34% and 99.9% correspondingly. The comparison of the results of simulation of the proposed model with the results of other existing algorithms demonstrates the efficiency of the presented FNN model. The experimental results indicate that the approach proposed offers reasonable accuracy of detection and has the potential to be applied in clinical practice.

Structural health management constitutes an essential factor in ensuring the durability of concrete structures. Cracks in concrete, reinforcement corrosion, alkali-silica reaction, and efflorescence attacks are commonly concrete defects that can be identified visually. However, detection and classification of these defects in concrete bridges and other high-rise concrete structures are difficult and expensive process in manual approaches. In this research, a deep learning application is applied to detect and classify concrete defects. Concrete images from the public repository were used to create the explored database. The database was divided into training and validation subsets. The visual geometry group (Vgg19), neural search architecture (nasnetlarge), and residual inception block (vinceptionresnetv2) algorithms were used in analysing the images.  The results of the overall performance show that Vgg19 algorithm recorded higher accuracy in the detection and classification of concrete defects as compared to nasnetlarge and inceptionresnetv2 algorithms. The efficiency of the proposed approach was evaluated using a new dataset containing images of concrete defects. The outcome of this research strongly shows that deep learning models could enhance the efficiency of concrete structural health monitoring in a multi-classification scenario.

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