Yapay Sinir Ağları ile Web İçeriklerini Sınıflandırma

Recent developments and widespread usage of the Internet have made business and processes to be completed faster and easily in electronic media. The increasing size of the stored, transferred and processed data brings many problems that affect access to information on the Web. Because of users’ need get to access to the information in electronic environment quickly, correctly and appropriately, different methods of classification and categorization of data are strictly needed. Millions of search engines should be supported with new approaches every day in order for users to get access to relevant information quickly. In this study, Multilayered Perceptrons (MLP) artificial neural network model is used to classify the web sites according to the specified subjects. A software is developed to select the feature vector, to train the neural network and finally to categorize the web sites correctly. It is considered that this intelligent approach will provide more accurate and secure platform to the Internet users for classifying web contents precisely

Explainable Credit Card Fraud Detection with Image Conversion

The increase in the volume and velocity of credit card transactions causes class imbalance and concept deviation problems in data sets where credit card fraud is detected. These problems make it very difficult for traditional approaches to produce robust detection models. In this study, a different perspective has been developed for this problem and a novel approach named Fraud Detection with Image Conversion (FDIC) is proposed. FDIC handles credit card transactions as time series and transforms them into images. These images, which comprise temporal correlations and bilateral relationships of features, are classified by a convolutional neural network architecture as fraudulent or legitimate. When the obtained results are compared with the related studies, FDIC has the best F1-score and recall values, which are 85.49% and 80.35%, respectively. Since the images created during the FDIC process are difficult to interpret, a new explainable artificial intelligence approach is also presented. In this way, feature relationships that have a dominant effect on fraud detection are revealed

Yapay Sinir Ağları ile Web İçeriklerini Sınıflandırma

Recent developments and widespread usage of the Internet have made business and processes to be completed faster and easily in electronic media. The increasing size of the stored, transferred and processed data brings many problems that affect access to information on the Web. Because of users’ need get to access to the information in electronic environment quickly, correctly and appropriately, different methods of classification and categorization of data are strictly needed. Millions of search engines should be supported with new approaches every day in order for users to get access to relevant information quickly. In this study, Multilayered Perceptrons (MLP) artificial neural network model is used to classify the web sites according to the specified subjects. A software is developed to select the feature vector, to train the neural network and finally to categorize the web sites correctly. It is considered that this intelligent approach will provide more accurate and secure platform to the Internet users for classifying web contents precisely

Neural Computing of the Bandwith of Resonant Rectangular Microstrip Antennas

A new method based on the backpropagation multilayered perceptron network for calculating the bandwidth of resonant rectangular microstrip patch antennas is presented The method can be used for a wide range of substrate thicknesses and permittivities, and is useful for the computer-aided design (CAD) of microstrip antennas. The results obtained by using this new method are in conformity with those reported elsewhere. This method may find wide applications in high-frequency printed antennas, especially at the millimeter-wave frequency range

Artificial Neural Networks in Robotic Applications

There are a number of problems that their analytical solutions are difficult to obtain using conventional techniques in robotics. This-paper examines the use of Artificial Neural Networks (ANNs) as a new technique to solve such problems in the field of robotics.<br/>This paper presents an overview on ANNs applications to robot kinematics, dynamics, control, trajectory and task planning, and sensing. Moreover, the advantages and disadvantages of using and implementing ANNs to the robotic problems are outlined

Use Of Big Data In Audits And An Evaluation On It

In today's, the golden age of digitalization, it is experienced change and development in the field of auditing as it is in almost every area. Big Datas that, occurs to define the exponential growth and usability of data generated by humans, applications and intelligent machines, affects the audit process. The audit of the future will be quite different from today’s audit. Broader data sets, new techniques and technologies, and data analysis methods should be used to generate more business value, better understand the business, identify key risk areas, and adapt to change. Also, to be successful, with the audit function, everyone needs to be open to change from supervised to regulator. In this study; big data and analytics have been examined, basic elements have been reviewed, answers have been sought for how to take advantage of the big data analysis in audits, how to integrate the big data analysis with audits with adequate planning and appropriate resources, and what potential adverse events could be encountered. In addition, the importance of big data was emphasized for institutions and concrete proposals were presented so that more effective, efficient and economic audit could be made an expected level. As a result; more comprehensive, faster, more productive, value-added audit activities can be achieved with big data analysis

Determining resonant frequencies of various microstrip antennas within a single neural model trained using parallel tabu search algorithm

Artificial neural networks (ANNs) are one of the popular intelligent techniques in solving engineering problems. In this paper, an intelligent new approach based on ANN trained with a parallel tabu search (PTS) algorithm to determine the resonant frequencies of microstrip antennas of regular geometries is presented. A single ANN model was used to determine the resonant frequencies of the rectangular, circular, and triangular microstrip antennas. The determination performance of a single neural model was improved with the help of PTS. The results obtained from the single neural model for the resonant frequencies of the rectangular, circular, and triangular microstrip antennas are in very good agreement with the experimental and other methods presented in the literature