The studies implemented with Electroencephalogram (EEG) signals are progressing very rapidly and brain computer interfaces (BCI) and disease determinations are carried out at certain success rates thanks to new methods developed in this field. The effective use of these signals, especially in disease detection, is very important in terms of both time and cost. Currently, in general, EEG studies are used in addition to conventional methods as well as deep learning networks that have recently achieved great success. The most important reason for this is that in conventional methods, increasing classification accuracy is based on too many human efforts as EEG is being processed, obtaining the features is the most important step. This stage is based on both the time-consuming and the investigation of many feature methods. Therefore, there is a need for methods that do not require human effort in this area and can learn the features themselves. Based on that, two-dimensional (2D) frequency-time scalograms were obtained in this study by applying Continuous Wavelet Transform to EEG records containing five different classes. Convolutional Neural Network structure was used to learn the properties of these scalogram images and the classification performance of the structure was compared with the studies in the literature. In order to compare the performance of the proposed method, the data set of the University of Bonn was used. The data set consists of five EEG records containing healthy and epilepsy disease which are labeled as A, B, C, D, and E. In the study, A-E and B-E data sets were classified as 99.50%, A-D and B-D data sets were classified as 100% in binary classifications, A-D-E data sets were 99.00% in triple classification, A-C-D-E data sets were 90.50%, B-C-D-E data sets were 91.50% in quaternary classification, and A-B-C-D-E data sets were in the fifth class classification with an accuracy of 93.60%.

Emotion plays an important role in human interaction. People can explain their emotions in terms of word, voice intonation, facial expression, and body language. However, brain–computer interface (BCI) systems have not reached the desired level to interpret emotions. Automatic emotion recognition based on BCI systems has been a topic of great research in the last few decades. Electroencephalogram (EEG) signals are one of the most crucial resources for these systems. The main advantage of using EEG signals is that it reflects real emotion and can easily be processed by computer systems. In this study, EEG signals related to positive and negative emotions have been classified with preprocessing of channel selection. Self-Assessment Manikins was used to determine emotional states. We have employed discrete wavelet transform and machine learning techniques such as multilayer perceptron neural network (MLPNN) and k-nearest neighborhood (kNN) algorithm to classify EEG signals. The classifier algorithms were initially used for channel selection. EEG channels for each participant were evaluated separately, and five EEG channels that offered the best classification performance were determined. Thus, final feature vectors were obtained by combining the features of EEG segments belonging to these channels. The final feature vectors with related positive and negative emotions were classified separately using MLPNN and kNN algorithms. The classification performance obtained with both the algorithms are computed and compared. The average overall accuracies were obtained as 77.14 and 72.92% by using MLPNN and kNN, respectively.

Remote Sensing is the acquisition of information about its physical properties without direct contact with an object. This information is obtained through sensors. These sensors do not come into contact with objects. There are two different systems for remote sensing. These are Active and Passive Sensor Systems. Passive Sensor Systems measure the energy of the rays reflected from the objects by the rays sent by the sun. On the other hand, Active Sensor Systems measure the energy reflected from the objects by transmitting their rays to the object. Passive Sensor Systems can be shown as an example of optical sensor systems. The Landsat-8 satellite works with an optical sensor system. Synthetic Aperture Radar (SAR) systems are examples of active sensor systems. SAR systems have a wide range of usage in all weather conditions and they are a radar system that displays the earth in high resolution. Radarsat-2 satellite has SAR sensor systems. The aim of this study is to compare each of the vegetation indices by using Landsat-8 and Radarsat-2 satellite images with two different types of sensors. In this study, Radar Vegetation Index (RVI) and Normalized Difference Vegetation Index (NDVI) were investigated. For the calculation of the RVI index, the back-scattering coefficient of the four different bands (HH, HV, VH, VV) of the multi-time full-polarimetric Radarsat-2 FQ satellite image dated 8 April 2015 was used. In the calculation of NDVI index, Band 5 (Near Infrared) and Band 4 (Red) of the Landsat-8 satellite image of May 25, 2015 were used. Dicle University agricultural areas were chosen as the study area. 100 different GPS points belonging to this agricultural area were determined and RVI and NDVI values of these points were calculated. A good correlation was observed between RVI and NDVI indices with the aid of statistically approach.