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Alzheimer's disease (AD), which occurs as a result of the loss of cognitive functions in the brain, causes near-forgetfulness in the case and dementia in subsequent processes. Dataset consists of MR images containing four phases of AD. The dataset was re-enhanced separately with DeepDream, fuzzy color image enhancement, hypercolumn techniques. Visual Geometry Group-16 (VGG-16) deep learning model is used in the enhancing process and deep features are combined. Linear Regression is used for the selection of efficient features. The Support Vector Machine is preferred as a classifier. With the proposed approach, the classification achievement was obtained as 100% in Mild Dementia, 99.94% in Moderate Dementia, 100% in non-Dementia, 99.94% in Very Mild Dementia. The overall accuracy was 99.94%. The proposed approach increased the prediction success in detecting Alzheimer's stages by re-enhancing MR images. Thus, an efficient early diagnosis model was realized at an affordable cost for individuals likely to progress with dementia.

The transmission of weather information of a location at certain time intervals affects the living conditions of the people there directly or indirectly. According to weather information, people shape their behavior in daily life. Besides, agricultural activities are carried out according to the weather conditions. Considering the importance of this subject, it is possible to make weather predictions based on the weather images in today’s technology exploiting the computer systems. However, the recent mention of the name of artificial intelligence technology in every field has made it compulsory for computer systems to benefit from this technology. The dataset used in the study has four classes: cloudy, rain, shine, and sunrise. In the study, GoogLeNet and VGG-16 models and the spiking neural network (SNN) were used together. The features extracted from GoogLeNet and VGG-16 models were combined and given to the SNNs as the input. As a result, the SNNs contributed to the success of classification with the proposed approach. The classification accuracy rates of cloudy, rain, shine, and sunrise classes were 98.48%, 97.58%, 97%, and 98.48%, respectively, together with SNN. Also, the use of SNNs in combination with deep learning models to obtain a successful result is proved in this study.

Pneumonia is one of the diseases that people may encounter in any period of their lives. Approximately 18% of infectious diseases are caused by pneumonia. This disease may result in death in the following stages. In order to diagnose pneumonia as a medical condition, lung X-ray images are routinely examined by the field experts in the clinical practice. In this study, lung X-ray images that are available for the diagnosis of pneumonia were used. The convolutional neural network was employed as feature extractor, and some of existing convolutional neural network models that are AlexNet, VGG-16 and VGG-19 were utilized so as to realize this specific task. Then, the number of deep features was reduced from 1000 to 100 by using the minimum redundancy maximum relevance algorithm for each deep model. Accordingly, we achieved 100 deep features from each deep model, and we combined these features so as to provide an efficient feature set consisting of totally 300 deep features. In this step of the experiment, this feature set was given as an input to the decision tree, k-nearest neighbors, linear discriminant analysis, linear regression, and support vector machine learning models. Finally, all models ensured promising results, especially linear discriminant analysis yielded the most efficient results with an accuracy of 99.41%. Consequently, the results point out that the deep features provided robust and consistent features for pneumonia detection, and minimum redundancy maximum relevance method was found a beneficial tool to reduce the dimension of the feature set.

Lung cancer is a disease caused by the involuntary increase of cells in the lung tissue. Early detection of cancerous cells is of vital importance in the lungs providing oxygen to the human body and excretion of carbon dioxide in the body as a result of vital activities. In this study, the detection of lung cancers is realized using LeNet, AlexNet and VGG-16 deep learning models. The experiments were carried out on an open dataset composed of Computed Tomography (CT) images. In the experiment, convolutional neural networks (CNNs) were used for feature extraction and classification purposes. In order to increase the success rate of the classification, the image augmentation techniques, such as cutting, zooming, horizontal turning and filling, were applied to the dataset during the training of the models. Because of the outstanding success of AlexNet model, the features obtained from the last fully-connected layer of the model were separately applied as the input to linear regression (LR), linear discriminant analysis (LDA), decision tree (DT), support vector machine (SVM), k -nearest neighbor (kNN) and softmax classifiers. A combination of AlexNet model and k NN classifier achieved the most efficient classification accuracy as 98.74 %. Then, the minimum redundancy maximum relevance (mRMR) feature selection method was applied to the deep feature set to choose the most efficient features. Consequently, the success rate was yielded as 99.51 % by reclassifying the dataset with the selected features and k NN model. The proposed model is consistent diagnosis model for lung cancer detection using chest CT images.

The white blood cells produced in the bone marrow and lymphoid tissue known as leucocytes are an important part of the immune system to protect the body against foreign invaders and infectious disease. These cells, which do not have color, have a few days or several weeks of life. A lot of clinic experience is required for a doctor to detect the amount of white blood cells in human blood and classify it. Thus, early and accurate diagnosis can be made in the formation of various disease types, including infection on the immune system, such as anemia and leukemia, while evaluating and determining the disease of a patient. The white blood cells can be separated into four subclasses, such as Eosinophil, Lymphocyte, Monocyte, and Neutrophil. This study focuses on the separation of the white blood cell images by the classification process using convolutional neural network models, which is a deep learning model. A deep learning network, which is slow in the training step due to the complex architecture, but fast in the test step, is used for the feature extraction instead of intricate methods. For the subclass separation of white blood cells, the experimental results show that the AlexNet architecture gives the correct recognition rate among the convolutional neural network architectures tested in the study. Various classifiers are performed on the features derived from the AlexNet architecture to evaluate the classification performance. The best performance in the classification of white blood cells is given by the quadratic discriminant analysis classifier with the accuracy of 97.78 %.

White blood cells are cells in the blood and lymph tissue produced by the bone marrow in the human body. White blood cells are an important part of the immune system. The most important task of these cells is to protect the human body against foreign invaders and infectious diseases. When the number of white blood cells in the blood is not enough for the human body, it can cause leukopenia. As a result of this situation, the resistance of the human body against infections and diseases decreases. In this respect, determining the number of these cells in the human body is a specialist task. Detection and treatment of this symptom is a labor-intensive process carried out by specialist doctors and radiologists. Image processing techniques have recently been widely used in biomedical systems for the diagnosis of various diseases. In this study, it is aimed to use image processing techniques to improve the classification performance of deep learning models in white blood cells classification. To perform the classification process more efficiently, the Maximal Information Coefficient and Ridge feature selection methods were used in conjunction with the Convolutional Neural Network models. The Maximal Information Coefficient and Ridge feature selection methods extracted the most relevant features. Afterward, the classification process was realized by using this feature set. In this study, AlexNet, GoogLeNet, and ResNet-50 were used as feature extractor and quadratic discriminant analysis was used as a classifier. As a result, the overall success rate was obtained as 97.95% in the classification of white blood cells. The experimental results showed that the use of the convolutional neural network models with feature selection methods contributed to improving the classification success of white blood cell types.

It is important for the sensitivity of ecological balance that image processing methods and techniques give better results day by day. Today, researchers use deep learning in image-based object recognition. Recently, the use of deep learning methods on plant species has increased. In this study, a hybrid method that is used together with feature selection methods and Convolutional Neural Network (CNN) models is presented. In the proposed model, CNN models are used for feature extraction. The features obtained from these models are combined and efficient features are selected with feature selection methods. The aim here is to subtract and classify intersecting features between the features obtained by feature selection methods. When the results of the experiments are compared, the intersection of the features obtained by feature selection methods are contributed to the classification performance. The classification success achieved by the Support Vector Machine (SVM) method was 98.91%.

In recent years, deep learning models have been widely used on remote sensing images. Deep learning is held on remote sensing images as well as in every area; is to be able to perform better performance classification than existing approaches and to perceive the feature inferences on its own. In remote sensing, more studies are made especially on hyperspectral images. The most important reason for this is that it can carry a large number of data features. The large number of data features means that there are a large number of attributes for that image. The most important disadvantage of hyperspectral images is; due to the influence of the environment of the device which is shooting the image, various noises may occur. There may be a variety of information loss on this image. Various algorithms techniques have been developed to prevent these losses, while hyperspectral images have been better classified by deep learning models. Recent advances in deep learning models in technological firms and the creation and development of their own deep learning model are evidence of how intense this interest is in this area. Our aim is to examine recent developments in deep learning activities on remote sensing images in this article; to compare the performances obtained by deep learning model and to give brief information about the methods used in this area.

Breast cancer is one of the most common cancer types diagnosed in the ladies worldwide. Statistics indicate that breast cancer rate is about 12 % in all cancer cases in the world. Also, approximately 25% of women have breast cancer. Therefore, rapid and accurate analysis of medical images obtained with breast cancer is extremely important for diagnosis. Many methods have been used to classify breast cancer. One of the most important methods among these methods is deep learning-based methods. The most important reason for choosing the deep learning model for breast cancer diagnosis is that it can give faster and more accurate results than the existing methods. Because early diagnosis is always important. In this study, a total of 700 images, including benign and malign variants of breast cancer images, were analyzed using the convolutional neural network method. The data set used in this study is publicly accessible. As a result, the histopathologic images of the breast cancer features were extracted using the AlexNet method which is one of the deep learning approaches. The classification process was performed with Support Vector Machines and accuracy of 93.4% was achieved.

Social networks are an increasingly popular medias today. On these medias, users are able to interact with each other or share their views on an up-to-date topic, enabling them to have information about their own characters and worldviews. In this study, using the twitter data, the political tendency or parties supported by the users were predicted using data mining techniques.

In this study, an SVM-based system is proposed for the classification of facial expressions that are represented in 3D. Distance based features are used as a feature vector, which are determined by the distances between the different key points on the image. Study was conducted on a subset (Happy, sadness, surprise) of Bosphorus 3D Face Database. 9 different fiducial points are used to calculate a total of 5 distance features. SVM classification was performed with K-fold cross validation thus mean classification performance of different training and test clusters were determined. %85 success rate has achieved as a result of the expression analysis performed on the 3D facial scans.

Unless adequate measures are taken for waste litter, the ecological balance may deteriorate over time. The wastes disposed of the trash can be divided into two classes that are organic and recycling types. In recent years, artificial intelligence is frequently mentioned in all areas of technology. In this study, the dataset used for the classification of waste is reconstructed with the AutoEncoder network. The feature sets are then extracted using two datasets by Convolutional Neural Network (CNN) architectures and these feature sets are combined. The Ridge Regression (RR) method performed on the combined feature set reduced the number of features and also revealed the efficient features. Support Vector Machines (SVMs) were used as classifiers in all experiments. The most successful classification accuracy in the experiments was 99.95%. In this study, it is seen that the proposed approach is successful in the classification of waste types.

Recently, the need for automatic identification has caused researchers to focus on biometric identification methods. Palmprint-based biometric identification has several advantages such as user friendliness, low-cost capturing devices, and robustness. In this paper, a method that integrates the discrete cosine transform (DCT) and an autoregressive (AR) signal modeling is proposed for biometric identification. The method provides scale invariance and produces a fixed-length feature vector. In particular, the Burg algorithm is used for the determination of the AR parameters used as a feature vector. Experimental results demonstrate that a small number of the AR parameters that are modeling the DCT coefficients of a palmprint are sufficient to constitute a practically applicable identification system achieving a correct recognition rate of 99.79%. The accuracy of the proposed approach is not overly dependent on the number of training samples, another advantage of the method.