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Öğe Classification of EMG Signals by LRF-ELM(Ieee, 2017) Ayaz, Furkan; Ari, Ali; Hanbay, DavutElectromyogram (EMG) signal can be defined as the electrical activity of muscles cells. It is commonly used in motion recognition, treatment of neuromuscular disorders and prosthetic hand control. In this study, classification of EMG signals obtained from 6 different hand shapes of holding object was proposed. At first Short Time Fourier Transform of the EMG signal were evaluated to obtain their Time-Frekans representation. After than these T-F images were segmented and their mean values were evaluated to reduce the dimension of the images. Local Receptive Fields based Extreme Learning Machines (ELM-LRF) used to classification of these hand shapes of holding object. Evaluated accuracy is 94.12 %.Öğe A CNN based real-time eye tracker for web mining applications(Springer, 2022) Donuk, Kenan; Ari, Ali; Hanbay, DavutEye gaze tracking is an increasingly important technology in the field of human-computer interaction. Individuals' preferences, tendencies, and attention can be measured by processing the data obtained from face and eye images. This technology is used in advertising, market research, web page design, education, learning methods, and various neurological-psychiatric studies of medical research. Many different methods have been used in eye gaze tracking tasks. Today, commonly model-shape and appearance-based methods are used. Model-shape based methods require less workload than appearance-based methods. But it is more sensitive to environmental conditions. Appearance-based methods require powerful hardware, but they are less susceptible to environmental conditions. Developments in technology have paved the way for applying appearance-based models in eye gaze tracking. In this paper, a CNN-based real-time eye tracking system was designed to overcome environmental problems in eye gaze tracking. The designed system is used to determine the areas of interest of the user in web pages. The performance of the designed CNN-based system is evaluated during the training and testing phases. In the training phase, the difference between the desired and determined points on the screen is 32 pixels and in testing phase, the difference between the desired and determined points on the screen is 53 pixels. The results of the test trials have shown that the proposed system could be used successfully in eye tracking studies on web pages.Öğe Comparison of Neural Style Transfer Performance of Deep Learning Models(Gazi Univ, 2021) Karadag, Batuhan; Ari, Ali; Karadag, MugeNeural style transfer is one of the most studied topics in both academic and industrial fields. Quality and performance enhancement are among the most targeted goals in the studies. In this study, the performance of different CNN models in neural style transfer was investigated. Deep features were obtained using VGG16, VGG19 and ResNet50 models. Thanks to these attributes, a new target image is created by taking the content of the content image and the style of the style image. Adam, RMSprop and SGD optimization algorithms are used. In neural transfer studies, the best visual performance was obtained from VGG19 network model by using SGD optimization algorithm. The fastest neural style transfer in terms of time was obtained using the SGD optimization algorithm in the ResNet50 convolutional neural network model.Öğe Computer-aided Tumor Detection System Using Brain MR Images(Ieee, 2015) Ari, Ali; Alpaslan, Nuh; Hanbay, DavutIn today's technology, computer assisted detection applications have managed to make great contributions to the field of medicine. Computer assisted detection systems aim to help radiologist about mass detection by using image processing systems. In this study, it's aimed to carry out mass detection process on the images of 3D brain MRI (Magnetic Resonance Imaging). The steps followed in this study are the stage of pre-processing the stage of segmentation, identification of the areas of interests and identification of tumor. As a result of processing's in the stages of preprocessing and segmentation, obtained areas of interests are labelled and attributes of these areas of interests are extracted during the stage of attributes extraction and in the last stage, the areas of interests are identified as whether they are mass or not according to these attributes. With this method applied on 845 number of magnetic resonance image sections belonging to 13 patients, it has been achieved classification success with 86.39%.Öğe Deep Feature Selection for Facial Emotion Recognition Based on BPSO and SVM(Gazi Univ, 2023) Donuk, Kenan; Ari, Ali; Ozdemir, Mehmet Fatih; Hanbay, DavutFacial expressions, which are important social communication tools in our daily life, provide important information about the mental state of people. Research is being done to obtain this information accurately. The importance of these researchs in the field of human-computer interaction is increasing. Many methods have been used for the recognition of universal facial expressions such as neutral, happiness, surprise, sadness, anger, disgust, and fear by intelligent systems with high accuracy. Emotion recognition is an example of difficult classification due to factors such as ambient light, age, race, gender, and facial position. In this article, a 3-stage system is proposed for emotion detection from facial images. In the first stage, the CNN-based network is trained with the Fer+ dataset. The Binary Particle Swarm Optimization algorithm is applied for feature selection to the feature vector in the fully connected layer of the CNN network trained in the second stage. Selected features are classified by Support Vector Machine. The performance of the proposed system has been tested with the Fer+ dataset. As a result of the test, 85.74% accuracy was measured. The results show that the combination of BPSO and SVM contributes to the classification accuracy and speed of the FER+ dataset.Öğe Determining the probability of juvenile delinquency by using support vector machines and designing a clinical decision support system(Churchill Livingstone, 2020) Ucuz, Ilknur; Cicek, Ayla Uzun; Ari, Ali; Ozcan, Ozlem Ozel; Sari, Seda AybukeIt is a known fact that individuals who engaged in delinquent behavior in childhood are more probable to carry on similar behavior in adulthood. If the factors that lead children to involve in delinquency are defined, the risk of dragging children into crime can be detected before they are involved in crime and delinquency can be prevented with appropriate preventive rehabilitation programs, in the early period. However, given that delinquent behavior occurs under the influence of multiple conditions and factors rather than a single risk factor; the need for diagnostic tools to evaluate multiple factors together is obvious. Artificial intelligence-based clinical decision support systems have already been used in the field of psychiatry as well as many other fields of medicine. In this study, we assume that thanks to artificial intelligence-based clinical decision support systems, children and adolescents at risk can be detected before the criminal behavior occurs by addressing certain factors. In this way, we anticipate that it can provide psychiatrists and other experts in the field.Öğe EMG Signal Classification Using Deep Learning and Time Domain Descriptors-Based Feature Extraction for Hand Grip Movement Recognition(Int Information & Engineering Technology Assoc, 2023) Ari, AliElectromyogram (EMG) signals are very important in recognizing hand and finger movements and controlling prosthesis movements. In recent years, EMG signals have become popular in designing and controlling human-machine interactions and rehabilitation equipment such as robotic prostheses. This study aims to develop an innovative model based on EMG signal in the classification of basic hand grip movements that can improve prosthetic hand movements for individuals who have lost some limbs for various reasons. The proposed approach consists of Time Domain Descriptors (TDD), convolutional neural network (CNN), Long short-term memory (LSTM) techniques, Selection Minimum Redundancy Maximum Relationship (MRMR), and Support Vector Machine (SVM). First, it is applied to TDD, CNN, and LSTM models to extract features from EMG signals. It is then applied as input to MRMR to select the most effective features from the obtained features. Finally, SVM is applied to classify different hand grip movements. The effectiveness of the proposed model was evaluated with the EMG hand gestures dataset in the publicly available UCI repository. In experimental studies, a 95.63% accuracy rate was achieved in the first two of the five subjects and 100% accuracy in the remaining three subjects. As a result, it achieved an average specificity of 99.66% and an accuracy of 98.34% for five subjects. In addition, the experimental results of the proposed hybrid model show that when compared to the most advanced methods using the same dataset, the model achieves a higher classification rate and produces superior results compared to several previous studies. Therefore, this study reveals that it can be used as a low-cost control unit that can accurately classify hand grips from EMG signals with high accuracy.Öğe Estimation of the Development of Depression and PTSD in Children Exposed to Sexual Abuse and Development of Decision Support Systems by Using Artificial Intelligence(Routledge Journals, Taylor & Francis Ltd, 2022) Ucuz, Ilknur; Ari, Ali; Ozcan, Ozlem Ozel; Topaktas, Ozgu; Sarraf, Merve; Dogan, OzlemThe most common diagnoses after childhood sexual abuse are Post-Traumatic Stress Disorder and depression. The aim of this study is to design a decision support system to help psychiatry physicians in the treatment of childhood sexual abuse. Computer aided decision support system (CADSS) based on ANN, which predicts the development of PTSD and Major Depressive Disorder, using different parameters of the act of abuse and patients was designed. The data of 149 girls and 21 boys who were victims of sexual abuse were included in the study. In the designed CADDS, the gender of the victim, the type of sexual abuse, the age of exposure, the duration until reporting, the time of abuse, the proximity of the abuser to the victim, number of sexual abuse, whether the child is exposed to threats and violence during the abuse, the person who reported the event, and the intelligence level of the victim are used as input parameters. The average accuracy values for all three designed systems were calculated as 99.2%. It has been shown that the system designed by using these data can be used safely in the psychiatric assessment process, in order to differentiate psychiatric diagnoses in the early post-abuse period.Öğe Feature Mapping and Deep Long Short Term Memory Network-Based Efficient Approach for Parkinson's Disease Diagnosis(Ieee-Inst Electrical Electronics Engineers Inc, 2021) Demir, Fatih; Sengur, Abdulkadir; Ari, Ali; Siddique, Kamran; Alswaitti, MohammedIn this paper, a novel approach was developed for Parkinson's disease (PD) diagnosis based on speech disorders. When the literature about the speech disorders-based PD diagnosis was reviewed, it was seen that the most of approaches were concentrated on the feature selection as the datasets contained a huge number of features. In contrast, in the proposed approach, instead of eliminating some of the features by using any feature selection method, all features were initially used for forming a mapping procedure where the input feature vectors were converted to the input images. Then, a deep Long Short Term Memory (LSTM) network was employed for PD detection where the obtained images were used. The deep LSTM network carried out both feature extraction and classification processes and its training was carried out in an end-to-end fashion. The activations in the convolutional layer were converted to sequence data through the sequence-folding and sequence-unfolding layers. The activations in the LSTM output with learning parameters were conveyed to the Softmax layer for the classification process. A publically available PD dataset was used in the experimental works and classification accuracy, sensitivity, specificity, precision, and F-score metrics were used for performance evaluation. The obtained accuracy, sensitivity, specificity, precision and F-score values were 94.27%, 0.960, 0.960, 0.910 and 0.930, respectively. The obtained results were also compared with some of the published results and it had seen that most of the achievements of the proposed method are better than the compared methods.Öğe InceptionV3 based enriched feature integration network architecture for pixel-level surface defect detection(Gazi Univ, Fac Engineering Architecture, 2023) Uzen, Huseyin; Turkoglu, Muammer; Ari, Ali; Hanbay, DavutIn this study, InceptionV3 based Enriched Feature Integration Network (Inc-EFIN) architecture was developed for automatic detection of surface defects. In the proposed architecture, features of all levels of the InceptionV3 architecture are extracted and the features with the same height and width are combined. As a result of merging, 5 feature maps were obtained. Channel-Based Squeeze and Excitation block has been applied to reveal important details in these feature maps. In Feature Pyramid Network module, information from low-level feature maps containing spatial details were transferred to high-level feature maps containing semantic details. Then, for the final feature map, features were combined using the Feature Integration and Signification (FIS) module. The feature map combined in the FIS module was passed through the Spatial and Channel-based Squeeze and Excitation block. Defect detection results were obtained by using convolution and sigmoid layers in the last layer of the Inc-EFIN architecture. MT, MVTec-Texture, and DAGM datasets were used to calculate the pixel-level defect detection success of the Inc-EFIN architecture. In experimental studies, Inc-EFIN architecture achieved higher performance than the latest technologies in the literature with 77.44% mIoU, 81.2% mIoU and 79.46% mIoU performance results in MT, MVTec-Texture and DAGM datasets, respectively.Öğe Iterative Hard Thresholding Based Extreme Learning Machine(Ieee, 2015) Alcin, Omer Faruk; Ari, Ali; Sengur, Abdulkadir; Ince, Melih CevdetExtreme Learning Machines (ELM) is a new learning algorithm for Single hidden Layer Feed-forward Networks (SLFNs). The ELM has better generalization, rapid training and lower complexity, however, the method suffer from singularity problem and obtaining optimum number of neurons in the hidden layer. In this paper, we considered an IHT for sparse approximation of the output weights vector of the ELM network. The performance evaluation of the proposed method which is called IHT-ELM, was chosen out on four commonly used medical dataset for prediction purposes. The results showed that IHT-ELM has several advantages against the original ELM methods such as obtaining optimum number of neurons and low complexity.Öğe Leaf Recognition based on Artificial Neural Network(Ieee, 2017) Ayaz, Furkan; Ari, Ali; Hanbay, DavutPlant recognition from their leaves has become a popular area in the machine learning and image processing. In this study 7 different types of apricot trees were determined and classified by using their leaves. At first leaves images were preprocessed. After than each image was scanned by 5x5 overlapping filter and median values of each filter process were recorded to represent the leaves. After than filtered each image was scanned by 2x2 overlapping filter and maximum values of each shifting step was recorded. The dimension of each image reduced to it half. Histogram of these uniform patterns were evaluated. These features were applied as input to the Artificial Neural Network (ANN) and 7 types of apricot were classified with the accuracy is 98.6 %.Öğe LSGP-USFNet: Automated Attention Deficit Hyperactivity Disorder Detection Using Locations of Sophie Germain's Primes on Ulam's Spiral-Based Features with Electroencephalogram Signals(Mdpi, 2023) Atila, Orhan; Deniz, Erkan; Ari, Ali; Sengur, Abdulkadir; Chakraborty, Subrata; Barua, Prabal Datta; Acharya, U. RajendraAnxiety, learning disabilities, and depression are the symptoms of attention deficit hyperactivity disorder (ADHD), an isogenous pattern of hyperactivity, impulsivity, and inattention. For the early diagnosis of ADHD, electroencephalogram (EEG) signals are widely used. However, the direct analysis of an EEG is highly challenging as it is time-consuming, nonlinear, and nonstationary in nature. Thus, in this paper, a novel approach (LSGP-USFNet) is developed based on the patterns obtained from Ulam's spiral and Sophia Germain's prime numbers. The EEG signals are initially filtered to remove the noise and segmented with a non-overlapping sliding window of a length of 512 samples. Then, a time-frequency analysis approach, namely continuous wavelet transform, is applied to each channel of the segmented EEG signal to interpret it in the time and frequency domain. The obtained time-frequency representation is saved as a time-frequency image, and a non-overlapping n x n sliding window is applied to this image for patch extraction. An n x n Ulam's spiral is localized on each patch, and the gray levels are acquired from this patch as features where Sophie Germain's primes are located in Ulam's spiral. All gray tones from all patches are concatenated to construct the features for ADHD and normal classes. A gray tone selection algorithm, namely ReliefF, is employed on the representative features to acquire the final most important gray tones. The support vector machine classifier is used with a 10-fold cross-validation criteria. Our proposed approach, LSGP-USFNet, was developed using a publicly available dataset and obtained an accuracy of 97.46% in detecting ADHD automatically. Our generated model is ready to be validated using a bigger database and it can also be used to detect other children's neurological disorders.Öğe A multi-division convolutional neural network-based plant identification system(Peerj Inc, 2021) Turkoglu, Muammer; Aslan, Muzaffer; Ari, Ali; Alcin, Zeynep Mine; Hanbay, DavutBackground. Plants have an important place in the life of all living things. Today, there is a risk of extinction for many plant species due to climate change and its environmental impact. Therefore, researchers have conducted various studies with the aim of protecting the diversity of the planet's plant life. Generally, research in this area is aimed at determining plant species and diseases, with works predominantly based on plant images. Advances in deep learning techniques have provided very successful results in this field, and have become widely used in research studies to identify plant species. Methods. In this paper, a Multi-Division Convolutional Neural Network (MD-CNN)-based plant recognition system was developed in order to address an agricultural problem related to the classification of plant species. In the proposed system, we divide plant images into equal nxn-sized pieces, and then deep features are extracted for each piece using a Convolutional Neural Network (CNN). For each part of the obtained deep features, effective features are selected using the Principal Component Analysis (PCA) algorithm. Finally, the obtained effective features are combined and classification conducted using the Support Vector Machine (SVM) method. Results. In order to test the performance of the proposed deep-based system, eight different plant datasets were used: Flavia, Swedish, ICL, Foliage, Folio, Flowerl7, Flower102, and LeafSnap. According to the results of these experimental studies, 100% accuracy scores were achieved for the Flavia, Swedish, and Folio datasets, whilst the ICL, Foliage, Flower17, Flower102, and LeafSnap datasets achieved results of 99.77%, 99.93%, 97.87%, 98.03%, and 94.38%, respectively.Öğe Multipath feature fusion for hyperspectral image classification based on hybrid 3D/2D CNN and squeeze-excitation network(Springer Heidelberg, 2023) Ari, AliHyperspectral Images (HSI) are commonly used for classification thanks to their rich spectral feature information along with their spatial feature information. Convolutional Neural Network (CNN) based deep learning methods are commonly used in HSI classification (HSIC) applications to process the high nonlinearity and high dimensionality of HSI. This study proposes a method consisting of a combination of multipath Hybrid CNN and a Squeeze and Excitation (SE) network for HSIC. Features extracted with different kernel sizes in the multipath method are used together to extract richer feature information from HSI in this proposed method (PM). In the Hybrid CNN used in PM, 3D CNN was used to extract the spectral-spatial features. However, computational complexity increases with 3D CNN. Computational complexity is decreased with the use of Hybrid CNN. In addition, 2D CNN used in Hybrid CNN provides more spatial feature information to be extracted. However, in this study, 2D depthwise separable convolution (DSC) was used instead of 2D CNN. By using 2D DSC instead of standard 2D CNN, computational cost and the number of trainable parameters is significantly decreased. Finally, the PM is combined with the SE network to advance the HSIC accuracies. The SE network is designed to enhance the representation quality of CNN. WHU-Hi-HongHu (WHHH), WHU-Hi-HanChuan (WHHC), and WHU-Hi-LongKou (WHLK) datasets were used to evaluate the classification accuracies of the PM. Using a 5% training sample with WHLK, WHHC and WHHH, OA values of 99.86%, 97.51% and 97.64% were obtained. Furthermore, the PM was compared with the latest technology methods in the literature and outperformed all methods.Öğe New algorithm for near-maximum independent set and its upper bounds in claw-free graphs(Gazi Univ, Fac Engineering Architecture, 2022) Karci, Seyda; Ari, Ali; Karc, AliPurpose: The aim of this paper is to develop algorithms for obtaining independent set in given graph and determine upper bounds for |I| in claw-free graphs. Theory and Methods: The main aim of this paper is to develop algorithms for obtaining near-optimal independent set for any type of graph. A special spanning tree (Kmin) is used for this aim. Kmin is used to obtain the fundamental cut-sets of graphs, and cut-set matrix. The multiplication of incidence matrix and transpose of cut-set matrix gives the first independent set element which has minimum independence number. The Kmin tree is also used to determine the upper bounds for size of independent set in term of minimum degree. Results: The developed algorithms are used for obtaining near-maximum independent set for any given graphs, and this case is the advantage of this algorithm. The Kmin spanning tree is used to obtain the upper bounds for size of independent set, and the obtained inequality is in term of minimum degree in graph. Conclusion: The developed method obtains the near-maximum independent set for any graph type. The upper bound for size of independent set is obtained based Kmin and minimum degree in graph.Öğe Object Detection with YOLOv7 Model on Smart Mobile Devices(Gazi Univ, 2023) Karadag, Batuhan; Ari, AliThe YOLOv7 model, which is one of the current object detection algorithms based on deep learning, achieved an average accuracy of 51.2% in the Microsoft COCO dataset, proving that it is ahead of other object detection methods. YOLO has been a preferred model for object detection problems in the commercial field since it was first introduced, due to its speed , accuracy. Generally, high-capacity hardware is needed to run deep learning-based systems. In this study, it is aimed to detect objects in smart mobile devices without using a graphic processor unit by activating the YOLOv7 model on the server in order to be able to detect objects in smart mobile devices, which have become one of the important tools of trade today. With the study, the YOLOv7 object detection algorithm has been successfully run on mobile devices with iOS operating system. In this way, an image taken on mobile devices or already in the gallery after any image is transferred to the server, it is ensured that the objects in the image are detected effectively in terms of accuracy and speed.Öğe Prediction of neuropathy, neuropathic pain and kinesiophobia in patients with type 2 diabetes and design of computerized clinical decision support systems by using artificial intelligence(Elsevier, 2020) Ozdemir, Filiz; Ari, Ali; Kilcik, Melek Havva; Hanbay, Davut; Sahin, IbrahimExercise is a key component for prevention and treatment of type 2 diabetes. However, diabetes complications affect exercise habits. Computerized clinical decision support systems (CCDSSs) may help specialists improve their decision-making abilities in the management of diseases. We hypothesized that patients' diabetic neuropathy, neuropathic pain, and kinesiophobia will quickly be identified in the early stages by using the designed CCDSSs. It is thought that such systems will help in planning exercise programs for patients with diabetes and in maintaining the appropriate programs. Based on our hypothesis, we conclude that CCDSSs will also be effective in managing complications and movement dysfunctions occurring in the musculoskeletal system.
