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U2-Net Segmentation And Multi-Label Cnn Classification Of Wheat Varieties
(Konya Teknik Univ, 2024) Argun, Mustafa Şamil; Türk, Fuat; Civelek, Zafer
There are many varieties of wheat grown around the world. In addition, they have different physiological states such as vitreous and yellow berry. These reasons make it difficult to classify wheat by experts. In this study, a workflow was carried out for both segmentation of wheat according to its vitreous/yellow berry grain status and classification according to variety. Unlike previous studies, automatic segmentation of wheat images was carried out with the U2-NET architecture. Thus, roughness and shadows on the image are minimized. This increased the level of success in classification. The newly proposed CNN architecture is run in two stages. In the first stage, wheat was sorted as vitreous-yellow berry. In the second stage, these separated wheats were grouped by multi-label classification. Experimental results showed that the accuracy for binary classification was 98.71% and the multi-label classification average accuracy was 89.5%. The results showed that the proposed study has the potential to contribute to making the wheat classification process more reliable, effective, and objective by helping the experts.
LSTM Network Based Sentiment Analysis for Customer Reviews
(Gazi Univ, 2022) Bilen, Burhan; Horasan, Fahrettin
Continuously increasing data bring new problems and problems usually reveal new research areas. One of the new areas is Sentiment Analysis. This field has some difficulties. The fact that people have complex sentiments is the main cause of the difficulty, but this has not prevented the progress of the studies in this field. Sentiment analysis is generally used to obtain information about persons by collecting their texts or expressions. Sentiment analysis can sometimes bring serious benefits. In this study, with singular tag-plural class approach, a binary classification was performed. An LSTM network and several machine learning models were tested. The dataset collected in Turkish, and Stanford Large Movie Reviews datasets were used in this study. Due to the noise in the dataset, the Zemberek NLP Library for Turkic Languages and Regular Expression techniques were used to normalize and clean texts, later, the data were transformed into vector sequences. The preprocessing process made 2% increase to the model performance on the Turkish Customer Reviews dataset. The model was established using an LSTM network. Our model showed better performance than Machine Learning techniques and achieved an accuracy of 90.59% on the Turkish dataset and an accuracy of 89.02% on the IMDB dataset.
A Linear Time Pattern Based Algorithm for N-Queens Problem
(Gazi Univ, 2022) Karabulut, Bergen; Erguzen, Atilla; Unver, Halil Murat
The n-queens problem is the placing of n number of queens on an nxn chessboard so that no two queens attack each other. This problem is important due to various usage fields such as VLSI testing, traffic control job scheduling, data routing, dead-lock or blockage prevention, digital image processing and parallel memory storage schemes mentioned in the literature. Besides, this problem has been used as a benchmark for developing new artificial intelligence search techniques. However, it is known that backtracking algorithms, one of the most frequently used algorithms to solve this problem, cannot produce all solutions in large n values due to the exponentially growing time complexity. Therefore, various methods have been proposed for producing one or more solutions, not all solutions for each n value. In this study, a pattern based approach that produces at least one unique solution for all n values (n>3) was detected. By using this pattern, a new algorithm that produces at least one unique solution for even very large n values in linear time was developed. The developed algorithm with.(n) time complexity produces quite faster solution to n-queens problem and even in some values, this algorithm produces (n-1)/2 unique solutions in linear time.
Design of a Tracking Welding Robot Automation System for Manufacturing of Steam and Heating Boilers
(2018) Ersöz, Süleyman; Türker, Ahmet Kürşad; Aktepe, Adnan; Atabaş, İrfan; Kokoç, Melda
For satisfying customers companies want to respond to customer requests on time. At the same time, they expect production process to be completed with low cost and low loss. For this reason, the importance of mechanization and automation in production sector has increased. As a result, companies have begun to give more importance to robotic systems, which are the basic components of automation systems. Despite the likelihood of mistakes caused by physiological and mental states of humans, these systems can perform operations precisely without any variability. In this study, an application was carried out for the automation of welding process of industrial type boilers in different sizes and features. For products of which standard measurements or welding operations are difficult to perform manually, a robotic system was proposed in which measurement and welding operations can be performed automatically. In addition, operators are prevented from exposure to gas and light via the proposed system which enables a safer working condition.
Motion Capture Technology in Sports Science: 3D Virtual Sports Platform with Kinect
(2017) Erbay, H; Kutlu, M
Düzenli spor yapmanın yaşam ve sağlık kalitesi için yararlı olduğu kabul edilmektedir. Fakat düzenli spor yapma olanaklarının özellikle gelişmekte olan ülkelerde yeterince yaygınlaşmadığı gözlenmektedir. İnternet ve Kinect gibi güncel ve gelişmiş teknolojilerin sağladığı olanaklar ile, spor yapamayanlara fiziksel olarak aynı ortamda bulunmaksızın, sanal eğitmen kontrolünde spor yapabilme olanağı sunmanın alternatif bir çözüm yaratabileceği düşünülmektedir. Bu doğrultudaki çalışmalarla, Kinect üç boyutlu sanal spor platformu tasarlanıp geliştirilmiştir. Geliştirilen platformun özellikleri, yeni teknolojilerin avantajlarının kullanıldığı platformlar ile alternatif spor yapma olanakları gözden geçirilmektedir
Bulut Tabanlı Öğrenme Yönetim Sistemi Seçiminde Bulanık Çok Kriterli Karar Analizi Yaklaşımı
(2020) Özcan, Hakan; Emiroğlu, Bülent Gürsel
Bulut bilişim teknolojisinin gelişmesiyle öğrenme yönetim sistemleri (ÖYS’ler) yeni özellikler ve servis seçenekleri kazanmıştır. Buna bağlı olarak artan ürün alternatifleri arasından seçim yapma süreci zorlaşmıştır. Belli kriterlere bağlı en uygun bulut tabanlı ÖYS’yi seçmek kurumlar için önemli bir karar verme sorunu olmuştur. Bu çalışmada, kurumların bir grup bulut tabanlı ÖYS arasından belli kriterlere uygun seçim yapabilmesini kolaylaştıracak Bulanık Analitik Hiyerarşi Süreci (BAHS) tabanlı bir model geliştirilmiştir. Bu modelde, bulut tabanlı ÖYS seçiminde etkili olabilecek içerik desteği, etkileşim ve iş birliği, ölçme ve değerlendirme, ders yapısı, arayüz, verimlilik araçları, platform esnekliği, ölçeklenebilirlik, güvenlik, destek ve lisanslama kriterleri literatüre ve uzman görüşlerine dayalı incelenmiş ve oluşturulan bir hiyerarşik yapı ile sunulmuştur. Çalışmada hem kriterler hem de durum çalışması kapsamında ele alınan altı alternatif, çevrim-içi eğitim alanında uzman yedi karar verici tarafından değerlendirilmiştir. Belirlenen kriterlere bağlı olarak, seçilen alternatifler arasında yapılan bulanık ikili karşılaştırmalar sonucu en uygun bulut tabanlı ÖYS, TalentLMS olarak belirlenmiştir.
Integration search strategies in tree seed algorithm for high dimensional function optimization
(SPRINGER HEIDELBERG, 2020) Gungor, Imral; Emiroglu, Bulent Gursel; Cinar, Ahmet Cevahir; Kiran, Mustafa Servet
The tree-seed algorithm, TSA for short, is a new population-based intelligent optimization algorithm developed for solving continuous optimization problems by inspiring the relationship between trees and their seeds. The locations of trees and seeds correspond to the possible solutions of the optimization problem on the search space. By using this model, the continuous optimization problems with lower dimensions are solved effectively, but its performance dramatically decreases on solving higher dimensional optimization problems. In order to address this issue in the basic TSA, an integration of different solution update rules are proposed in this study for solving high dimensional continuous optimization problems. Based on the search tendency parameter, which is a peculiar control parameter of TSA, five update rules and a withering process are utilized for obtaining seeds for the trees. The performance of the proposed method is investigated on basic 30-dimensional twelve numerical benchmark functions and CEC (congress on evolutionary computation) 2015 test suite. The performance of the proposed approach is also compared with the artificial bee colony algorithm, particle swarm optimization algorithm, genetic algorithm, pure random search algorithm and differential evolution variants. Experimental comparisons show that the proposed method is better than the basic method in terms of solution quality, robustness and convergence characteristics.
The Picard and Gauss-Weierstrass Singular Integrals in (p, q)-Calculus
(MALAYSIAN MATHEMATICAL SCIENCES SOC, 2020) Aral, A.; Deniz, E.; Erbay, H.
The vast development of the techniques in both the quantum calculus and the post-quantum calculus leads to a significant increase in activities in approximation theory due to applications in computational science and engineering. Herein, we introduce (p, q)-Picard and (p, q)-Gauss-Weierstrass integral operators in terms of the (p, q)-Gamma integral. We give a general formula for the monomials under both (p, q)-Picard and (p, q)-Gauss-Weierstrass operators as well as some special cases. We discuss the uniform convergence properties of them. We show that both operators have optimal global smoothness preservation property via usual modulus of continuity. Finally, we establish the rate of approximation using the weighted modulus of smoothness. Depending on the choices of parameters p and q in the integrals, we are able to obtain better error estimation than classical ones.
Detection of rheumatoid arthritis from hand radiographs using a convolutional neural network
(SPRINGER LONDON LTD, 2020) Ureten, Kemal; Erbay, Hasan; Maras, Hadi Hakan
Introduction Plain hand radiographs are the first-line and most commonly used imaging methods for diagnosis or differential diagnosis of rheumatoid arthritis (RA) and for monitoring disease activity. In this study, we used plain hand radiographs and tried to develop an automated diagnostic method using the convolutional neural networks to help physicians while diagnosing rheumatoid arthritis. Methods A convolutional neural network (CNN) is a deep learning method based on a multilayer neural network structure. The network was trained on a dataset containing 135 radiographs of the right hands, of which 61 were normal and 74 RA, and tested it on 45 radiographs, of which 20 were normal and 25 RA. Results The accuracy of the network was 73.33% and the error rate 0.0167. The sensitivity of the network was 0.6818; the specificity was 0.7826 and the precision 0.7500. Conclusion Using only pixel information on hand radiographs, a multi-layer CNN architecture with online data augmentation was designed. The performance metrics such as accuracy, error rate, sensitivity, specificity, and precision state shows that the network is promising in diagnosing rheumatoid arthritis.
Blind video quality assessment via spatiotemporal statistical analysis of adaptive cube size 3D-DCT coefficients
(INST ENGINEERING TECHNOLOGY-IET, 2020) Cemiloglu, Enes; Yilmaz, Gokce Nur
There is an urgent need for a robust video quality assessment (VQA) model that can efficiently evaluate the quality of a video content varying in terms of the distortion and content type in the absence of the reference video. Considering this need, a novel no reference (NR) model relying on the spatiotemporal statistics of the distorted video in a three-dimensional (3D)-discrete cosine transform (DCT) domain is proposed in this study. While developing the model, as the first contribution, the video contents are adaptively segmented into the cubes of different sizes and spatiotemporal contents in line with the human visual system (HVS) properties. Then, the 3D-DCT is applied to these cubes. Following that, as the second contribution, different efficient features (i.e. spectral behaviour, energy variation, distances between spatiotemporal frequency bands, and DC variation) associated with the contents of these cubes are extracted. After that, these features are associated with the subjective experimental results obtained from the EPFL-PoliMi video database using the linear regression analysis for building the model. The evaluation results present that the proposed model, unlike many top-performing NR-VQA models (e.g. V-BLIINDS, VIIDEO, and SSEQ), achieves high and stable performance across the videos with different contents and distortions.
Detection of breast cancer via deep convolution neural networks using MRI images
(SPRINGER, 2020) Yurttakal, Ahmet Hasim; Erbay, Hasan; Ikizceli, Turkan; Karacavus, Seyhan
Breast cancer is the type of cancer that develops from cells in the breast tissue. It is the leading cancer in women. Early detection of the breast cancer tumor is crucial in the treatment process. Mammography is a valuable tool for identifying breast cancer in the early phase before physical symptoms develop. To reduce false-negative diagnosis in mammography, a biopsy is recommended for lesions with greater than a 2% chance of having suspected malignant tumors and, among them, less than 30 percent are found to have malignancy. To decrease unnecessary biopsies, recently, Magnetic Resonance Imaging (MRI) has also been used to diagnose breast cancer. MRI is the highly recommended test for detecting and monitoring breast cancer tumors and interpreting lesioned regions since it has an excellent capability for soft tissue imaging. However, it requires an experienced radiologist and time-consuming process. On the other hand, convolutional neural networks (CNNs) have demonstrated better performance in image classification compared to feature-based methods and show promising performance in medical imaging. Herein, CNN was employed to characterize lesions as malignant or benign tumors using MRI images. Using only pixel information, a multi-layer CNN architecture with online data augmentation was designed. Later, the CNN architecture was trained and tested. The accuracy of the network is 98.33% and the error rate 0.0167. The sensitivity of the network is 1.0 whereas specificity is 0.9688. The precision is 0.9655.
Bibliometric analysis of publications on house dust mites during 1980-2018
(ELSEVIER ESPANA SLU, 2020) Demir, E.; Akmese, O. F.; Erbay, H.; Taylan-Ozkan, A.; Mumcuoglu, K. Y.
Background: The global prevalence of allergic diseases has increased dramatically in recent years and are now recognized as significant chronic diseases worldwide. One of the most important allergens that causes allergic diseases is house dust mites. Objective: This study aims to present a bibliometric overview of research published on dust mites between 1980 and 2018. Methods: Articles published from 1980 to 2018 were analyzed using bibliometric methods. The keywords ?Dust mite*,? and ? Dermatophagoides ? were used in the Web of Science (WoS). Simple linear regression analysis was used to estimate the number of future publications on this subject. Results: A total of 4742 publications were found, 2552 (53.8%) of them were articles. Most of the articles were on subjects related to immunology (1274; 49.9%) and allergy (1229; 48.1%). Clinical and Experimental Allergy (222; 8.7%) was the journal with the most publications. The USA was the country that most contributed to the literature with 461 (18.1%) articles. The countries producing the most publications on this subject were developed countries. The most active author was W.R. Thomas (66; 2.5%). The most productive institution was the University of Western Australia (91; 3.6%). The most cited article was published in the New England Journal of Medicine .Conclusion: According to the findings, developed countries were the most productive in publishing on house dust mites. By planning multinational research rather than regional studies, it may be suggested that researchers in underdeveloped or developing countries could also-conduct more research on this subject.(C) 2019 SEICAP. Published by Elsevier Espana, S.L.U. All rights reserved.
Android malware detection based on image-based features and machine learning techniques
(SPRINGER INTERNATIONAL PUBLISHING AG, 2020) Unver, Halil Murat; Bakour, Khaled
In this paper, a malware classification model has been proposed for detecting malware samples in the Android environment. The proposed model is based on converting some files from the source of the Android applications into grayscale images. Some image-based local features and global features, including four different types of local features and three different types of global features, have been extracted from the constructed grayscale image datasets and used for training the proposed model. To the best of our knowledge, this type of features is used for the first time in the Android malware detection domain. Moreover, the bag of visual words algorithm has been used to construct one feature vector from the descriptors of the local feature extracted from each image. The extracted local and global features have been used for training multiple machine learning classifiers including Random forest, k-nearest neighbors, Decision Tree, Bagging, AdaBoost and Gradient Boost. The proposed method obtained a very high classification accuracy reached 98.75% with a typical computational time does not exceed 0.018 s for each sample. The results of the proposed model outperformed the results of all compared state-of-art models in term of both classification accuracy and computational time.
VisDroid: Android malware classification based on local and global image features, bag of visual words and machine learning techniques
(SPRINGER LONDON LTD, 2020) Bakour, Khaled; Unver, Halil Murat
In this paper, VisDroid, a novel generic image-based classification method has been suggested and developed for classifying the Android malware samples into its families. To this end, five grayscale image datasets each of which contains 4850 samples have been constructed based on different files from the contents of the Android malware samples sources. Two types of image-based features have been extracted and used to train six machine learning classifiers including Random Forest, K-nearest neighbour, Decision trees, Bagging, AdaBoost and Gradient Boost classifiers. The first type of the extracted features is local features including Scale-Invariant Feature Transform, Speeded Up Robust Features, Oriented FAST and Rotated BRIEF (ORB) and KAZE features. The second type of the extracted features is global features including Colour Histogram, Hu Moments and Haralick Texture. Furthermore, a hybridized ensemble voting classifier has been proposed to test the efficiency of using a number of machine learning classifiers trained using local and global features as voters to make a decision in an ensemble voting classifier. Moreover, two well-known deep learning model, i.e. Residual Neural Network and Inception-v3 have been tested using some of the constructed image datasets. Furthermore, when the results of the proposed model have been compared with the results of some state-of-art works it has been revealed that the proposed model outperforms the compared previous models in term of classification accuracy, computational time, generality and classification mode.
Context-dependent model for spam detection on social networks
(SPRINGER INTERNATIONAL PUBLISHING AG, 2020) Ghanem, Razan; Erbay, Hasan
Social media platforms are getting an important communication medium in our daily life, and their increasing popularity makes them an ideal platform for spammers to spread spam messages, known as spam problems. Moreover, messages on social media are vague and messy, so a good representation of the text may be the first step to address spam problem. While traditional weighting methods suffer from both high dimensionality and high sparsity problems, traditional word embedding methods suffer from context independence and out of vocabulary problems. To overcome these problems, in this study, we propose a novel architecture based on a context-dependent representation of text using the BERT model. The model was tested using the Twitter dataset, and experimental results show that the proposed method outperforms traditional weighting methods, traditional word embedding based methods as well as the existing state of the art methods used to detect spam on the twitter platform.
Prediction of Glioma Grades Using Deep Learning with Wavelet Radiomic Features
(MDPI, 2020) Çınarer, Gökalp; Emiroğlu, Bülent Gürsel; Yurttakal, Ahmet Haşim
Gliomas are the most common primary brain tumors. They are classified into 4 grades (Grade I-II-III-IV) according to the guidelines of the World Health Organization (WHO). The accurate grading of gliomas has clinical significance for planning prognostic treatments, pre-diagnosis, monitoring and administration of chemotherapy. The purpose of this study is to develop a deep learning-based classification method using radiomic features of brain tumor glioma grades with deep neural network (DNN). The classifier was combined with the discrete wavelet transform (DWT) the powerful feature extraction tool. This study primarily focuses on the four main aspects of the radiomic workflow, namely tumor segmentation, feature extraction, analysis, and classification. We evaluated data from 121 patients with brain tumors (Grade II,n= 77; Grade III,n= 44) from The Cancer Imaging Archive, and 744 radiomic features were obtained by applying low sub-band and high sub-band 3D wavelet transform filters to the 3D tumor images. Quantitative values were statistically analyzed with MannWhitney U tests and 126 radiomic features with significant statistical properties were selected in eight different wavelet filters. Classification performances of 3D wavelet transform filter groups were measured using accuracy, sensitivity, F1 score, and specificity values using the deep learning classifier model. The proposed model was highly effective in grading gliomas with 96.15% accuracy, 94.12% precision, 100% recall, 96.97% F1 score, and 98.75% Area under the ROC curve. As a result, deep learning and feature selection techniques with wavelet transform filters can be accurately applied using the proposed method in glioma grade classification.
Kidney and Renal Tumor Segmentation Using a Hybrid V-Net-Based Model
(MDPI, 2020) Turk, Fuat; Luy, Murat; Barisci, Necaattin
Kidney tumors represent a type of cancer that people of advanced age are more likely to develop. For this reason, it is important to exercise caution and provide diagnostic tests in the later stages of life. Medical imaging and deep learning methods are becoming increasingly attractive in this sense. Developing deep learning models to help physicians identify tumors with successful segmentation is of great importance. However, not many successful systems exist for soft tissue organs, such as the kidneys and the prostate, of which segmentation is relatively difficult. In such cases where segmentation is difficult, V-Net-based models are mostly used. This paper proposes a new hybrid model using the superior features of existing V-Net models. The model represents a more successful system with improvements in the encoder and decoder phases not previously applied. We believe that this new hybrid V-Net model could help the majority of physicians, particularly those focused on kidney and kidney tumor segmentation. The proposed model showed better performance in segmentation than existing imaging models and can be easily integrated into all systems due to its flexible structure and applicability. The hybrid V-Net model exhibited average Dice coefficients of 97.7% and 86.5% for kidney and tumor segmentation, respectively, and, therefore, could be used as a reliable method for soft tissue organ segmentation.
On Using Structural Patterns In Data For Classification
(PUSHPA PUBLISHING HOUSE, 2020) Arslan, G.; Karabulut, B.; Unver, H. M.
There are some interesting approaches for classification such as semi-supervised algorithms, algorithms that learn distance functions, and various extensions and generalizations of support vector machines. In this study, we propose a new clustering algorithm that uses similarities only and is used as an intermediate step for classification. The motivation for this combined approach is to obtain information from the data set that can be used for classification. After obtaining a clustering of the data set with the proposed clustering algorithm, we apply different strategies for classification. The results on some data sets show that this approach can have some advantages. For example, when using support vector machines, the size of the training set is reduced, while at the same time, comparable performance results are obtained with a smaller number of support vectors.
Crop pest classification with a genetic algorithm-based weighted ensemble of deep convolutional neural networks
(ELSEVIER SCI LTD, 2020) Ayan, Enes; Erbay, Hasan; Varcin, Fatih
Insects are among the important causes of significant losses in crops such as rice, wheat, corn, soybeans, sugarcane, chickpeas, potatoes. Identification of insect species in the early period is crucial so that the necessary precautions can be taken to keep losses at a low level. However, accurate identification of various types of crop insects is a challenging task for the farmers due to the similarities among insect species and also their lack of knowledge. To address this problem, computerized methods, especially based on Convolutional Neural Networks (CNNs), can be employed. CNNs have been used successfully in many image classification problems due to their ability to learn data-dependent features automatically from the data. Throughout the study, seven different pre-trained CNN models (VGG-16, VGG-19, ResNet-50, Inception-V3, Xception, MobileNet, SqueezeNet) were modified and re-trained using appropriate transfer learning and finetuning strategies on publicly available D0 dataset with 40 classes. Later, the top three best performing CNN models, Inception-V3, Xception, and MobileNet, were ensembled via sum of maximum probabilities strategy to increase the classification performance, the model was named SMPEnsemble. After that, these models were ensembled using weighted voting. The weights were determined by the genetic algorithm that takes the success rate and predictive stability of three CNN models into account, the model was named GAEnsemble. GAEnsemble achieved the highest classification accuracy of 98.81% for D0 dataset. For the sake of robustness ensembled model, without changing the initial best performing CNN models on D0, the process was repeated by using two more datasets such that SMALL dataset with 10 classes and IP102 dataset with 102 classes. The accuracy values for GAEnsemble are 95.15% for SMALL dataset and 67.13% for IP102. In terms of performance metrics, GAEnsemble is competitive compared to the literature for each of these three datasets.
The Use of Machine Learning Approaches for the Diagnosis of Acute Appendicitis
(Hindawi Ltd, 2020) Akmese, Omer F.; Dogan, Gul; Kor, Hakan; Erbay, Hasan; Demir, Emre
Acute appendicitis is one of the most common emergency diseases in general surgery clinics. It is more common, especially between the ages of 10 and 30 years. Additionally, approximately 7% of the entire population is diagnosed with acute appendicitis at some time in their lives and requires surgery. The study aims to develop an easy, fast, and accurate estimation method for early acute appendicitis diagnosis using machine learning algorithms. Retrospective clinical records were analyzed with predictive data mining models. The predictive success of the models obtained by various machine learning algorithms was compared. A total of 595 clinical records were used in the study, including 348 males (58.49%) and 247 females (41.51%). It was found that the gradient boosted trees algorithm achieves the best success with an accurate prediction success of 95.31%. In this study, an estimation method based on machine learning was developed to identify individuals with acute appendicitis. It is thought that this method will benefit patients with signs of appendicitis, especially in emergency departments in hospitals.

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