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Öğe Classification of multi-carrier digital modulation signals using NCM clustering based feature-weighting method(Elsevier Science Bv, 2019) Daldal, Nihat; Polat, Kemal; Guo, YanhuiThis work presents a novel digital modulation signal classification model by combining Neutrosophic c-means (NCM) based feature weighting (NCMBFW) and classifier algorithms. As the digital modulation signal, the multi-carrier amplitude shift keying (MC-ASK), frequency shift keying (MC-FSK), and phase shift keying (MC-PSK) modulation types are employed. In the first step, the feature extraction process has been conducted from the raw digital modulation signals and thereby extracted time, frequency, and timefrequency domain features from the multi-carrier ASK, FSK, and PSK signals. After that, these features have been weighted by using NCMBFW. Finally, classifier algorithms including Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), k-nearest neighbor (k-NN), AdaBoostM1, and Random Forest, have been used to determine the types of digital modulation signals automatically. Many metrics are used to evaluate the performance in the experiments. The proposed method in the classification of MC digital modulation signals is the first work with respect to the classification of MC modulation signals in the literature. For worst case (in 5 dB), while the obtained f-measure values are 0.842, 0.848, 0.863, 0.842, and 0.894 using LDA, SVM, k-NN, AdaBoostM1, and Random Forest classifiers without NCMBFW, respectively, while the f-measure values by combining NCMBFW with classifier algorithms are 0.983, 0.976, 0.992, 0.988, and 0.991, respectively. The experimental results show that the proposed NCMBFW can be considered as a promising tool to improve the classification performance of digital multi-carrier modulation signals. (C) 2019 Elsevier B.V. All rights reserved.Öğe Graph-based link prediction between human phenotypes and genes(Hindawi Ltd, 2022) Patel, Rushabh; Guo, Yanhui; Alhudhaif, Adi; Alenezi, Fayadh; Althubiti, Sara A.; Polat, KemalDeep phenotyping is defined as learning about genotype-phenotype associations and the history of human illness by analyzing phenotypic anomalies. It is significant to investigate the association between phenotype and genotype. Machine learning approaches are good at predicting the associations between abnormal human phenotypes and genes. A novel framework based on machine learning is proposed to estimate the links between human phenotype ontology (HPO) and genes. The Orphanet's annotation parses the human phenotype-gene associations. An algorithm node2vec generates the embeddings for the nodes (HPO and genes). It performs node sampling on the graph using random walks and learns features on these sampled nodes for embedding. These embeddings were used downstream to predict the link between these nodes by supervised classifiers. Results show the gradient boosting decision tree model (LightGBM) has achieved an optimal AUROC of 0.904 and an AUCPR of 0.784, an optimal weighted F1 score of 0.87. LightGBM can detect more accurate interactions and links between human phenotypes and gene pairs.Öğe Guest editorial: new trends in data pre-processing methods for signal and image classification(Springer, 2017) Polat, Kemal; Muthusamy, Hariharan; Acharya, Rajendra; Guo, YanhuiA special issue of the Neural Computing and Applications (NCAA) is dedicated to ‘‘New trends in data pre-processing methods for signal and image classification.’’Data pre-processing is crucial for effective data mining. Low-quality data usually produce inaccurate and unpredictable outcomes. Today’s real-world data are greatly vulnerable to noise and getting lost due to either large data size or the sources of origin. Real-world data are often inconsistent and incomplete, and are possible to have several errors. These poor-quality data will result in poorquality mining outcomes. Data pre-processing enhances the data standard and subsequently aids to refine the value of data mining outcomes. Data pre-processing performs certain processing on raw original data to prepare it for further processing or analysis. In short, data pre-processing prepares original raw data for further processing. Data preprocessing converts the data into a form acceptable easily for further processing by the user.Öğe A hybrid dermoscopy images segmentation approach based on neutrosophic clustering and histogram estimation(Elsevier, 2018) Ashour, Amira S.; Guo, Yanhui; Küçükkülahlı, Enver; Erdoğmuş, Pakize; Polat, KemalIn this work, a novel skin lesion detection approach, called HBCENCM, is proposed using histogram-based clustering estimation (HBCE) algorithm to determine the required number of clusters in the neutrosophic c-means clustering (NCM) method. Initially, the dermoscopic images are mapped into the neutrosophic domain over three memberships, namely true, indeterminate, and false subsets. Then, an NCM algorithm is employed to group the pixels in the dermoscopy images, where the number of clusters in the dermoscopy images is determined using the HBCE algorithm. Lastly, the skin lesion is detected based on its intensity and morphological features. The public dataset (ISIC 2016) of 900 images for training and 379 images for testing are used in the present work. A comparative study of the original NCM clustering method is conducted on the same dataset. The results showed the superiority of the proposed approach to detect the lesion with 96.3% average accuracy compared to the average accuracy of 94.6% using the original NCM without HBCE algorithm. (C) 2018 Elsevier B.V. All rights reserved.Öğe KNCM: Kernel Neutrosophic c-Means clustering(Elsevier, 2017) Akbulut, Yaman; Şengür, Abdulkadir; Guo, Yanhui; Polat, KemalData clustering is an important step in data mining and machine learning. It is especially crucial to analyze the data structures for further procedures. Recently a new clustering algorithm known as 'neutrosophic c-means' (NCM) was proposed in order to alleviate the limitations of the popular fuzzy c-means (FCM) clustering algorithm by introducing a new objective function which contains two types of rejection. The ambiguity rejection which concerned patterns lying near the cluster boundaries, and the distance rejection was dealing with patterns that are far away from the clusters. In this paper, we extend the idea of NCM for nonlinear-shaped data clustering by incorporating the kernel function into NCM. The new clustering algorithm is called Kernel Neutrosophic c-Means (KNCM), and has been evaluated through extensive experiments. Nonlinear-shaped toy datasets, real datasets and images were used in the experiments for demonstrating the efficiency of the proposed method. A comparison between Kernel FCM (KFCM) and KNCM was also accomplished in order to visualize the performance of both methods. According to the obtained results, the proposed KNCM produced better results than KFCM. (C) 2016 Elsevier B.V. All rights reserved.Öğe A novel framework of two successive feature selection levels using weight-based procedure for voice-loss detection in parkinson's disease(Ieee-Inst Electrical Electronics Engineers Inc, 2020) Ashour, Amira S.; Nour, Majid Kamal A.; Polat, Kemal; Guo, Yanhui; Alsaggaf, Wafaa; El-Attar, AmiraParkinson & x2019;s disease (PD) is one of the public neuro-degenerative disorders. Speech/voice disorder is considered one of the symptoms at an early stage. Acoustic and speech signal processing methods can potentially evaluate and measure PD-related vocal impairment. The present work proposed a novel feature selection framework using two levels of the feature selection procedure for voice-loss detection in PD patients. At the first level selection, the principal component analysis (PCA) and the eigenvector centrality feature selection (ECFS) methods are initially calculated independently, and the selected features from each method are considered as a separated sublist, namely ECFS selected features sublist, and PCA selected features sublist, in the first set. Accordingly, the first set, which is the first level selection set, is generated from the union of these two sublists using the top-selected features from both methods. In the training phase, a second level selection, which forms the second set (which is a subset from the first set), is generated to calculate the proposed weight of each selection method. Since in the present work, the ECFS provided superior performance to the PCA in the first level selection, the ECFS is applied to the first set in order to find weight values based on the contribution/impact of the top-selected PCA- and ECFS- features in the second level. This weight is determined by finding a proposed ratio, which is multiplied directly by the selected ECFS features in the first level. The selected weighted ECFS features are then combined with the same PCA features to avoid ignoring any of the top-ranked features from the first level. This combination includes the final weighted-hybrid selected features that fed to a support vector machine (SVM) classifier to evaluate the proposed weighted hybrid selected features. Hence, in the test phase, the generated weight is used directly without any further need for the second level selection. Several comparative studies were conducted to evaluate the proposed feature selection performance for PD voice-loss detection. The experimental results established the superiority of the proposed procedure using cubic kernel-SVM with 94 & x0025; accuracy for voice-loss detection in PD, while, with the same classifier, 88 & x0025; accuracy was achieved without using the proposed selection method.Öğe A novel image segmentation approach based on neutrosophic c-means clustering and indeterminacy filtering(Springer, 2017) Guo, Yanhui; Xia, Rong; Sengu, Abdulkadir; Polat, KemalThis paper presents a novel image segmentation algorithm based on neutrosophic c-means clustering and indeterminacy filtering method. Firstly, the image is transformed into neutrosophic set domain. Then, a new filter, indeterminacy filter is designed according to the indeterminacy value on the neutrosophic image, and the neighborhood information is utilized to remove the indeterminacy in the spatial neighborhood. Neutrosophic c-means clustering is then used to cluster the pixels into different groups, which has advantages to describe the indeterminacy in the intensity. The indeterminacy filter is employed again to remove the indeterminacy in the intensity. Finally, the segmentation results are obtained according to the refined membership in the clustering after indeterminacy filtering operation. A variety of experiments are performed to evaluate the performance of the proposed method, and a newly published method neutrosophic similarity clustering (NSC) segmentation algorithm is utilized to compare with the proposed method quantitatively. The experimental results show that the proposed algorithm has better performances in quantitatively and qualitatively.Öğe OCE-NGC: A neutrosophic graph cut algorithm using optimized clustering estimation algorithm for dermoscopic skin lesion segmentation(Elsevier, 2020) Hawas, Ahmed Refaat; Guo, Yanhui; Du, Chunlai; Polat, Kemal; Ashour, Amira S.Automated skin lesion segmentation is one of the most crucial stages in dermoscopic images based diagnosis. To guarantee efficient unsupervised clustering-based segmentation, a histogram-based clustering estimation (HBCE) algorithm can be used to obtain the initial number of clusters with their corresponding centroids. Accordingly, the present work introduced a novel skin lesion segmentation algorithm, called optimized clustering estimation for neutrosophic graph cut algorithm (OCE-NGC). Firstly, the genetic algorithm (GA) is used to optimize the HBCE procedure by finding its optimal threshold values which are functions of a factor, called beta to be optimized. This optimization process guarantees the optimal determination of the initial number of clusters and their corresponding centroids for further use in the proposed clustering process. Thus, the skin lesion dermoscopic images are then mapped into the neutrosophic set (NS) domain which is computed by the neutrosophic c-means (NCM). The NCM groups the pixels in the dermoscopic images using the pre-determined optimal number of clusters obtained by the optimized HBCE. Finally, a cost function of the graph cut (GC) algorithm is defined in the NS domain for the segmentation process. The experimental results established the superiority of the proposed OCE-NGC approach in comparison with the traditional HBCE with NCM only, the traditional HBCE with the NGC, and the typical GC. In a public dataset, the proposed approach achieved 97.12% and 86.28% average accuracy and average Jaccard (JAC) values, respectively. (C) 2019 Elsevier B.V. All rights reserved.Öğe Retraction Note: A novel image segmentation approach based on neutrosophic c-means clustering and indeterminacy filtering (Neural Computing and Applications, (2017), 28, 10, (3009-3019), 10.1007/s00521-016-2441-2)(Springer Science and Business Media Deutschland GmbH, 2024) Guo, Yanhui; Xia, Rong; Şengür, Abdulkadir; Polat, KemalCorrection to: Neural Comput & Applic (2017) 28:3009–3019https://doi.org/10.1007/s00521-016-2441-2. The Editor-in-Chief and the publisher have retracted this article. The article was submitted to be part of a guest-edited issue. An investigation by the publisher found a number of articles, including this one, with a number of concerns, including but not limited to compromised editorial handling and peer review process, inappropriate or irrelevant references or not being in scope of the journal or guest-edited issue. Based on the investigation's findings the Editor-in-Chief therefore no longer has confidence in the results and conclusions of this article. The authors disagree with this retraction. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.
