Yazar "Heydarov, Saddam" seçeneğine göre listele

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    Accuracy comparison of dimensionality reduction techniques to determine significant features from IMU sensor-based data to diagnose vestibular system disorders
    (ELSEVIER SCI LTD, THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND, 2020) İkizoğlu, Serhat; Heydarov, Saddam
    This study is a significant step gone to develop Machine Learning (ML) algorithm to apply to gait sensory information collected from people to identify Vestibular System (VS) disorders. Although ML is widely used as diagnostic tool in medical decision-making, there is not much research done on application of ML methods to identify VS imperfections. In this paper, we compared the accuracies of two dimensionality-reduction techniques to use with SVM with Gaussian Kernel: Feature Selection (FS) and Feature Transformation (FT) methods. T-test and Sequential Backward Selection (SBS) were used for FS and Principal Component Analysis (PCA) and Kernel Principal Component Analysis (KPCA) with polynomial and Gaussian kernels were used as FT method. Both methods were applied to the dataset formed by 22 features collected from 37 people, of whom 21 were healthy and 16 subjects had VS-disorders. The highest accuracy among FT methods was 89.2%, while it was 81.1% for FS method. SVM with Gaussian Kernel, trained with the dataset of reduced dimensionality, had computation time of few hundreds of milliseconds, which makes real-time data processing possible. The importance of this work will obviously increase with the increase in the number of initial features. As a next step, we aim to increase dataset and use additional features extracted from pressure sensors placed under the feet. We also aim to use time domain characteristics of the features to increase overall accuracy as a next step.
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    Low-cost VIS/NIR range hand-held and portable photospectrometer and evaluation of machine learning algorithms for classification performance
    (ELSEVIER - DIVISION REED ELSEVIER INDIA PVT LTD17-A/1 MAIN RING ROAD, LAJPAT NAGAR IV, NEW DELHI 110024, INDIA, 2023) Heydarov, Saddam; Aydın, Musa; Faydacı, Çağrı; Tuna, Suha; Öztürk, Sadullah
    In this study, the electronic design of a low-cost and portable spectrophotometer device capable of analyzing in the visible-near infrared region was established. The design of C#.NET-based user-friendly device control software and the development of machine learning algorithms for data classification as well as the comparison of the results were presented. When the spectrophotometer design and implementation studies are reviewed in the literature, two groups of subjects become prominent: (i) a new device fabrication, (ii) solution approaches to current problems by combining commercial portable spectrometer systems and devices with artificial intelligence applications. This work encompasses both groups, and a supportive approach has been followed on how to transform the theoretical knowledge into practice in device development and supportive software with the help of machine learning approaches from design to production. Three commercial spectral sensors, each with six photodiode arrays, were adopted in the spectrophotometer. Thus, 18 features belonging to each sample were acquired in the optical spectral region in the 410 nm to 940 nm band range. The spectral analyses were conducted with 9 different food types of powder or flake structures. A Support Vector Machines (SVM) and Convolutional Neural Network (CNN) approaches were employed for data classification. As a result, SVM and CNN achieved 97% and 95% accuracies, respectively. Moreover, we provided the spectral measurement data, the electronic circuit designs, the API files containing the artificial intelligence algorithms and the graphical user interface (GUI).
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    Rapid determination of colistin resistance in Klebsiella pneumoniae by MALDI-TOF peak based machine learning algorithm with MATLAB
    (Elsevier Science Inc, 2023) Iskender, Secil; Heydarov, Saddam; Yalcin, Metin; Faydaci, Cagri; Kurt, Ozge; Surme, Serkan; Kucukbasmaci, Omer
    Introduction: To date, limited data exist on demonstrating the usefulness of machine learning (ML) algorithms applied to MALDI-TOF in determining colistin resistance among Klebsiella pneumoniae. We aimed to detect colistin resistance in K. pneumoniae using MATLAB on MALDI-TOF database.Materials and methods: A total of 260 K. pneumoniae isolates were collected. Three ML models, namely, linear discriminant analysis (LDA), support vector machine, and Ensemble were used as ML algorithms and applied to training data set.Results: The accuracies for the training phase with 200 isolates were found to be 99.3%, 93.1%, and 88.3% for LDA, support vector machine, and Ensemble models, respectively. Accuracy, sensitivity, specificity, and precision values for LDA in the application test set with 60 K. pneumoniae isolates were 81.6%, 66.7%, 91.7%, and 84.2%, respectively.Conclusion: This study provides a rapid and accurate MALDI-TOF MS screening assay for clinical practice in identifying colistin resistance in K. pneumoniae.(c) 2023 Elsevier Inc. All rights reserved.