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Article Citation - WoS: 5Citation - Scopus: 8A Novel Hybrid Machine Learning-Based System Using Deep Learning Techniques and Meta-Heuristic Algorithms for Various Medical Datatypes Classification(Mdpi, 2024) Kadhim, Yezi Ali; Guzel, Mehmet Serdar; Mishra, AlokMedicine is one of the fields where the advancement of computer science is making significant progress. Some diseases require an immediate diagnosis in order to improve patient outcomes. The usage of computers in medicine improves precision and accelerates data processing and diagnosis. In order to categorize biological images, hybrid machine learning, a combination of various deep learning approaches, was utilized, and a meta-heuristic algorithm was provided in this research. In addition, two different medical datasets were introduced, one covering the magnetic resonance imaging (MRI) of brain tumors and the other dealing with chest X-rays (CXRs) of COVID-19. These datasets were introduced to the combination network that contained deep learning techniques, which were based on a convolutional neural network (CNN) or autoencoder, to extract features and combine them with the next step of the meta-heuristic algorithm in order to select optimal features using the particle swarm optimization (PSO) algorithm. This combination sought to reduce the dimensionality of the datasets while maintaining the original performance of the data. This is considered an innovative method and ensures highly accurate classification results across various medical datasets. Several classifiers were employed to predict the diseases. The COVID-19 dataset found that the highest accuracy was 99.76% using the combination of CNN-PSO-SVM. In comparison, the brain tumor dataset obtained 99.51% accuracy, the highest accuracy derived using the combination method of autoencoder-PSO-KNN.Conference Object An Rfid Based Location Finding and Tracking With Guidance(Ieee, 2008) Oktem, Rusen; Aydin, Elif Uray; Cagiltay, Nergiz ErcilThis paper tackles an RFID based location finding and tracking system. The system is an integral part of a navigation aid being developed for guiding visually disabled people in a store. The aid is composed of a portable hardware interface unit, a standalone RFID unit, and a central processing unit. The units interact via wireless communication to locate the position of the user in a known indoor environment and tracking his/her movement. An active RFID tag helps to estimate the location of a user and the user is guided to follow a route accordingly, via a tactile compass. The system uses RF signal strengths and is based on Bayes Decision Theory. Initial simulation results with the system prove promising for location finding and tracking, especially when the tracked person is guided by a system.Article Citation - WoS: 2Citation - Scopus: 2Classification of Different Recycled Rubber-Epoxy Composite Based on Their Hardness Using Laser-Induced Breakdown Spectroscopy (libs) With Comparison Machine Learning Algorithms(Mdpi, 2023) Yilmaz, Vadi Su; Yılmaz, Vadi Su; Eseller, Kemal Efe; Aslan, Ozgur; Aslan, Özgür; Bayraktar, Emin; Eseller, Kemal Efe; Yılmaz, Vadi Su; Aslan, Özgür; Eseller, Kemal Efe; Electrical-Electronics Engineering; Department of Electrical & Electronics Engineering; Mechanical Engineering; Electrical-Electronics Engineering; Mechanical Engineering; Department of Electrical & Electronics EngineeringThis paper aims toward the successful detection of harmful materials in a substance by integrating machine learning (ML) into laser-induced breakdown spectroscopy (LIBS). LIBS is used to distinguish five different synthetic polymers where eight different heavy material contents are also detected by LIBS. Each material intensity-wavelength graph is obtained and the dataset is constructed for classification by a machine learning (ML) algorithm. Seven popular machine learning algorithms are applied to the dataset which include eight different substances with their wavelength-intensity value. Machine learning algorithms are used to train the dataset, results are discussed and which classification algorithm is appropriate for this dataset is determined.Data Paper Citation - WoS: 42Citation - Scopus: 62A Database for the Radio Frequency Fingerprinting of Bluetooth Devices(Mdpi, 2020) Uzundurukan, Emre; Dalveren, Yaser; Kara, AliRadio frequency fingerprinting (RFF) is a promising physical layer protection technique which can be used to defend wireless networks from malicious attacks. It is based on the use of the distinctive features of the physical waveforms (signals) transmitted from wireless devices in order to classify authorized users. The most important requirement to develop an RFF method is the existence of a precise, robust, and extensive database of the emitted signals. In this context, this paper introduces a database consisting of Bluetooth (BT) signals collected at different sampling rates from 27 different smartphones (six manufacturers with several models for each). Firstly, the data acquisition system to create the database is described in detail. Then, the two well-known methods based on transient BT signals are experimentally tested by using the provided data to check their solidity. The results show that the created database may be useful for many researchers working on the development of the RFF of BT devices.Article Citation - WoS: 7Citation - Scopus: 7Classification of Intermediate and Novice Surgeons' Skill Assessment Through Performance Metrics(Sage Publications inc, 2019) Topalli, Damla; Cagiltay, Nergiz ErcilBackground. Endoscopic surgeries have become an alternative for open procedures whenever possible. For such types of operations, surgeons are required to gain several skills, whose development needs hands-on practice. Accordingly, gaining these skills today is a challenge for surgical education programs. Despite the development of several technology-enhanced training environments, there are still problems to better integrate these technologies into educational programs. For an appropriate integration, it is critical to assess the skill levels and adapt the training content according to the trainees' requirements. In the literature, there exist several methods for assessing these skill levels. However, there are still problems in practice for objective and repetitive assessment. Methods. The present study aims to estimate the skill levels of participants in surgical training programs in an objective manner by collecting experimental data from residents in an endoscopic surgical simulation environment and gathering performance metrics. Results. It is shown that, by comparing the results of a number of classification algorithms for the best accuracy estimation and feature set, the "novice" and "intermediate" skill levels can be estimated with an accuracy of 86%. Conclusions. The outcomes help surgical educators and instructional system designers to better assess the skill levels of the trainees and guide them accordingly. In addition, objective assessments as highlighted in this study can be beneficial when designing technology-enhanced adaptive learning environments.Article Citation - WoS: 55Citation - Scopus: 70Assessment of Features and Classifiers for Bluetooth Rf Fingerprinting(Ieee-inst Electrical Electronics Engineers inc, 2019) Ali, Aysha M.; Uzundurukan, Emre; Kara, AliRecently, network security has become a major challenge in communication networks. Most wireless networks are exposed to some penetrative attacks such as signal interception, spoofing, and stray. Radio frequency (RF) fingerprinting is considered to be a promising solution for network security problems and has been applied with various improvements. In this paper, extensive data from Bluetooth (BT) devices are utilized in RF fingerprinting implementation. Hilbert-Huang transform (HHT) has been used, for the first time, for RF fingerprinting of Bluetooth (BT) device identification. In this way, time-frequency-energy distributions (TFED) are utilized. By means of the signals' energy envelopes, the transient signals are detected with some improvements. Thirteen features are extracted from the signals' transients along with their TFEDs. The extracted features are pre-processed to evaluate their usability. The implementation of three different classifiers to the extracted features is provided for the first time in this paper. A comparative analysis based on the receiver operating characteristics (ROC) curves, the associated areas under curves (AUC), and confusion matrix are obtained to visualize the performance of the applied classifiers. In doing this, different levels of signal to noise ratio (SNR) levels are used to evaluate the robustness of the extracted features and the classifier performances. The classification performance demonstrates the feasibility of the method. The results of this paper may help readers assess the usability of RF fingerprinting for BT signals at the physical layer security of wireless networks.Article Citation - WoS: 25Citation - Scopus: 33Hybrid Eeg-Fnirs Bci Fusion Using Multi-Resolution Singular Value Decomposition (msvd)(Frontiers Media Sa, 2020) Khan, Muhammad Umer; Hasan, Mustafa A. H.Brain-computer interface (BCI) multi-modal fusion has the potential to generate multiple commands in a highly reliable manner by alleviating the drawbacks associated with single modality. In the present work, a hybrid EEG-fNIRS BCI system-achieved through a fusion of concurrently recorded electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals-is used to overcome the limitations of uni-modality and to achieve higher tasks classification. Although the hybrid approach enhances the performance of the system, the improvements are still modest due to the lack of availability of computational approaches to fuse the two modalities. To overcome this, a novel approach is proposed using Multi-resolution singular value decomposition (MSVD) to achieve system- and feature-based fusion. The two approaches based up different features set are compared using the KNN and Tree classifiers. The results obtained through multiple datasets show that the proposed approach can effectively fuse both modalities with improvement in the classification accuracy.Article Citation - WoS: 10Citation - Scopus: 15An Rfid Based Indoor Tracking Method for Navigating Visually Impaired People(Tubitak Scientific & Technological Research Council Turkey, 2010) Oktem, Rusen; Aydin, ElifThis paper tackles the RFID based tracking problem in an obscured indoor environment. The proposed solution is an integral part of a navigation aid for guiding visually impaired people in a store. It uses RF signal strengths and is based on the Bayes Decision Theory. An observation vector is formed by received radio signal strength indication values, transmitted from three transmitters at distinct frequencies in the UHF band. The indoor area is divided into square grids, where each grid is considered as a class. The problem of tracking is expressed as classifying the observed radio signal strengths to the most likely class. A classification rule is formulated by incorporating a priori assumptions appropriate for the studied model. The proposed approach is tested in a laboratory environment. The results prove that the proposed approach is promising in tracking especially when the tracked person is guided by a system.
