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Sürdürülebilir Kalkınma Amaçları

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• MakaleYalnız künye2026/r/gumushane/13834
  Machine learning-based multi-class classification of rock mass permeability using integrated geotechnical and geophysical parameters

  Sari, Mahmut; Alemdag, Selcuk; Melek, Mesut; Seren, Aysel

  In engineering applications, machine learning (ML) provides a promising approach when direct characterization of subsurface properties is challenging, time-consuming, and costly. This study developed a multi-class ML framework for classifying rock mass permeability based on Lugeon values using 86 observations from drilling and geophysical investigations at the Kırklartepe dam site in Bayburt, Türkiye. Seven predictors were considered: depth, Rock Quality Designation (RQD), joint frequency, Schmidt hammer hardness, P-wave velocity, S-wave velocity, and electrical resistivity. The observations were assigned to four permeability classes: very low, low, moderate, and high. Six classifiers were evaluated: Polynomial Kernel Support Vector Machine (P-SVM), Linear Discriminant Analysis, k-Nearest Neighbors (k-NN), AdaBoostM2, Bagged Trees, and Radial Basis Function SVM. Sequential Forward Feature Selection and Leave-One-Out Cross-Validation were used for feature selection and model evaluation, respectively. Robustness was further assessed using bootstrap confidence intervals, class-imbalance treatments, feature-selection stability, and learning curves. Among the baseline models, P-SVM using all seven predictors achieved the highest accuracy (79.07%), with a macro-F1 of 0.774 and the lowest Golden Distance (GD = 0.4372). When balanced class-wise performance was prioritized, k-NN with in-fold SMOTE achieved the lowest overall GD (0.4126) and a macro-F1 of 0.777. S-wave velocity and electrical resistivity were the most influential predictors. Overall, the findings demonstrate that the proposed framework effectively integrates geotechnical and geophysical data for rock mass permeability classification, providing a robust, data-driven decision-support approach that supports subsurface characterization and complements conventional permeability assessment in engineering applications.

  İncele
• MakaleAçık Erişim2025/r/gumushane/19565
  EOG Artefaktları Kullanılarak Yörünge Tabanlı Sınıflandırma: Felçli Bireyler İçin Alternatif Bir İnsan-Makine Arayüz Yaklaşımı

  Melek, Mesut; Gökrem, Levent; Aydin, Sefa

  Günümüzde, doğuştan veya omurilik yaralanması gibi sinir sistemi problemleri sebebiyle tamamen felç olmuş bireylerin günlük ihtiyaçlarını karşılayabilmeleri için Beyin-bilgisayar arayüz (BBA) sistemleri geliştirilmiştir. BBAlar, çeşitli cihazları kullanarak topladığı beyin sinyallerini bilgisayarlar aracılığıyla işleyerek, çevre cihazları harekete geçirmek için çıktı komutları üreten sistemlerdir. Fakat BBA sistemlerinin kullanıcılar üzerinde bazı olumsuz etkileri vardır. Bu çalışmada, Görsel uyaran potansiyel (GUP) ve P300 gibi görsel uyaran tabanlı BBA sistemlerinde kullanılan farklı frekans değerlerinde titreşen uyaranların sistem kullanıcılarının göz sağlığı üzerinde meydana getirdiği olumsuz etkiyi azaltmak amacıyla; hareketli nesneler yaklaşımını kullanan bir İnsan-Makine Arayüz (İMA) sistemi önerilmiştir. Sistem, kullanıcının hareketli topları odaklanarak takip etmesi sonucunda Elektrosefalografi (EEG) sinyallerinde meydana gelen Elektrookülografi (EOG) artefaktlarını kullanarak hareket yörüngelerini makine öğrenme algoritmaları ile sınıflandırmayı amaçlamaktadır. Çalışma, Emotiv EPOC EEG cihazı aracılığıyla dört farklı rotada hareket eden toplar içeren yaklaşım kullanılarak sağlıklı 8 denek üzerinde gerçekleştirilmiştir. Kaydedilen ham verilere ön işleme aşamaları uygulanmıştır ve etkin kanal seçimi yapılmıştır. Tespit edilen etkin kanallara (AF3, AF4) ait verilere Güç Spektral Yoğunluğu (GSY) ve Ayrık Dalgacık Dönüşümü (ADD) yöntemleri uygulanmıştır. Her yöntem için öznitelikler çıkartılmış ve k-en yakın komşu (k-EYK) ve Lineer Diskriminant Analiz (LDA) algoritmaları kullanılarak sınıflandırılmıştır. GSY yöntemi için LDA ve k-EYK algoritmaları ile sırasıyla 93.86%, 92.62% doğruluk oranları ve 31.42 (bit/dk), 30.10 (bit/dk) ITR değerleri hesaplanmıştır. ADD yöntemi için ise sırasıyla 92.84%, 92.17% doğruluk oranları ve 30.36 (bit/dk), 29.62 (bit/dk) ITR değerleri elde edilmiştir. Karşılaştırılan iki yöntemden GSY’nin ADD’ye göre daha yüksek doğruluk oranları ve ITR değerleri verdiği, LDA algoritmasının k-EYK algoritmasından daha başarılı olduğu gözlemlenmiştir.

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  İncele
• MakaleYalnız künye2025/r/gumushane/14301
  Golden Distance: A New and Comprehensive Metric Definition Study Facilitating Classification Performance Evaluations

  Melek, Mesut; Melek, Negin

  Machine/deep learning has great potential in many classification studies that make our lives easier. Performance evaluation of the classification algorithm is of great importance for making accurate predictions in machine learning, which, itself, learns from data without any human intervention. In these learning models, high-dimensional data in classes with complex features belonging to balanced or unbalanced distributions are analysed with different classification algorithms and take their place in the decision stage. While the difficulties in implementing classification algorithms continue today, evaluating the superiority of developed systems over each other continues to be both a time-consuming and very difficult process. In models proposed in the literature, only a single parameter, such as accuracy, is not sufficient to determine model adequacy. In this context, among different classification algorithm performance parameters, some metrics such as sensitivity, specificity, F-measure (F1), Jaccard index, area under the curve, and kappa coefficient are used for system evaluation. As these parameters are placed in the analysis tables, the study should be evaluated at length by including large tables in the article. Instead of using various parameters that make performance analysis difficult in system evaluation, this study defines a new Golden Distance-based parameter that summarizes the model comprehensively from all dimensions. To understand this new metric’s working principle, a detailed comparative analysis of research conducted on three datasets was performed. Based on the final evaluation, the proposed inclusive metric demonstrated successful and fair behaviour in all conditions. Thus, a fair and simple comparison will be achieved with a single metric, and long analyses can be avoided by presenting many performance parameters in systems’ superiority indicator tables.

  İncele
• MakaleAçık Erişim2025/r/gumushane/14150
  Intersession Robust Hybrid Brain–Computer Interface: Safe and User-Friendly Approach with LED Activation Mechanism

  Aydın, Sefa; Melek, Mesut; Gökrem, Levent

  This study introduces a hybrid Brain–Computer (BCI) system with a robust and secure activation mechanism between sessions, aiming to minimize the negative effects of visual stimulus-based BCI systems on user eye health. The system is based on the integration of Electroencephalography (EEG) signals and Electrooculography (EOG) artefacts, and includes an LED stimulus operating at a frequency of 7 Hz for safe activation and objects moving in different directions. While the LED functions as an activation switch that reduces visual fatigue caused by traditional visual stimuli, moving objects provide command generation depending on the user’s intention. In order to evaluate the stability of the system against physiological and psychological conditions, data were collected from 15 participants in two different sessions. The Correlation Alignment (CORAL) method was applied to the data to reduce the variance between sessions and to increase stability. A Bootstrap Aggregating algorithm was used in the classification processes, and with the CORAL method, the system accuracy rate was increased from 81.54% to 94.29%. Compared to similar BCI approaches, the proposed system offers a safe activation mechanism that effectively adapts to users’ changing cognitive states throughout the day by reducing visual fatigue, despite using a low number of EEG channels, and demonstrates its practicality and effectiveness by performing on par or superior to other systems in terms of high accuracy and robust stability.

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  İncele
• MakaleAçık Erişim2025/r/gumushane/11572
  A Safe and Efficient Brain–Computer Interface Using Moving Object Trajectories and LED-Controlled Activation

  Aydin, Sefa; Melek, Mesut; Gökrem, Levent

  Nowadays, brain–computer interface (BCI) systems are frequently used to connect individuals who have lost their mobility with the outside world. These BCI systems enable individuals to control external devices using brain signals. However, these systems have certain disadvantages for users. This paper proposes a novel approach to minimize the disadvantages of visual stimuli on the eye health of system users in BCI systems employing visual evoked potential (VEP) and P300 methods. The approach employs moving objects with different trajectories instead of visual stimuli. It uses a light-emitting diode (LED) with a frequency of 7 Hz as a condition for the BCI system to be active. The LED is assigned to the system to prevent it from being triggered by any involuntary or independent eye movements of the user. Thus, the system user will be able to use a safe BCI system with a single visual stimulus that blinks on the side without needing to focus on any visual stimulus through moving balls. Data were recorded in two phases: when the LED was on and when the LED was off. The recorded data were processed using a Butterworth filter and the power spectral density (PSD) method. In the first classification phase, which was performed for the system to detect the LED in the background, the highest accuracy rate of 99.57% was achieved with the random forest (RF) classification algorithm. In the second classification phase, which involves classifying moving objects within the proposed approach, the highest accuracy rate of 97.89% and an information transfer rate (ITR) value of 36.75 (bits/min) were achieved using the RF classifier.

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  İncele
• MakaleYalnız künye2022/r/gumushane/14755
  Detection of 2D and 3D Video Transitions Based on EEG Power

  Manshouri, Negin; Melek, Mesut; Kaylkcloglu, Temel

  Despite the long and extensive history of 3D technology, it has recently attracted the attention of researchers. This technology has become the center of interest of young people because of the real feelings and sensations it creates. People see their environment as 3D because of their eye structure. In this study, it is hypothesized that people lose their perception of depth during sleepy moments and that there is a sudden transition from 3D vision to 2D vision. Regarding these transitions, the EEG signal analysis method was used for deep and comprehensive analysis of 2D and 3D brain signals. In this study, a single-stream anaglyph video of random 2D and 3D segments was prepared. After watching this single video, the obtained EEG recordings were considered for two different analyses: the part involving the critical transition (transition state) and the state analysis of only the 2D versus 3D or 3D versus 2D parts (steady state). The main objective of this study is to see the behavioral changes of brain signals in 2D and 3D transitions. To clarify the impacts of the human brain's power spectral density (PSD) in 2D-to-3D (2D_3D) and 3D-to-2D (3D_2D) transitions of anaglyph video, nine visual healthy individuals were prepared for testing in this pioneering study. Spectrogram graphs based on short time Fourier transform (STFT) were considered to evaluate the power spectrum analysis in each EEG channel of transition or steady state. Thus, in 2D and 3D transition scenarios, important channels representing EEG frequency bands and brain lobes will be identified. To classify the 2D and 3D transitions, the dominant bands and time intervals representing the maximum difference of PSD were selected. Afterward, effective features were selected by applying statistical methods such as standard deviation, maximum (max) and Hjorth parameters to epochs indicating transition intervals. Ultimately, k-nearest neighbors, support vector machine and linear discriminant analysis (LDA) algorithms were applied to classify 2D_3D and 3D_2D transitions. The frontal, temporal and partially parietal lobes show 2D_3D and 3D_2D transitions with a good classification success rate. Overall, it was found that Hjorth parameters and LDA algorithms have 71.11% and 77.78% classification success rates for transition and steady state, respectively.

  İncele
• MakaleYalnız künye2022/r/gumushane/11506
  Roza: a new and comprehensive metric for evaluating classification systems

  Melek, Mesut; Melek, Negin

  Many metrics such as accuracy rate (ACC), area under curve (AUC), Jaccard index (JI), and Cohen’s kappa coefficient are available to measure the success of the system in pattern recognition and machine/deep learning systems. However, the superiority of one system to one other cannot be determined based on the mentioned metrics. This is because such a system can be successful using one metric, but not the other ones. Moreover, such metrics are insufficient when the number of samples in the classes is unequal (imbalanced data). In this case, naturally, by using these metrics, a sensible comparison cannot be made between two given systems. In the present study, the comprehensive, fair, and accurate Roza (Roza means rose in Persian. When different permutations of the metrics used are superimposed in a polygon format, it looks like a flower, so we named it Roza.) metric is introduced for evaluating classification systems. This metric, which facilitates the comparison of systems, expresses the summary of many metrics with a single value. To verify the stability and validity of the metric and to conduct a comprehensive, fair, and accurate comparison between the systems, the Roza metric of the systems tested under the same conditions are calculated and comparisons are made. For this, systems tested with three different strategies on three different datasets are considered. The results show that the performance of the system can be summarized by a single value and the Roza metric can be used in all systems that include classification processes, as a powerful metric.

  İncele
• MakaleAçık Erişim2021/r/gumushane/16899
  Eeg sinyallerini kullanarak 2B ve 3B hibrit bir videonun ayrıntılı kanal/lob analizi

  Melek, Negin; Melek, Mesut; Kayikcioglu, Temel

  İnsan gözünün yapısına göre 2 Boyutlu (2B) ve 3B parçalardan oluşan video analizinde ani geçiş ve sabit durumların değerlendirilmesi önemlidir. Bu çalışmada, insan beyin sinyallerinin güç spektrum yoğunluğu (GSY), 2B ve 3B hibrit video izleme sonucunda analiz için dikkate alınmıştır. İnsanların yorulunca derinlik algısının kaybettiğini daha önceki çalışmalarımızda iddia etmiştik. Bu çalışmada, rastgele 2B ve 3B parçalardan oluşan, tek akışlı bir anaglif video sağlanmıştır. 2B ve 3B hibrit video çalışmasında, beyin sinyal analizinde kısa zamanlı Fourier dönüşümüne (KZFD) dayalı GSY ve spektrogram adı verilen görsel temsil kullanılmıştır. Tüm EEG frekans bantları test edildikten sonra, spektrogram çizelgesindeki 2B ve 3B parçaların karşılaştırılmasında GSY farkı dikkate alınarak, delta bandı baskın bant olarak seçilmiştir. Bu banttan çıkarılan öznitelikler iki popüler sınıflandırıcı tarafından sınıflandırılmıştır. Bunlar destek vektör makinesi (DVM) ve doğrusal ayırma analizi (DAA) algoritmalarıdır. Sonuç olarak, frontal ve temporal loblar, 2B ve 3B geçişlerin sınıflandırılmasında diğer loblara göre daha iyi sonuçlar göstermektedir. Öznitelik çıkarma yöntemi olarak kullanılan istatistiksel fonksiyonlar ve Hjorth parametreleri sonucunda DVM ve DAA algoritmaları için sınıflandırma başarısı sırasıyla %68 ve %79 olarak hesaplanmıştır.

  İncele
• BildiriAçık Erişim2021/r/gumushane/16475
  Automatic Sleep Scoring System Based On Autoregressive Model

  Melek, Mesut

  Electroencephalography (EEG) signals recorded during sleep are divided into 30-second segments and analyzed by experts and classified into sleep stages, and in this way, diagnosis and treatment of diseases are made. Computer-assisted sleep scoring systems can effectively reduce the burden of experts by classifying sleep stages. In this study, after preprocessing of the EEG signals, four features were obtained for each EEG band (20 features in total) with the autoregressive model and then classified with three different classifiers common in machine learning. The proposed method was tested on the data set called ISRUC sleep (Subgroup 3, ISRUC3) and the results were compared with the results of other studies. The results show that the proposed method is more successful than other existing methods, despite the use of fewer features.

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  İncele
• MakaleYalnız künye2021/r/gumushane/14912
  Brain-computer interface systems for smart homes-a review study

  Maleki, Masoud; Manshouri, Negin; Kayikcioglu, Temel

  Brain-computer Interface (BCI) systems, usually using signals taken from users' brain through electroencephalography (EEG), control various devices around and provide the user's com-mand by interacting. Improving the quality of life of people with disabilities is the main goal of BCI systems. The importance of BCI-based smart home systems is further increasing as a smart home system directly affects the life of a disabled individual. On the other hand, few BCI systems can be run directly using smart home systems. The importance of the BCI-based smart home and the few existing systems require more work in this vital field. In addition, no reviews have described BCI systems in a smart home. In this study, we reviewed all the papers on BCI-based smart home systems published in the last 6 years. These studies investigated and evaluated BCI systems from nine different perspectives. In addition, all studies were examined in terms of signal processing methods. Finally, the problems and challenges of these systems were discussed and new solutions were pro-posed.

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