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    Automated Classification of Skin Diseases Using Microscopic Images: A Machine Learning Approach
    (Wiley, 2025) Karapinar Senturk, Zehra; Guler, Recep; Ozcan, Yunus; Gamsizkan, Mehmet
    This study presents a machine learning-based approach for the automated classification of skin diseases, specifically targeting morphea and lichen sclerosus, using microscopic images. The proposed method involves a systematic workflow, including image preprocessing techniques such as resizing, Reinhard normalization, Gaussian filtering, and CLAHE histogram equalization to enhance image quality. Feature extraction was performed using Gray-Level Co-occurrence Matrix (GLCM) and histogram-based statistical methods, capturing texture and intensity characteristics of skin tissues. Several classification models, including Support Vector Machine (SVM), Artificial Neural Network (ANN), Decision Tree (DT), Random Forest (RF), K-Nearest Neighbors (K-NN), and Logistic Regression (LR), were evaluated using accuracy, precision, recall, and F1 score, with hyperparameter optimization via grid search. The experimental results revealed that the combined feature set (GLCM + Histogram) achieved the highest performance, with the RF and K-NN models yielding a 100% in all performance metrics, including accuracy, sensitivity, recall, and F1-score. The study introduces a novel approach by examining these two diseases simultaneously, offering a reliable tool to support dermatologists with accurate and quick diagnoses. Future work will focus on expanding the dataset, exploring advanced deep learning techniques, and integrating clinical metadata to enhance model generalizability.
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    Machine Learning-Based Hand Gesture Recognition via EMG Data
    (Ediciones Univ Salamanca, 2021) Karapinar Senturk, Zehra; Bakay, Melahat Sevgul
    Gestures are one of the most important agents for human-computer interaction. They play a mediator role between human intention and the control of machines. Electromyography (EMG) pattern recognition has been studied for gesture recognition for years to control of prostheses and rehabilitation systems. EMG data gives information about the electrical activity related to muscles. It is obtained from the arm and helps to understand hand gestures. For this work, hand gesture data taken from UCI2019 EMG dataset obtained from myo Thalmic armband were classified with six different machine learning algorithms. Artificial Neural Network (ANN), Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Naive Bayes (NB), Decision Tree (DT) and Random Forest (RF) methods were applied for comparison based on several performance metrics which are accuracy, precision, sensitivity, specificity, classification error, kappa, root mean squared error (RMSE) and correlation. The data belongs to seven hand gestures. 700 samples from 7 classes (100 samples per group) were used in the experiments. Splitting ratio in the classification was 0.8-0.2, i.e. 80% of the samples were used in training and 20% of data were used in the testing phase of the classifier. NB was found to be the best among other methods because of high accuracy. Classification accuracy varies between 97.52% to 100% for each gesture. Considering the results of the performance metrics, it can be said that this study recognizes and classifies seven hand gestures successfully in comparison with the literature. The proposed method can easily be used for human-machine interaction and smart device controlling like prosthesis, wheelchair, and smart entertainment applications.