In recent years, the volume and variety of biological data being acquired have increased significantly. Among these data types, the diagnosis of Parkinson's disease holds a critical place in medical research. For this study, speech signals were recorded from patients and healthy controls in a controlled environment at the Neurology Department of Fırat University Hospital. 28 healthy controls, 22 Med Off patients and 30 Med On patients constituted our data set. Participants were asked to read a standardized text in a quiet room using a high-quality H1N Zoom microphone. 19 features were extracted from the obtained sounds. The dataset was categorized into three distinct classes: Healthy Control, Med Off (patients without medication), and Med On (patients medication). To evaluate classification performance, we used a three-layer deeep neural network (DNN) model as well as classical machine learning algorithms in MATLAB. Various classification scenarios have been considered, including many different combinations. For benchmarking, the DNN results were compared with those from commonly used algorithms in the literature: K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT), and Naive Bayes (NB). Furthermore, the DNN model’s performance was assessed using the NeuroParkNet architecture. The comparative analysis revealed that the DNN model generally provided a more accurate and efficient classification process. However, in some specific cases, its performance was partially outperformed by traditional classification algorithms. These findings highlight the DNN's potential while also underscoring areas for optimization in Parkinson’s disease classification systems. In addition, the effects of pharmacological treatments were also evaluated in this study.

The design and performance analysis of relay lenses that provide high-performance image transmission for target acquisition and tracking in military optical systems. Relay lenses are critical components for clear and lossless image transmission over long distances. In this study, the optical performance of a relay lens system designed and optimized using ZEMAX software is investigated in detail. The analysis focuses on important optical properties such as modulation transfer function (MTF), spot diagrams, Seidel diagram, field curvature and distortion. The results show that the lens has significant potential in military applications for target detection and tracking with high resolution and low aberration.

Parkinson's disease (PD) is a progressive neurodegenerative disorder that impacts motor functions and speech characteristics This study focuses on differentiating individuals with Parkinson's disease from healthy controls through the extraction and classification of speech features. Patients were further divided into 2 groups. Med On represents the patient with medication, while Med Off represents the patient without medication. The dataset consisted of patients and healthy individuals who read a predefined text using the H1N Zoom microphone in a suitable recording environment at Fırat University Neurology Department. Speech recordings from PD patients and healthy controls were analyzed, and 19 key features were extracted, including jitter, luminance, zero-crossing rate (ZCR), root mean square (RMS) energy, entropy, skewness, and kurtosis.These features were visualized in graphs and statistically evaluated to identify distinctive patterns in PD patients. Using MATLAB's Classification Learner toolbox, several machine learning classification algorithm models were applied to classify groups and significant accuracy rates were achieved. The accuracy of our 3-layer artificial neural network architecture was also compared with classical machine learning algorithms. This study highlights the potential of noninvasive voice analysis combined with machine learning for early detection and monitoring of PD patients. Future research can improve diagnostic accuracy by optimizing feature selection and exploring advanced classification techniques.

Brushless DC (BLDC) motors are widely used in industrial and automotive systems due to their high efficiency, low maintenance requirements, and compact structure. However, they are vulnerable to various electrical and mechanical faults, such as bearing wear and rotor imbalance, which can lead to unexpected downtimes. To address this issue, this study proposes ETNeXt, a lightweight, self-organizing fault detection framework based on acoustic signal analysis. The method applies a 7-level Multilevel Discrete Wavelet Transform (MDWT) with the ‘sym4’ wavelet to extract frequency-domain features, followed by triadic histogram feature generation using signum, upper ternary, and lower ternary functions. A hybrid feature selection process based on Neighborhood Component Analysis (NCA) and Chi-square (Chi2) methods identifies the most discriminative features. Classification is performed using Fine k-NN and Cubic SVM with tenfold cross-validation. The proposed ETNeXt model achieved up to 100% accuracy with Cubic SVM and 99.80% with kNN on a benchmark dataset, and maintained 99.95% accuracy on a separate test dataset, demonstrating strong generalizability. Compared to deep learning models, ETNeXt offers significantly reduced computational complexity, making it highly suitable for real-time, edge-based deployment thanks to its lightweight design.