Using multivariate control chart instead of establishing univariate control chart for all variables in processes provides time and labor advantage. In addition, it is considered in the relations between variables. However, the statistical calculation of the measured values of all variables is seen as a single value in the control chart. Therefore, it is necessary to determine which variable(s) is the cause of the out of control signal. Effective corrective measures can only be developed when the causes of the fault(s) are determined correctly. The aim of the study is to determine the machine learning techniques that will accurately estimate the type of fault. With the Hotelling T2 chart, out of control signals are identified and the types of faults affected by the variables are defined. Various machine learning techniques are used to compare classification performances. The developed model was applied in the evaluation of the paint quality in a painting process. ANN was determined as the most successful techniques according to performance criteria. The novelty of the study is to classify the fault according to the types of faults, not the variables. Defining the faults according to its types will enable to take effective corrective actions quickly. Scientia Iranica (SCI)

Handwriting is a preferred identifier in detecting Alzheimer’s disease that enables diagnosis about people. The aim of this study is to evaluate the handwriting and make the early detection and diagnosis of Alzheimer’s disease with the highest possible prediction rates. In this regard, 9 machine learning algorithms were used. Seven feature selection methods were used to determine the most effective features for Alzheimer’s disease prediction to eliminate unnecessary ones and increase model prediction performance. The models were trained and tested on the DARWIN dataset with both train - test split and cross-validation methods. According to the results, it has been evaluated that the highest performance criterion values are generally achieved when the SHAP is used as the feature selection method. According to the results, the appropriate model that achieved the highest performance values was determined as the hybrid SHAP-Support Vector Machine model with 0.9623 accuracy, 0.9643 precision, 0.9630 recall and 0.9636 F1-Score.

Chicken meat plays an important role in the healthy diets of many people and has a large global trade volume. In the chicken meat sector, in some production processes, traditional methods are used. Traditional chicken part sorting methods are often manual and time-consuming, especially during the packaging process. This study aimed to identify and classify the chicken parts for their input during the packaging process with the highest possible accuracy and speed. For this purpose, deep-learning-based object detection models were used. An image dataset was developed for the classification models by collecting the image data of different chicken parts, such as legs, breasts, shanks, wings, and drumsticks. The models were trained by the You Only Look Once version 8 (YOLOv8) algorithm variants and the Real-Time Detection Transformer (RT-DETR) algorithm variants. Then, they were evaluated and compared based on precision, recall, F1-Score, mean average precision (mAP), and Mean Inference Time per frame (MITF) metrics. Based on the obtained results, the YOLOv8s model outperformed the other models developed with other YOLOv8 versions and the RT-DETR algorithm versions by obtaining values of 0.9969, 0.9950, and 0.9807 for the F1-score, mAP@0.5, and mAP@0.5:0.95, respectively. It has been proven suitable for real-time applications with an MITF value of 10.3 ms/image.