Abstract Demographic age range prediction from voice assistant data is crucial for developing personalized user applications. In this context, this study aims to introduce the Adaptive Extremely Random Trees via Reinforcement Learning (AERT-RL) model, a new method that uses adaptive hyperparameter optimization to improve prediction performance. In this paper, categorical verbal data from voice assistants was first converted to numerical representations to facilitate processing. Borderline-Synthetic Minority Oversampling Technique was then used to address class imbalance. Also, mutual information (MI) was then used to identify effective features. Seven ensemble models were run during the classification phase, and the results were compared. Ultimately, the proposed new AERT-RL model outperformed all classifiers with ~90% accuracy, with Kappa (0.86) and F-score (0.89). The results of the study demonstrate that reinforcement learning overcomes the limitations of traditional optimization techniques, enabling adaptive and robust parameter optimization of the models. These results also demonstrate that the integration of the three stages—data quantization, MI-based feature extraction, and the developed AERT-RL model—achieves more effective performance outcomes. Briefly, this research presents an efficient computational model for demographic age range detection from categorical voice data. Furthermore, the proposed AERT-RL model will be a powerful alternative to traditional natural language processing methods.

Real-time polymerase chain reaction (RT-PCR) known as the swab test is a diagnostic test that can diagnose COVID-19 disease through respiratory samples in the laboratory. Due to the rapid spread of the coronavirus around the world, the RT-PCR test has become insufficient to get fast results. For this reason, the need for diagnostic methods to fill this gap has arisen and machine learning studies have started in this area. On the other hand, studying medical data is a challenging area because the data it contains is inconsistent, incomplete, difficult to scale, and very large. Additionally, some poor clinical decisions, irrelevant parameters, and limited medical data adversely affect the accuracy of studies performed. Therefore, considering the availability of datasets containing COVID-19 blood parameters, which are less in number than other medical datasets today, it is aimed to improve these existing datasets. In this direction, to obtain more consistent results in COVID-19 machine learning studies, the effect of data preprocessing techniques on the classification of COVID-19 data was investigated in this study. In this study primarily, encoding categorical feature and feature scaling processes were applied to the dataset with 15 features that contain blood data of 279 patients, including gender and age information. Then, the missingness of the dataset was eliminated by using both K-nearest neighbor algorithm (KNN) and chain equations multiple value assignment (MICE) methods. Data balancing has been done with synthetic minority oversampling technique (SMOTE), which is a data balancing method. The effect of data preprocessing techniques on ensemble learning algorithms bagging, AdaBoost, random forest and on popular classifier algorithms KNN classifier, support vector machine, logistic regression, artificial neural network, and decision tree classifiers have been analyzed. The highest accuracies obtained with the bagging classifier were 83.42% and 83.74% with KNN and MICE imputations by applying SMOTE, respectively. On the other hand, the highest accuracy ratio reached with the same classifier without SMOTE was 83.91% for the KNN imputation. In conclusion, certain data preprocessing techniques are examined comparatively and the effect of these data preprocessing techniques on success is presented and the importance of the right combination of data preprocessing to achieve success has been demonstrated by experimental studies.

Summary This article aimed to automatically determine the power density and application time values of the coagulation state obtained as a result of this application according to the relevant texture properties. For this purpose, these target values of the laser system were estimated by using some properties obtained from the examined tissue. Particle swarm optimization based artificial neural networks hybrid algorithm was used for this pre‐determination process. As a result of the applications, test accuracy rates of mean squared error percentages were obtained as 99.88% and 98.47% for the power density and application time targets, respectively. Furthermore, the correlation coefficients between actual and predicted data for both targets were calculated as 0.99. According to the literature search, as a result of giving some laser measurements for coagulation state as input to the proposed system or the other artificial intelligence algorithms, any study has not been encountered in which the power density and application time of the laser system are detected automatically in advance. When the research is evaluated from this novel perspective, it is thought that it will contribute to the literature and present ideas with different innovations to other researchers.

Summary Autism spectrum disorder (ASD) is a neurodevelopmental disorder caused by several nervous system problems that change the function of the brain. It is either congenital or occurs in the early years of life. Although the cause of ASD is not known exactly, it is generally thought to be genetic. Today, the relationship of ASD with other diseases is under investigation. One of these diseases is jaundice. It has been shown in different medical studies that the probability of children, who were jaundiced in the newborn period and/or had a family history of autism, having this disorder is higher than the others. However, studies in this area using artificial intelligence techniques are limited. For this reason, in this study, the relationship between ASD with jaundice and/or family history of autism in children was emphasized by using current machine learning techniques and analyses. Datasets were established by digitizing verbal data obtained from children between the ages of 4 and 11: (1) subjects with a family history of autism, (2) subjects with a history of jaundice, (3) subjects with both history of jaundice and family autism, and (4) subjects with no conditions. Since the verbal datasets created by the answers from the children (with or without ASD) or the people around them were converted into numerical form with 0–1 coding, mathematical operations could be performed using these datasets. The four subgroups of data mentioned above were given to the sequential minimal optimization‐support vector machine hybrid classifier after they were separated into training and test data via the stratified cross‐validation method. The results were analyzed with various statistical parameters. In addition, the sequential forward floating selection algorithm was used to determine which features were not effective for ASD detection. Obtained results from all datasets can provide a new perspective for the literature. As a result, it was determined with a 100% success rate for dataset1 and dataset3. In addition, the ASD detection rate was calculated at 95.52% in children with a history of jaundice. Finally, considerable and meaningful interpretations were made about which features according to the history of jaundice and family autism for each dataset are more effective in ASD detection in children.