Nursing care during dialysis involves managing symptoms and preventing complications among patients undergoing hemodialysis or peritoneal dialysis. In this regard, to improve the quality of nursing care during dialysis, several approaches were developed to enhance hemodialysis adequacy and prevent complications,however, Machine learning (ML) emerged as a methodological approach for eval-uating hemodialysis adequacy and complications. The current study aims to analyze ML approach in predicting and managing hemo-dialysis by R programming language analysis to provide a therapeutic concept for hemodialysis management in critical nursing care. An R programming language was used to perform the logical analysis of the data. ML algorithms based on usage rate included logistic regression (LR), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Random Forest (RF), Complement Naive Bayes (CNB), Takagi-Sugeno-Kang fuzzy system (G-TSK-FS), k-nearest neighbors' classifier (KNN), Stochastic gradient descent (SGD), Linear Discriminant Analysis (LDA), and Multi-adaptive neural-fuzzy system (MANFIS). Also, the use of ML in nursing care during hemodialysis is categorized into three indications for predicting hemodialysis adequacy, complications, and vascular access performance. Using ML in hemodialysis nursing care is a growing research interest. The main application areas are the prediction of hemodialysis adequacy, complications, and vascular access performance. LR and SVM are practical ML algorithms for constructing AI tools to improve hemodialysis management.

Distributed Denial of Service (DDoS) attacks are among the primary concerns in internet security today. Machine learning can be exploited to detect such attacks. In this paper, a multi-layer perceptron model is proposed and implemented using deep Machine learning to distinguish between malicious and normal traffic based on their behavioral patterns. The proposed model is trained and tested using the CICDDoS2019 dataset. To remove irrelevant and redundant data from the dataset and increase learning accuracy, feature selection is used to select and extract the most effective features that allow us to detect these attacks. Moreover, we use the grid search algorithm to acquire optimum values of the model’s hyperparameters among the parameters’ space. In addition, the sensitivity of accuracy of the model to variations of an input parameter is analyzed. Finally, the effectiveness of the presented model is validated in comparison with some state-of-the-art works.

In this study, the groundwater level of the Kabodarahang aquifer located in Hamadan Province, Iran, is simulated using MODFLOW, Extreme learning Machine (ELM), and Wavelet-Extreme learning Machine (WA-ELM) Models. The correlation coefficient and scatter index values for the MODFLOW model are calculated 0. 917 and 0. 0004, respectively. Then, by different input combination and using the stepwise selection, 10 different models are introduced for the ELM and WA-ELM models with different lags. By evaluating all activation functions of the ELM model, the sigmoid activation function predicts groundwater level values with more accuracy. Also, Daubechies2 is selected as the mother wavelet of the WA-ELM models. According to different numerical models results, the WA-ELM model is selected as the superior model in prediction of groundwater level. For the superior model, the correlation coefficient and Nash-Sutcliffe efficiency coefficient are calculated 0. 959 and 0. 915, respectively. These values for ELM model was respectively computed as 0. 828 and 0. 672.

As an artificial intelligence (AI) branch, Machine learning has pioneering applications in public health, ranging from disease diagnosis to epidemic prediction. Machine learning (ML) is a strategic lever to improve care services’ access, quality, and efficiency and create health systems based on learning and value. In the following, we mention only a part of ML assistance in public health.