This research on quantum Machine learning (QML) represents the culmination of a thorough investigation spanning four months and drawing upon insights from more than 20 reputable scholarly articles. Quantum Machine learning stands at the convergence of quantum computing and Machine learning, offering a unique avenue to revolutionize traditional algorithms by leveraging principles from quantum mechanics. This abstract delves into the foundational aspects of QML, elucidating key components such as qubits, quantum gates, superposition, and entanglement. Additionally, it explores various QML algorithms, including quantum neural networks, quantum support vector Machines, and quantum clustering, which exploit quantum properties to tackle intricate computational challenges. The abstract further discusses the wide spectrum of applications where QML holds promise, ranging from quantum chemistry and optimization problems to cryptography and big data analysis. Despite the considerable potential, QML confronts obstacles such as scalability issues, noise, and error correction. To surmount these hurdles and unlock the full potential of quantum Machine learning, sustained research efforts and collaborative endeavours are imperative, poised to drive transformative advancements across diverse industries.

Each of the various uses of water, such as agriculture, drinking, industry, etc., require water with a specific quality that is characterized by repeated sampling, testing, and analysis of the results. However, the cost of sampling surface water, measuring quality parameters in the laboratory environment, human errors are the most important problems in estimating the concentration of water qualitative parameters. For this purpose, there are several methods for modeling the water quality parameters. In this regard, the data mining methods have been considered by the researchers in recent decades. Therefore, in this research, the main purpose is to estimating and modeling water quality parameters using modern data mining methods and improve the performance of data mining methods with the aim of wavelet theory and compare them with other commonly used data mining methods. In other words, extreme learning Machines (ELM) and multi-layer perceptron (MLP) method will be used to model water quality parameters. The evaluation of these two models was performed by statistical criteria Correlation Coefficient (R), root mean square error (RMSE) and mean absolute error (MAE) and relative standard error (RSE) for statistical data of 20 years. According to the results, it was found that the ELM method has been able to averagely provide a correlation coefficient of 0. 97. Although both models yielded acceptable results, the results showed that the ELM model has higher accuracy than the MLP model for prediction of water quality parameters.

Objective Supply chain management is a modern organizational management mode that organizes and plans information, capital flow, and business partnerships in the supply chain and requires complete business and market information (Quinn et al., 2012). However, the cost of acquiring supply chain companies and product information by traditional methods is very high. Information technology provides the power for companies to implement supply chain management and share the supply chain easily, and all companies in the supply chain can create value through information management (Shawaki et al., 2023). The utilization of intelligent approaches to predict prices and demand quantities enhances supplier delivery performance. It also refines demand forecasting accuracy, improves factory planning precision, forecasts demand for new products, and minimizes supplier risks, transportation costs, inventory, operational expenses, and time (Tirklai et al., 2021). In supply chain management, accurate forecasting of demand reflects the price. It is a critical issue that can reduce inventory costs and achieve the desired service level (Zouqaq et al., 2020). Intelligent supply chain pricing approaches can help supply chain companies to adapt the quality of their product offerings in supply chain management according to the knowledge gained (Kotsiopoulos et al., 2021). Identifying and modeling steel market fluctuations is very important in the steel industry and supply chain management. Considering the vertical chain in this industry and the interaction between the players of this industry, game theory has been used to model the optimal price. Neural network models were employed to replicate the game, as interaction and repeated gameplay are required for achieving balance among players. Taking into account Iran's unique circumstances, notably its confrontations with substantial sanctions in the metal industry, the sanctions variable was integrated as an adjusting factor in the pricing model for this sector. Methods This is a practical study. The research time frame for predicting steel prices and calculating the sanctions index spans from 2011 to 2020, with quarterly data. The MATLAB software was used. Results Three Bayesian neural networks, support vectors, and Grassberg's anti-diffusion were used to predict the price of steel. The results showed that the Grossberg anti-diffusion model is more accurate in predicting steel prices. Next, the predicted price entered the game theory process and the Nash equilibrium point of the model was determined. According to the country's specific conditions, the sanctions variable was introduced in the game theory model. The results showed that the inclusion of sanctions in the model led to price increases and production reductions within the steel industry. The present study delved into price fluctuations resulting from shifts in supply and demand, particularly in the context of sanctions. The findings reveal that a reduction in supply coupled with escalated sanctions led to substantial price hikes, surpassing the impact of supply changes. Consequently, steel exhibits a heightened susceptibility to input constraints, where any disruption in its supply chain triggers significant price spikes, thus unsettling the market. This amplifies the sensitivity of supply chain management for steel. Consequently, a systemic and dynamic approach is essential for market regulation policies, raw material supply, transportation strategies, and warehousing considerations. It should be noted that the use of intelligent approaches and Machine learning can play a significant role in coordinating such issues. Conclusion Considering that Stackelberg's approach was used in the current research, the sequence of players' entry into the game holds significance with respect to the Nash equilibrium. The development of market entry monitoring rules and regulations in this industry should be investigated because the steel industry is one of the industries that face high entry and exit costs. As a result, Policymakers and industry managers should monitor the entry and exit of players within this sector. They should endeavor to establish norms and regulations governing interactions among market participants to foster a structured and well-defined competitive environment.

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.

One of the factors that can be the link between our intentions and actions and their external consequences is human agency, which indicates the conscious design and intentional execution of actions by the individual in order to influence future events.Objective and Method: This research with a developmental approach of psychometric method and method 1, examines the psychometric indices of the Human Factor Characteristics Scale using the classical theory of test score measurement and the graduated question-answer theory. The purpose of this study, which included high school students in Tehran, was selected by cluster sampling of 500 people as a sample size and statistical analysis was performed on 481 data. To collect the data, the ion Human Agent Characteristics Scale (2011) was used and the research questions were evaluated using IRTPRO and SPSS software.Results:The assumption of local independence based on Pearson x2 index was established by applying Simjima's calibrated question-answer theory and the assumption of being one-dimensional based on the analysis of multidimensional question-answer theory. Diagnosis parameters with question-answer approach and classical approach Test score Both item 25 approach had the lowest and item 2 had the highest diagnosis parameter. The answer thresholds for all the questions were so far apart that no option was covered by the other option, and the options were independently selected by individuals at intervals of theta. The total scale was calculated with Cronbach's alpha of 0.945, intentionality of 0.894, foresight of 0.780, self-reactivity of 0.871 and rethinking of 0.762. Also, the role of each item in internal consistency was investigated by the loop method, which all questions had a favorable role in internal consistency of this scale. The value of the validity coefficient obtained from the question-answer theory was obtained by marginal method for intentionality 0.92, forethought 0.85, self-reaction 0.91, rethinking 0.83..

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.