Decision tree

Empirical s tudies in the field of the design process s tarted in the 60s. Protocol analysis is among the empirical research methods that have been developed simultaneously with the growth of empirical s tudies. Concurrent with the use of protocol analysis for researching in the field of the design process, analysis methods have been presented by some researchers, which can be used with the protocol analysis method in order to analyze the s tructure of the design process. Among these analysis methods, problem behavior graph, decision tree, linkography, and extended linkography could be mentioned. The problem behavior graph is based on problem-solving theories. In the decision tree method, the extracted data from protocol analysis is used for the perception of decision-making processes. Linkography is another method for analyzing the s tructure of the design process. In this method, the design process of a designer is unfolding by drawing a graph, which is called linkograph. This paper considers making a s tudy and comparison of these different analysis methods by the use of sys tematic review. By comparison of diverse analysis methods, two approaches could be recognized, formal and informal ones. In the formal approach, the design is mentioned as a logical research process of solving the design problem. The second approach is informal. In this one, the design process is mentioned as a reflective conversation with the situation. In this approach, which is based on Donald Schon’s theories, the design process is referred as an argumentative process.

Todays, Intrusion Detection Systems (IDS) are considered as key components of security networks. However, high false positive and false negative rates are the important problems of these systems. On the other hand, many of the existing solutions in the articles are restricted to class datasets due to the use of a specific technique, but in real applications they may have multi-variant datasets. With the impetus of the facts, this paper presents a new anomaly based intrusion detection system using J48 Decision Tree, Support Vector Classifier (SVC) and k-means clustering algorithm in order to reduce false alarm rates and enhance the system performance. J48 decision tree algorithm is used to select the best features and optimize the dataset. Also, an SVM classifier and a modified k-means clustering algorithm are used to build a profile of normal and anomalous behaviors of dataset. Simulation results on benchmark NSL-KDD, CICIDS2017 and synthetic datasets confirm that the proposed method has significant performance in comparison with previous approaches.

Urban retail markets are state-owned retail markets that were recently established in Iran to increase the welfare of consumers and producers. To achieve this goal and expand its presence in the Iranian retail sector, it is essential to gain a comprehensive understanding of consumer behavior within these markets. This study examines the various socio-economic factors influencing consumers' decisions in the retail market by using the C4.5 algorithm. The data were collected using a random sampling method through a survey of 189 consumers, focusing on the population of Mashhad, Iran, during 2019-2020. Results revealed that awareness of available discounts significantly drives consumer choices in urban retail markets. Despite existing discounts, awareness among consumers remains low, suggesting a need to review promotional strategies within the marketing mix. The study also identifies previous purchases from urban markets, household income, and education as influential factors. Findings offer valuable insights for policymakers, market strategists, and stakeholders seeking to enhance the effectiveness of local retail markets in Iran. By leveraging insights into consumer behavior and market dynamics, these markets can thrive, benefiting Iran's retail sector and overall economy. Following the study, recommendations such as enhanced promotional campaigns, education-oriented strategies, loyalty programs, collaborations with local producers, and inclusive marketing policies was made aim to improve access for all consumers to urban retail markets.

Record linkage is vital for consolidating data from different sources, particularly in Persian records where diverse data structures and formats present challenges. To tackle these complexities, an expert system with decision tree algorithms is crucial for ensuring precise record linkage and data aggregation. Adaptation operations are created based on predefined rules by incorporating decision trees into an expert system framework, simplifying the aggregation of disparate data sources. This method surpasses traditional approaches like IF-THEN rules in effectiveness and ease of use and improves accessibility for non-technical users due to its intuitive nature. Integrating probabilistic record linkage results into the decision tree model within the expert system automates the linkage process, allowing users to customize string metrics and thresholds for optimal outcomes. The model’s accuracy rate of over 95% on test data highlights its effectiveness in predicting and adjusting to data variations, confirming its reliability in various record linkage scenarios. The innovative utilization of machine learning decision trees alongside probabilistic record linkage in an expert system represents a significant advancement in the field, providing a robust solution for data aggregation in intricate environments and large-scale projects involving Persian records. Combining decision tree algorithms and probabilistic record linkage within an expert system offers a powerful tool for handling complex data integration tasks. This approach not only streamlines the process of consolidating diverse data sources but also enhances the accuracy and efficiency of record linkage operations By leveraging machine learning techniques and automated decision-making processes, organizations can achieve significant improvements in data quality and consistency, paving the way for more reliable and insightful analytical results in implementing statistical registers. In conclusion, integrating decision trees and probabilistic record linkage in an expert system represents a cutting-edge solution for addressing data aggregation challenges in Persian records and beyond.

This study compares three machine learning algorithms (Multilayer Perceptron Neural Network (MLP), Random Forest (RF), and Decision Tree (DT)) for modeling biodiesel production. For this purpose the synthesis methods (UIMS, MS, FPUI, PUI), the methanol to oil ratio (3:1 to 15:1) and reaction times (5–50 minutes), were considered as input parameters and the percentage of biodiesel production was considered as the output of the model. According to the results, the MLP model demonstrated superior predictive performance, with an R² score of 0.9800, RMSE of 3.28, and MAE of 2.35, significantly outperforming RF (R² = 0.8892) and DT (R² = 0.8500). Also, the neural network model represents that all parameters (reaction time, methanol to oil ratio, and synthesis method) hold nearly equal importance. Based on the neural network model, the optimal synthesis conditions are: the UIMS method, a reaction time of 47 minutes, and a methanol-to-oil ratio of 5.8:1, yielding a predicted conversion of 98%.