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Class noise is a common issue that affects the performance of classification techniques on real-world data sets. Class noise appears when a class variable in data sets has incorrect class labels. In the case of noisy data, the robustness of classification techniques against noise could be more important than the performance results on noise-free data sets. The decision tree method is one of the most popular techniques for classification tasks. The C4.5, CART, and random forest (RF) algorithms are considered to be three of the most used algorithms in decision trees. The aim of this paper is to reach conclusions on which decision tree algorithm is better to use for building decision trees in terms of its performance and robustness against class noise. In order to achieve this aim, we study and compare the performance of the models when applied to class variables with noise. The results obtained indicate that the RF algorithm is more robust to data sets with noisy class variable than other algorithms. [ABSTRACT FROM AUTHOR]

Renewable energy has become popular compared with traditional energy like coal. The relative demand for renewable energy compared to traditional energy is an important index to determine the energy supply structure. Forecasting the relative demand index has become quite essential. Data mining methods like decision trees are quite effective in such time series forecasting, but theory behind them is rarely discussed in research. In this paper, some theories are explored about decision trees including the behavior of bias, variance, and squared prediction error using trees and the prediction interval analysis. After that, real UK grid data are used in interval forecasting application. In the renewable energy ratio forecasting application, the ratio of renewable energy supply over that of traditional energy can be dynamically forecasted with an interval coverage accuracy higher than 80% and a small width around 22, which is similar to its standard deviation. [ABSTRACT FROM AUTHOR]

This research created hot-pressed composites of the AA6063 matrix with varying concentrations of ZrO2 (0.25, 0.5, and 1 wt %). At sliding speeds of 80, 120, and 150 mm/s, the wear performance of the specimen was studied at loads of 10 N, 15 N, 20 N, and 25 N. The authors analyzed the counter-face material, the wear debris, and the worn surfaces to learn about the wear mechanisms. Developing these three machine learning (ML) algorithms was to evaluate the ability to predict wear behavior using the same small dataset collected using varying test processes. A thorough examination of each model hyperparameter tuning phase was performed. The predictive performance was analyzed using several statistical tools. The most effective decision-making algorithms for this data collection were those based on trees. Predictions made by the decision tree algorithm for the test and validation measurements have an accuracy of 86% and 99.7%, respectively. The best model was picked out based on the results of the predictions. [ABSTRACT FROM AUTHOR]

The main purpose of the present study is to investigate the possible application of decision tree in landslide susceptibility assessment. The study area having a surface area of 174.8 km² locates at the northern coast of the Sea of Marmara and western part of Istanbul metropolitan area. When applying data mining and extracting decision tree, geological formations, altitude, slope, plan curvature, profile curvature, heat load and stream power index parameters are taken into consideration as landslide conditioning factors. Using the predicted values, the landslide susceptibility map of the study area is produced. The AUC value of the produced landslide susceptibility map has been obtained as 89.6%. According to the results of the AUC evaluation, the produced map has exhibited a good enough performance. [ABSTRACT FROM AUTHOR]

Nowadays, more and more college students' physical health is getting worse because of their living habits and self-consciousness. In order to improve the physical quality of college students as much as possible, the experiment uses the improved iterative dichotomiser III (ID3) decision tree to make decisions on the physical condition of some college students and the corresponding sports mode recommendation, and compares the results with the traditional ID3 algorithm. In the experimental results, the information entropy ratio of the improved ID3 algorithm is 89.5%, the operation information loss rate is 4.136%, and the accuracy of motion mode decision is 92.58%. The average relative time is 12.7, and the accuracy of physical health decision making is 90.02%. The above two values are not significant compared with the traditional ID3 algorithm. The experimental results show that the improved ID3 algorithm has significant optimization in the stability of information transmission and the accuracy of sports recommendation decision making, and can be applied to the physical health evaluation and sports recommendation of college students in a certain range. [ABSTRACT FROM AUTHOR]

China's economy has become more globalized in the twenty-first century. English has become the primary means of communication and trade between people and other countries as the economy has rapidly developed. As a result, colleges and universities have increased the English proficiency and teaching quality criteria for college students. The success of college instructors' English instruction is judged using teaching quality as a primary metric. The procedure of assessing the quality of English instruction is complicated. Using appropriate assessment indexes and procedures, a scientific and sensible evaluation system for English education should be developed. This study uses the AHP and fuzzy decision tree algorithms to investigate the quality of teaching (preferably English) and gives teaching evaluation data to university administrators, which is useful to enhance the quality of teaching (preferably English) in colleges. First, a hierarchical analysis-based evaluation index system (EIS) for English teaching is established, followed by a detailed description of the decision tree algorithm's calculation process and the application of the fuzzy decision tree algorithm to the evaluation of teaching (preferably English) quality. Finally, the practical application effect of the algorithm is tested. The results show that the accuracy of this algorithm is higher than other algorithms, and it is helpful to improve the efficiency of evaluating English teaching quality. [ABSTRACT FROM AUTHOR]

The innovative development of an enterprise is a necessary factor for its long-term development, and science and technology is a necessary driving force for its development. An effective management of human resources management will find more high-quality talent, and it will also be efficient management of the enterprise staff. The efficiency of the traditional human resource management is relatively low; the approach will produce larger errors for the enterprise. This study combines the Hadoop distributed platform and big data technology to conduct a decision recommendation research on the relevant factors of human resource management. In this study, the decision tree is used to classify the data related to human resource management. The data feature of human resource will be predicted by the neural network method, and human resource managers will refer to the prediction data from the output layer of neural network. Human resource managers will make corresponding decisions and recommendations. The research results show that Hadoop technology and big data technology have better accuracy in human resource management recommendation system, which contains ConvLSTM method and decision tree method in this study. The maximum prediction error of the ConvLSTM algorithm in predicting the relevant factors of the human resource decision-making system is only 2.67%, and this part of the error comes from the employee-job matching index. The smallest forecast is only 1.98%. [ABSTRACT FROM AUTHOR]

In the era of the rise of big data, if insurance companies can effectively and reasonably use existing data to tap more potential information, it can not only improve work efficiency but also achieve precise marketing to customers, thereby saving costs. At the same time, China pays more and more attention to financial insurance, so it is of great significance to use customer information to explore the purchase behavior of financial insurance. This paper mainly analyzes the factors that affect the purchase of financial insurance from the aspects of individuals and families and provides references for insurance marketing. This paper selects the results of the China Comprehensive Social Survey in 2020 as the empirical sample data of this paper and sets whether to purchase financial insurance as the target variable. The characteristic variables were screened, and the 10 variables selected after screening were finally used to build the model. Next, the data are divided into training set and test set, and a decision tree learning model is established on the two data sets at the same time. The classification results of the model are evaluated according to the classification evaluation indicators. Finally, the decision tree analysis results provide suggestions and strategies for the construction of insurance marketing recommendation methods. [ABSTRACT FROM AUTHOR]

In this modern era, each and everything is computerized, and everyone has their own smart gadgets to communicate with others around the globe without any range limitations. Most of the communication pathways belong to smart applications, call options in smartphones, and other multiple ways, but e-mail communication is considered the main professional communication pathway, which allows business people as well as commercial and noncommercial organizations to communicate with one another or globally share some important official documents and reports. This global pathway attracts many attackers and intruders to do a scam with such innovations; in particular, the intruders generate false messages with some attractive contents and post them as e-mails to global users. This kind of unnecessary and not needed advertisement or threatening mails is considered as spam mails, which usually contain advertisements, promotions of a concern or institution, and so on. These mails are also considered or called junk mails, which will be reflected as the same category. In general, e-mails are the usual way of message delivery for business oriented as well as any official needs, but in some cases there is a necessity of transferring some voice instructions or messages to the destination via the same e-mail pathway. These kinds of voice-oriented e-mail accessing are called voice mails. The voice mail is generally composed to deliver the speech aspect instructions or information to the receiver to do some particular tasks or convey some important messages to the receiver. A voice-mail-enabled system allows users to communicate with one another based on speech input which the sender can communicate to the receiver via voice conversations, which is used to deliver voice information to the recipient. These kinds of mails are usually generated using personal computers or laptops and exchanged via general e-mail pathway, or separate paid and nonpaid mail gateways are available to deal with certain mail transactions. The above-mentioned e-mail spam is considered in many past researches and attains some solutions, but in case of voice-based e-mail aspect, there will be no options to manage such kind of security parameters. In this paper, a hybrid data processing mechanism is handled with respect to both text-enabled and voice-enabled e-mails, which is called Genetic Decision Tree Processing with Natural Language Processing (GDTPNLP). This proposed approach provides a way of identifying the e-mail spam in both textual e-mails and speech-enabled e-mails. The proposed approach of GDTPNLP provides higher spam detection rate in terms of text extraction speed, performance, cost efficiency, and accuracy. These all will be explained in detail with graphical output views in the Results and Discussion. [ABSTRACT FROM AUTHOR]

This study aimed to establish a method for fast and accurate determination of body constitution types from the body constitution questionnaire (BCQ) by employing a decision tree model. The model was trained for 4 classes, namely, Yin-Xu, Yang-Xu, Phlegm and Blood Stasis, and Normal, and it achieved 67% accuracy for the testing dataset. The model also reduced the required number of BCQ questions from 44 to 3–6, depending on the responses. Lastly, we developed the Traditional Chinese Medicine (TCM) body constitution online diagnosis system using our model to collect data digitally and use it more practically and efficiently. This system can assist doctors to improve the diagnosis and treatment in TCM practice. [ABSTRACT FROM AUTHOR]

Improving the employment rate and quality of women's employment has been a hot spot of social concern in recent years. Traditional female employment information has not been fully utilized; only simple storage and query functions have been completed. In order to mine and use female employment information, we find the rules that exist in it, and to better understand the current female employment problems, this article focuses on the data mining of female employment information data. This paper introduces in detail the current employment trends of women and the advantages of decision tree algorithms, the hot content and key technical points of data mining technology, the research progress of this technology at home and abroad, etc. Decision tree analysis algorithm for association rules: According to the formula calculus, the advantages of the decision tree algorithm and the association rule analysis method are displayed more clearly. The model of female employment information is mainly constructed, and the process of data collection and data model establishment is introduced. According to a set of female employment information data, the algorithm is analyzed through the decision tree analysis algorithm based on association rules, and the reliability of the algorithm is verified. Finally, the whole article is summarized, and its basic contents are explained. The decision tree analysis method based on association rules can effectively dig out the information hidden in the female employment information data, and it has an important role in guiding the self planning of college women. This algorithm has the characteristics of high operating efficiency and stable results, and it also has a positive effect on other types of data mining. [ABSTRACT FROM AUTHOR]

When traditional methods analyze the audit data of enterprise financing alliance, there are some problems, such as long algorithm modeling time and low accuracy of interest distribution algorithm of enterprise financing alliance. Therefore, this paper proposes an analysis method of interest distribution of enterprise audit data financing alliance based on the decision tree algorithm. The audit data collection process of enterprise financing alliance is given, and the continuous attributes of audit data are discretized by the C4.5 algorithm. We perform enterprise financing alliance audit data analysis, remove inconsistencies from audit data through data cleaning, and finally realize enterprise financing alliance audit data analysis based on the improved C4.5 algorithm. The experimental results show that this method can shorten the modeling time and improve the accuracy of interest distribution algorithm of enterprise financing alliance. We achieved an average accuracy of 84.7% with the C4.5 algorithm while 84.35% with NBTree. [ABSTRACT FROM AUTHOR]

To investigate and compare the lane changing behavior of passenger cars and heavy vehicles during the implementation period (defined as the interval from the start time to the end time of a lane change maneuver), this study applies the gradient boosting decision tree (GBDT) method to model the lane changing behavior of heavy vehicles and passenger cars, respectively. Results show that the lane change models vary with the vehicle types and lane change directions. Different factors are considered by the drivers of passenger cars and heavy vehicles when implementing lane changes to different directions. Partial dependence plots of GBDT models reveal that the influence of independent variables on lane changing behavior is nonlinear and complicated, which means that the same variable leads to various effects on the lane change decision across different vehicle types and lane change directions. In contrast with other state-of-the-art methods, the proposed method can obtain more accurate results. The findings indicate that it is necessary to build specific lane change models based on vehicle types and lane change directions for microscopic traffic simulators and autonomous vehicles. [ABSTRACT FROM AUTHOR]

Aims: To determine the main driving factors affecting the senior nursing students in their decision to migrate and to evaluate the effect of attitude towards migration in career planning. Background: In order to promote the nursing workforce, it is important to understand the factors affecting the decisions to migrate. Method: A cross‐sectional study was conducted with 1,410 Turkish nursing students. The data, which were collected using the Descriptive Form and Attitude Scale for Brain Drain (BD‐s), were analysed with the multiple regression and decision tree analysis. Results: The mean score of attitudes towards migration was 56.30 ± 12.09 (min 16‐ max 80). The main push–pull drivers to migrate were the socio‐political factors and working conditions. The BD‐s score was higher in the participants, who had overseas experience, had career plans and studied in a metropolitan city. The strongest variable predicting career planning was the attitude towards migration. Conclusion: The opinions of the students on career planning and their intentions to migrate indicated that the shortage of nurses would continue in Turkey in the future. Implications for Nursing Management: The prospects of the nursing candidates regarding the nursing profession should be addressed considering their expectations for initial salaries, career development and salary increases. [ABSTRACT FROM AUTHOR]

This study is to present acceptable measurement values through decision tree analysis for the seepage, which is an important measuring item of the fill dam. The seepage of the dam under study increases rapidly when rainfall occurs, when the direct inflow of rainfall from the downstream slope and both sides occur. Therefore, the allowable seepage of fill dam considering rainfall and water level is required. Decision tree analysis was conducted for one domestic fill dam by setting the seepage as a response variable and setting rainfall and water level as explanatory variables. At this time, in order to analyze the effects of rainfall on the seepage more closely, the data subject was classified into two groups consisting of a rainfall-free group and a rainfall-occurring group. Group A, which is a rainfall-free group, had 97.7% of the seepage data distributed under the conditions of 98.50 mm/day of the antecedent 5-day rainfall selected as the first explanatory variable. The average seepage of the group was between 12.01 L/min and 26.35 L/min. Group B, which is a rainfall-occurring group, had 85.7% of the water leakage data distributed under conditions of 38.50 mm/day of daily rainfall selected as the first explanatory variable, with an average of 23.70 L/min. [ABSTRACT FROM AUTHOR]

In recent years, transportation, medical care, energy, and other fields have relied on a number of smart technologies such as the Internet of Things and cloud computing, and the degree of intelligence has continued to increase. As a key part of the future development of cities, education is facing tremendous pressure and challenges. How to use all kinds of smart technologies to promote the informatization of education and create a new era of smarter education is an important mission of current smart education. Decision tree classification is a frequently used technique in data mining. It has the characteristics of fast classification and high efficiency. It is not only used to analyze data but also to make predictions. Professional art education occupies an important position in college education. How to combine decision tree algorithms to develop targeted and effective professional art education and to strengthen the teaching reform and research method innovation of art design has become the current teaching work of colleges and universities. The purpose of this paper is to optimize the teaching mode of art courses in colleges and universities, improve the teaching effect of art classes, and realize the teaching goal of art courses. In this paper, art curriculum teaching experience and the reality of teaching research, through analyzing three local questionnaire surveys of college students, based on the principle of students' subjectivity from the topic content, organization form, evaluation mode of college art teaching in our student autonomy, initiative, and the role of creative mechanisms are discussed. According to the practical teaching activities of art courses conducted in this paper, the satisfaction degree of college students with art courses is up to 92%, which indicates that this experiment is still relatively successful and significant, and arouses students' subjectivity to some extent. [ABSTRACT FROM AUTHOR]

After the reform and the opening, the economy of our country has developed rapidly, and the living conditions of the people have become better and better. As a result, they have a lot of time to pay attention to their health, which has promoted the rapid development of the sports and fitness industry in my country. In response to the increasing development of the sports and fitness sector of my country, the current state of the administration of members of the sports fitness industry does not keep pace with the development of the sports and fitness industry of my country. Based on this, this article uses a fuzzy decision tree algorithm to establish a decision tree based on the characteristics of customer data and loses existing customers. Analyzing the situation is of strategic significance for improving the competitiveness of the club. This article selects the 7 most commonly used data sets from the UCI data set as the initial experimental data for model training in three different formats and then uses the data of a specific club member to conduct experiments, using these data files as training samples to construct a vague analysis of the decision tree to overturn the customer to analyze the main factors of customer change. Experiments show that the fuzzy decision tree ID3 algorithm based on mobile computing has the highest accuracy in the Iris data set, reaching 97.8%, and the accuracy rate in the Wine data set is the smallest, only 65.2%. The mobile computing-based fuzzy decision tree ID3 algorithm proposed in this paper obtained the highest correct rate (86.32%). This shows that, compared to traditional analysis methods, the blurred decision tree obtained for churn client analysis has the advantages of high classification accuracy and is understandable so that ideal classification accuracy can be achieved when the tree is small. [ABSTRACT FROM AUTHOR]

In order to explore the application analysis of the decision tree algorithm in enterprise HRM effectiveness evaluation, we discuss data mining, decision tree algorithm, and decision tree model construction. In view of the continuous growth of big data information in the information age, we use the mean clustering algorithm, expert system algorithm, and decision tree algorithm to verify the accuracy and time efficiency of enterprise HRM effectiveness. Finally, it is considered that the application of the decision tree algorithm of data mining not only is in line with the enterprise HRM efficiency evaluation but also can promote the integration and effective allocation of human resources and transform the generated decision tree algorithm into classification rules, which has been widely studied and applied, and finally has important strategic significance for the development of enterprises. [ABSTRACT FROM AUTHOR]

RFID can automatically read the information stored on the tag. When multiple tags send data to the reader and writer at the same time, multiple tag collision will occur, resulting in the reader and writer being unable to successfully identify the tag. To solve this problem, an RFID multitag identification method based on decision tree is proposed. The Manchester code is used to represent different level states of voltage. The reader extracts the collision bits in the tag ID by using the Manchester code, and these collision bits form the new tag ID. In the process of decision tree search, the "XOR" command operation is added, and the query prefix is selectively sent to the tag to mark all collision bits. The process will continue until one tag is identified; then, reset the remaining unrecognized tags and continue to participate in a new round of identification. The simulation results show that the RFID multitag identification method based on decision tree can reduce the time complexity and communication overhead and improve the throughput, so it has high search efficiency. [ABSTRACT FROM AUTHOR]

Summary: Different mass timber structures require a specific range of wood moisture. Thus, moisture monitoring is a crucial quality control task during timber drying that impacts the properties and quality of the subsequent timber structures. Variations in the average and distribution of the final moisture can result in significant variations in the timber properties and thus negatively affect performance in service. This necessitates rigorous moisture monitoring and timber grading during and after kiln drying. This study aims to estimate the kiln‐dried population final moisture distribution based on green timber characteristics and machine learning. This will facilitate the timber grading process by reducing the variation in the mechanical properties of the kiln‐dried timber, as timbers with a higher chance of moisture variation will be identified before the drying process. Timber initial moisture, initial weight, basic density, and target moisture were used to train machine learning models. Linear discriminant analysis and decision tree were used for moisture classification. Principal component and variable clustering analysis were performed to study the critical parameters affecting the timber overdrying and underdrying. The results indicated that initial moisture level and weight are the essential variables, while density had the least significant effect on the performance of classification models. The decision tree approach exhibited better performance than discriminant analysis with ~91% classification accuracy proving the effectiveness of using initial timber properties for quality control and grading of kiln‐dried timber. [ABSTRACT FROM AUTHOR]

With the continuous development of China's cultural industry, people's health has become one of the topics of the highest concern. Therefore, all the application models of physical health test data in the actual analysis have become the current research focus and trend direction of healthy constitution. This paper summarizes the significant problems in the analysis of physical health test data, through the comprehensive analysis and investigation of physical health test data, combined with the measurement of the test indicators, through the analysis and processing system of youth physical health data, the use process of national youth group physical health standard data management software, and decision tree intelligent algorithm in physical health. The research steps of test data analysis and application model summarize the application characteristics of physical health test data in the application process. Based on this, a decision tree intelligent algorithm is proposed, and the corresponding functions and optimization formulas of the algorithm are substituted. In the process of actual sample checking calculation, each weight range and corresponding errors are inferred and analyzed by combining examples. This paper summarizes the application model and optimization model of health test data analysis based on decision tree intelligent algorithm. Through the repeated test of the research data, the feasible area and application scope of the algorithm are obtained, and the practical optimization scheme and application ideas under the algorithm are obtained. [ABSTRACT FROM AUTHOR]

Standard classification algorithms are often inaccurate when used in a wireless sensor network (WSN), where the observed data occur in imbalanced classes. The imbalanced data classification problem occurs when the number of samples in one class, usually the class of interest, is much lower than the number in the other classes. Many classification models have been studied in the data-mining research community. However, they all assume that the input data are stationary and bounded in size, so that resampling techniques and postadjustment by measuring the classification cost can be applied. In this paper, we devise a new scheme that extends a popular stream classification algorithm to the analysis of WSNs for reducing the adverse effects of the imbalanced class in the data. This new scheme is resource light at the algorithm level and does not require any data preprocessing. It uses weighted naïve Bayes predictors at the decision tree leaves to effectively reduce the impact of imbalanced classes. Experiments show that our modified algorithm outperforms the original stream classification algorithm. [ABSTRACT FROM AUTHOR]

The existing teaching evaluation system partially reflects the teaching effect and other related conditions through statistical reports, but it is difficult to find the useful knowledge hidden in the database, and it cannot effectively assist the decision-making support. In order to improve the evaluation effect of college classroom teaching mode, this paper mainly uses decision tree algorithm and data mining technology of association rules to construct the effectiveness evaluation system of college classroom teaching mode based on decision tree analysis. Moreover, this paper analyzes the teaching information and evaluation data to extract the potentially useful knowledge contained in it, which can help decision-makers find the rules and explore various factors that affect the teaching effect of teachers, thereby improving teaching management and optimizing resource allocation. In addition, this paper uses experimental teaching methods to verify the performance of the system model constructed in this paper. The research results show that the system constructed in this paper has certain reliability and practicability. [ABSTRACT FROM AUTHOR]

Although ranking (ordinal classification/regression) based on criteria is related closely to classification based on attributes, the development of methods for learning a ranking on the basis of data is lagging far behind that for learning a classification. Most of the work being done focuses on maintaining monotonicity (sometimes even only on the training set). We argue that in doing so, an essential aspect is mostly disregarded, namely, the importance of the role of the decision maker who decides about the acceptability of the generated rule base. Certainly, in ranking problems, there are more factors besides accuracy that play an important role. In this article, we turn to the field of multicriteria decision aid (MCDA) in order to cope with the aforementioned problems. We show that by a proper definition of the notion of partial dominance, it is possible to avoid the counter-intuitive outcomes of classification algorithms when applied to ranking problems. We focus on tree-based approaches and explain how the tree expansion can be guided by the principle of partial dominance preservation, and how the resulting rule base can be graphically represented and further refined. © 2003 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

With the rapid development of the Internet, various forms of network attack have emerged, so how to detect abnormal behavior effectively and to recognize their attack categories accurately have become an important research subject in the field of cyberspace security. Recently, many hot machine learning-based approaches are applied in the Intrusion Detection System (IDS) to construct a data-driven model. The methods are beneficial to reduce the time and cost of manual detection. However, the real-time network data contain an ocean of redundant terms and noises, and some existing intrusion detection technologies have lower accuracy and inadequate ability of feature extraction. In order to solve the above problems, this paper proposes an intrusion detection method based on the Decision Tree-Recursive Feature Elimination (DT-RFE) feature in ensemble learning. We firstly propose a data processing method by the Decision Tree-Based Recursive Elimination Algorithm to select features and to reduce the feature dimension. This method eliminates the redundant and uncorrelated data from the dataset to achieve better resource utilization and to reduce time complexity. In this paper, we use the Stacking ensemble learning algorithm by combining Decision Tree (DT) with Recursive Feature Elimination (RFE) methods. Finally, a series of comparison experiments by cross-validation on the KDD CUP 99 and NSL-KDD datasets indicate that the DT-RFE and Stacking-based approach can better improve the performance of the IDS, and the accuracy for all kinds of features is higher than 99%, except in the case of U2R accuracy, which is 98%. [ABSTRACT FROM AUTHOR]

With the advent of the Big Data era, information and data are growing in spurts, fueling the deep application of information technology in all levels of society. It is especially important to use data mining technology to study the industry trends behind the data and to explore the information value contained in the massive data. As teaching and learning in higher education continue to advance, student academic and administrative data are growing at a rapid pace. In this paper, we make full use of student academic data and campus behavior data to analyze the data inherent patterns and correlations and use these patterns rationally to provide guidance for teaching activities and teaching management, thus further improving the quality of teaching management. The establishment of a data-mining-technology-based college repetition warning system can help student management departments to strengthen supervision, provide timely warning information for college teaching management as well as leaders and counselors' decision-making, and thus provide early help to students with repetition warnings. In this paper, we use the global search advantage of genetic algorithm to build a GABP hybrid prediction model to solve the local minimum problem of BP neural network algorithm. The data validation results show that Recall reaches 95% and F1 result is about 86%, and the accuracy of the algorithm prediction results is improved significantly. It can provide a solid data support basis for college administrators to predict retention. Finally, the problems in the application of the retention prediction model are analyzed and corresponding suggestions are given. [ABSTRACT FROM AUTHOR]

Preeclampsia affects from 5% to 14% of all pregnant women and is responsible for about 14% of maternal deaths per year in the world. This paper is focused on the use of a decision analysis tool for the early detection of preeclampsia in women at risk. This tool applies a fuzzy linguistic approach implemented in a wearable device. In order to develop this tool, a real dataset containing data of pregnant women with high risk of preeclampsia from a health center has been analyzed, and a fuzzy linguistic methodology with two main phases is used. Firstly, linguistic transformation is applied to the dataset to increase the interpretability and flexibility in the analysis of preeclampsia. Secondly, knowledge extraction is done by means of inferring rules using decision trees to classify the dataset. The obtained linguistic rules provide understandable monitoring of preeclampsia based on wearable applications and devices. Furthermore, this paper not only introduces the proposedmethodology, but also presents a wearable application prototype which applies the rules inferred from the fuzzy decision tree to detect preeclampsia in women at risk. The proposed methodology and the developed wearable application can be easily adapted to other contexts such as diabetes or hypertension. [ABSTRACT FROM AUTHOR]

We wish to credit our own Research Integrity and Research Publishing teams and anonymous and named external researchers and research integrity experts for contributing to this investigation. This article has been retracted by Hindawi following an investigation undertaken by the publisher [[1]]. [Extracted from the article]

Integrated Deterministic-Probabilistic Safety Assessment (IDPSA) combines deterministic model of a nuclear power plant with a method for exploration of the uncertainty space. Huge amount of data is generated in the process of such exploration. It is very difficult to “manually” process and extract from such data information that can be used by a decision maker for risk-informed characterization, understanding, and eventually decision making on improvement of the system safety and performance. Such understanding requires an approach for interpretation, grouping of similar scenario evolutions, and classification of the principal characteristics of the events that contribute to the risk. In this work, we develop an approach for classification and characterization of failure domains. The method is based on scenario grouping, clustering, and application of decision trees for characterization of the influence of timing and order of events. We demonstrate how the proposed approach is used to classify scenarios that are amenable to treatment with Boolean logic in classical Probabilistic Safety Assessment (PSA) from those where timing and order of events determine process evolution and eventually violation of safety criteria. The efficiency of the approach has been verified with application to the SARNET benchmark exercise on the effectiveness of hydrogen management in the containment. [ABSTRACT FROM AUTHOR]

Rough set theory offers new perspective on Bayes' theorem. The look on Bayes' theorem offered by rough set theory reveals that any data set (decision table) satisfies the total probability theorem and Bayes' theorem. These properties can be used directly to draw conclusions from objective data without referring to subjective prior knowledge and its revision if new evidence is available. Thus, the rough set view on Bayes' theorem is rather objective in contrast to subjective “classical” interpretation of the theorem. © 2003 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Summary: Adverse situations such as prolonged downtime of a structure, unnecessary inspections, expensive allocation of personal and equipment, deficient structural performance, or failure can be avoided by using structural health monitoring (SHM). Enhanced structural safety is the leading reason for its implementation, but one of the remaining obstacles to fully implement SHM systems deals with justifying their economic benefit. At any point in time, the preference towards one particular action depends on factors such as the probability of the triggered events and their consequences. All the possible decisions and relevant information can be illustrated by decision tree models, and the optimal decision corresponds to the one with the highest utility. Applying the Bayesian Theorem, the assumed prior probabilities of the structural state are updated in the light of new information provided by a system and the optimal decision is revised. This paper proposes a dynamic decision‐making framework to manage civil engineering structures, where the ultimate goal is to achieve greater overall economy without jeopardizing safety. This paper covers a case study of a bridge where the optimal SHM and maintenance decisions are determined in the context of different scenarios in which the event probabilities and associated costs are made‐up. [ABSTRACT FROM AUTHOR]

Intrusion detection (ID) is critical in securing computer networks against various malicious attacks. Recent advancements in machine learning (ML), deep learning (DL), federated learning (FL), and explainable artificial intelligence (XAI) have drawn significant attention as potential approaches for ID. DL-based approaches have shown impressive performance in ID by automatically learning relevant features from data but require significant labelled data and computational resources to train complex models. ML-based approaches require fewer computational resources and labelled data, but their ability to generalize to unseen data is limited. FL is a relatively new approach that enables multiple entities to train a model collectively without exchanging their data, providing privacy and security benefits, making it an attractive option for ID. However, FL-based approaches require more communication resources and additional computation to aggregate models from different entities. XAI is critical for understanding how AI models make decisions, improving interpretability and transparency. While existing literature has explored the strengths and weaknesses of DL, ML, FL, and XAI-based approaches for ID, a significant gap exists in providing a comprehensive analysis of the specific use cases and scenarios where each approach is most suitable. This paper seeks to fill this void by delivering an in-depth review that not only highlights strengths and weaknesses but also offers guidance for selecting the appropriate approach based on the unique ID context and available resources. The selection of an appropriate approach depends on the specific use case, and this work provides insights into which method is best suited for various network sizes, data availability, privacy, and security concerns, thus aiding practitioners in making informed decisions for their ID needs. [ABSTRACT FROM AUTHOR]

This study aims to survey the bibliometric properties of distributed decisions, decentralized decisions, and fuzzy articles published between 1995 and 2023 in the Web of Science (WoS) database. During the analysis process, the keywords "distributed decision, decentralized decision, and fuzzy" were scanned in all languages, both in the titles and the content of all publication types. As a result of the analysis, 79 articles in all fields comprised the dataset. The most used keywords in the articles were related to the distributed decision, decentralized decision, and fuzzy logic, and the most frequently cited publications were examined using the social network analysis method, which uses VOSviewer (version 1.6.19) to visualize the relationships. The study's goal on "active researchers, active journals, journal metrics, title document type, active countries, and active institutions" was to look at the words most frequently used in articles on distributed, decentralized, and fuzzy logic. The social network analysis represented the relationships between these keywords and the most frequently cited publications. The findings demonstrated a significant correlation between using these keywords in academic literature and their contribution to this field's research. These results can assist researchers in finding potential partners and keeping up with current research trends. Overall, this study offers important new perspectives on the state of research on fuzzy logic, distributed decision making, and decentralized decision making. [ABSTRACT FROM AUTHOR]

In the context of data analysis, missing data imputation is a vital issue due to the typically large scale and complexity of the datasets. It often results in a higher incidence of missing data. So, addressing missing data through the imputation technique is essential to ensure the integrity and completeness of the data. It will ultimately improve the accuracy and validity of the data analysis. The prime objective of this study is to propose an imputation model. This paper presents a method for imputing missing employee data through a combination of features and probability calculations. The study utilized employee datasets that were collected from the Kaggle along with primary data collected from RMG factories located in Chittagong. The suggested algorithm demonstrated a notable level of accuracy on the datasets, and the average accuracy for each identified technique was also quite satisfactory. This study contributes to the existing body of research on missing data imputation in big data analysis and offers practical implications for handling missing data in different datasets. Usage of this technique will enhance the accuracy of data analysis and decision-making in organizations. [ABSTRACT FROM AUTHOR]

When applying reinforcement learning in domains with very large or continuous state spaces, the experience obtained by the learning agent in the interaction with the environment must be generalized. The generalization methods are usually based on the approximation of the value functions used to compute the action policy and tackled in two different ways. On the one hand by using an approximation of the value functions based on a supervized learning method. On the other hand, by discretizing the environment to use a tabular representation of the value functions. In this work, we propose an algorithm that uses both approaches to use the benefits of both mechanisms, allowing a higher performance. The approach is based on two learning phases. In the first one, a learner is used as a supervized function approximator, but using a machine learning technique which also outputs a state space discretization of the environment, such as nearest prototype classifiers or decision trees do. In the second learning phase, the space discretization computed in the first phase is used to obtain a tabular representation of the value function computed in the previous phase, allowing a tuning of such value function approximation. Experiments in different domains show that executing both learning phases improves the results obtained executing only the first one. The results take into account the resources used and the performance of the learned behavior. © 2008 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

This paper investigates predictive process monitoring problems in emergency treatment by combining the fields of process management and artificial intelligence. The objective is to predict the next activity and its timestamp in the treatment of emergency patients who have undergone surgery at the gastroenterology or urology surgery units in a hospital in Norway. To achieve this goal, three models were developed using different algorithms, and the best performing model was identified using various performance metrics. The results demonstrate the potential of predictive process monitoring to accurately forecast the outcome of patient treatments. By leveraging the insights gained from predictive process monitoring, hospitals can make more informed decisions. The findings of this study suggest that predictive process monitoring holds significant promise as a tool for improving the efficiency and effectiveness of emergency patient treatment processes. This research has significant implications for the field of decision sciences, particularly regarding resource allocation, reducing waiting times, and improving patient outcomes. The ability to predict the outcomes of patient treatment processes has important implications for hospitals, allowing the streamlining and acceleration of the treatment process. Overall, this study provides a promising framework for predicting patient treatment processes by using the predictive process monitoring method. This could be expanded upon in future research, ultimately leading to improved patient outcomes and better decision-making in healthcare. [ABSTRACT FROM AUTHOR]

Machine learning has become increasingly popular for modeling dam behavior due to its ability to capture complex relationships between input parameters and dam behavior responses. However, the use of sophisticated machine learning methods for monitoring dam behaviors and making decisions is often hindered by model uncertainty and a lack of interpretability. This paper introduces a novel model for dam health monitoring, focused on monitoring radial displacement and seepage, using optimized sparse Bayesian learning and sensitivity analysis. The model hyperparameters are optimized using an intelligent optimization method integrating the multi-population Rao algorithm and blocked cross-validation, while sensitivity analysis is employed to calculate the relative importance of input variables for a better understanding of the dam's state. The effectiveness of the proposed model is verified by using long-term monitoring data of a prototype concrete arch dam. The results confirm that the proposed model provides satisfactory performance on both the point predictions and the interval predictions for dam structural behaviors while obtaining effective explainability. [ABSTRACT FROM AUTHOR]

Pyroptosis is widely involved in many diseases, including periodontitis. Nonetheless, the functions of pyroptosis-related genes (PRGs) in periodontitis are still not fully elucidated. Therefore, we aimed to investigate the role of PRGs in periodontitis. Three datasets (GSE10334, GSE16134, and GSE173078) from the Gene Expression Omnibus (GEO) were selected to analyze the differences in expression values of the PRGs between nonperiodontitis and periodontitis tissue samples using difference analysis. Following this, five hub PRGs (charged multivesicular body protein 2B, granzyme B, Z-DNA-binding protein 1, interleukin-1β, and interferon regulatory factor 1) predicting periodontitis susceptibility were screened by establishing a random forest model, and a predictive nomogram model was constructed on the basis of these genes. Decision curve analysis suggested that the PRG-based predictive nomogram model could provide clinical benefits to patients. Three distinct PRG patterns (cluster A, cluster B, and cluster C) in the periodontitis samples were revealed according to the 48 significant PRGs, and the difference in the immune cell infiltration among the three patterns was explored. We observed that all infiltrating immune cells, except type 2 T helper cells, differ significantly among the three patterns. To quantify the PRG patterns, the PRG score was calculated by principal component analysis. According to the results, cluster B had the highest PRG score, followed by cluster A and cluster C. In conclusion, PRGs significantly contribute to the development of periodontitis. Our study of PRG patterns might open up a new avenue to guide individualized treatment plans for patients with periodontitis. [ABSTRACT FROM AUTHOR]

Appropriate data analysis technology can make people use the online degree education, obtain the data and information generated in the learning management system, and provide a useful decision basis for optimizing the teaching and management process of online degree education. Data analysis technology can help English teachers better grasp students' learning situations and progress and optimize management. First, data analysis methods and decision tree algorithms are analyzed. Second, in data mining technology, the C4.5 decision tree method is used to construct an English score prediction model. Through the analysis of English learning-related information such as questionnaires and collected student test score data, the prediction of English teaching performance is analyzed from the perspective of teachers' in-depth intervention. The survey results are shown as follows: (1) The model is simulated and tested. The model's prediction accuracy is 98.20%, 99.10%, 99.40%, 98.70%, and 98.90%, higher than the standard accuracy of 97.5%. Additionally, the average response efficiency of the model is 99.42%, which can be used. (2) The failure rate of boys' final grades is 11%, and the failure rate of female students' final grades is 10%. There is only a 1% difference in the final grade failure rate between male and female students. The effect of gender on teaching performance is less pronounced. (3) As the number of practice questions increases, the rate of failing grades decreases. Thus, the data suggest that the number of practice questions affects instructional performance. (4) Teachers' intervention can improve students' English achievement. Increasing the intensity of the intervention also improves student achievement. Therefore, the follow-up research should increase the number of practice questions and teacher intervention in English teaching. The English teaching achievement prediction suggestion based on big data analysis is put forward, providing a reference for prediction management. [ABSTRACT FROM AUTHOR]

Cardiovascular disease (CVD) is a life-threatening disease rising considerably in the world. Early detection and prediction of CVD as well as other heart diseases might protect many lives. This requires tact clinical data analysis. The potential of predictive machine learning algorithms to develop the doctor's perception is essential to all stakeholders in the health sector since it can augment the efforts of doctors to have a healthier climate for patient diagnosis and treatment. We used the machine learning (ML) algorithm to carry out a significant explanation for accurate prediction and decision making for CVD patients. Simple random sampling was used to select heart disease patients from the Khyber Teaching Hospital and Lady Reading Hospital, Pakistan. ML methods such as decision tree (DT), random forest (RF), logistic regression (LR), Naïve Bayes (NB), and support vector machine (SVM) were implemented for classification and prediction purposes for CVD patients in Pakistan. We performed exploratory analysis and experimental output analysis for all algorithms. We also estimated the confusion matrix and recursive operating characteristic curve for all algorithms. The performance of the proposed ML algorithm was estimated using numerous conditions to recognize the best suitable machine learning algorithm in the class of models. The RF algorithm had the highest accuracy of prediction, sensitivity, and recursive operative characteristic curve of 85.01%, 92.11%, and 87.73%, respectively, for CVD. It also had the least specificity and misclassification errors of 43.48% and 8.70%, respectively, for CVD. These results indicated that the RF algorithm is the most appropriate algorithm for CVD classification and prediction. Our proposed model can be implemented in all settings worldwide in the health sector for disease classification and prediction. [ABSTRACT FROM AUTHOR]

Blood centers are an essential component of the healthcare system, as timely blood collection, processing, and efficient blood dispatch are critical to the treatment of patients and the performance of the entire healthcare system. At the same time, an efficient blood dispatching system through the high-precision predictive capability of artificial intelligence is crucial for the efficiency improvement of the blood centers. However, the current artificial intelligence (AI) models for predicting blood usage do not meet the needs of blood centers. The challenges of AI models mainly include lower generalization ability in different hospitals, limited stability under missing values, and low interpretability. An artificial neural network-based model named the blood usage prediction neural network (BUPNN) has been developed to address these challenges. BUPNN includes a novel similarity-based manifold regularizer that aims to enhance network mapping consistency and, thus, overcome the domain bise of different hospitals. Moreover, BUPNN diminishes the performance degradation caused by missing values through data enhancement. Experimental results on a large amount of accurate data demonstrate that BUPNN outperforms the baseline method in classification and regression tasks and excels in generalization and consistency. Moreover, BUPNN has solid potential to be interpreted. Therefore, the decision-making process of BUPNN is explored to the extent that it acts as an aid to the experts in the blood center. [ABSTRACT FROM AUTHOR]

In Dempster–Shafer evidence theory (DST), some classical evidence combination rules can be used to fuse the multiple pieces of evidence, respectively abstracted from different attributes (features) so as to increase the accuracy of multiattribute classification decision making. However, most of them have not yet considered the interdependence among multiple pieces of evidence. The newly proposed maximum likelihood evidential reasoning (MAKER) rule measures such ubiquitous interdependence by introducing correlation factors into evidence combination. Hence, this paper designs a MAKER-based classifier to mine more correlation information for data classification. Finally, some numerical analysis (classification) experiments are carried out using five popular benchmark databases from the University of California, Irvine (UCI) to illustrate that the refined measure for evidence interdependence can aggregate the fused probability (belief degree) into real class label of a sample and further improve classification accuracy. [ABSTRACT FROM AUTHOR]

Introduction: In patients undergoing high‐risk cardiac surgery, the uncertainty of outcome may complicate the decision process to intervene. To augment decision‐making, a machine learning approach was used to determine weighted personalized factors contributing to mortality. Methods: American College of Surgeons National Surgical Quality Improvement Program was queried for cardiac surgery patients with predicted mortality ≥10% between 2012 and 2019. Multiple machine learning models were investigated, with significant predictors ultimately used in gradient boosting machine (GBM) modeling. GBM‐trained data were then used for local interpretable model‐agnostic explanations (LIME) modeling to provide individual patient‐specific mortality prediction. Results: A total of 194 patient deaths among 1291 high‐risk cardiac surgeries were included. GBM performance was superior to other model approaches. The top five factors contributing to mortality in LIME modeling were preoperative dialysis, emergent cases, Hispanic ethnicity, steroid use, and ventilator dependence. LIME results individualized patient factors with model probability and explanation of fit. Conclusions: The application of machine learning techniques provides individualized predicted mortality and identifies contributing factors in high‐risk cardiac surgery. Employment of this modeling to the Society of Thoracic Surgeons database may provide individualized risk factors contributing to mortality. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive water quality monitoring system that employs a smart network management, nano-enriched sensing framework, and intelligent and efficient data analysis and forwarding protocols for smart and system-aware decision making. The presented system comprises two main subsystems, a data sensing and forwarding subsystem (DSFS), and Operation Management Subsystem (OMS). The OMS operates based on real-time learned patterns and rules of system operations projected from the DSFS to manage the entire network of sensors. The main tasks of OMS are to enable real-time data visualization, managed system control, and secure system operation. The DSFS employs a Hybrid Intelligence (HI) scheme which is proposed through integrating an association rule learning algorithm with fuzzy logic and weighted decision trees. The DSFS operation is based on profiling and registering raw data readings, generated from a set of optical nanosensors, as profiles of attribute-value pairs. As a case study, we evaluate our implemented test bed via simulation scenarios in a water quality monitoring framework. The monitoring processes are simulated based on measuring the percentage of dissolved oxygen and potential hydrogen (PH) in fresh water. Simulation results show the efficiency of the proposed HI-based methodology at learning different water quality classes. [ABSTRACT FROM AUTHOR]

With the growing development of the era of big data, data acquisition and analysis have become hot spots, and Python-based crawler technology is one of the most widely used tools in data analysis work at present. In this paper, we apply Python crawler key technology to acquire data of movie list and hot movies on Cat's Eye movie network, analyze data based on Python development environment Spyder, use the Numpy system to store and process large data, Chinese Jieba word separation tool to crawl data for word separation text processing, Snownlp library to process text sentiment, and finally by the word cloud map and web dynamic map display information such as viewers' emotional tendency and movie rating statistics, and provide decision support for users' movie viewing. [ABSTRACT FROM AUTHOR]

In this paper, we construct a model of convolutional neural network speech emotion algorithm, analyze the classroom identified by the neural network with a certain degree of confidence together with the school used in the dataset, find the characteristics and rules of teachers' control of classroom emotion nowadays using big data, find the parts of classroom emotion, and design a classroom emotion recognition model based on convolutional neural network speech emotion algorithm according to these characteristics. This paper will investigate the factors and patterns of teachers' emotional control in the classroom. In this paper, the existing neural network is adapted and improved, and some preprocessing is performed on the current dataset to train the network. The network used in this paper is a combination of convolutional neural network (CNN) and recurrent neural network (RNN), which takes advantage of both CNN for feature extraction and RNN for memory capability in the sequence model. This network has a good effect on both object labeling and speech recognition. For the problem of extracting emotion features of whole-sentence speech, we propose an attention mechanism-based emotion recognition algorithm for variable-length speech and design a spatiotemporal attention module for the speech emotion algorithm and a convolutional channel attention module for the CNN network to reduce the contribution of the spatiotemporal data of the speech emotion algorithm and the unimportant parts of the CNN convolutional channel feature data in the subsequent recognition by the attention mechanism. In turn, the weight of core key data and features is increased to improve the model recognition accuracy. [ABSTRACT FROM AUTHOR]

Activity-based travel demand modeling is the approach with most relation and need for intelligent solutions as it directly aims at reproducing human decision making in daily life. Therefore, the way to implement the selected intelligent solution plays an important role in the successful application of the models. FEATHERS (the Forecasting 16 Evolutionary Activity-Travel of Households and their Environmental RepercussionS) is an activity-based microsimulation modeling framework used for transport demand forecasting. Currently, this framework is implemented for the Flanders region of Belgium and the most detailed travel demand data can be obtained at the Subzone level, which consists of 2,386 virtual units with an average area of 5.8 km2. For the sake of more detailed travel demand forecasting, we investigated in this study the extension of the FEATHERS framework from the Subzone zoning system to a more disaggregated zoning system, i.e., Building block (BB), which is the most detailed geographical level currently applicable in Belgium consisting of 10,521 units with an average area of 1.3 km2. In this paper, we elaborated the data processing procedure to implement the FEATHERS framework under the BB zoning system. The observed as well as the predicted travel demand in Flanders based on the two zoning systems was compared. The extended modeling system was further applied to investigate the potential impact of light rail initiatives on travel demand at a local network in Flanders. [ABSTRACT FROM AUTHOR]

With the sustained and rapid development of China's economy, the business environment of enterprises is becoming more and more complicated. Coupled with the increasing international competitive pressure of enterprises, the management of enterprises needs more attention. The emergence of DSS provides an opportunity for enterprise management intelligent decision-making. Based on the in-depth study of RF algorithm, this paper proposes a new intelligent decision-making model for enterprise management, aiming at the shortcomings of traditional decision-making systems. In this paper, the decision-making process of the system for decision support is given. Through the quantification and evaluation of various data by subsystems, the best decision-making scheme with strong operability is provided for enterprises. And this model can effectively solve the problem of heterogeneity, duplication and clutter of data in traditional database, and avoid the loss of historical data. Through simulation, the accuracy of this system can reach 96%, which is about 17% higher than other systems. It has certain practicability and feasibility. It is hoped that the research in this paper will play an important role and service support for enterprise intelligent decision-making and further promote the development of intelligent enterprise management decision-making system in China. [ABSTRACT FROM AUTHOR]

With the increasing interest devoted to dynamic environments, a crucial aspect is revealed in context-aware systems to deal with the rapid changes occurring in users' surrounding environments at runtime. However, most context-aware systems with predefined context-aware rules may not support effective decision-making in dynamic environments. These context-aware rules, which take into account different context information to reach an appropriate decision, could lose their efficiency at runtime. Therefore, a growing need is emerging to address the decision-making issue leveraged by dynamic environments. To tackle this issue, we present an approach that relies on improving decision-making in the wake of dynamic environments through automatically enriching a rule knowledge base with new context-aware rules discovered at runtime. The major features of the presented approach are as follows: (i) a hybridization of two machine learning algorithms for rule generation, (ii) an extended genetic algorithm (GA) for rule optimization, and (iii) a rule transformation for the knowledge base enrichment in an automated manner. Furthermore, extensive experiments on different datasets are performed to assess the effectiveness of the presented approach. The obtained experimental results depict that this approach exhibits better effectiveness compared to some algorithms and state-of-the-art works. [ABSTRACT FROM AUTHOR]