Decision Tree Analysis Method Research Articles

Strategies for optimising chemical instrumental analysis methods based on the ADDIE model

Abstract In order to study the optimization strategy of chemical instrumentation analysis methods, it can make the chemical instrumentation analysis methods more optimized. This paper proposes an improved sparrow search algorithm MSSA based on the ADDIE model, and a decision tree analysis method under random forest is introduced to assist chemical instrumentation analysis. The optimal prediction value of the algorithm is judged analytically, and adaptive operations change the size of the neighbourhood space to obtain the optimal strategy of the algorithm by a merit-seeking mechanism. The decision tree and evaluation indicators are then constructed with the decision tree under a random forest algorithm, and the indicators are used to select the optimisation path. From the experiments, it can be seen that the improved sparrow search algorithm MSSA strategy based on the ADDIE model can improve the optimisation ability of the algorithm. Furthermore, the MSSA algorithm also shows excellent performance in the experiments and obtains the best coverage effect. The coverage rate of the optimised chemical instrumentation analysis method reached 94.55%, which was 9.87%, 4.15%, 6.68%, 3.22% and 7.28% higher than other types of algorithms, respectively. It illustrates that the improved MSSA algorithm under the ADDIE model can also obtain better optimisation capability for practical chemical instrumental analysis method optimisation problems. The evaluation index complements this under the decision tree, which shows that the model is more conducive to the analytical capability of chemical instruments. It also provides a direction for solving the problem of choosing the optimal analytical method in chemical instrumentation analysis.

Exploring Factors Influencing School Adaptation of At-Risk Learners in Blind Spot Education with Using Decision Tree Analysis Method across Elementary School Students

Exploring Factors Influencing School Adaptation of At-Risk Learners in Blind Spot Education with Using Decision Tree Analysis Method across Elementary School Students

Factors Influencing Aggressive Adolescent Behavior: An Analysis Using the Decision Tree Method

Aggression among adolescents is a significant public health concern worldwide. To safeguard adolescents’ physical and mental health, controlling the incidence of aggressive behavior and its triggers and drivers is necessary. Different risk factors in each environment drive adolescent aggression. These factors are interconnected, making the environment an ecosystem of aggression. This study aims to analyze the factors that influence adolescents’ aggressive behavior and provide a basis for formulating measures to reduce such behavior in the future. This multicenter cross-sectional study was conducted in five middle schools in Lingbao City. The participants were junior high school students aged 11–15 years. Data were analyzed from January to March 2020 using the decision tree analysis method. The aggressive behavior scale, adolescent emotion regulation scale, family intimacy and adaptation scale, parental education participation scale, and a simple parenting style questionnaire were used for the investigation. Of the 581 participants, 25.5% demonstrated a tendency for aggressive behavior, while the remaining did not. The decision tree analysis showed that the incidence of adolescent aggression was the highest (54.8%) when the maternal overprotective score was greater than or equal to 17, the adolescent had low emotional control, and the mother’s intelligence score was higher than 30. The decision tree model reflects the influencing factors of adolescent aggressive behavior from multiple levels and provides a reference for formulating effective intervention measures to control such behavior and its influencing factors.

Modelling lifestyle-based segments of Indian wine consumers using the latent profile analysis approach

PurposeThis study aims to identify and validate the different clusters of wine consumers in India based on the wine-related lifestyle (WRL) instrument. It also investigates how the identified clusters differ in terms of socio-demographic characteristics, such as age, gender, income, education, employment and marital status.Design/methodology/approachThe authors conducted a survey using a structured questionnaire to collect data from wine consumers in India. The number of participants totalled to 432. The authors first identified the clusters using latent profile analysis. The authors then used the decision tree analysis based on a recursive partitioning algorithm to validate the clusters. Finally, the authors analysed the relationship between the identified clusters and socio-demographic characteristics using correspondence analysis.FindingsThree distinct segments emerged after data were subjected to latent profile analysis, namely, curious, ritualistic and casual. The authors found that the curious cluster had a high mean score for situational and social consumption while the ritualistic cluster had a high mean for ritualistic consumption. The findings also suggest that the casual cluster had more female wine consumers.Originality/valueThis study makes methodological contributions to the wine consumer segmentation approach. First, it adopts a latent profile analysis to profile Indian wine consumers. Second, it validates the obtained clusters using the decision tree analysis method. Third, it analyses the relationship between the identified clusters and socio-demographic variables using correspondence analysis, a technique far superior to the Chi-square methods.

Female Employment Data Analysis Based on Decision Tree Algorithm and Association Rule Analysis Method

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.

A survey of new orientations in the field of vehicular cybersecurity, applying artificial intelligence based methods

AbstractNowadays, cybersecurity is an emerging research area in the automotive industry, and it is investigated by many different perspectives. Our article is a review of existing vehicular security solutions that covers the state‐of‐the‐art and future research directions. This article is a new contribution to tutorials/surveys related to the vehicular cybersecurity domain with the latest details. We developed a database from 140 articles from the field of automotive security. In the database, we assigned specific attributes to every article (such as Web of Science Impact Factor or the number of citations). The data set was analyzed by the K‐means clustering and decision tree analysis methods to identify and characterize the generated groups of papers. Following this, the article highlights the research areas that might receive more attention in the future. Accordingly, the result of the current research can be applied by the decision‐makers, researchers, and Original Equipment Manufacturers to allocate additional resources to those domains, which is expected to shape the future of vehicular security.

Application of the decision tree method to lithology identification of volcanic rocks-taking the Mesozoic in the Laizhouwan Sag as an example

The decision tree method can be used to identify complex volcanic rock lithology by dividing lithology sample data layer by layer and establishing a tree structure classification model. Mesozoic volcanic strata are widely developed in the Bohai Bay Basin, the rock types are complex and diverse, and the logging response is irregular. Taking the D oilfield of the Laizhouwan Sag in the Bohai Bay Basin as an example, this study selects volcanic rocks with good development scales and single-layer thicknesses of more than 0.2 m as samples. Based on a comparison of various lithology identification methods and both coring and logging data, using the decision tree analysis method and the probability density characteristics of logging parameters, six logging parameters with good sensitivity to the response of the volcanic rocks of the above formation are selected (resistivity (RD), spontaneous potential (SP), density (ZDEN), natural gamma ray (GR), acoustic (DT), and compensated neutron correction (CNCF) curves), which are combined to form a lithology classifier with a tree structure similar to a flow chart. This method can clearly express the process and result of identifying volcanic rock lithology with each logging curve. Additionally, crossplots and imaging logging are used to identify the volcanic rock structure, and the core data are used to correct the identified lithology. A combination of conventional logging, imaging logging and the decision tree method is proposed to identify volcanic rock lithology, which substantially improves the accuracy of rock identification.

의사결정나무 분석 방법을 활용한 중 · 고등학교 3학년 학생의 진로정체성 예측모형 비교 연구: 개인-환경 상호작용을 중심으로

의사결정나무 분석 방법을 활용한 중 · 고등학교 3학년 학생의 진로정체성 예측모형 비교 연구: 개인-환경 상호작용을 중심으로

의사결정나무분석을 이용한 외국인유학생 생활만족도 분석 : 중국유학생을 중심으로

의사결정나무분석을 이용한 외국인유학생 생활만족도 분석 : 중국유학생을 중심으로

Fast nutritional characterization of different pigmented rice grains using a combination of NMR and decision tree analysis

ABSTRACTDifferent rice cultivars contain various metabolites which are closely related with their nutritional and functional effects. In the current study, NMR and chemometrics methods – decision tree analysis (DTA) were applied to explore the chemical characteristics in different rice cultivars. The pigmented rice were completely discriminated from each other, and the key metabolites that mostly contributed to the rice discrimination were explored, including sugars compounds (sucrose, fructose and glucose) and non-sugar compounds (γ-amino butyric acid (GABA), asparagine, citric acid and malic acid) with DTA approach and 1HNMR metabolite fingerprints. The purple rice has the highest concentrations of sucrose, citric acid and asparagine (p < 0.05), but lowest fructose (p < 0.05); red rice has the lowest GABA concentration comparing with the other three groups (p < 0.05). This study implies that DTA method is a powerful tool to discriminate different pigmented rice varieties. Furthermore, it also reveals the crucial nutrients based on 1H-NMR metabolic profiling.

Analysing multiple combinations of the conditions affecting local residents’ life satisfaction: Application of decision tree analysis method

Analysing multiple combinations of the conditions affecting local residents’ life satisfaction: Application of decision tree analysis method

A study on the impact of considerations factors on suspended sentence for sexual offence using decision tree analysis method

A study on the impact of considerations factors on suspended sentence for sexual offence using decision tree analysis method

Cost-effectiveness of routine computed tomography in the evaluation of idiopathic unilateral vocal fold paralysis.

To evaluate the cost-effectiveness of routine computed tomography (CT) in individuals with unilateral vocal fold paralysis (UVFP) STUDY DESIGN: Health Economics Decision Tree Analysis METHODS: A decision tree was constructed to determine the incremental cost-effectiveness ratio (ICER) of CT imaging in UVFP patients. Univariate sensitivity analysis was utilized to calculate what the probability of having an etiology of the paralysis discovered would have to be to make CT with contrast more cost-effective than no imaging. We used two studies examining findings in UVFP patients. The decision pathways were utilizing CT neck with intravenous contrast after diagnostic laryngoscopy versus laryngoscopy alone. The probability of detecting an etiology for UVFP and associated costs were extracted to construct the decision tree. The only incorrect diagnosis was missing a mass in the no-imaging decision branch, which rendered an effectiveness of 0. The ICER of using CT was $3,306, below most acceptable willingness-to-pay (WTP) thresholds. Additionally, univariate sensitivity analysis indicated that at the WTP threshold of $30,000, obtaining CT imaging was the most cost-effective choice when the probability of having a lesion was above 1.7%. Multivariate probabilistic sensitivity analysis with Monte Carlo simulations also showed that at the WTP of $30,000, CT scanning is more cost-effective, with 99.5% certainty. Particularly in the current healthcare environment characterized by increasing consciousness of utilization defensive medicine, economic evaluations represent evidence-based findings that can be employed to facilitate appropriate decision making and enhance physician-patient communication. This economic evaluation strongly supports obtaining CT imaging in patients with newly diagnosed UVFP. 2c. Laryngoscope, 2016 127:440-444, 2017.

A retrospective analysis to identify the factors affecting infection in patients undergoing chemotherapy

PurposeThis study compares the performance of the logistic regression and decision tree analysis methods for assessing the risk factors for infection in cancer patients undergoing chemotherapy. MethodThe subjects were 732 cancer patients who were receiving chemotherapy at K university hospital in Seoul, Korea. The data were collected between March 2011 and February 2013 and were processed for descriptive analysis, logistic regression and decision tree analysis using the IBM SPSS Statistics 19 and Modeler 15.1 programs. ResultsThe most common risk factors for infection in cancer patients receiving chemotherapy were identified as alkylating agents, vinca alkaloid and underlying diabetes mellitus. The logistic regression explained 66.7% of the variation in the data in terms of sensitivity and 88.9% in terms of specificity. The decision tree analysis accounted for 55.0% of the variation in the data in terms of sensitivity and 89.0% in terms of specificity. As for the overall classification accuracy, the logistic regression explained 88.0% and the decision tree analysis explained 87.2%. ConclusionsThe logistic regression analysis showed a higher degree of sensitivity and classification accuracy. Therefore, logistic regression analysis is concluded to be the more effective and useful method for establishing an infection prediction model for patients undergoing chemotherapy.

Understanding Japanese tourists’ shopping preferences using the Decision Tree Analysis method

This study was designed to assess the factors affecting Japanese tourists’ shopping preference and intention to revisit Korea. The analytical method applied in this study was Decision Tree Analysis, which is under-utilized in tourism studies. A total of 300 questionnaires were collected on the basis of on-site survey method and used for data analysis. Among interesting findings, three groups including ‘ respondents who were satisfied, accompanied, and spent US$50–300 on shopping’, ‘ respondents who were satisfied, accompanied, and had a shopping expenditure of US$300–1000’ and ‘ respondents who were satisfied, accompanied, and had a shopping expenditure of US$1000–5000,’ showed a high level of intention to return to Korea for the purpose of shopping. In addition, two groups ‘ those who were interested in shopping in Korea, preferred a shopping mall as a shopping destination, and had an educational level of below high school’ and ‘ those who were interested in shopping in Korea, preferred a shopping mall as a shopping destination, and had an educational level of a college graduate or above’ showed a higher level interest in merchandise than in shopping venue attractiveness.

Valuing Technology Investments: Use Real Options Thinking But Forget Real Options Valuation

Real options valuation (ROV) has recently been proposed as a promising solution to the deficiencies of traditional valuation methods when facing risky technology investments or ventures. Indeed 'real option thinking' - the managerial flexibility to capitalise on opportunities when they arise and/or to minimise the impact of threats - is precisely what is needed when faced with the uncertain future of technology investments. Notwithstanding this we argue that traditional decision tree analysis methods are preferred to ROV techniques when valuing technology investments that contain 'real options'. Our reasoning is twofold. First, ROV techniques provide a sophisticated treatment of market risks, but do not deal with firm-specific risks. However, the elevated risk facing technology ventures is predominantly firm-specific risk, and these ventures face only about average levels of market risk. Second, ROV has a severe practical limitation in the context of new technology ventures. Normally, the favoured approach when using ROV to value investments is to use the 'market asset disclaimer' assumption. The starting point is to value the venture/project in the absence of the 'real options' using traditional discounted cash flow techniques (to establish the value of the underlying asset). ROV analysis then adjusts this valuation to take account of the real options. But the first step makes no sense for technology ventures because these 'real options' are an integral part of the venture.

Population screening for glucose intolerant subjects using decision tree analyses

The purpose of this study was to develop a method of screening for impaired glucose tolerance and previously undiagnosed NIDDM that could be used preliminary to the administration of an oral glucose tolerance test (OGTT) for final classification of glucose tolerance status. The purpose of a preliminary screening of this type would be to reduce the number of OGTT's needed to identify cases of IGT and NIDDM in the population. We used NIDDM risk indicators and decision tree analysis methods (CART software) to identify subgroups of the population at increased risk. We examined a population of Hispanic ( n = 583) and non-Hispanic white ( n = 768) subjects without a prior history of diabetes. Subjects were classified as normal, IGT or NIDDM (WHO criteria) based on results from a 75 g oral glucose tolerance test (OGTT). Sensitivity (SEN) and specificity (SPE) of the CART models were calculated using the OGTT as the ‘gold standard.’ Two approaches to screening were simulated. In the simultaneous approach all risk variables were entered into CART models at once. In the serial approach, risk variables were grouped according to degree of effort required for data collection, and were entered into CART models in stages. Fasting glucose, age and body mass index (BMI) were selected as risk variables by CART when simulating the simultaneous approach (SEN = 91%, SPE = 55%). In the serial approach, CART used age and BMI to eliminate 35% of the population from further screening, and then used fasting glucose, glycohemoglobin, age and BMI to classify the remaining higher risk subjects (SEN = 85%, SPE = 64%). These models suggest that screening for IGT and previously undiagnosed NIDDM can be based on measurement of relatively simple indicators, and yet maintain a level of both sensitivity and specificity acceptable for this type of preliminary screening.

Caisson Wellheads Allow For Future Use Of Offshore Exploration Wells

Abstract Exploration wells on the iceberg infested Grand Banks have traditionally been abandoned after testing, mainly because the wellheads have no protection against iceberg impact. The cost of drilling wells on the Grand Banks and the likelihood of future subsea development now justify retaining certain exploration wells for further testing or completion. Caissons have recently been used to place wellheads submudline below iceberg scour depth. This paper discusses the reasons for employing caisson wellheads, mentions other iceberg protection methods, explains the decision tree analysis method used to identify candidate wells and outlines the drilling and completion equipment installed. Introduction Typical offshore drilling practice is to plug and cement exploration and delineation wells on the completion of drilling and testing operations. They may then be abandoned by removing the subsea wellhead, or they may be left suspended to allow for future re-entry. In most areas of the world, subsequent development wells are drilled from fixed platforms. The exploration and delineation wells are therefore abandoned rather than re-entered for production because a replacement development well is cheaper to drill and maintain, However, as smaller offshore fields are being developed, floating production systems have become more common (Fig. 1). Such a development system requires subsea wells, making suspended delineation wells candidates for production. On the Grand Banks, other than the Hibernia field, no fields have yet been discovered which would warrant a fixed production platform. This is due to the high reserve threshold required to justify a fixed platform designed to withstand iceberg impact. Floating production systems are likely to be the only means by which these smaller discoveries can be developed. Unfortunately, icebergs pose major problems for subsea developments. Floating production systems will have to be designed to disconnect to avoid iceberg collision. Also, seafloor equipment should be placed below scour depth, where possible, to avoid damage to equipment such as manifolds, flowlines and wellheads. This is crucial for wellheads as an iceberg collision could result in a subsea well blowout if the only remaining means of well control is the subsurface safety valve. Furthermore, ice conditions would hamper immediate well control operations. Delineation wells with conventional subsea wellheads are therefore of little value as ‘keeper’ wells without wellhead protection. Various conceptual methods to protect wellheads from iceberg scour damage exist. For reasons of flexibility and compatibility with the drilling program and cost, the most appropriate system for protection of delineation wells is an arrangement whereby the wellhead (and ultimately the "Xmas " tree) is placed below scour depth in a caisson. Caisson wellhead systems were originally conceived to protect wellheads from fishing gear damage and are relatively recent developments. There are additional costs associated with using a caisson wellhead, both in equipment and rig time. A decision analysis must therefore be undertaken for each delineation well to justify the use of a caisson wellhead on that specific location. This involves estimating future cost savings and determining the risks associated with the delineation well and ultimate field development.