CART Decision Tree Research Articles

A fast detection method for metering anomalies of three-phase energy meters based on sliding filter and decision tree

In the actual measurement of three-phase electricity meters, the presence of electromagnetic interference, environmental temperature, and other factors increases the noise in the measurement data, which affects the accuracy of anomaly detection. In this regard, a fast detection method for three-phase meter measurement anomalies based on sliding filters and decision trees is studied. Firstly, sliding filtering and sliding window matrix methods are used to reduce the dimensionality and filter the measurement data of three-phase electric meters. Then, a cascaded random forest is constructed using a CART decision tree, and the filtered three-phase meter measurement data is input into the cascaded random forest. The CART decision tree uses a binary method to partition the nodes. Finally, using the Gini coefficient as a measurement indicator, a cascaded structure is formed through layered stacking to output rapid detection results of outliers in three-phase electricity meter measurements. The experimental results show that this method can quickly detect measurement abnormalities of three-phase energy meters, timely detect meter flying anomalies and unexpected mutations, and improve the fault handling efficiency of three-phase energy meters.

Monitoring the Landscape Pattern Dynamics and Driving Forces in Dongting Lake Wetland in China Based on Landsat Images

Dongting Lake wetland is a typical lake wetland in the Middle and Lower Yangtze River Plain in China. Due to the influence of natural and human activities, the landscape pattern has changed significantly. This study used 12 Landsat images from 1991 to 2022 and applied three common classification methods (support vector machine, maximum likelihood, and CART decision tree) to extract and classify the landscape information, with the latter having a superior annual accuracy of over 90%. Based on the CART decision tree classification results, the dynamic characteristics of wetland spatial patterns were analyzed through the landscape pattern index, dynamic degree model, and transition matrix model. Redundancy and grey correlation analysis were employed to investigate the driving factors. The results showed increased landscape fragmentation, reduced heterogeneity, and increased complexity from 1991 to 2022. The water and mudflat areas exhibited three distinct stages: gradual decline until 2001 (−3.06 km2/a); sharp decrease until 2014 (−19.44 km2/a); and steady increase (22.93 km2/a). Vegetation conversion, particularly between sedge and reed, dominated the change in landscape pattern. Reed area initially increased (18.88 km2/a), then decreased (−35.89 km2/a), while sedge showed the opposite trend. Woodland area fluctuated, peaking in 2016 and declined by 2022. The construction of the Three Gorges Dam significantly altered landscape dynamics through water level changes, reflected by a 4.03% comprehensive dynamic degree during 2001–2004. Potential evaporation also emerged as a significant natural factor, exhibiting a negative correlation with the landscape index. During 1991–2001 and 2004–2022, the comprehensive explanatory rates of temperature, precipitation, potential evaporation, and water level on landscape pattern dynamics were 88.56% and 52.44%, respectively. Other factors like policies and socio-economic factors played a crucial role in wetland change. These findings offer valuable insights into the dynamic evolution and driving mechanisms of Dongting Lake wetland.

Price Prediction and Determination of the Affecting Variables of the Real Estate by Using X-Means Clustering and CART Decision Trees

The use of machine learning in real estate is quite new. When the working area is large, the factors affecting the price may vary according to the geographical regions and socioeconomic factors. It is thought that the price prediction performance of a model that will reflect these differences will be more successful than a general model. Unsupervised learning methods can be used both to increase performance and to show the variation of different factors affecting the price according to regions. With this aim, a hybrid model of X-Means clustering and CART decision trees was established in this study.  This model successfully learned the geographical and physical variables that affect the price. The prediction performance of the model was compared with the direct capitalization method, which is the gold standard in the domain. The hybrid model has a superior performance over direct capitalization in terms of mean square error, root mean square error and adjusted R-Squared metrics. The scores were 72.86, 0.0057 and 0.978, respectively. The effect of clustering was also examined. Clustering increased the prediction performance by 36%. 

Editorial

Dear Readers,  It gives me great pleasure to announce the fourth regular issue of 2024. I would like to thank all the authors for their sound research papers and the editorial board and our guest reviewers for their extremely valuable reviews and suggestions for improvement. These contributions and the generous support of the consortium members enable us to run our journal and maintain its quality. I would also like to thank our broader community for reading and incorporating sound J.UCS papers into their research. I am also very proud to announce that our journal has been included in the Free Journal Network as one of the listed high-quality journals in this field. Still, I would like to expand our editorial board: If you are a tenured associate professor or above with a good publication record, please apply to join our editorial board. We are also interested in receiving high-quality proposals for special issues on new topics and emerging trends. In this regular issue, I am very pleased to introduce six papers by 22 authors from nine countries: Brazil, Czech Republic, Germany, Iran, Jordan, Portugal, Thailand, Turkey, and the USA. In a collaboration between researchers from the Czech Republic and Germany, Sivaramasamy Elayaraja, Sunil Yeruva Vlastimil Stejskal, and Satish Nandipati look into multi-class microscopic image analysis of protozoan parasites using convolutional neural network and perform a study on 4740 microscopic images. André Diegues Fernandes, João Cruz, Miguel Mira da Silva, and Rúben Pereira from Portugal cover a systematic literature review for mapping and integrating security and risk standards. In a collaborative research between Jordan and the USA, Sahar Idwan, Junaid Zubairi, Syed Ali Haider, and Wael Etaiwi introduce their reactive traffic congestion control method based on a hierarchical graph approach. Shahrzad Riahi, Ramtin Khosravi, and Fatemeh Ghassemi from Iran conduct a study on knowledge-related policy analysis in an inference-enabled actor model. Josival Silva, Nelson Rosa, and Fernando Aires from Brazil research and present UP-Home, a self-adaptive solution that manages the security of smart homes by industry standards, and identifies and mitigates smart home security vulnerabilities. Last but not least, Sait Can Yucebas, Sukran Yalpir, Levent Genc, and Melike Dogan from Turkey discuss their research on price prediction and determination of the factors influencing real estate prices using X-Means clustering and CART decision trees. Enjoy Reading! Cordially, Christian Gütl, Managing Editor Graz University of Technology, Graz, Austria

Analysis of the characteristics and causes of city gas explosion accidents based on DT-AR algorithm

In order to effectively prevent and control accidents, it is essential to trace back the causes of gas explosions in cities. The DT-AR(decision tree-association rule) algorithm is proposed as a quantitative analysis of gas accident features and causality. First, 210 gas explosion accident investigation reports were taken as samples. The gas accident causation system is divided into three aspects, including environmental factors, management factors and physical factors. Management factors were sorted into organizational-level and individual-level factors from the investigation reports. Second, the CART decision tree model was used to compare location features, organizational causality features, and individual causality features of the piped and bottled gas accidents, and a decision tree model with the gas system fault site as the root node was built to filter the key feature variables. In order to reveal factor correlations and deep-level causation, the Apriori algorithm is used to mine accident association rules. The combinations on the branches of the decision tree are used as constraints to filter the critical causality rule, which improves the efficiency of association rule screening and enhances prediction accuracy. The results demonstrate that the DT-AR algorithm can evaluate the importance of variables, quickly locate effective combinations of factors, and mine the complete causal chain. The association rule is screened based on the constraint of the key element combination of the decision tree, which compensates for the low efficiency of the Apriori algorithm for association rule mining. In addition, the accident-caused excavation results provide an effective path for gas companies, outsourced service companies and administrative departments to implement gas safety chain supervision, which can address the problem of gas accident safety management failures and provide decision support for accident prevention.

A (mérsékelten rizikós) koraszülöttség az IQ hátrány ellenére nem akadályozza az olvasási és helyesírási képességek fejlődését

Background and aims: It is well established that the impacts of prematurity are often long-lasting in the various domains of development, however, the published findings concerning the prevalence, severity, and background of disorders are inconsistent. As yet research into the development of abilities contributing to the academic progress in Hungarian preterm children has been scarce. Our study aimed to find out to what extent prematurity impeded the reading and spelling abilities in school age and to contribute some information on the cognitive background of the performances. Methods: Three groups of 7 – 12 year-old children participated in the study. The target group consisted of moderate-risk preterms. For comparison two groups of full-term children were tested: typically developing good readers and dyslexics. To assess the reading and spelling abilities the Hungarian version of the 3DM (Dyslexia Differential Diagnosis) was used. Cognitive abilities were assessed using the Hungarian adaptation of the WISC-IV and the Rey Complex Figure Test. Results: The reading and spelling performances of the full-term good readers and the preterms did not differ and the preterms scored higher than the dyslexics. The cognitive tests did not offer any direct explanation to this, since the preterms lagged behind the full-term good readers in the WISC-IV full-scale IQ as well as in several specific cognitive measures. The CART Decision Tree served to analyse the relationships, allowing to reveal the interactions and the moderation effects. Discussion: The CHC model as a theoretical framework and the CART Decision Tree statistical analysis proved to be fruitful to disentangle the complex interplay of the cognitive background factors. There are various cognitive paths of acquiring the reading and spelling skills, e.g., in case of relatively lower IQs the sufficient levels of memory and processing speed may ensure success.

Factors Affecting the Impact of Autonomous Vehicles on Freeway Operations – An Exploratory Analysis Using PCEs

Autonomous vehicles (AVs) and human-driven vehicles (HDVs) will share the roads for a long time, hence the need to study traffic flows mixing AVs and HDVs, especially during the AV introduction period. This paper aims to investigate the roadway and traffic characteristics that affect the impact of AVs on freeway traffic operations, using an adapted version of the HCM6 truck passenger-car equivalent (PCE) methodology. A large number of scenarios comprising different roadway characteristics, AV types and traffic flow compositions were simulated using Vissim to obtain AV PCEs. The results indicated that, for all scenarios considered, an AV has a 20% lower impact on the quality of service and operation than an HDV. A CART decision tree indicated that the most important factors affecting the AVs’ impact on traffic operations are vehicle-to-vehicle connectivity level and the capability of travelling in platoons. Maximum platoon length did not matter, and the increase in the number of traffic lanes reduced the positive impact of AVs on service quality.

Research on Influencing Factors of Passenger Evacuation Behavior Decision Making Based on CART Decision Tree

Research on Influencing Factors of Passenger Evacuation Behavior Decision Making Based on CART Decision Tree

Path Updating of the Legal Ontology Model for Personal Information Protection in the Age of Big Data

Abstract This paper proposes a legal ontology model for personal information protection to extract structured information on the high-frequency elements of common privacy leakage and data security cases as key information of judicial documents. The prediction model of relevant sentences based on the CART decision tree is used to predict the outcome of the defendant’s sentence. The application scenarios of the legal ontology model of personal information protection in judicial guarantee are explored to guarantee the fairness of adjudication of personal information protection cases in terms of reliability of legal counseling, accuracy of sentencing assistance, and categorization of cases. The results show that 113 sample cases were retrieved by keyword search, excluding duplicate cases, with Beijing having the highest number of 42. With the development of the big data era, there has been a significant increase in personal information protection cases after 2019. The legal ontology model constructed in this paper has only 56 instances of classification error, the Macro-F1-Score and Micro-F1-Score indicator values are above 70%, and the utility values of personal information protection-related cases are all higher than 0.5. Therefore, the legal ontology model for personal information protection constructed in this paper is accurate in classification and sentencing prediction, with high reliability, and realizes the path of the big data era.

Extraction Methods for Small-Scale Features on a Large Scale: Investigating Object-Oriented Cart Decision Tree for Gravel Information Extraction

Extraction Methods for Small-Scale Features on a Large Scale: Investigating Object-Oriented Cart Decision Tree for Gravel Information Extraction

Research on the Reform and Practice of Informatization Mode of Adult Higher and Continuing Education Academic Records Management in the Context of Three-Whole Parenting

Abstract Information technology reform is an important way for schools to realize efficient management and information technology should be introduced into the management of student registration files to accelerate the process of informationization of registration file management. This paper builds an informatization database of adult higher continuing education student registration file management based on informatization technology and carries out the design of the main business process of student registration files. The multi-layer stacked LSTM network in the ELMo model is utilized to learn the preprocessing of the text of the school records data, and the CART decision tree algorithm is improved based on the reinforcement learning mechanism in order to improve the classification prediction accuracy of the decision tree in the dataset. In order to verify the feasibility of the school registration file informationization database constructed in this paper, a test analysis is conducted for the application of data mining technology and informationization database. The results show that the check accuracy of the CART decision tree algorithm improved by the reinforcement learning mechanism is around 80%, and the throughput of this paper’s database reaches 4827ops/s when the data writing volume is 1*105GB. When the number of threads reaches 128, the throughput of this paper’s informative database is around 5486ops/s, and when the data volume reaches 12*105 entries, the database in this paper the query response time is around 602ms. Under the background of three-round education, it is necessary to fully integrate information technology with the management of school registration files, which will lead to the innovation of the informationization mode of the management of school registration files in adult higher education.

Supervised and unsupervised learning models for pharmaceutical drug rating and classification using consumer generated reviews

Optimization of medication therapy depends on maximizing benefits and minimizing side effects of medications. This research showed how a joint approach using text mining, natural language processing, and machine learning can provide information for personalized and optimized medication therapy. Reviews on the benefits and side effects of prescription and over-the-counter medications were used to determine how well an integrated supervised and unsupervised learning could learn medication satisfaction. Supervised learning with naïve-Bayes, non-linear support vector machine with radial basis function kernels, and random forests with CART decision trees was measured by a micro-aggregated Matthews correlation coefficient and a macro-averaged F1 measure. Random forests outperformed support vector machines by almost 250% and naive-Bayes by 600% on the two evaluation metrics. All models did better with three rating levels, instead of five. Topic modeling and stacked cluster analysis were coupled with parts-of-speech tagging and text mining operations to establish a robust data preprocessing procedure to eliminate noisy features from the data. Unsupervised topic modeling and clustering represented an exploratory validation of how easy supervised classification would be. Well-defined latent topics were discovered including topics on “sleep quality”, “the opportunity to get back to work”, and “weight gain”. Overlapping clusters revealed that incorporating more information on social, demographic, or medical history variables could improve classifier performance. This research provided evidence that medication satisfaction can be learned with carefully designed joint supervised, unsupervised, and natural language learning techniques.

Comparative Study on Object-Oriented Identification Methods of Plastic Greenhouses Based on Landsat Operational Land Imager

The rapid and precise acquisition of the agricultural plastic greenhouse (PG) spatial distribution is essential in understanding PG usage and degradation, ensuring agricultural production, and protecting the ecological environment and human health. It is of great practical significance to realize the effective utilization of remote sensing images in the agricultural field and improve the extraction accuracy of PG remote sensing data. In this study, Landsat operational land imager (OLI) remote sensing images were used as data sources, and Shandong Province, which has the largest PG distribution in China, was selected as the study area. PGs in the study area were identified by means of contour recognition, feature set construction of the spatial structure, and machine learning. The results were as follows. (1) Through an optimal segmentation parameter approach, it was determined that the optimal segmentation scale for size, shape, and compactness should be set at 20, 0.8, and 0.5, respectively, which significantly improved PG contour recognition. (2) Among the 72 feature variables for PG spatial recognition, the number of features and classification accuracy showed a trend of first gradually increasing and then decreasing. Among them, fifteen feature variables, including the mean of bands 2 and 5; six index features (NDWI, GNDVI, SWIR1_NIR, NDVI, and PMLI); two shape features, the density and shape index; and two texture features, the contrast and standard deviation, played an important role. (3) According to the recall rate, accuracy rate, and F-value of three machine learning methods, random forest (RDF), CART decision tree (CART), and support vector machine (SVM), SVM had the best classification effect. The classification method described in this paper can accurately extract continuous plastic greenhouses through remote sensing images and provide a reference for the application of facility agriculture and non-point-source pollution control.

Comparison of Regression Algorithms for Predicting Students' Academic Performance

In recent years, higher education has reached the peak level in growth. Many Universities, educational institutes and colleges are being set up at private level and public level for the growth of the education sector and for the well being of students [1]. Each of the institutions aims at providing higher knowledge to the students by grooming their faculties and implementing best teaching and educational practices. But still the problem persists as most of the students are at risk of unemployment due to poor academic performance. DM techniques can be used to analyse vast quantity of data that is readily available in bulk, extract usable knowledge, and help decision-making in order to forecast the academic success of students. [2].
 The amount of data present in the education sector is examined, and by extracting the data from it, beneficial patterns are found. This is known as educational data mining. This paper discusses numerous regression techniques that can be used in educational institutions to forecast students' academic achievement. To forecast student performance, regression techniques such as Bayesian networks and decision trees can be used. Applications of the CART, J48, Random Forest, and ID3 decision tree algorithms are made to undergraduate student data obtained from private colleges in order to forecast how well they will perform on the final exam of the semester and determine whether or not they will be promoted for the following year. [3].

Algorithm Research for Bubble Detection in Capacitive Liquid Level Sensor of Fully Automated Immunoassay Analyzer

Fully automated immunoassay analyzers are integral to clinical testing, with liquid-level detection crucial to mitigate false measurements and cross-contamination. This study introduces a bubble detection algorithm based on the Random Forest machine learning model to address bubble detection in analyzers using capacitive sensors. The algorithm processes collected capacitance data, accurately identifying bubbles and ensuring sampling accuracy. Data preprocessing, feature extraction, and classification enable precise bubble detection. Compared to the traditional CART decision tree algorithm, this algorithm demonstrates superior bubble detection performance, indicating its significant research value and potential for liquid-level detection in fully automated immunoassay analyzers.

An Improved Multi-Objective Particle Swarm Optimization-Based Hybrid Intelligent Algorithm for Index Screening of Underwater Manned/Unmanned Cooperative System of Systems Architecture Evaluation

An improved multi-objective particle swarm optimization algorithm is combined with a machine learning classifier to meet the needs of underwater manned/unmanned cooperative warfare architecture evaluation. Firstly, based on the traditional Cauchy variation method, the particles in the population are disturbed in a dynamic way so that the particles trapped in the local optimal can jump out of the local optimal, and the convergence performance of the particle swarm optimization is improved. Secondly, the accuracy of the index set is analyzed based on the CART decision tree algorithm and the IWRF algorithm. A screening method of key indexes with fewer evaluation indexes and high evaluation accuracy is developed to solve the problem of a large number of evaluation indexes and unclear correlation of the underwater combat system. Through simulation, the extraction results of key indicators were verified, and the reliability coefficient of the final simulation experiment was 0.93, which can be considered as high reliability and effectiveness of the key indicators extracted in this study. By combining multi-objective optimization with machine learning and weighing evaluation efficiency and accuracy, a high-precision and rapid evaluation of a few indicators is achieved, which provides support for establishing an evaluation model of SoS architecture for underwater manned/unmanned cooperative operations. This research result can provide inspiration for the evaluation and evaluation of the system in order to analyze the accuracy of indicators and the evaluation effect and carry out research by simulating the actual system with tools with a high simulation degree.

Machine learning-based product quality classification of the enterprise producing aluminum flat coil

A fundamental research challenge in organizations is determining product quality by constructing prediction models considering the parameters that affect product quality. By solving this problem, shipment planning can be organized quickly based on product quality status, the line can be stopped when necessary, or when it is expected that the required product quality will not be achieved, such products can be planned in the following productions production plan. When such forecasts are not made, the product quality can be recognized much later in the production process through quality control operations. Re-planning and rework expenditures are incurred as a result of this condition. This paper proposes a data-mining-based methodological framework for predicting the quality of flat coils produced by an aluminum flat coil casting company. In the study, the quality of aluminum flat coil was classified considering the data such as OperatorSidePrint, RollEntranceWaterTemperature, UpRollDiameter, DownRollDiameter, UpSpreyVelocity, DownRollExitWaterTemperature, SteelPlateLineVelocity, SteelPlateOperatorSideBending, DownSpreyBulk that affect the product quality by the use of K-means, CLARANS, BIRCH, Ward's Hierarchical Agglomerative Clustering, Clustering, and Logistic Regression, KNN, Artificial Neural Network, CART (Decision Tree), Random Forest (RF), Gradient Boosting Machines, Feed-Forward Neural Network, Naive Bayes, XGBoost and RF, Gradient Boosting RF and AdaBoost-LSTM ensemble models, and Simple RNN classification techniques. Following the application of algorithms to 23 different semi-finished products, RNN provided the best results in 35% of the semi-finished products, and AdaBoost-LSTM provided the best results in 18% of the semi-finished products.

A study of algorithms for solving nonlinear two-level programming problems oriented to decision tree models

Abstract In this paper, the original two-level planning problem is transformed into a single-level optimization problem by combining the penalty function method for the large amount of data processing involved in the training process of the decision tree model, setting the output as a classification tree in the iterative process of the CART decision tree, and recursively building the CART classification tree with the training set to find the optimal solution set for the nonlinear two-level planning problem. It is verified that the proposed solution method is also stable at a convergence index of 1.0 with a maximum accuracy of 95.37%, which can provide an efficient solution method for nonlinear two-level programming problems oriented to decision tree models.

The effect of fitness qigong exercise on college students’ physical fitness based on big data technology

Abstract In recent years, the physical quality of college students has been declining year by year, and it is important to study the influence of fitness qigong exercise on physical fitness. In this paper, we study the principle of the CART decision tree algorithm under big data technology, sort out its algorithm steps, and propose the optimization based on the genetic algorithm to improve the accuracy of the CART algorithm by using two-layer GA for the disadvantage that CART decision tree is easy to fall into local optimum. Then, the GA-CART algorithm is used to explore the relationship between college students’ physical fitness and physical function changes and fitness qigong exercise. After 12 weeks of performing fitness qigong exercise, the BMI of female college students decreased by 2.98 on average, the BMI of male college students decreased by 2.57 on average, and the Velvec index decreased by 0.286 on average. Regarding the effect of physical function, the heart rate of female college students decreased by 2.11, 2.66, 5.97, and 12 weeks after 3, 6, 9, and 12 weeks of fitness qigong exercise on average, respectively. 6.31 beats/minute, while male college students decreased by 2.18, 1.81, 4.97, and 5.51 beats/minute, respectively. The study based on big data can provide more scientific guidance for college students to participate in fitness qigong exercises and help them to develop their overall physical fitness.

Comparative study on risk prediction model of type 2 diabetes based on machine learning theory: a cross-sectional study

ObjectivesTo compare the prediction effects of six models based on machine learning theories, which can provide a methodological reference for predicting the risk of type 2 diabetes mellitus (T2DM).Setting and...