J48 Decision Tree Algorithm Research Articles

Application of Bayesian Family Classifiers for Cutting Tool Inserts Health Monitoring on CNC Milling

The customized usage of tool inserts plays an imperative role in the economics of machining operations. Eventually, any in-process defects in the cutting tool lead to deterioration of complete machining activity. Such defects are untraceable by the conventional practices of condition monitoring. The characterization of such in-process tool defects needs to be addressed smartly. This would also assist the requirement of ‘self-monitoring’ in Industry 4.0. In this context, induction of supervised Machine Learning (ML) classifiers to design empirical classification models for tool condition monitoring is presented herein. The variation in faulty and fault-free tool condition is collected in terms of vibrations during the face milling process on CNC (Computer Numerically Controlled) machine tool. The statistical approach is incorporated to extract attributes and the dimensionality of the attributes is reduced using the J48 decision tree algorithm. The various conditions of tool inserts are then classified using two supervised algorithms viz. Bayes Net and Naïve Bayes from the Bayesian family.

Development of a monitoring system for grain loss of paddy rice based on a decision tree algorithm

China has the world’s largest planting area of paddy rice, but large quantities of paddy rice fall to the ground and are lost during harvesting with a combine harvester. Reducing grain loss is an effective way to increase production and revenue. In this study, a monitoring system was developed to monitor the grain loss of the paddy rice and this approach was tested on the test bench for verifying the precision. The development of the monitoring system for grain loss included two stages: the first stage was to collect impact signals using a piezoelectric film, extract the four features of Root Mean Square, Peak number, Frequency and Amplitude (fundamental component), and identify the kernel impact signals using the J48 (C4.5) Decision Tree algorithm. In the second stage, the precision of the monitoring system was tested for the paddy rice at three different moisture contents (10.4%, 19.6%, and 30.4%) and five different grain/impurity ratios (1/0.5, 1/1, 1/1.5, 1/2, and 1/2.5). According to the results, the highest monitoring accuracy was 99.3% (moisture content 30.8% and grain/impurity ratio 1/2.5), the average accuracy of the monitoring tests was 92.6%, and monitoring of grain/impurity ratios between 1/1 and 1/1.5 (>95.4%) had higher accuracy than monitoring the other grain/impurity ratios. Monitoring accuracy decreased as impurities increased. The lowest accuracy for grain loss monitoring was obtained when the grain/impurity ratio was 1/2.5, with monitoring accuracies of 88.2%, 75.7% and 78.8% at moisture contents of 10.4%, 19.6% and 30.4%. Keywords: monitoring system, combine harvester, paddy rice, grain loss, sensor, data mining, decision tree, development DOI: 10.25165/j.ijabe.20211401.5731 Citation: Lian Y, Chen J, Guan Z H, Song J. Development of a monitoring system for grain loss of paddy rice based on a decision tree algorithm. Int J Agric & Biol Eng, 2021; 14(1): 224–229.

Binary Classification of Network-Generated Flow Data Using a Machine Learning Algorithm

This study proposes a model for building intrusion detection systems. The dataset used, CICIDS 2017, contains 14 different attacks with 85 features for each attack. This high dimensionality of the data is a major challenge when building efficient intrusion detection systems, especially in today's big data environment, since a lot of the features are redundant. The main goal in this paper was to reduce the number of features and present a detailed discussion of the important features. For feature selection, information gain was used in an iterative way, and for classification, a machine learning algorithm, the J48 decision tree algorithm, was used. The important features for the classification of each attack were identified, and the features that were important for classifying multiple attacks were also identified and discussed.

A Bayes learning approach for monitoring the condition of suspension system using vibration signals

The suspension system is an important component in an automobile, aimed to provide better comfort and good road handling characteristics during vehicle motion on uneven road conditions. Varying load conditions, prolonged operations, continuous road shocks absorption, and time-based degradation of internal components can create faults in a suspension system. Fault occurrences in the suspension system can damage the internal components that lead to malfunctioning of the system causes failure and endanger vehicle safety. Thus, condition monitoring has become an essential part of identifying fault occurrences in a suspension system. This paper is focussed on condition monitoring of suspension system using vibration signal analysis and faults are classified with the help of machine learning-based Bayesian classifiers. A test setup is fabricated to simulate the working of the suspension system under different load conditions with one good and six faulty conditions. Vibration signals are recorded and used to extract statistical features. Further J48 decision tree algorithm was utilized to identify the most significant features during feature selection process. Bayes classifiers such as NaiveBayes and BayesNet classifiers were used to find the type of fault occurrence from the selected features. The overall results of the aforementioned classifiers are compared and the best in the Bayesian classifier is suggested for real-time application.

Health monitoring of automotive clutch system by using Bayes algorithms

Clutch is an essential component in an automotive transmission system that helps in power transmission from the engine to the gearbox. Continuous operation of the clutch leads to degradation, damage, and reduced lifetime of internal components. Such factors can provoke the occurrence of various faults, which left unmonitored will result in clutch damage and seizure. Hence, continuous monitoring of the clutch is necessary to minimize unwanted breakdowns. This paper presents a condition monitoring technique based on vibration analysis to monitor the fault occurrence of different components. A machine learning approach is carried out to classify various faults. Such as fingers worn, pressure plate broken, pressure plate worn, friction material loss, and tangential strip bent. Feature extraction is performed on the acquired vibration signals using statistical learning process. The most important and significant features are selected from the extracted features by J48 decision tree algorithms. Further, feature classification is done with the help of Bayes based classifiers and the obtained results are compared to predict the best in class classifier for real-time.

Predicting change in newly created files in a software product line project

AbstractAt the beginning of the testing phase and before the deployment phase of a project's development cycle, we need to predict files with a high chance of change. Software products are always prone to change due to several reasons, including fixing errors or improvements. In this work, we used the Eclipse (releases from 2.0 to 3.5) to investigate how prediction models can perform when learning from a release and predicting in the subsequent one, which contains new files that models have not seen. We compared the performance of these models with models that are trained and tested on the same release. We found no differences between predicting the same release or subsequent release on two pre Europa releases. Predicting change in newly created files helps improve maintenance planning for software project managers and reduce cost. It will also help to enhance the quality of software by improving the practices of developers. This study used the Adaptive Boost classifier with the decision tree J48 algorithm and combined it with the re‐sampling method. We find this to be better than using a meta classifier alone or combine the re‐sampling with the standard classification. We compared our results with related works and found that our results are outperforming.

THE ENSEMBLE METHOD DEVELOPMENT OF CLASSIFICATION OF THE COMPUTER SYSTEM STATE BASED ON DECISIONS TREES

The subject of this article is exploration of methods for identifying the status of a computer system. The purpose of the article is development of a method for classifying a computer system anomalous state based on ensemble methods. Task: To investigate the usage of algorithms for building decision trees: REPTree, Random Tree, J48, HoeffdingTree, DecisionStump and bagging and boosting decision tree ensembles to identify a computer system anomalous state by analyzing operating system events. The methods used are artificial intelligence, machine learning and ensemble classification methods. The following results were obtained: the methods of identifying the computer systems anomalous state based on ensemble methods were investigated, namely, bagging, boosting, and classifiers: REPTree, Random Tree, J48, HoeffdingTree, DecisionStump to identify a computer system anomalous state. The different classifiers set and classifiers ensembles were developed. Training and cross-validation on each algorithm was performed. The developed classifiers performance has been evaluated. The research suggests an ensemble method of a computer system state classifying based on the J48 decision tree algorithm. Conclusions. The scientific novelty of the obtained results consists in creating an ensemble method for classifying the state of a computer system based on a decision tree, which makes it possible to increase the reliability and speed of classification.

Rough Set Theory Based Blade Condition Classification on Wind Turbine through Statistical Features

Wind energy has become a one of the alternative energy source due to fossil fuel crisis. These wind energies are being harvested from the wind through wind turbines. These wind turbines are subjected to various environmental factors and prone to severe vibration on blade. This vibration lead to the catastrophic calamities and cause severe capital loss and wind production loss. This study proposes a data processing and analysis of wind turbine blade faults using rough set theory based feature classification. The feature extraction (statistical features) and the feature selection (J48 decision tree algorithm) methods were used to identify the best features for fault classification. Using rough set theory, with five statistical features, 75.5% of classification accuracy have been obtained for the fault identification on wind turbine blade.

Labelling Data for Correlation Pollution Dataset by Using Machine Learning

The significant problem in the environment is the pollution of air which affects all the areas in our world. The solution of this problem is excluding by using Internet of Things (IoT) technology which designed system that monitors the parameters that affect the pollution in the environment. The handling and operation of large quantities of information from a wide range of sources is challenges in IoT technology. The purpose of this study is to decrease the dimensions in IoT systems so we suggest two models for IoT system and also aims to build rules for Air quality classification by defining pollution levels. In this study, we suggested machine learning techniques to predict an air pollution levels based on a dataset consisting of daily weather, also we simplified our classification models to solve this problem by using binary classification which is to classify the air pollution level into “High pollution” and “Simple pollution” class labels. The decision tree J48 Algorithm gives the best outcomes in the first model than the second models.

Classifying the Level of Energy-Environmental Efficiency Rating of Brazilian Ethanol

The present study aimed to assess and classify energy-environmental efficiency levels to reduce greenhouse gas emissions in the production, commercialization, and use of biofuels certified by the Brazilian National Biofuel Policy (RenovaBio). The parameters of the level of energy-environmental efficiency were standardized and categorized according to the Energy-Environmental Efficiency Rating (E-EER). The rating scale varied between lower efficiency (D) and high efficiency + (highest efficiency A+). The classification method with the J48 decision tree and naive Bayes algorithms was used to predict the models. The classification of the E-EER scores using a decision tree using the J48 algorithm and Bayesian classifiers using the naive Bayes algorithm produced decision tree models efficient at estimating the efficiency level of Brazilian ethanol producers and importers certified by the RenovaBio. The rules generated by the models can assess the level classes (efficiency scores) according to the scale discretized into high efficiency (Classification A), average efficiency (Classification B), and standard efficiency (Classification C). These results might generate an ethanol energy-environmental efficiency label for the end consumers and resellers of the product, to assist in making a purchase decision concerning its performance. The best classification model was naive Bayes, compared to the J48 decision tree. The classification of the Energy Efficiency Note levels using the naive Bayes algorithm produced a model capable of estimating the efficiency level of Brazilian ethanol to create labels.

Adaptive Virtual Learning Environment based on Learning Styles for Personalizing E-learning System: Design and Implementation

Most virtual learning environment fails to recognize that students have different needs when it comes to learning. With the evolving characteristics and tendencies of students, these learning environments must provide adaptation and personalization features for adaptive learning materials, course content and navigational designs to support student’s learning styles. Based from the data mining results of learner behavioral features of five hundred seven (507) tertiary students, an accurate model for classification of student’s learning styles were derived using J48 decision tree algorithm. The model was implemented in a prototype using a framework and a proposed system architectural design of an adaptive virtual learning environment. The study resulted in the development of an adaptive virtual learning environment prototype where learner’s preferences are dynamically diagnosed to intelligently personalize the course content design and user interfaces for them.

Logistic Model Tree and Decision Tree J48 Algorithms for Predicting the Length of Study Period

One point to be assessed in the accreditation process in an institution is the length of the student's study period. The Informatics department in XYZ college has been accredited by the national accreditation bureau for higher education (BAN-PT), but the accreditation has the potential to be improved. One thing that affects the accreditation value is many students did not graduate on time. Therefore, the current study used available student data, both academic and non-academic, using data mining. Two model classifications were used, i.e. Logistic Model Tree (LMT) and Decision Tree J48. The study was aimed to compare LMT and Decision Tree J48 algorithm in predicting the length of student’s study and to find out the influence factors. The data were Informatics Engineering students who have graduated in February 2018 to February 2019 (135 records). Results showed that the LMT algorithm produced an accuracy rate of 71% better than Decision Tree J48 (62.8% accuracy) in predicting the length of the student’s study. The factors influencing the length of study of students are temporary grade point average (GPA) of the first semester, temporary GPA of the second semester, organizational status, and employment status.

A machine leaning approach for abdominal aortic aneurysm severity assessment using geometric, biomechanical, and patient-specific historical clinical features.

Recent studies monitoring severity of abdominal aortic aneurysm (AAA) suggested that reliance on only the maximum transverse diameter ( D max ) may be insufficient to predict AAA rupture risk. Moreover, geometric indices, biomechanical parameters, material properties, and patient-specific historical data affect AAA morphology, indicating the need for an integrative approach that incorporates all factors for more accurate estimation of AAA severity. We implemented a machine learning algorithm using 45 features extracted from 66 patients. The model was generated using the J48 decision tree algorithm with the aim of maximizing model accuracy. Three different feature sets were used to assess the prediction rate: i) using D max as a single-feature set, ii) using a set of all features, and, lastly iii) using a feature set selected via the BestFirst feature selection algorithm. Our results indicate that BestFirst feature selection yielded the highest prediction accuracy. These results indicate that a combination of several specific parameters that comprehensively capture AAA behavior may enable a suitable assessment of AAA severity, suggesting the potential benefit of machine learning for this application.

Analyzing Student Performance in Programming Education Using Classification Techniques

In this research, we aggregated students log data such as Class Test Score (CTS), Assignment Completed (ASC), Class Lab Work (CLW) and Class Attendance (CATT) from the Department of Mathematics, Computer Science Unit, Usmanu Danfodiyo University, Sokoto, Nigeria. Similarly, we employed data mining techniques such as ID3 & J48 Decision Tree Algorithms to analyze these data. We compared these algorithms on 239 classification instances. The experimental results show that the J48 algorithm has higher accuracy in the classification task compared to the ID3 algorithm. The important feature attributes such as Information Gain and Gain Ratio feature evaluators were also compared. Both the methods applied were able to rank search method and the experimental results confirmed that the two methods derived the same set of attributes with a slight deviation in the ranking. From the results analyzed, we discovered that 67.36 percent failed the course titled Introduction to Computer Programming, while 32.64 percent passed the course. Since the CATT has the highest gain value from our analysis; we concluded that it is largely responsible for the success or failure of the students.

Prediction of Wheat Rust Diseases Using Data Mining Application

Stem rust caused by Puccinia graminis f.sp. tritici and stripe rust caused by Puccinia striiformis are the most destructive wheat rust diseases when environment conditions are favorable in regions where wheat crops are grown. An early prediction mechanism can play a great role in forecasting the occurrence of the rust disease. It assists proactive control and early decision making. However, in the absence of prediction mechanism of wheat rust disease hurried wheat production yield loss more. Hence, to overcome these issues, this study was conducted to develop wheat stripe rust and stem rust diseases prediction model using data mining application. The meteorological and disease data for the year 2010 to 2019 of national meteorological agency southern Oromia region Bale Robe service center and Oromia Seed enterprise Sinana farm unit II were obtained and used for the study. Two bread wheat varieties, namely Kakaba and Danda’a were involved in the study. Fist, daily meteorological data mean values of three-day consecutive interval were computed. Then, mean value of meteorological data has been integrated with disease incidence and severity date observed during the most critical infection period (mid-august to November-30) was used to develop the model. WEKA software machine learning tool with J48 decision tree algorithm was used for data preprocess and experiments. It is open source software containing various machine learning algorithms for data mining tasks. The study results showed that, stripe rust predictive model trained and tested with using training set test option achieved accuracy 75.70% and stem rust predictive model achieved accuracy 90.045% with cross-validation fold 10 test option. The results found were much promised to forecast occurrence of wheat stripe and stem rust disease.

A Model for Prediction of Kidney Cancer Using Data Analytics Technique

Across the globe, kidney cancer and other cancerous diseases has been a threat to human lives. The incidence and mortality rate represent a significant and growing threat to both developed and developing countries especially in Africa, where most cancers are diagnosed at an advanced stage. This typically contributes to its complications and high rate of mortality, and has been attributed to limited awareness of early signs and symptoms of the disease, lack of detective mechanism and inaccessible cancer care in our health care centres. To preclude the harm and mortality caused by the disease, an intelligent mechanism for early prediction and prognosis of the syndrome is vital. However, early detection and prognosis requires an accurate information and analytic procedure that will assist and equip the health-care providers/public with the skills to identify early the indicators of the disease. Efforts in this work, produced a model for early prediction of kidney cancer using data analytic approach. Dataset and reports pertaining to the disease were acquired from selected private and public hospitals in fifty-two (52) selected LGA in Nigeria. A two-layered classifier system consisting of Artificial Neural Networks (ANN) and Decision Tree (DT) designed for the work was successfully employed in the model building. Waikato Environment for Knowledge Analysis (WEKA) platform was used for the experiment. The performance of the classifiers considered was compared using standard metrics of accuracy and time taken as benchmark. Experimental results show that the J48 decision tree algorithm outperform all other algorithms in the classifier family with correctly classified instances of 74.7%, F-Measure of 0.614, TP rate of 0.747, FP rate of 0.135, precision and recall of 0.687 and 0.714 respectively. It took the algorithm, 0.03 seconds to build the model. The performance of this algorithm proved its suitability as a valuable tool for the research purpose. The model will in no small measure support the efforts of the national health scheme in preventing the disease mortality rate.

A lazy learning approach for condition monitoring of wind turbine blade using vibration signals and histogram features

The main objective of the proposed research study is to discriminate different blade fault conditions which affect the wind turbine blades under operating condition through machine learning approach. A three bladed wind turbine was chosen and the faults like blade bend, blade cracks, blade erosion, hub-blade loose connection and pitch angle twist were considered in the study. This problem is formulated as a machine learning problem which consists of three phases, namely feature extraction, feature selection and feature classification. Histogram features were extracted from vibration signals and feature selection was carried out using J48 decision tree algorithm. Feature classification was performed using lazy classifiers like nearest neighbour, k-nearest neighbours, locally weighted learning and K-star classifier. The results of these classifiers were compared with respect to their correctly classified instances (accuracy percentage) and found that, locally weighted learning yielded a maximum accuracy of 93.83% with a computational time of 0.07 s.

Analisis Kinerja Algoritma J48 Decision Tree untuk Pengambilan Keputusan Beli/Jual pada Saham PT Harum Energi Tbk. (HRUM)

The problem that is often faced by investors in selling / buying stocks is the difficulty in analyzing a dataset of stock prices in large quantities.This analysis aims to predict the rise or fall of stock prices based on data obtained. To assist investors in determining buying / selling decisions on stock analysis based on technical and equipped with classification techniques in data mining. This study analyzes the performance of the J48 Decision Tree algorithm in the Waikato Environmental Software for Knowledge Analysis (WEKA) version 3.8.2 for PT. Harum Energi Tbk. (HRUM). The results showed in the testing data, the percentage of testing on data without normalization was higher by 87.3 (non-aggressive) and 88.8 (aggressive) compared to normalized data 84.2 (non-aggressive) and 85 (aggressive ). The biggest stock profit generated is in non-aggressive type data without normalized by 48.75 or Rp. 48,750.00.

Implementation of Rule based Classifiers for Wind Turbine Blade Fault Diagnosis using Vibration Signals

The fast developing wind industry has revealed a requirement for more multifaceted fault diagnosis system in the segments of a wind turbine. “Present wind turbine researches concentrate on enhancing their dependability quality and decreasing the cost of energy production, especially when wind turbines are worked in off-shore places. Wind turbine blades are ought to be an important component among the other basic segments in the wind turbine framework since they transform dynamic energy of wind into useable power and due to environmental conditions, it get damage often and cause lack in productivity. The main objective of this study is to carry out a fault identification model for wind turbine blade using a machine learning approach through vibration data to classify the blade condition. Here five faults namely, blade bend, hub-blade loose connection, blade cracks, blade erosion and pitch angle twist have been considered. Machine learning approach has three steps namely feature extraction, feature selection and feature classification. Feature extraction was carried out by statistical analysis followed by feature selection using J48 decision tree algorithm. Feature classification was done using twelve rule based classifiers using WEKA. The results were compared with respect to the classification accuracy and the computational time of the classifier.”

Email Spam Detection using Ensemble Methods

The swiftly growth of spam email has escalated the need to upgrade the existing spam detection and filtration methods. There is the existence of several machine learning methods for the classification and detection of email spam but these lacks in some cases. In this research work ensemble methods are adapted to detect the email spam. The machine learning methods of Multinomial Naïve Bayes and J48 Decision Tree algorithms are considered and ensembled. The considered ensemble methods are bagging and boosting. The experimentation is conducted on the dataset of CSDMC2010 Spam corpus. The results for the considered dataset are evaluated using individual classifiers, bagging, and boosting ensemble approaches. The system performance is accessed in terms of precision, recall, f-measure, and accuracy. The experimental outcomes indicates the distinguish results for the detection of email spam using ensemble methods.