J48 Decision Tree Algorithm Research Articles

Predicting the Poverty Alleviation in the Province of Eastern Samar using Data Mining Techniques

Poverty has been a main concern for century in any part of the world. The abrupt increase of population in the country and the inevitable rise of the inflation rate due to the economic challenges and other factors, it is clearly manifested that poverty is a problem that needs to be addressed seriously. With the available various advanced-technology nowadays, this problem on poverty maybe reduced with the aide of Data Mining which is a part of Data Science. This paper focused on predicting the poverty alleviation using Data Mining techniques based from all available data from the Philippine Statistic’s Authority, National Economic Development Authority, and Department of Social Welfare and Development. The application of supervised learning in Data Mining specifically, NaiveBayes Algorithm, Decision Tree J48 Algorithm, and K- Nearest Neighbour Algorithm has been utilized for the prediction of poverty alleviation in the province of Eastern Samar. The results of this study unveil that among the core indicators in identifying poverty, it is the “Economic Sector” with the attribute “Income” is the most significant factor that affects poverty alleviation in the province.

Classification of Autism Spectrum Disorder Data using Machine Learning Techniques

Autism is a neuro-developmental disability that affects human communication and behaviour. It is a condition that is associated with the complex disorder of the brain which can lead to significant changes in social interaction and behaviour of a human being.Machine learning techniques are being applied to autism data sets to discover useful hidden patterns and to construct predictive models for detecting its risk.This paper focuses on finding the best machine learning classifier on the UCI autism disorder data set for identifying the main factors associated with autism. The results obtained using Multilayer Perceptron, Naive Bayes Classifier and Bayesian Networkwere compared with J48 Decision tree algorithm. The superiority of MultilayerPerceptron over the well known classification algorithms in predicting the autism risk is established in this paper.

Intelligent Fault Diagnosis of Synchromesh Gearbox Using Fusion of Vibration and Acoustic Emission Signals for Performance Enhancement

Condition monitoring system monitors the system degradation and it identifies common failure modes. Several sensor signals are available for monitoring the changes in system components. Vibration signal is one of the most extensively used technique for monitoring rotating components as it identifies faults before the system fails. Early fault detection is the significant factor for condition monitoring, where Acoustic Emission ( AE ) sensor signals have been applied for early fault detection due to their high sensitivity and high frequency. In this paper, vibration and acoustic emission signals are acquired under various simulated gear and bearing fault conditions from the synchromesh gearbox. Then the statistical features are extracted from vibration and AE signals and then the prominent features are selected using J48 decision tree algorithm respectively. The best features from the vibration and AE signals are then fused using feature-level fusion strategy and it is classified using Support Vector Machine ( SVM ) and Proximal Support Vector Machine ( PSVM ) classifiers and it is compared with individual signals for fault diagnosis of the synchromesh gearbox. From the experiments, it is observed that the performance of the fault diagnosis system has been improved for the proposed feature level fusion technique compared to the performance of unfused vibration and AE feature sets.

Identification of Potential Type II Diabetes in a Chinese Population with a Sensitive Decision Tree Approach.

Background Diabetes mellitus is a chronic disease with a steadfast increase in prevalence. Due to the chronic course of the disease combining with devastating complications, this disorder could easily carry a financial burden. The early diagnosis of diabetes remains as one of the major challenges medical providers are facing, and the satisfactory screening tools or methods are still required, especially a population- or community-based tool. Methods This is a retrospective cross-sectional study involving 15,323 subjects who underwent the annual check-up in the Department of Family Medicine of Shengjing Hospital of China Medical University from January 2017 to June 2017. With a strict data filtration, 10,436 records from the eligible participants were utilized to develop a prediction model using the J48 decision tree algorithm. Nine variables, including age, gender, body mass index (BMI), hypertension, history of cardiovascular disease or stroke, family history of diabetes, physical activity, work-related stress, and salty food preference, were considered. Results The accuracy, precision, recall, and area under the receiver operating characteristic curve (AUC) value for identifying potential diabetes were 94.2%, 94.0%, 94.2%, and 94.8%, respectively. The structure of the decision tree shows that age is the most significant feature. The decision tree demonstrated that among those participants with age ≤ 49, 5497 participants (97%) of the individuals were identified as nondiabetic, while age > 49, 771 participants (50%) of the individuals were identified as nondiabetic. In the subgroup where people were 34 < age ≤ 49 and BMI ≥ 25, when with positive family history of diabetes, 89 (92%) out of 97 individuals were identified as diabetic and, when without family history of diabetes, 576 (58%) of the individuals were identified as nondiabetic. Work-related stress was identified as being associated with diabetes. In individuals with 34 < age ≤ 49 and BMI ≥ 25 and without family history of diabetes, 22 (51%) of the individuals with high work-related stress were identified as nondiabetic while 349 (88%) of the individuals with low or moderate work-related stress were identified as not having diabetes. Conclusions We proposed a classifier based on a decision tree which used nine features of patients which are easily obtained and noninvasive as predictor variables to identify potential incidents of diabetes. The classifier indicates that a decision tree analysis can be successfully applied to screen diabetes, which will support clinical practitioners for rapid diabetes identification. The model provides a means to target the prevention of diabetes which could reduce the burden on the health system through effective case management.

Improvement in wind energy production through condition monitoring of wind turbine blades using vibration signatures and ARMA features: a data-driven approach

The main objective of this study is to improve the wind energy productivity by implementing the condition monitoring technique for wind turbine blades through vibration source. The fault detection and the isolation of the fault which affects the wind energy productivity were carried using machine learning algorithms. In this study, a three bladed horizontal axis wind turbine was chosen and the faults like blade bend, blade cracks, hub-blade loose connection, blade erosion and pitch angle twist were considered as these are the faults which affect the turbine blade. Initially, vibration sources were collected from the wind turbine using piezoelectric accelerometer and from that vibration source; needed features are extracted using ARMA through MATLAB. From the extracted feature, the dominating feature is selected using J48 decision tree algorithm and with the selected features, fault classification has been carried out. The fault classifications were carried out using Bayesian, function and lazy classifiers.

Using decision trees to analyse the customers' shopping location preferences

Customers' shopping location preferences were analysed by implementing J48 decision tree algorithm. Findings revealed that the existence of discounts at shopping centres and customers' awareness of these discounts is the most important socioeconomic factor that affects customers' behaviour in the context of shopping location preferences. The findings also revealed that the majority of high-income households prefer shopping centres, whereas households in low- or middle-income groups are more inclined toward shopping at individual scattered stores. Finally, the results indicated that J48 decision tree algorithm can be used as a reliable tool for analysing the consumer behaviour due to its high accuracy.

Vibration Based Tool Insert Health Monitoring Using Decision Tree and Fuzzy Logic

The productivity and quality in the turning process can be improved by utilizing the predicted performance of the cutting tools. This research incorporates condition monitoring of a non-carbide tool insert using vibration analysis along with machine learning and fuzzy logic approach. A non-carbide tool insert is considered for the process of cutting operation in a semi-automatic lathe, where the condition of tool is monitored using vibration characteristics. The vibration signals for conditions such as heathy, damaged, thermal and flank were acquired with the help of piezoelectric transducer and data acquisition system. The descriptive statistical features were extracted from the acquired vibration signal using the feature extraction techniques. The extracted statistical features were selected using a feature selection process through J48 decision tree algorithm. The selected features were classified using J48 decision tree and fuzzy to develop the fault diagnosis model for the improved predictive analysis. The decision tree model produced the classification accuracy as 94.78% with five selected features. The developed fuzzy model produced the classification accuracy as 94.02% with five membership functions. Hence, the decision tree has been proposed as a suitable fault diagnosis model for predicting the tool insert health condition under different fault conditions.

Predicting Tuberculosis Treatment Relapse: A Decision Tree Analysis of J48 for Data Mining

Tuberculosis remains an important problem in public health that threatens the world, including the Philippines. Treatment relapse continues to place a severe problem on patients and TB programs worldwide. A significant reason for the development of decline is poor compliance with medical treatments. The objectives of this research are to generate a predictive data mining model to classify the treatment relapse of TB patients and to identify the features influencing the category of treatment relapse. The TB patient dataset is applied and tested in decision tree J48 algorithm using WEKA. The J48 model identified the three (3) significant independent variables (DSSM Result, Age, and Sex) as predictors of category treatment relapse.

Condition Monitoring of Two Stroke IC Engine Ball Bearing Based on Vibration Signals Using Machine Learning Techniques

Ball bearing faults are foremost cause to the breakdown of rotating machine components. In general, ball bearing in the IC engine is one of the vital machine elements and plays major role in the rotating machine as it takes different kinds of stresses and dynamic loads under various working conditions. Monitoring of such ball bearing continuously is extremely needed to avoid damage to the components of IC Engine. Vibration analysis of IC engine is most commonly used to recognise the fault in ball bearing and to differentiate the types of ball bearing defects. In the present work, study has been conducted on condition monitoring of ball bearing of 2-stroke single cylinder IC engine using vibration signals and explains the use of statistical features and support vector machine as a tool for classification of the ball bearing conditions. The different cases of faults considered in the diagnosis of ball bearing are outer race (OR) defect, inner race (IR) defect, combined faults in outer and inner race (OR & IR) and ball defect. The vibration signals under good and different simulated defective conditions of ball bearing are collected using an accelerometer. The descriptive statistical set of features are found from the collected vibration signals. The significant features are carefully chosen by means of J48 decision tree technique and the features chosen from tree are given as input to the support vector machine (SVM). The experimental results show that, the SVM model provides 96% classification accuracy for the given vibration signals. The obtained result from the model is good and can be considered for diagnosis of rotating machinery. Hence, the J48 decision tree and SVM algorithm can be proposed for applications of condition monitoring of ball bearings.

A Comparative Study for Condition Monitoring on Wind Turbine Blade using Vibration Signals through Statistical Features: a Lazy Learning Approach

This study is to identify whether the wind turbine blades are in good or faulty conditions. If faulty, then the objective to find which fault condition are the blades subjected to. The problem identification is carried out by machine learning approach using vibration signals through statistical features. In this study, a three bladed wind turbine was chosen and faults like blade cracks, hub-blade loose connection, blade bend, pitch angle twist and blade erosion were considered. Here, the study is carried out in three phases namely, feature extraction, feature selection and feature classification. In phase 1, the required statistical features are extracted from the vibration signals which obtained from the wind turbine through accelerometer. In phase 2, the most dominating or the relevant feature is selected from the extracted features using J48 decision tree algorithm. In phase 3, the selected features are classified using machine learning classifiers namely, K-star (KS), locally weighted learning (LWL), nearest neighbour (NN), k-nearest neighbours (kNN), instance based K-nearest using log and Gaussian weight kernels (IBKLG) and lazy Bayesian rules classifier (LBRC). The results were compared with respect to the classification accuracy and the computational time of the classifier.

Acoustic Emission-Based Tool Condition Classification in a Precision High-Speed Machining of Titanium Alloy: A Machine Learning Approach

Mechanical and chemical properties of titanium alloy have led to its wide range of applications in aerospace and biomedical industries. The heat generation and its transfer from the cutting zone are critical in machining of titanium alloys. The process of transferring heat from the primary cutting zone is difficult due to poor thermal conductivity of titanium alloy, and it will lead to rapid tool wear and poor surface finish. An effective tool monitoring system is essential to predict such variations during machining process. In this study, using a high-speed precision mill, experiments are conducted under optimum cutting conditions with an objective of maximizing the life of tungsten carbide tool. Tool wear profile is established and tool conditions are arrived on the basis of the surface roughness. Acoustic emission (AE) signals are captured using an AE sensor during machining of titanium alloy. Statistical features are extracted in time and frequency domain. Features that contain rich information about the tool conditions are selected using J48 decision tree (DT) algorithm. Tool condition classification abilities of DT and support vector machines are studied in time and frequency domains.

Appraisal of the Classification Technique in Data Mining of Student Performance using J48 Decision Tree, K-Nearest Neighbor and Multilayer Perceptron Algorithms

Appraisal of the Classification Technique in Data Mining of Student Performance using J48 Decision Tree, K-Nearest Neighbor and Multilayer Perceptron Algorithms

A machine learning approach for condition monitoring of wind turbine blade using autoregressive moving average (ARMA) features through vibration signals: a comparative study

In wind turbine, blades are the major component for capturing the wind, however, due to environmental conditions it prompts to get a fault. To overcome this problem, a machine learning based condition monitoring technique is incorporated into the wind turbine to identify the fault classification which occurred in the blade. In this study, a three-bladed horizontal axis wind turbine was chosen and the faults like blade bend, blade cracks, hub-blade loose connection, blade erosion and pitch angle twist were considered as these are the faults mostly affect the turbine blade. In this study, the autoregressive-moving-average (ARMA) features have been extracted from the raw signal and the dominating feature was selected through J48 decision tree algorithm followed by the fault classification using machine learning classifiers. The results were compared with respect to the classification accuracy and their computational time of the classifier.

Detection and Elimination of Spyware and Ransomware by Intercepting Kernel-Level System Routines

Spyware is the most complex, obfuscated, and targeted class of malware, which has grown dramatically in recent years. Spyware is designed for secret, long-term, and persistent missions. This paper provides a novel method for detection, tracking, and confronting the stealth and obfuscated spyware and ransomware, including keyloggers, screen recorders, and blockers. The proposed method of this paper is based on a dynamic behavioral analysis through deep and transparent hooking of kernel-level routines. We used linear regression, JRIP, and J48 decision tree algorithms as a classifier to recognize three classes of malware. This paper presents the main architectural plan of an anti-spyware application to track spyware footprints in order to detect and force terminate running processes, eliminate executable files, and restrict network communications. The efficiency of the proposed method was evaluated from the viewpoint of accuracy in detecting real-world samples of spyware by ROC curve analysis and from the viewpoint of success rate to confront effectively with active spyware. Our proposed method was able to recognize spyware with an accuracy of about 93% and an error rate near 7%. In addition, the proposed system can disinfect an operating system from infection by spyware with a hit rate of about 82%.

Misfire detection in I.C. engine through ARMA features using machine learning approach

One of the prime problems engines are facing is Misfire, as it leads to the power loss along with the exhaust gas containing air pollutants like CO and NOx. This paper proposes a predictive model for misfire detection using machine learning approach. For the present study, vibration signals acquired using the piezoelectric accelerometer were taken into consideration as a pattern of a misfire for each cylinder is specific in nature. Then, ARMA features were extracted from acquired vibration signals followed by Feature selection using J48 decision tree algorithm. For feature classification, the functional tree classifier was used. In this study, the classification accuracy of 92.2% was achieved. The proposed model was tested on the engine test rig wherein every cylinder misfire tests were conducted. This work can be improved by using different classifier algorithms for more accurate misfire detection.

Bound Model of Clustering and Classification (BMCC) for Proficient Performance Prediction of Didactical Outcomes of Students

In this era of High-Performance High computing systems, Large-scale Data Mining methodologies in the field of education have become a convenience to discover and extract knowledge from Databased of their respective educational archives. Typically, all educational institutions around the world maintain student data repositories. Attributes of students such as the name of the student, gender of student, age group (date of birth), religion, eligibility details, academic assessment details, etc. are kept in it. With this knowledge, in this paper, didactical data mining (DDM) is used to leverage the performance prediction of student and to analyse it proactively. As it is known, Classification and Clustering are the liveliest techniques in mining the required data. Hence, Bound Model of Clustering and Classification (BMCC) have been proposed in this research for most proficient educational data mining. Classification is one of the distinguished options in Data Mining to assign an object under some pre-defined classes according to their attributes, and hence it comes under a supervised learning problem. On the other side, clustering is considered as a non-supervised learning problem that involves in grouping up of objects with respect to some similarities. Moreover, this paper uses the dataset collected from Kerala Technological University-SNG College of Engineering (KTU_SNG) for performing the BMCC. An efficient J48 decision tree algorithm is used for classification and the k-means algorithm is incorporated for clustering here and is optimised with Bootstrap Aggregation (Bagging). The implementation has been done and analysed with a data mining tool called WEKA (Waikato Environment for Knowledge Analysis), and the results are compared with some most used classifications such as Bayes Classifier (NB), Neural Network (Multilayer Perceptron MLP) and J48. It is provable from the results that the model, proposed in this provides high Precision Rate (PR), accuracy and robustness with less computational time, though the sample data set includes some missing values.

Early prediction of systemic lupus erythematosus using hybrid K-Means J48 decision tree algorithm

The objective of the paper is to propose an enhanced algorithm for the prediction of chronic, autoimmune disease called Systemic Lupus Erythematosus (SLE). The Hybrid K-means J48 Decision Tree algorithm (HKMJDT) has been proposed for the effective and early prediction of the SLE. The reason for combining both the clustering and classification algorithms is to obtain the best accuracy and to predict the disease in the early stage. The performance of algorithms such as Naïve Bayes, decision tree, random forest, J48 and Hoeffding tree has been combined with K-means clustering algorithm and compared in an effort to find the best algorithm for diagnosing SLE disease. The results of the statistical evaluation with the comparative study show that the effectiveness of different classification techniques depends on the nature and intricacy of the dataset used. K-means combined with J48 algorithm shows the best accuracy rate of 82.14% on the pre-processed data. The work-flow has been proposed to show the execution of the algorithm.

Predicting the Seroprevalence of HBV, HCV, and HIV Based on National Blood of Addis Ababa Ethiopia Using Data Mining Technology

Recent advancements in communication technologies, on the one hand, and computer hardware and database technologies, on the other hand, have made it easy for organizations to collect, store and manipulate massive amounts of data. As the volume of data increases, the proportion of information in which people could understand decreases substantially. The applications of learning algorithms in knowledge discovery are promising and they are relevant area of research offering new possibilities and benefits in real-world applications such as blood bank data warehouse. The availability of optimal blood in blood banks is a critical and important aspect in a Blood transfusion service. Blood banks are typically based on a healthy person voluntarily donating blood used for transfusions. The ability to identify regular blood donors enables blood bank and voluntary organizations to plan systematically for organizing blood donation camps in an efficient manner. The objective of this study was to explore the immense applicability of data mining technology in the Ethiopian national blood bank service by developing a predictive model that could help in the donor recruitment strategies by identifying donors that are at risk of TTIs which can help in the collection of safe blood group which in turn assists in maintaining optimal blood. The analysis has been carried out on 14575 blood donor’s dataset that has at least one pathogen using the J48 decision tree and Naive bayes algorithm implemented in Weka. J48 decision tree algorithm with the overall model accuracy of 94% has offered interesting rules. From the total of 156729 consecutive blood donors, 14757 (9.41%) had serological evidence of infection with at least one pathogen and 29 (0.19%) had multiple infections. The overall seroprevalence of HIV, HBV and HCV was 2.29%, 5.23%, and 2.30% respectively. The seropositivity of TTIs was significant in business owners, students, civil servants, unemployed individuals, drivers and age groups 25 to 34 and 35 to 44 years.

A data driven approach for condition monitoring of wind turbine blade using vibration signals through best-first tree algorithm and functional trees algorithm: A comparative study

Wind energy is one of the important renewable energy resources available in nature. It is one of the major resources for production of energy because of its dependability due to the development of the technology and relatively low cost. Wind energy is converted into electrical energy using rotating blades. Due to environmental conditions and large structure, the blades are subjected to various vibration forces that may cause damage to the blades. This leads to a liability in energy production and turbine shutdown. The downtime can be reduced when the blades are diagnosed continuously using structural health condition monitoring. These are considered as a pattern recognition problem which consists of three phases namely, feature extraction, feature selection, and feature classification. In this study, statistical features were extracted from vibration signals, feature selection was carried out using a J48 decision tree algorithm and feature classification was performed using best-first tree algorithm and functional trees algorithm. The better algorithm is suggested for fault diagnosis of wind turbine blade.

Student performance and time-to-degree analysis by the study of course-taking patterns using J48 decision tree algorithm

Higher educational institutions (HEIs) are striving to improve their student's performance. Classification of students to predict student's performance could be useful in identifying bad performers or late completers to improve at an early stage by forecasting their final outcome. In this study, three student's performance prediction models were investigated. J48 decision tree algorithm was used for the study. When the performance of these models was evaluated, it was found that the third prediction model provided better performance compared to the other models. Also, there was variation in the course-taking patterns of students and contextual attribute course difficulty depending on their completion time. This demonstrates the significance of students course-taking patterns on performance and time-to-degree. The most significant finding is that unlike easy course delivering high GPAs interestingly some very difficult courses too lead to optimum time and GPA which could be attributed to other hidden factors which need to be investigated.