REPTree Algorithm Research Articles

An Artificial Intelligence Approach to Monitor and Predict Student Academic Performance

Recent progress in technology has altered the learning behaviours of students. It can easily be said that the improvements in technologies especially from the area of artificial intelligence and data mining can empower students to learn more efficiently, effectively, and contentedly. The purpose of this paper is aimed at developing a prediction model that gives a guide to the stakeholders likes university, student’s sponsor and parents that can monitor and interpret the on-going student’s performance. The proposed system will help the user to identify students who have a high risk or low risk to ending the semester with unsatisfactory results through discovering the essential features that can influence student’s academic performance. The methodology used were mixed method approach which is qualitative and quantitative. For the part of quantitative technique, the data set for this research will be collected from the academic records section. Meanwhile, the part of qualitative will go through a few main stages which are focused on developmental phase. The prediction model used in this research are RepTree, k-NN and Naïve Bayes. The finding states that RepTree had the highest accuracy compared to k-NN and Naïve Bayes techniques. It also suggests that the RepTree algorithm would be the best prediction model which has focused the research in term of developing the system of monitoring students’ academic performance at UPTM.

Assessment of groundwater potential and determination of influencing factors using remote sensing and machine learning algorithms: A study of Nainital district of Uttarakhand state, India

Exponential increase in population, rapid urbanization and industrialization have increased the demand of water globally. Groundwater is an important resource in hilly and mountainous regions during dry spells. Thus, identifying prospective groundwater zones is crucial for conserving and managing groundwater. The study makes an attempt to assess groundwater potential in the Nainital district of a hill state in India. Random forest, multi-layer perceptron, M5P and REPTree algorithms were used for preparing groundwater potential maps. Sensitivity analysis was carried out to examine the influence of site-specific parameters on groundwater potential. Each model was evaluated through performance successors and receiver operating characteristic curve (ROC) for its effectiveness in groundwater potential assessment. Multi-layer perceptron was found best fit model for groundwater potential assessment. Largest area was found under high to very high groundwater potential zones in the plain area of the district (48 %), followed by very low to low in the hilly area (46 %) and moderate in the transition zone (7 %). Rainfall, lineament density and drainage density were found significant factors for influencing groundwater potential. The methodology adopted in this study has proved effective in groundwater potential assessment. The other geographical regions may find this methodology useful for groundwater management.

Eucalyptus Species Discrimination Using Hyperspectral Sensor Data and Machine Learning

The identification of tree species is very useful for the management and monitoring of forest resources. When paired with machine learning (ML) algorithms, species identification based on spectral bands from a hyperspectral sensor can contribute to developing technologies that enable accurate forest inventories to be completed efficiently, reducing labor and time. This is the first study to evaluate the effectiveness of classification of five eucalyptus species (E. camaldulensis, Corymbia citriodora, E. saligna, E. grandis, and E. urophyla) using hyperspectral images and machine learning. Spectral readings were taken from 200 leaves of each species and divided into three dataset sizes: one set containing 50 samples per species, a second with 100 samples per species, and a third set with 200 samples per species. The ML algorithms tested were multilayer perceptron artificial neural network (ANN), decision trees (J48 and REPTree algorithms), and random forest (RF). As a control, a conventional approach by logistic regression (LR) was used. Eucalyptus species were classified by ML algorithms using a randomized stratified cross-validation with 10 folds. After obtaining the percentage of correct classification (CC) and F-measure accuracy metrics, the means were grouped by the Scott–Knott test at 5% probability. Our findings revealed the existence of distinct spectral curves between the species, with the differences being more marked from the 700 nm range onwards. The most accurate ML algorithm for identifying eucalyptus species was ANN. There was no statistical difference for CC between the three dataset sizes. Therefore, it was determined that 50 leaves would be sufficient to accurately differentiate the eucalyptus species evaluated. Our study represents an important scientific advance for forest inventories and breeding programs with applications in both forest plantations and native forest areas as it proposes a fast, accurate, and large-scale species-level classification approach.

Comparative Performance Analysis of Ensemble Learning Algorithms for Rock Classification

Knowing the physical and mechanical properties of rocks is important for engineering studies. Because determining the properties and type of rocks affects the safety of engineering structures. Automatic detection of rock types reduces the workload of engineers. In this study, the types of rocks were determined by using some physical and mechanical properties of rocks measured in the laboratory. Rep tree algorithm and ensemble learning algorithms were used in the study. The success of ensemble learning algorithms in classification was compared. As a result, it was understood that ensemble learning algorithms increase success. The most successful algorithm in rock classification was the Logistboost algorithm. The highest performance metrics were obtained in the classification made with the Logistboost algorithm. In addition, 4 different metric types were calculated to determine the error rates of the algorithms. Logistboost algorithm classified with the lowest error rate.

Predicting Software Cohesion Metrics with Machine Learning Techniques

The cohesion value is one of the important factors used to evaluate software maintainability. However, measuring the cohesion value is a relatively difficult issue when tracing the source code manually. Although there are many static code analysis tools, not every tool measures every metric. The user should apply different tools for different metrics. In this study, besides the use of these tools, we predicted the cohesion values (LCOM2, TCC, LCC, and LSCC) with machine learning techniques (KNN, REPTree, multi-layer perceptron, linear regression (LR), support vector machine, and random forest (RF)) to solve them alternatively. We created two datasets utilizing two different open-source software projects. According to the obtained results, for the LCOM2 and TCC metrics, the KNN algorithm provided the best results, and for LCC and LSCC metrics, the REPTree algorithm was the best. However, out of all the metrics, RF, REPTree, and KNN had close performances with each other, and therefore any of the RF, REPTree, and KNN techniques can be used for software cohesion metric prediction.

Meta-learning algorithm development to predict outcomes in patients with hepatitis C virus-related hepatocellular carcinoma

Background and study aimsPrediction of prognosis and treatment outcomes for patients with hepatocellular carcinoma (HCC) is complex for most patients. Machine learning predictive analysis can be used to explore the rich information in electronic health records to discover hidden patterns and relationships. We aimed to develop a noninvasive algorithm for predicting outcome treatment options for patients with HCC. Patients and methodsThis cross-sectional study included 1298 patients with Hepatitis C virus-related HCC attending an HCC multidisciplinary clinic, Kasr Al-Aini Hospital, Cairo University, between 2009 and 2016. Using machine learning analysis, we constructed Reduced Error Pruning (REP) decision tree algorithms and applied Auto-WEKA to select the best classifier out of 39 algorithms. ResultsThe REP-tree algorithm predicted HCC management outcomes with a recall (sensitivity) of 0.658 and a precision (specificity) of 0.653 using only routine data. 854 (65.8%) instances were correctly identified, and 444 (34.2%) instances were incorrectly classified. Out of 31 attributes, liver decompensation was selected by REP-tree as the best predictor of HCC outcome (root node). With Auto-WEKA, the random subspace classifier was chosen as the best predictive algorithm with a recall (sensitivity) of 0.750 and a precision (specificity) of 0.75. There were 974 (75%) correctly classified instances and 324 (25%) incorrectly classified instances, which was better than REP-tree. ConclusionMachine learning analysis explores data to discover hidden patterns and trends and enables the development of models to predict HCC treatment outcomes utilizing simple laboratory data. The random subspace classifier predicted the outcome more accurately than REP-tree.

Analytical Formulation for Diesel Engine Fueled with Fusel Oil/Diesel Blends

The experiments related to reduction of gases from the exhaust emissions of internal combustion engines, usually conducted in laboratory conditions, are quite laborious and costly. For these purposes, modelling engine experiments with algorithms have emerged as a way forward. In this paper, the operation of diesel engine is modelled through experimental dataset, which has input variables such as engine load, fuel type and output variables such as carbon monoxide (CO), carbon dioxide (CO2), oxides of nitrogen (NOx), hydrocarbon (HC), smoke, Brake Specific Energy Consumption (BSEC) and maximum in-cylinder pressure (Cpmax). Artificial intelligence based Symbolic Regression (SR) algorithms have been used to derive analytical equations of each output variable. The derived equations and experimental results are plotted on the same graph to show the accuracy of the obtained equations. The coefficient of determination (R2) is between 0.98 and 0.99 in all equations. In addition, Mean Error Percentage (MEP) value is less than 10 in all equations. The performance of SR algorithms is compared with Artificial Neural Network (ANN), Support Vector Machines (SVM), instance-based and K nearest based classifier (IBk), ensemble method-based bagging algorithm, and decision tree-based REPTree algorithms. SR algorithms exhibit the best performance for all output variables. IBk algorithm exhibits the second-best performance for the BSEC, CO, CO2, HC and NOx output variable. SVM algorithm exhibits the second-best performance for the Cpmax output variable and Bagging algorithms exhibits the second-best performance for the smoke output variable. The operation of diesel engine can be predicted using these equations and algorithms for further research.

Assessment of groundwater arsenic contamination using machine learning in Varanasi, Uttar Pradesh, India.

This paper presents a machine learning approach for classification of arsenic (As) levels as safe and unsafe in groundwater samples collected from the Indo-Gangetic region. As water is essential for sustaining life, heavy metals like arsenic pose a public health concern. In this study, various tree-based machine learning models namely Random Forest, Optimized Forest, CS Forest, SPAARC, and REP Tree algorithms have been applied to classify water samples. As per the guidelines of the World Health Organization (WHO), the arsenic concentration in water should not exceed 10 μg/L. The groundwater quality parameter was ranked using a classifier attribute evaluator for training and testing the models. Parameters obtained from the confusion matrix, such as accuracy, precision, recall, and FPR, were used to analyze the performance of models. Among all models, Optimized Forest outperforms other classifier as it has a high accuracy of 80.64%, a precision of 80.70%, recall of 97.87%, and a low FPR of 73.33%. The Optimized Forest model can be used to test new water samples for classification of arsenic in groundwater samples.

Opinion Mining Analytics for Spotting Omicron Fear-Stimuli Using REPTree Classifier and Natural Language Processing

Abstract: Data has indisputably proven overtime to have a better idea and with the surge of big data in the era of coronavirus, research initiatives in the field of data mining continues to leverage computational methodologies. Owing to the dreadful nature of the Omicron-variant, a fight or flight dilemma readily pervades college communities far reaching implications on work ethics of academic front-liners. This study therefore aim to gain insights from academia-sourced data to unravel fear-stimulus in college communities. The predictive analytics is carried out on college-based opinion poll. The Valence Aware Dictionary for Sentiment Reasoning algorithm is deployed for emotion analytics while the Latent Dirichlet Allocation (LDA) and Latent Semantic Indexing (LSI) are employed for topic modelling. The REPTree algorithm models the fear-spotting decision tree using 10-fold cross-validation. Experimental results shows a high performance metrics of 94.68% on Recall and Precision as the hand-washing attribute is returned as the most significant variable with highest information gain. Results of topic modelling likewise returns non-clinical precautionary measures as fear stimulus while the Vader sentiment analysis shows a 22.47%, 25.8%, and 51.73% positive, negative, and neutral polarity scores respectively, indicative of the academic front-liners’ pessimism towards effective safety measure compliance with non-clinical regulations. Keywords: COVID-19, Omicron, Sentiment Analysis, Topic Modelling.

E-learning and COVID-19: Predicting Student Academic Performance Using Data Mining Algorithms

The satisfaction of E-learners has the main effect on the success of the E-learning process and leads to improvements in the E-learning system's quality and several factors affect this satisfaction. Based on the dimensions of e-learning, the main objective of this study was to evaluate the factors that contributed to students' satisfaction with e-learning during pandemic the Covid-19 and to give a thorough understanding and knowledge of different data mining techniques that have been used to predict student performance and development, as well as how these techniques help in the identification of the most relevant student attribute for prediction. Currently, to search for information in large databases, data mining techniques have become very popular and proven itis effective. Because of the performance and effectiveness of data mining techniques, it has been adopted by many areas such as telecommunication, education, sales management, banking, etc. In this paper, data mining algorithms were relied on to build e-learning classification models for a "student performance" data set, the proposed model includes 1000 instances with 35 attributes. Data mining algorithms have been implemented on the student performance data set in E-learning. Among these algorithms are the Decision Tree algorithm, Random Tree algorithm, Naive Bayes algorithm, Random Forest algorithm, REP Tree algorithm, Bagging algorithm and KNN algorithm. After comparing the results and conducting the assessment, the impact of the proposed features in e-learning on the student's performance was clarified. The final result of this study is important for providing greater insight into evaluating student performance in the COVID-19 pandemic and underscores the importance of data mining in education.

Use of Reflection Coefficients and Decision Tree Algorithm for Rapid Classification of Hazardous Chemical Liquids

The purpose of occupational health and safety studies is to protect employees from work accidents and occupational diseases and to ensure that they work in a healthy environment. Most of the work accidents happen as a result of wrong storage, transportation and use of chemicals. In order to protect employees from chemical hazards and eliminate their possible risks, a risk assessment should first be carried out. Based on the results of the risk assessment, if not done before, bottles that contain hazardous chemicals must be classified and labelled according to their risk levels. The labels of bottles that contain chemical liquids must be checked and if the labels are worn or unreadable, they must be renewed. After these have been done, hazardous chemical liquids must be classified, stored and transported according to these labels. In this study, a non-contact, liquid measurement system based on microwave data is proposed to detect hazardous liquids. In order to select the most suitable algorithm for use in this measurement system, 3 different classification algorithms have been used and the performance analysis of the algorithms has made. In the study, 3 different classification processes have been applied according to the chemical properties of the liquids. It has been observed that Random Tree algorithm has achieved the best performance while Rep Tree algorithm has done the worst performance. Using this system, hazardous chemical liquids can be detected without opening the cover of the bottles that contain the liquids. Therefore, it can be used to quickly label hazardous liquids for their safe storage and transportation.

Assessing the chemical-induced estrogenicity using in silico and in vitro methods

Multiple substances are considered endocrine disrupting chemicals (EDCs). However, there is a significant gap in the early prioritization of EDC’s effects. In this work, in silico and in vitro methods were used to model estrogenicity. Two Quantitative Structure-Activity Relationship (QSAR) models based on Logistic Regression and REPTree algorithms were built using a large and diverse database of estrogen receptor (ESR) agonism. A 10-fold external validation demonstrated their robustness and predictive capacity. Mechanistic interpretations of the molecular descriptors (C-026, nArOH,PW5, B06[Br-Br]) used for modelling suggested that the heteroatomic fragments, aromatic hydroxyls, and bromines, and the relative bond accessibility areas of molecules, are structural determinants in estrogenicity. As validation of the QSARs, ESR transactivity of thirteen persistent organic pollutants (POPs) and suspected EDCs was tested in vitro using the MMV-Luc cell line. A good correspondence between predictions and experimental bioassays demonstrated the value of the QSARs for prioritization of ESR agonist compounds.

Classification and Prediction based Enhanced J48 and REPTree Algorithms to Predict Corona Virus Pandemic

Classification and Prediction based Enhanced J48 and REPTree Algorithms to Predict Corona Virus Pandemic

Leveraging mist and fog for big data analytics in IoT environment

AbstractInternet of Things (IoT) emerged as one of the leading technological advancements of our days. IoT generates enormous quantities of valuable data that need on time processing, resulting in reliable, and accurate decisions based on the Internet of Things vision. The quality of the generated data is inadequate, incomplete, uncertain, and produced from multiple sources. Although cloud servers can analyze and store enormous data, they need a lot of time to send full‐size data for storage and analysis as well as the high overhead they have that not satisfactory in many applications. This article provides a systematic way to review the IoT environment according to big data analytics together with limitations and challenges. Moreover, a cloud‐fog‐mist combination for handling IoT data concerning centralized and distributed data mining is explained. A proposed hybrid real‐time remote patient monitoring framework introduced that consists of the integration among the mist, fog, and cloud for healthcare treatment, which remote‐monitors patients continuously. In addition, Reduced‐Error Pruning tree (REPtree), MultiLayer Perceptron, naïve Bayes, and Sequential Minimal Optimization algorithms have applied to “Gas sensors for home activity monitoring” dataset to demonstrate the feasibility of traditional data mining algorithms to IoT data. The results showed that the REPtree algorithm achieved better accuracy against others with accuracy ranged between 90.66% and 93.6% according to the size of the data used in the study. Still, for the time metric, naive Bayes outperformed them with the lowest time between 1 and 18 seconds for building the model.

Predicting Student Failure in University Examination using Machine Learning Algorithms

Student Performance Management is one of the key pillars of the higher education institutions since it directly impacts the student’s career prospects and college rankings. This paper follows the path of learning analytics and educational data mining by applying machine learning techniques in student data for identifying students who are at the more likely to fail in the university examinations and thus providing needed interventions for improved student performance. The Paper uses data mining approach with 10 fold cross validation to classify students based on predictors which are demographic and social characteristics of the students. This paper compares five popular machine learning algorithms Rep Tree, Jrip, Random Forest, Random Tree, Naive Bayes algorithms based on overall classifier accuracy as well as other class specific indicators i.e. precision, recall, f-measure. Results proved that Rep tree algorithm outperformed other machine learning algorithms in classifying students who are at more likely to fail in the examinations.

Decision Tree Based Interference Recognition for Fog Enabled IOT Architecture

The cyber-attacks nowadays are becoming more and more erudite causing challenges in distinguishing them and confining. These attacks affect the sensitized information’s of the network by penetrating into the network and behaving normally. The paper devises a system for such interference recognition in the internet of things architecture that is aided by the FOG. The proposed system is a combination of variety of classifiers that are founded on the decision tree as well as the rule centered conceptions. The system put forth involves the JRip and the REP tree algorithm to utilize the features of the data set as input and distinguishes between the benign and the malicious traffic in the network and includes an decision forest that is improved with the penalizing attributes of the previous trees in the final stage to classify the traffic in the network utilizing the initial data set as well as the outputs of the classifiers that were engaged in the former stages. The proffered system was examined using the dataset such BOT-Internet of things and the CICIDS2017 to evince its competence in terms of rate of false alarm, detection, and accuracy. The attained results proved that the performance of the proposed system was better compared to the exiting methodologies to recognize the interference.

Pattern recognition analysis on long noncoding RNAs: a tool for prediction in plants.

Long noncoding RNAs (lncRNAs) correspond to a eukaryotic noncoding RNA class that gained great attention in the past years as a higher layer of regulation for gene expression in cells. There is, however, a lack of specific computational approaches to reliably predict lncRNA in plants, which contrast the variety of prediction tools available for mammalian lncRNAs. This distinction is not that obvious, given that biological features and mechanisms generating lncRNAs in the cell are likely different between animals and plants. Considering this, we present a machine learning analysis and a classifier approach called RNAplonc (https://github.com/TatianneNegri/RNAplonc/) to identify lncRNAs in plants. Our feature selection analysis considered 5468 features, and it used only 16 features to robustly identify lncRNA with the REPTree algorithm. That was the base to create the model and train it with lncRNA and mRNA data from five plant species (thale cress, cucumber, soybean, poplar and Asian rice). After an extensive comparison with other tools largely used in plants (CPC, CPC2, CPAT and PLncPRO), we found that RNAplonc produced more reliable lncRNA predictions from plant transcripts with 87.5% of the best result in eight tests in eight species from the GreeNC database and four independent studies in monocotyledonous (Brachypodium) and eudicotyledonous (Populus and Gossypium) species.

Risk prediction in life insurance industry using supervised learning algorithms

Risk assessment is a crucial element in the life insurance business to classify the applicants. Companies perform underwriting process to make decisions on applications and to price policies accordingly. With the increase in the amount of data and advances in data analytics, the underwriting process can be automated for faster processing of applications. This research aims at providing solutions to enhance risk assessment among life insurance firms using predictive analytics. The real world dataset with over hundred attributes (anonymized) has been used to conduct the analysis. The dimensionality reduction has been performed to choose prominent attributes that can improve the prediction power of the models. The data dimension has been reduced by feature selection techniques and feature extraction namely, Correlation-Based Feature Selection (CFS) and Principal Components Analysis (PCA). Machine learning algorithms, namely Multiple Linear Regression, Artificial Neural Network, REPTree and Random Tree classifiers were implemented on the dataset to predict the risk level of applicants. Findings revealed that REPTree algorithm showed the highest performance with the lowest mean absolute error (MAE) value of 1.5285 and lowest root-mean-squared error (RMSE) value of 2.027 for the CFS method, whereas Multiple Linear Regression showed the best performance for the PCA with the lowest MAE and RMSE values of 1.6396 and 2.0659, respectively, as compared to the other models.

Predicting Student Performance in Higher Education Institutions Using Decision Tree Analysis

The overall success of educational institutions can be measured by the success of its students. Providing factors that increase success rate and reduce the failure of students is profoundly helpful to educational organizations. Data mining is the best solution to finding hidden patterns and giving suggestions that enhance the performance of students. This paper presents a model based on decision tree algorithms and suggests the best algorithm based on performance. Three built classifiers (J48, Random Tree and REPTree) were used in this model with the questionnaires filled in by students. The survey consists of 60 questions that cover the fields, such as health, social activity, relationships, and academic performance, most related to and affect the performance of students. A total of 161 questionnaires were collected. The Weka 3.8 tool was used to construct this model. Finally, the J48 algorithm was considered as the best algorithm based on its performance compared with the Random Tree and RepTree algorithms.

Data Mining and Machine Learning Algorithms Using IL28B Genotype and Biochemical Markers Best Predicted Advanced Liver Fibrosis in Chronic Hepatitis C.

IL28B single nucleotide polymorphism (rs12979860) is an etiology-independent predictor of hepatitis C virus (HCV)-related hepatic fibrosis. Data mining is a method of predictive analysis which can explore tremendous volumes of information from health records to discover hidden patterns and relationships. The current study aims to evaluate and compare the prediction accuracy of scoring system like aspartate aminotransferase-to-platelet ratio index (APRI) and fibrosis-4 (FIB-4) index versus data mining for the prediction of HCV-related advanced fibrosis. This retrospective study included 427 patients with chronic hepatitis C. We used data mining analysis to construct a decision tree by reduced error (REP) technique, followed by Auto-WEKA tool to select the best classifier out of 39 algorithms to predict advanced fibrosis. APRI and FIB-4 had sensitivity-specificity parameters of 0.523-0.831 and 0.415-0.917, respectively. REPTree algorithm was able to predict advanced fibrosis with sensitivity of 0.749, specificity of 0.729, and receiver operating characteristic (ROC) area of 0.796. Out of the 16 attributes, IL28B genotype was selected by the REPTree as the best predictor for advanced fibrosis. Using Auto-WEKA, the multilayer perceptron (MLP) neural model was selected as the best predictive algorithm with sensitivity of 0.825, specificity of 0.811, and ROC area of 0.880. Thus, MLP is better than APRI, FIB-4, and REPTree for predicting advanced fibrosis for patients with chronic hepatitis C.