REP Tree Research Articles

A Study on Enhancing Crop Yield Prediction with Accuracy through Machine and Deep Learning Models

Accurate crop yield prediction is essential for optimizing agricultural resource allocation and supporting farmers in making informed crop management decisions. This study investigates the effectiveness of various machine learning and deep learning models for predicting crop yields based on key agricultural parameters, including Nitrogen (N), Phosphorous (P), Potassium (K), temperature, humidity, pH, and rainfall. We apply multiple algorithms—Logistic Regression, Multilayer Perceptron, Sequential Minimal Optimization (SMO), J48, Random Forest, REP Tree, and a proposed deep learning model—to evaluate their predictive accuracy in classifying agricultural items. Each model’s performance is assessed using metrics such as Kappa statistic, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Relative Absolute Error (RAE), Root Relative Squared Error (RRSE), and the time taken to build the model. Findings reveal that while traditional machine learning models like Random Forest and REP Tree show robust performance, the proposed deep learning model demonstrates enhanced accuracy and efficiency, making it highly promising for crop yield prediction applications. This study underscores the potential of advanced learning algorithms to improve agricultural productivity by providing actionable insights for crop planning and resource management.

Harnessing machine learning for accurate estimation of compressive strength of high-performance self-compacting concrete from non-destructive tests: A comparative study

This study investigates the compressive strength (CS-28d) of high-performance self-compacting concrete. To accurately estimate the CS-28d, the study employed regression techniques (linear regression (LR) and stepwise polynomial regression (SPR)) as well as machine learning techniques (M5Prime, Random Forest, Random Tree and REP Tree). The study was based on 600 experimental results were collected from literature, with the rebound number (R), and ultrasonic pulse velocity (Vp) used to predict the CS-28d. The results indicate that the M5Prime model performed the best, with the highest coefficient of determination (Pearson R of 95.06 %) and the lowest root mean square error (RMSE of 4.84516 %). Among the tested regression models, the SPR model demonstrated the best performance and an empirical equation was introduced to estimate the CS-28d of a high-performance self-compacting concrete with high accuracy. An in-depth exploration of the model's sensitivity to its input parameters was conducted to quantify their effect on the CS-28d outcome. The results showed that the rebound number (R) had the most significant impact, with a sensitivity analysis parameter of 95 %. This was followed by the ultrasonic pulse velocity (Vp) had a relatively insignificant effect of only 5 % on the CS-28d values.

Modeling of Strength Properties of Porous Concrete

According to previous research, porous concrete has lesser compressive strength than normal concrete and can only withstand mild traffic loads. The demand for high-strength porous concrete is urgent. By balancing the elements of porous concrete, high strength porous concrete may be produced. This may be accomplished by comprehending the complicated interaction between porous concrete's constituents. Using artificial intelligence approaches, the present research offers a study on the prediction of 7, 28-day compressive strengths and 28, 90- day flexural strengths of porous concrete. Random tree (RT), M5 Rules, REP Tree, and ensemble with Bagging were used to estimate the compressive strength of porous concrete in this work, and a comparison of performance and practicality of artificial intelligence result is shown. A total of 111 datasets were extracted from prior research and publications. As input parameters for modelling, cement, natural coarse aggregates, recycled coarse aggregates, water, silica fume, plastic/steel/rubber fibres, and density were employed. Properties such as mean absolute error, root mean square error, and correlation coefficient were used to evaluate and compare the behavior of projected models. The results demonstrated a high level of capacity and potential for predicting the compressive of porous concrete.

Tree based Regression Models for Predicting the Compressive Strength of Concrete at High Temperature

Predicting the compressive strength of concrete is a complicated process due to the heterogeneous mixture of concrete and high variable materials. Researchers have predicted the compressive strength of concrete for various mixes using soft computing models. In this research, compressive strength of concrete at high temperature with fly ash, super plasticizers, and fibre is predicted using three regression tree-based soft computing models (Random Forest, Random Tree, and Reduced-Error Pruning Tree (REP Tree)). The data used in this study is collected from the literature, and two-thirds of the total data is used for model training, while the remaining third is reserved for testing the prepared model. The model’s performance is evaluated based on scatter plots, variation plots, box plots, and prediction error rates, i.e., R, RMSE, and MAE. The results highlight the highest performance of the Random Forest model, with R of 0.9142; RMSE of 9.6285 MPa and MAE of 6.7931 MPa, outperforming the other competing models. Furthermore, the most influential parameter is determined using sensitivity analysis. Thus, the Random Forest model is the model that can be used for predicting the compressive strength of concrete at high temperatures.

Predictive modelling of nitrogen dioxide using soft computing techniques in the Agra, Uttar Pradesh, India

Nitrogen dioxide (NO2) is one of the air pollutants which aggravates the human health as well as causes environmental issues. It is more causes respiratory problems due to acid rains. Agra is a major tourist destination spot in India also similarly air pollution also increased growing urbanization and traffic reflux. The current study aims to predicted the NO2 episodes in the Agra city using soft computing models namely, M5P, Random Forest (RF), Group method of data handling (GMDH), Multivariate adaptive regression (MARS), Reduced error pruning tree (REP Tree) and Random tree (RT). The models were generated using 1116 observations, from 2015 to 2020 with input parameters such as Particulate matter (PM2.5), Nitrogen monoxide (NO), Oxides of nitrogen (NOX), Sulphur dioxide (SO2), Carbon monoxide (CO), Ozone (O), Benzene (Be), Toluene, Relative humidity (RH), Wind speed (WS), Wind direction (WD), Solar radiation (SR), Barometric pressure (BP) and Xylene. The performance of each model was evaluated based on the six statistical indices, namely correlation coefficient (CC), root mean square error (RMSE), mean absolute error (MAE), normalized root of mean squared relative error (NRMSE), Willmott's Index (WI), and Legates and McCabe's Index (LMI). The performance evaluation models results of study area, Box plot and Taylor's diagram indicated that M5P is the outperforming model among others with testing CC = 0.9543, RMSE = 5.8006, MAE = 3,9204, NRMSE = 0.1512, WI = 0.9744, and LMI = 0.7549. Based on the sensitivity analysis indicated that NOx is the most influential parameter followed by WD and CO. These results of study area can be helpful to understanding the air pollution causes, health issues, and future NO2 levels around the study area with useful results for air pollution monitoring policy and development.

Use of Machine Learning Algorithms in Location Determination for Safe Construction

Disasters are events that affect life activities that cause physical, economic and social losses. These events cause loss of life and property, as well as damage to structures such as schools and hospitals that will affect the continuation of education and health services. There are two types of disasters. The first is man-made disasters and the second is natural disasters. Natural disasters occur as a result of natural events. Earthquake is a natural disaster. Disaster management is a process that covers pre-disaster, disaster and post-disaster. This study focuses on pre-earthquake disaster management. Safe construction is necessary to reduce the effects of earthquakes. Soil class is very important in a safe construction. Soil classification was made according to TBDY-2018 by using machine learning techniques for a safe construction in the Mediterranean region. 12 different machine learning algorithms were used for Classification and the results were analyzed. As a result of the analysis, the accuracy values of the algorithms are respectively: Naive Bayes 87%, LDA 88%, KNN 84%, Adaboost 96%, Logit boost 95%, Ultraboost 92%, BF Tree 98%, Extra Tree 84%, Random Forest 93%, Random Tree%. 95, Rep Tree 96%, SimpleCart 98%. The most successful algorithms in classification are Simle Cart and BT tree. The least successful algorithm is the Extra Tree algorithm.

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.

A Study on Variable Selections and Prediction for Diabetes Pregnancy Dataset Using Data Mining With Machine Learning Approaches

Data mining aims to convert raw data into valuable insights that inform decision-making, predictions, and business-related research. Machine learning, a branch of artificial intelligence (AI), creates algorithms and models that empower computers to learn from data and make data-driven predictions or decisions. This paper considers diabetes-related parameters, namely gender, age, hypertension, heart disease, smoking history, BMI, HbA1c level, and blood glucose, for applying machine learning techniques to find diabetes and suitable variables for future predictions in diabetes using Gaussian Process, Linear Regression, Multilayer Perceptron, Simple Linear Regression, SMOreg, decision stump, M5P, random forest, random tree, and REP tree. Numerical illustrations are provided to prove the proposed results with test statistics or accuracy parameters.

A Study on Prediction for Crop Area, Production, and Yield Analys is Using Machine Learning Approaches

Analyzing crop area, production, and yield typically involves data collection from farmers, the application of remote sensing technologies like satellite imagery, and on-site surveys. This gathered data is then used to assess crop performance, identify trends, and offer recommendations to improve agricultural practices.Data mining finds extensive application across diverse sectors such as business, healthcare, finance, marketing, and scientific research, enabling the extraction of knowledge and insights from data that may not be readily discernible using conventional approaches.This paper considers Crops Production between 2006 to 2011. The machine learning approaches which is used to analysis and predict the dataset usinglinear regression, multilayer perceptron, SMOreg, random forest, random tree, and REP tree. Numerical illustrations are provided to prove the proposed results with test statistics or accuracy parameters.

A Study on Parkinson's Disease Parameters Using Data Mining with Machine Learning Approaches

Parkinson's disease is a long-term condition that demands continuous attention and care. While it can substantially affect an individual's quality of life, with appropriate treatment and support, people diagnosed with Parkinson's can enjoy meaningful lives for numerous years. Research into Parkinson's disease remains active, with ongoing progress in comprehending the condition and discovering innovative treatment options. Data mining, also known as Knowledge Discovery in Database (KDD), is a highly valuable technique employed by entrepreneurs, researchers, and individuals for extracting valuable insights from extensive data collections. The knowledge discovery process encompasses several key steps, including data cleaning, data integration, data selection, data transformation, data mining, pattern evaluation, and knowledge presentation.This paper considers Parkinson’s disease Data Set.The machine learning approaches which is used to analysis and predict the dataset usinglinear regression, multilayer perceptron, SMOreg, random forest, random tree, and REP tree. Numerical illustrations are provided to prove the proposed results with test statistics or accuracy parameters.

A Study on Variable Selections and Prediction for Covid-19 and Delta Dataset Using Machine Learning Approaches

Ongoing initiatives to combat COVID-19 prioritize widespread vaccination, ongoing research, and implementing public health measures to mitigate disease transmission and its impact. Staying informed about the most current guidance provided by local health authorities and the World Health Organization (WHO) is crucial for safeguarding your well-being and that of your community. Data mining entails exploring patterns, trends, relationships, and valuable insights within extensive datasets by employing various techniques and algorithms. This process aims to extract useful information from structured and unstructured data sources. This paper considers COVID-19-related dataset like state name, state code, district, confirmed, Active, deceased, recovered, delta confirmed, delta deceased, delta recovered. The machine learning approaches are used to analyze and predict the dataset using linear regression, multilayer perceptron, SMOreg, random forest, random tree, and REP tree. Numerical illustrations are provided to prove the proposed results with test statistics or accuracy parameters.

A Study on Variable Selections and Prediction for Crop Recommender System with Soil Nutrients Using Stochastic Model and Machine Learning Approaches

To develop a crop recommendation system using soil nutrient data, you'll need a dataset containing details on soil nutrients and the crops that thrive in particular soil conditions. While I can't supply a specific dataset, I can offer guidance on the types of data you should seek or gather for building such a system. Machine learning, a subset of artificial intelligence (AI) and computer science, centers on harnessing data and algorithms to replicate human learning processes, steadily enhancing its precision over time. This paper considers crop recommender dataset with soil nutrients-related dataset like N, P, K, ph, EC, S, Cu, Fe, Mn, Zn, B, label. The machine learning approaches are used to analyze and predict the dataset using Logistic, Multilayer Perceptron, Simple Logistic, Hoeffding Tree, random forest, random tree, and REP tree. Numerical illustrations are provided to prove the proposed results with test statistics or accuracy parameters.

Analysis and Prediction for IT Sector Growth Using Machine Learning and Stochastic Modeling Approaches

Data mining involves extracting valuable insights, patterns, correlations, and trends from large datasets stored in database repositories. Machine learning and data mining approaches are used to analyze and predict various research areas with the help of statistical, mathematical, and computational modeling or techniques. The main objective is to convert raw data into actionable knowledge. In this paper considers the information technology sector data for analysis and prediction using data mining, machine learning, stochastic models, and test statistics. It is used to find future predictions based on four different parameters: open, high, low, and close using familiar machine learning approaches and stochastic model are linear regression, multilayer perceptron, M5P, random forest, random tree, REP tree and proposed stochastic model. Numerical illustrations are provided to prove the proposed results with test statistics or accuracy parameters.

Optimal Variable Selections and Prediction Methods for Soil Chemical Property Using Data Mining and Machine Learning Approaches

The main objective of the research is to convert raw data into actionable knowledge. Creating a decision tree involves recursively dividing the data into subsets based on different attributes, aiming to achieve homogeneity or minimize variance within each subset. This paper considers agriculture and its soil chemical-related dataset for applying data mining techniques to find suitable variables for future predictions. The tenfamiliar ML approaches are Gaussian processes,linear regression, multilayer perceptron, simple linear regression, SMOreg, decision stump, M5P, random forest, random tree, and REP tree. Numerical illustrations are provided to prove the proposed results with test statistics or accuracy parameters.

Low-energy residential building optimisation for energy and comfort enhancement in semi-arid climate conditions

The application of energy-efficient strategies in buildings, such as the Green Building Concept, can significantly impact human comfort and resource consumption. However, due to the complexity of decision-making factors and the variety of available materials, computational models are necessary to identify the most effective solutions and optimise building energy performance. This study presents an integrated framework that uses machine learning algorithms and a Petri Net control system to optimise the thermal, comfort, and energy efficiency of both vertical and horizontal building envelopes in semi-arid climate zones. The framework incorporates several passive techniques for building energy parameters, including material thickness and melting point, window types, wall insulation thickness and thermal emissivity, wall solar absorbance, window wall ratio, fenestration position, air tightness, roof solar reflectance, roof insulation thickness and conductivity (W/(m·°C)), and floor insulation thickness. An experiment design was developed using Box-Behnken Design-Response Surface Methodology (BBD-RSM) for statistical optimisation, which was coupled with Design Builder simulation model. The methodology was demonstrated by applying it to a residential building in Mexico. Meta Additive Regression was used to analyse the output factors, which showed higher confidence compared to REP Tree and M5P Tree algorithms in green buildings. The results demonstrate that an annual energy reduction of 50 kW/m2 per household can be achieved by using an optimised building envelope.

Heterogeneous Ensemble Feature Selection for Network Intrusion Detection System

Intrusion detection systems get more attention to secure the computers and network systems. Researchers propose different network intrusion detection systems using machine learning techniques. However, the massive amount of data that contain irrelevant and redundant features is still challenging the intrusion detection systems. The redundancy and irrelevance of features may slow the processing time and decrease prediction performance. This paper proposes a Heterogeneous Ensemble Feature Selection (HEFS) method to select the relevant features while achieving better attack detection performance. The proposed method fuses the output feature subsets of five filter feature selection methods, using a union combination method, to obtain an ensemble features subset. HEFS method uses merit-based evaluation to avoid the internal redundancy of the obtained ensemble features subset and acquire the final optimal features. We evaluate the HEFS method with random forest, J48, random tree, and REP tree. In a multi-class NSL-KDD dataset, the experimental results show that the proposed method achieves better prediction performance than the specific feature selection methods and other frameworks.

Radiomics and Artificial Intelligence Can Predict Malignancy of Solitary Pulmonary Nodules in the Elderly.

Solitary pulmonary nodules (SPNs) are a diagnostic and therapeutic challenge for thoracic surgeons. Although such lesions are usually benign, the risk of malignancy remains significant, particularly in elderly patients, who represent a large segment of the affected population. Surgical treatment in this subset, which usually presents several comorbidities, requires careful evaluation, especially when pre-operative biopsy is not feasible and comorbidities may jeopardize the outcome. Radiomics and artificial intelligence (AI) are progressively being applied in predicting malignancy in suspicious nodules and assisting the decision-making process. In this study, we analyzed features of the radiomic images of 71 patients with SPN aged more than 75 years (median 79, IQR 76-81) who had undergone upfront pulmonary resection based on CT and PET-CT findings. Three different machine learning algorithms were applied-functional tree, Rep Tree and J48. Histology was malignant in 64.8% of nodules and the best predictive value was achieved by the J48 model (AUC 0.9). The use of AI analysis of radiomic features may be applied to the decision-making process in elderly frail patients with suspicious SPNs to minimize the false positive rate and reduce the incidence of unnecessary surgery.

Machine Learning Applied to Datasets of Human Activity Recognition: Data Analysis in Health Care.

In order to remain active and productive, older adults with poor health require a combination of advanced methods of visual monitoring, optimization, pattern recognition, and learning, which provide safe and comfortable environments and serve as a tool to facilitate the work of family members and workers, both at home and in geriatric homes. Therefore, there is a need to develop technologies to provide these adults autonomy in indoor environments. This study aimed to generate a prediction model of daily living activities through classification techniques and selection of characteristics in order to contribute to the development in this area of knowledge, especially in the field of health. Moreover, the study aimed to accurately monitor the activities of the elderly or people with disabilities. Technological developments allow predictive analysis of daily life activities, contributing to the identification of patterns in advance in order to improve the quality of life of the elderly. The vanKasteren, CASAS Kyoto, and CASAS Aruba datasets were used to validate a predictive model capable of supporting the identification of activities in indoor environments. These datasets have some variation in terms of occupation and the number of daily living activities to be identified. Twelve classifiers were implemented, among which the following stand out: Classification via Regression, OneR, Attribute Selected, J48, Random SubSpace, RandomForest, RandomCommittee, Bagging, Random Tree, JRip, LMT, and REP Tree. The classifiers that show better results when identifying daily life activities are analyzed in the light of precision and recall quality metrics. For this specific experimentation, the Classification via Regression and OneR classifiers obtain the best results. The efficiency of the predictive model based on classification is concluded, showing the results of the two classifiers, i.e., Classification via Regression and OneR, with quality metrics higher than 90% even when the datasets vary in occupation and number of activities.

Experimental investigation and prediction of free fall jet scouring using machine learning models

The current study deals with the depth of scour at the location of impact between a free fall jet and a riverbed. The current study is based on extensive laboratory experiments that were designed to mimic full-scale behavior. The literature review shows that relations among hydraulic parameters for predicting the depth of scour are complex; therefore, six artificial intelligence techniques are used in the current study to capture these complex relation. A total of 120 observations are used for the analysis. Results from the experiments show that with increasing downstream water depth (h), the impinging jet causes increasingly turbulent currents and large vortices that increase the scouring of the riverbed. Increasing discharge per unit width (q) enhances the relative scour depth (D/H) while increasing the average diameter of the riverbed materials (d) decreases D/H, where D is maximum scour depth and H is the height of the falling jet. With increasing (particle Froude number Fr), the relative scour depth increases. In the current study the prediction accuracy of Gene Expression Programming (GEP), Multivariate Adaptive Regression Spline (MARS), M5P Tree, Random Forest (RF), Random Tree (RT), and Reduces Error Pruning Tree (REP Tree) techniques are evaluated using the relative scour depth (D/(H−h)). The performance evaluation indices and graphical methods suggest that the GEP based model is more accurate than other prediction methods for the relative scour depth with a coefficient of determination (R2) equal to 0.8330 and 0.8270, a mean absolute error (MAE) equal to 0.1125 and 0.0902, root mean square error (RMSE) values of 0.1463 and 0.1116, and Willmott's Index (WI) equal to 0.8998 and 0.9014, for the training and testing stages.

Using Principal Component Analysis to Identify Mortality Rates of Covid-19 Patients and Patients at High Risk of Death

The Covid-19 virus emerged in 2019 and spread all over the world in a short time. It caused millions of people to be infected and hundreds of thousands to die. The number of cases is increasing day by day and new variants of the virus are emerging. Polymerase Chain Reaction (PCR) tests are used to detect people with this disease. It is very important to examine the conditions of the people with the disease and to determine the intensive care and mortality rates in advance. In this study, Principal Component Analysis (PCA) was used as a feature extraction method to determine mortality rates from Covid-19 patients, and the successful results of the method were demonstrated with the most popular machine learning techniques. Machine learning techniques used in the study are K-Nearest Neighbor (KNN), Linear Discrimination Analysis (LDA), Extra Trees, Random Tree, Rep Tree and Naive Bayes algorithms. In the performance evaluation of these techniques, Accuracy, Precision, Sensitivity, Rms, F-score values were calculated. In addition, ROC Curves and Confusion matrices were examined and the results were compared. As a result, it was seen that the best performance was obtained with the use of Linear Discrimination Analysis (PCA+LDA) after applying Principal component analysis. With the PCA+LDA application, an accuracy rate of 96.39% was obtained. In the article, it has also been revealed that Pneumonia, Diabetes, COPD and Asthma patients, Pregnant, Elderly and Intubated people are more affected and the risk of death is higher from the Covid- 19 virus by using feature extraction. This study is important in terms of examining the lethality of virus variants, taking the necessary precautions for the treatment of risky patients isolation of patients at risk of death, and improving hospital capacity planning.