Random Tree Research Articles

Machine Learning Prediction of Quality of Life: Insight from Property Crime and Tropical Climate Analysis

<p align="center">This study delves into predicting the quality of life, a widely researched concept spanning various domains, employing mixed methodologies and indicators. Common predictors of overall quality of life encompass health status, socioeconomic factors, subjective well-being, and environmental conditions. Consequently, this research investigates the potential of utilizing machine learning prediction models for quality-of-life prediction, focusing on two key indicators.Five machine learning algorithms—Generalized Linear Model, Random Forest, Decision Tree, Gradient Boosted Tree, and Support Vector Machine—are empirically compared. Their performance in predicting quality of life is assessed based on property crime and tropical climate (temperature) attributes. Despite an initially low correlation value, temperature yields valuable insights for specific algorithms, enhancing predictive accuracy. This underscores the intricate and nuanced impact of seemingly unrelated factors in machine learning. The Support Vector Machine emerges as the top-performing algorithm, followed by Random Forest and Decision Tree. This paper offers a foundational research framework to aid educators and researchers in exploring quality of life prediction in-depth, utilizing property crime and temperature as key factors.</p>

Application of classification algorithms for smishing detection on mobile devices: literature review

Smishing is a form of phishing carried out via mobile devices to steal confidential information from victims. The number of smishing attacks has increased in recent years due to the large number of users acquiring these easy-to-use and functional devices. This literature review objective is to examine the techniques and methods used in smishing attacks using classification algorithms. To do so, we conducted a manual search process and selected 155 articles from Scopus and 29 articles from access to research for development and innovation (ARDI). Of these, 36 articles met the inclusion criteria. In addition, the algorithms most commonly used by the studies were random forest classification techniques, decision trees, and neural networks. These studies analyzed various machine learning models for detecting phishing and smishing messages. The attack simulation scenarios included generating web pages, sending fake links (URLs), and installing malicious applications. The analysis evaluated web pages and SMS messages using a database containing legitimate as well as smishing messages. Based on the results, it is suggested to combine these methods to improve detection performance, making it more robust and promising.

Insightful database-driven prediction and sensitivity analysis of electrical resistivity of nano-graphite-modified cementitious composites: a supervised machine learning regression

This study presents a novel approach to assessing electrical resistivity (ER) in nano-graphite-modified cementitious composites, a key factor for enhancing their durability and integrity. This innovative framework establishes a new paradigm in the application of ML to material science, highlighting the ability to bridge experimental and computational methods for more reliable and efficient outcomes. Leveraging a dataset of 539 cement-based materials, advanced machine learning (ML) techniques were applied, including the extremely randomized trees (XRF) algorithm, which outperformed traditional models with minimal error metrics (MAE: 37.093, MSE: 4992.581, RMSE: 8.406). The integration of Shapley’s additive explanations (SHAP) analysis further highlights the novelty of this work, revealing testing protocol (MM), ultrasonication time (SN), and graphene dosage (GN) as the most significant factors influencing ER. Longer SN times and higher GN doses reduced ER, while smaller graphene particles (2µm) increased it. Additionally, the water-to-binder (W-B) ratio and sand-to-cement (S-C) ratio showed peak ER values at 0.47 and 2.8, respectively. The four-probe method and lime-water curing produced more reliable and lower ER values compared to other methods. This data-driven methodology offers unprecedented insights into the complex interactions between material properties and ER, providing a powerful tool for optimizing composite formulations. Future research may focus on scaling this approach to larger datasets, investigating the long-term durability of composites, and refining ultrasonication techniques for better graphene dispersion.

Optimization of Motion Planning for Quadrotor-Equipped Robotic Manipulators

The focus of the work is on optimizing motion planning for quadcopter-equipped robotic manipulators by integrating quadcopter design, modeling, and path planning for a robotic arm mounted on a quadcopter through MATLAB. Recently, quadcopters have also been used in many application fields with increased viability. This paper will attempt at bringing a complete framework that enhances the operational capabilities of quadcopters through optimized path planning algorithms. The approach will rely on obstacle avoidance techniques for safe operation with satisfactory efficiency in unstructured environments. Advanced algorithms such as Rapidly-exploring Random Trees and dynamic window approaches provide solutions to this challenge of redundancy in the path followed and adaptability in its surroundings. Simulations prove that the proposed optimization methods strongly improve the accuracy and efficiency of the path planned by the robotic arm as it adheres to the dynamic constraints imposed. This work can apply practical insight into many real-world fields such as logistics, surveillance, and search-and-rescue applications aside from contributing to the theoretical understanding of quadcopter dynamics.

Determination of Organic Carbon Content of the Soils within the Greenhouses Built on Pyroclastic Deposits in Isparta Settlement Area

Soil organic carbon (SOC) is an important indication of soil health and helps to sustain soil fertility. As a result, determining its composition and the factors that influence it is critical for long-term soil nutrient management, especially in controlled conditions such as greenhouses. This study utilizes machine learning to classify SOC content in greenhouses built on pyroclastic deposits in the Isparta region. A dataset of 276 samples and eight variables—clay (%), silt (%), sand (%), soil electrical conductivity (EC), pH, elevation, slope, and aspect—were used to model SOC values. SOC content was classified into five classifications: very low (2.3%). In this study, five machine learning models—Logistic Regression (LR), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF)—were evaluated using cross-validation to determine their classification accuracy, precision, recall, F-score, and ROC area. Random Forest (RF) and Decision Tree (DT) outperformed the other models, with RF achieving the highest overall accuracy (76.4%), precision (77.3%), and AUC (0.904), followed by DT at 75.4% and AUC of 0.874. This study shows the practicality of machine learning models in categorizing SOC content, highlighting their importance for long-term soil health and fertility control in greenhouse conditions. To improve model efficacy, future studies should include more auxiliary variables, such as soil physical and chemical qualities and lithological data, as well as a wider range of soil types.

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.

How to arrange the robotic environment? Leveraging experience in both motion planning and environment optimization

This study presents an experience-based hierarchical-structure optimization algorithm to address the robotic system environment design problem, which combines motion planning and environment arrangement problems together. The motion planning problem, which could be defined as a multiple-degree-of-freedom (m-DOF) problem, together with the environment arrangement problem, which could be defined as a free DOF problem, is a high-dimensional optimization problem. Therefore, the hierarchical structure was established, with the higher layer solving the environment arrangement problem and lower layer solving the problem of motion planning. Previously planned trajectories and past results for this design problem were first constructed as datasets; however, they cannot be seen as optimal. Therefore, this study proposed an experience-reuse manner, which selected the most “useful” experience from the datasets and reused it to query new problems, optimize the results in the datasets, and provide better environment arrangement with shorter path lengths within the same time. Therefore, a hierarchical structural caseGA-ERTC algorithm was proposed. In the higher layer, a novel approach employing the case-injected genetic algorithm (GA) was implemented to tackle optimization challenges in robot environment design, leveraging experiential insights. Performance indices in the arrangement of the robot system’s environment were determined by the robotic arm’s motion and path length calculated using an experience-driven random tree (ERT) algorithm. Moreover, the effectiveness of the proposed method is illustrated with the 12.59% decrease in path lengths by solving different settings of hard problems and 5.05% decrease in easy problems compared with other state-of-the-art methods in three small robots.

A UAV path planning method based on the framework of multi-objective jellyfish search algorithm.

Multi-constraint UAV path planning problems can be viewed as many-objective optimization problems that can be solved by meta-heuristic algorithms with good self-organizing optimization capabilities. However, such algorithms mostly use random initializing methods, resulting in low-quality initial paths that reduce the efficiency of subsequent algorithmic searches. Moreover, as the number of objective functions increases, meta-heuristic algorithms face inadequate selection pressure and convergence capability, which lead to poor solution. In order to address these issues, this paper proposes a UAV path planning method based on the framework of multi-objective jellyfish search algorithm (UMOJS). Firstly, an initializing strategy based on Rapidly-exploring Random Trees (RRT) is proposed to achieve higher quality initial paths. Secondly, a jellyfish updating strategy guided by the class-optimal individual is designed to enhance the convergence ability of the algorithm. Furthermore, a set of predefined reference points is imported to obtain Pareto optimal solutions with better convergence and distribution in many-objective optimization problems. To evaluate the superiority of the proposed UMOJS algorithm, three different difficulties of simulated flight environments are constructed to verify its performance. The experimental results show that UMOJS is not only able to gain more UAV paths with shorter length, but also more evenly distributed Pareto optimal solutions compared to five meta-heuristic algorithms when the constraint conditions are satisfied.

Utilizing Artificial Intelligence for Microbiome Decision-Making: Autism Spectrum Disorder in Children from Bosnia and Herzegovina

Background/Objectives: The study of microbiome composition shows positive indications for application in the diagnosis and treatment of many conditions and diseases. One such condition is autism spectrum disorder (ASD). We aimed to analyze gut microbiome samples from children in Bosnia and Herzegovina to identify microbial differences between neurotypical children and those with ASD. Additionally, we developed machine learning classifiers to differentiate between the two groups using microbial abundance and predicted functional pathways. Methods: A total of 60 gut microbiome samples (16S rRNA sequences) were analyzed, with 44 from children with ASD and 16 from neurotypical children. Four machine learning algorithms (Random Forest, Support Vector Classification, Gradient Boosting, and Extremely Randomized Tree Classifier) were applied to create eight classification models based on bacterial abundance at the genus level and KEGG pathways. Model accuracy was evaluated, and an external dataset was introduced to test model generalizability. Results: The highest classification accuracy (80%) was achieved with Random Forest and Extremely Randomized Tree Classifier using genus-level taxa. The Random Forest model also performed well (78%) with KEGG pathways. When tested on an independent dataset, the model maintained high accuracy (79%), confirming its generalizability. Conclusions: This study identified significant microbial differences between neurotypical children and children with ASD. Machine learning classifiers, particularly Random Forest and Extremely Randomized Tree Classifier, achieved strong accuracy. Validation with external data demonstrated that the models could generalize across different datasets, highlighting their potential use.

Cyber Hacking Breaches Detection Using Machine Learning

Cyber hacking breach prediction is one of the emerging technologies and detecting and predicting breaches through computer algorithms has become a very challenging task To make malware detection more effective, scalable and more efficient than traditional system calls human involvement Machine learning to be used for breach detection and prediction The main goal is a series of cyber hacking attacks each of which will harm the person’s information and financial reputation. Government and non-profit organizations’ data, such as user and company information,, can be compromised, posing a risk to their finances and reputation if they collect information from websites and social networks it can trigger a cyber attack. Organizations such as the healthcare sector are capable of holding sensitive information that must be handled discreetly and securely. Data breaches can lead to identity theft, fraud, and other losses. The findings show that 70% of breaches affect a wide range of organisations, including healthcare providers. The investigation indicates a possible data breach. Due to the heavy usage of computer programs and security on the host and network, there is a risk of data breach. Machine learning can be used to detect these attacks. Research uses machine learning models to protect against web security flaws. The data set is available from the Privacy Rights Clearing House. Teaching employees how to use modern security measures can reduce data breaches. This can help to understand attack detection and data security. Machine learning models such as Random Forest, Decision Tree, k-means and Multi- layer Perceptron are used to predict data violations

Comparative Analysis of Cryptocurrency Prediction based on Deep Learning, Decision Tree, Gradient Boosted Tree, Random Tree, and k-NN Model

Comparative Analysis of Cryptocurrency Prediction based on Deep Learning, Decision Tree, Gradient Boosted Tree, Random Tree, and k-NN Model

COVID-19 pandemic microplastics environmental impacts predicted by deep random forest (DRF) predictive model

BackgroundMicroplastic pollution is a pressing issue with far-reaching environmental and public health consequences. This study delves into the intricacies of predicting microplastic pollution during the COVID-19 pandemic in Tehran, Iran.MethodsThe research introduces a rigorous comparative analysis that evaluates the predictive prowess of the Deep Random Forest algorithm and established benchmarks, such as Random Forest, Decision Trees, Gradient Boosting, AdaBoost, and Support Vector Machine. The evaluation process encompasses a meticulous 70–30 training–testing split of the main data set. Performance is assessed by analysis metrics, including ROC and statistical errors. The primary data set encompasses distinct categories, including household wastes, hospital wastes, clinics wastes, and unknown-originated susceptible waste which is categorized in Infected items, PPEs, SUPs, Test kits, Medical packages, Unknown-originated pandemic mircoplastic waste. Deliberately, this data set was partitioned into training and testing subsets, ensuring the robustness and reliability of subsequent analyses. Approximately 70% of the main database was allocated to the training data set, with the remaining 30% constituting the testing data set.ResultsThe findings underscore the proposed algorithm’s supremacy, boasting an impressive AUC = 0.941. This exceptional score reflects the model’s precision in categorizing microplastics. These results have profound implications for environmental management and public health during pandemics.ConclusionsThe study positions the proposed model as a potent tool for microplastic pollution prediction, encouraging further research to refine predictive models and tap into new data sources for a more comprehensive understanding of microplastic dynamics in urban settings.

Acoustic detection rate can outperform traditional survey approaches in estimating relative densities of breeding waders

Passive acoustic devices are increasingly being used to monitor biodiversity. However, few studies have compared the accuracy of acoustic surveys and traditional surveys against ground‐truthed data. Here, we assess whether acoustic recorders used in conjunction with an artificial intelligence (AI) classifier can predict the relative breeding density of four wader species better than traditional fieldworker transect surveys. In a 27‐km2 upland study site, acoustic data were collected at 83 sampling points and analysed using the BirdNet bird‐sound classifier to estimate vocal detection rate at each location; we also carried out concurrent transect bird surveys. To ground‐truth these approaches, intensive field surveys were undertaken to identify each breeding territory of our focal species. With both the acoustic dataset and the transect dataset, we used similar analytical approaches (random forest regression trees) to predict relative territory density across the study site, and then compared these predictions with the territory density obtained from the intensive field surveys. The classifier performed well at identifying the presence of target species' vocalizations within 3‐s periods for Lapwing (accuracy = 0.911), Curlew (0.826) and Oystercatcher (0.841), but less well for Golden Plover (0.699). For Curlew and Oystercatcher, the predictions obtained from the acoustic approach were a better fit to actual territory density than the transect approach. In contrast, for Lapwing and Golden Plover, the transect predictions outperformed the acoustic predictions, with the acoustic model particularly poor for Golden Plover. We attributed these differences to the performance of the classifier, species' ecology and vocal behaviour. Data gathering for the acoustic approach was more time‐efficient than the transect surveys, requiring less than a quarter of the fieldworker days. We conclude that there is high potential for acoustic approaches to augment traditional methods, although species' ecological characteristics should be considered: species that vocalize more frequently, at higher amplitudes and hold larger territories will be better‐suited to sampling‐based acoustic methods.

Estimating coffee crop parameters through multispectral imaging and machine learning algorithms

Abstract. Brazil plays a crucial role in the global economy due to its significant contribution to the agricultural sector, particularly in coffee production, where it stands out as the largest producer and exporter of processed coffee. Various disturbances can influence coffee plants, causing abnormalities that can hinder their successful growth. Parameters such as plant height and canopy diameter play an essential role in assessing the health and productivity of the plants, reflecting their growth, development, and ability to capture sunlight. Additionally, height is also related to the balanced distribution of nutrients and water, providing valuable information about overall performance and the capacity for healthy production. In this regard, the application of methodologies involving remote sensing and machine learning algorithms has shown promising results in the rapid and safe acquisition of information about agricultural systems. This study evaluates different machine learning algorithms, using radiometric values from multispectral images obtained by remote sensing platforms as input datasets for estimating plant height and canopy diameter in coffee cultivation. The best performance was observed for architectures that showed lower RMSE and RMSE% values. For the plant height parameter (m), the RGB sensor exhibited the best performance using the Random Tree algorithm, with an RMSE (0.27) and RMSE% (8.80). For the canopy diameter (m), the sensor showed the best performance using the Random Forest algorithm, with an RMSE (0.15) and RMSE% (8.16).

Experimental and modelling analysis of waste material-based geopolymer concrete incorporated with crumb rubber particles

Rubberized geopolymer concrete (RuGPC) emerges as an eco-friendly alternative to conventional concrete, significantly reducing greenhouse gas emissions. This study investigates the use of steel slag (SS) in varying proportions (30 %, 35 %, and 40 %) as a replacement for ground granulated blast furnace slag (GGBS) in geopolymer concrete, with crumb rubber (CR) replacing crusher dust (CD) as fine aggregate due to its increasing demand. The research focuses on understanding the impact of aluminosilicate materials on the mechanical, thermal, and microstructural properties of geopolymer concrete cured at 60°C. Advanced characterization techniques, including Scanning Electron Microscopy (SEM), Energy-Dispersive X-ray Spectroscopy (EDX), X-ray Diffraction (XRD), and Fourier Transform Infrared Spectroscopy (FTIR), were employed. SEM and EDX analyses revealed that the microstructural properties of GGBS and SS materials, mainly Na/Si, Si/Al, H₂O/Na₂O, and Na/Al ratios, significantly influence RuGPC performance through gel formation. FTIR analysis indicates a shift in the stretching vibrations of GGBS and SS to lower wavenumbers due to geopolymerization changes. XRD results show the formation of C-S-H gel at around 27–30° 2theta, attributed to increased GGBS and SS content. Despite efforts to incorporate CR into geopolymer matrices, challenges in mitigating strength degradation persist. To address this, a predictive model was developed to understand the key factors affecting RuGPC performance. Six machine learning techniques—M5P (pruned and unpruned), random forest, random tree, linear regression, and support vector machine with various kernels (PUK, RBF, PK, and NPK), and artificial neural network (ANN)—were employed to predict the physical and thermal behavior of RuGPC. The analysis identified ANN-based models as the most effective. Sensitivity analysis highlighted the grade of rubber and the CR replacement percentage by volume of CD as the most influential parameters determining RuGPC compressive strength, density, and thermal conductivity. Moreover, The economic analysis revealed that RuGPC mixtures were 1.2–11.61 % more cost-effective than OPC concrete. These findings underscore the importance of predictive model development in optimizing RuGPC properties for practical applications, offering valuable insights for decision-making processes.

Random trees have height O(n)

Random trees have height O(n)

A Combined Strategy for Path Planning of Tensegrity Manipulators Considering Structural Stability

Abstract A strategy combining an improved rapidly exploring random tree (RRT) method with the artificial potential field (APF) method is proposed for the path planning of flexible tensegrity manipulators considering typical complex constraints, such as structural stability, obstacle avoidance, and cable no-slackening. The relationship between the node displacements and the elongations of active members is established for the kinematic analysis of tensegrity manipulators. The rest lengths of active members are taken as the design variables for the generation of a random tree. The guide node is utilized for goal-biased samplings to expedite the exploration toward the final configuration, while the APF method is introduced to ensure that the obtained elongations of active members can satisfy the constraint of obstacle avoidance. The proportion of random and goal-biased samplings is adaptively adjusted based on the sampling success rate. Futile random samplings can be significantly reduced by dynamically modifying the random sampling range according to the obtained configuration nearest to the final configuration. The computational procedure of the proposed method is presented. An illustrative flexible tensegrity manipulator is employed to demonstrate the adaptability of the proposed path planning method to complex constraints, especially structural stability.

Bayesian Optimization Driven Strategy for Detecting Credit Card Fraud with Extremely Randomized Trees

Bayesian Optimization Driven Strategy for Detecting Credit Card Fraud with Extremely Randomized Trees

An Artificial Intelligence-Based Hybrid Approach to Detect the Type of Buried Objects with Broad Frequency Band Antenna Systems

Knowing the type of buried object before excavation prevents unnecessary excavation. Moreover, it saves time and money. In this study, an experiment set was prepared for the detection of buried objects. The experimental set was composed of an antenna that sends and receives electromagnetic waves in a wide frequency band, software that records and processes reflections, and a sandbox. In the study, metallic and non-metallic objects with different depths, sizes and shapes were buried in this sand pool and measurements were taken along a profile. 2D images were created from the measurements and image processing techniques were applied to these images. Classification algorithms were used to detect the type of bruied object from processed images. To increase the success of the algorithms, correlation-based attribute selection (CFS) and Principal Component Analysis (PCA) were used as attribute selection techniques. Genetic algorithm (GA), Particle Swarm Optimization (PSO), Harmony search (HA), and Evolutionary search (EA), which are among the metaheuristic optimization algorithms, were preferred as search methods in attribute selection with CFS. The performance of the algorithms was analyzed using the 10-fold cross-validation method. As a result, it was understood that the use of the PCA algorithm in attribute selection increases the classification success more than metaheuristic algorithms. The most successful among the classification algorithms used is the Random tree algorithm. After PCA, the accuracy value of this algorithm was 95.8 Therefore, a hybrid approach is proposed in which PCA and Random tree algorithms are used in the software embedded in the measurement system.

Fruit and Vegetable Image Recognition Based on Multiple Tree Models: Applications of Random Forest, XGBoost and Decision Tree

The primary objective of this study is to evaluate and compare the performance of three machine learning models— Random Forest, XGBoost, and Decision Tree—in the context of fruit and vegetable image classification. This research aims to identify which model best handles the challenges associated with imbalanced datasets and complex data structures. The ultimate goal is to contribute to the development of more efficient and accurate automated systems for agricultural applications, thereby improving productivity and reducing operational costs in the industry. This study utilized a dataset of 3,825 images covering 36 fruit and vegetable classes. Images were resized, normalized, and augmented to enhance diversity. Three models—Random Forest, XGBoost, and Decision Tree—were trained on this dataset. Performance was evaluated using accuracy, precision, recall, and F1-score to assess classification effectiveness and handling of class imbalances. The evaluation revealed that XGBoost outperformed Random Forest and Decision Tree in fruit and vegetable image classification, achieving the highest accuracy of 96.66%. XGBoost demonstrated superior handling of class imbalances and complex data structures, reflected in its precision and recall scores across various classes. Random Forest also performed well, closely following XGBoost, while Decision Tree exhibited more variability in results, indicating potential overfitting in certain classes. In conclusion, this study highlights the effectiveness of ensemble methods, particularly XGBoost, in agricultural image classification tasks. These findings suggest that XGBoost is a robust model for similar applications, offering improved accuracy and reliability