Nature-inspired Optimization Research Articles

A New Single-Parameter Bees Algorithm.

Based on bee foraging behaviour, the Bees Algorithm (BA) is an optimisation metaheuristic algorithm which has found many applications in both the continuous and combinatorial domains. The original version of the Bees Algorithm has six user-selected parameters: the number of scout bees, the number of high-performing bees, the number of top-performing or "elite" bees, the number of forager bees following the elite bees, the number of forager bees recruited by the other high-performing bees, and the neighbourhood size. These parameters must be chosen with due care, as their values can impact the algorithm's performance, particularly when the problem is complex. However, determining the optimum values for those parameters can be time-consuming for users who are not familiar with the algorithm. This paper presents BA1, a Bees Algorithm with just one parameter. BA1 eliminates the need to specify the numbers of high-performing and elite bees and other associated parameters. Instead, it uses incremental k-means clustering to divide the scout bees into groups. By reducing the required number of parameters, BA1 simplifies the tuning process and increases efficiency. BA1 has been evaluated on 23 benchmark functions in the continuous domain, followed by 12 problems from the TSPLIB in the combinatorial domain. The results show good performance against popular nature-inspired optimisation algorithms on the problems tested.

Nature-inspired optimization of weighted-feature ensemble model to predict the deflection of corroded reinforced concrete beam

Steel corrosion is a significant concern for the structural integrity of reinforced concrete (RC) beams, leading to a decline in durability throughout a building's lifespan. Assessing the deflection of corroded RC beams is essential for maintaining structural safety and longevity. This study introduces a novel nature-inspired weighted-feature ensemble model (NiwE) that integrates two distinct machine learning algorithms: least squares support vector regression and radial basis function neural network, both optimized by beluga whale optimization to predict the deflection of corroded RC beams. The primary objective is to enhance the predictive accuracy of deflection measurements, offering a robust tool for evaluating structural performance under corrosion-induced damage. The model was developed and validated using a comprehensive dataset comprising 180 samples collected from aging residential buildings in southern Vietnam. A cross-validation approach was employed in this process to ensure a robust and reliable evaluation of the models. The results show that the NiwE model achieved superior predictive accuracy compared to existing methods, with an RMSE of 1.739 mm, MAE of 1.115 mm, and R2 of 0.926 in testing. Remarkably, the NiwE model was able to determine weight values that facilitate the discovery of new feature combinations, achieving significantly higher accuracy than existing models. The proposed model offers civil engineers a valuable tool for estimating deflection in corroded RC beams and opens new pathways for optimizing building structures, developing early warning systems, and implementing timely maintenance measures to ensure safety and resilience.

Optimizing breast cancer diagnosis: Harnessing the power of nature-inspired metaheuristics for feature selection with soft voting classifiers

Breast cancer is a widespread and serious condition that poses a significant threat to women's health globally, contributing significantly to mortality rates. Machine learning tools play a critical role in both the effective management and early detection of this disease. Feature selection (FS) methods are essential for identifying the most impactful features to improve breast cancer diagnosis. These methods reduce data dimensionality, eliminate irrelevant information, enhance learning accuracy, and improve the comprehensibility of results. However, the increasing complexity and dimensionality of cancer data pose substantial challenges to many existing FS methods, thereby reducing their efficiency and effectiveness. To overcome these challenges, numerous studies have demonstrated the success of nature-inspired optimization (NIO) algorithms across various domains. These algorithms excel in mimicking natural processes and efficiently solving complex optimization problems. Building on these advancements, we propose an innovative approach that combines powerful feature selection methods based on NIO techniques with a soft voting classifier. The NIO techniques employed include the Genetic Algorithm, Cuckoo Search, Salp Swarm, Jaya, Flower Pollination, Whale Optimization, Sine Cosine, Harris Hawks, and Grey Wolf Optimization algorithms. The Soft Voting Classifier integrates various machine learning models, including Support Vector Machines, Gaussian Naive Bayes, Logistic Regression, Decision Tree, and Gradient Boosting. These are used to improve the effectiveness and accuracy of breast cancer diagnosis. The proposed approach has been empirically evaluated using a variety of evaluation measures, such as F1 score, precision, recall, accuracy and Area Under the Curve (AUC), for performance comparison with individual machine learning techniques. The results demonstrate that the soft-voting ensemble technique, particularly when combined with feature selection based on the Jaya algorithm, outperforms all individual classifiers on the breast cancer dataset. It achieves the highest scores for accuracy, precision, recall, F1-score, and AUC, with values of 99.6 %, 99.21 %, 100 %, 99.6 %, and 99.6 %, respectively. Following closely, both the Genetic Algorithm and Salp Swarm feature selection demonstrate strong performance, with scores of 99.5 %, 100 %, 99 %, 99.5 %, and 99.5 %, respectively. Furthermore, the approach's effectiveness in identifying the minimal set of relevant features has been evaluated. Given the critical importance of selecting relevant features in complex cancer datasets, the proposed method proves to be a valuable tool for achieving early detection and improved interpretability. This method can also support healthcare professionals in making well-informed decisions regarding cancer diagnosis and treatment strategies.

An Integration of Adaptive Control Techniques with Portable Solar-Powered EV Charging with Particle Swarm Optimization (PSO)

Abstract: This paper presents a novel approach utilizing Particle Swarm Optimization (PSO) for enhancing the efficiency of solar-powered electric vehicle (EV) charging systems. With the increasing adoption of EVs and the imperative to transition towards renewable energy sources, optimizing the utilization of solar power for charging becomes crucial. The proposed system integrates photovoltaic (PV) panels to harness solar energy, providing a sustainable and renewable power source for EV charging. The key innovation lies in the application of PSO, a nature-inspired optimization algorithm, to dynamically adjust charging parameters based on real-time environmental conditions, grid electricity prices, and EV battery requirements. PSO offers a robust and efficient method for solving optimization problems by simulating the behavior of swarms in nature. In the context of solar-powered EV charging, the PSO algorithm iteratively adjusts charging rates and schedules to maximize solar energy utilization, minimize charging costs, and ensure optimal battery health. The integration of PSO optimization with solarpowered EV charging represents a promising step towards achieving sustainable and intelligent transportation solutions. Future research directions may include further optimization refinements, scalability studies, and real-world deployment to validate the practicality and effectiveness of the proposed approach in diverse environments and usage scenarios.

Hybrid raven roosting intelligence framework for enhancing efficiency in data clustering

The field of data exploration relies heavily on clustering techniques to organize vast datasets into meaningful subgroups, offering valuable insights across various domains. Traditional clustering algorithms face limitations in terms of performance, often getting stuck in local minima and struggling with complex datasets of varying shapes and densities. They also require prior knowledge of the number of clusters, which can be a drawback in real-world scenarios. In response to these challenges, we propose the "hybrid raven roosting intelligence framework" (HRIF) algorithm. HRIF draws inspiration from the dynamic behaviors of roosting ravens and computational intelligence. What distinguishes HRIF is its effective capacity to adeptly navigate the clustering landscape, evading local optima and converging toward optimal solutions. An essential enhancement in HRIF is the incorporation of the Gaussian mutation operator, which adds stochasticity to improve exploration and mitigate the risk of local minima. This research presents the development and evaluation of HRIF, showcasing its unique fusion of nature-inspired optimization techniques and computational intelligence. Extensive experiments with diverse benchmark datasets demonstrate HRIF's competitive performance, particularly its capability to handle complex data and avoid local minima, resulting in accurate clustering outcomes. HRIF's adaptability to challenging datasets and its potential to enhance clustering efficiency and solution quality position it as a promising solution in the world of data exploration.

Optimal Path Planning Algorithm with Built-In Velocity Profiling for Collaborative Robot.

This paper proposes a method for solving the path planning problem for a collaborative robot. The time-optimal, smooth, collision-free B-spline path is obtained by the application of a nature-inspired optimization algorithm. The proposed approach can be especially useful when moving items that are delicate or contain a liquid in an open container using a robotic arm. The goal of the optimization is to obtain the shortest execution time of the production cycle, taking into account the velocity, velocity and jerk limits, and the derivative continuity of the final trajectory. For this purpose, the velocity profiling algorithm for B-spline paths is proposed. The methodology has been applied to the production cycle optimization of the pick-and-place process using a collaborative robot. In comparison with point-to-point movement and the solution provided by the RRT* algorithm with the same velocity profiling to ensure the same motion limitations, the proposed path planning algorithm decreased the entire production cycle time by 11.28% and 57.5%, respectively. The obtained results have been examined in a simulation with the entire production cycle visualization. Moreover, the smoothness of the movement of the robotic arm has been validated experimentally using a robotic arm.

Volume Optimization for a Single-Phase Air-Core Reactor – 2D Finite Element Method and Particle Swarm Optimization Approach

This paper describes the optimization of an air core reactor using Particle Swarm Optimization (PSO) combined with the finite element analysis. The aim is to reduce material volume costs in manufacturing and maintenance. PSO is a nature-inspired optimization algorithm that simulates swarm behavior in search of the best solution in a multidimensional search space. This article employs the PSO optimization method as an initial contribution to the design of single-phase air-core reactors, suggesting the possibility of further studies comparing its efficacy with other heuristic algorithms on the subject in the future.

Piranha predation optimization algorithm (PPOA) for global optimization and engineering design problems

A new nature-inspired optimization algorithm, Piranha predation optimization algorithm (PPOA), is proposed based on the unique foraging and predation behaviors of piranhas. Briefly, PPOA consists of three optimization operations, i.e., narrowing down to tear prey, swimming in a straight line, and swimming in a spiral. In this paper, various mathematical models for simulating the behavioral operators are presented in detail to solve different optimization challenges effectively. In this paper, the performance of PPOA is rigorously tested on 23 benchmark optimization functions, CEC2017 competition test set, CEC2020 real-world engineering optimization problems and four engineering design applications to show the applicability of the algorithm in different applications. Comparison experiments with other good and advanced competitive algorithms are conducted to reveal the advantages and performance of PPOA by using performance metrics such as Wilcoxon rank sum test and Friedman mean rank. The comparative results of this paper demonstrate the effectiveness of the proposed algorithmic strategy and its potential in applying it to solving optimization real-world engineering optimization problems.

Rapid quantitative and qualitative analysis of talcum powder in wheat flour by microwave detection combined with multivariate analysis

Rapid and accurate monitoring of talcum powder in wheat flour is of great practical importance for maintaining market order, public health and food safety. In this study, the potential of a home-made microwave detection system for rapid quantitative and qualitative detection of talcum powder adulteration in wheat flour at 2.5–11.5 GHz was investigated. Useful microwave spectral information is selected using three feature selection strategies, the bootstrapping soft shrinkage (BOSS), the multiple feature spaces ensemble with least absolute shrinkage and selection operator (MEF-LASSO), and the competitive adaptive reweighted sampling (CARS). The eXtreme Gradient Boosting (XGBoost) classification and regression models are constructed and evaluated, and three nature-inspired optimization algorithms (NIOA) are used for parameter optimization: the Osprey Optimization Algorithm (OOA), the Crested Porcupine Optimizer (CPO), and the Sparrow Search Algorithm (SSA). The experimental results show that both feature selection strategies and NIOA algorithms can effectively improve the model performance. The SSA-XGBoost classification model has the highest prediction accuracy, and its prediction accuracy and F1-score can reach 100 % and 1, respectively. The MEF-LASSO-SSA-XGBoost regression model has strong robustness. The root mean square error (RMSE) was 1.76 %, the prediction coefficient of determination (R2) was 0.98, and the ratio of performance to deviation (RPD) was 8.66. The results of this study showed that the combination of microwave technology and machine learning model with multivariate analysis method can realize the rapid and accurate determination of qualitative and quantitative talcum powder in wheat flour.

Colon Cancer Disease Diagnosis Based on Convolutional Neural Network and Fishier Mantis Optimizer.

Colon cancer is a prevalent and potentially fatal disease that demands early and accurate diagnosis for effective treatment. Traditional diagnostic approaches for colon cancer often face limitations in accuracy and efficiency, leading to challenges in early detection and treatment. In response to these challenges, this paper introduces an innovative method that leverages artificial intelligence, specifically convolutional neural network (CNN) and Fishier Mantis Optimizer, for the automated detection of colon cancer. The utilization of deep learning techniques, specifically CNN, enables the extraction of intricate features from medical imaging data, providing a robust and efficient diagnostic model. Additionally, the Fishier Mantis Optimizer, a bio-inspired optimization algorithm inspired by the hunting behavior of the mantis shrimp, is employed to fine-tune the parameters of the CNN, enhancing its convergence speed and performance. This hybrid approach aims to address the limitations of traditional diagnostic methods by leveraging the strengths of both deep learning and nature-inspired optimization to enhance the accuracy and effectiveness of colon cancer diagnosis. The proposed method was evaluated on a comprehensive dataset comprising colon cancer images, and the results demonstrate its superiority over traditional diagnostic approaches. The CNN-Fishier Mantis Optimizer model exhibited high sensitivity, specificity, and overall accuracy in distinguishing between cancer and non-cancer colon tissues. The integration of bio-inspired optimization algorithms with deep learning techniques not only contributes to the advancement of computer-aided diagnostic tools for colon cancer but also holds promise for enhancing the early detection and diagnosis of this disease, thereby facilitating timely intervention and improved patient prognosis. Various CNN designs, such as GoogLeNet and ResNet-50, were employed to capture features associated with colon diseases. However, inaccuracies were introduced in both feature extraction and data classification due to the abundance of features. To address this issue, feature reduction techniques were implemented using Fishier Mantis Optimizer algorithms, outperforming alternative methods such as Genetic Algorithms and simulated annealing. Encouraging results were obtained in the evaluation of diverse metrics, including sensitivity, specificity, accuracy, and F1-Score, which were found to be 94.87%, 96.19%, 97.65%, and 96.76%, respectively.

Nature-inspired optimization prey–predator algorithm for soil slope stability analysis with physically informed initial population generation

In soil slope stability, locating the critical slip surface with the minimum safety factor is a difficult and complicated optimization problem. Most methods fail when proper bounds are not applied to the decision variable. For the first time, the paper uses the prey–predator algorithm to analyze slopes stability with circular slip surfaces making use of meaningful rules, from an engineering point of view, to define these bounds. The Fellenius method based on limit equilibrium technique also serves as the constraint of the mathematical problem adopted to solve the task. A pseudo-analytical study is also carried out on four benchmark literature problems to test the performance and to certify the accuracy of the results obtained with the new application of the prey–predator algorithm. We show that the solutions obtained are accurate and should be assumed as a reference comparing to other methods.

Multilevel Thresholding-based Medical Image Segmentation using Hybrid Particle Cuckoo Swarm Optimization

Background: The most important aspect of medical image processing and analysis is image segmentation. Fundamentally, the outcomes of segmentation have an impact on all subsequent image testing methods, including object representation and characterization, measuring of features, and even higher-level procedures. The problem with image segmentation is recognition and perceptual completion while segmenting the image. However, these issues can be resolved by multilevel optimization techniques. However, multilevel thresholding will become more computationally intensive with increasing thresholds. Optimization algorithms can resolve these issues. Therefore, hybrid optimization is used for image segmentation in this research work. Methods: The researchers propose a Multilevel Thresholding-based Segmentation using a Hybrid Optimization approach with an adaptive bilateral filter to resolve the optimization challenges in medical image segmentation. The proposed model utilizes Kapur's entropy as the objective function in the nature-inspired optimization algorithm. Results: The result is evaluated using parameters such as the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Feature Similarity Index (FSIM). The researchers perform result analysis with variable thresholding levels on KAU-BCMD and mini-MIAS datasets. The highest PSNR, SSIM, and FSIM achieved were 31.9672, 0.9501, and 0.9728 respectively. The results of the hybrid model are compared with state-of-the-art models, demonstrating its efficiency. Conclusion: The research concludes that the proposed Multilevel thresholding-based segmentation using a Hybrid Optimization approach effectively solves optimization challenges in medical image segmentation. The results indicate its efficiency compared to existing models. The research work highlights the potential of the proposed hybrid model for improving image processing and analysis in the medical field.

An Ensemble Model for Predicting Cardiovascular Disease utilizing Nature Inspired Optimization

قدم هذا البحث نموذجًا تنبؤيًا مبتكرًا لأمراض القلب، تم تحسينه من خلال تطبيق منهجية تحسين أفضل التقنيات المتاحة. من المعترف به عالميًا أن انتشار أمراض القلب والأوعية الدموية، خاصة في الدول التي تواجه تحديات اقتصادية، هو مساهم كبير في معدلات الوفيات العالمية.يتم التأكيد على ضرورة التنبؤ الدقيق بالنتائج في الوقت المناسب من خلال ضرورة التخفيف من العواقب الوخيمة المحتملة المرتبطة بهذا التحدي الصحي المحدد. تتضمن المنهجية اختيار الميزات من خلال المعلومات المتبادلة، يليها التخلص من العناصر الخارجية باستخدام نهج Inter Quartile Range أثناء تنقية البيانات.لمعالجة التحيز المحتمل من اختلافات المقياس المتغير، يتم تنفيذ توحيد الميزات باستخدام طريقة Standard Scaler. يتم استخدام تقنية تعزيز التدرج لتطوير النماذج، المعروفة بقدرتها على إدارة البيانات المفقودة وإنشاء تنبؤات دقيقة. لمزيد من تعزيز الأداء، تم تقديم خوارزمية BAT، مع الاستفادة من التحسين المستوحى من الطبيعة. وقد أدى تطبيق أسلوب أفضل التقنيات المتاحة في هذا النموذج بالذات إلى تحسن ملحوظ في الأداء، مما أدى إلى معدل دقة قدره 84.94%. وكانت درجات الدقة والنوعية والحساسية للنموذج 76.47%، 81.88%، و89.65%، على التوالي. وتشير هذه المقاييس مجتمعة إلى أداء متوازن.

Hybridized machine learning models for phosphate pollution modeling in water systems for multiple uses

Phosphate pollution in water bodies is a significant environmental concern, especially in regions with extensive agricultural practices. Hence, a tool for accurately assessing the phosphate concentration is essential. This research paper explores the effectiveness of machine learning (ML) models combined with nature-inspired optimization algorithms for predicting phosphate levels in water systems. The novelty consists of integrating the power of machine learning models, which have been presenting excellent performance at capturing complex relationships in environmental pollution data, with the Harris Hawks Optimizer (HHO) optimization capabilities inspired by hawks' hunting behavior. Four hybrid implementations combining the HHO were evaluated, and four feature subsets were assessed to identify the most influential variables in the modeling process. Using water quality data from Brazilian upstream watersheds, the hybrid models were trained and validated, enabling accurate and robust predictions of phosphate concentrations. The elastic net (EN) model optimized by Harris Hawk Optimizer (HHO-EN) produced the best-averaged performance among all experiments (R = 0.825, R2 = 0.670, Root Mean Square Error (RMSE) = 0.049 mg/L, Mean Absolute Error (MAE) = 0.037 mg/L). A parametric and feature importance analysis identified the most influential parameters in the contamination modeling process. Hybrid machine learning models represent a novel and efficient strategy for water quality monitoring and environmental management, supporting the preservation of aquatic ecosystems.

Fine-tuning digital FIR filters with gray wolf optimization for peak performance

The design of optimum filters constitutes a fundamental aspect within the realm of signal processing applications. The process entails the calculation of ideal coefficients for a filter in order to get a passband with a flat response and an unlimited level of attenuation in the stopband. The objective of this work is to solve the FIR filter design problem and to compare the optimal solutions obtained from evolutionary algorithms. The design of optimal FIR low pass (LP), high pass (HP), and band stop (BS) filters is achieved by the utilization of nature-inspired optimization approaches, namely gray wolf optimization ,cuckoo search, particle swarm optimization, and genetic algorithm. The filters are evaluated in terms of their stop band attenuation, pass band ripples, and departure from the anticipated response. In addition, this study compares the optimization strategies applied in the context of algorithm execution time which is achievement of global optimal outcomes for the design of digital finite impulse response (FIR) filters. The results indicate that when the Gray wolf algorithm is applied to the development of a finite impulse response (FIR) filter, it produces a higher level of performance than other approaches, as supported by enhanced design precision, decreased execution time, and achievement of an optimal solution.

Evaluation of heating load energy performance in residential buildings through five nature-inspired optimization algorithms

This study evaluates a new hybrid approach for determining homes’ heating load (HL). The crow search algorithm (CSA), heap-based optimizer (HBO), seeker optimization algorithm (SOA), political optimizer (PO), and harmony search (HS) are the five components of the suggested paradigm. A nonlinear analysis of the effects of eight independent factors on the HL was conducted using the best structure identified in each model. The assessment procedure for the HS technique in this study consisted of three parts. The appropriate population size to utilize in the first phase was found to be the one that yields the best coefficient of determination (R2) value and the lowest root mean squared error (RMSE) value. For CSA-MLP, HBO-MLP, SOA-MLP, PO-MLP, and HS-MLP, respectively, the first phase yielded R2 = 0.96473, 0.95618, 0.96931, 0.97048, and 0.96702, and RMSE = 2.57119, 2.85968, 2.40125, 2.3554, and 2.46872. A battery of tests using a range of different nNew values (between 10 and 100) was applied to the HS-MLP with a population size of 50 in the second phase. The data indicates that the most reliable results are obtained with a nNew-value of 60. For training and testing, this value has RMSE values of 2.61518 and 2.4387 and R2 values of 0.9669 and 0.96783. In the third stage, an experiment with a population size of 50 and nNew of 60 was examined using a range of HMCR values (between 0.5 and 1.4). Concerning training and testing, the results indicate that the HMCR value 1.1 produces the most reliable results; its R2 values are 0.9739 and 0.97207, and its RMSE values are 2.32691 and 2.27488. Lastly, the results demonstrate that the accuracy of the HS-MLP method has been improved by the 3-phase analysis method.

A comprehensive comparison of different regression techniques and nature-inspired optimization algorithms to predict carbonation depth of recycled aggregate concrete

A comprehensive comparison of different regression techniques and nature-inspired optimization algorithms to predict carbonation depth of recycled aggregate concrete

Experimental investigation on fiber laser micro drilling of Titanium grade 5: fabrication, nature-inspired optimization and analysis through image processing

This investigation showed that micro holes were created on Titanium grade 5 substrate surface using a 30 W fiber laser. The impact of the control factors such as scan speed, frequency, number of passes and power were studied on the responses namely heat affected zone (HAZ), hole circularity (HC) and deviation in diameter (DIV). The control factors were optimized using firefly algorithm. Mathematical models were developed for each response having significant R-square value. 3D surface plots were used to examine how the control parameters affected the response. The firefly algorithm identifies the optimal conditions for micro drilling as scan speed of 210 mm s−1, frequency of 40 kHz, power of 8 W and total of 40 passes which improved experimental findings i.e. HC-0.974, DIV-37.02 μm and HAZ-19.53. After comparing the predicted values with the experimental findings, it was observed that the prediction error is lowest for HC (1.23%) followed by DIV (13.9%) and HAZ (16.9%). Image processing technique was used to convert regular images into a digital format to extract useful information.

Fast EM-driven nature-inspired optimization of antenna input characteristics using response features and variable-resolution simulation models

Utilization of optimization technique is a must in the design of contemporary antenna systems. Often, global search methods are necessary, which are associated with high computational costs when conducted at the level of full-wave electromagnetic (EM) models. In this study, we introduce an innovative method for globally optimizing reflection responses of multi-band antennas. Our approach uses surrogates constructed based on response features, smoothing the objective function landscape processed by the algorithm. We begin with initial parameter space screening and surrogate model construction using coarse-discretization EM analysis. Subsequently, the surrogate evolves iteratively into a co-kriging model, refining itself using accumulated high-fidelity EM simulation results, with the infill criterion focusing on minimizing the predicted objective function. Employing a particle swarm optimizer (PSO) as the underlying search routine, extensive verification case studies showcase the efficiency and superiority of our procedure over benchmarks. The average optimization cost translates to just around ninety high-fidelity EM antenna analyses, showcasing excellent solution repeatability. Leveraging variable-resolution simulations achieves up to a seventy percent speedup compared to the single-fidelity algorithm.

Nature-inspired optimization algorithms based feature selection: Application in credit scoring

Nature-inspired algorithms as problem-solving methodologies are extremely effective in discovery of optimized solutions in multi-dimensional and multi-modal problems. Because of qualities like “self-optimization”, “flexibility” and etc., nature-inspired algorithms for problem solving are effectively optimal. Feature selection is an approach to find approximate optimal subset of the features which are more relevant towards the particular outcome. In this study, we focused on how feature selection may improve the credit scoring model’s performance for prediction. Nature-inspired algorithms are applied for feature selection to improve the predictive performance of the credit scoring model. Additionally, four benchmark credit scoring datasets collected from the UCI repository are used to test feature selection by several Nature-inspired algorithms aggregated with “Random Forest (RF)”, “Logistic Regression (LR),” and “Multi-layer Perceptron (MLP)” for classification and results are compared in terms of classification accuracy and G-measures.