Forest Optimization Algorithm Research Articles

Refining heart disease prediction accuracy using hybrid machine learning techniques with novel metaheuristic algorithms

Early diagnosis of heart disease is crucial, as it's one of the leading causes of death globally. Machine learning algorithms can be a powerful tool in achieving this goal. Therefore, this article aims to increase the accuracy of predicting heart disease using machine learning algorithms. Five classification models are explored: eXtreme Gradient Boosting (XGBC), Random Forest Classifier (RFC), Decision Tree Classifier (DTC), K-Nearest Neighbors Classifier (KNNC), and Logistic Regression Classifier (LRC). Additionally, four optimizers are evaluated: Slime mold Optimization Algorithm, Forest Optimization Algorithm, Pathfinder algorithm, and Giant Armadillo Optimization. To ensure robust model selection, a feature selection technique utilizing k-fold cross-validation is employed. This method identifies the most relevant features from the data, potentially improving model performance. The top three performing models are then coupled with the optimization algorithms to potentially enhance their generalizability and accuracy in predicting heart failure. In the final stage, the shortlisted models (XGBC, RFC, and DTC) were assessed using performance metrics like accuracy, precision, recall, F1-score, and Matthews Correlation Coefficient (MCC). This rigorous evaluation identified the XGGA hybrid model as the top performer, demonstrating its effectiveness in predicting heart failure. XGGA achieved impressive metrics, with an accuracy, precision, recall, and F1-score of 0.972 in the training phase, underscoring its robustness. Notably, the model's predictions deviated by less than 5.5 % for patients classified as alive and by less than 1.2 % for those classified as deceased compared to the actual outcomes, reflecting minimal error and high predictive reliability. In contrast, the DTC base model was the least effective, with an accuracy of 0.840 and a precision of 0.847. Overall, the optimization using the GAO algorithm significantly enhanced the performance of the models, highlighting the benefits of this approach.

Wrapper-Filter Feature Selection using Multi-Objective Forest Optimization Algorithm: A Fusion Methodology

The prevailing data upsurge has empowered the use of data for enhanced decision making process. Prior processing of data is a vital task to certify that the appropriate traits are measured during the study. To reduce the data extents, Feature Selection (FS) is an essential stage in the data pre-processing process along with stabilizing the significant data features. This paper introduces a Hybrid Wrapper Filter Multi-Objective Forest Optimization Algorithm with Local Search model (HWF-MOFOA-LS) algorithm for FS problem. The FS is performed using the hybrid wrapper-filter approach which are optimized concurrently. Initially a Multi-objective approach simultaneously optimizes the hybrid wrapper-filter fitness functions. The aim is to reduce the number of features and identify the related information to improve the classification accuracy. Next, the classified population with Pareto front solutions are enhanced by applying a local search selection strategy. Finally, the performance of the Multi-objective proposed technique are performed on 12 datasets from UCI repository. The results of the proposed approach is optimal when compared to the other multi-objective techniques. The proposed algorithms outperforms the other techniques by reducing the classification error along with selecting ¬minimum features.

Optimization of Spectral and Energy Efficiencies Trade-Off Using Multi-objective Forest Optimization Algorithm in Massive MIMO Systems

Optimization of Spectral and Energy Efficiencies Trade-Off Using Multi-objective Forest Optimization Algorithm in Massive MIMO Systems

Enhancing forest optimization algorithm with gravitational search for nonlinear continuous optimization

Meta-heuristic algorithms have great role in solving problems related to optimization. Meta-heuristic method cannot solve problems related to optimization due to No Free Lunch theory. Hence different optimization methods are proposed by various researchers each year in order to solve optimization problems. Forest Optimization Algorithm (FOA) is an evolutionary optimization algorithm that is appropriate for continuous nonlinear optimization problems. The algorithm drawbacks include entrapment in local optimum and failure in achieving global optimum. The paper proposes hybrid algorithm called FOAGSA, in which the Gravitational Search Algorithm (GSA) is employed to improve the FOA performance in order to solve nonlinear continuous problems. The FOAGSA was evaluated through 39 benchmark optimization functions and two engineering problems. The experimental results proved that the FOAGSA exhibited acceptable results compared to state-of-art and well-known Meta-heuristic algorithms. Friedman ranking algorithm was utilized to compare FOAGSA with existing methods. The FOAGSA was ranked first on that basis.

Automated road crack classification using a novel forest optimization algorithm for otsu thresholding and hybrid feature extraction

Automated road crack classification using a novel forest optimization algorithm for otsu thresholding and hybrid feature extraction

Comparative Analysis of Low Voltage Ride Through Techniques of DFIG Connected to Grid using AI Techniques

The requirement for clean and eco-friendly energy initiated the production of renewable energy. Wind energy promises attractive features such as bulk power production and reduced maintenance cost. Advancement of technologies in the field of wind turbines and generators made the gates open for attractive investments. Adjustable-speed wind turbines tied to Doubly Fed Induction Generators (DFIG) became the most excellent choice of power utilities due to their low-cost power converters and four-quadrant control of useful and wattless powers. The ability to extract a high amount of power for a specified wind speed is a major advantage of DFIG which attracted Power system Operators (PSOs). Low voltage ride-through and wattless Spower support to the grid are estimated and compared using conventional PI controllers, Artificial Neural Network (ANN), and Random Forest Optimization (RFO) algorithm.

DBRF: Random Forest Optimization Algorithm Based on DBSCAN

DBRF: Random Forest Optimization Algorithm Based on DBSCAN

Construction of Reliability Evaluation Model of Cigarette Quality Supervision Measuring Instrument Based on Random Forest Algorithm

Abstract This paper combines the cigarette quality supervision measurement project, summarizes the key instruments for cigarette quality supervision, constructs the reliability evaluation model of cigarette quality supervision measurement instruments, adopts the random forest method and carries out model optimization so as to solve the weights of quality evaluation indexes of cigarette quality supervision measurement instruments. The QTM comprehensive test bench is selected to measure the quality and circumference of the traceability comparison stick of cigarette physical inspection equipment and combined with the Random Forest optimization algorithm to analyze the impact of measurement repeatability on the moisture content of cigarettes. Using the normalized deviation evaluation method, we can compare the consistency of the detection results of the evaluation model of this paper with the detection results of the parent company. It is analyzed that the maximum permissible error of cigarette circumference is ±0.05, and the maximum permissible deviation of comparison D max is ±0.06. The circumference deviation of sample A is 0.04<0.06, the sample is qualified, and it is consistent with the testing results of the parent company.

A disease diagnosis system for smart healthcare based on fuzzy clustering and battle royale optimization

The ongoing growth of the Internet of Things and machine learning technology have provided increased motivation for the development of smart healthcare. In this study, a disease diagnosis system is proposed for remote identification and early prediction in smart healthcare environments. The originality of this study resides in the innovative implementation of ensuing modules to improve diagnostic accuracy of the system. First, fuzzy clustering based on the forest optimization algorithm is employed to detect outliers and a self-organizing fuzzy logic classifier is applied to supplement missing data in electronic medical records (EMRs). A feature selection technique using the battle royale optimization algorithm is then developed to remove redundant information and identify optimal EMR features. The refined and fused data are further classified using an eigenvalue-based machine learning algorithm to determine whether a patient exhibits a certain disease. Simulation experiments are conducted with widely used heart disease and diabetes datasets to evaluate the performance of the proposed system, using accuracy, precision, recall, and F-measure as evaluation metrics.

Selective Mapping scheme based on Modified Forest Optimization Algorithm for PAPR reduction in FBMC system

Filter Bank Multicarrier(FBMC)is considered as one of the most standardized waveform for fifth generation (5G) mobile communication system application FBMC endures lot of nonlinear effects which occurs because of high Peak Average Power Ratio (PAPR). High value of PAPR due to the large dynamic range of multicarrier signal is one of the most significant issues in FBMC multicarrier based modulation technique. This paper presents one investigated PAPR reduction technique named as Selected Mapping (SLM) to minimize high PAPR by utilizing the complex signal divide into real and imaginary parts and then select minimum PAPR signal based on Modified Forest Optimization Algorithm (MFOA)to achieve good PAPR which can maintain the FBMC based system performance with a required Bit Error Rate (BER). The associated method was produced with the aim of optimize the phase factors so that the phase rotation operation is accomplished to minimize PAPR by fixing the MFOA into the conventional SLM. The simulation results demonstrate that the proposed technique gives better performance in terms of BER and PAPR compared to other SLM based optimization techniques.

Generating the structural graph‐based model from a program source‐code using chaotic forrest optimization algorithm

AbstractOne of the most important and costly stages in software development is maintenance. Understanding the structure of software will make it easier to maintain it more efficiently. Clustering software modules is thought to be an effective reverse engineering technique for deriving structural models of software from source code. In software module clustering, the most essential objectives are to minimize connections between produced clusters, maximize internal connections within created clusters, and maximize clustering quality. Finding the appropriate software system clustering model is considered an NP‐complete task. The previously proposed approaches' key limitations are their low success rate, low stability, and poor modularization quality. In this paper, for optimal clustering of software modules, Chaotic based heuristic method using a forest optimization algorithm is proposed. The impact of chaos theory on the performance of the other SFLA‐GA and PSO‐GA has also been investigated. The results show that using the logistic chaos approach improves the performance of these methods in the software‐module clustering problem. The performance of chaotic based FOA, SFLA‐GA and PSO‐GA is superior to the other heuristic methods in terms of modularization quality and stability of the results.

Automated software effort estimation for agile development system by heuristically improved hybrid learning

SummaryIn the process of modern software development, an important role is played by software effort estimation. The failure or success of the projects is mostly based on the schedule outcomes and effort estimation accuracy. The effort estimation problem still remains as a challenging issue because of the limitations of various standard measures for forecasting the efforts in the plan‐driven software development. This article emphasizes on novel software effort estimation framework using heuristically improved hybrid learning model. This article proposes heuristically improved hybrid learning (HI‐HL) with deep belief network and artificial neural network for software effort estimation. The weight optimization strategy of DBN and ANN to attain highly accurate estimation. Here, the weight optimization is performed by integrating two standard optimization algorithms such as forest optimization algorithm and moth‐flame optimization, so called as solution index‐based forest moth flame optimization with the purpose of solving the fitness function concerning the “Magnitude of Relative Error and Mean Absolute Error (MAE).” From the table results, for dataset 1, the SMAPE of SI‐FMFO‐HI‐HL is correspondingly secured 60.75%, 34.26%, 56.77%, and 61.44% improved than ELM, LSTM, DNN, and fuzzy. The simulation findings indicated that the recommended estimation model outperformed the baseline approaches on the three publically available datasets.

Improving mobile mass monitoring in the IoT environment based on Fog computing using an improved forest optimization algorithm

In the IoT-based users monitor tasks in the network environment by participating in the data collection process by smart devices. Users monitor their data in the form of fog computing (mobile mass monitoring). Service providers are required to pay user rewards without increasing platform costs. One of the NP-Hard methods to maximise the coverage rate and reduce the platform costs (reward) is the Cooperative Based Method for Smart Sensing Tasks (CMST). This article uses chaos theory and fuzzy parameter setting in the forest optimisation algorithm. The proposed method is implemented with MATLAB. The average findings show that the network coverage rate is 31% and the monitoring cost is 11% optimised compared to the CMST scheme and the mapping of the mobile mass monitoring problem to meta-heuristic algorithms. And using the improved forest optimisation algorithm can reduce the costs of the mobile crowd monitoring platform and has a better coverage rate.

Application of Forest Optimization Algorithm for the Resource Constraint Project Scheduling

Project scheduling is one of the most complex yet important issues in project-based planning. Scheduling is a topic that has entered the project from the production space and has been considered by many researchers in recent years. This problem has always faced many challenges due to its complex limitations in the real world. One of these issues is resource scheduling, known in optimization as the RCPSP problem. The study

An Ensemble Framework to Forest Optimization Based Reduct Searching

Essentially, the solution to an attribute reduction problem can be viewed as a reduct searching process. Currently, among various searching strategies, meta-heuristic searching has received extensive attention. As a new emerging meta-heuristic approach, the forest optimization algorithm (FOA) is introduced to the problem solving of attribute reduction in this study. To further improve the classification performance of selected attributes in reduct, an ensemble framework is also developed: firstly, multiple reducts are obtained by FOA and data perturbation, and the structure of those multiple reducts is symmetrical, which indicates that no order exists among those reducts; secondly, multiple reducts are used to execute voting classification over testing samples. Finally, comprehensive experiments on over 20 UCI datasets clearly validated the effectiveness of our framework: it is not only beneficial to output reducts with superior classification accuracies and classification stabilities but also suitable for data pre-processing with noise. This improvement work we have performed makes the FOA obtain better benefits in the data processing of life, health, medical and other fields.

Product Review-Based Customer Sentiment Analysis Using an Ensemble of mRMR and Forest Optimization Algorithm (FOA)

This research presents a way of feature selection problem for classification of sentiments that use ensemble-based classifier. This includes a hybrid approach of minimum redundancy and maximum relevance (mRMR) technique and Forest Optimization Algorithm (FOA) (i.e. mRMR-FOA) based feature selection. Before applying the FOA on sentiment analysis, it has been used as feature selection technique applied on 10 different classification datasets publically available on UCI machine learning repository. The classifiers for example k-Nearest Neighbor (k-NN), Support Vector Machine (SVM) and Naïve Bayes used the ensemble based algorithm for available datasets. The mRMR-FOA uses the Blitzer’s dataset (customer reviews on electronic products survey) to select the significant features. The classification of sentiments has noticed to improve by 12 to 18%. The evaluated results are further enhanced by the ensemble of k-NN, NB and SVM with an accuracy of 88.47% for the classification of sentiment analysis task.

Mining social media text for disaster resource management using a feature selection based on forest optimization

Resource management is an essential task that needs to be performed by the government or any disaster management agency during natural disasters. During these critical circumstances, people mostly depend upon a social media platform to share and collect information about the situation of the affected localities. The huge volume of real-time data can be useful in disaster assessment, response, and relief activities. We have presented a system which analyzes tweets during natural disasters and categorizes them according to the availability or need for general or medical resources along with their location information (if any) mentioned in the tweets. Several statistical classifiers are applied to show their usefulness for a better solution. Optimal feature representation is the heart of any machine learning based classification model. Here, we have applied a forest optimization-based wrapper feature selection algorithm to improve the classification accuracy. FIRE, SMERP, and CrisisLex dataset are used to evaluate our system and its effectiveness is demonstrated for smooth management of the resources. From the experimentation, it is found that forest optimization algorithm (FOA) wrapped multinomial naive bayes classifier gives an accuracy of 91.41 percent and f-measure of 88.33 percent on the FIRE dataset. The execution time of the model is quite less which will be very helpful for this challenging task.

Talent management by predicting employee attrition using enhanced weighted forest optimization algorithm with improved random forest classifier

Talent management by predicting employee attrition using enhanced weighted forest optimization algorithm with improved random forest classifier

Forest Optimization-Based Test Case Generation for Multiple Paths With Metamorphic Relations

In general multiple paths are covered by multiple runs which is a time consuming task. Now a days, metaheuristic techniques are widely used for path coverage. In order to reduce the time, an efficient method is proposed based on Forest Optimization Algorithm (FOA) with Metamorphic Relations (MRs) that cover multiple paths at a time in one run unlike the traditional search based testing. In the proposed approach, initial test case is generated using FOA, the successive test cases are generated using MRs without undergoing several runs. The motive of using FOA is that the searching mechanism of this algorithm having resemblance with the branch / path coverage techniques of testing. To the best of our knowledge, FOA has not been implemented in software testing. The experimental results are compared with three existing work. The efficiency of simply FOA is also shown how it able to cover multiple paths. The results show that FOA with MRs is more efficient in terms of time consumption and number of paths covered.

An energy-aware approach for resources allocating in the internet of things using a forest optimization algorithm

PurposeInternet of things (IoT) is essential in technical, social and economic domains, but there are many challenges that researchers are continuously trying to solve. Traditional resource allocation methods in IoT focused on the optimal resource selection process, but the energy consumption for allocating resources is not considered sufficiently. This paper aims to propose a resource allocation technique aiming at energy and delay reduction in resource allocation. Because of the non-deterministic polynomial-time hard nature of the resource allocation issue and the forest optimization algorithm’s success in complex problems, the authors used this algorithm to allocate resources in IoT.Design/methodology/approachFor the vast majority of IoT applications, energy-efficient communications, sustainable energy supply and reduction of latency have been major goals in resource allocation, making operating systems and applications more efficient. One of the most critical challenges in this field is efficient resource allocation. This paper has provided a new technique to solve the mentioned problem using the forest optimization algorithm. To simulate and analyze the proposed technique, the MATLAB software environment has been used. The results obtained from implementing the proposed method have been compared to the particle swarm optimization (PSO), genetic algorithm (GA) and distance-based algorithm.FindingsSimulation results show that the proper performance of the proposed technique. The proposed method, in terms of “energy” and “delay,” is better than other ones (GA, PSO and distance-based algorithm).Practical implicationsThe paper presents a useful method for improving resource allocation methods. The proposed method has higher efficiency compared to the previous methods. The MATLAB-based simulation results have indicated that energy consumption and delay have been improved compared to other algorithms, which causes the high application of this method in practical projects. In the future, the focus will be on resource failure and reducing the service level agreement violation rate concerning the number of resources.Originality/valueThe proposed technique can solve the mentioned problem in the IoT with the best resource utilization, low delay and reduced energy consumption. The proposed forest optimization-based algorithm is a promising technique to help enterprises participate in IoT initiatives and develop their business.