Cuckoo Search Algorithm Research Articles

Optimization of MQL-Turning Process Parameters to Produce Environmentally-Benign AISI 4340 Alloy with Nano-Lubricants using Cuckoo Search Algorithm

The current research consists of a machining process involving AISI steel where the input parameters are the cutting depth, feed rate and cutting speed while the responses include the cutting force, surface roughness and tool wear. Usually, heat is generated during the turning process and various machining processes, and to reduce it, coolants are considered. In this work, CuO and Al2O3 were used as nano lubricants (MQL). Data obtained from the machining process were inserted into Minitab 18 software where quadratic objective functions were formulated as related to each output concerning the input parameters. Objective functions were optimized with the aid of C++ programming code. The cuckoo search algorithm was used for the optimization process of the work. This work clearly shows a reduction of the output parameters that is, cutting force from 243N to 127.20N, surface roughness from 0.66µm to 0.368µm and tool wear from 0.069mm to 0.0046mm using CuO as the nano lubricant. While using Al2O3, cutting force was lowered from 363N to 197.63N, surface roughness from 1.98µm to 0.148µm and tool wear from 0.219mm to 0.063mm. This clearly shows that using CuO helps to obtain a better cutting force coupled with elongation of the tool life but Al2O3 best gives a better surface finish.

Optimisation of laser surface ablation of alumina/GO coating on AZ31D magnesium alloy using Cuckoo Search Algorithm method

ABSTRACT Graphene oxide (GO) and alumina (Al2O3) work in concert to give magnesium alloys advantageous surface qualities and protection from adverse environments. This paper aims to investigate the effect of laser surface treatment on plasma sprayed alumina-graphene oxide (GO) coating on AZ31D magnesium alloy. Key ablation quality responses; HAZ width, surface roughness and ablation rate were correlated with laser parameters using the Response Surface Methodology and optimised using the metaheuristic Cuckoo Search Algorithm Method. Laser power significantly influenced all ablation characteristics of the composite coating. The addition of 1.5 wt% GO in the alumina coating caused a significant reduction in HAZ width (43.32%), surface roughness (25.11%) and ablation rate (76.58%) when treated with optimal combinations of laser power (8 W), scanning speed (950 mm/s) and frequency (78.2 kHz). The surface quality was characterised by smooth textures, and resolidified splats along with deep pits when ablated with lower and intermediate power whereas surfaces ablated with higher power showed debonded splats with peripheral surface destruction. While a prominent 2D peak at 16 W confirmed the presence of GO layers during high-power laser contacts, the ID/IG ratio below unity across all specimens indicated minimal defect concentrations.

Bayesian structural damage identification using fraction function regularisation model based on natural frequency and mode shape curvature

ABSTRACT The existing models based on Bayesian inference and L 1 regularisation provide a feasible scheme to the uncertainty problem in structural damage identification. However, L 1 regularisation cannot accurately describe the sparsity of damage due to excessive punishment of large damage parameters, and the mode shape data used by such models are insensitive to the damage. This compromises the accuracy of damage identification. Compared with L 1 regularisation, the fraction function regularisation avoids excessive punishment for larger elements in the damage parameters, resulting in a more accurate sparse solution. The mode shape curvature is more sensitive to damage because it can be viewed as the differential form of the mode shape. Inspired by this, a fraction function regularisation model based on natural frequency and mode shape curvature data is proposed in this paper to further improve the accuracy of Bayesian damage identification. In addition, an improved cuckoo search algorithm is proposed to solve the model by introducing a ranking-based mutation strategy. The results obtained from both the numerical investigation and the experimental study consistently indicate that the proposed method can provide accurate and reliable identification results.

A cuckoo search algorithm to improve a routing problem adapted to last mile e-commerce logistics

A cuckoo search algorithm to improve a routing problem adapted to last mile e-commerce logistics

RBFNN based fixed time sliding mode control for PEMFC air supply system with input delay

Ensuring rapid regulation of the oxygen excess ratio (OER) in proton exchange membrane fuel cells (PEMFC) during load changes is an important challenge. In this paper, fixed time sliding mode control of a PEMFC with input delay has been investigated. First, a simplified fourth-order nonlinear dynamical model with input disturbance and input delay is considered and a cascade structure is selected for the control design. A radial basis function neural network (RBFNN) is designed to estimate the input disturbance. To achieve precise estimation, a Cuckoo Search Algorithm is utilized to calculate the parameters of the RBFNN. Then, a new sliding mode controller is proposed for trajectory tracking within a fixed time. To ensure the effectiveness of the proposed controller, the fixed time convergence of both sliding and reaching phases is investigated and proven. Finally, a robust prediction based sliding mode control is designed for the PEMFC system that by incorporating the disturbance estimation, can eliminate the effect of input delay. The effectiveness and robustness of the proposed controller are validated via comparative simulations. It is noteworthy that this is the first study to propose predictor based control for input delay PEMFCs.

Soft Switching Technique in a Modified SEPIC Converter with MPPT using Cuckoo Search Algorithm

: MPPT refers to the process of continuously tracking and adjusting the operating point of a photovoltaic (PV) system to maximize the power output from the solar panels. The operating point at which a PV system produces the most power under a specific set of environmental circumstances is known as the maximum power point (MPP). The Maximum power point tracking (MPPT) technique is used to overcome the challenges of PV arrays with variable irradiance levels, which collects the greatest power from the PV array. Standalone PV systems, as well as gridconnected systems, can benefit from a DC-DC converter with MPPT. This study compares the standard perturbation and observation (P&O) technique with the soft-switching method used in the SEPIC converter utilizing the cuckoo search (CS) algorithm using MATLAB/Simulink. Reviewing the simulation results and assessing the SEPIC converter's performance. Background: SEPIC soft-switching converter, the soft-switching technique is applied to the SEPIC converter topology. It typically uses additional components, such as inductor and capacitors, to enable soft-switching operation. These additional components help to control the voltage and current waveforms across the main switching devices, reducing switching losses. Methods: The proposed method uses a soft switching technique to reduce switching losses and to improve efficiency. Soft switching is used in SEPIC (Single Ended Primary Inductor Converter) converters, a type of DC-DC converter, to enhance their performance. The projected solution further addresses the maximum power point tracking (MPPT) issue in PV systems using the Cuckoo Search (CS) method. Results: Soft switching technique is implemented in SEPIC converters to reduce these switching losses. In this system, Zero Voltage Switching (ZVS) involves turning on the switch when the voltage across it is zero. This allows the current to flow through the switch without creating any significant switching losses. CS algorithm can track the MPP quickly and accurately, it exhibits less prone to oscillations, easily monitor the MPP under a variety of weather circumstances. However, it exhibits negligible oscillation in a steady state, which results in significant power savings. Conclusion: The modified SEPIC converter with a soft-switching MPPT cuckoo algorithm is used with a solar-powered system. A prototype comprising a solar panel, a SEPIC converter, a driver, and a controller circuit was created. A soft-switching SEPIC converter with an MPPT cuckoo algorithm for the PV system is implemented, as are the converter operation, converter analysis, and theoretical analysis, and a controller circuit is analysed. The PV system, MPPT controller with tracking algorithm, and PMSM load were used in the system simulation. A 60-watt SEPIC converter prototype is built, and the outcomes are also tested experimentally.

Enhancing Sustainability in Agriculture using Hybrid Grey Wolf and Cuckoo Search Algorithm

Enhancing Sustainability in Agriculture using Hybrid Grey Wolf and Cuckoo Search Algorithm

Hybridization of CSA and PSO improves the efficacy of MPPT for solar photovoltaic array with partial shading

AbstractOne critical issue in the tracking systems based on photovoltaic (PV) is how to harvest highest power of the photovoltaic array; particularly when the system is operating in partially shaded conditions (PSCs) or varying irradiances. This study proposes particle swarm optimization (PSO) hybridization and cuckoo search algorithm (CSA) methods for maximum power point tracking (MPPT). The effectiveness of the proposed algorithm is validated and examined under various irradiance patterns. A comparison study in performance has been conducted between the proposed hybrid CSA-PSO method with the conventional P&O and PSO techniques. Several tests have been performed based on numerical simulations utilizing the programming software MATLAB/Simulink. The results demonstrated that the suggested hybrid technique yields smaller tracking time, higher power and greater efficiency than those of other traditional algorithms.

Data-driven forecasting framework for daily reservoir inflow time series considering the flood peaks based on multi-head attention mechanism

Accurate and reliable daily reservoir inflow forecast plays an essential role in several applications involving the management and planning of water resources, such as hydroelectric generation, flood control, water supply, and basin ecological dispatching. Runoff usually exhibits strong non-linearity, high uncertainty, and spatial and temporal variability. Existing techniques fail to capture complete dynamics change processes effectively. A data-driven forecasting framework for daily reservoir inflow time series considering the flood peaks based on a multi-head attention mechanism was developed, referred to as the GWOCS-VMD-CNN-Transformer (GCVCT). First, the model utilize Grey Wolf Optimizer coupled with Cuckoo Search (GWO-CS) algorithms to optimize parameters in variational mode decomposition model (VMD). This approach helps obtain highly correlated intrinsic mode function (IMF) components, enhancing the frequency resolution of the input dataset. The proposed method overcomes the bottleneck of other available methods by decomposing the time series to capture the main long-term and short-term properties of hydrological processes. Second, the convolution neural network and Transformer (CNN-Transformer) are based on a multi-head attention mechanism as the objective predictive method. Finally, six evaluation indicators verify the performance of the proposed approach. The approach’s reliability was evaluated using the historical daily reservoir inflow data from the Xiluodu (XLD) and Wudongde (WDD) reservoirs in the Jinsha River Basin, China. Several single and hybrid models were developed for comparative analysis. The results indicate that the proposed ensemble approach fits better than other developed model methods. The GCVCT model showed excellent performance in forecasting the inflows of XLD and WDD reservoirs, with NSE values of 0.985 and 0.984, respectively. Furthermore, the GCVCT framework forecast capacity for peak inflow was further verified through discussion and analysis of the 48 peak flows during the validation period, consistently outperforming other models in predicting peak flow for both study reservoirs. This framework provides an effective method for the scientific optimal scheduling of hydropower reservoirs, enabling more sustainable and efficient management practices. It also demonstrates the potential of powerful deep-learning models in intelligent hydrological forecasting.

An Improved Cuckoo Search Algorithm and Its Application in Robot Path Planning

This manuscript introduces an improved Cuckoo Search (CS) algorithm, known as BASCS, designed to address the inherent limitations of CS, including insufficient search space coverage, premature convergence, low search accuracy, and slow search speed. The proposed improvements encompass four main areas: the integration of tent chaotic mapping and random migration in population initialization to reduce the impact of random errors, the guidance of Levy flight by the directional determination strategy of the Beetle Antennae Search (BAS) algorithm during the global search phase to improve search accuracy and convergence speed, the adoption of the Sine Cosine Algorithm for local exploitation in later iterations to enhance local optimization and accuracy, and the adaptive adjustment of the step-size factor and elimination probability throughout the iterative process to convergence. The performance of BASCS is validated through ablation experiments on 10 benchmark functions, comparative experiments with the original CS and its four variants, and application to a robot path planning problem. The results demonstrate that BASCS achieves higher convergence accuracy and exhibits faster convergence speed and superior practical applicability compared to other algorithms.

Application of improved Jellyfish search algorithm for 9-parameters cell extraction and GMPPT in PV systems

Optimization algorithms are essential tools used to address real-world problems through minimization or maximization processes. In the context of photovoltaic (PV) systems, various optimization algorithms have been employed to extract solar cell parameters using minimization techniques, and to identify the maximum power point (MPP) using maximization approaches. However, under partial shading conditions or during rapidly changing irradiance, many of these algorithms tend to get trapped in local minima, failing to locate the global maximum power point (GMPPT). This shortcoming leads to significant energy losses from PV cells. Similarly, the extraction of solar cell parameters is often compromised by the random search behavior and inadequate exploration and exploitation capabilities of many existing algorithms. The Jellyfish Search Optimization (JSO) algorithm is a recent, parameter-free method developed to tackle a wide range of optimization problems. Despite its innovative design, JSO is prone to premature convergence and exhibits a longer convergence time. To address these limitations, this paper proposes a modified version of the JSO algorithm, which integrates the state-of-the-art features of JSO with the Lévy flight characteristic from the cuckoo search algorithm. This combination aims to achieve a better balance between exploration and exploitation. To validate the effectiveness of the proposed Improved Jellyfish Search Optimization (IJSO) algorithm in PV systems, extensive experiments were conducted. These experiments included benchmarking with complex test functions, extracting cell parameters using various solar cell models, and maximizing energy output from PV systems under different environmental conditions. The results demonstrate that the proposed IJSO algorithm effectively enhances parameter extraction and global maximum power point tracking, making it a promising solution for optimizing energy extraction from PV cells in dynamic conditions.

Power Loss Minimization and Voltage Profile Improvement on Nigeria Distribution Network Using Whale Optimization Algorithm

The power grid has significant power losses, unstable voltage, and large voltage drops during high demand due to its high resistance. One way to reduce power loss is by strategically placing and sizing Distributed Generation (DG) within the distribution network. However, improper installation of DG can increase power loss. To address this issue, various optimization techniques have been used, but finding the best solution and dealing with complex issues has been challenging. It is important to make efforts to optimally place and size DG in the distribution network to minimize power loss. To this end, a research study aimed to minimize power loss and improve the voltage profile on the DG network using the Whale Optimization Algorithm (WOA) was done. Objective functions were formulated and integrated into WOA, and power flow analysis on the standard IEEE 33-bus and 33-bus Ilorin industrial distribution feeder with and without DG was conducted using the forward and backward sweep distribution load flow algorithm. The optimal size and location of DG for power network loss reduction using sensitivity analysis (SA) and the whale optimization algorithm were determined. The results of the power flow analysis indicated that the total active losses and reactive power losses were reduced to varying extents with the integration of DG using both SA and WOA. The validation results showed that WOA is more efficient and provides high-quality solutions in terms of system loss reduction and best placement for DG in power systems compared to the application of SA. Additionally, WOA was found to offer accurate and high-quality solutions for power loss reduction compared to other existing techniques such as Loss Sensitivity Analysis (LSA), Grey Wolf Optimizer (GWO), Adaptive Shuffled Frogs Leaping Algorithm (ASFLA), and One Rank Cuckoo Search Algorithm (ORCSA). Keywords: Optimization, Distributed Generation (DG), Whale Optimization Algorithm (WOA), Sensitivity Analysis (SA)

A Fresh Manner Cuckoo Search Algorithm to Hydrothermal Scheduling in Short Term

The main goal of the short-term hydrothermal scheduling (HS) challenge is to drastically reduce the large fuel cost of producing power by scheduling the hydrothermal energy producers while taking power balance restrictions, the reservoir's storage restrictions, the water's gross discharge, and the thermal power generators' and hydropower plants' operating restrictions into account. Many algorithms have been employed to solve this similar problem, and relevant research have been published in the literature; nevertheless, their scope is limited in terms of the number of 16 iterations required to achieve the solution state and the solution state itself. In order to solve the HS problem, this article suggests applying an improved cuckoo search (CS) algorithm known as the fresh manner cuckoo search (FMCS) algorithm, a modified variant of the conventional CS. The suggested FMCS

Leveraging Classifier Performance Using Heuristic Optimization for Detecting Cardiovascular Disease from PPG Signals.

Photoplethysmography (PPG) signals, which measure blood volume changes through light absorption, are increasingly used for non-invasive cardiovascular disease (CVD) detection. Analyzing PPG signals can help identify irregular heart patterns and other indicators of CVD. This research involves a total of 41 subjects sourced from the CapnoBase database, consisting of 21 normal subjects and 20 CVD cases. In the initial stage, heuristic optimization algorithms, such as ABC-PSO, the Cuckoo Search algorithm (CSA), and the Dragonfly algorithm (DFA), were applied to reduce the dimension of the PPG data. Next, these Dimensionally Reduced (DR) PPG data are then fed into various classifiers such as Linear Regression (LR), Linear Regression with Bayesian Linear Discriminant Classifier (LR-BLDC), K-Nearest Neighbors (KNN), PCA-Firefly, Linear Discriminant Analysis (LDA), Kernel LDA (KLDA), Probabilistic LDA (ProbLDA), SVM-Linear, SVM-Polynomial, and SVM-RBF, to identify CVD. Classifier performance is evaluated using Accuracy, Kappa, MCC, F1 Score, Good Detection Rate (GDR), Error rate, and Jaccard Index (JI). The SVM-RBF classifier for ABC PSO dimensionality reduced values outperforms other classifiers, achieving the highest accuracy of 95.12% along with the minimum error rate of 4.88%. In addition to that, it provides an MCC and kappa value of 0.90, a GDR and F1 score of 95%, and a Jaccard Index of 90.48%. This study demonstrated that heuristic-based optimization and machine learning classification of PPG signals are highly effective for the non-invasive detection of cardiovascular disease.

Forecasting Daily Air Quality Index and Early Warning System for Estimating Ambient Air Pollution on Road Networks Using Gaussian Dispersion Model with Deep Learning Algorithm

The rapid growth of the vehicle population is a major factor in heavy air pollution and public health issues. Traffic-related air pollutants (TRAPs) on roads are often much higher than ambient values, leading to high exposure levels in vehicles. This research proposes a hybrid forecasting model for early detection and early warning systems (EWS) of road networks during real-world travels. Data is collected from Kannur, Calicut, Palakkad, and Coimbatore using real-time sensors, including surrounding discussion information, activity information, vehicle speed, and stopping events. The study predicts ambient air quality (AAQ) levels on the road network using the Gaussian Dispersion model (GDM) and measures the risk sensitivity of PM10 and PM2.5 in selected regions. This helps formulate powerful prevention strategies and prevent negative health impacts. The air pollution module for predicting concentration has an innovative hybridization model that combines an improved cuckoo search (CS) and differential evolution (DE) algorithm with a stacked LSTM model to increase forecasting accuracy of six major environmental pollution levels. This model predicts the AAQ level and is effective and robust for warning one day before the pollutant event occurs based on the risk level of an ambient air pollutant from the RN.

Modeling and optimization of two-stage emergency supply chain network under uncertain environment

In order to formulate an effective emergency supply chain network planning scheme, improve the efficiency of emergency organization rescue and realize the reasonable allocation of resources and materials, considering the uncertainty of supply and demand, a distribution mode combining multiple transportation modes is adopted, and the optimization objectives are to minimize network response time, cost and carbon emissions. A two-stage emergency supply chain mixed integer programming model is constructed. At the same time, an adjustable robust optimization model is constructed based on robust optimization theory to enhance the network's ability to cope with uncertain factors, and the constraints with uncertain parameters are transformed through linear duality theory. In order to improve the solution effect of the model, an optimize cuckoo search (OCS) algorithm is proposed, and a benchmark example is introduced to verify the superiority and applicability of the OCS algorithm in solving multi-objective functions. Finally, the emergency material distribution data during the COVID-19 epidemic in Wuhan is used to study the emergency supply chain network decision-making problem with uncertain parameters, and the effective suppression of the robust control coefficient on uncertain disturbances is proved through sensitivity analysis.

Pipeline Leak Identification and Prediction of Urban Water Supply Network System with Deep Learning Artificial Neural Network

Pipeline leakage, which leads to water wastage, financial losses, and contamination, is a significant challenge in urban water supply networks. Leak detection and prediction is urgent to secure the safety of the water supply system. Relaying on deep learning artificial neural networks and a specific optimization algorithm, an intelligential detection approach in identifying the pipeline leaks is proposed. A hydraulic model is initially constructed on the simplified Net2 benchmark pipe network. The District Metering Area (DMA) algorithm and the Cuckoo Search (CS) algorithm are integrated as the DMA-CS algorithm, which is employed for the hydraulic model optimization. Attributing to the suspected leak area identification and the exact leak location, the DMA-CS algorithm possess higher accuracy for pipeline leakage (97.43%) than that of the DMA algorithm (92.67%). The identification pattern of leakage nodes is correlated to the maximum number of leakage points set with the participation of the DMA-CS algorithm, which provide a more accurate pathway for identifying and predicting the specific pipeline leaks.

An EfficientNet integrated ResNet deep network and explainable AI for breast lesion classification from ultrasound images

AbstractBreast cancer is one of the major causes of deaths in women. However, the early diagnosis is important for screening and control the mortality rate. Thus for the diagnosis of breast cancer at the early stage, a computer‐aided diagnosis system is highly required. Ultrasound is an important examination technique for breast cancer diagnosis due to its low cost. Recently, many learning‐based techniques have been introduced to classify breast cancer using breast ultrasound imaging dataset (BUSI) datasets; however, the manual handling is not an easy process and time consuming. The authors propose an EfficientNet‐integrated ResNet deep network and XAI‐based framework for accurately classifying breast cancer (malignant and benign). In the initial step, data augmentation is performed to increase the number of training samples. For this purpose, three‐pixel flip mathematical equations are introduced: horizontal, vertical, and 90°. Later, two pre‐trained deep learning models were employed, skipped some layers, and fine‐tuned. Both fine‐tuned models are later trained using a deep transfer learning process and extracted features from the deeper layer. Explainable artificial intelligence‐based analysed the performance of trained models. After that, a new feature selection technique is proposed based on the cuckoo search algorithm called cuckoo search controlled standard error mean. This technique selects the best features and fuses using a new parallel zero‐padding maximum correlated coefficient features. In the end, the selection algorithm is applied again to the fused feature vector and classified using machine learning algorithms. The experimental process of the proposed framework is conducted on a publicly available BUSI and obtained 98.4% and 98% accuracy in two different experiments. Comparing the proposed framework is also conducted with recent techniques and shows improved accuracy. In addition, the proposed framework was executed less than the original deep learning models.

Adaptive feature mode decomposition method for bearing fault diagnosis under strong noise

In practical mechanical equipment operation, bearing vibration signals are challenging to analyze due to their non-stationarity and low signal-to-noise ratio for traditional detection methods. As a new signal decomposition method, the feature mode decomposition (FMD) method has been successfully applied to bearing fault diagnostics. However, FMD’s decomposition efficiency is easily influenced by the input parameter settings, and the efficiency of the decomposed signals is related to the number of initialized filter banks. For this reason, this paper proposes an adaptive parameterized feature mode decomposition method. Firstly, based bearing fault diagnosis method using a cuckoo search algorithm with logarithmic decline of nonlinear inertial weights and random adjustment discovery probability (DWCS), which is used to optimize the three parameters of the FMD. Then, a new approach to finding out the maximum feature fault frequency and its multiplicative feature frequency is proposed, called the feature frequency ratio (FFR), obtained from the envelope spectrum of bearing fault signal. Finally, this paper uses the DWCS based on the maximum FFR to adaptively select the best FMD parameter combination, which is verified by simulation, the results of the proposed AFMD can effectively extract bearing fault features under strong background noise with a signal-to-noise ratio of −15 dB, and actual experiments further verify the superiority of the method, which not only improves the accuracy of fault diagnosis under strong background noise, but also contributes to the overall maintenance and operational efficiency of the mechanical system.

Machine Learning based Lung Cancer Diagnostic System using Optimized Feature Subset Selection

Lung is a vital organ that plays a major role in respiration. Without breathing, one may not survive in this world. Hence lung is an important organ that acts as filter to absorb oxygen and supply it to heart where pumping takes place through blood vessel in the circulatory system .The pumped blood takes oxygen and other nutrients to every other parts of the body. Hence one must take care of lung. There are various diseases associated with lungs. Lung Cancer is a deadly disease that spread across the countries all over the world. An early detection of lung cancer has been proved to improve the survival rate of human life. There are various resources are available to detect the lung cancer disease. They are low dose CT-scans, X-rays, blood-based screening, pathology slide reading, biopsy’s test, survey data(clinical dataset) etc. helps to predict the disease well in advance. Our proposed work uses two clinical datasets that has various features to detect how likely the persons get affected from the lung disease. Dataset1 includes features such as age, gender, smoking, yellow fingers, anxiety, peer-pressure, chronic disease, fatigue, allergy, wheezing, alcohol, coughing, shortness of breath, swallowing difficulty, and chest pain. Also, the work has experimented with another dataset2 that represents causes of lung cancer due to exposure of pesticide. Our proposed diagnostic system consider all these features in total and perform feature selection to extract optimal feature subsets using cuckoo search algorithm then perform classification using machine learning algorithms such as Linear Support Vector Machine, Logistic Regression and Random Forest algorithm. It is observed that with the cuckoo search algorithm, dataset 1 achieves an accuracy of 100%, precision of 100%, recall of 100%, and F1-score of 100% by LR Classifier. The Linear SVC classifier achieves an accuracy of 90%, a precision of 88%, a recall of 86%, and an F1-score of 87%.The Random forest Classifier achieves an accuracy of 91%, precision of 86%, recall of 93%, and F1-score of 90%. For dataset 2, both the LR classifier and Linear SVC classifier outperform with an accuracy of 100%, precision of 100%, recall of 100%, and F1-score of 100%. Whereas Random Forest provides accuracy of 97%, precision of 97%, recall of 96%, and F1-score of 97%.