Multiple Base Classifiers Research Articles

Identification of Parkinson’s disease using MRI and genetic data from the PPMI cohort: an improved machine learning fusion approach

ObjectiveThis study aim to leverage advanced machine learning techniques to develop and validate novel MRI imaging features and single nucleotide polymorphism (SNP) gene data fusion methodologies to enhance the early identification and diagnosis of Parkinson’s disease (PD).MethodsWe leveraged a comprehensive dataset from the Parkinson’s Progression Markers Initiative (PPMI), which includes high-resolution neuroimaging data, genetic single-nucleotide polymorphism (SNP) profiles, and detailed clinical information from individuals with early-stage PD and healthy controls. Two multi-modal fusion strategies were used: feature-level fusion, where we employed a hybrid feature selection algorithm combining Fisher discriminant analysis, an ensemble Lasso (EnLasso) method, and partial least squares (PLS) regression to identify and integrate the most informative features from neuroimaging and genetic data; and decision-level fusion, where we developed an adaptive ensemble stacking (AE_Stacking) model to synergistically integrate the predictions from multiple base classifiers trained on individual modalities.ResultsThe AE_Stacking model achieving the highest average balanced accuracy of 95.36% and an area under the receiver operating characteristic curve (AUC) of 0.974, significantly outperforming feature-level fusion and single-modal models (p < 0.05). Furthermore, by analyzing the features selected across multiple iterations of our models, we identified stable brain region features [lh 6r (FD) and rh 46 (GI)] and key genetic markers (rs356181 and rs2736990 SNPs within the SNCA gene region; rs213202 SNP within the VPS52 gene region), highlighting their potential as reliable early diagnostic indicators for the disease.ConclusionThe AE_Stacking model, trained on MRI and genetic data, demonstrates potential in distinguishing individuals with PD. Our findings enhance understanding of the disease and advance us toward the goal of precision medicine for neurodegenerative disorder.

Enhanced Diagnosis of Lung Cancer through an Ensemble Learning Model leveraging an Adaptive Optimization Algorithm

Early and accurate diagnosis of lung cancer is crucial to improving patient outcomes and survival rates. Machine and deep learning models have emerged as promising tools to improve the accuracy and efficiency of disease diagnosis. However, achieving optimal diagnostic performance remains a challenging task in medical research. This study integrates ensemble learning techniques with an adaptive optimization algorithm to enhance the accuracy of lung cancer diagnosis. By combining the predictive potential of multiple base classifiers, the ensemble-learning model improves overall performance and mitigates the weaknesses of individual classifiers. Additionally, the adaptive optimization algorithm dynamically adjusts the model parameters to optimize the classification performance. The effectiveness of the approach was evaluated using a comprehensive dataset that includes lung cancer images. Rigorous evaluation and comparison with state-of-the-art models showed that the proposed method achieved superior diagnostic performance, reaching an overall accuracy of 99%.

Improving multi-view ensemble learning with Round-Robin feature set partitioning

Multi-view Ensemble Learning (MEL) techniques have shown remarkable success in improving the accuracy and resilience of classification algorithms by combining multiple base classifiers trained over different perspectives of a dataset, known as views. One crucial factor affecting ensemble performance is the selection of diverse and informative feature subsets. Feature Set Partitioning (FSP) methods address this challenge by creating distinct views of features for each base classifier. In this context, we propose the Round-Robin Feature Set Partitioning (RR-FSP) technique, which introduces a novel approach to feature allocation among views. This novel approach evenly distributes highly correlated features across views, thereby enhancing ensemble diversity, promoting balanced feature utilization, and encouraging the more equitable distribution of correlated features, RR-FSP contributes to the advancement of MEL techniques. Through experiments on various datasets, we demonstrate that RR-FSP offers improved classification accuracy and robustness, making it a valuable addition to the arsenal of FSP techniques for MEL.

Prediction of aviation safety incident risk level based on ECSDNN

Prediction of aviation safety incident risk level is an important means of active risk management. Considering the high-dimensional complexity and class imbalance of massive aviation safety incident data, a prediction method for aviation safety incident risk level based on integrated cost-sensitive deep neural network (ECSDNN) is proposed. The feature representation of aviation safety incident data is realized by using the method of embedded coding of categorical attributes and splicing of numerical attributes; the cost-sensitive matrix and cost-sensitive loss function are designed by comprehensively considering the misclassification ratio and fixed cost, and the base classifier model based on cost-sensitive deep neural network (CSDNN) is constructed; the hard voting method is used to integrate multiple base classifiers with different parameters and performances to construct the integrated prediction model ECSDNN for aviation safety incident risk level. The experimental results on the Aviation Safety Incident Reporting System (ASRS) dataset show that compared with the optimal prediction ability of the baseline algorithm, the prediction accuracy of the ECSDNN model is improved 4.51%; compared with the single CSDNN base classifier, the prediction accuracy is improved 3.17%.

Classification of an Individual's Vaccination Status Using Ensemble Hard Voting Classifier

Vaccination is a proactive medical immunization procedure where an inactivated form of a disease-causing agent (such as a virus) is administered to boost the body's defense systems. Efficient management of vaccination status is crucial in healthcare management, disease eradication, community immunity ("herd immunity"), disease prevention, and global health security. Ensuring precise monitoring and validation of an individual's vaccination status is indispensable, especially in the context of emerging diseases and epidemics. This study evaluates the likelihood of individuals obtaining vaccination for the H1N1 virus and the seasonal flu vaccine. Ensemble methods combine the predictions of multiple base classifiers to enhance overall performance. One such method, the hard voting classifier, aggregates the votes from each base classifier and selects the class with the majority vote as the final prediction. This approach leverages the strengths of different classifiers, reducing the risk of individual model biases and improving generalization using metrics such as precision, recall, accuracy, and F1-score are employed to assess the system's effectiveness. The results demonstrate how data-driven methods can address population wellness and improve vaccination rates using an ensemble method. The proposed ensemble hard voting classifier achieved accuracies of 0.905 and 0.907 on the H1N1 and seasonal vaccine datasets, respectively. Using an ensemble approach like the hard voting classifier enhances prediction accuracy and robustness, ultimately leading to better decision making in public health initiatives.

A recommendation attack detection approach integrating CNN with Bagging

Due to their open architecture, collaborative filtering recommender systems are susceptible to recommendation attacks, in which attackers inject fake rating data into the system to affect the accuracy of recommendation results. To detect these attacks, numerous detection methods have been designed and proven effective. However, in recent years, deep learning-based recommendation attack models such as GSA-GAN have shown higher concealment, posing new challenges to existing detection methods. Motivated by the need for improved detection, in this paper we propose a new approach called CNN-BAG, which integrates convolutional neural network (CNN) and Bagging (BAG) techniques. CNN-BAG can enhance the detection performance by simultaneously leveraging the deep learning capabilities of CNN and the ensemble learning strengths of Bagging. Firstly, we construct a deep neural network based on CNN as the base learner to automatically extract and learn features of recommendation attacks. Secondly, we use the Bagging algorithm to perform bootstrap sampling on the training data to generate multiple diverse training subsets. The above constructed base learners are then trained on these generated training subsets to produce multiple base classifiers for classifying recommendation attacks. Finally, we combine the base classifiers’ outputs using a majority voting method to obtain the final detection results. To assess the performance of CNN-BAG in detecting recommendation attacks, we compared it against several well-established detection methods on the Movielens-10M and Amazon datasets. Our experiments revealed that CNN-BAG is adept at identifying various attack types, including the deep learning-based recommendation attack models.

Predicting stroke events with a proactive fusion system: a comprehensive study on imbalance class handling in computational biomechanics

Stroke, as a critical global health concern and the second leading cause of death, occurs when blood flow to the brain is interrupted. Although machine learning has advanced in medical safety, there is limited research on stroke prediction using information fusion systems. This study presents a fusion framework that combines multiple base classifiers and a Meta classifier to improve stroke prediction performance. The research utilizes Grid Search optimized models, such as Random Forest, Support Vector Machine, K Nearest Neighbors, AdaBoost, Gradient Boosting, Light Gradient Boosting, Categorical Boosting, and eXtreme Gradient Boosting for in-depth stroke analysis. Since stroke events are rare and unpredictable, classification outcomes can be deceptive due to imbalanced data. This article examines stroke probability by comparing three data balancing methods: over-sampling, under-sampling, and tomek-link synthetic minority over-sampling (SMOTE-TL) to enhance prediction accuracy. The findings reveal that AdaBoost as a meta-classifier attains the highest performance in the fusion framework, with a peak of 88.09% Recall and 83.66% F1 score. This innovative approach provides crucial insights into stroke prediction and can be a valuable resource for strengthening intervention efforts in advanced healthcare systems.

Robust network anomaly detection using ensemble learning approach and explainable artificial intelligence (XAI)

Intrusion Detection Systems, specifically Network Anomaly Detection Systems (NADSs) are vital tools in network security. The NADSs are affected by data imbalance issues in classifying minority classes. Also, designing an efficient detection framework is sought after to achieve a higher detection rate for minority classes, especially when utilizing ensemble learning methods. To solve the issue of imbalanced data, a hybrid method of sampling techniques is proposed. This imbalance processing tool integrates the Synthetic Minority Oversampling Technique (SMOTE) and the K-means clustering algorithm (SKM). SMOTE over-samples the minority class, and K-means is used to perform a cluster-based under-sampling. We use Denoising Autoencoder (DAE) to select the top 15 features to reduce data dimensionality based on their higher weights. For anomaly detection, the XGBoost algorithm is deployed and the SHapley Additive exPlanation (SHAP) approach is deployed to provide explanations of the proposed techniques. The performance of the SKM-XGB model is assessed using the NSL-KDD and UNSW-NB15 datasets. A comparative analysis and series of experiments were carried out using several ensemble models with multiple base classifiers. The experimental findings indicate that the model's detection rate for binary classification and multiclass classification using the UNSW-NB15 dataset is 99.01% and 97.49%, respectively. The model achieves a 99.37% detection rate for binary classification and a 99.22% detection rate for multiclass classification on the NSL-KDD dataset. We conducted a comparative analysis of various ensemble models with multiple base classifiers. The results indicate that SKM-XGB outperforms the other investigated models and outperforms the performance of state-of-the-art models.

Prediction of microvascular invasion in hepatocellular carcinoma based on multi-phase dynamic enhanced CT radiomics feature and multi-classifier hierarchical fusion model

To predict microvascular invasion (MVI) in hepatocellular carcinoma (HCC) using a model based on multiphase dynamic-enhanced CT (DCE-CT) radiomics feature and hierarchical fusion of multiple classifiers. We retrospectively collected preoperative DCE-CT images from 111 patients with pathologically confirmed HCC in Guangzhou First People's Hospital between January, 2016 and April, 2020. The volume of interest was outlined in the early arterial phase, late arterial phase, portal venous phase and equilibrium phase, and radiomics features of these 4 phases were extracted. Seven classifiers based on different algorithms were trained using the filtered feature subsets to obtain multiple base classifiers under each phase. According to the hierarchical fusion strategy, a multi-criteria decision-making-based weight assignment algorithm was used for fusing each base classifier under the same phase with the model after extracting the phase information to obtain the prediction model. The proposed model was evaluated using a 5-fold cross-validation and assessed for area under the ROC curve (AUC), accuracy, sensitivity, and specificity. The prediction model was also compared with the fusion models using a single phase or multiple phases, models based on a single phase with a single classifier, models with different base classifier diversities, and 8 classifier models based on other ensemble methods. The experimental results showed that the performance of the proposed model for predicting HCCMVI was optimal after incorporating the 4 phases and 7 classifiers, with AUC, accuracy, sensitivity, and specificity of 0.828, 0.766, 0.877, and 0.648, respectively. Comparative experiments showed that this prediction model outperformed the models based on a single phase with a single classifier and other ensemble models. The proposed prediction model is effective for predicting MVI in HCC with superior performance to other models.

MSFSS: A whale optimization-based multiple sampling feature selection stacking ensemble algorithm for classifying imbalanced data

<abstract> <p>Learning from imbalanced data is a challenging task in the machine learning field, as with this type of data, many traditional supervised learning algorithms tend to focus more on the majority class while damaging the interests of the minority class. Stacking ensemble, which formulates an ensemble by using a meta-learner to combine the predictions of multiple base classifiers, has been used for solving class imbalance learning issues. Specifically, in the context of class imbalance learning, a stacking ensemble learning algorithm is generally considered to combine with a specific sampling algorithm. Such an operation, however, might suffer from suboptimization problems as only using a sampling strategy may make it difficult to acquire diverse enough features. In addition, we also note that using all of these features may damage the meta-learner as there may exist noisy and redundant features. To address these problems, we have proposed a novel stacking ensemble learning algorithm named MSFSS, which divides the learning procedure into two phases. The first stage combined multiple sampling algorithms and multiple supervised learning approaches to construct meta feature space by means of cross combination. The adoption of this strategy satisfied the diversity of the stacking ensemble. The second phase adopted the whale optimization algorithm (WOA) to select the optimal sub-feature combination from the meta feature space, which further improved the quality of the features. Finally, a linear regression classifier was trained as the meta learner to conduct the final prediction. Experimental results on 40 benchmarked imbalanced datasets showed that the proposed MSFSS algorithm significantly outperformed several popular and state-of-the-art class imbalance ensemble learning algorithms. Specifically, the MSFSS acquired the best results in terms of the F-measure metric on 27 datasets and the best results in terms of the G-mean metric on 26 datasets, out of 40 datasets. Although it required consuming more time than several other competitors, the increment of the running time was acceptable. The experimental results indicated the effectiveness and superiority of the proposed MSFSS algorithm.</p> </abstract>

Heart Disease Prediction Using Concatenated Hybrid Ensemble Classifiers

Heart disease is a leading global cause of mortality, demanding early detection for effective and timely medical intervention. In this study, we propose a machine learning-based model for early heart disease prediction. This model is trained on a dataset from the UC Irvine Machine Learning Repository (UCI) and employs the Extra Trees Classifier for performing feature selection. To ensure robust model training, we standardize this dataset using the StandardScaler method for data standardization, thus preserving the distribution shape and mitigating the impact of outliers. For the classification task, we introduce a novel approach, which is the concatenated hybrid ensemble voting classification. This method combines two hybrid ensemble classifiers, each one utilizing a distinct subset of base classifiers from a set that includes Support Vector Machine, Decision Tree, K-Nearest Neighbor, Logistic Regression, Adaboost and Naive Bayes. By leveraging the concatenated ensemble classifiers, the proposed model shows some promising performance results; in particular, it achieves an accuracy of 86.89%. The obtained results highlight the efficacy of combining the strengths of multiple base classifiers in the problem of early heart disease prediction, thus aiding and enabling timely medical intervention.

A hybrid quantum annealing method for generating ensemble classifiers

Quantum annealing has been widely used to optimize machine learning such as ensemble classifiers. This ensemble classifier enhances classification performance through the combination of multiple accurate and diverse base classifiers. It benefits from the “perturb and combine” strategy which involves using the random subspace as the prevalent approach to perturb input data. There are several methods to generate a random subspace which include bagging, boosting, clustering, and, more recently, clustering balancing. The main contribution of this research was to improve the accuracy of the ensemble classifier using the hybrid quantum annealing method. The process involved making larger, stronger, and more balanced clusters through 1) the modification of the range of values from K in incremental clustering, 2) the adjustment of the clusters to be stronger and more balanced, and 3) the optimization of pure-class clusters using quantum annealing. The proposed method was tested and evaluated on 10 benchmark datasets from the UCI repository and the results were compared with current approaches. The results showed that the proposed method has better accuracy than the others due to the larger, stronger, and more balanced clusters generated as well as the better trade-off between accuracy and diversity.

Neural Network Ensemble With Evolutionary Algorithm for Highly Imbalanced Classification

Imbalanced data is a major challenge in classification tasks. Most classification algorithms tend to be biased toward the samples in the majority class but fail to classify the samples in the minority class. Recently, ensemble learning, as a promising method, has been rapidly developed in solving highly imbalanced classification. However, the design of the base classifier for the ensemble is still an open question because the optimization problem of the base classifier is gradientless. In this study, the evolutionary algorithm (EA) technique is adopted to solve a wide range of optimization design problems in highly imbalanced classification without gradient information. A novel EA-based classifier optimization design method is proposed to optimize the design of multiple base classifiers automatically for the ensemble. In particular, an EA method with a neural network (NN) as the base classifier termed NN ensemble with EA (NNEAE) is developed for highly imbalanced classification. To verify the performance of NNEAE, extensive experiments are designed for testing. Results illustrate that NNEAE outperforms other compared methods.

A comprehensive machine learning model for lithology identification while drilling

A comprehensive machine learning model is established for lithology identification while drilling. Easily available field data, including well trajectory parameters and real-time drilling parameters, are chosen as feature parameters. First, kernel principal component analysis is used to convert the original feature space, enhancing the association between feature parameters and corresponding lithology. Subsequently, clustering is performed in a novel way to magnify the differences between data points, which ameliorates the ability of the model to resist signal noise and reduces the difficulty of lithology identification. Finally, for each data cluster, multiple base classifiers are integrated to constitute strong sub-classifiers through the Stacking method, which improves the classification accuracy. The model is validated with field data of a shale gas reservoir in East Sichuan. With the optimal model parameters, the overall identification accuracy of the comprehensive machine learning model is 0.87. The specific identification accuracies are 0.88 for sandstone, 0.85 for mudstone, 0.86 for limestone, and 0.89 for shale. The current comprehensive machine learning model is also demonstrated to have better performance than the single-classifier models and the deep neural network model. Real-time field application of the model during the drilling process of a new well shows an accuracy of 0.92, and the rate of penetration is increased by 22.74% through the matching of drilling parameters and formation lithology. It is proved that the present comprehensive machine learning model can be employed for lithology identification while drilling, and provide guidance for adjusting drilling parameters in real time.

A deep multi-view imbalanced learning approach for identifying informative COVID-19 tweets from social media

Social media platforms such as Twitter are home ground for rapid COVID-19-related information sharing over the Internet, thereby becoming the favorable data resource for many downstream applications. Due to the massive pile of COVID-19 tweets generated every day, it is significant that the machine-learning-supported downstream applications can effectively skip the uninformative tweets and only pick up the informative tweets for their further use. However, existing solutions do not specifically consider the negative effect caused by the imbalanced ratios between informative and uninformative tweets in training data. In particular, most of the existing solutions are dominated by single-view learning, neglecting the rich information from different views to facilitate learning. In this study, a novel deep imbalanced multi-view learning approach called D-SVM-2K is proposed to identify the informative COVID-19 tweets from social media. This approach is built upon the well-known multiview learning method SVM-2K to incorporate different views generated from different feature extraction techniques. To battle against the class imbalance problem and enhance its learning ability, D-SVM-2K stacks multiple SVM-2K base classifiers in a stacked deep structure where its base classifiers can learn from either the original training dataset or the shifted critical regions identified using the well-known k-nearest neighboring algorithm. D-SVM-2K also realises a global and local deep ensemble learning on the multiple views’ data. Our empirical experiments on a real-world labeled tweet dataset demonstrate the effectiveness of D-SVM-2K in dealing with the real-world multi-view class imbalance issues.

An ensemble method with DenseNet and evidential reasoning rule for machinery fault diagnosis under imbalanced condition

Fault diagnosis is of significant importance for intelligent manufacturing as it can increase production efficiency and decrease the uncertain breakdown risk of machines. Previous studies have extensively utilized different types of shallow statistical features as well as deep representation features to comprehensively describe the fault information for achieving better performances of fault diagnosis. However, making full use of the combination of such shallow statistical features and deep representation features under the class-imbalance situation in real-world applications becomes a challenge in current research of fault diagnosis. To remedy the above issue, an Imbalanced Ensemble Method with DenseNet and Evidential Reasoning Rule, namely IEMD-ER, is proposed to incorporate both human experience and machine wisdom for machinery fault diagnosis under the class-imbalance situation. To this end, the shallow statistical features with human experience are extracted by several signal processing techniques, while the modified DenseNet is adopted to extract the deep representation features with machine wisdom. Based on these features, multiple diverse base classifiers are produced by leveraging the importance of different features. The outputs of each base classifier are adaptively fused using the Evidential Reasoning Rule as the final fault diagnosis results. Extensive experiments are conducted on the real-world bearing datasets collected by Paderborn University to validate the proposed method. The experimental results proved the superiority of the proposed IEMD-ER.

Hybrid Representation and Decision Fusion towards Visual-textual Sentiment

The rising use of online media has changed social customs of the public. Users have become gradually accustomed to sharing daily experiences and publishing personal opinions on social networks. Social data carrying with emotions and attitudes have provided significant decision support for numerous tasks in sentiment analysis. Conventional sentiment analysis methods only concern about textual modality and are vulnerable to the multimodal scenario, while common multimodal approaches only focus on the interactive relationship between modalities without considering unique intra-modal information. A hybrid fusion network is proposed in this work to capture both the inter-modal and intra-modal features. First, in the intermediate fusion stage, a multi-head visual attention is proposed to extract accurate semantic and sentimental information from textual embedding representations with the assistance of visual features. Then, multiple base classifiers are trained to learn independent and diverse discriminative information from different modal representations in the late fusion stage. The final decision is determined based on fusing the decision supports from base classifiers via a decision fusion method. To improve the generalization of our hybrid fusion network, a similarity loss is employed to inject decision diversity into the whole model. Empirical results on multimodal datasets have demonstrated the proposed model achieves a higher accuracy and better generalization compared with baselines for multimodal sentiment analysis.

Performance Evaluation of Machine Learning Methods for Heart Failure Prediction

Heart failure is a syndrome of cardiac circulation disorder. Due to the dysfunction of the systolic function or diastolic function of the heart, the venous blood volume cannot be fully discharged from the heart, resulting in blood stasis in the venous system and insufficient perfusion in the arterial system. The symptoms of this disorder are concentrated in pulmonary congestion and vena cava congestion. The correlation between the inducement of heart failure and the incidence of heart failure is a subject that needs to be studied in the medical field. In recent years, with the development of data mining technology, more and more analytical models and algorithms have been applied in the medical field, which greatly improve the efficiency of medical data analysis and enable medical workers to cure diseases better. In this study, an ensemble learning model is applied to analyze the data of heart failure. First, the data is preprocessed and normalized, and features that are not associated with death rate of heart failure are removed. Secondly, multiple base classifiers are trained and compared. Finally, the competent base classifiers are selected and integrated with the Stacking-based ensemble learning algorithm for final classification. Comparative analysis showed that the prediction results of ensemble model are better than that of base classifiers in evaluation indexes such as accuracy, precision, AUC, Balanced accuracy and F1-score for the heart failure data.

Tomato leaf diseases classification using image processing and weighted ensemble learning

AbstractIn ensemble methods, multiple base classifiers with different performances are used to increase classification accuracy. This study proposes a novel weighted majority voting ensemble approach to classify red green blue images to identify the bacterial spot, late blight, leaf mold, sectorial leaf spot, target spot, early blight, and healthy tomato leaves. Color, textural, and shape features of the images were extracted to be used as inputs for base classifiers, followed by selecting the effective features using the relief method to increase the prediction performance. Six machine learning methods, that is, support vector machine, decision tree, random forest, k‐nearest neighbors, naïve Bayes, and discriminative analysis, were used as base classifiers, which resulted in 89.81%, 79.81%, 91.53%, 85.91%, 44.42%, and 82.84% accuracies, respectively. Then, simple majority and weighted majority voting ensemble methods were utilized to improve disease classification, the accuracies of which were 93.49% and 95.58%, respectively. The performance of the proposed method was compared with that of two well‐known deep learning methods, namely GoogLeNet and AlexNet, which exerted poor results. The findings of this study showed that the proposed framework based on weighted majority voting outperformed the base machine learning models in classifying tomato diseases.

Detection of DoH Traffic Tunnels Using Deep Learning for Encrypted Traffic Classification

Currently, the primary concerns on the Internet are security and privacy, particularly in encrypted communications to prevent snooping and modification of Domain Name System (DNS) data by hackers who may attack using the HTTP protocol to gain illegal access to the information. DNS over HTTPS (DoH) is the new protocol that has made remarkable progress in encrypting Domain Name System traffic to prevent modifying DNS traffic and spying. To alleviate these challenges, this study explored the detection of DoH traffic tunnels of encrypted traffic, with the aim to determine the gained information through the use of HTTP. To implement the proposed work, state-of-the-art machine learning algorithms were used including Random Forest (RF), Gaussian Naive Bayes (GNB), Logistic Regression (LR), k-Nearest Neighbor (KNN), the Support Vector Classifier (SVC), Linear Discriminant Analysis (LDA), Decision Tree (DT), Adaboost, Gradient Boost (SGD), and LSTM neural networks. Moreover, ensemble models consisting of multiple base classifiers were utilized to carry out a series of experiments and conduct a comparative study. The CIRA-CIC-DoHBrw2020 dataset was used for experimentation. The experimental findings showed that the detection accuracy of the stacking model for binary classification was 99.99%. In the multiclass classification, the gradient boosting model scored maximum values of 90.71%, 90.71%, 90.87%, and 91.18% in Accuracy, Recall, Precision, and AUC. Moreover, the micro average ROC curve for the LSTM model scored 98%.