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Journal articles on the topic 'Advanced Ensemble Learning'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Yaganteeswarudu, Akkem, Saroj Kumar Biswas, and Varanasi Aruna. "Streamlit Application for Advanced Ensemble Learning Methods in Crop Recommendation Systems – A Review and Implementation." Indian Journal of Science and Technology 16, no. 48 (2023): 4688–702. https://doi.org/10.17485/IJST/v16i48.2850.

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Abstract <strong>Objectives:</strong> This article explores the integration of advanced ensemble machine learning methods within precision agriculture, aiming to enhance the reliability and practical utility of crop recommendation systems. The incorporation of the Streamlit framework in the development process underpins our objective to deliver a user-friendly tool that provides farmers and agricultural analysts with actionable insights. <strong>Methods:</strong> A thorough literature review of artificial intelligence applications in agriculture serves as the foundation of our s

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Adamu, Yusuf Aliyu. "MALARIA PREDICTION MODEL USING ADVANCED ENSEMBLE MACHINE LEARNING TECHNIQUES." Journal of Medical pharmaceutical and allied sciences 10, no. 6 (2021): 3794–801. http://dx.doi.org/10.22270/jmpas.v10i6.1701.

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Malaria is a life-threatening disease that leads to death globally, its early prediction is necessary for preventing the rapid transmission. In this work, an enhanced ensemble learning approach for predicting malaria outbreaks is suggested. Using a mean-based splitting strategy, the dataset is randomly partitioned into smaller groups. The splits are then modelled using a classification and regression tree, and an accuracy-based weighted aging classifier ensemble is used to construct a homogenous ensemble from the several Classification and Regression Tree models. This approach ensures higher p

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Nandhini, A. Sunitha, J. Balakrishna, R. Bala Manikandan, and S. Bharath Kumar. "Advanced flood severity detection using ensemble learning models." Journal of Physics: Conference Series 1916, no. 1 (2021): 012048. http://dx.doi.org/10.1088/1742-6596/1916/1/012048.

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Abuassba, Adnan O. M., Dezheng Zhang, Xiong Luo, Ahmad Shaheryar, and Hazrat Ali. "Improving Classification Performance through an Advanced Ensemble Based Heterogeneous Extreme Learning Machines." Computational Intelligence and Neuroscience 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/3405463.

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Extreme Learning Machine (ELM) is a fast-learning algorithm for a single-hidden layer feedforward neural network (SLFN). It often has good generalization performance. However, there are chances that it might overfit the training data due to having more hidden nodes than needed. To address the generalization performance, we use a heterogeneous ensemble approach. We propose an Advanced ELM Ensemble (AELME) for classification, which includes Regularized-ELM, L2-norm-optimized ELM (ELML2), and Kernel-ELM. The ensemble is constructed by training a randomly chosen ELM classifier on a subset of train

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B M, Rakshitha. "Ensemble Learning Frameworks in Cardiovascular Prognostics: Advancements in Predictive Analytics." International Journal for Research in Applied Science and Engineering Technology 13, no. 6 (2025): 2048–58. https://doi.org/10.22214/ijraset.2025.72558.

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Cardiovasculardisease remains a pervasive and serious global health concern, underscoring the necessity of accurate and timely risk assessment. Within the field of machine learning, ensemble methods have gained significant traction for their ability to predict cardiovascular outcomes. Established algorithms—such as Support Vector Machines, Random Forests, and Gradient Boosting—continue to serve as reliable mainstays. Recently, however, advanced ensemble approaches like stacking and CatBoost have garnered increased attention. Emerging research suggests these newer methodologies may, in some ins

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Alserhani, Faeiz, and Alaa Aljared. "Evaluating Ensemble Learning Mechanisms for Predicting Advanced Cyber Attacks." Applied Sciences 13, no. 24 (2023): 13310. http://dx.doi.org/10.3390/app132413310.

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With the increased sophistication of cyber-attacks, there is a greater demand for effective network intrusion detection systems (NIDS) to protect against various threats. Traditional NIDS are incapable of detecting modern and sophisticated attacks due to the fact that they rely on pattern-matching models or simple activity analysis. Moreover, Intelligent NIDS based on Machine Learning (ML) models are still in the early stages and often exhibit low accuracy and high false positives, making them ineffective in detecting emerging cyber-attacks. On the other hand, improved detection and prediction

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Krishnamoorthy, Latha, and Ammasandra Sadashivaiah Raju. "An ensemble approach for electrocardiogram and lip features based biometric authentication by using grey wolf optimization." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 3 (2024): 1524. http://dx.doi.org/10.11591/ijeecs.v33.i3.pp1524-1535.

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In the pursuit of fortified security measures, the convergence of multimodal biometric authentication and ensemble learning techniques have emerged as a pivotal domain of research. This study explores the integration of multimodal biometric authentication and ensemble learning techniques to enhance security. Focusing on lip movement and electrocardiogram (ECG) data, the research combines their distinct characteristics for advanced authentication. Ensemble learning merges diverse models, achieving increased accuracy and resilience in multimodal fusion. Harmonizing lip and ECG modalities establi

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Krishnamoorthy, Latha, and Ammasandra Sadashivaiah Raju. "An ensemble approach for electrocardiogram and lip features based biometric authentication by using grey wolf optimization." Indonesian Journal of Electrical Engineering and Computer Science 33, no. 3 (2024): 1524–35. https://doi.org/10.11591/ijeecs.v33.i3.pp1524-1535.

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In the pursuit of fortified security measures, the convergence of multimodal biometric authentication and ensemble learning techniques have emerged as a pivotal domain of research. This study explores the integration of multimodal biometric authentication and ensemble learning techniques to enhance security. Focusing on lip movement and electrocardiogram (ECG) data, the research combines their distinct characteristics for advanced authentication. Ensemble learning merges diverse models, achieving increased accuracy and resilience in multimodal fusion. Harmonizing lip and ECG modalities establi

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Darji, Pinesh Arvindbhai. "Utilizing an Ensemble of Extra Tree Model for Classifying Mesothelioma Cancer." African Journal of Biological Sciences 6, no. 12 (2024): 535–45. http://dx.doi.org/10.48047/afjbs.6.12.2024.535-545.

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Objectives: Explore the potential of ensemble learning techniques like Bagging Tree, Random Forest, and Ensemble Extra Tree in transforming mesothelioma diagnosis.Overcome challenges associated with late-stage detection and limited treatment options using advanced machine learning algorithms.Enhance predictive power and feature extraction capabilities through the combination of diverse ensemble algorithms.

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Nyaramneni, Sarika, Tejaswi Potluri, and Jahnavi Somavarapu. "Advanced Ensemble Machine Learning Models to Predict SDN Traffic." Procedia Computer Science 230 (2023): 417–26. http://dx.doi.org/10.1016/j.procs.2023.12.097.

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PATEL, RAM MANOHAR, and KAMAL BUNKAR. "Soybean yield prediction leveraging advanced ensemble machine learning models." Journal of Agrometeorology 27, no. 2 (2025): 227–29. https://doi.org/10.54386/jam.v27i2.2971.

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El Kaim Billah, Mohammed, and Abdelfettah Mabrouk. "Advanced Traffic Flow Optimization Using Hybrid Machine Learning and Deep Learning Techniques." Journal of Electronics, Electromedical Engineering, and Medical Informatics 7, no. 3 (2025): 817–34. https://doi.org/10.35882/jeeemi.v7i3.948.

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Road traffic congestion remains a persistent and critical challenge in modern urban environments, adversely affecting travel times, fuel consumption, air quality, and overall urban livability. To address this issue, this study proposes a hybrid ensemble learning framework for accurate short-term traffic flow prediction across signalized urban intersections. The model integrates Random Forest, Gradient Boosting, and Multi-Layer Perceptron within a weighted voting ensemble mechanism, wherein model contributions are dynamically scaled based on individual validation performance. Benchmarking is pe

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Akhi, Sharmin Sultana, Sonjoy Kumar Dey, Mazharul Islam Tusher, et al. "Enhancing Banking Cybersecurity: An Ensemble-Based Predictive Machine Learning Approach." American Journal of Engineering and Technology 07, no. 03 (2025): 88–97. https://doi.org/10.37547/tajet/volume07issue03-07.

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In this study, we propose a predictive cybersecurity framework for the banking sector by integrating ensemble-based machine learning models. Our approach leverages heterogeneous datasets—including internal firewall and intrusion detection system logs, banking transaction records, user behavior data, and external threat intelligence—to capture a comprehensive view of the cyber threat landscape. Following rigorous data preprocessing, feature selection, and feature engineering, we evaluated multiple models, including Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, and Deep N

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Akkem, Yaganteeswarudu, Biswas Saroj Kumar, and Aruna Varanasi. "Streamlit Application for Advanced Ensemble Learning Methods in Crop Recommendation Systems – A Review and Implementation." Indian Journal Of Science And Technology 16, no. 48 (2023): 4688–702. http://dx.doi.org/10.17485/ijst/v16i48.2850.

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Milind Ruparel. "Enhancing Student Placement Predictions with Advanced Machine Learning Techniques." Journal of Information Systems Engineering and Management 10, no. 1s (2024): 275–88. https://doi.org/10.52783/jisem.v10i1s.121.

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Optimal management of student placement mechanisms is pivotal to cost-effective distribution and individualized aid for learning establishments. The study presents a novel ensemble methodology to anticipate the outcomes of student placements, integrating manifold machine learning (ML) algorithms — logistic regression, naive Bayes, gradient boosting, linear discriminant analysis (LDA), k-nearest neighbours (KNN), random forest, and support vector machines (SVM). The data set has been constructed with an extensive scope covering various attributes from demographic details through socioeconomic s

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Hu, Zhengbing, Ivan Dychka, Kateryna Potapova, and Vasyl Meliukh. "Augmenting Sentiment Analysis Prediction in Binary Text Classification through Advanced Natural Language Processing Models and Classifiers." International Journal of Information Technology and Computer Science 16, no. 2 (2024): 16–31. http://dx.doi.org/10.5815/ijitcs.2024.02.02.

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Sentiment analysis is a critical component in natural language processing applications, particularly for text classification. By employing state-of-the-art techniques such as ensemble methods, transfer learning and deep learning architectures, our methodology significantly enhances the robustness and precision of sentiment predictions. We systematically investigate the impact of various NLP models, including recurrent neural networks and transformer-based architectures, on sentiment classification tasks. Furthermore, we introduce a novel ensemble method that combines the strengths of multiple

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MOHAMMED, ATEEQUR RAHAMAN. "Enhancing Diabetes Mellitus Onset Prediction through Advanced Ensemble Learning Techniques." Journal of Statistical Modelling and Analytics 6, no. 2 (2024): 1–18. https://doi.org/10.22452/josma.vol6no2.2.

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Type 2 diabetes is a major worldwide health issue, necessitating accurate and effective prediction models for timely intervention. Traditional machine learning (ML) models often underperform with imbalanced datasets and complex data relationships, resulting in suboptimal predictive accuracy. This study applies advanced ensemble methods, such as random forest, boosting, bagging, and stacking, to enhance diabetes onset prediction using a synthetic minority over-sampling technique (SMOTE)-balanced data from the Pima Indians Diabetes Database. The research involves extensive data processing, featu

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Al. Abri, Khoula, and Manjit Singh Sidhu. "Machine Learning Approaches to Advanced Outlier Detection in Psychological Datasets." International journal of electrical and computer engineering systems 15, no. 1 (2024): 13–20. http://dx.doi.org/10.32985/ijeces.15.1.2.

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The core aim of this study is to determine the most effective outlier detection methodologies for multivariate psychological datasets, particularly those derived from Omani students. Due to their complex nature, such datasets demand robust analytical methods. To this end, we employed three sophisticated algorithms: local outlier factor (LOF), one-class support vector machine (OCSVM), and isolation forest (IF). Our initial findings showed 155 outliers by both LOF and IF and 147 by OCSVM. A deeper analysis revealed that LOF detected 55 unique outliers based on differences in local density, OCSVM

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Tsai, Chih-Fong, and Chihli Hung. "Modeling credit scoring using neural network ensembles." Kybernetes 43, no. 7 (2014): 1114–23. http://dx.doi.org/10.1108/k-01-2014-0016.

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Purpose – Credit scoring is important for financial institutions in order to accurately predict the likelihood of business failure. Related studies have shown that machine learning techniques, such as neural networks, outperform many statistical approaches to solving this type of problem, and advanced machine learning techniques, such as classifier ensembles and hybrid classifiers, provide better prediction performance than single machine learning based classification techniques. However, it is not known which type of advanced classification technique performs better in terms of financial dist

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Tama, Bayu Adhi, and Marco Comuzzi. "Leveraging a Heterogeneous Ensemble Learning for Outcome-Based Predictive Monitoring Using Business Process Event Logs." Electronics 11, no. 16 (2022): 2548. http://dx.doi.org/10.3390/electronics11162548.

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Outcome-based predictive process monitoring concerns predicting the outcome of a running process case using historical events stored as so-called process event logs. This prediction problem has been approached using different learning models in the literature. Ensemble learners have been shown to be particularly effective in outcome-based business process predictive monitoring, even when compared with learners exploiting complex deep learning architectures. However, the ensemble learners that have been used in the literature rely on weak base learners, such as decision trees. In this article,

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Dash, Sujata, and Bichitrananda Patra. "Genetic Diagnosis of Cancer by Evolutionary Fuzzy-Rough based Neural-Network Ensemble." International Journal of Knowledge Discovery in Bioinformatics 6, no. 1 (2016): 1–16. http://dx.doi.org/10.4018/ijkdb.2016010101.

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High dimension and small sample size is an inherent problem of gene expression datasets which makes the analysis process more complex. The present study has developed a novel learning scheme that encapsulates a hybrid evolutionary fuzzy-rough feature selection model with an adaptive neural net ensemble. Fuzzy-rough method deals with uncertainty and impreciseness of real valued gene expression dataset and evolutionary search concept optimizes the subset selection process. The efficiency of the hybrid-FRGSNN model is evaluated by the proposed neural net ensemble learning algorithm. Again to prov

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Sebastianelli, Alessandro, Dario Spiller, Raquel Carmo, et al. "A reproducible ensemble machine learning approach to forecast dengue outbreaks." Scientific Reports 14 (February 15, 2024): 3807. https://doi.org/10.1038/s41598-024-52796-9.

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Dengue fever, a prevalent and rapidly spreading arboviral disease, poses substantial public health and economic challenges in tropical and sub-tropical regions worldwide. Predicting infectious disease outbreaks on a countrywide scale is complex due to spatiotemporal variations in dengue incidence across administrative areas. To address this, we propose a machine learning ensemble model for forecasting the dengue incidence rate (DIR) in Brazil, with a focus on the population under 19 years old. The model integrates spatial and temporal information, providing one-month-ahead DIR estimates at the

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Hasan, Prof Zohaib, Prof Abhishek Singh, Prof Vishal Paranjape, Gourav Mourya, and Arjita Sarkar. "Improving App Rating Predictions through Robust Machine Learning Models." International Journal of Innovative Research in Science,Engineering and Technology 11, no. 01 (2022): 1–14. http://dx.doi.org/10.15680/ijirset.2022.1101134.

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In this research, we develop a machine learning model to predict the ratings of Google Play Store applications using a comprehensive dataset. Our approach includes extensive data preprocessing, feature engineering, and model optimization using Random Forest Regressor and ensemble methods. The proposed model demonstrates significant improvements in prediction accuracy, emphasizing the importance of advanced preprocessing techniques and ensemble learning in regression tasks.

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Albehadili-Murtdha Saadoon Balasim. "Deep Feature Mapping and Ensemble Learning for Advanced IoT Malware Detection and Classification." Journal of Information Systems Engineering and Management 10, no. 21s (2025): 769–76. https://doi.org/10.52783/jisem.v10i21s.3409.

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Introduction20.4 With the exponential growth of Internet of Things (IoT) devices, security threats have become a major concern. Traditional malware detection techniques struggle to keep up with the ever-evolving attack landscape due to their reliance on predefined signatures and static rule-based detection. This paper explores the use of deep learning-based feature mapping combined with ensemble learning techniques to enhance IoT malware detection and classification. The proposed approach leverages convolutional neural networks (CNNs) for automatic feature extraction and ensemble models to imp

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Albehadili-Murtdha saadoon Balasim. "Deep Feature Mapping and Ensemble Learning for Advanced IoT Malware Detection and Classification." Journal of Information Systems Engineering and Management 10, no. 21s (2025): 871–77. https://doi.org/10.52783/jisem.v10i21s.3451.

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Introduction20.4 With the exponential growth of Internet of Things (IoT) devices, security threats have become a major concern. Traditional malware detection techniques struggle to keep up with the ever-evolving attack landscape due to their reliance on predefined signatures and static rule-based detection. This paper explores the use of deep learning-based feature mapping combined with ensemble learning techniques to enhance IoT malware detection and classification. The proposed approach leverages convolutional neural networks (CNNs) for automatic feature extraction and ensemble models to imp

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Rana, Md Shohel, and Andrew H. Sung. "Evaluation of Advanced Ensemble Learning Techniques for Android Malware Detection." Vietnam Journal of Computer Science 07, no. 02 (2020): 145–59. http://dx.doi.org/10.1142/s2196888820500086.

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Android is the most well-known portable working framework having billions of dynamic clients worldwide that pulled in promoters, programmers, and cybercriminals to create malware for different purposes. As of late, wide-running inquiries have been led on malware examination and identification for Android gadgets while Android has likewise actualized different security controls to manage the malware issues, including a User ID (UID) for every application, framework authorizations. In this paper, we advance and assess various kinds of machine learning (ML) by applying ensemble-based learning sys

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Aktas, Abdulsamet, Taha Cap, Gorkem Serbes, Hamza Osman Ilhan, and Hakkı Uzun. "Advanced Multi-Level Ensemble Learning Approaches for Comprehensive Sperm Morphology Assessment." Diagnostics 15, no. 12 (2025): 1564. https://doi.org/10.3390/diagnostics15121564.

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Introduction: Fertility is fundamental to human well-being, significantly impacting both individual lives and societal development. In particular, sperm morphology—referring to the shape, size, and structural integrity of sperm cells—is a key indicator in diagnosing male infertility and selecting viable sperm in assisted reproductive technologies such as in vitro fertilisation (IVF) and intracytoplasmic sperm injection (ICSI). However, traditional manual evaluation methods are highly subjective and inconsistent, creating a need for standardized, automated systems. Objectives: This study aims t

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Karakaya, İrem. "Evaluation of Machine Learning and Ensemble Learning Models for Classification Using Delivery Data." Verimlilik Dergisi, PRODUCTIVITY FOR LOGISTICS (February 3, 2025): 89–104. https://doi.org/10.51551/verimlilik.1526436.

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Purpose: This study aims to evaluate the performance of various machine learning and ensemble learning models in classifying delivery times using Amazon delivery data. Fast deliveries' role in providing a competitive advantage and boosting customer loyalty highlights the importance of this study. Methodology: The research employs a dataset of 43,739 delivery records with 15 features. Data preprocessing steps include handling missing values, encoding categorical variables, calculating geospatial distances, and normalizing data. Advanced machine learning techniques (e.g., KNN, SVM, Logistic Regr

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Mia, Md Uzzal, Tahmida Naher Chowdhury, Rabin Chakrabortty, et al. "Flood Susceptibility Modeling Using an Advanced Deep Learning-Based Iterative Classifier Optimizer." Land 12, no. 4 (2023): 810. http://dx.doi.org/10.3390/land12040810.

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We developed a novel iterative classifier optimizer (ICO) with alternating decision tree (ADT), naïve Bayes (NB), artificial neural network (ANN), and deep learning neural network (DLNN) ensemble algorithms to build novel ensemble computational models (ADT-ICO, NB-ICO, ANN-ICO, and DLNN-ICO) for flood susceptibility (FS) mapping in the Padma River basin, Bangladesh. The models consist of environmental, topographical, hydrological, and tectonic circumstances, and the final result was chosen based on the causative attributes using multicollinearity analysis. Statistical techniques were utilized

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Sreelatha, Sreelatha, and Shivashetty Vrinda. "Deep ensemble learning with uncertainty aware prediction ranking for cervical cancer detection using Pap smear images." IAES International Journal of Artificial Intelligence (IJ-AI) 14, no. 2 (2025): 1450–60. https://doi.org/10.11591/ijai.v14.i2.pp1450-1460.

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This paper proposes a novel deep ensemble learning framework designed for the efficient detection and classification of cervical cancer from Pap smear images. The proposed study implements three advanced learning models namely DenseNet201, Xception, and a classical convolutional neural network (CNN) customized with optimal hyperparameters to automate feature extraction and cervical cancer detection process. The proposed study also introduces a novel ensemble learning to enhance the classification of cervical cancer. The proposed ensemble mechanism is based on the confidence aggregation followe

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Alharbi, Njud S., Hadi Jahanshahi, Qijia Yao, Stelios Bekiros, and Irene Moroz. "Enhanced Classification of Heartbeat Electrocardiogram Signals Using a Long Short-Term Memory–Convolutional Neural Network Ensemble: Paving the Way for Preventive Healthcare." Mathematics 11, no. 18 (2023): 3942. http://dx.doi.org/10.3390/math11183942.

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In the rapidly evolving field of medical diagnosis, the accurate and prompt interpretation of heartbeat electrocardiogram (ECG) signals have become increasingly crucial. Despite the presence of recent advances, there is an exigent need to enhance the accuracy of existing methodologies, especially given the profound implications such interpretations can have on patient prognosis. To this end, we introduce a novel ensemble comprising Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) models to enable the enhanced classification of heartbeat ECG signals. Our approach capitalizes

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Raju, Akella Subrahmnaya Narasimha, and K. Venkatesh. "EnsemDeepCADx: Empowering Colorectal Cancer Diagnosis with Mixed-Dataset Features and Ensemble Fusion CNNs on Evidence-Based CKHK-22 Dataset." Bioengineering 10, no. 6 (2023): 738. http://dx.doi.org/10.3390/bioengineering10060738.

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Colorectal cancer is associated with a high mortality rate and significant patient risk. Images obtained during a colonoscopy are used to make a diagnosis, highlighting the importance of timely diagnosis and treatment. Using techniques of deep learning could enhance the diagnostic accuracy of existing systems. Using the most advanced deep learning techniques, a brand-new EnsemDeepCADx system for accurate colorectal cancer diagnosis has been developed. The optimal accuracy is achieved by combining Convolutional Neural Networks (CNNs) with transfer learning via bidirectional long short-term memo

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Du, Ke-Lin, Rengong Zhang, Bingchun Jiang, Jie Zeng, and Jiabin Lu. "Foundations and Innovations in Data Fusion and Ensemble Learning for Effective Consensus." Mathematics 13, no. 4 (2025): 587. https://doi.org/10.3390/math13040587.

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Ensemble learning and data fusion techniques play a crucial role in modern machine learning, enhancing predictive performance, robustness, and generalization. This paper provides a comprehensive survey of ensemble methods, covering foundational techniques such as bagging, boosting, and random forests, as well as advanced topics including multiclass classification, multiview learning, multiple kernel learning, and the Dempster–Shafer theory of evidence. We present a comparative analysis of ensemble learning and deep learning, highlighting their respective strengths, limitations, and synergies.

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El Hajla, Salah, El Mahfoud Ennaji, Yassine Maleh, and Soufyane Mounir. "Enhancing IoT network defense: advanced intrusion detection via ensemble learning techniques." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 3 (2024): 2010. http://dx.doi.org/10.11591/ijeecs.v35.i3.pp2010-2020.

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The Internet of Things (IoT) has evolved significantly, automating daily activities by connecting numerous devices. However, this growth has increased cybersecurity threats, compromising data integrity. To address this, intrusion detection systems (IDSs) have been developed, mainly using predefined attack patterns. With rising cyber-attacks, improving IDS effectiveness is crucial, and machine learning is a key solution. This research enhances IDS capabilities by introducing binary attack identification and multiclass attack categorization for IoT traffic, aiming to improve IDS performance. Our

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Salah, El Hajla El Mahfoud Ennaji Yassine Maleh Soufyane Mounir. "Enhancing IoT network defense: advanced intrusion detection via ensemble learning techniques." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 3 (2024): 2010–20. https://doi.org/10.11591/ijeecs.v35.i3.pp2010-2020.

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The Internet of Things (IoT) has evolved significantly, automating daily activities by connecting numerous devices. However, this growth has increased cybersecurity threats, compromising data integrity. To address this, intrusion detection systems (IDSs) have been developed, mainly using predefined attack patterns. With rising cyber-attacks, improving IDS effectiveness is crucial, and machine learning is a key solution. This research enhances IDS capabilities by introducing binary attack identification and multiclass attack categorization for IoT traffic, aiming to improve IDS performance. Our

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Abdullah, Sherwan A., Mohammed I. Salih, and Omar M. Ahmed. "Improving Sentiment Classification using Ensemble Learning." International Journal of Informatics, Information System and Computer Engineering (INJIISCOM) 6, no. 2 (2024): 200–211. https://doi.org/10.34010/injiiscom.v6i2.13921.

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This paper presents an ensemble learning-based approach to improve sentiment classification accuracy in the IMDB movie reviews dataset. To this end, we tap three diversified models: Logistic Regression, Random Forest, and a Bidirectional Long Short-Term Memory neural network. Each one contributes its unique strengths to the ensemble, enhancing the overall performance. The text data has been processed using a statistical formula that converts the text document into a vector from the relevancy of the word with bigrams; data have been transformed to make it useful for Logistic Regression and Rand

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Tiago, Pinto, Praça Isabel, Vale Zita, and Silva Jose. "Ensemble learning for electricity consumption forecasting in office buildings." Neurocomputing 423 (May 8, 2020): 747–55. https://doi.org/10.1016/j.neucom.2020.02.124.

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This paper presents three ensemble learning models for short term load forecasting. Machine learning has evolved quickly in recent years, leading to novel and advanced models that are improving the forecasting results in multiple fields. However, in highly dynamic fields such as power and energy systems, dealing with the fast acquisition of large amounts of data from multiple data sources and taking advantage from the correlation between the multiple available variables is a challenging task, for which current models are not prepared. Ensemble learning is bringing promising results in this sen

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Shakil, Farhan, Sadia Afrin, Abdullah Al Mamun, et al. "HYBRID MULTI-MODAL DETECTION FRAMEWORK FOR ADVANCED PERSISTENT THREATS IN CORPORATE NETWORKS USING MACHINE LEARNING AND DEEP LEARNING." International Journal of Computer Science & Information System 10, no. 02 (2025): 6–20. https://doi.org/10.55640/ijcsis/volume10issue02-02.

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This study addresses the challenge of detecting Advanced Persistent Threats (APTs) in corporate networks by developing a hybrid multi-modal detection framework. We combine traditional machine learning models, deep learning architectures, and transformer-based models to improve the detection of sophisticated and stealthy cyber threats. A comprehensive dataset, consisting of network traffic and event logs, was processed through rigorous data preprocessing, feature engineering, and model development. The results show that the hybrid ensemble model, integrating Gradient Boosting and Transformer-ba

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Chan, Hong Ru. "Rent Price Prediction with Advanced Machine Learning Methods: A Comparison of California and Texas." Highlights in Science, Engineering and Technology 85 (March 13, 2024): 501–10. http://dx.doi.org/10.54097/84vvv580.

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The forecast of rent prices in dynamic housing markets is of fundamental importance to renters, landlords, investors, and politicians alike. Machine learning models offer flexibility, excel at modeling complex relationships, and provide outstanding forecast precision. This study compares advanced machine learning models, extreme gradient boosting regressor (XGBoost), light gradient boosting machine (LightGBM), random forest, ridge regression, and two ensemble approaches, to predict California and Texas rent prices. The two ensemble approaches include a hybrid regression of averaging base model

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Ab Rahman, Noor Fadzilah, Shir Li Wang, and Nurkaliza Khalid. "ENSEMBLE LEARNING IN EDUCATIONAL DATA ANALYSIS FOR IMPROVED PREDICTION OF STUDENT PERFORMANCE: A LITERATURE REVIEW." International Journal of Modern Education 7, no. 24 (2025): 887–902. https://doi.org/10.35631/ijmoe.724064.

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The integration of advanced technology and digital platforms in modern education is essential for enhancing educational outcomes. Ensemble learning has emerged as a prominent approach in educational data analysis, demonstrating its effectiveness in improving student performance predictions. The study reviews the application of ensemble learning methods in educational data analysis to improve student performance prediction. The primary objective of this review is to highlight the effectiveness of ensemble approaches in achieving superior prediction accuracy compared to individual classifiers. A

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Michael Ehiedu Usiagwu, Mayowa Timothy Adesina, and Johnson Chinonso. "Advanced machine learning models for real-time decision making in dynamic data environments." International Journal of Science and Research Archive 14, no. 2 (2025): 852–65. https://doi.org/10.30574/ijsra.2025.14.2.0441.

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Dynamic dataenvironments presentsignificant challengesdue to their continuousevolution, highvelocity, and heterogeneity. This study explores the application of advanced ensemble machine learning (ML) models for real-time decision-making in these settings. A comprehensive methodology is employed, incorporating ensemble techniques such as XGBoost, LightGBM, CatBoost, and Random Forest to enhance decisionaccuracy, adaptability, and robustness. The research integrates real-time data processing frameworks, featuring micro- batch processing, feature engineering, noise filtering, and synthetic data b

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Tiwari, Rajesh, Satyanand Singh, G. Shanmugaraj, et al. "Leveraging Advanced Machine Learning Methods to Enhance Multilevel Fusion Score Level Computations." Fusion: Practice and Applications 14, no. 2 (2024): 76–88. http://dx.doi.org/10.54216/fpa.140206.

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This research introduces a novel technique for determining numerous fusion score levels that works with many datasets and purposes. Each of the four system pieces works together. These are Feature Engineering, Ensemble Learning, deep neural networks (DNNs), and Transfer Learning. In feature engineering, raw data is totally transformed. This stage stresses the importance of PCA and MI for predictive power. AdaBoost is added during ensemble learning. It repeatedly teaches weak learners and adjusts weights depending on errors to create a strong ensemble model. Weighted input processing, ReLU acti

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Paul, M. Robin Raj, and Dr K. Santhi Sree. "Ensemble Based Detection of Phishing URLs Using Hybrid, Deep Learning and Machine Learning Models." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 6402–15. https://doi.org/10.22214/ijraset.2025.71708.

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Abstract: Phishing attacks pose a serious cybersecurity threat, requiring advanced detection mechanisms. This study proposes an ensemble-based phishing Uniform Resource Locator(URL) detection framework integrating both machine learning and deep learning models. The first phase employs Adaboost, Naïve Bayes(NB), Random Forest(RF), Logistic Regression(LR), Support Vector Machine(SVM), Artificial Neural Network(ANN), Convolutional Neural Network(CNN), Recurrent Neural Network(RNN), Long Short TermMemory(LSTM) and Stacked Gated Recurrent Unit(Stacked GRU), combined using voting ensemble. The secon

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Oner, Mahir, and Alp Ustundag. "Combining predictive base models using deep ensemble learning." Journal of Intelligent & Fuzzy Systems 39, no. 5 (2020): 6657–68. http://dx.doi.org/10.3233/jifs-189126.

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Since information science and communication technologies had improved significantly, data volumes had expanded. As a result of that situation, advanced pre-processing and analysis of collected data became a crucial topic for extracting meaningful patterns hidden in the data. Therefore, traditional machine learning algorithms generally fail to gather satisfactory results when analyzing complex data. The main reason of this situation is the difficulty of capturing multiple characteristics of the high dimensional data. Within this scope, ensemble learning enables the integration of diversified si

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Livieris, Ioannis, Andreas Kanavos, Vassilis Tampakas, and Panagiotis Pintelas. "A Weighted Voting Ensemble Self-Labeled Algorithm for the Detection of Lung Abnormalities from X-Rays." Algorithms 12, no. 3 (2019): 64. http://dx.doi.org/10.3390/a12030064.

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During the last decades, intensive efforts have been devoted to the extraction of useful knowledge from large volumes of medical data employing advanced machine learning and data mining techniques. Advances in digital chest radiography have enabled research and medical centers to accumulate large repositories of classified (labeled) images and mostly of unclassified (unlabeled) images from human experts. Machine learning methods such as semi-supervised learning algorithms have been proposed as a new direction to address the problem of shortage of available labeled data, by exploiting the expli

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Naema, Shaikh Haque. "Advanced Deep Learning Methodologies in the Diagnosis of Parkinson's Disease: A Comprehensive Review." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 06 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem36005.

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This study of the literature explores the field of sophisticated deep techniques for diagnosing Parkinson's illness and severity evaluation. The research looks into the use of machine learning algorithms as potential markers of Parkinson's illness in manual illustrations and speech impairments. These algorithms include XGBoost, Neural Networks with Recurrence, and ensemble models. Numerous feature extraction techniques, model comparison assessments, and preprocessing methodologies are investigated in an effort to improve precision and efficacy of Parkinson's illness diagnosis. In order to impr

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Symeonidis, Panagiotis, Thanasis Vafeiadis, Dimosthenis Ioannidis, and Dimitrios Tzovaras. "Wildfire Susceptibility Mapping in Greece Using Ensemble Machine Learning." Earth 6, no. 3 (2025): 75. https://doi.org/10.3390/earth6030075.

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This study explores the use of ensemble machine learning models to develop wildfire susceptibility maps (WFSMs) in Greece, focusing on their application as regressors. We provide a continuous assessment of wildfire risk, enhancing the interpretability and accuracy of predictions. Two key metrics were developed: Ensemble Mean and Ensemble Max. This dual-metric approach improves predictive robustness and provides critical insights for wildfire management strategies. The ensemble mode effectively handles complex, high-dimensional data, addressing challenges such as over fitting and data heterogen

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Purnawansyah, Purnawansyah, Adam Adnan, Herdianti Darwis, Aji Prasetya Wibawa, Triyanna Widyaningtyas, and Haviluddin Haviluddin. "Ensemble semi-supervised learning in facial expression recognition." International Journal of Advances in Intelligent Informatics 11, no. 1 (2025): 1. https://doi.org/10.26555/ijain.v11i1.1880.

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Facial Expression Recognition (FER) plays a crucial role in human-computer interaction, yet improving its accuracy remains a significant challenge. This study aims to enhance the robustness and effectiveness of FER systems by integrating multiple machine learning techniques within a semi-supervised learning framework. The primary objective is to develop a more effective ensemble model that combines Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), Support Vector Classifier (SVC), and Random Forest classifiers, utilizing both labeled and unlabeled data. The research implements

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Irfan, Muhammad, Nasir Ayub, Faisal Althobiani, et al. "Ensemble learning approach for advanced metering infrastructure in future smart grids." PLOS ONE 18, no. 10 (2023): e0289672. http://dx.doi.org/10.1371/journal.pone.0289672.

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Typically, load forecasting models are trained in an offline setting and then used to generate predictions in an online setting. However, this approach, known as batch learning, is limited in its ability to integrate new load information that becomes available in real-time. On the other hand, online learning methods enable load forecasting models to adapt efficiently to new incoming data. Electricity Load and Price Forecasting (ELPF) is critical to maintaining energy grid stability in smart grids. Existing forecasting methods cannot handle the available large amount of data, which are limited

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Duwadi, Navin, and Dr Bhoj Raj Ghimire. "AUTOMOBILE INSURANCE FRAUD DETECTION USING ENSEMBLE LEARNING MODELS." International Journal of Engineering Applied Sciences and Technology 09, no. 04 (2024): 187–99. http://dx.doi.org/10.33564/ijeast.2024.v09i04.024.

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Automobile insurance fraud is a universal problem that has negative effects on both insurance companies and policyholders. This research proposes a novel ensemble learning model to accurately detect potential fraudulent vehicle insurance claims. By leveraging advanced machine learning techniques and addressing the challenge of imbalanced data, our model aims to enhance fraud detection efficiency and reduce financial losses. Our approach combines a stacking ensemble learner with carefully selected base classifiers, meta-classifier and data pre-processing techniques. We evaluate the model's perf

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