Hybrid Machine Learning Algorithms Research Articles

An efficient healthcare system by cloud computing and clustering-based hybrid machine learning algorithm

Cloud computing, deep learning, clustering, genetic, and ensemble algorithms in healthcare are gaining popularity. This research highlights the relevance and complex repercussions of this integration. Cloud computing is transforming healthcare by providing scalable data storage and application access. It streamlines data exchange between hospitals, researchers, and institutions. Deep learning allows healthcare systems to use artificial intelligence for diagnostics, predictive analytics, and customized medication. Clustering algorithms segment patients, improving therapy and intervention customization. Genetic algorithms can optimize healthcare processes like treatment planning and resource allocation. Ensemble algorithms combine multiple models to improve predicted accuracy, enabling strong healthcare decision-making. This connection has several benefits. Healthcare systems become more efficient and scalable, resulting in cost-effective resource allocation. Access to patient data and apps promotes collaborative research and real-time healthcare. Deep learning algorithms can recognize complex medical data patterns, improving illness diagnosis and treatment results. Clustering algorithms streamline customized healthcare by stratifying individuals by clinical variables. Genetic algorithms optimize resource allocation, assuring healthcare resource efficiency. Ensemble algorithms improve predicted accuracy and clinical decision support system dependability. Its efficiency, accessibility, and prediction accuracy are positives, but security, resource constraints, interpretability, and ethical issues are obstacles.

Efficient Faba Bean Leaf Disease Identification through Smart Detection using Deep Convolutional Neural Networks

Background: Legumes, such as lentils, field peas, Faba beans and chickpeas, are high in vitamins, fiber, important minerals and protein and can help avoid obesity and cardiovascular illnesses. They also contribute to ecosystem services, such as nitrogen fixation and resilience to environmental stresses. Despite a 60% increase in global pulse production from 2000 to 2021, a demand-supply gap, especially in South Asia, raises concerns about nutritional access. Since illnesses are currently an issue to the food security of faba beans, machine learning is required for efficient disease identification. Methods: This research employs Convolutional Neural Networks (CNNs) for robust Faba bean leaf disease identification. The CNN model is trained with diverse images representing specific diseases. The study focuses on diseases like Chocolate Spot, Faba Bean Gall, Rust and Healthy leaves. Image processing involves resizing, grayscale conversion and labeling. The CNN architecture includes eight convolutional layers, four max-pooling layers and three dropout layers. The model is trained using 80% of the dataset, validated using 20% and tested for accuracy. Result: The CNN model achieves an accuracy of 99.37% during training and 89.69% during validation after 75 epochs. Confusion matrix and classification report illustrate the model’s performance. It shows high precision, recall and F1 scores for each class, indicating balanced performance. Chocolate Spot and Rust exhibit the highest precision and F1 scores. The overall accuracy is 91%, comparable to other studies on Faba bean disease detection. The study presents a CNN-based disease identification system for Faba beans, demonstrating high accuracy and balanced performance across different diseases. The model’s effectiveness is comparable to other advanced techniques. The research highlights the potential of machine learning in optimizing disease management for Faba beans. Future work could explore a broader range of diseases and incorporate hybrid machine learning algorithms for further improvement.

A Novel Approaches to Crop Variety Development for Sustainable Agriculture using Hybrid Machine Learning Model

The growing global population and changing climate need sustainable agriculture practices and new crop varieties. This study proposes a hybrid machine learning technique that combines linear regression and logistic regression to identify and extract unique traits from a broad range of crop species. The system integrates these qualities to create new crop types with a variety of benefits. The system begins by collecting detailed data on multiple crop species, including production potential, drought resilience, disease tolerance, nutritional content, and adaptation to different climates. Preprocessing ensures data quality. The hybrid machine learning algorithm uses the revised dataset. Linear regression identifies variables with strong connections with the required attributes, making it easier to find good candidates for the new crop variety. Logistic regression analyzes categorical data, including disease resistance genes and nutritional variables that affect crop quality. The method efficiently identifies and ranks the best characteristics across varied crop species by combining regression model findings. The traits have been strategically combined to create a pioneering cultivar that optimizes crop productivity, robustness, nutritional composition, and versatility while reducing vulnerability to pathogenic diseases and ecological challenges. The innovative crop variety is tested in various agricultural situations to determine the system's effectiveness. The topic under inquiry is compared to current kinds in terms of yield, quality, and sustainability. This unique hybrid machine learning technology may combine the best qualities from multiple sources to develop cutting-edge agricultural cultivars. The system's speed and accuracy in identifying beneficial traits promotes sustainable agriculture by reducing monospecific breeding and increasing crop resilience to environmental challenges. This work advances global food security and resource-efficient crop varieties.

Hybrid Machine Learning Algorithms for Prediction of Failure Modes and Punching Resistance in Slab-Column Connections with Shear Reinforcement

This study presents a data-driven model for identifying failure modes (FMs) and predicting the corresponding punching shear resistance of slab-column connections with shear reinforcement. An experimental database that contains 328 test results is used to determine nine input variables based on the punching shear mechanism. A comparison is conducted between three typical machine learning (ML) approaches: random forest (RF), light gradient boosting machine (LightGBM), extreme gradient boosting (XGBoost) and two hybrid optimized algorithms: grey wolf optimization (GWO) and whale optimization algorithm (WOA). It was found that the XGBoost classifier had the highest accuracy rate, precision, and recall values for FM identification. In testing, WOA-XGBoost has the best accuracy in predicting punching shear resistance, with R2, MAE, and RMSE values of 0.9642, 0.087 MN, and 0.126 MN, respectively. However, a comparison between experimental values and calculated values derived from classical analytical methods clearly demonstrates that existing design codes need to be improved. Additionally, Shapley additive explanations (SHAP) were applied to explain the model’s predictions, with factors categorized according to their impact on failure modes and punching shear resistance. By modifying these parameters, punching resistance can be improved while reducing unpredictable failure. With the proposed hybrid algorithms, it is possible to determine the failure modes and the punching shear resistance of slabs during the preliminary stages of the construction.

Abstract 1843: Integrating machine learning with microfluidic technologies for proteomic profiling of extracellular vesicles in triple-negative breast cancer

Abstract Accurate diagnosis of breast cancer using circulating biomarkers present in plasma remains an important challenge. In particular, protein changes in tumor-derived extracellular vesicles (tdEVs) have emerged as potential biomarkers for breast cancer diagnosis because they accurately reflect dynamic changes in tumors. In this study, we compared the proteomes of extracellular vesicle (EV) isolated from the plasma of 100 breast cancer patients and 30 healthy individuals who visited Severance Hospital between Mar 2010 and Dec 2015. Microfluidic chip-based protocol that facilitates the removal of contaminants such as albumin and immunoglobulins was used for the extraction of enriched tdEVs with small amounts of plasma. Comparative analysis of the proteomes identified 26 significant biomarkers that could indicate differences between breast cancer patients and healthy individuals. Using the LsBoost-CNN-SVM hybrid machine learning algorithm, we especially identified key EV protein biomarkers for detecting triple-negative breast cancer (TNBC), a breast cancer subtype with a poor prognosis and a high recurrence rate, as well as lacking therapeutic and diagnostic targets. Remarkably, a signature consisting of three EV proteins, specifically extracellular matrix protein 1 (ECM1), mannose-binding lectin 2 (MBL2), and biotinidase (BTD), effectively distinguished TNBC patients from healthy individuals. In our proteomic analysis set (n=73), this signature exhibited impressive performance, achieving a sensitivity of 93.3% and specificity of 93% in the accurate discrimination of TNBC from the control group. The validation set (n=40) confirmed these findings, with 100% sensitivity and 80% specificity. This signature not only served as a diagnostic tool but also provided valuable insights into the risk of recurrence and patient prognosis. We found a novel diagnostic approach that holds the potential to revolutionize breast cancer diagnostics by enhancing the reliability of tumor-related information obtained from blood samples. Citation Format: Min Woo Kim, Jee Ye Kim, Young Kim, Suji Lee, Sol Moon, Ju-yong Hyon, Kyung-A Hyun, Yeji Yang, Seongmin Ha, Sunyoung Park, Hogyeong Gawk, Haeji Lee, Eun Hee Han, Jin Young Kim, Hyo-Il Jung, Young-Ho Chung, Seung Il Kim. Integrating machine learning with microfluidic technologies for proteomic profiling of extracellular vesicles in triple-negative breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1843.

CEEMDAN-Based Hybrid Machine Learning Models for Time Series Forecasting Using MARS Algorithm and PSO-Optimization

Accurate prediction of time series data is crucial for informed decision-making and economic development. However, predicting noisy time series data is a challenging task due to their irregularity and complex trends. In the past, several attempts have been made to model complex time series data using both stochastic and machine learning techniques. This study proposed a CEEMDAN-based hybrid machine learning algorithm combined with stochastic models to capture the volatility of weekly potato price in major markets of India. The smooth decomposed component is predicted using stochastic models, while the coarser components, selected using MARS, are fitted into two different machine learning algorithms. The final predictions for the original series are obtained using optimization techniques such as PSO. The performance of the proposed algorithm is measured using various metrics, and it is found that the optimization-based combination of models outperforms the individual counterparts. Overall, this study presents a promising approach to predict price series using a hybrid model combining stochastic and machine learning techniques, with feature selection and optimization techniques for improved performance.

Hybrid Machine Learning Algorithms for Email and Malware Spam Filtering: A Review

In this paper, we presented a review of the state-of-the-art hybrid machine learning algorithms that were being used for email effective computing. For this reason, three research questions were formed, and the questions were answered by studying and analyzing related papers collected from some well-established scientific databases (Springer Link, IEEE Explore, Web of Science, and Scopus) based on some exclusion and inclusion criteria. The result presented the common Hybrid ML algorithms used to enhance email spam filtering. Also, the state-of-the-art datasets used for email and malware spam filtering were presented.

Prediction of Porosity in Atmospheric Plasma Sprayed Thermal Barrier Coatings Using Terahertz Pulse and Hybrid Machine Learning Algorithms

Prediction of Porosity in Atmospheric Plasma Sprayed Thermal Barrier Coatings Using Terahertz Pulse and Hybrid Machine Learning Algorithms

Explainable Software Defects Classification Using SMOTE and Machine Learning

Software defect prediction is a critical task in software engineering that aims to identify and mitigate potential defects in software systems. In recent years, numerous techniques and approaches have been developed to improve the accuracy and efficiency of the defect prediction model. In this research paper, we proposed a comprehensive approach that addresses class imbalance by utilizing stratified splitting, explainable AI techniques, and a hybrid machine learning algorithm. To mitigate the impact of class imbalance, we employed stratified splitting during the training and evaluation phases. This method ensures that the class distribution is maintained in both the training and testing sets, enabling the model to learn from and generalize to the minority class examples effectively. Furthermore, we leveraged explainable AI methods, Lime and Shap, to enhance interpretability in the machine learning models. To improve prediction accuracy, we propose a hybrid machine learning algorithm that combines the strength of multiple models. This hybridization allows us to exploit the strength of each model, resulting in improved overall performance. The experiment is evaluated using the NASA-MD datasets. The result revealed that handling the class imbalanced data using stratify splitting approach achieves a better overall performance than the SMOTE approach in Software Defect Detection (SDD).

Hybrid Model-Based Cauchy and Machine Learning Algorithms for IoTIntrusion Detection System

Hybrid Model-Based Cauchy and Machine Learning Algorithms for IoTIntrusion Detection System

Healthcare Cost Prediction Based on Hybrid Machine Learning Algorithms

Healthcare cost is an issue of concern right now. While many complex machine learning algorithms have been proposed to analyze healthcare cost and address the shortcomings of linear regression and reliance on expert analyses, these algorithms do not take into account whether each characteristic variable contained in the healthcare data has a positive effect on predicting healthcare cost. This paper uses hybrid machine learning algorithms to predict healthcare cost. First, network structure learning algorithms (a score-based algorithm, constraint-based algorithm, and hybrid algorithm) for a Conditional Gaussian Bayesian Network (CGBN) are used to learn the isolated characteristic variables in healthcare data without changing the data properties (i.e., discrete or continuous). Then, the isolated characteristic variables are removed from the original data and the remaining data used to train regression algorithms. Two public healthcare datasets are used to test the performance of the proposed hybrid machine learning algorithm model. Experiments show that when compared to popular single machine learning algorithms (Long Short Term Memory, Random Forest, etc.) the proposed scheme can obtain similar or higher prediction accuracy with a reduced amount of data.

Energy-efficient buffer and service rate allocation in manufacturing systems using hybrid machine learning and evolutionary algorithms

Energy-efficient buffer and service rate allocation in manufacturing systems using hybrid machine learning and evolutionary algorithms

Electricity demand forecasting with hybrid classical statistical and machine learning algorithms: Case study of Ukraine

This article presents a novel hybrid approach using classic statistics and machine learning to forecast the national demand of electricity. As investment and operation of future energy systems require long-term electricity demand forecasts with hourly resolution, our mathematical model fills a gap in energy forecasting. The proposed methodology was constructed using hourly data from Ukraine’s electricity consumption ranging from 2013 to 2020. To this end, we analysed the underlying structure of the hourly, daily and yearly time series of electricity consumption. The long-term yearly trend is evaluated using macroeconomic regression analysis. The mid-term model integrates temperature and calendar regressors to describe the underlying structure, and combines ARIMA and LSTM “black-box” pattern-based approaches to describe the error term. The short-term model captures the hourly seasonality through calendar regressors and multiple ARMA models for the residual. Results show that the best forecasting model is composed by combining multiple regression models and a LSTM hybrid model for residual prediction. Our hybrid model is very effective at forecasting long-term electricity consumption on an hourly resolution. In two years of out-of-sample forecasts with 17520 timesteps, it is shown to be within 96.83% accuracy.

Advanced Soil Moisture Predictive Methodology in the Maize Cultivation Region using Hybrid Machine Learning Algorithms

The moisture level in the soil in which maize is grown is crucial to the plant's health and production. And over 60% of India's maize cultivation comes from the states of South India. Therefore, forecasting the soil moisture of maize will emerge as a crucial factor for regulating the cultivation of maize crops with optimal irrigation. In light of this, this research provides a unique Improved Hybridized Machine Learning (IHML) model, which combines and optimizes several ML models (base learners-BL). The convergence rate of all the considered BL approaches and the preciseness of the proposed approach significantly enhances the process of determining the appropriate parameters to attain the desirable outcome. Consequently, IHML contributes to an improvement in the accuracy of the overall forecast. This research collects data from districts in South India that are primarily committed to maize agriculture to develop a model. The correlation evaluations served as the basis for the model's framework and the parameter selection. This research compares the outcomes of BL models to the IHML model in depth to ensure the model's accuracy. Results reveal that the IHML performs exceptionally well in forecasting soil moisture, comprising Correlation Coefficient (R2) of 0.9762, Root Mean Square Error (RMSE) of 0.293, and Mean Absolute Error (MAE) of 0.731 at a depth of 10 cm. Conceptual IHML models could be used to make smart farming and precise irrigation much better.

Efficient Prediction of Seasonal Infectious Diseases Using Hybrid Machine Learning Algorithms with Feature Selection Techniques

Early seasonal disease risk identification is the most challenging task in the medical industry. The capacity to detect patients at risk of improvement throughout their hospital stay is critical for effective patient allocation and care among patients with seasonal diseases. Patient risk factor prediction is the most important issue in reducing victim mortality. Machine learning plays a vital role in identifying the risk level of patients. In this research, we consider four seasonal diseases such as dengue, malaria, typhoid, and pneumonia. In this research, the researcher finds the dangerous symptoms of victims based on their age group. The real-time dataset was used in this study. The dataset for this study was gathered from hospitals in the Madurai district between 2019 and 2020. Feature selection is the prime element of patient risk recognition. It is used to pick the most accurate attributes for prediction. The dataset is divided into 70% training data and 30% testing data. This research proposes the feature selection method Boruta-XGBoost for improving accuracy. In this research, we discuss various attribute selection algorithms, including the Boruta algorithm, the XGBoost algorithm, the recursive feature elimination method (RFE), and the PRF-BXGBoost (Patient Risk Factor-Boruta-XGBoost) algorithm. The proposed method provides greater accuracy when compared to other variable selection methods. The time complexity of the proposed method is low when compared to other algorithms.

Experimentally trained hybrid machine learning algorithm for predicting turbulent particle-laden flows in pipes

A hybrid learning algorithm consisting of a preprocessor, a k-nearest neighbors regressor, a noise generator, and a particle–wall collision model is introduced for predicting features of turbulent single-phase and particle–liquid flows in a pipe. The hybrid learning algorithm has the ability to learn and predict the behavior of such complex fluid dynamic systems using experimental dynamic databases. Given a small amount of typical training data, the algorithm is able to reliably predict the local liquid and particle velocities as well as the spatial distribution of particle concentration within and without the limits of the range of training data. The algorithm requires an order of magnitude less training data than a typical full set of experimental measurements to give predictions on the same level of accuracy (typically, 20 cf. 100 trajectories for phase velocity distribution and 40 cf. 500 trajectories for phase concentration distribution), thus leading to huge reductions in experimentation and simulation. A feature importance analysis revealed the effects of the different experimental variables on the particle velocity field in a two-phase particulate flow, with particle–liquid density ratio and particle vertical radial position being the most influential and particle concentration the least. The algorithm is amenable to extension by using more complex databanks to address a much more comprehensive range of flow situations.

Building information modelling-enabled multi-objective optimization for energy consumption parametric analysis in green buildings design using hybrid machine learning algorithms

Green buildings (GB) have been widely promoted in various nations. However, the post occupancy evaluation suggests many GB cannot well fulfill the expected targets. To overcome the mismatch among GB’s multi-objectives, this paper develops an efficient and intelligent hybrid method using BIM-DesignBuilder (BIM-DB), Grey wolf optimization (GWO), random forest (RF) and non-dominated sorting genetic algorithm II (NSGA-II) to achieve the optimization of design parameters. The BIM model and DB simulation tool were used to obtain data samples of envelope and air conditioning system design parameters, and their life cycle carbon emission (LCCE), economic cost (EC) and predicted mean vote (PMV). The RF model was used to achieve high precision prediction. The GWO was used in the hyper-parameter optimization. The NSGA-II algorithm was applied to multi-objective optimization to obtain optimal design parameters. A building case shows: (1) The RF model had an excellent prediction performance for LCCE, EC and PMV. (2) BIM-DB can be used to obtain low error and high reliability building simulation data sets. (3) The RF-NSGA-II intelligent algorithm can reduce the LCCE of the building in the entire cycle by 16.6%, reduce the EC per square meter by 2.0%, and greatly improve the thermal comfort by 18.3%, representing good application value. This research provides a way of thinking for the multiobjective optimization of green buildings from the perspective of data mining and guidance for the parameter selection of the envelopes and air conditioning systems of new and existing buildings to more scientifically and effectively design green buildings.

A novel method for detecting URLs phishing using hybrid machine learning algorithm

Abstract— The phishing attack is the type of cyberattack that targets people’s trust by masking the malicious intent of the attack as communications from reputable sources. The goal is to steal sensitive data from the victim(s) (banking information, social identification, credentials, etc.) for various purposes (selling for monetary gain, performing identity thief, using as a lever for escalation attack). In 2022, the number of reported phishing attacks will reach a whopping 255 million cases, an increment of 61% compared to 2021. Existing methods of phishing URL detection have limitations. The article proposes a method to increase the accuracy of detecting malicious URL by using machine learning methods Linear Support Vector Classification and multinomial Naive Bayes with voting mechanisms.

Breast Cancer Prediction Using Hybrid Machine Learning and Nature-Inspired Adaptive Optimization Algorithms

Breast Cancer Prediction Using Hybrid Machine Learning and Nature-Inspired Adaptive Optimization Algorithms

A hybrid machine learning algorithm for studying magnetized nanofluid flow containing gyrotactic microorganisms via a vertically inclined stretching surface.

The novelty of the present work is to acquire continuous functions as solutions rather than the discrete ones that traditional numerical methods generally produce and to minimize simulation times and higher computation costs that are the fundamental barriers to employing any numerical method. In this study, a novel hybrid finite element-based machine learning algorithm utilizing the Levenberg-Marquardt scheme with backpropagation in a neural network (LMBNN) is presented to analyze the nanofluid flow in the presence of magnetohydrodynamics and gyrotactic microorganisms through a vertically inclined stretching surface in a porous medium. Finite Element Method is used to generate the minimum reference dataset for LMBNN by varying six flow parameters in the form of six scenarios. Surface plots are utilized to understand how these scenarios affect velocity, temperature, concentration of nanoparticles, and density of motile microorganisms. Regression analysis, error histogram analysis, and fitness curves based on mean square error all support the LMBNN's effectiveness and dependability. Results reveal that temperature increases with the rise in Brownian motion and thermophoresis parameter, whereas the reverse trend has been noticed for Prandtl number. The motile microorganism density number decreases with the rise in Prandtl numbers but improves with the porosity parameter.