Machine Learning Fusion Research Articles

Fusion of machine learning and explainable AI for enhanced rice classification: a case study on Cammeo and Osmancik species

Fusion of machine learning and explainable AI for enhanced rice classification: a case study on Cammeo and Osmancik species

Averaging method for the existence of singular canards in a two-cell inhibitory neural network

Abstract Studies of firing rate neuronal competition models, as observed in binocular rivalry, showed that the variation of the stimulus parameters can lead to rich and interesting dynamics. We use the two-cell inhibitory neural network model to address the canards accompanied by the L^s-typed mixed-mode oscillations (MMOs). Specifically, we prove the existence and asymptotic expression of canards for this two-cell model by the average method. Meanwhile, our simulation indicates that the results are in accordance with the theory. Our solution can provide ideas for determining and selecting weights in model fusion of machine learning.

Generating Artistic Images Through Neural Style Transfer Using Machine Learning and Image Manipulation Techniques

Neural Style Transfer (NST) is a computational technique that merges the content of one image with the stylistic characteristics of another, creating visually appealing compositions. Leveraging deep learning and image processing, this method separates andcombines the content and style of input images using convolutional neural networks (CNNs). The content representation of an image is captured by the high-level features extracted from a pre-trained CNN, while its style is characterized by the statistical correlations of features at multiple network layers. It explores the fusion of machine learning and image processing techniques in NST. Initially introduced by Gatys et al., NST has gained popularity due to its ability to generate artistic renditions and novel visualizations. The process involves extracting content and style features from separate images and optimizing a generated image to minimize differences in content and style representations. In this work, explored into the underlying concepts and technical aspectsof NST. This proceedings discussed the utilization of pre-trained CNN models like VGG19 or ResNet for feature extraction and how optimization algorithms, such as gradient descent, iteratively refine the generated image to achieve a fusion of content and style. Additionally, This development has been examined the impact of hyper parameters and layer choices on the quality of stylized outputs.Mainly this technology focuses on reduction of total variation loss, which we incur during the style transfer.These proceedings has carried on work with VGG19, XCEPTION, EfficientnetB7. These findings show how Convolutional Neural Networks can learn deep image representations and show how they may be used for sophisticated image synthesis andmodification.

The Cloud Security Revolution: Unlocking the Potential of AI and Machine Learning to Stay Ahead of Threats

As we navigate the digital world, cybersecurity has become a top priority. With each technological advancement, new vulnerabilities emerge, making robust defenses essential. The fusion of machine learning and artificial intelligence has become a game-changer in the fight against cyber threats. This paper delves into the latest applications of these technologies in network security, shedding light on their critical roles in addressing pressing concerns and identifying areas for further exploration. We also examine the ethical and legal implications of implementing these technologies. Our research highlights current challenges and open questions, with a focus on recent breakthroughs in network security leveraging AI and ML. The findings are promising, suggesting that further innovation in integrating AI and ML into network security frameworks holds significant potential. Exciting applications include bolstering network security, detecting malware, and responding to intrusions. Interestingly, while 45% of organizations recognize the need to adopt these technologies, half have already done so, while 5% remain hesitant.

Development and validation of a prediction model for malignant sinonasal tumors based on MR radiomics and machine learning.

This study aimed to utilize MR radiomics-based machine learning classifiers on a large-sample, multicenter dataset to develop an optimal model for predicting malignant sinonasal tumors and tumor-like lesions. This study included 1711 adult patients (875 benign and 836 malignant) with sinonasal tumors or tumor-like lesions from three institutions. Patients from institution 1 (n = 1367) constituted both the training and validation cohorts, while those from institution 2 and 3 (n = 158/186) made up the test cohorts. Manual segmentation of the region of interest of the tumor was performed on T1WI, T2WI, and contrast-enhanced T1WI (CE-T1WI). Data normalization, dimensional reductions, feature selection, and classifications were performed using ten machine-learning classifiers. Four fusion models, namely T1WI + T2WI, T1WI + CE-T1WI, T2WI + CE-T1WI, and T1WI + T2WI + CE-T1WI, were constructed using the top ten features with the highest contribution in feature selection in the optimal models of T1WI, T2WI, and CE-T1WI. The Delong test compared areas under the curve (AUC) between models. The AUCs of training/validation/test1/test2 datasets for T1WI, T2WI, and CE-T1WI were 0.900/0.842/0.872/0.839, 0.876/0.789/0.842/0.863, and 0.899/0.824/0.831/0.707, respectively. The fusion model from T1WI + T2WI + CE-T1WI had the highest AUC. The AUCs of training/validation/test1/test2 datasets were 0.947/0.849/0.871/0.887. The T1WI + T2WI + CE-T1WI model demonstrated a significantly higher AUC than the T2WI + CE-T1WI model in both cohorts (p < 0.05) and outperformed the T2WI model in test 1 (p = 0.008) and the T1WI model in test 2 (p = 0.006). This fusion model based on radiomics from T1WI + T2WI + CE-T1WI images and machine learning can improve the power in predicting malignant sinonasal tumors with high accuracy, resilience, and robustness. Our study proposes a radiomics-based machine learning fusion model from T1- and T2-weighted images and contrast-enhanced T1-weighted images, which can non-invasively identify the nature of sinonasal tumors and improve the performance in predicting malignant sinonasal tumors. Differentiating benign and malignant sinonasal tumors is difficult due to similar clinical presentations. A radiomics model from T1 + T2 + contrast-enhanced T1 images can identify the nature of sinonasal tumors. This model can help distinguish benign and malignant sinonasal tumors.

Establish TIIC signature score based the machine learning fusion in bladder cancer

BackgroundBladder cancer is a prevalent malignant tumor with high heterogeneity. Current treatments, such as transurethral resection of bladder tumor (TURBT) and intravesical Bacillus Calmette-Guérin (BCG) therapy, still have limitations, with approximately 30% of non-muscle-invasive bladder cancer (NMIBC) progressing to muscle-invasive bladder cancer (MIBC), and a substantial number of MIBC patients experiencing recurrence after surgery. Immunotherapy has shown potential benefits, but accurate prediction of its prognostic effects remains challenging.MethodsWe analyzed bladder cancer RNA-seq data and clinical information from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and used various machine learning algorithms to screen for feature RNAs related to tumor-infiltrating immune cells (TIICs) from single-cell data. Based on these RNAs, we established a TIIC signature score and evaluated its relationship with overall survival (OS) and immunotherapy response in bladder cancer patients.ResultsThe study identified 171 TIIC-RNAs and selected 11 TIIC-RNAs with prognostic value through survival analysis. The TIIC signature score established using a machine learning fusion method was significantly associated with OS and showed good predictive performance in different datasets. Additionally, the signature score was negatively correlated with immunotherapy response, with patients with low TIIC feature scores showing better survival outcomes after immunotherapy. Further biological functional analysis revealed a close association between the TIIC signature score and immune regulation processes, cellular metabolism, and genetic variations.ConclusionThis study successfully constructed and validated an RNA signature scoring system based on tumor-infiltrating immune cell (TIIC) features, which can effectively predict OS and the effectiveness of immunotherapy in bladder cancer patients.

Fusion of machine learning and blockchain-based privacy-preserving approach for healthcare data in the Internet of Things

Fusion of machine learning and blockchain-based privacy-preserving approach for healthcare data in the Internet of Things

Ozone, nitrogen dioxide, and PM2.5 estimation from observation-model machine learning fusion over S. Korea: Influence of observation density, chemical transport model resolution, and geostationary remotely sensed AOD

High-resolution multi-component estimates of ground-level air pollutants are necessary for assessing their impacts to human health, agriculture, and ecosystems. We demonstrate a high-resolution fusion and downscaling approach over South Korea for May 2016 and May 2021. Daily 1 km fine particulate matter (PM2.5), ozone (O3), and nitrogen dioxide (NO2) concentrations are calculated at ground level using a random forest machine learning (ML) algorithm, with predictors including reanalysis meteorology, satellite aerosol optical depth (AOD), and gridded surface fields from chemical transport models (CTM). The ML model is tested for May 2016, coinciding with the Korea-United States Air Quality Study (KORUS-AQ) intensive field campaign, and for May 2021, to allow incorporation of observations from the Geostationary Environment Monitoring Spectrometer (GEMS). In the tests for May 2016, the correlation coefficients (R) and root mean squared errors (RMSE) relative to withheld observations of daily-averaged pollutants in 10-fold cross-validation are promising: 0.93 (5.5 μg/m3), 0.90 (5.5 ppbv), and 0.95 (4.7 ppbv) for PM2.5, O3, and NO2, respectively. Relative performance is assessed for alternate choices of predictors: (a) 80-km global reanalysis Copernicus Atmosphere Monitoring Service (CAMS) vs. 4-km regional Weather Research and Forecasting model coupled with Chemistry (WRF-Chem); (b) AOD polar-orbiting Moderate Resolution Image Spectroradiometer (MODIS) Multi-Angle Implementation of Atmospheric Correction (MAIAC) vs. geostationary GEMS; and (c) variations in surface observation density. This study is among the very first to incorporate both CTM and GEMS AOD for building surface high resolution multiple air pollution predictions over South Korea.

Survey on Blind Assist System using ML and Image Processing

This paper presents a Blind Assist System (BAS) leveraging Machine Learning (ML) and Image Processing (IP) techniques to enhance the autonomy and safety of visually impaired individuals. The system utilizes a convolutional neural network (CNN) to process real-time image inputs from a wearable camera device. Through ML classification, it identifies objects, obstacles, and environmental cues. The IP module further refines data, providing depth perception and spatial awareness. Leveraging ML's adaptability, the system continuously learns and improves its recognition accuracy. Integration with auditory feedback facilitates intuitive interaction, conveying vital information to users. In evaluations, the BAS demonstrates promising results in aiding navigation and increasing users' independence. The fusion of ML and IP offers a robust solution for empowering the visually impaired in navigating complex environments

Integrative BNN-LHS Surrogate Modeling and Thermo-Mechanical-EM Analysis for Enhanced Characterization of High-Frequency Low-Pass Filters in COMSOL.

This paper pioneers a novel approach in electromagnetic (EM) system analysis by synergistically combining Bayesian Neural Networks (BNNs) informed by Latin Hypercube Sampling (LHS) with advanced thermal-mechanical surrogate modeling within COMSOL simulations for high-frequency low-pass filter modeling. Our methodology transcends traditional EM characterization by integrating physical dimension variability, thermal effects, mechanical deformation, and real-world operational conditions, thereby achieving a significant leap in predictive modeling fidelity. Through rigorous evaluation using Mean Squared Error (MSE), Maximum Learning Error (MLE), and Maximum Test Error (MTE) metrics, as well as comprehensive validation on unseen data, the model's robustness and generalization capability is demonstrated. This research challenges conventional methods, offering a nuanced understanding of multiphysical phenomena to enhance reliability and resilience in electronic component design and optimization. The integration of thermal variables alongside dimensional parameters marks a novel paradigm in filter performance analysis, significantly improving simulation accuracy. Our findings not only contribute to the body of knowledge in EM diagnostics and complex-environment analysis but also pave the way for future investigations into the fusion of machine learning with computational physics, promising transformative impacts across various applications, from telecommunications to medical devices.

Machine learning empowered geographic information systems: Advancing Spatial analysis and decision making

In this era of technological innovation, the integration of machine learning techniques with Geographic Information Systems (GIS) has emerged as a transformative approach to spatial analysis and decision-making. This abstract explores the synergy between machine learning and GIS, highlighting their combined potential to unlock new insights from spatial data, automate analytical processes, and enhance predictive modeling capabilities. By leveraging machine learning algorithms such as neural networks, random forests, and support vector machines, GIS practitioners can address complex spatial challenges more effectively, ranging from land cover classification and urban growth modeling to environmental monitoring and disaster response. Through case studies and examples, this abstract demonstrates the practical applications of machine learning in GIS, illustrating its role in advancing our understanding of spatial phenomena and informing evidence-based decision-making across diverse domains. As the field continues to evolve, embracing the fusion of machine learning and GIS holds immense promise for unlocking the full potential of spatial analysis and shaping a more sustainable and resilient future. Illustrative case studies and examples showcase the practical applications of machine learning in GIS across diverse domains. From land cover classification and urban growth modeling to environmental monitoring and disaster response, machine learning algorithms offer versatile solutions to address a wide spectrum of spatial challenges. Through the fusion of machine learning and GIS, researchers and practitioners gain unprecedented insights into complex spatial phenomena, enabling them to make data-driven decisions that are both informed and actionable. Looking ahead, the fusion of machine learning and GIS holds immense promise for advancing our understanding of spatial dynamics and shaping a more sustainable and resilient future. As the field continues to evolve, embracing this interdisciplinary approach is essential for unlocking the full potential of spatial analysis, fostering innovation, and addressing pressing societal challenges at local, regional, and global scales. By leveraging the synergies between machine learning and GIS, we can chart a path towards a more data-driven, informed, and equitable world.

Seismic Fragility Estimation Based on Machine Learning and Particle Swarm Optimization

In seismic performance assessment, the development of building fragility curves is critical for performance-based engineering. Traditional methods for time history analysis, reliant on detailed ground motion (GM) inputs, often suffer from inefficiency and a lack of automation. This study proposes an accurate fragility assessment methodology, which is assisted by machine learning (ML) and particle swarm optimization (PSO), adept at handling scenarios with both scarce and sufficient fragility data. Under scenarios of scarce data, the integrated algorithms of PSO and ML are utilized, focusing on selecting GMs that may induce maximum inter-story drifts. When the dataset is sufficient, an ML fusion model is utilized to predict engineering demand parameters (EDPs), facilitating the generation of more accurate fragility curves. The effectiveness of this method is demonstrated through a case study on a high-rise reinforced concrete (RC) building, revealing a marked improvement in the precision of GM selection and the estimated range of fragility curves over traditional approaches. The proposed methodology aids in advancing structural optimization and the development of early-warning systems for seismic events, thus holding the potential to enhance current seismic risk mitigation strategies.

A fusion of machine learning and cryptography for fast data encryption through the encoding of high and moderate plaintext information blocks

AbstractWithin the domain of image encryption, an intrinsic trade-off emerges between computational complexity and the integrity of data transmission security. Protecting digital images often requires extensive mathematical operations for robust security. However, this computational burden makes real-time applications unfeasible. The proposed research addresses this challenge by leveraging machine learning algorithms to optimize efficiency while maintaining high security. This methodology involves categorizing image pixel blocks into three classes: high-information, moderate-information, and low-information blocks using a support vector machine (SVM). Encryption is selectively applied to high and moderate information blocks, leaving low-information blocks untouched, significantly reducing computational time. To evaluate the proposed methodology, parameters like precision, recall, and F1-score are used for the machine learning component, and security is assessed using metrics like correlation, peak signal-to-noise ratio, mean square error, entropy, energy, and contrast. The results are exceptional, with accuracy, entropy, correlation, and energy values all at 97.4%, 7.9991, 0.0001, and 0.0153, respectively. Furthermore, this encryption scheme is highly efficient, completed in less than one second, as validated by a MATLAB tool. These findings emphasize the potential for efficient and secure image encryption, crucial for secure data transmission in rea-time applications.

Integrating Traditional Machine Learning and Deep Learning for Precision Screening of Anticancer Peptides: A Novel Approach for Efficient Drug Discovery.

The rapid and effective identification of anticancer peptides (ACPs) by computer technology provides a new perspective for cancer treatment. In the identification process of ACPs, accurate sequence encoding and effective classification models are crucial for predicting their biological activity. Traditional machine learning methods have been widely applied in sequence analysis, but deep learning provides a new approach to capture sequence complexity. In this study, a two-stage ACPs classification model was innovatively proposed. Three novel coding strategies were explored; two mainstream Natural Language Processing (NLP) models and 11 machine learning models were fused to identify ACPs, which significantly improved the prediction accuracy of ACPs. We analyzed the correlation between peptide chain amino acids and evaluated the relevant performance of the model by the ROC curve and t-SNE dimensionality reduction technique. The results indicated that the deep learning and machine learning fusion models of M3E-base and KNeighborsDist models, especially when considering the semantic information on amino acid sequences, achieved the highest average accuracy (AvgAcc) of 0.939, with an AUC value as high as 0.97. Then, in vitro cell experiments were used to verify that the two ACPs predicted by the model had antitumor efficacy. This study provides a convenient and effective method for screening ACPs. With further optimization and testing, these strategies have the potential to play an important role in drug discovery and design.

Micro-electro-mechanical acoustofluidic mixing system: A response surface-metaheuristic machine learning fusion framework

Micro-mixing is an essential step in various biological and chemical analyses and synthesis processes. In the present study, an acoustofluidic micromixer is designed, modeled, and its functionality has been investigated. Intelligent acoustofluidic is proposed as an evaluation-optimization method, based on Machine Learning algorithms. The first step involves designing a parametric geometry with multiple sharp edges. Following that, mathematical modeling of the system is carried out using Response Surface Methodology (RSM) and computational fluid dynamics (CFD). Geometrical parameters including the sharp edge tip angle and the channel thickness, along with the peak-to-peak actuation voltage were investigated. It was revealed that, due to the inherent nonlinearity of acoustic streaming, each design parameter could significantly impact the flow patterns and mixing performance of the acoustofluidic mixer. The mathematical surrogate model obtained from data-based modeling procedure is then employed for the artificial intelligence multi-objective optimization algorithms, i.e. Differential Evolution (DE) and Non-dominated Sorting Genetic Algorithm two (NSGA-II). The two objective functions are the mixing index and the mechanical energy consumption index (ME). The derived optimum solutions had low Mehcanical Energy (ME) values while yielding homogeneity in a wide range of applied voltages. This ensures the controllability of the proposed system, which is an invaluable feature in synthesis processes to determine the critical physical properties of the nanoparticles. Through the algorithm evaluation process, which was conducted to distinguish the more accurate and robust optimization method, it was revealed that the DE algorithm outperformed NSGA-II in generating optimum solutions with superior functionalities. In the final section, three optimum models, operating under three different Vp-p values, 10, 20, 30, were selected, and their acoustofluidic characteristics were analyzed. It was concluded that utilizing baffles with three sharp edges at low Vp-p values results in a 96% Mixing Index and 48% decrease in the power consumption of the system. However, at high Vp-p values, baffles with one sharp edge demonstrated superior mixing functionality, with a MI of 98%. Moreover, it is revealed that utilizing baffles with three-sharp edges results in an 80% increase of Mixing Index of the acoustofluidic micromixer.

Approaches of Combining Machine Learning with NMR-Based Pore Structure Characterization for Reservoir Evaluation

Tight gas, a category of unconventional natural gas, relies on advanced intelligent monitoring methods for their extraction. Conventional logging for reservoir evaluation relies on logging data and the manual setting of evaluation criteria to classify reservoirs. However, the complexity and heterogeneity of tight reservoirs pose challenges in accurately identifying target layers by using traditional well-logging techniques. Machine learning may hold the key to solving this problem, as it enables computers to learn without being explicitly programmed and manually adding rules. Therefore, it is possible to make reservoir evaluations using machine learning methods. In this paper, the reservoir quality index (RQI) and porous geometric parameters obtained from the optimized inversion of the spherical–tubular model are adopted to evaluate the reservoir. Then, three different machine learning approaches, the random forest (RF) algorithm, support vector machine (SVM) algorithm, and extreme gradient boosting (XGB) algorithm, are utilized for reservoir classification. The selected dataset covers more than 7000 samples from five wells. The data from four wells are arranged as the training dataset, and the data of the remaining one well is designed as the testing dataset to calculate the prediction accuracies of different machine learning algorithms. Among them, accuracies of RF, SVM, and XGB are all higher than 90%, and XGB owns the highest result by reaching 97%. Machine learning based approaches can greatly assist reservoir prediction by implementing the well-logging data. The research highlights the application of reservoir classification with a higher prediction accuracy by combining machine learning algorithms with NMR-logging-based pore structure characterization, which can provide a guideline for sweet spot identification within the tight formation. This not only optimizes resource extraction but also aligns with the global shift towards clean and renewable energy sources, promoting sustainability and reducing the carbon footprint associated with conventional energy production. In summary, the fusion of machine learning and NMR-logging-based reservoir evaluation plays a crucial role in advancing both energy efficiency and the transition to cleaner energy sources.

Spatiotemporal aerosol prediction model based on fusion of machine learning and spatial analysis

This study examined long-term aerosol optical thickness (AOT) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) to quantify aerosol conditions on the Korean Peninsula. Time-series machine learning (ML) techniques and spatial interpolation methods were used to predict future aerosol trends. This investigation utilized AOT data from Terra MODIS and meteorological data from Automatic Weather System (AWS) in eight selected cities in Korea (Gangneung, Seoul, Busan, Wonju, Naju, Jeonju, Jeju, and Baengyeong) to assess atmospheric aerosols from 2000 to 2021. A machine-learning-based AOT prediction model was developed to forecast future AOT using long-term observations. The accuracy analysis of the AOT prediction results revealed mean absolute error of 0.152 ± 0.15, mean squared error of 0.048 ± 0.016, bias of 0.002 ± 0.011, and root mean squared error of 0.216 ± 0.038, which are deemed satisfactory. By employing spatial interpolation, gridded AOT values within the observation area were generated based on the ML prediction results. This study effectively integrated the ML model with point-measured data and spatial interpolation for an extensive analysis of regional AOT across the Korean Peninsula. These findings have substantial implications for regional air pollution policies because they provide spatiotemporal AOT predictions.

Novelty detection in the design of synthesis of garnet-structured solid electrolytes

Recent decades have shown arising growth-on-demand fusion of machine learning into all of the areas of chemistry and materials science. In this study, we examine one aspect of using these technologies to gain an advantage in the search for new knowledge in increased amounts of experimental data. The novelty detection approaches are aimed to identify the data artefacts. The analysis of such artefacts, “outliers”, in details of synthesis of garnet-structured solid electrolytes was chosen as an example of the practical application of this methodology. Particular attention was paid to the choice of the precursors. The thermodynamic data such as the heat of formation from the pure oxides as well as the results of drop solution calorimetry for simple oxides were involved as the descriptors for the studied systems. The overall performance of novelty/outlier detection of “outliers” was characterized using the AUC (area under the curve) value and assessed to be 0.71 – 0.72 varying the complexity of data description. It was found that all “outlier” due to the precursors choice compounds were successfully identified. The regression analysis was performed to analyze the impact of outlier/novelty detection on model predictive performance as well as to elucidate the relationship with the data diversity and the complexity of data description.

Mitigating ransomware attacks through cyber threat intelligence and machine learning

&lt;div&gt;In the face of escalating cyber threats, particularly the rampant and sophisticated nature of ransomware attacks, organizations are compelled to adopt a proactive and multi-faceted strategy for mitigation. The fusion of machine learning (ML) algorithms enables the system to dynamically adapt and evolve in response to evolving attack vectors and tactics employed by cybercriminals. This paper presents a comprehensive approach that synergistically integrates ML and cyber threat intelligence (CTI) to fortify defenses against ransomware assaults. The proposed methodology incorporates three distinct machine learning techniques, namely random forest (RF), extreme gradient boosting (XGBoost), and adaptive boosting (AdaBoost). Empirical evidence derived from the study affirms the efficacy of this approach in effectively discriminating between malicious and ransom software, achieving a notable identification rate of 98.55%. The incorporation of CTI enhances the strategic posture by providing actionable insights into the threat landscape. The proposed focuses on identifying and neutralizing ransomware, aligning with contemporary cybersecurity imperatives, offering a proactive defense against ransomware attacks, ultimately safeguarding critical assets, and preserving the integrity of digital ecosystems.&lt;/div&gt;

Sarcopenia prediction using shear-wave elastography, grayscale ultrasonography, and clinical information with machine learning fusion techniques: feature-level fusion vs. score-level fusion

This study aimed to develop and evaluate a sarcopenia prediction model by fusing numerical features from shear-wave elastography (SWE) and gray-scale ultrasonography (GSU) examinations, using the rectus femoris muscle (RF) and categorical/numerical features related to clinical information. Both cohorts (development, 70 healthy subjects; evaluation, 81 patients) underwent ultrasonography (SWE and GSU) and computed tomography. Sarcopenia was determined using skeletal muscle index calculated from the computed tomography. Clinical and ultrasonography measurements were used to predict sarcopenia based on a linear regression model with the least absolute shrinkage and selection operator (LASSO) regularization. Furthermore, clinical and ultrasonography features were combined at the feature and score levels to improve sarcopenia prediction performance. The accuracies of LASSO were 70.57 ± 5.00–81.54 ± 4.83 (clinical) and 69.00 ± 4.52–69.73 ± 5.47 (ultrasonography). Feature-level fusion of clinical and ultrasonography (accuracy, 70.29 ± 6.63 and 83.55 ± 4.32) showed similar performance with clinical features. Score-level fusion by AdaBoost showed the best performance (accuracy, 73.43 ± 6.57–83.17 ± 5.51) in the development and evaluation cohorts, respectively. This study might suggest the potential of machine learning fusion techniques to enhance the accuracy of sarcopenia prediction models and improve clinical decision-making in patients with sarcopenia.