Ensemble Learning Techniques Research Articles

Cutting-Edge Deep Learning Strategies for Precise Ovarian Disease Detection: A Comprehensive Review

Accurate and timely diagnosis of ovary-related diseases is of paramount importance in the realm of medical imaging. Utilizing deep learning technology has emerged as a viable method to improve the precision and effectiveness of medical image segmentation, which directly impacts the detection of ovarian diseases. Ovarian disease detection through medical image segmentation is critical for early diagnosis and effective treatment. This study evaluates cutting-edge deep learning strategies using the MMOTU ovarian tumour ultrasound dataset, which includes OTU 2D and OTU CEUS subsets. We implemented various models, including U-Net, its variants with ResNet and DenseNet backbones, CR-Unet, and Ocys-Net, along with ensemble learning and transfer learning techniques. Results show that while the baseline U-Net is effective, advanced models, particularly CR-Unet and DenseNet, significantly improve segmentation accuracy. The best performance was achieved using an ensemble model and a fine-tuned pre-trained network, highlighting the potential of these approaches in enhancing the precision and reliability of ovarian disease detection.

Ensemble deep learning techniques for time series analysis: a comprehensive review, applications, open issues, challenges, and future directions

Ensemble deep learning techniques for time series analysis: a comprehensive review, applications, open issues, challenges, and future directions

An Effective Ensemble Approach for Preventing and Detecting Phishing Attacks in Textual Form

Phishing email assaults have been a prevalent cybercriminal tactic for many decades. Various detectors have been suggested over time that rely on textual information. However, to address the growing prevalence of phishing emails, more sophisticated techniques are required to use all aspects of emails to improve the detection capabilities of machine learning classifiers. This paper presents a novel approach to detecting phishing emails. The proposed methodology combines ensemble learning techniques with various variables, such as word frequency, the presence of specific keywords or phrases, and email length, to improve detection accuracy. We provide two approaches for the planned task; The first technique employs ensemble learning soft voting, while the second employs weighted ensemble learning. Both strategies use distinct machine learning algorithms to concurrently process the characteristics, reducing their complexity and enhancing the model’s performance. An extensive assessment and analysis are conducted, considering unique criteria designed to minimize biased and inaccurate findings. Our empirical experiments demonstrates that using ensemble learning to merge attributes in the evolution of phishing emails showcases the competitive performance of ensemble learning over other machine learning algorithms. This superiority is underscored by achieving an F1-score of 0.90 in the weighted ensemble method and 0.85 in the soft voting method, showcasing the effectiveness of this approach.

MACHINE LEARNING, DEEP LEARNING AND ENSEMBLE LEARNING BASED APPROACHES FOR INTRUSION DETECTION ENHANCEMENT

With the extensive application of Internet of Things and wireless sensor networks (WSNs) in various real-time fields, they have become increasingly vulnerable to several types of attacks that could have a serious impact in their functionalities. Recently, intrusion detection systems have become one of the crucial security components. We propose in this work an approach based on machine and deep learning for DoS and DDoS attack detection. We have evaluated, analyzed, and compared the efficiency of three learning models separately, including deep neural network (DNN), random forest, and decision tree, using the standard metrics of evaluation such as accuracy, precision, F1-score, and recall. In our contribution, an approach ensemble learning was introduced in order to enhance the accuracy rate of classification. Our study was carried out using two well-known benchmark and real-time datasets, CICIoT-2023 and WSN-DS, intended for wireless sensor networks and IoT. These datasets containing various types of attacks. CICIoT2023 dataset contains 33 attacks divided into 7 classes, while WSN-DS dataset contains four types of DoS attacks, including Blackhole, Grayhole, Flooding, and TDMA. The experiment result demonstrate the effectiveness of our approach in attacks detection with high accuracy achieved close to perfect. Our approach result demonstrates that using the ensemble learning technique works better than each ML architecture independently.

Ensemble learning and deep learning-based defect detection in power generation plants

Abstract One of the key factors driving a country’s economic development and ensuring the sustainability of its industries is the constant availability of electricity. This is normally provided by the national grid. However, the power supply is not always stable in developing countries where new businesses, including the telecommunications industry, are constantly emerging. Therefore, they must rely on generators to ensure their full functionality. These generators rely on fuel to function, and consumption is usually high if not properly monitored. Monitoring is usually done by a (non-expert) human. This can sometimes be a tedious process, as some companies have reported excessively high consumption rates. For anomaly detection in power generating plants, the studies by Mulongo et al. and Atemkeng and Jimoh used the same dataset to train a multilayer perceptron (MLP) and generative adversarial networks (GANs), respectively, achieving an accuracy of 96.1% with MLP and 98.9% with GAN. Through comparative analysis and the use of ensemble learning techniques, we found that ensemble learning models outperform both MLP and GAN as proposed by Mulongo et al. and Atemkeng and Jimoh using the same dataset. Furthermore, we investigated the potential of autoencoders to outperform MLPs, GANs, and ensemble learning models. To this end, we have introduced a label-assisted autoencoder approach for detecting anomalies in power-generating plants. This model includes a labelling assistance module that adjusts the thresholds. Our results indicate that the label-assisted autoencoder outperforms the MLP. However, GANs and all ensemble learning models outperformed the label-assisted autoencoder. Nevertheless, the use of a label-assisted autoencoder offers a distinct advantage in categorizing anomalies based on their severity, a capability not present in ensemble learning models and GANs.

Stacking Ensemble Technique Using Optimized Machine Learning Models with Boruta–XGBoost Feature Selection for Landslide Susceptibility Mapping: A Case of Kermanshah Province, Iran

Landslides cause significant human and financial losses in different regions of the world. A high-accuracy landslide susceptibility map (LSM) is required to reduce the adverse effects of landslides. Machine learning (ML) is a robust tool for LSM creation. ML models require large amounts of data to predict landslides accurately. This study has developed a stacking ensemble technique based on ML and optimization to enhance the accuracy of an LSM while considering small datasets. The Boruta–XGBoost feature selection was used to determine the optimal combination of features. Then, an intelligent and accurate analysis was performed to prepare the LSM using a dynamic and hybrid approach based on the Adaptive Fuzzy Inference System (ANFIS), Extreme Learning Machine (ELM), Support Vector Regression (SVR), and new optimization algorithms (Ladybug Beetle Optimization [LBO] and Electric Eel Foraging Optimization [EEFO]). After model optimization, a stacking ensemble learning technique was used to weight the models and combine the model outputs to increase the accuracy and reliability of the LSM. The weight combinations of the models were optimized using LBO and EEFO. The Root Mean Square Error (RMSE) and Area Under the Receiver Operating Characteristic Curve (AUC-ROC) parameters were used to assess the performance of these models. A landslide dataset from Kermanshah province, Iran, and 17 influencing factors were used to evaluate the proposed approach. Landslide inventory was 116 points, and the combined Voronoi and entropy method was applied for non-landslide point sampling. The results showed higher accuracy from the stacking ensemble technique with EEFO and LBO algorithms with AUC-ROC values of 94.81% and 94.84% and RMSE values of 0.3146 and 0.3142, respectively. The proposed approach can help managers and planners prepare accurate and reliable LSMs and, as a result, reduce the human and financial losses associated with landslide events.

A spatially explicit multi-hazard framework for assessing flood, landslide, wildfire, and drought susceptibilities

Sustainable development goals require evaluating vulnerabilities and examining natural and climatic hazards for effective planning that reduces their impact on economic, social, and developmental efforts. Key hazards like floods, landslides, wildfires, and droughts have significantly affected terrestrial ecosystems and human societies, emphasizing the importance of comprehending these hazards. This study aimed to predict and spatially map multi-hazard, identifying historical and potential risks to inform sustainable development and construction programs that mitigate risks and promote resilience. A 34-year drought magnitude map was generated using long-term data, and ensemble and individual machine learning techniques were used to produce maps of flood, landslide, and wildfire hazards in a northwest region of Iran. Results demonstrated that ensemble learning models outperformed individual models, with the top-performing models being the weighted average (WA) of the two best models, random forest, extreme gradient boosting, WA models with over 80% accuracy, and WA incorporating all models, respectively. The CART model performed best among individual models. Variable importance analysis revealed that slope and precipitation were crucial factors for identifying high-hazard landslide areas, distance from waterways, vegetation cover, and topographic humidity index emerged as the most crucial factor for identifying flood hazard areas, while vegetation, rainfall, and proximity to roads significantly impacted wildfire hazard. The multi-hazard map produced by our study indicated that about 30% of the study area was highly and very highly susceptible to floods, landslides, wildfires, and droughts and the hazards mitigation efforts should be primarily directed to these specific portions of the study area. Our study underscored the importance of integrating long-term data and machine learning techniques in multi-hazard prediction and mapping, ultimately guiding mitigation efforts and promoting resilience in the face of natural and climatic hazards.

Automatic categorization of medical documents in Afaan Oromo using ensemble machine learning techniques

Automatic medical document classification using machine learning techniques can enhance the productivity of healthcare services by reducing processing time and cost. This work proposes an ensemble learning approach to develop a model that classifies electronic medical documents in Afaan Oromo. The main tasks in this work are preparing the corpus, pre-processing, training the models, and the classification process. We used the term frequency-inverse document frequency (TF-IDF) and bag of words (BOW) feature extraction methods. An ensemble technique in this work is that it creates multiple individual classifier predictions from naïve Bayes, random forest, SVM, and logistic regression and then combines them to advance a reliable and more accurate classifier. Evaluation measures were employed using accuracy, F1-score, recall, and precision for performance comparison. The efficiency of the proposed method is compared with the two existing boosting approaches, namely gradient boosting and adaboost. The experimental result shows the efficiency of BOW feature extraction over TF-IDF in this work on our dataset. These results also illustrated the effectiveness of the proposed model by scoring 94.81% accuracy and 94.84% F1-score. This work significantly contributes to the technological enhancement of service delivery, managing documents through classification methods, and advancing the data processing systems in healthcare sectors.

Enhancing spatial resolution of satellite soil moisture data through stacking ensemble learning techniques

Soil moisture (SM) is a critical variable influencing various environmental processes, but traditional microwave sensors often lack the spatial resolution needed for local-scale studies. This study develops a novel stacking ensemble learning framework to enhance the spatial resolution of satellite-derived SM data to 1 km in the Urmia basin, a region facing significant water scarcity. We integrated in-situ SM measurements (obtained using time-domain reflectometry [TDR]), Soil Moisture Active Passive (SMAP) and Advanced Microwave Scanning Radiometer 2 (AMSR2) SM products, Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature and vegetation indices, precipitation records, and topography data. Ten base machine-learning models were evaluated using the Complex Proportional Assessment (COPRAS) method, and the top-performing models were selected as base learners for the stacking ensemble. The ensemble model, incorporating Random Forest, Gradient Boosting, and XGBoost, significantly improved SM estimation accuracy and resolution compared to individual models. The XGBoost and Gradient Boosting meta-models achieved the highest accuracy, with an unbiased root mean square error (ubRMSE) of 1.23% m3/m3 and a coefficient of determination (R2) of 0.97 during testing, demonstrating the exceptional predictive capabilities of our approach. SHapley Additive exPlanations (SHAP) analysis revealed the influence of each base model on the ensemble’s predictions, highlighting the synergistic benefits of combining diverse models. This study establishes new benchmarks for soil moisture monitoring by showcasing the potential of ensemble learning to improve the spatial resolution and accuracy of satellite-derived SM data, providing crucial insights for environmental science and agricultural planning, particularly in water-stressed regions.

Differential evolution optimization based ensemble framework for accurate cervical cancer diagnosis

Cervical cancer remains a significant global health concern, demanding accurate and efficient diagnostic approaches for early detection and treatment. However, there are situations where a single deep learning model might not be sufficient to adequately capture the pertinent information required for precise disease prediction from intricate datasets. In this paper, we introduce an innovative ensemble learning framework that leverages the Differential Evolution (DE) algorithm to enhance the detection of cervical cancer. Our innovative approach combines the collective strength of a diverse set of base models using feature fusion techniques to achieve precise and robust classification. We have developed three base models, denoted as Fusion 1, Fusion 2, and Fusion 3, where each fusion model incorporates two pre-trained models using a feature fusion technique. To capture sequential dependencies and improve feature attention, we have integrated ConvLSTM layers and Squeeze-and-Excitation (SE) blocks into these base models. Subsequently, we've amalgamated the predictions of each base model using a weighted aggregation scheme, with the weights optimized using the DE algorithm. Our ensemble framework introduces a novel method for dynamic weight adjustment, utilizing advanced fusion techniques to improve cervical cancer detection accuracy. This approach ensures robust diagnostic performance across cancer cells datasets, enhancing patient outcomes and community health. Our evaluation covers two datasets, SIPaKMeD and Mendeley LBC, demonstrating the remarkable performance and robustness of our ensemble model. It consistently outperforms individual base models and even surpasses state-of-the-art methods, achieving an impressive accuracy rate of 98.96 % on the Mendeley LBC dataset and 95.68 % on the SIPaKMeD dataset. The incorporation of ConvLSTM layers, SE blocks, and DE optimization significantly contributes to the model's exceptional accuracy, its ability to generalize effectively, and its adaptability to diverse scenarios. Additionally, we employ Grad-CAM to visualize how the base models focus on crucial regions for accurate predictions, providing insights into the model's decision-making process. Our ensemble model exhibits outstanding Area under the Curve (AUC) values in Receiver Operating Characteristic (ROC) analysis, further confirming its effectiveness. This research represents a significant advancement in cervical cancer detection, underscoring the potential of ensemble learning techniques optimized through DE for enhancing diagnostic accuracy and advancing patient care.

Predicting smartphone prices using machine learning algorithms

This study investigates the prediction of smartphone prices using machine learning algorithms, motivated by the increasing demand for accurate pricing information in the highly competitive and rapidly evolving mobile phone market. With the proliferation of smartphones and the constant introduction of new models with varying specifications, it is crucial for consumers, retailers, and manufacturers to have a reliable method for estimating prices based on technical features. Accurate price prediction can assist consumers in making informed purchasing decisions, retailers in setting competitive prices, and manufacturers in optimizing production costs and pricing strategies. Utilizing Decision Tree Regression, Support Vector Regression (SVR), Random Forest Regression and Convolution Neural Network (CNN), the research investigates the efficacy of these methods in estimating prices accurately. Principal Component Analysis (PCA) is applied to reduce dimensionality and enhance model performance. Through rigorous experimentation and hyperparameter tuning, Random Forest Regression emerges as the most effective method, achieving the lowest Mean Squared Error (MSE) and the highest R-squared (R) score. The findings highlight the potential of ensemble learning techniques in addressing complex prediction tasks.

Prediction of Ionospheric Scintillations Using Machine Learning Techniques during Solar Cycle 24 across the Equatorial Anomaly

Ionospheric scintillation is a pressing issue in space weather studies due to its diverse effects on positioning, navigation, and timing (PNT) systems. Developing an accurate and timely prediction model for this event is crucial. In this work, we developed two machine learning models for the prediction of ionospheric scintillation events at the equatorial anomaly during the maximum and minimum phases of solar cycle 24. The models developed in this study are the Random Forest (RF) algorithm and the eXtreme Gradient Boosting (XGBoost) algorithm. The models take inputs based on the solar wind parameters obtained from the OMNI Web database from the years 2010–2017 and Pc5 wave power obtained from the Bear Island (BJN) magnetometer station. We retrieved data from the Scintillation Network and Decision Aid (SCINDA) receiver in Egypt from which the S4 index was computed to quantify amplitude scintillations that were utilized as the target in the model development. Out-of-sample model testing was performed to evaluate the prediction accuracy of the models on unseen data after training. The similarity between the observed and predicted scintillation events, quantified by the R2 score, was 0.66 and 0.74 for the RF and XGBoost models, respectively. The corresponding Root Mean Square Errors (RMSEs) associated with the models were 0.01 and 0.01 for the RF and XGBoost models, respectively. The similarity in error shows that the XGBoost model is a good and preferred choice for the prediction of ionospheric scintillation events at the equatorial anomaly. With these results, we recommend the use of ensemble learning techniques for the study of the ionospheric scintillation phenomenon.

Optimized biomedical entity relation extraction method with data augmentation and classification using GPT-4 and Gemini.

Despite numerous research efforts by teams participating in the BioCreative VIII Track 01 employing various techniques to achieve the high accuracy of biomedical relation tasks, the overall performance in this area still has substantial room for improvement. Large language models bring a new opportunity to improve the performance of existing techniques in natural language processing tasks. This paper presents our improved method for relation extraction, which involves integrating two renowned large language models: Gemini and GPT-4. Our new approach utilizes GPT-4 to generate augmented data for training, followed by an ensemble learning technique to combine the outputs of diverse models to create a more precise prediction. We then employ a method using Gemini responses as input to fine-tune the BioNLP-PubMed-Bert classification model, which leads to improved performance as measured by precision, recall, and F1 scores on the same test dataset used in the challenge evaluation. Database URL: https://biocreative.bioinformatics.udel.edu/tasks/biocreative-viii/track-1/.

Deep adaptive ensemble learning for imbalanced credit card fraud detection

ABSTRACT Traditional techniques for credit card fraud detection often struggle with highly imbalanced datasets, which compounds the complexity of fraudulent transaction detection. To tackle this issue, the current paper puts forth a novel technique known as deep adaptive ensemble learning (DAEL). Initially, we employ the ensemble-based under-sampling method to bring equilibrium to the data. This is followed by the application of a deep autoencoder for the purpose of feature extraction and dimensionality reduction. Then, a bidirectional long short-term memory model is trained using the features extracted from the autoencoder. Finally, an adaptive weighting technique is applied to unify multiple models. The empirical results demonstrate that the proposed DAEL technique significantly surpasses individual models and conventional ensemble learning techniques in performance.

Groundnut (ARACHIS HYPOGAEA L.) seed defect classification using ensemble deep learning techniques

Groundnut is an oil seed cash crop, that can be consumed directly by humans and animals. It is also used as an intelligence in the industrial production of butter and other products. However, Groundnut seeds can become infected with fungi, viruses, pests, physical damage, and high heat, which can negatively impact crop yield, quality, and economic value, and pose health hazards. To address these challenges, computer vision technology detects and classifies defects. This study introduced an ensemble deep learning defected classification model that combines VGG16 and InceptionV3 using seed images. To enhance model performance, collected images are preprocessed using novel techniques tailored to different image types. Preprocessing techniques are chosen based on image-quality evaluation metrics. Watershed segmentation is applied followed by detecting the region of interest in the image using YOLOv3. The image dataset is augmented and balanced using a Generative Adversarial Network (GAN). The model development involves a combination of classical and deep-based features, comparing features extracted with (HOG and GLCM) to those extracted with InceptionV3 and VGG16. The ensemble model achieves an accuracy of 96.25 % with a split ratio of 10 % for testing, 10 % for validation, and 80 % for training set. This research benefits farmers looking to improve yield, consumers of groundnut products, and researchers studying seed defects. The work provides valid insights for future researchers regarding dataset creation, augmentation, methods, and feature selection for modeling. However, the model experienced misclassifications due to the similar appearance of sample images in different classes. Future researchers should address these challenges and consider factors such as oil content and defect grading.

Improving predictions of rock tunnel squeezing with ensemble Q-learning and online Markov chain

Predicting rock tunnel squeezing in underground projects is challenging due to its intricate and unpredictable nature. This study proposes an innovative approach to enhance the accuracy and reliability of tunnel squeezing prediction. The proposed method combines ensemble learning techniques with Q-learning and online Markov chain integration. A deep learning model is trained on a comprehensive database comprising tunnel parameters including diameter (D), burial depth (H), support stiffness (K), and tunneling quality index (Q). Multiple deep learning models are trained concurrently, leveraging ensemble learning to capture diverse patterns and improve prediction performance. Integration of the Q-learning-Online Markov Chain further refines predictions. The online Markov chain analyzes historical sequences of tunnel parameters and squeezing class transitions, establishing transition probabilities between different squeezing classes. The Q-learning algorithm optimizes decision-making by learning the optimal policy for transitioning between tunnel states. The proposed model is evaluated using a dataset from various tunnel construction projects, assessing performance through metrics like accuracy, precision, recall, and F1-score. Results demonstrate the efficiency of the ensemble deep learning model combined with Q-learning-Online Markov Chain in predicting surrounding rock tunnel squeezing. This approach offers insights into parameter interrelationships and dynamic squeezing characteristics, enabling proactive planning and support measures implementation to mitigate tunnel squeezing hazards and ensure underground structure safety. Experimental results show the model achieves a prediction accuracy of 98.11%, surpassing individual CNN and RNN models, with an AUC value of 0.98.

Comparison and integration of physical and interpretable AI-driven models for rainfall-runoff simulation

Precise streamflow forecasting in river systems is crucial for water resources management and flood risk assessment. The Tagus Headwaters River Basin (THRB) in Spain is a key hydrological hub, providing regulated flow for agricultural, urban, and energy sectors, and facilitating water transfer to the Segura River Basin, a key semi-arid region. Given the basin's near-total allocation of water resources, accurate streamflow simulations are essential to optimize the socio-economic distribution and ensure sustainable management across both interconnected basins. This study conducts a comprehensive evaluation of the Soil and Water Assessment Tool (SWAT+), support vector regression (SVR), feed forward neural network (FFNN), and long short-term memory (LSTM) models in simulating the rainfall-runoff process at four gauging stations within the THRB. An ensemble machine learning technique is then applied to assess improvements in streamflow estimation. Results revealed that AI-based models significantly surpassed the SWAT+ model in performance. Furthermore, the application of an ensemble technique enhanced the precision of rainfall-runoff modeling by 18 to 26% during the calibration period and 4.1 to 9.2% during the validation period, compared to individual AI-based models. Additionally, the SWAT+ model's precision improved by 44 to 74% and 40 to 55% for the respective periods. The use of Shapley Additive Explanations (SHAP) methodology allowed the results of the ensemble with machine learning to be more interpretable by explaining how each model contributes to the prediction. This research offers significant contributions to hydrological modeling, highlighting the importance of ensemble techniques in elevating predictive accuracy for various river basins.

Machine learning techniques for predicting the peak response of reinforced concrete beam subjected to impact loading

To meet the growing need for resilient structures in seismic and high-impact zones, accurate prediction of the response of reinforced concrete (RC) beams under impact loads is essential. Traditional methods, such as experimental testing and high fidelity finite element models, are often time consuming and resource intensive. To address these challenges, this study investigates various ensemble and non-ensemble machine learning techniques—including support vector machine, gaussian process regression (GPR), k-nearest neighbor (KNN), gene expression programming, random forest, decision tree, boosted tree, adaptive boosting tree, gradient boosting algorithm, stochastic gradient descent, and artificial neural network—for predicting the peak response of RC beams under impact loads. A set of 145 experimental data points from 12 different sources is used to train and evaluate these machine learning models. Key parameters in the data include beam width and depth, span, reinforcement ratios, concrete strength, steel yield strength, deflection, and impact characteristics. Except for KNN, all models showed satisfactory generalization capabilities with R2 values over 0.8. Statistical errors such as RMSE, a-10 index, MAE, and a-20 index are within acceptable limits. The GPR model is the most effective with R2 value of 0.95. Moreover, Shapely analysis identified beam depth, impact velocity, and beam breadth as critical factors. Overall, this study demonstrates the efficacy of machine learning in accurately predicting the behavior of RC structures under impact loads, providing valuable tools for civil engineers in design and analysis.

Harnessing interpretable and ensemble machine learning techniques for precision fabrication of aligned micro-fibers

Electrospinning is a robust technique for producing micro/nano-scale fibrous structures, influenced by intricate interplays of fluid dynamics, aerodynamics, and electromagnetic forces. Depending on the desired outcome, these fibers can adopt various morphologies, including solid, tubular, concentric, and gradient. Such morphologies are modulated by parameters such as collector configuration, flow rate, voltage, solution properties, and nozzle dimensions. However, the task of modeling and predicting these multifaceted morphologies remains complex. Aligned microfibers with 3D orientation hold promise in tissue engineering, regenerative medicine, and drug delivery, necessitating meticulous control over the fabrication parameters. In our research, we tapped into machine learning (ML) to address these challenges. Classification ML models were designed to predict fibrous patterns—aligned, random, or jet branching—based on determinants like voltage, flow rate, and collector configurations. Notably, the Random Forest (RF) and Support Vector Machine (SVM) models, especially with radial kernel-trick, displayed outstanding predictive capabilities on the test data. Furthermore, regression-based ML was harnessed to discern fiber alignment coherency and inter-fiber distances. Models such as Lasso and Ridge regression elucidated predictive coefficients for these characteristics, while ensemble models, like gradient-boosting (GB) decision trees (DT), showcased prowess in regression scenarios. Key findings spotlighted the significance of parameters like plate gap for alignment coherency and needle-to-collector distance for inter-fiber spacing. As we strive to gain granular control over micro/nano feature morphology in electrospinning, understanding predictor-response dynamics is imperative. Our investigation underscores the essential role of ML in enhancing both qualitative and quantitative precision in fabricating advanced fibrous structures. Moreover, fusing ML with real-time process monitoring offers groundbreaking potential, particularly in Bio-Fabrication, regenerative medicine, and tissue engineering, where high-precision manufacturing remains a top priority.

Predictions of mechanical properties of Fiber Reinforced Concrete using ensemble learning models

Fiber Reinforced Concrete (FRC) significantly improves the tensile, crack resistance, and durability of concrete by adding fiber materials, making it highly promising for applications in structural reinforcement, repair and new construction projects. The strength of FRC is a crucial indicator of its mechanical properties. This study utilizes ensemble learning techniques to predict and analyze the compressive strength, splitting tensile strength, and flexural strength of FRC. A total of 1589 sets of compressive strength data, 1137 sets of splitting tensile strength data, and 1061 sets of flexural strength data were collected from existing literature. Four ensemble models (GBDT, Xgboost, LightGBM, RF) were used to establish prediction models and analyze influencing factors for these three FRC mechanical properties, and the results were compared with traditional models. The results show that the ensemble models can effectively solve the FRC strength prediction problems compared to traditional models. The GBDT regression model performed best in predicting compressive and flexural strength, with R2 values of 0.939 and 0.867, respectively. The Xgboost regression model performed best in predicting splitting tensile strength, with an R2 value of 0.944. Further analysis using SHAP revealed that cement and water significantly impact FRC strength. The inclusion of fibers significantly affects the splitting tensile strength and flexural strength of FRC but has a minimal impact on compressive strength. The study not only demonstrates the feasibility of using machine learning algorithms for FRC strength prediction but also provides a fast and reliable means for FRC strength prediction, offering more reliable data support for the design and application of FRC materials, aiding practical engineering applications.