Specific Learning Algorithm Research Articles

Pushing the boundaries of aphid detection: An investigation into mmWaveRadar and machine learning synergy

Agriculture, essential for global sustenance and economic vitality, faces significant threats from pest-induced damages, resulting in substantial crop losses and affecting food supply if not detected on time. Traditional pest control methods, primarily reliant on pesticides. However, blindly applying pesticide may cause environmental issue. Therefore detecting the infested crops at early stage is crucial for application of sustainable pest management solutions. This study innovatively employs the IWR1443BOOST FMCW Millimeter Wave Radar (mmWaveRadar) in conjunction with machine learning algorithms such as SVM, Random Forest, Adaboost, Lightgbm, Catboost, and edRVFL for enhanced pest detection in crops. Our novel framework encompasses the collection and pre-processing of mmWaveRadar data from both healthy and infested crops, followed by comprehensive feature extraction. Decision tree-based methods exhibited a remarkable detection accuracy of 98%. EdRVFL demonstrated a 95% detection accuracy. SVM, post-feature selection, achieved a 90% accuracy. The research reveals the efficacy of the mmWaveRadar as a robust tool, overcoming the environmental and concealment limitations of conventional image-based pest detection methods. The integration of curated features with machine learning algorithms has shown promising empirical results, establishing a connection between the discerned features and the real-world attributes of healthy and infested crops. This study underscores the potential of mmWaveRadar, coupled with specific machine learning algorithms, as a significant stride towards sustainable and effective pest management strategies in agricultural technology.

Association between stent implantation results and target vessel failure after IVUS-Guided PCI: A FLAVOUR substudy

Abstract Background Despite substantial advancements in coronary stent systems and implantation techniques, target vessel failure (TVF) remains a clinical issue to be addressed. Purpose We used deep learning algorithms for automatic stent analysis of intravascular ultrasound (IVUS) data to identify stent implantation characteristics associated with TVF. Methods In patients from the IVUS-guided group of the FLAVOUR trial who had undergone successful stenting, validated deep learning algorithms were applied to IVUS images to delineate plaque and stent strut for the quantification of stent expansion, apposition, and residual plaque burden. Stent overexpansion was defined as stent area exceeding 120% of the hypothetical healthy lumen area. Murray law-based quantitative flow ratio (μQFR) was also assessed from angiographic images. The primary endpoint was TVF at 2 years, defined as a composite of cardiac death, target vessel-related myocardial infarction, and ischemia-driven target vessel revascularization. Results A total of 459 vessels in 441 patients were included, with TVF events occurred in 15 vessels (3.3%) during the 2-year follow-up. Stent analysis was performed in 687,379 stent struts from 70,369 IVUS image frames of the interrogated vessels with the assistance of specific deep learning algorithms. On vessel-level multivariate analysis adjusting for procedural covariates, stent overexpansion length >5 mm (hazard ratio, 4.57; 95% confidence interval [CI], 1.31 to 15.92; P=0.017), residual plaque burden at proximal stent edge >50% (hazard ratio, 3.34; 95% CI, 1.19 to 9.33; P=0.022), and post-PCI μQFR <0.90 (hazard ratio, 5.09; 95% CI, 1.62 to 15.97; P=0.005) were more likely to be present in vessels with TVF events than in those without. Conclusions Besides the established indexes of post-procedural residual plaque burden and low μQFR, deep learning-powered IVUS stent overexpansion was significantly associated with increased risk of TVF.Suboptimal stenting results and TVF

Quantitative analysis and machine learning-based interpretation of EEG signals in coma and brain-death diagnosis.

Electroencephalography (EEG) reflects brain activity and is crucial for diagnosing states such as coma and brain-death. However, the clinical interpretation of EEG signals faces challenges due to the patients' faint brain activity and the complexities of the intensive care unit environment, further compounded by the absence of quantified standards for signal analysis. This study developed an improved denoise method tailored to the characteristics of Coma/Brain-Death EEG signals. The spectral feature map derived from the EEG signal via Variational Mode Decomposition (VMD) with a mode number (K) of 5, represents the frequency-based energy distribution. Subsequently, by integrating the Recursive Feature Elimination (RFE) algorithm with Support Vector Machine (SVM) algorithm employing cross-validation method, distinctive energy features in the 4-9Hz frequency band of coma patients compared to brain-death patients are identified. An accuracy of 99.59% and an F1-score of 99.61% for the SVM classifier demonstrate the high precision and reliability of the method. The application of specific machine learning algorithms provides robust theoretical support for the nuanced clinical interpretation of EEG signals across different levels of consciousness. This approach not only deepens scientific understanding of EEG signal variations associated with distinct consciousness levels but also establishes a solid foundation for future research aimed at quantifying EEG signal characteristics for the diagnosis and monitoring of brain diseases like epilepsy, Alzheimer's, and sleep disorders.

Utilizing Machine Learning for Intelligent Data Management in Event-Driven Microservices Architectures

The following research paper is a case study of three specific machine learning algorithms, LSTM networks, GBMs and RL for managing data in event-driven microservices architectures. This work assesses the performance of these algorithms using factors including anomaly detection, resource consumption prediction, and service coordination concerning such challenges as anomalies. LSTM networks was used in assessment of the anomalous patterns with accuracy reaching 92%, and false positive rate of 5%. The ability of the GBMs was evaluated for its capacity to accurately predict resource requirements, and in turn, minimize resource over-commitment and under-commitment that occurred, achieving 89% accuracy, with a percentage variation of 18% and 14% accordingly. The RL algorithms proved their potential to enhance the decision-making process governing the orchestration of services and failure recovery with the decision-makers achieving 22% increase in decision making accuracy and failure recovery time was reduced to 4. 5 minutes. These algorithms are discussed separately in the next sections with reference to their applications in intelligent data management in business event processing system. These findings are useful to improve the application of these machine learning techniques to increase the performance, utilization of resources and reliability of the system.

Optimization of English Teaching Model Based on Deep Learning Algorithm

With the vigorous development of artificial intelligence technology, especially in the field of deep learning, English education and teaching models are facing unprecedented opportunities and challenges. In order to enable students to master English knowledge more efficiently and align with international standards, it is particularly important to study the optimization of English teaching models. This paper uses specific deep learning algorithms and techniques, such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), to model and optimize English teaching modes, aiming to solve many problems in English teaching and improve the quality of English teaching. Through deep learning algorithms, we can analyze students’ learning behavior, habits and grades, thereby providing them with personalized learning resources and teaching strategies. Deep learning technologies such as CNN and RNN are used to recognize keywords and phrases in text, as well as to process sequence data such as speech and text, helping teachers better understand students’ learning needs and interests, thereby adjusting teaching content and methods. In addition, the adaptive nature of deep learning algorithms allows automatic adjustment of teaching content and difficulty according to the actual situation of students, providing them with learning resources and support that better meet their learning needs. This study not only applies deep learning algorithms to optimize English teaching modes but also delves into how these algorithms affect the learning outcomes of students and the teaching efficiency of teachers. Through empirical research and case analysis, we hope to provide new ideas and methods for the future development of English education.

The Impact of Machine Learning in Agriculture: Revolutionizing Farming Practices

Machine learning (ML) and big data analytics have emerged as transformative technologies in the agricultural sector, offering innovative solutions to enhance productivity, sustainability, and profitability. This abstract provides an overview of how ML and big data are revolutionizing traditional farming practices by leveraging advanced algorithms and data analytics. It discusses the potential of ML and big data in optimizing crop yield and quality, promoting sustainable practices, enabling precision agriculture, and addressing crop diseases through advanced predictive modelling. The abstract emphasizes the potential of ML and big data to reshape the future of agriculture, fostering resilience and prosperity for farmers and stakeholders worldwide. The application of machine learning in the field of agriculture and its correlation with weather patterns delves into various ways in which machine learning techniques can be used to analyze and predict the impact of weather conditions on agricultural productivity. The paper discusses specific machine learning algorithms and models that have been successfully applied in this domain, providing insights into the potential benefits and challenges of integrating machine learning into agricultural practices.

Selecting reliable instances based on evidence theory for transfer learning

The aim of transfer learning is to improve the performance of learning models in the target domain by transferring knowledge from the related source domain. However, not all data instances in the source domain are reliable for the learning task in the target domain. Unreliable source-domain data may lead to negative transfer. To address this problem, we propose a novel strategy for selecting reliable data instances from the source domain based on evidence theory. Specifically, a mass function is formulated to measure the degree of ignorance and reliability of the source domain data with respect to the learning task in the target domain. By selecting reliable instances with low degree of ignorance from the source domain, the domain adaptation of the transfer learning models is enhanced. Moreover, the proposed data-selection strategy is independent of specific learning algorithms and can be regarded as a common preprocessing technique for transfer learning. Experiments on both simulated and real-world datasets validated that the proposed data selection strategy can improve the performance of various types of transfer learning methods.

Breast Cancer and Lesion Detection using Machine Learning : A Review

Breast Breast cancer poses a significant global health concern, with approximately 2.2 million new cases and 700,000 deaths reported in 2020. Traditional diagnostic approaches which predominantly depend on expert judgement, have been associated with substantial variability in accuracy. To bridge this gap ML models are used to improve diagnostic out of which the present research investigates the potential of specific machine learning algorithms—D cancer remains one of the most common cancers among women globally, necessitating early detection to improve prognosis and survival rates. Recent advancements in machine learning (ML) have shown promise in enhancing the accuracy and efficiency of breast cancer and lesion detection. This review paper discusses the current methodologies, challenges, and future directions of ML applications in the detection and diagnosis of breast cancer. We analyze various ML algorithms used for analyzing mammograms, ultrasound, and MRI data, evaluating their effectiveness and applicability in clinical settings.

Machine learning algorithms for in-line monitoring during yeast fermentations based on Raman spectroscopy

Given the intricacies and nonlinearity inherent to industrial fermentation systems, the application of process analytical technology presents considerable benefits for the direct, real-time monitoring, control, and assessment of synthetic processes. In this study, we introduce an in-line monitoring approach utilizing Raman spectroscopy for ethanol production by Saccharomyces cerevisiae. Initially, we employed feature selection techniques from the realm of machine learning to reduce the dimensionality of the Raman spectral data. Our findings reveal that feature selection results in a noteworthy reduction of over 90% in model training time, concurrently enhancing the predictive performance of glycerol and cell concentration by 14.20% and 17.10% at the root mean square error (RMSE) level. Subsequently, we conducted model retraining using 15 machine learning algorithms, with hyperparameters optimized through grid search. Our results illustrate that the post-hyperparameter adjustment model exhibits improvements in RMSE for ethanol, glycerol, glucose, and biomass by 9.73%, 4.33%, 22.22%, and 13.79%, respectively. Finally, specific machine learning algorithms, namely BaggingRegressor, Support Vector Regression, BayesianRidge, and VotingRegressor, were identified as suitable models for predicting glucose, ethanol, glycerol, and cell concentrations, respectively. Notably, the coefficient of determination (R2) ranged from 0.89 to 0.97, and RMSE values ranged from 0.06 to 2.59 g/L on the testing datasets. The study highlights machine learning's effectiveness in Raman spectroscopy data analysis for improved industrial fermentation monitoring, enhancing efficiency, and offering novel modeling insights.

Machine learning can predict anterior elevation after reverse total shoulder arthroplasty: A new tool for daily outpatient clinic?

The aim of the present study was to individuate and compare specific machine learning algorithms that could predict postoperative anterior elevation score after reverse shoulder arthroplasty surgery at different time points. Data from 105 patients who underwent reverse shoulder arthroplasty at the same institute have been collected with the purpose of generating algorithms which could predict the target. Twenty-eight features were extracted and applied to two different machine learning techniques: Linear regression and support vector regression (SVR). These two techniques were also compared in order to define to most faithfully predictive. Using the extracted features, the SVR algorithm resulted in a mean absolute error (MAE) of 11.6° and a classification accuracy (PCC) of 0.88 on the test-set. Linear regression, instead, resulted in a MAE of 13.0° and a PCC of 0.85 on the test-set. Our machine learning study demonstrates that machine learning could provide high predictive algorithms for anterior elevation after reverse shoulder arthroplasty. The differential analysis between the utilized techniques showed higher accuracy in prediction for the support vector regression. Level of Evidence III: Retrospective cohort comparison; Computer Modeling.

Performance Comparison of Decision Tree and Support Vector Machine Algorithms for Heart Failure Prediction

Heart failure is a significant global cause of mortality and morbidity. This research article aims to evaluate the performance of decision tree (DTree) and support vector machine (SVM) methods in predicting heart disease. The study utilizes a dataset with diverse features and employs exploratory data analysis (EDA), clustering, and classification techniques to gain insights and evaluate the performance of the two methods. The results demonstrate the effectiveness of both DTree and SVM in predicting heart disease. Notably, SVM outperforms DTree in terms of accuracy, precision, recall, and F1-score. However, the performance of these methods is influenced by the preprocessing steps applied, indicating the importance of selecting appropriate data preprocessing techniques for optimal performance with specific machine learning algorithms. This study emphasizes the potential of machine learning algorithms in predicting heart disease and underscores the significance of thoughtful preprocessing technique selection to enhance performance.

A Comprehensive Review of Reinforcement Learning Applications in Gaming

Reinforcement learning is one of the most popular models for building agents that deal with the real world but are not distinctly told which actions to perform. In the context of gaming, the application of reinforcement learning thus spans many different categories, from classic arcade games to modern simulations. The aim of this review paper is to present a comprehensive review of reinforcement learning in gaming, its core methods or algorithms, and the results obtained. This paper first lays the groundwork by discussing the basics of reinforcement learning, among which are agents, environments, rewards, and policies. Then we discussed the mathematical framework of reinforcement learning, the Markov decision process, and the Bellman equation. Thereafter, it discusses specific reinforcement learning algorithms that have been successfully implemented in video games. A variety of algorithms has been adapted from the field of reinforcement learning and has shown huge success, like Q-learning, deep Q-networks, and policy gradients. Then we compare the different games and algorithms associated with them and their outcomes. There are also some major challenges with RL in video games, such as computational complexity, environment design, and many more. Finally, the conclusion and future aspects of applications of reinforcement learning in video games are discussed.

Machine learning approach for predicting the fracture toughness of Nb[sbnd]Si based alloys

Machine learning based data analysis was used to quantitatively examine the effects of constituent elements on the room-temperature fracture toughness of NbSi based alloys. Correlation between the room-temperature fracture toughness and alloying elements was analyzed using the machine learning model developed in this study based on the collected NbSi based alloy data. In addition, using a reasonably trained data analysis approach, the accuracies of several machine learning algorithms were compared to determine the specific machine learning algorithm with showing the highest accuracy. The results of the correlation analysis between the input and output features coincided with widely accepted facts regarding the effects of alloying elements additions on the room-temperature fracture toughness of NbSi based alloys. It was confirmed that the random forest algorithm outperformed other three representative machine learning algorithms.

Spectroscopic Identification of Bacteria Resistance to Antibiotics by Means of Absorption of Specific Biochemical Groups and Special Machine Learning Algorithm.

FTIR (Fourier transform infrared spectroscopy) is one analytical technique of the absorption of infrared radiation. FTIR can also be used as a tool to characterize profiles of biomolecules in bacterial cells, which can be useful in differentiating different bacteria. Considering that different bacterial species have different molecular compositions, it will then result in unique FTIR spectra for each species and even bacterial strains. Having this important tool, here, we have developed a methodology aimed at refining the analysis and classification of the FTIR absorption spectra obtained from samples of Staphylococcus aureus, with the implementation of machine learning algorithms. In the first stage, the system conforming to four specified species groups, Control, Amoxicillin induced (AMO), Gentamicin induced (GEN), and Erythromycin induced (ERY), was analyzed. Then, in the second stage, five hidden samples were identified and correctly classified as with/without resistance to induced antibiotics. The total analyses were performed in three windows, Carbohydrates, Fatty Acids, and Proteins, of five hundred spectra. The protocol for acquiring the spectral data from the antibiotic-resistant bacteria via FTIR spectroscopy developed by Soares et al. was implemented here due to demonstrating high accuracy and sensitivity. The present study focuses on the prediction of antibiotic-induced samples through the implementation of the hierarchical cluster analysis (HCA), principal component analysis (PCA) algorithm, and calculation of confusion matrices (CMs) applied to the FTIR absorption spectra data. The data analysis process developed here has the main objective of obtaining knowledge about the intrinsic behavior of S. aureus samples within the analysis regions of the FTIR absorption spectra. The results yielded values with 0.7 to 1 accuracy and high values of sensitivity and specificity for the species identification in the CM calculations. Such results provide important information on antibiotic resistance in samples of S. aureus bacteria for potential application in the detection of antibiotic resistance in clinical use.

Noninvasive early differential diagnosis and progression monitoring of ovarian cancer using the copy number alterations of plasma cell-free DNA

Ovarian cancer (OV) is the most lethal gynecological malignancy and requires improved early detection methods and more effective intervention to achieve a better prognosis. The lack of sensitive and noninvasive biomarkers with clinical utility remains a challenge. Here, we conducted a genome-wide copy number variation (CNV) profiling analysis using low-coverage whole genome sequencing (LC-WGS) of plasma cfDNA in patients with nonmalignant and malignant ovarian tumors and identified 10 malignancy-specific and 12 late-stage-specific CNV markers from plasma cfDNA LC-WGS data. Concordance analysis indicated a significant correlation of identified CNV markers between CNV profiles of plasma cfDNA and tissue DNA (Pearson's r = 0.64, P = 0.006 for the TCGA cohort and r = 0.51, P = 0.04 for the Dariush cohort). By leveraging these specific CNV markers and machine learning algorithms, we developed robust predictive models showing excellent performance in distinguishing between malignant and nonmalignant ovarian tumors with F1-scores of 0.90 and ranging from 0.75 to 0.99, and prediction accuracy of 0.89 and ranging from 0.66 to 0.98, respectively, as well as between early- and late-stage ovarian tumors with F1-scores of 0.84 and ranging from 0.61 to 1.00, and prediction accuracy of 0.82 and ranging from 0.63 to 0.96 in our institute cohort and other external validation cohorts. Furthermore, we also discovered and validated certain CNV features associated with survival outcomes and platinum-based chemotherapy response in multicenter cohorts. In conclusion, our study demonstrated the clinical utility of CNV profiling in plasma cfDNA using LC-WGS as a cost-effective and accessible liquid biopsy for OV.

SMOTE vs. KNNOR: An evaluation of oversampling techniques in machine learning

The increasing availability of big data has led to the development of applications that make human life easier. In order to process this data correctly, it is necessary to extract useful and valid information from large data warehouses through a knowledge discovery process in databases (KDD). Data mining is an important part of this and it involves discovering data and developing models that extract unknown patterns. The quality of the data used in supervised machine learning algorithms plays a significant role in determining the success of predictions. One factor that improves the quality of data is a balanced dataset, where the input values are distributed close to each other. However, in practice, many datasets are unbalanced. To overcome this problem, oversampling techniques are used to generate synthetic data that is as close to real data as possible. In this study, we compared the performance of two oversampling techniques, SMOTE and KNNOR, on a variety of datasets using different machine learning algorithms. Our results showed that the use of SMOTE and KNNOR did not always improve the accuracy of the model. In fact, on many datasets, these techniques resulted in a decrease in accuracy. However, on certain datasets, both SMOTE and KNNOR were able to increase the accuracy of the model. Our results indicate that the effectiveness of oversampling techniques varies depending on the specific dataset and machine learning algorithm being used. Therefore, it is crucial to assess the effectiveness of these methods on a case-by-case basis to determine the best approach for a given dataset and algorithm.

Effect of Sensor Size, Number and Position under the Foot to Measure the Center of Pressure (CoP) Displacement and Total Center of Pressure (CoPT) Using an Anatomical Foot Model

Ambulatory instrumented insoles are widely used in real-time monitoring of the plantar pressure in order to calculate balance indicators such as Center of Pressure (CoP) or Pressure Maps. Such insoles include many pressure sensors; the required number and surface area of the sensors used are usually determined experimentally. Additionally, they follow the common plantar pressure zones, and the quality of measurement is usually strongly related to the number of sensors. In this paper, we experimentally investigate the robustness of an anatomical foot model, combined with a specific learning algorithm, to measure the static displacement of the center of pressure (CoP) and the center of total pressure (CoPT), as a function of the number, size, and position of sensors. Application of our algorithm to the pressure maps of nine healthy subjects shows that only three sensors per foot, with an area of about 1.5 × 1.5 cm2, are needed to give a good approximation of the CoP during quiet standing when placed on the main pressure areas.

Application of Machine Learning in Disease Prediction

Abstract: Healthcare is a sector that is always changing. Healthcare professionals may find it challenging to stay current with the constant development of new technologies and treatments. As a result, the purpose of this research paper is to try and implement machine learning features in a specific system for health facilities. Knowing if we are ill at an early stage rather than finding out later is crucial. The entire process of treatment can be made much more effective if the disease is predicted ahead using specific machine learning algorithms as opposed to directly treating the patient. In this work, disease is predicted based on symptoms using machine learning. Machine learning algorithms like Naive Bayes, Decision Tree, Random Forest, and KNN are used to forecast the disease on the provided dataset. As you can see, there are numerous potential applications of machine learning in clinical care in the areas of patient data improvement, diagnosis, and treatment, cost reduction, and improved patient safety.

A Review of Embedded Machine Learning Based on Hardware, Application, and Sensing Scheme.

Machine learning is an expanding field with an ever-increasing role in everyday life, with its utility in the industrial, agricultural, and medical sectors being undeniable. Recently, this utility has come in the form of machine learning implementation on embedded system devices. While there have been steady advances in the performance, memory, and power consumption of embedded devices, most machine learning algorithms still have a very high power consumption and computational demand, making the implementation of embedded machine learning somewhat difficult. However, different devices can be implemented for different applications based on their overall processing power and performance. This paper presents an overview of several different implementations of machine learning on embedded systems divided by their specific device, application, specific machine learning algorithm, and sensors. We will mainly focus on NVIDIA Jetson and Raspberry Pi devices with a few different less utilized embedded computers, as well as which of these devices were more commonly used for specific applications in different fields. We will also briefly analyze the specific ML models most commonly implemented on the devices and the specific sensors that were used to gather input from the field. All of the papers included in this review were selected using Google Scholar and published papers in the IEEExplore database. The selection criterion for these papers was the usage of embedded computing systems in either a theoretical study or practical implementation of machine learning models. The papers needed to have provided either one or, preferably, all of the following results in their studies-the overall accuracy of the models on the system, the overall power consumption of the embedded machine learning system, and the inference time of their models on the embedded system. Embedded machine learning is experiencing an explosion in both scale and scope, both due to advances in system performance and machine learning models, as well as greater affordability and accessibility of both. Improvements are noted in quality, power usage, and effectiveness.

SAMA: Spatially-Aware Model-Agnostic Machine Learning Framework for Geophysical Data

Geophysical data is a form of spatial data that suffers from various limitations when applying conventional machine learning algorithms and evaluation techniques. A key limitation facing models trained on geophysical data is their inability to generalize well when deployed to predict from new unseen data. We address the problem of inaccurate performance assessments of machine learning models, that stems from violating independence assumptions during the feature selection and evaluation phases of the learning process. Our proposed spatially-aware and model-agnostic (SAMA) framework provides a suite of spatially-aware feature generation, feature selection, and model validation algorithms that account for spatial characteristics of geophysical data. The framework is model agnostic, as it tackles data-related challenges that are not affected by the specific machine learning algorithm used to fit the data. To demonstrate the effectiveness of the proposed approach, it is applied to the water saturation mapping problem using a novel geophysical dataset to train a prediction model. The proposed spatially-aware models obtains an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> of 0.620, an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$RMSE$ </tex-math></inline-formula> of 0.220 for predicting water saturation for the Whole Region of the reservoir model box and an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> of 0.161, an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$RMSE$ </tex-math></inline-formula> of 0.263 for the Interwell Region. Extensive experiments on 5 additional unseen datasets show that the model maintains stable performance across different datasets, which demonstrates the ability of the SAMA framework to produce robust models that are transferable to new datasets.