Robust Machine Learning Approach Research Articles

MieAI: a neural network for calculating optical properties of internally mixed aerosol in atmospheric models

Aerosols influence weather and climate by interacting with radiation through absorption and scattering. These effects heavily rely on the optical properties of aerosols, which are mainly governed by attributes such as morphology, size distribution, and chemical composition. These attributes undergo continuous changes due to chemical reactions and aerosol micro-physics, resulting in significant spatio-temporal variations. Most atmospheric models struggle to incorporate this variability because they use pre-calculated tables to handle aerosol optics. This offline approach often leads to substantial errors in estimating the radiative impacts of aerosols along with posing significant computational burdens. To address this challenge, we introduce a computationally efficient and robust machine learning approach called MieAI. It allows for relatively inexpensive calculation of the optical properties of internally mixed aerosols with a log-normal size distribution. Importantly, MieAI fully incorporates the variability in aerosol chemistry and microphysics. Our evaluation of MieAI against traditional Mie calculations, using number concentrations from the ICOsahedral Nonhydrostatic model with Aerosol and Reactive Trace gases (ICON-ART) simulations, demonstrates that MieAI exhibits excellent predictive accuracy for aerosol optical properties. MieAI achieves this with errors well within 10%, and it operates more than 1000 times faster than the benchmark approach of Mie calculations. Due to its generalized nature, the MieAI approach can be implemented in any chemistry transport model which represents aerosol size distribution in the form of log-normally distributed internally mixed modes. This advancement has the potential to replace frequently employed look-up tables and plays a substantial role in the ongoing attempts to reduce uncertainties in estimating aerosol radiative forcing.

MicroRNA classification and discovery for major depressive disorder diagnosis: Towards a robust and interpretable machine learning approach

BackgroundMajor depressive disorder (MDD) is notably underdiagnosed and undertreated due to its complex nature and subjective diagnostic methods. Biomarker identification would help provide a clearer understanding of MDD aetiology. Although machine learning (ML) has been implemented in previous studies to study the alteration of microRNA (miRNA) levels in MDD cases, clinical translation has not been feasible due to the lack of interpretability (i.e. too many miRNAs for consideration) and stability. MethodsThis study applied logistic regression (LR) model to the blood miRNA expression profile to differentiate patients with MDD (n = 60) from healthy controls (HCs, n = 60). Embedded (L1-regularised logistic regression) feature selector was utilised to extract clinically relevant miRNAs, and optimized for clinical application. ResultsPatients with MDD could be differentiated from HCs with the area under the receiver operating characteristic curve (AUC) of 0.81 on testing data when all available miRNAs were considered (which served as a benchmark). Our LR model selected miRNAs up to 5 (known as LR-5 model) emerged as the best model because it achieved a moderate classification ability (AUC = 0.75), relatively high interpretability (feature number = 5) and stability (ϕ̂Z=0.55) compared to the benchmark. The top-ranking miRNAs identified by our model have demonstrated associations with MDD pathways involving cytokine signalling in the immune system, the reelin signalling pathway, programmed cell death and cellular responses to stress. ConclusionThe LR-5 model, which is optimised based on ML design factors, may lead to a robust and clinically usable MDD diagnostic tool.

D-TrAttUnet: Toward hybrid CNN-transformer architecture for generic and subtle segmentation in medical images

Over the past two decades, machine analysis of medical imaging has advanced rapidly, opening up significant potential for several important medical applications. As complicated diseases increase and the number of cases rises, the role of machine-based imaging analysis has become indispensable. It serves as both a tool and an assistant to medical experts, providing valuable insights and guidance. A particularly challenging task in this area is lesion segmentation, a task that is challenging even for experienced radiologists. The complexity of this task highlights the urgent need for robust machine learning approaches to support medical staff. In response, we present our novel solution: the D-TrAttUnet architecture. This framework is based on the observation that different diseases often target specific organs. Our architecture includes an encoder–decoder structure with a composite Transformer-CNN encoder and dual decoders. The encoder includes two paths: the Transformer path and the Encoders Fusion Module path. The Dual-Decoder configuration uses two identical decoders, each with attention gates. This allows the model to simultaneously segment lesions and organs and integrate their segmentation losses.To validate our approach, we performed evaluations on the Covid-19 and Bone Metastasis segmentation tasks. We also investigated the adaptability of the model by testing it without the second decoder in the segmentation of glands and nuclei. The results confirmed the superiority of our approach, especially in Covid-19 infections and the segmentation of bone metastases. In addition, the hybrid encoder showed exceptional performance in the segmentation of glands and nuclei, solidifying its role in modern medical image analysis.

A Comprehensive Approach to Safeguard Credit Card Transactions and Fraud Prevention

The escalating prevalence of financial fraud within the financial sector poses profound challenges. Detecting credit card fraud in online transactions necessitates data mining due to inherent complexities. Addressing two key issues—evolving patterns in legitimate and fraudulent behaviors and highly skewed datasets of credit card frauds—renders the task challenging. This paper scrutinizes the performance of naive Bayes, KNN, and logistic regression on significantly imbalanced credit card fraud data comprising 284,807 transactions from European cardholders. The dataset's skewness is addressed through a hybrid under-sampling and oversampling approach. The three techniques are applied to both unprocessed and preprocessed data. Fraud detection, defined as a set of activities thwarting illicit acquisition of assets or funds through deceptive means, varies across industries and methods. Credit card fraud, particularly susceptible due to its ease and prevalence in e-commerce and online platforms, prompted the adoption of diverse machine learning strategies to combat rising fraud rates. This paper employs machine learning algorithms for credit card fraud detection, utilizing a publicly available credit card dataset for model evaluation. While acknowledging that achieving 100% accuracy in fraud detection is elusive, the paper emphasizes the real-world applicability of its findings through the analysis of credit card data from a financial institution. In addition to assessing model efficacy, the study introduces noise into the data samples to evaluate algorithm robustness. Experimental outcomes underscore the effectiveness of the majority voting method, achieving commendable accuracy rates in detecting credit card fraud cases. The study sheds light on the pressing issue of credit card fraud, emphasizing the importance of deploying robust machine learning approaches for timely and accurate detection in real-world scenarios.

Enhancing unconfined compressive strength of stabilized soil with lime and cement prediction through a robust hybrid machine learning approach utilizing Naive Bayes Algorithm

The unconfined compressive strength (UCS) of stabilized soil with lime and cement is a crucial mechanical factor in developing accurate geomechanical models. In the past, determining UCS required laborious laboratory testing of core samples or complex well-log analysis, both of which consumed many resources. This study introduces a novel method for real-time UCS prediction while acknowledging the need for efficiency. This method makes use of Specific Naive Bayes (NB) predictive models that are strengthened by the smell agent optimization (SAO) and the Dynamic Arithmetic Optimization Algorithm (DAOA), two reliable meta-heuristic algorithms. Combining these algorithms improves prediction precision while streamlining the process. By examining UCS samples from various soil types obtained from earlier stabilization tests, these models are validated. This study identifies three different models: NBDA, NBSA, and a single NB. The individual insights each model provides work in concert to increase the overall UCS prediction accuracy. This approach represents a significant advancement in UCS prediction methodologies, revealing a quick and effective method with wide-ranging implications for various geomechanical applications. Meta-heuristic algorithms combined with particular NB models produce promising results, opening up new possibilities for real-time UCS estimation across various geological scenarios. Especially noteworthy are the NBDA model’s impressive performance metrics. The entire dataset achieves an R2 value of 0.992 during testing. The RMSE of 108.69 for the NBDA model during the training phase also shows that it has the best performance overall. It consistently exhibits commendable generalization and predictive abilities that outperform those of the developed NB and NBSA models, highlighting its usefulness and effectiveness in practical applications.

Privacy-Preserving Federated Survival Support Vector Machines for Cross-Institutional Time-To-Event Analysis: Algorithm Development and Validation

Background Central collection of distributed medical patient data is problematic due to strict privacy regulations. Especially in clinical environments, such as clinical time-to-event studies, large sample sizes are critical but usually not available at a single institution. It has been shown recently that federated learning, combined with privacy-enhancing technologies, is an excellent and privacy-preserving alternative to data sharing. Objective This study aims to develop and validate a privacy-preserving, federated survival support vector machine (SVM) and make it accessible for researchers to perform cross-institutional time-to-event analyses. Methods We extended the survival SVM algorithm to be applicable in federated environments. We further implemented it as a FeatureCloud app, enabling it to run in the federated infrastructure provided by the FeatureCloud platform. Finally, we evaluated our algorithm on 3 benchmark data sets, a large sample size synthetic data set, and a real-world microbiome data set and compared the results to the corresponding central method. Results Our federated survival SVM produces highly similar results to the centralized model on all data sets. The maximal difference between the model weights of the central model and the federated model was only 0.001, and the mean difference over all data sets was 0.0002. We further show that by including more data in the analysis through federated learning, predictions are more accurate even in the presence of site-dependent batch effects. Conclusions The federated survival SVM extends the palette of federated time-to-event analysis methods by a robust machine learning approach. To our knowledge, the implemented FeatureCloud app is the first publicly available implementation of a federated survival SVM, is freely accessible for all kinds of researchers, and can be directly used within the FeatureCloud platform.

Detecting video forgery: A machine learning approach for consistency analysis of video frames

Video forgery, a prevalent concern in today’s digital age, involves the deliberate manipulation of video content, often carried out using sophisticated video editing software. In response to this challenge, the need for an automated approach to detect forged video footage has become increasingly pressing. Our proposed methodology addresses this need by employing a multi-faceted strategy. It begins with the classification of video frames as either originating from genuine sources or having undergone manipulation. To assess the authenticity, the Δ r ¯ s metric is applied to evaluate the coherence of frame sequences. Additionally, we’ve harnessed the power of machine learning, training a model on a diverse dataset, namely the VIFFD dataset. This robust machine learning approach, particularly the suggested Support Vector Machine (SVM) method, consistently achieves an impressive average accuracy of 94.4%, showcasing its potential as a dependable and effective solution for video forgery detection. In an era where the trustworthiness of video content is of paramount importance, our method emerges as a pivotal safeguard, contributing significantly to the preservation of the integrity and credibility of visual media.

On the use of a new probabilistic model and machine learning methods with applications to reliability and music engineering

In this paper, we considered a new probability distribution with new applications in the field of engineering, in particular, in music engineering. The new probability distribution is mainly based on the Weibull distribution and cosine function. We call this the weighted cosine flexible Weibull distribution. The point estimators for the new distribution are obtained. We evaluate these point estimators for three sets of parameter values. The illustration of the weighted cosine flexible Weibull distribution is provided for two practical data set which are drawn from the reliability and music engineering. In addition, to dual robust machine learning approaches, we implement the Lasso (Least Absolute Shrinkage and Selection Operator) and Elastic Net (ENet) to improve the predictive performance of the data. This was done in response to the existence of outliers in the same datasets. We provide comparative analysis to see how well these approaches performed in comparison to the Step Indicator Saturation (SIS) method.

Machine learning-based approach KEVOLVE efficiently identifies SARS-CoV-2 variant-specific genomic signatures.

Machine learning was shown to be effective at identifying distinctive genomic signatures among viral sequences. These signatures are defined as pervasive motifs in the viral genome that allow discrimination between species or variants. In the context of SARS-CoV-2, the identification of these signatures can assist in taxonomic and phylogenetic studies, improve in the recognition and definition of emerging variants, and aid in the characterization of functional properties of polymorphic gene products. In this paper, we assess KEVOLVE, an approach based on a genetic algorithm with a machine-learning kernel, to identify multiple genomic signatures based on minimal sets of k-mers. In a comparative study, in which we analyzed large SARS-CoV-2 genome dataset, KEVOLVE was more effective at identifying variant-discriminative signatures than several gold-standard statistical tools. Subsequently, these signatures were characterized using a new extension of KEVOLVE (KANALYZER) to highlight variations of the discriminative signatures among different classes of variants, their genomic location, and the mutations involved. The majority of identified signatures were associated with known mutations among the different variants, in terms of functional and pathological impact based on available literature. Here we showed that KEVOLVE is a robust machine learning approach to identify discriminative signatures among SARS-CoV-2 variants, which are frequently also biologically relevant, while bypassing multiple sequence alignments. The source code of the method and additional resources are available at: https://github.com/bioinfoUQAM/KEVOLVE.

Cascading Machine Learning to Monitor Volcanic Thermal Activity Using Orbital Infrared Data: From Detection to Quantitative Evaluation

Several satellite missions are currently available to provide thermal infrared data at different spatial resolutions and revisit time. Furthermore, new missions are planned thus enabling to keep a nearly continuous ‘eye’ on thermal volcanic activity around the world. This massive volume of data requires the development of artificial intelligence (AI) techniques for the automatic processing of satellite data in order to extract significant information about volcano conditions in a short time. Here, we propose a robust machine learning approach to accurately detect, recognize and quantify high-temperature volcanic features using Sentinel-2 MultiSpectral Instrument (S2-MSI) imagery. We use the entire archive of high spatial resolution satellite data containing more than 6000 S2-MSI scenes at ten different volcanoes around the world. Combining a ‘top-down’ cascading architecture, two different machine learning models, a scene classifier (SqueezeNet) and a pixel-based segmentation model (random forest), we achieved a very high accuracy, namely 95%. These results show that the cascading approach can be applied in near-real time to any available satellite image, providing a full description of the scene, with an important contribution to the monitoring, mapping and characterization of volcanic thermal features.

Improving preoperative prediction of surgery duration

BackgroundOperating rooms (ORs) are one of the costliest units in a hospital, therefore the cumulative consequences of any kind of inefficiency in OR management lead to a significant loss of revenue for the hospital, staff dissatisfaction, and patient care disruption. One of the possible solutions to improving OR efficiency is knowing a reliable estimate of the duration of operations. The literature suggests that the current methods used in hospitals, e.g., a surgeon’s estimate for the given surgery or taking the average of only five previous records of the same procedure, have room for improvement.MethodsWe used over 4 years of elective surgery records (n = 52,171) from one of the major metropolitan hospitals in Australia. We developed robust Machine Learning (ML) approaches to provide a more accurate prediction of operation duration, especially in the absence of surgeon’s estimation. Individual patient characteristics and historic surgery information attributed to medical records were used to train predictive models. A wide range of algorithms such as Extreme Gradient Boosting (XGBoost) and Random Forest (RF) were tested for predicting operation duration.ResultsThe results show that the XGBoost model provided statistically significantly less error than other compared ML models. The XGBoost model also reduced the total absolute error by 6854 min (i.e., about 114 h) compared to the current hospital methods.ConclusionThe results indicate the potential of using ML methods for reaching a more accurate estimation of operation duration compared to current methods used in the hospital. In addition, using a set of realistic features in the ML models that are available at the point of OR scheduling enabled the potential deployment of the proposed approach.

A flexible framework to coordinate debris clearance and relief distribution operations: A robust machine learning approach

This study aims to offer an integrated, flexible action plan to bridge two vital operations in the response phase of disaster management, namely debris clearance and relief items distribution. On this subject, a broad range of associated decisions, including location-allocation, inventory, scheduling, and routing, are reflected in a multi-period environment. Also, so as to provide a pliable decision chain, two substantial kinds of flexibility are considered, including network design and due dates. So, a new bi-objective mathematical model is proposed, where the first objective minimizes the total cost, and the second one minimizes the total operation time. Since travel time information accuracy has an undeniable influence on system performance, a new two-phase data-driven methodology, including a hybrid machine learning model and the distributionally robust optimization with φ- divergence, is offered to estimate reliable travel time and tackle its uncertainty. Additionally, the application and validation of the recommended model and solution methodology are investigated in a real case study. What is more, in order to explain the superiority of the proposed solution approach concerning robustness solution, various simulation experiments are executed. Ultimately, several numerical experiments are implemented, and captured results manifest that the proposed framework can enhance the system’s performance. The obtained results reveal that considering the flexibility in network design and delivery date reduces the total costs and operation time by 7 and 6 percent.

A new approach for real-time prediction of stick–slip vibrations enhancement using model agnostic and supervised machine learning: a case study of Norwegian continental shelf

Efficient and safe drilling operations require real-time identification and mitigation of downhole vibrations like stick-slip, which can significantly diminish performance, reliability, and efficiency. This pioneering research introduces a robust machine learning approach combining model-agnostic regression techniques with Bayesian Optimized Extra Trees (BO_ET) to accurately predict stick-slip events in real-time using downhole sensor data. The model is rigorously tested and validated on a substantial offshore dataset comprising over 78,000 data points from a Norwegian continental shelf (NCS) oil field. The key input features encompassing real-time downhole and surface drilling parameters are carefully selected, including critical variables such as collar rotational speed, shock risks, annular pressure, torque, mud flow rate, drill string vibration severity, and other relevant measurements. These parameters offer significant insights into the occurrence of harmful stick-slip vibrations. Among several sophisticated machine learning models, the Extra Trees (ET) algorithm demonstrates superior performance with the lowest errors of 5.5056 revolutions per minute (r/min) Mean Absolute Error (MAE) and 9.9672 r/min Root Mean Square Error (RMSE) on out-of-sample test data. Further hyperparameter tuning of the ET algorithm via Bayesian Optimization dramatically reduces errors down to 0.002156 MAE and 0.024495 RMSE, underscoring the significant innovation and advantages of the proposed approach. By seamlessly incorporating real-time downhole sensor data and drill string mechanics, the model enables reliable identification of stick-slip events as they occur downhole. This grants opportunities to optimize critical drilling parameters including revolutions per minute (RPM), weight-on-bit (WOB), mud flow rates, and more to effectively mitigate stick-slip severity and improve the rate of penetration (ROP). Integrating the approach into automatic driller systems on offshore rigs offers immense benefits for drilling operations through substantially increased efficiency, fewer premature failures, lower costs, and significantly improved productivity and safety. Overall, this research strongly emphasizes the immense transformative potential of advanced data analytics and machine learning in enabling more efficient, economical, and sustainable drilling practices. The proposed model demonstrates clear superiority over existing methods and establishes a robust and reliable platform for real-time stick-slip prediction and mitigation, maximizing drilling performance.Graphical abstract

Structure-based modeling of critical micelle concentration (CMC) of anionic surfactants in brine using intelligent methods

Critical micelle concentration (CMC) is one of the main physico-chemical properties of surface-active agents, also known as surfactants, with diverse theoretical and industrial applications. It is influenced by basic parameters such as temperature, pH, salinity, and the chemical structure of surfactants. Most studies have only estimated CMC at fixed conditions based on the surfactant’s chemical parameters. In the present study, we aimed to develop a set of novel and applicable models for estimating CMC of well-known anionic surfactants by considering both the molecular properties of surfactants and basic affecting factors such as salinity, pH, and temperature as modeling parameters. We employed the quantitative-structural property relationship technique to employ the molecular parameters of surfactant ions. We collected 488 CMC values from literature for 111 sodium-based anionic surfactants, including sulfate types, sulfonate, benzene sulfonate, sulfosuccinate, and polyoxyethylene sulfate. We computed 1410 optimized molecular descriptors for each surfactant using Dragon software to be utilized in the modelling processes. The enhanced replacement method was used for selecting the most effective descriptors for the CMC. A multivariate linear model and two non-linear models are the outputs of the present study. The non-linear models were produced using two robust machine learning approaches, stochastic gradient boosting (SGB) trees and genetic programming (GP). Statistical assessment showed highly applicable and acceptable accuracy of the newly developed models (RSGB2 = 0.999395 and RGP2 = 0.954946). The ultimate results showed the superiority and greater ability of the SGB method for making confident predictions.

Retracted: Breast Tumor Detection Using Robust and Efficient Machine Learning and Convolutional Neural Network Approaches.

[This retracts the article DOI: 10.1155/2022/6333573.].

Unraveling the regulatory role of MYC2 on ASMT gene expression in wheat: Implications for melatonin biosynthesis and drought tolerance.

Recognized for its multifaceted functions, melatonin is a hormone found in both animals and plants. In the plant kingdom, it plays diverse roles, regulating growth, development, and stress responses. Notably, melatonin demonstrates its significance by mitigating the effects of abiotic stresses like drought. However, understanding the precise regulatory mechanisms controlling melatonin biosynthesis genes, especially during monocots' response to stresses, requires further exploration. Seeking to understand the molecular basis of drought stress tolerance in wheat, we analyzed RNA-Seq libraries of wheat exposed to drought stress using bioinformatics methods. In light of our findings, we identified that the Myelocytomatosis oncogenes 2 (MYC2) transcription factor is a hub gene upstream of a main melatonin biosynthesis gene, N-acetylserotonin methyltransferase (ASMT), in the wheat drought response-gene network. Promoter analysis of the ASMT gene suggested that it might be a target gene of MYC2. We conducted a set of molecular and physiochemical assays along with robust machine learning approaches to elevate those findings further. MYC2 and ASMT were co-regulated under Jasmonate, drought, and a combination of them in the leaf tissues of wheat was detected. A meaningful correlation was observed among gene expression profiles, melatonin contents, photosynthetic activities, antioxidant enzyme activities, H2 O2 levels, and plasma membrane damage. The results indicated an evident relationship between jasmonic acid and the melatonin biosynthesis pathway. Moreover, it seems that the MYC2-ASMT module might contribute to wheat drought tolerance by regulating melatonin contents.

A robust radiomic-based machine learning approach to detect cardiac amyloidosis using cardiac computed tomography.

Cardiac amyloidosis (CA) shares similar clinical and imaging characteristics (e.g., hypertrophic phenotype) with aortic stenosis (AS), but its prognosis is generally worse than severe AS alone. Recent studies suggest that the presence of CA is frequent (1 out of 8 patients) in patients with severe AS. The coexistence of the two diseases complicates the prognosis and therapeutic management of both conditions. Thus, there is an urgent need to standardize and optimize the diagnostic process of CA and AS. The aim of this study is to develop a robust and reliable radiomics-based pipeline to differentiate the two pathologies. Thirty patients were included in the study, equally divided between CA and AS. For each patient, a cardiac computed tomography (CCT) was analyzed by extracting 107 radiomics features from the LV wall. Feature robustness was evaluated by means of geometrical transformations to the ROIs and intra-class correlation coefficient (ICC) computation. Various correlation thresholds (0.80, 0.85, 0.90, 0.95, 1), feature selection methods [p-value, least absolute shrinkage and selection operator (LASSO), semi-supervised LASSO, principal component analysis (PCA), semi-supervised PCA, sequential forwards selection] and machine learning classifiers (k-nearest neighbors, support vector machine, decision tree, logistic regression and gradient boosting) were assessed using a leave-one-out cross-validation. Data augmentation was performed using the synthetic minority oversampling technique. Finally, explainability analysis was performed by using the SHapley Additive exPlanations (SHAP) method. Ninety-two radiomic features were selected as robust and used in the further steps. Best performances of classification were obtained using a correlation threshold of 0.95, PCA (keeping 95% of the variance, corresponding to 9 PCs) and support vector machine classifier reaching an accuracy, sensitivity and specificity of 0.93. Four PCs were found to be mainly dependent on textural features, two on first-order statistics and three on shape and size features. These preliminary results show that radiomics might be used as non-invasive tool able to differentiate CA from AS using clinical routine available images.

Development of a Robust Consensus Modeling Approach for Identifying Cellular and Media Metabolites Predictive of Mesenchymal Stromal Cell Potency.

Mesenchymal stromal cells (MSCs) have shown promise in regenerative medicine applications due in part to their ability to modulate immune cells. However, MSCs demonstrate significant functional heterogeneity in terms of their immunomodulatory function because of differences in MSC donor/tissue source, as well as non-standardized manufacturing approaches. As MSC metabolism plays a critical role in their ability to expand to therapeutic numbers ex vivo, we comprehensively profiled intracellular and extracellular metabolites throughout the expansion process to identify predictors of immunomodulatory function (T-cell modulation and indoleamine-2,3-dehydrogenase (IDO) activity). Here, we profiled media metabolites in a non-destructive manner through daily sampling and nuclear magnetic resonance (NMR), as well as MSC intracellular metabolites at the end of expansion using mass spectrometry (MS). Using a robust consensus machine learning approach, we were able to identify panels of metabolites predictive of MSC immunomodulatory function for 10 independent MSC lines. This approach consisted of identifying metabolites in 2 or more machine learning models and then building consensus models based on these consensus metabolite panels. Consensus intracellular metabolites with high predictive value included multiple lipid classes (such as phosphatidylcholines, phosphatidylethanolamines, and sphingomyelins) while consensus media metabolites included proline, phenylalanine, and pyruvate. Pathway enrichment identified metabolic pathways significantly associated with MSC function such as sphingolipid signaling and metabolism, arginine and proline metabolism, and autophagy. Overall, this work establishes a generalizable framework for identifying consensus predictive metabolites that predict MSC function, as well as guiding future MSC manufacturing efforts through identification of high-potency MSC lines and metabolic engineering.

Public subsidies and innovation: a doubly robust machine learning approach leveraging deep neural networks

Public subsidies and innovation: a doubly robust machine learning approach leveraging deep neural networks

Uncertainty in Study of Social Networks: Robust Optimization and Machine Learning

In the study of social networks, there exist many uncertainties which were ignored in earlier research efforts. However, they are now getting more and more attentions. Therefore, more and more robust optimization and machine learning approaches are getting involved. In this small survey, we would like to accumulate those uncertainties and related research works in robust optimization and machine learning.