Data-driven Machine Learning Algorithms Research Articles

A systematic review of optimization methods for recovery planning in cyber–physical infrastructure networks: Current state and future trends

Cyber–Physical Systems are increasingly complex and frequently integrated into modern societies via critical infrastructure systems, products, and services. Consequently, there is a need for reliable functionality of these complex systems under various scenarios, from physical failures due to aging to cyber attacks. However, the development of effective strategies to restore disrupted cyber–physical infrastructure systems continues to be a major challenge. Even though there have been an increasing number of papers evaluating recovery planning in cyber–physical infrastructures networks, a comprehensive literature review focusing on mathematical modeling and optimization methods is still lacking. Thus, this study critically analyzes the literature on optimization techniques for recovery planning of cyber–physical infrastructure networks after a disruption, to synthesize key findings on the current methods in this domain. A total of 152 relevant research papers are reviewed following an extensive assessment of all major scientific databases. The main mathematical modeling practices and optimization methods are identified for both deterministic and stochastic formulations, categorizing them based on the solution approach (exact, heuristic, metaheuristic), objective function, and network size. Having identified the gaps, a set of future trends for both the methodology and application of optimization algorithms is presented. Overall, there is a need to shift toward scalable optimization solution algorithms, empowered by data-driven methods and machine learning algorithms, to provide reliable and computationally efficient decision-support systems for decision-makers and practitioners.

Association between wrist-worn free-living accelerometry and hand grip strength in middle-aged and older adults

IntroductionWrist-worn activity monitors have seen widespread adoption in recent times, particularly in young and sport-oriented cohorts, while their usage among older adults has remained relatively low. The main limitations are in regards to the lack of medical insights that current mainstream activity trackers can provide to older subjects. One of the most important research areas under investigation currently is the possibility of extrapolating clinical information from these wearable devices.MethodsThe research question of this study is understanding whether accelerometry data collected for 7-days in free-living environments using a consumer-based wristband device, in conjunction with data-driven machine learning algorithms, is able to predict hand grip strength and possible conditions categorized by hand grip strength in a general population consisting of middle-aged and older adults.ResultsThe results of the regression analysis reveal that the performance of the developed models is notably superior to a simple mean-predicting dummy regressor. While the improvement in absolute terms may appear modest, the mean absolute error (6.32 kg for males and 4.53 kg for females) falls within the range considered sufficiently accurate for grip strength estimation. The classification models, instead, excel in categorizing individuals as frail/pre-frail, or healthy, depending on the T-score levels applied for frailty/pre-frailty definition. While cut-off values for frailty vary, the results suggest that the models can moderately detect characteristics associated with frailty (AUC-ROC: 0.70 for males, and 0.76 for females) and viably detect characteristics associated with frailty/pre-frailty (AUC-ROC: 0.86 for males, and 0.87 for females).Conclusions The results of this study can enable the adoption of wearable devices as an efficient tool for clinical assessment in older adults with multimorbidities, improving and advancing integrated care, diagnosis and early screening of a number of widespread diseases.

Association Between Sleep Efficiency Variability and Cognition Among Older Adults: Cross-Sectional Accelerometer Study.

Sleep efficiency is often used as a measure of sleep quality. Getting sufficiently high-quality sleep has been associated with better cognitive function among older adults; however, the relationship between day-to-day sleep quality variability and cognition has not been well-established. We aimed to determine the relationship between day-to-day sleep efficiency variability and cognitive function among older adults, using accelerometer data and 3 cognitive tests. We included older adults aged >65 years with at least 5 days of accelerometer wear time from the National Health and Nutrition Examination Survey (NHANES) who completed the Digit Symbol Substitution Test (DSST), the Consortium to Establish a Registry for Alzheimer's Disease Word-Learning subtest (CERAD-WL), and the Animal Fluency Test (AFT). Sleep efficiency was derived using a data-driven machine learning algorithm. We examined associations between sleep efficiency variability and scores on each cognitive test adjusted for age, sex, education, household income, marital status, depressive symptoms, diabetes, smoking habits, alcohol consumption, arthritis, heart disease, prior heart attack, prior stroke, activities of daily living, and instrumental activities of daily living. Associations between average sleep efficiency and each cognitive test score were further examined for comparison purposes. A total of 1074 older adults from the NHANES were included in this study. Older adults with low average sleep efficiency exhibited higher levels of sleep efficiency variability (Pearson r=-0.63). After adjusting for confounding factors, greater average sleep efficiency was associated with higher scores on the DSST (per 10% increase, β=2.25, 95% CI 0.61 to 3.90) and AFT (per 10% increase, β=.91, 95% CI 0.27 to 1.56). Greater sleep efficiency variability was univariably associated with worse cognitive function based on the DSST (per 10% increase, β=-3.34, 95% CI -5.33 to -1.34), CERAD-WL (per 10% increase, β=-1.00, 95% CI -1.79 to -0.21), and AFT (per 10% increase, β=-1.02, 95% CI -1.68 to -0.36). In fully adjusted models, greater sleep efficiency variability remained associated with lower DSST (per 10% increase, β=-2.01, 95% CI -3.62 to -0.40) and AFT (per 10% increase, β=-.84, 95% CI -1.47 to -0.21) scores but not CERAD-WL (per 10% increase, β=-.65, 95% CI -1.39 to 0.08) scores. Targeting consistency in sleep quality may be useful for interventions seeking to preserve cognitive function among older adults.

Machine learning-based algorithms applied to drug prescriptions and other healthcare services in the Sicilian claims database to identify acromegaly as a model for the earlier diagnosis of rare diseases

Acromegaly is a rare disease characterized by a diagnostic delay ranging from 5 to 10 years from the symptoms’ onset. The aim of this study was to develop and internally validate machine-learning algorithms to identify a combination of variables for the early diagnosis of acromegaly. This retrospective population-based study was conducted between 2011 and 2018 using data from the claims databases of Sicily Region, in Southern Italy. To identify combinations of potential predictors of acromegaly diagnosis, conditional and unconditional penalized multivariable logistic regression models and three machine learning algorithms (i.e., the Recursive Partitioning and Regression Tree, the Random Forest and the Support Vector Machine) were used, and their performance was evaluated. The random forest (RF) algorithm achieved the highest Area under the ROC Curve value of 0.83 (95% CI 0.79–0.87). The sensitivity in the test set, computed at the optimal threshold of predicted probabilities, ranged from 28% for the unconditional logistic regression model to 69% for the RF. Overall, the only diagnosis predictor selected by all five models and algorithms was the number of immunosuppressants-related pharmacy claims. The other predictors selected by at least two models were eventually combined in an unconditional logistic regression to develop a meta-score that achieved an acceptable discrimination accuracy (AUC = 0.71, 95% CI 0.66–0.75). Findings of this study showed that data-driven machine learning algorithms may play a role in supporting the early diagnosis of rare diseases such as acromegaly.

Small Target Detection in a Radar Surveillance System Using Contractive Autoencoders

With the rapid development of Unpiloted Aerial Vehicles (UAVs), also known as drones, in recent years, the need for surveillance systems that are able to detect drones has grown as well. Radar is technology with the potential to fulfill this task and several previous publications show examples of radar detection and classification schemes. The purpose of this paper is related to the detection scheme used in these radars. Most surveillance systems use a background subtraction and a threshold to detect targets. This threshold often depends on a model of the radar noise and the background, which is imperfect by nature. The approach presented here uses a data driven machine learning algorithm that is trained with measured background profiles of the radar and is applied afterwards to the given background for target detection. This scheme can in general be applied to any detection problem in a fixed area, but is shown here with examples from measurements of drones and persons. The results show that the chosen approach gives better detection rates for low false alarm rates with real data than background subtraction.

Extrapolating tipping points and simulating non-stationary dynamics of complex systems using efficient machine learning

Model-free and data-driven prediction of tipping point transitions in nonlinear dynamical systems is a challenging and outstanding task in complex systems science. We propose a novel, fully data-driven machine learning algorithm based on next-generation reservoir computing to extrapolate the bifurcation behavior of nonlinear dynamical systems using stationary training data samples. We show that this method can extrapolate tipping point transitions. Furthermore, it is demonstrated that the trained next-generation reservoir computing architecture can be used to predict non-stationary dynamics with time-varying bifurcation parameters. In doing so, post-tipping point dynamics of unseen parameter regions can be simulated.

Identifying nonlinear effects of factors on hit-and-run crashes using interpretable machine learning techniques

Previous research on hit-and-run crashes employed regression methods or machine learning techniques. However, regression methods necessitate preestablished model formulations, making it challenging to accommodate intricate nonlinear effects. In contrast, machine learning methods are characterized as black box systems, lacking interpretability. Thus, we propose an innovative analytical framework that combines data-driven machine learning algorithms with emerging interpretation techniques. The complex nonlinear effects of various factors on hit-and-run crashes are investigated by employing post hoc interpretation techniques, specifically, Shapley Additive exPlanations and accumulated local effect. The results demonstrate that machine learning algorithms are superior in accounting for complex relationships among influencing factors and identifying hit-and-run crashes. The quantitative importance of various factors is estimated and compared to reveal key determinants such as visibility, road location, and accident liability. The complex effects of different factors on hit-and-run crashes are unveiled, delineating quantitative piecewise nonlinear patterns. These patterns, which are difficult to capture using conventional regression models with predefined formulations, shed light on the nuanced dynamics of hit-and-run crashes. This research offers quantitative analysis and data-supported insights for transportation agencies and police departments to proactively mitigate hit-and-run crashes.

Predicting hospital disposition for trauma patients: application of data-driven machine learning algorithms

<abstract> <p>As a consequence of road accidents, around 1.3 million people die, and between 20 to 50 million have nonfatal injuries. Therefore, hospitals are receiving a high volume of patients in their urgent care, and a quick decision must be made regarding their treatment plans. At the admission stage, there is no information or probability about the patient's final result, regardless of if the patient will mostly die or be safely discharged from the hospital. To address this issue, this study proposed a machine learning-based framework that can predict the hospital disposition for trauma patients. The framework was developed to anticipate whether the patient would be safely discharged from the hospital or die based on a set of features collected at the admission time. In this study, the data used was collected from the King Abdulaziz Medical City (KAMC) in Riyadh, Saudi Arabia, and the performance of different machine learning algorithms was investigated, including eXtreme gradient boost (XGBoost), K-nearest neighbor, random forest, logistic regression, BRR, and support vector machine. Results show that the XGBoost algorithm demonstrated a high degree of detection and prediction accuracy for disposed-to-home patients; of the 6059 patients that were sent home, the XGBoost correctly predicted 5944 (98%) of the total. Finally, the developed framework could accurately predict hospital disposition for trauma patients with high accuracy and sensitivity levels. This system can benefit healthcare teams and insurance companies by providing them with a quick decision-making tool to determine the best treatment plan for patients.</p> </abstract>

Predicting real-time within-vehicle air pollution exposure with mass-balance and machine learning approaches using on-road and air quality data

Modelling the air pollutant concentrations within-vehicles is an essential step to estimate our daily exposure to air pollution. This is a challenging issue however, since the processes that affect the exposures within-vehicles change with different driving patterns and ventilation settings. This study introduces an innovative approach that combines mass-balance principles and machine learning techniques, leveraging ambient air quality, on-road and within-vehicle measurements of particulate matter (PM10, PM2.5, PM1), nitrogen dioxide (NO2), nitrogen oxides (NOx), aerosol lung surface deposited area (LSDA) and ultrafine particles (UFP) under different ventilation settings to estimate air pollution exposure levels within vehicles. The first model (MB) includes basic physical and chemical processes and follows a mass-balance approach to estimate the within-vehicle concentrations. The second model (ML) applies data driven machine learning algorithms to a training set of observations to predict unseen within-vehicle concentrations. By using a number generator, the whole observational dataset was divided to 80:20 and 80% was used to build and train the ML model, while 20% was used for validation. Both models demonstrated good predictions of observations apart from an underestimation in UFP and LSDA. The ML model showed better predictive power than the MB model and had skill in predicting the unseen within-vehicle exposures. The ML model predictions were as good as the MB model for most of the species and improved for NO2. The ML model demonstrated good index of agreement (IOA >0.69) and Pearson correlation coefficient (r > 0.80) for all the species. The inclusion of air quality data from nearby monitoring stations instead of on-road (sampled while driving), in the ML model showed promising and new capabilities to within-vehicle exposure predictions. In an era where air pollution is a growing concern, understanding and predicting within-vehicle air pollution exposure is of great importance for public health and environmental research. This research not only advances the field of exposure assessment but (at no extra cost) also demonstrates practical implications for real-time exposure mapping and health impact assessment of vehicle occupants with existing infrastructure.

Deciphering the Differential Diagnosis of Inherited Versus Acquired Bone Marrow Failure Syndromes: A Large Cohort Study

The wide overlaps in clinical and hematologic features of inherited (iBMF) and acquired (aBMF) bone marrow failures are often the cause of diagnostic difficulties and delay in proper therapeutic approaches. Screening tests for iBMF are complex and expensive and have limited availability. Data-driven machine learning algorithms may reveal useful tools in clinical practice to guide differential diagnosis. We collected data about clinical, laboratory and natural history of 597 patients with BMF (55 iBMF; 542 aBMF). iBMFs were typically younger (30 y, 0.1-71 vs 44 y, 2-85, p <0.001) and presented higher baseline blood counts (reticulocytes 62x10^9/L, 31-205 vs 35x10^9/L, 0-216; neutrophiles 1.4x10^9/L, 0-4.3 vs 0.6x10^9/L, 0-4.6; platelets 93x10^9/L, 7-356 vs 23x10^9/L, 1-355, p <0.001), and more cytogenetic aberrations (32% vs 13%, p 0.002). Conversely, PNH clones were detected and confirmed overtime mainly in aBMF (51% vs 9% p <0.001), with median clone size of 0.6% (range 0.02-3.8) in iBMF. By NGS analysis, no differences in acquired myeloid gene mutations incidence were detected between the groups (25% vs 13%, p 0.1). Evolution to MDS occurred in 20% of iBMF vs 11% of aBMF (p 0.04), and to AML in 3.6% vs 1.5% (p 0.2). Time between diagnosis and evolution was shorter in iBMF (2 y, 0-14 vs 5.4 y, 0.2-32 p 0.01). Survival was similar between the 2 groups with median values of 7 y (95%CI, 6.1-7.6), reaching 8 y (95%CI, 6.9-9.2) in transplanted patients. Factors significantly associated with a better survival were female gender (HR: 0.73, 95%CI: 0.59-0.90, p 0.004), BMT (HR: 0.62, 95%CI: 0.47-0.83, p 0.001), number of therapy lines (HR: 0.67, 95%CI: 0.51-0.87, p 0.003), response to 1 st line (HR: 0.53, 95%CI: 0.41-0.69, p < 0.001), and evolution to PNH (HR: 0.61, 95%CI: 0.40-0.96, p 0.03). Conversely, a lower survival was associated with older age: patients aged 31-60 had 2-fold risk of death compared to those under 30 (HR 1.8, 95%CI 1.15-2.75, p <0.001); patients over 60 had a 3.2 times higher risk compared to under 30 (HR 3.2, 95%CI 2.35-4.45, p <0.001). Presence of trilineage cytopenia also predicted poorer survival (HR: 2.92, 95%CI: 2.25-3.79, p <0.001 versus single- or bi-lineage cytopenias). No clear association with survival was found regarding karyotype and presence of mutations at myeloid NGS panel (Table 1). Finally, via a 10-folds cross-validation, we built a multivariate logistic model to predict the probability of having an iBMF. A dataset composed of 139 patients (120 aBMF and 19 iBMF) was used to train and validate the model, while subjects with missing information were excluded from the analysis. The obtained classifier was tested on an independent test set, resulting in 96% (81-100) accuracy, with 26/27 patients correctly assigned to their true class, 100% sensitivity and 96% specificity and having area under the ROC curve (AUC) equal to 0.97. Factors showing an impact on the predictive model were as follows: higher Hb levels (OR 2.35, range 0.98-5.60, p 0.05), reticulocytes (OR 1.01, range 0.99-1.04, p 0.29), platelets (OR 1.05, range 1.01-1.09, p 0.005), and erythropoietin (OR 1.0, range 1.00-1.01, p 0.009) were associated with IBMF; whilst older age (OR 0.91, range 0.84-0.99, p 0.03), normal NGS myeloid panel (OR 0.09, range 0.01-1.52, p 0.09), and the presence of a PNH clone (OR 0.46, range 0.04-4.83, p 0.52) showed a negative association (Figure 1). In conclusion, iBMF and aBMF differ in clinical presentation and risk of clonal evolution, and deserve specific treatments (IST in aBMF vs BMT and androgens in iBMF) and special considerations (i.e. choice of BMT donor and conditioning in iBMF). The proposed model includes few and routinely available variables and may be a practical tool to be implemented in the clinic, though it will require validation on an independent cohort to confirm its feasibility and prediction accuracy.

Performance of Decision-Tree-Based Ensemble Classifiers in Predicting Fog Frequency in Ungauged Areas

Abstract Fog is a phenomenon that exerts significant impacts on transportation, aviation, air quality, agriculture, and even water resources. While data-driven machine learning algorithms have shown promising performance in capturing nonlinear fog events at point locations, their applicability to different areas and time periods is questionable. This study addresses this issue by examining five decision-tree-based classifiers in a South Korean region, where diverse fog formation mechanisms are at play. The five machine learning algorithms were trained at point locations and tested with other point locations for time periods independent of the training processes. Using the ensemble classifiers and high-resolution atmospheric reanalysis data, we also attempted to establish fog occurrence maps in a regional area. Results showed that machine learning models trained on the local datasets exhibited superior performance in mountainous areas, where radiative cooling predominantly contributes to fog formation, compared to inland and coastal regions. As the fog generation mechanisms diversified, the tree-based ensemble models appeared to encounter challenges in delineating their decision boundaries. When they were trained with the reanalysis data, their predictive skills were significantly decreased, resulting in high false alarm rates. This prompted the need for postprocessing techniques to rectify overestimated fog frequency. While postprocessing may ameliorate overestimation, caution is needed to interpret the resultant fog frequency estimates, especially in regions with more diverse fog generation mechanisms. The spatial upscaling of machine learning–based fog prediction models poses challenges owing to the intricate interplay of various fog formation mechanisms, data imbalances, and potential inaccuracies in reanalysis data.

Geomechanical reservoir modelling with Thermodynamics-based Artificial Neural Networks (TANNs)

Geological subsurface storage is a promising strategy for large-scale, cost-efficient energy storage systems. Stress sensitivity has notably influence on the long-term stability and serviceability of subsurface reservoirs set for energy storage use. The evolution of the field stress of stress-sensitive reservoirs and the associated structural deformation, often a consequence of fluid production and injection, can cause significant changes to the reservoir stress-dependent properties [1]. These properties include pore and void compressibility, and porosity. For fractured and faulted reservoirs, field stress variation may also impact fracture conductivity, alter pre-existing fractures, and reactivate faults [4].
 Current geomechanical reservoir simulators incorporate the evolution of the reservoir mechanical state by means of material constitutive modelling, typically within a Finite Element Modelling (FEM) framework. In efforts to honour the heterogeneity and multi-scale nature of subsurface reservoirs, multi-scale solution methods are commonly adopted. For a reservoir multi-scale simulation, the micro-scale problem must be iteratively solved for different input parameters, render-ing the solution method computationally exhaustive, particularly for two-way coupled problems.
 The combination of machine learning-based solution methods and FEM frameworks can address the computational inefficiency of conventional multi-scale reservoir modelling schemes. In principle, a machine learning algorithm trained to learn the constitutive behaviour of a material can replace in-built material constitutive models in a FEM framework. We resort to Thermodynamics-based Artificial Neural Networks (TANNs), a physics-based data-driven machine learning algorithm introduced in [5] for material constitutive modelling. TANNs have been shown to guarantee the thermodynamic consistency of learnt material constitutive models. The first and second laws of thermodynamics are directly encoded in the architecture of TANNs through the definition of two scalar functions, an energy potential and a dissipation function, and the computation of their differentials [3].
 TANNs can be incorporated in FEM tools, in what is referred to as TANNxFEM in [6]. We present the application of the TANNxFEM framework to geomechanical reservoir modelling. This work aims to introduce a computationally efficient reservoir modelling framework, for which uncertainty quantification is possible. The proposed framework can maximise the use of information inherently contained in high-dimensional data, and significantly reduce computation demands necessary for accurate statistical evaluation, while warranting physical and geological realism.
 TANNs are first trained on analytical material data, computed by numerical integration of an incremental, thermodynamically consistent material model presented in [2]. The trained TANNs are then imported into a user material subroutine for the finite element package Abaqus. Through an input file, the parameters of the trained neural network are read as a set of material properties. The material subroutine then evaluates the incorporated trained neural network to construct the stress and elasticity tensors necessary for large-scale finite element reservoir simulation. The above proposed framework is validated against a large-scale finite element simulation with a user material subroutine implementing the constitutive model used to first generate the training data for TANNs. The free-energy, dissipation, and the stress-strain response of the two finite element simulations are compared for model verification.

Bandgap prediction of ABX3-type perovskites using Broad Learning System

Perovskite materials have attracted significant attention of scholars at home and abroad due to their excellent photoelectric properties, with the bandgap being a crucial factor that directly determines the performance of perovskite solar cells. However, density functional theory (DFT) calculations of bandgap are time-consuming and costly. To address this issue, Broad learning System (BLS), a data-driven machine learning algorithm, is proposed to predict the bandgap of hybrid organic-inorganic halide perovskites using a dataset of 227 ABX3-type perovskites with experimental bandgap values, and the stoichiometric ratio of positions A, B, X of perovskite as input features. The results demonstrate that BLS algorithm provides superior predictions compared to Support Vector Regression (SVR), Multi-Layer Perceptron (MLP) and Random Forest (RF). BLS algorithm has a lower root mean square error (RMSE), and a higher correlation coefficient (R2), with RMSE being 0.0855 eV and R2 reaching 92.24 %. This work indicates that BLS can be a powerful and potential tool for predicting the bandgap of perovskites.

An Assessment of Contemporary Methods and Data-Enabled Approaches for Early Cataract Detection

Background: Cataracts are common causes of visual impairment. Preventing blindness requires an early and accurate diagnosis. This review examines current cataract diagnosis strategies, explores data-driven machine learning algorithms for early detection, investigates the use of artificial intelligence (AI) approaches, assesses improvements in cataract detection accuracy, identifies research gaps, and provides recommendations for future studies. Methods: We gathered labelled cataract and non-cataract fundus data from the Kaggle. Scholarly publications were sourced from reliable databases such as ProQuest, IEEE, ELSEVIER, Google Scholar, and PubMed. A detailed literature search with specific terms expanded the scope of this review. We included studies that used cataract and non-cataract fundus eye images from cross-sectional, retrospective, and prospective studies. The quality assessment used the AMSTAR tool, considering factors such as literature search comprehensiveness, study selection criteria, data extraction methodologies, and study validity (Table 1). Results: This study encompassed 130 research publications, focusing on machine learning models and clinical-based diagnostic approaches for early-stage cataract identification. The performance of machine-learning models is influenced by factors such as dataset noise and limited reliable data. Barriers to the successful implementation of AI for cataract diagnosis were identified. Conclusions: This review emphasises the obstacles hindering the broad application of AI in cataract diagnosis. Addressing these findings is vital for developing strategies to overcome these challenges and enhance cataract detection systems. To achieve improved accuracy and efficiency in cataract diagnosis, future research should prioritise efforts to enhance dataset availability and quality, reduce data noise, and refine machine-learning algorithms. Unlocking the full potential of AI and/or machine learning can lead to significant breakthroughs in cataract diagnosis, ultimately resulting in better patient outcomes and reduced visual impairments.

GLCM FEATURES FOR LEARNING FLOODED VEGETATION FROM SENTINEL-1 AND SENTINEL-2 DATA

Abstract. Efforts on flood mapping from active and passive satellite Earth Observation sensors increased in the last decade especially due to the availability of free datasets from European Space Agency’s Sentinel-1 and Sentinel-2 platforms. Regular data acquisition scheme also allows observing areas prone to natural hazards with a small temporal interval (within a week). Thus, before and after datasets are often available for detecting surface changes caused by flooding. This study investigates the contribution of textural variables to the predictive performance of a data-driven machine learning algorithm for detecting the effects of a flooding caused by Sardoba Dam break in Uzbekistan. In addition to the spectral channels of Sentinel-2 and polarization bands of Sentinel-1, two spectral indices (normalized difference vegetation index and modified normalized difference water index), and textural features of gray-level co-occurrence matrix (GLCM) were used with the Random Forest. Due to high dimensionality of input variables, principal component (PC) analysis was applied to the GLCM features and only the most significant PCs were used for modeling. The feature stacks used for learning were derived from both pre- and post-event Sentinel-1 and Sentinel-2 images. The models were validated through model test measures and external reference data obtained from PlanetScope imagery. The results show that the GLCM features improve the classification of flooded areas (from 82% to 93%) and flooded vegetation (from 17% to 78%) expressed in user’s accuracy. As an outcome of the study, the use of textural features is recommended for accurate mapping of flooded areas and flooded vegetation.

A data-driven simulation-optimization framework for generating priority dispatching rules in dynamic job shop scheduling with uncertainties

Modeling and optimizing dynamic job shop scheduling problems (DJSSP) without ample assumptions is inherently challenging due to the increasing complexity and uncertainty arising from various disruptive events. Moreover, establishing job due dates in dynamic job shop environments has become increasing difficult due to unforeseen events, while it remains a pivotal factor in determining tardiness-related objectives. A research gap exists in exploring the potential synergy between the two aforementioned aspects and its resulting implications. In this study, we propose a novel framework that combines data-driven simulation, machine learning, and evolutionary algorithm to generate new Priority Dispatching Rules (PDRs) for optimizing makespan and total tardiness. The framework incorporates a data-exchange mechanism for real-time updates of the simulation model based on the shop floor. Moreover, an online due date prediction model using XGBoost is developed, replacing the previous rough estimation of the Total Work Content (TWK) rule. Simulation experiments of various scales demonstrate that XGBoost-assisted due date prediction leads to an average reduction of 1.6% in makespan and 65.0% in total tardiness compared to the TWK rule. The proposed method outperforms existing well-known dispatching rules, yielding considerable improvements with average reductions of 90.8%, 40.3%, and 44.6% in total tardiness for vary-sized experiments. Finally, the approach is validated through a real industrial case study.

A model for identifying the feeder-transformer relationship in distribution grids using a data-driven machine-learning algorithm

With the increasing demand for reliable power supply and the widespread integration of distributed energy sources, the topology of distribution networks is subject to frequent changes. Consequently, the dynamic alterations in the connection relationships between distribution transformers and feeders occur frequently, and these changes are not accurately monitored by grid companies in real-time. In this paper, we present a data-driven machine learning approach for identifying the feeder-transformer relationship in distribution networks. Initially, we preprocess the collected three-phase voltage magnitude data of distribution transformers, addressing data quality and enhancing usability through three-phase voltage normalization. Subsequently, we derive the correlation coefficient calculations between distribution transformers, as well as between distribution transformers and feeders. To tackle the challenging task of determining the correlation coefficient threshold, we propose a multi-feature fusion approach. We extracted additional features from the feeders and combined them with the correlation coefficients to create a feature matrix. Machine learning algorithms were then applied to calculate the results. Through experimentation on a real distribution network in Jiangxi province, we demonstrated the effectiveness of the proposed method. When compared to other approaches, our method achieved outstanding results with an F1 score of 0.977, indicating high precision and recall. The precision value was 0.973 and the recall value was 0.981. Importantly, our method eliminates the need for additional measurement installations, as the required data can be obtained using existing collection devices. This significantly reduces the application cost associated with implementing our approach.

An intelligent system control method based on visual sensor

In order to solve the complexity problem caused by the uncertainty of control system models, this paper utilizes visual sensors and intelligent control technology, and uses data-driven machine learning algorithms to extract representations from the original video for video representation learning, providing crucial semantic features for related tasks. Convolutional Neural Network (CNN) greatly improves the utilization efficiency of visual data and model performance, and realizes the recognition of complex application scenarios. In this paper, an intelligent system control method of time-domain vision sensor is proposed. The proposed method locate, track and measure the speed of moving objects based on CNN and image acquisition device, Asynchronous Temporal Vision Sensor (ATVS). The experimental results show that our proposed algorithm has improved its overall performance through video feature learning and clustering. It not only pays more attention to video spatial information to enhance the discrimination ability of learned video representations, such as scenes and objects, but also improves the tracking performance of visual sensors under various interference attributes.

Use of semi-synthetic data for catheter segmentation improvement.

In the era of data-driven machine learning algorithms, data is the new oil. For the most optimal results, datasets should be large, heterogeneous and, crucially, correctly labeled. However, data collection and labeling are time-consuming and labor-intensive processes. In the field of medical device segmentation, present during minimally invasive surgery, this leads to a lack of informative data. Motivated by this drawback, we developed an algorithm generating semi-synthetic images based on real ones. The concept of this algorithm is to place a randomly shaped catheter in an empty heart cavity, where the shape of the catheter is generated by forward kinematics of continuum robots. Having implemented the proposed algorithm, we generated new images of heart cavities with various artificial catheters. We compared the results of deep neural networks trained purely on real datasets with respect to networks trained on both real and semi-synthetic datasets, highlighting that semi-synthetic data improves catheter segmentation accuracy. A modified U-Net trained on combined datasets performed the segmentation with a Dice similarity coefficient of 92.6±2.2%, while the same model trained only on real images achieved a Dice similarity coefficient of 86.5±3.6%. Therefore, using semi-synthetic data allows for the decrease of accuracy spread, improves model generalization, reduces subjectivity, shortens the labeling routine, increases the number of samples, and improves the heterogeneity.

Performance Assessment of Four Data-Driven Machine Learning Models: A Case to Generate Sentinel-2 Albedo at 10 Meters

High-resolution albedo has the advantage of a higher spatial scale from tens to hundreds of meters, which can fill the gaps of albedo applications from the global scale to the regional scale and can solve problems related to land use change and ecosystems. The Sentinel-2 satellite provides high-resolution observations in the visible-to-NIR bands, giving possibilities to generate a high-resolution surface albedo at 10 m. This study attempted to evaluate the performance of the four data-driven machine learning algorithms (i.e., random forest (RF), artificial neural network (ANN), k-nearest neighbor (KNN), and XGBoost (XGBT)) for the generation of a Sentinel-2 albedo over flat and rugged terrain. First, we used the RossThick-LiSparseR model and the 3D discrete anisotropic radiative transfer (DART) model to build the narrowband surface reflectance and broadband surface albedo, which acted as the training and testing datasets over flat and rugged terrain. Second, we used the training and testing datasets to drive the four machine learning models, and evaluated the performance of these machine learning models for the generation of Sentinel-2 albedo. Finally, we used the four machine learning models to generate a Sentinel-2 albedo and compared them with in situ albedos to show the models’ application potentials. The results show that these machine learning models have great performance in estimating Sentinel-2 albedos at a 10 m spatial scale. The comparison with in situ albedos shows that the random forest model outperformed the others in estimating a high-resolution surface albedo based on Sentinel-2 datasets over the flat and rugged terrain, with an RMSE smaller than 0.0308 and R2 larger than 0.9472.