Can a Hebbian-like learning rule be avoiding the curse of dimensionality in sparse distributed data?

Machine learning (ML) models provide more choices to patients with diabetes mellitus (DM) to more properly manage blood glucose (BG) levels. However, because of numerous types of ML algorithms, choosing an appropriate model is vitally important. In a systematic review and network meta-analysis, this study aimed to comprehensively assess the performance of ML models in predicting BG levels. In addition, we assessed ML models used to detect and predict adverse BG (hypoglycemia) events by calculating pooled estimates of sensitivity and specificity. PubMed, Embase, Web of Science, and Institute of Electrical and Electronics Engineers Explore databases were systematically searched for studies on predicting BG levels and predicting or detecting adverse BG events using ML models, from inception to November 2022. Studies that assessed the performance of different ML models in predicting or detecting BG levels or adverse BG events of patients with DM were included. Studies with no derivation or performance metrics of ML models were excluded. The Quality Assessment of Diagnostic Accuracy Studies tool was applied to assess the quality of included studies. Primary outcomes were the relative ranking of ML models for predicting BG levels in different prediction horizons (PHs) and pooled estimates of the sensitivity and specificity of ML models in detecting or predicting adverse BG events. In total, 46 eligible studies were included for meta-analysis. Regarding ML models for predicting BG levels, the means of the absolute root mean square error (RMSE) in a PH of 15, 30, 45, and 60 minutes were 18.88 (SD 19.71), 21.40 (SD 12.56), 21.27 (SD 5.17), and 30.01 (SD 7.23) mg/dL, respectively. The neural network model (NNM) showed the highest relative performance in different PHs. Furthermore, the pooled estimates of the positive likelihood ratio and the negative likelihood ratio of ML models were 8.3 (95% CI 5.7-12.0) and 0.31 (95% CI 0.22-0.44), respectively, for predicting hypoglycemia and 2.4 (95% CI 1.6-3.7) and 0.37 (95% CI 0.29-0.46), respectively, for detecting hypoglycemia. Statistically significant high heterogeneity was detected in all subgroups, with different sources of heterogeneity. For predicting precise BG levels, the RMSE increases with a rise in the PH, and the NNM shows the highest relative performance among all the ML models. Meanwhile, current ML models have sufficient ability to predict adverse BG events, while their ability to detect adverse BG events needs to be enhanced. PROSPERO CRD42022375250; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=375250.

Accurate wind speed forecasting is essential for daily activities and socio‐economic production. This study employs an explainable machine learning (ML) model to correct wind speed forecasts from a numerical weather prediction (NWP) model over weather stations across lead times of 1–24 hr in Zhejiang Province, China. Eleven meteorological variables derived from the NWP model are used as predictors. The results demonstrate that ML model substantially improves the original NWP forecasts, reducing the root mean square error of approximately 20%–40% at most stations and about 10% at certain coastal stations. The corrected forecasts also consistently achieve higher Kling‐Gupta Efficiency scores and Forecast Accuracy across all lead times. To enhance model interpretability, we apply Split Count and SHapley Additive exPlanation analyses. The results indicate that the friction velocity (UST) is the most influential predictor and reveal a threshold beyond which the ML model's correction performance improves markedly. The ML model performance slightly declines in coastal regions, likely due to the inconsistent representation of UST between the land and water surface in the NWP model. Monthly analysis further shows superior model performance in November and December, when UST values are higher. This study underscores the value of explainable ML models in improving NWP wind forecasts and provides insights for correcting meteorological variables in NWP models.

The development of machine learning (ML) approaches in materials science offers the opportunity to exploit existing engineering and developmental alloy datasets, such as Oak Ridge National Laboratory (ORNL)’s consistently measured creep-rupture dataset for alumina-forming austenitic (AFA) alloys, to accelerate their further development. As a first step toward achieving ML insights for improved alloy design, the potential sources of uncertainty and their impacts on ML output are examined. It is observed that the selection of algorithms and features as well as data sampling significantly affects the performance of ML models, either positively or negatively. The performance of various ML models in predicting the creep properties of AFA alloys is compared, with further evaluation by assessment of a small set of new developmental AFA alloys that were not part of the training dataset. The present study demonstrates that uncertainty quantification (UQ) is essential in materials science for evaluating the performance of ML algorithms with specifically selected feature sets and obtaining a comprehensive understanding of their limitations and the resultant capability of effective prediction in complex materials systems.

Background Peripheral Arterial Disease(PAD) is a prevalent cardiovascular condition affecting millions worldwide, and the advent of machine learning(ML) techniques offers promising opportunities to enhance risk prediction and personalise patient management in this complex disease. This systematic review and meta-analysis aimed to evaluate the performance of ML models in predicting adverse outcomes in PAD patients compared to traditional statistical approaches. Methods A comprehensive search of major databases(MEDLINE,EMBASE,CINAHL,Cochrane CENTRAL) was conducted for studies published between 2000 and 2024. Two reviewers independently screened studies, extracted data, and assessed risk of bias using the PROBAST tool. The quality of included studies was also assessed using criteria outlined by Qiao et al. A modified Hierarchical Summary Receiver Operating Characteristic(HSROC) analysis was performed to compare the predictive performance of various ML models and traditional regression methods. Results Thirteen studies met the inclusion criteria. Gradient boosted models demonstrated the highest predictive performance with a diagnostic odds ratio(DOR) of 36.593(95% CI:24.44-54.79), sensitivity of 0.853, and specificity of 0.863. The best performing ML models for outcomes within 2 years, showed a DOR of 34.169(95% CI:22.840-51.117). Traditional regression models consistently underperformed compared to ML approaches, with the lowest DOR of 3.326(95% CI:1.814-6.097). The quality assessment revealed a mix of methodological rigor, with 54% of studies rated as low risk of bias and 46% as unclear. Conclusion Advanced ML techniques demonstrate superior predictive power for adverse outcomes in PAD patients compared to traditional regression methods.

SummaryAccurate prediction of the solubility of chemical substances in solvents remains a challenge. The sparsity of high-quality solubility data is recognized as the biggest hurdle in the development of robust data-driven methods for practical use. Nonetheless, the effects of the quality and quantity of data on aqueous solubility predictions have not yet been scrutinized. In this study, the roles of the size and the quality of data sets on the performances of the solubility prediction models are unraveled, and the concepts of actual and observed performances are introduced. In an effort to curtail the gap between actual and observed performances, a quality-oriented data selection method, which evaluates the quality of data and extracts the most accurate part of it through statistical validation, is designed. Applying this method on the largest publicly available solubility database and using a consensus machine learning approach, a top-performing solubility prediction model is achieved.

Understanding the Performance of Machine Learning Models to Predict Credit Default: A Novel Approach for Supervisory Evaluation

Deep learning (also known as deep structured learning, hierarchical learning, or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using a deep graph with multiple processing layers, composed of multiple linear and nonlinear transformations. Deep learning has been characterized as a class of machine learning algorithms with the following characteristics [257]: • They use a cascade of many layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The algorithms may be supervised or unsupervised and applications include pattern analysis (unsupervised) and classification (supervised). • They are based on the (unsupervised) learning of multiple levels of features or representations of the data. Higher-level features are derived from lower-level features to form a hierarchical representation. • They are part of the broader machine learning field of learning representations of data. • They learn multiple levels of representations that correspond to different levels of abstraction; the levels form a hierarchy of concepts. These definitions have in common: multiple layers of nonlinear processing units and the supervised or unsupervised learning of feature representations in each layer, with the layers forming a hierarchy from low-level to high-level features. Various deep learning architectures such as deep neural networks, convolutional deep neural networks, deep belief networks (DBN), and recurrent neural networks have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition, and bioinformatics where they have been shown to produce state-of-the-art results on various tasks. In this chapter we start in Section 7.1 with an introduction, giving a brief history of this field, the relevant literature, and its applications. Then we study some basic concepts of deep learning such as convolutional neural networks, recurrent neural networks, backpropagation algorithm, restricted Boltzmann machines, and deep learning networks in Section 7.2. Then we illustrate three examples for Apache Spark implementation for mobile big data (MBD), user moving pattern extraction, and combination with nonparametric Bayesian learning, respectively, in Sections 7.3 through 7.5. Finally, we have summary in Section 7.6.

To enhance the performance of machine learning (ML) models for the post-embolization recanalization of cerebral aneurysms, we evaluated the impact of hemodynamic feature derivation and selection method on six ML algorithms. We utilized computational fluid dynamics (CFD) to simulate hemodynamics in 66 cerebral aneurysms from 65 patients, including 57 stable and nine recanalized aneurysms. We derived a total of 107 features for each aneurysm, encompassing four clinical features, 12 morphological features, and 91 hemodynamic features. To investigate the influence of feature derivation and selection methods on the ML models, we employed two derivation methods, simplified and fully derived, in combination with four selection methods: all features, statistically significant analysis, stepwise multivariate logistic regression analysis (stepwise-LR), and recursive feature elimination (RFE). Model performance was assessed using the area under the receiver operating characteristic curve (AUROC) and precision-recall curve (AUPRC) on both the training and testing datasets. The AUROC values on the testing dataset exhibited a wide-ranging spectrum, spanning from 0.373 to 0.863. Fully derived features and the RFE selection method demonstrated superior performance in intra-model comparisons. The multi-layer perceptron (MLP) model, trained with RFE-selected fully derived features, achieved the best performance on the testing dataset, with an AUROC value of 0.863 (95% CI: 0.684- 1.000). Our study demonstrated the importance of feature derivation and selection in determining the performance of ML models. This enabled the development of accurate decision-making models without the need to invade the patient.

MP07-06 EVALUATING THE EFFICACY OF ARTIFICIAL INTELLIGENCE IN PREDICTING OUTCOMES FOR PEDIATRIC HYDRONEPHROSIS: A SYSTEMATIC REVIEW

Understanding the BRAF alterations preoperatively could remarkably assist in predicting tumor behavior, which leads to a more precise prognostication and management strategy. Recent advances in artificial intelligence (AI) have resulted in effective predictive models. Therefore, for the first time, this study aimed to review the performance of machine learning and deep learning models in predicting the BRAF alterations in low-grade gliomas (LGGs)using imaging data. PubMed/MEDLINE, Embase, and the Cochrane Library were systematically searched for studies published up to June 1, 2024, and evaluated the performance of AI models in predicting BRAF alterations in LGGs using imaging data. Pooled sensitivity, specificity, and area under the receiver operating curve (AUROC) were meta-analyzed. A total of 6 studies with 951 patients were included in this systematic review. The pooled AUROC of internal validation cohorts for their best-performing model was 84.44%, with models detecting BRAF mutation, BRAF fusion, BRAF fusion from mutation, and BRAF wild type producing similar AUROCs of 90.75%, 84.59%, 82.33%, and 82%, respectively. The best-performing models had pooled sensitivities of 80.3%, 87.51%, and 74.14% and pooled specificities of 88.57%, 70.41%, and 83.98% for detection of BRAF fusion from mutation, BRAF fusion, and BRAF mutation, respectively. AI models may perform relatively well in predicting BRAF alterations in LGG using imaging data and appear to be capable of high sensitivities and specificities. However, future studies with larger sample sizes implementing different machine learning or deep learning algorithms are required to reduce imprecision.

Optimizing asphalt mix design through predicting effective asphalt content and absorbed asphalt content using machine learning

PurposeThe purpose of this paper is to propose an approach to incorporate contextual information into collaborative filtering (CF) based on the restricted Boltzmann machine (RBM) and deep belief networks (DBNs). Traditionally, neither the RBM nor its derivative model has been applied to modeling contextual information. In this work, the authors analyze the RBM and explore how to utilize a user’s occupation information to enhance recommendation accuracy.Design/methodology/approachThe proposed approach is based on the RBM. The authors employ user occupation information as a context to design a context-aware RBM and stack the context-aware RBM to construct DBNs for recommendations.FindingsThe experiments on the MovieLens data sets show that the user occupation-aware RBM outperforms other CF models, and combinations of different context-aware models by mutual information can obtain better accuracy. Moreover, the context-aware DBNs model is superior to baseline methods, indicating that deep networks have more qualifications for extracting preference features.Originality/valueTo improve recommendation accuracy through modeling contextual information, the authors propose context-aware CF approaches based on the RBM. Additionally, the authors attempt to introduce hybrid weights based on information entropy to combine context-aware models. Furthermore, the authors stack the RBM to construct a context-aware multilayer network model. The results of the experiments not only convey that the context-aware RBM has potential in terms of contextual information but also demonstrate that the combination method, the hybrid recommendation and the multilayer neural network extension have significant benefits for the recommendation quality.

Hidden-layer visible deep stacking network optimized by PSO for motor imagery EEG recognition

In recent years, infections and damage caused by malware have increased at exponential rates. At the same time, machine learning (ML) techniques have shown tremendous promise in many domains, often out performing human efforts by learning from large amounts of data. Results in the open literature suggest that ML is able to provide similar results for malware detection, achieving greater than 99% classification accuracy [49]. However, the same detection rates when applied in deployed settings have not been achieved. Malware is distinct from many other domains in which ML has shown success in that (1) it purposefully tries to hide, leading to noisy labels and (2) often its behavior is similar to benign software only differing in intent, among other complicating factors. This report details the reasons for the difficultly of detecting novel malware by ML methods and offers solutions to improve the detection of novel malware. We propose to detect malware by detecting behaviors commonly exhibited by malware such as DLL injection, and process hollowing. This is based on the assumption that there is a set of behaviors that are common to most malware samples and detecting them will generalize to novel malware. Additionally, detected behaviors point analysts toward appropriate handling and mitigation strategies, which is not the case with a binary benign/malicious classification. A behavior labeling method was developed and was used to label an existing malware dataset. Results show that detecting malicious behaviors is much more difficult than simply classifying malware and goodware?achieving 80% accuracy compared to reported 99% accuracy from classifying malware and goodware. This drop is due to several reasons which are detailed in the report. We also propose to evaluate the performance of detecting novel malware by holding out a malware family for testing and training on the other families. Traditional ML evaluation will shuffle the data and then split the data into training and testing. Our method addresses the use-case when novel malware families are encountered and they require more than just a malicious or benign designation. Our results suggest that this type of evaluation is much more difficult than traditional methods and provides more realistic results, albeit, significantly worse. For our behavior detection, accuracy decreases from 80% to 68% across all behaviors when holding out a malware family from training. We show that the degradation in performance is because each malware family has distinct characteristics resulting in high extrapolations by an ML model. Here, an ML model should return an "I do not know" response and request further analysis from an analyst. We run a number of experiments that compare novel malware families to the training data using different feature representations including a genomics-inspired distance measure and features extracted by deep learning. Generally, held-out families are significantly different from the training data, resulting in unpredictable results. This has been observed generally in the ML community [22, 9]. We empirically demonstrate this in the domain of malware detection. In an attempt to improve the detection of malware behaviors, we examine the impact that additional synthetic data has on the performance of an ML model in detecting behaviors in novel malware families. We find that while synthetic data does improve the performance of ML models, often simpler methods perform better than more complicated ones. Two generative modeling techniques were examined to produce synthetic malware samples such that the behaviors present are able to be specified externally. The difficulty is due to finer grained analysis of the executable and modifying the problem from a binary classification problem to a multi-label problem. The addition of synthetic data increases the overall accuracy from 68% to 70%. While far less accurate than measures presented in academic analyses, we believe that this is more representative of real-world performance and allows models to be properly placed within a malware detection system. We suggest that in highly dynamic environments ML pipelines should determine whether an ML model is competent in the area of new data and should involve mechanisms to improve over time with a human in the loop.

Ground truthing from multi-rater labeling with three-way decision and possibility theory