Robust Machine Learning Algorithmic Rules for Detecting Air Pollution in the Lower Parts of the Atmosphere

Aim This study aims to enhance safety in large diameter tunnel construction by integrating robust optimization and machine learning (ML) techniques with Building Information Modeling (BIM). By acquiring and preprocessing various datasets, implementing feature engineering, and using algorithms like SVM, decision trees, ANN, and random forests, the study demonstrates the effectiveness of ML models in risk prediction and mitigation, ultimately advancing safety performance in civil engineering projects. Background Large diameter tunnel construction presents significant safety challenges. Traditional methods often fall short of effectively predicting and mitigating risks. This study addresses these gaps by integrating robust optimization and machine learning (ML) approaches with Building Information Modeling (BIM) technology. By acquiring and preprocessing diverse datasets, implementing feature engineering, and employing ML algorithms, the study aims to enhance risk prediction and safety measures in tunnel construction projects. Objective The objective of this study is to improve safety in large diameter tunnel construction by integrating robust optimization and machine learning (ML) techniques with Building Information Modeling (BIM). This involves acquiring and preprocessing diverse datasets, using feature engineering to extract key parameters, and applying ML algorithms like SVM, decision trees, ANN, and random forests to predict and mitigate risks, ultimately enhancing safety performance in civil engineering projects. Methods The study's methods include acquiring and preprocessing various datasets (geological, structural, environmental, operational, historical, and simulation). Feature engineering techniques are used to extract key safety parameters for tunnels. Machine learning algorithms, such as decision trees, support vector machines (SVM), artificial neural networks, and random forests, are employed to analyze the data and predict construction risks. The SVM algorithm, with a 98.76% accuracy, is the most reliable predictor. Results The study found that the Support Vector Machine (SVM) algorithm was the most accurate predictor of risks in large diameter tunnel construction, achieving a 98.76% accuracy rate. Other models, such as decision trees, artificial neural networks, and random forests, also performed well, validating the effectiveness of ML-based solutions for risk assessment and mitigation. These predictive models enable stakeholders to monitor construction, allocate resources, and implement preventative measures effectively. Conclusion The study concludes that integrating machine learning (ML) approaches with Building Information Modeling (BIM) significantly improves safety in large diameter tunnel construction. The Support Vector Machine (SVM) algorithm, with 98.76% accuracy, is the most reliable predictor of risks. Other models, like decision trees, artificial neural networks, and random forests, also perform well, validating ML-based solutions for risk assessment. Adopting these ML approaches enhances safety performance and resource management in civil engineering projects.

Hypertension or elevated blood pressure is a serious medical condition that significantly increases the risks of cardiovascular disease, heart disease, diabetes, stroke, kidney disease, and other health problems, that affect people worldwide. Thus, hypertension is one of the major global causes of premature death. Regarding the prevention and treatment of hypertension with no or few side effects, antihypertensive peptides (AHTPs) obtained from natural sources might be useful as nutraceuticals. Therefore, the search for alternative/novel AHTPs in food or natural sources has received much attention, as AHTPs may be functional agents for human health. AHTPs have been observed in diverse organisms, although many of them remain underinvestigated. The identification of peptides with antihypertensive activity in the laboratory is time- and resource-consuming. Alternatively, computational methods based on robust machine learning can identify or screen potential AHTP candidates prior to experimental verification. In this paper, we propose Ensemble-AHTPpred, an ensemble machine learning algorithm composed of a random forest (RF), a support vector machine (SVM), and extreme gradient boosting (XGB), with the aim of integrating diverse heterogeneous algorithms to enhance the robustness of the final predictive model. The selected feature set includes various computed features, such as various physicochemical properties, amino acid compositions (AACs), transitions, n-grams, and secondary structure-related information; these features are able to learn more information in terms of analyzing or explaining the characteristics of the predicted peptide. In addition, the tool is integrated with a newly proposed composite feature (generated based on a logistic regression function) that combines various feature aspects to enable improved AHTP characterization. Our tool, Ensemble-AHTPpred, achieved an overall accuracy above 90% on independent test data. Additionally, the approach was applied to novel experimentally validated AHTPs, obtained from recent studies, which did not overlap with the training and test datasets, and the tool could precisely predict these AHTPs.

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.

As next-generation sequencing has become an integral part of clinical lab and molecular diagnostics services, identifying true somatic variants from sequencing data is crucial for targeted treatments as well as for cancer research. Traditional, rule-based methods, offering a systemic approach to filter out noise and artifacts, often rely on domain knowledge. Additionally, variant callers such as Mutect or highly sensitive Mutect2 reports SNVs based on their internal probabilistic models can pass noise and sequencing errors as true variants, hence requiring manual inspection. Here we propose a robust ensemble approach involving a series of feature selection algorithms, combining with an ensemble of machine learning (ML) models, to identify true somatic calls from the false positive calls. This approach, in conjunction with clinical workflow, can potentially eliminate manual inspection, minimize human errors and, in turn, reduce turnaround time. A cohort of 79056 SNVs from clinical sequencing of tumor-matched normal pairs were collected and divided into 80% for training, 20% for validation. These SNVs, analyzed through MSK-IMPACT, were manually reviewed individually as part of our clinical workflow and labeled as either reported (real) or dropped (artifacts). Using the training set, we constructed an array of feature elimination and selection algorithms, cross validated, and then fine-tuned on the validation set, to yield an optimal feature combination which was then used to train a binary super-learner consisting of 12 different ML models. To maximize the predictive confidence of the ML models, each individual model was recalibrated based on the probabilities of the respective labels and calibration thresholds, and finally a confidence interval was calculated for each prediction to reflect the certainty of the classifications. Our model demonstrated 0.99 (+- 0.01) accuracy, 0.98 (+- 0.01) recall, and 0.97 (+- 0.01) precision on a set of unseen SNVs (N=7085). Of these 7085 SNVs, 5000 were classified as real, 1943 were labeled as artifact, and only 142 calls were misclassified. These 142 uncertain calls were from contaminated samples, which would trigger manual inspection, and from SNVs that were dropped from merged events, which are real somatic events. We show here that combining a multitude of feature selection techniques and an ensemble of machine learning layers optimizes detection of variant artifacts identified from sequencing data. The finale ensemble model was pitted against its constituent models, on a validation set with 5-fold cross validation, and the model demonstrated consistency in prediction and improved classification stability. In our test set, 98% of the SNVs received a correct label and therefore would be exempt from manual review. The remaining 2% would be caught by traditional rule methods (contamination and merging). It is our goal to use this framework to improve quality and efficiency of the variant review process in clinical labs, leading to a potential improved clinical workflow for diagnosis and treatment of cancer. Citation Format: YunTe David. Lin, Pallavi Akella, Anita Bowman, Erika Gedvilaite, Omkar Adhali, Scott Eckert, Angela Rose. Brannon. A robust ensemble-feature selection and machine learning approach to identify true somatic variants [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr A034.

Kernel Risk-Sensitive Loss based Hyper-graph Regularized Robust Extreme Learning Machine and Its Semi-supervised Extension for Classification

Current machine learning (ML) applications in atmospheric science focus on forecasting and bias correction for numerical modeling estimations, but few studies examined the nonlinear response of their predictions to precursor emissions. This study uses ground-level maximum daily 8-hour ozone average (MDA8 O3) as an example to examine O3 responses to local anthropogenic NOx and VOC emissions in Taiwan by Response Surface Modeling (RSM). Three different datasets for RSM were examined, including the Community Multiscale Air Quality (CMAQ) model data, ML-measurement-model fusion (ML-MMF) data, and ML data, which respectively represent direct numerical model predictions, numerical predictions adjusted by observations and other auxiliary data, and ML predictions based on observations and other auxiliary data.The results show that both ML-MMF (r = 0.93–0.94) and ML predictions (r = 0.89–0.94) present significantly improved performance in the benchmark case compared with CMAQ predictions (r = 0.41–0.80). While ML-MMF isopleths exhibit O3 nonlinearity close to actual responses due to their numerical base and observation-based correction, ML isopleths present biased predictions concerning their different controlled ranges of O3 and distorted O3 responses to NOx and VOC emission ratios compared with ML-MMF isopleths, which implies that using data without support from CMAQ modeling to predict the air quality could mislead the controlled targets and future trends. Meanwhile, the observation-corrected ML-MMF isopleths also emphasize the impact of transboundary pollution from mainland China on the regional O3 sensitivity to local NOx and VOC emissions, which transboundary NOx would make all air quality regions in April more sensitive to local VOC emissions and limit the potential effort by reducing local emissions.Future ML applications in atmospheric science like forecasting or bias correction should provide interpretability and explainability, except for meeting statistical performance and providing variable importance. Assessment with interpretable physical and chemical mechanisms and constructing a statistically robust ML model should be equally important.

BackgroundApproximately 90% of the 65,000 human diseases are infrequent, collectively affecting ~400 million people, substantially limiting cohort accrual. This low prevalence constrains the development of robust transcriptome-based machine learning (ML) classifiers. Standard data-driven classifiers typically require cohorts of more than 100 participants per group to achieve clinical accuracy while managing high-dimensional input (~25,000 transcripts). These requirements are infeasible for microcohorts of ~20 individuals, where overfitting becomes pervasive.ObjectiveTo overcome these constraints, we developed a classification method that integrates three enabling strategies: (i) paired-sample transcriptome dynamics, (ii) N-of-1 pathway-based analytics, and (iii) reproducible machine learning operations (MLOps) for continuous model refinement.MethodsUnlike ML approaches relying on a single transcriptome per subject, within-subject paired-sample designs—such as pre- versus post-treatment or diseased versus adjacent-normal tissue—effectively control intraindividual variability under isogenic conditions and within-subject environmental exposures (eg, smoking history, other medications, etc), improve signal-to-noise ratios, and, when pre-processed as single- studies (N-of-1), can achieve statistical power comparable with that obtained in animal models. Pathway-level N-of-1 analytics further reduces each sample’s high-dimensional profile into ~4000 biologically interpretable features, annotated with effect sizes, dispersion, and significance. Complementary MLOp practices—automated versioning, continuous monitoring, and adaptive hyperparameter tuning—improve model reproducibility and generalization.ResultsIn two case studies of distinct diseases, human rhinovirus infection (HRV) versus matched healthy controls (n=16 training; n=3 test) and breast cancer tissues harboring TP53 or PIK3CA mutations versus adjacent normal tissue (n=27 training; n=9 test)—this approach achieved 90% precision and recall on an unseen breast cancer test set and 92% precision with 90% recall in rhinovirus fivefold cross-validation. Incorporating paired-sample dynamics boosted precision by up to 12% and recall by 13% in breast cancer and by 5% each in HRV. MLOps workflows yielded an additional ~14.5% accuracy improvement compared to traditional pipelines. Moreover, our method identified 42 critical gene sets (pathways) for rhinovirus response and 21 for breast cancer mutation status, selected as the most important features (mean decrease impurity) of the best-performing model, with retroactive ablation of top 20 features reducing accuracy by ~25%.ConclusionsThese proof-of-concept results support the utility of integrating intrasubject dynamics, “biological knowledge”-based feature reduction (pathway-level feature reduction grounded in prior biological knowledge; eg, N-of-1-pathway analytics), and reproducible MLOp workflows can overcome cohort size limitations in infrequent disease, offering a scalable, interpretable solution for high-dimensional transcriptomic classification. Future work will extend these advances across various therapeutic and small cohort designs.

BackgroundRecently, there has been a growing interest in combining causal inference with machine learning algorithms. Double machine learning model (DML), as an implementation of this combination, has received widespread attention for their expertise in estimating causal effects within high-dimensional complex data. However, the DML model is sensitive to the presence of outliers and heavy-tailed noise in the outcome variable. In this paper, we propose the robust double machine learning (RDML) model to achieve a robust estimation of causal effects when the distribution of the outcome is contaminated by outliers or exhibits symmetrically heavy-tailed characteristics.ResultsIn the modelling of RDML model, we employed median machine learning algorithms to achieve robust predictions for the treatment and outcome variables. Subsequently, we established a median regression model for the prediction residuals. These two steps ensure robust causal effect estimation. Simulation study show that the RDML model is comparable to the existing DML model when the data follow normal distribution, while the RDML model has obvious superiority when the data follow mixed normal distribution and t-distribution, which is manifested by having a smaller RMSE. Meanwhile, we also apply the RDML model to the deoxyribonucleic acid methylation dataset from the Alzheimer’s disease (AD) neuroimaging initiative database with the aim of investigating the impact of Cerebrospinal Fluid Amyloid β\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upbeta$$\\end{document}42 (CSF Aβ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\upbeta$$\\end{document}42) on AD severity.ConclusionThese findings illustrate that the RDML model is capable of robustly estimating causal effect, even when the outcome distribution is affected by outliers or displays symmetrically heavy-tailed properties.

BACKGROUND Ninety percent of the 65,000 human diseases are infrequent, collectively affecting ~ 400 million people, substantially limiting cohort accrual. This low prevalence constrains the development of robust transcriptome-based machine learning (ML) classifiers. Standard data-driven classifiers typically require cohorts of over 100 subjects per group to achieve clinical accuracy while managing high-dimensional input (~25,000 transcripts). These requirements are infeasible for micro-cohorts of ~20 individuals, where overfitting becomes pervasive OBJECTIVE To overcome these constraints, we developed a classification method that integrates three enabling strategies: (i) paired-sample transcriptome dynamics, (ii) N-of-1 pathway-based analytics, and (iii) reproducible machine learning operations (MLOps) for continuous model refinement. METHODS Unlike ML approaches relying on a single transcriptome per subject, within-subject paired-sample designs — such as pre- versus post-treatment or diseased versus adjacent-normal tissue —effectively control intra-individual variability under isogenic conditions and within-subject environmental exposures (e.g. smoking history, other medications, etc.), improve signal-to-noise ratios, and, when pre-processed as single-subject studies (N-of-1), can achieve statistical power comparable to that obtained in animal models. Pathway-level N-of-1 analytics further reduces each sample’s high-dimensional profile into ~4,000 biologically interpretable features, annotated with effect sizes, dispersion, and significance. Complementary MLOps practices—automated versioning, continuous monitoring, and adaptive hyperparameter tuning—improve model reproducibility and generalization. RESULTS In two case studies—human rhinovirus infection versus matched healthy controls (n=16 training; 3 test) and breast cancer tissues harboring TP53 or PIK3CA mutations versus adjacent normal tissue (n=27 training; 9 test)—this approach achieved 90% precision and recall on an unseen breast cancer test set and 92% precision with 90% recall in rhinovirus fivefold cross-validation. . Incorporating paired-sample dynamics boosted precision by up to 12% and recall by 13% in BC, and by 5% each in HRV. MLOps workflows yielded an additional ~14.5% accuracy improvement compared to traditional pipelines. Moreover, our method identified 42 critical gene-sets (pathways) for rhinovirus response and 21 for breast cancer mutation status, with retroactive ablation of top features reducing accuracy by ~25%. CONCLUSIONS These proof-of-concept results support the utility of integrating intra-subject dynamics, “biological knowledge”-based feature reduction (pathway-level feature reduction grounded in prior biological knowledge; e.g., N-of-1-pathways analytics), and reproducible MLOps workflows can overcome cohort-size limitations in infrequent disease, offering a scalable, interpretable solution for high-dimensional transcriptomic classification. Future work will extend these advances across various therapeutic and small-cohort designs. CLINICALTRIAL not applicable

BackgroundNinety percent of the 65,000 human diseases are infrequent, collectively affecting ∼ 400 million people, substantially limiting cohort accrual. This low prevalence constrains the development of robust transcriptome-based machine learning (ML) classifiers. Standard data-driven classifiers typically require cohorts of over 100 subjects per group to achieve clinical accuracy while managing high-dimensional input (∼25,000 transcripts). These requirements are infeasible for micro-cohorts of ∼20 individuals, where overfitting becomes pervasive.ObjectiveTo overcome these constraints, we developed a classification method that integrates three enabling strategies: (i) paired-sample transcriptome dynamics, (ii) N-of-1 pathway-based analytics, and (iii) reproducible machine learning operations (MLOps) for continuous model refinement.MethodsUnlike ML approaches relying on a single transcriptome per subject, within-subject paired-sample designs — such as pre-versus post-treatment or diseased versus adjacent-normal tissue — effectively control intra-individual variability under isogenic conditions and within-subject environmental exposures (e.g. smoking history, other medications, etc.), improve signal-to-noise ratios, and, when pre-processed as single-subject studies (N-of-1), can achieve statistical power comparable to that obtained in animal models. Pathway-level N-of-1 analytics further reduces each sample’s high-dimensional profile into ∼4,000 biologically interpretable features, annotated with effect sizes, dispersion, and significance. Complementary MLOps practices—automated versioning, continuous monitoring, and adaptive hyperparameter tuning—improve model reproducibility and generalization.ResultsIn two case studies—human rhinovirus infection versus matched healthy controls (n=16 training; 3 test) and breast cancer tissues harboring TP53 or PIK3CA mutations versus adjacent normal tissue (n=27 training; 9 test)—this approach achieved 90% precision and recall on an unseen breast cancer test set and 92% precision with 90% recall in rhinovirus fivefold cross-validation. Incorporating paired-sample dynamics boosted precision by 8.8% and recall by 6%, while the MLOps workflow yielded additional gains of 14.5% and 12.5%, respectively. Moreover, our method identified 42 critical gene sets (pathways) for rhinovirus response and 21 for cancer mutation status.ConclusionsThese proof-of-concept results support the utility of integrating intra-subject dynamics, “biological knowledge”-based feature reduction (pathway-level feature reduction grounded in prior biological knowledge; e.g., N-of-1-pathways analytics), and reproducible MLOps workflows can overcome cohort-size limitations in infrequent disease, offering a scalable, interpretable solution for high-dimensional transcriptomic classification. Future work will extend these advances across various therapeutic and small-cohort designs.

The growing prevalence of acute lymphoblastic leukemia cancer worldwide underlines the critical need for early and more precise detection to counter this deadly disease. This study presents a robust SqueezeNet-enhanced machine learning framework for automatically screening and classifying histopathological images for acute lymphoblastic leukemia. This work employs a deep learning (DL)-based SqueezeNet integrated with three machine learning (ML) models including neural network (NN), logistic regression (LR), and random forest (RF) for diagnosis. Combining DL and ML algorithms addresses the complexity of understanding histopathological images and the classification process. Evaluation metrics computed for acute lymphoblastic leukemia reveal a good classification accuracy (CA) of 99.3%. Results are further validated by confusion matrix (CM), calibration plot (CP), receiver operating characteristic (ROC) analysis, and comparative analysis with previous techniques. The proposed method has the potential to transform healthcare with more accurate diagnosis. It provides a robust framework for the classification of acute lymphoblastic leukemia, facilitating timely treatment options for patients.

Global cybersecurity threats have grown as a result of the evolving digital transformation. Cybercriminals have more opportunities as a result of digitization. Initially, cyberthreats take the form of phishing in order to gain confidential user credentials.As cyber-attacks get more sophisticated and sophisticated, the cybersecurity industry is faced with the problem of utilising cutting-edge technology and techniques to combat the ever-present hostile threats. Hackers use phishing to persuade customers to grant them access to a company’s digital assets and networks. As technology progressed, phishing attempts became more sophisticated, necessitating the development of tools to detect phishing.Machine learning is unsupervised one of the most powerful weapons in the fight against terrorist threats. The features used for phishing detection, as well as the approaches employed with machine learning, are discussed in this study.In this light, the study’s major goal is to propose a unique, robust ensemble machine learning model architecture that gives the highest prediction accuracy with the lowest error rate, while also recommending a few alternative robust machine learning models.Finally, the Random forest algorithm attained a maximum accuracy of 96.454 percent. But by implementing a hybrid model including the 3 classifiers- Decision Trees,Random forest, Gradient boosting classifiers, the accuracy increases to 98.4 percent.

Towards a robust and trustworthy machine learning system development: An engineering perspective

Machine Learning (ML) algorithms, specifically supervised learning, are widely used in modern real-world applications, which utilize Computational Intelligence (CI) as their core technology, such as autonomous vehicles, assistive robots, and biometric systems. Attacks that cause misclassifications or mispredictions can lead to erroneous decisions resulting in unreliable operations. Designing robust ML with the ability to provide reliable results in the presence of such attacks has become a top priority in the field of adversarial machine learning. An essential characteristic for rapid development of robust ML is an arms race between attack and defense strategists. However, an important prerequisite for the arms race is access to a well-defined system model so that experiments can be repeated by independent researchers. This paper proposes a fine-grained system-driven taxonomy to specify ML applications and adversarial system models in an unambiguous manner such that independent researchers can replicate experiments and escalate the arms race to develop more evolved and robust ML applications. The paper provides taxonomies for: 1) the dataset, 2) the ML architecture, 3) the adversary's knowledge, capability, and goal, 4) adversary's strategy, and 5) the defense response. In addition, the relationships among these models and taxonomies are analyzed by proposing an adversarial machine learning cycle. The provided models and taxonomies are merged to form a comprehensive system-driven taxonomy, which represents the arms race between the ML applications and adversaries in recent years. The taxonomies encode best practices in the field and help evaluate and compare the contributions of research works and reveals gaps in the field.

Energy forecasting is crucial in scheduling and planning future electric load, so as to improve the reliability and safeness of the power grid. Despite recent developments of forecasting algorithms in the machine learning community, there is a lack of general and advanced algorithms specifically considering requirements from the power industry perspective. In this paper, we present eForecaster, a unified AI platform including robust, flexible, and explainable machine learning algorithms for diversified energy forecasting applications. Since October 2021, multiple commercial bus load, system load, and renewable energy forecasting systems built upon eForecaster have been deployed in seven provinces of China. The deployed systems consistently reduce the average Mean Absolute Error (MAE) by 39.8% to 77.0%, with reduced manual work and explainable guidance. In particular, eForecaster also integrates multiple interpretation methods to uncover the working mechanism of the predictive models, which significantly improves forecasts adoption and user satisfaction.