Abstract: Background: Understanding cardiovascular artery disease risk factors, the leading global cause of mortality, is crucial for influencing its etiology, prevalence, and treatment. This study aims to evaluate prognostic markers for coronary artery disease in Mashhad using Naive Bayes, REP Tree, J48, CART, and CHAID algorithms. Methods: Using data from the 2009 MASHAD STUDY, prognostic factors for coronary artery disease were determined with Naive Bayes, REP Tree, J48, CART, CHAID, and Random Forest algorithms using R 3.5.3 and WEKA 3.9.4. Model efficiency was compared by sensitivity, specificity, and accuracy. Cases were patients with coronary artery disease; each had three controls (totally 940). Results: Prognostic factors for coronary artery disease in Mashhad residents varied by algorithm. CHAID identified age, myocardial infarction history, and hypertension. CART included depression score and physical activity. REP added education level and anxiety score. NB included diabetes and family history. J48 highlighted father's heart disease and weight loss. CHAID had the highest accuracy (0.80). Conclusion: Key prognostic factors for coronary artery disease in CART and CHAID models include age, myocardial infarction history, hypertension, depression score, physical activity, and BMI. NB, REP Tree, and J48 identified numerous factors. CHAID had the highest accuracy, sensitivity, and specificity. CART offers simpler interpretation, aiding physician and paramedic model selection based on specific. Keywords: RF, Na\"ive Bayes, REP, J48 algorithms, Coronary Artery Disease (CAD).

Detecting object-level changes between two images across possibly different views is a core task in many applications that involve visual inspection or camera surveillance. Existing change-detection approaches suffer from three major limitations: (1) lack of evaluation on image pairs that contain no changes, leading to unreported false positive rates; (2) lack of correspondences (i.e., localizing the regions before and after a change); and (3) poor zero-shot generalization across different domains. To address these issues, we introduce a novel method that leverages change correspondences (a) during training to improve change detection accuracy, and (b) at test time, to minimize false positives. That is, we harness the supervision labels of where an object is added or removed to supervise change detectors, improving their accuracy over previous work by a large margin. Our work is also the first to predict correspondences between pairs of detected changes using estimated homography and the Hungarian algorithm. Our model demonstrates superior performance over existing methods, achieving state-of-the-art results in change detection and change correspondence accuracy across both in-distribution and zero-shot benchmarks.

Diagnosis of ice accretion on wind turbine blades is all the time a hard nut to crack in condition monitoring of wind farms. Existing methods focus on mechanism analysis of icing process, deviation degree analysis of feature engineering. However, there have not been deep researches of neural networks applied in this field at present. Supervisory control and data acquisition (SCADA) makes it possible to train networks through continuously providing not only operation parameters and performance parameters of wind turbines but also environmental parameters and operation modes. This paper explores the possibility that using convolutional neural networks (CNNs), generative adversarial networks (GANs) and domain adaption learning to establish intelligent diagnosis frameworks under different training scenarios. Specifically, PGANC and PGANT are proposed for sufficient and non-sufficient target wind turbine labeled data, respectively. The basic idea is that we consider a two-stage training with parallel GANs, which are aimed at capturing intrinsic features for normal and icing samples, followed by classification CNN or domain adaption module in various training cases. Model validation on three wind turbine SCADA data shows that two-stage training can effectively improve the model performance. Besides, if there is no sufficient labeled data for a target turbine, which is an extremely common phenomenon in real industrial practices, the addition of domain adaption learning makes the trained model show better performance. Overall, our proposed intelligent diagnosis frameworks can achieve more accurate detection on the same wind turbine and more generalized capability on a new wind turbine, compared with other machine learning models and conventional CNNs.

In high-dimensional regression, feature selection methods, such as sequential feature selection (SeqFS), are commonly used to identify relevant features. When data is limited, domain adaptation (DA) becomes crucial for transferring knowledge from a related source domain to a target domain, improving generalization performance. Although SeqFS after DA is an important task in machine learning, none of the existing methods can guarantee the reliability of its results. In this paper, we propose a novel method for testing the features selected by SeqFS-DA. The main advantage of the proposed method is its capability to control the false positive rate (FPR) below a significance level $\alpha$ (e.g., 0.05). Additionally, a strategic approach is introduced to enhance the statistical power of the test. Furthermore, we provide extensions of the proposed method to SeqFS with model selection criteria including AIC, BIC, and adjusted R-squared. Extensive experiments are conducted on both synthetic and real-world datasets to validate the theoretical results and demonstrate the proposed method's superior performance.

The F-measure is an important and commonly used performance metric for binary prediction tasks. By combining precision and recall into a single score, it avoids disadvantages of simple metrics like the error rate, especially in cases of imbalanced class distributions. The problem of optimizing the F-measure, that is, of developing learning algorithms that perform optimally in the sense of this measure, has recently been tackled by several authors. In this paper, we study the problem of F-measure maximization in the setting of online learning. We propose an efficient online algorithm and provide a formal analysis of its convergence properties.

Given an unsupervised novelty detection task on a new dataset, how can we automatically select a ''best'' detection model while simultaneously controlling the error rate of the best model? For novelty detection analysis, numerous detectors have been proposed to detect outliers on a new unseen dataset based on a score function trained on available clean data. However, due to the absence of labeled data for model evaluation and comparison, there is a lack of systematic approaches that are able to select a ''best'' model/detector (i.e., the algorithm as well as its hyperparameters) and achieve certain error rate control simultaneously. In this paper, we introduce a unified data-driven procedure to address this issue. The key idea is to maximize the number of detected outliers while controlling the false discovery rate (FDR) with the help of Jackknife prediction. We establish non-asymptotic bounds for the false discovery proportions and show that the proposed procedure yields valid FDR control under some mild conditions.

Classifying genome sequences based on metadata has been an active area of research in comparative genomics for decades with many important applications across the life sciences. Established methods for classifying genomes can be broadly grouped into sequence alignment-based and alignment-free models. Conventional alignment-based models rely on genome similarity measures calculated based on local sequence alignments or consistent ordering among sequences. However, such methods are computationally expensive when dealing with large ensembles of even moderately sized genomes. In contrast, alignment-free (AF) approaches measure genome similarity based on summary statistics in an unsupervised setting and are efficient enough to analyze large datasets. However, both alignment-based and AF methods typically assume fixed scoring rubrics that lack the flexibility to assign varying importance to different parts of the sequences based on prior knowledge. In this study, we integrate AI and network science approaches to develop a comparative genomic analysis framework that addresses these limitations. Our approach, termed the Genome Misclassification Network Analysis (GMNA), simultaneously leverages misclassified instances, a learned scoring rubric, and label information to classify genomes based on associated metadata and better understand potential drivers of misclassification. We evaluate the utility of the GMNA using Naive Bayes and convolutional neural network models, supplemented by additional experiments with transformer-based models, to construct SARS-CoV-2 sampling location classifiers using over 500,000 viral genome sequences and study the resulting network of misclassifications. We demonstrate the global health potential of the GMNA by leveraging the SARS-CoV-2 genome misclassification networks to investigate the role human mobility played in structuring geographic clustering of SARS-CoV-2.

Introduction: Substance use disorders (SUDs) have emerged as a pressing public health crisis in the United States, with adolescent substance use often leading to SUDs in adulthood. Effective strategies are needed to prevent this progression. To help in filling this need, we develop a novel and the first-ever absolute risk prediction model for cannabis use disorder (CUD) for adolescent or young adult cannabis users. Methods: We train a Bayesian machine learning model that provides a personalized CUD absolute risk for adolescent or young adult cannabis users using data from the National Longitudinal Study of Adolescent to Adult Health. Model performance is assessed using 5-fold cross-validation (CV) with area under the curve (AUC) and ratio of the expected to observed number of cases (E/O). External validation of the final model is conducted using two independent datasets. Results: The proposed model has five risk factors: biological sex, delinquency, and scores on personality traits of conscientiousness, neuroticism, and openness. For predicting CUD risk within five years of first cannabis use, AUC and E/O, computed via 5-fold CV, were 0.68 and 0.95, respectively. For the same type of prediction in external validation, AUC values were 0.64 and 0.75, with E/O values of 0.98 and 1, indicating good discrimination and calibration performances of the model. Discussion and Conclusion: The proposed model is the first absolute risk prediction model for an SUD. It can aid clinicians in identifying adolescent/youth substance users at a high risk of developing CUD in future for clinically appropriate interventions.

Accurate molecular quantification is essential for advancing research and diagnostics in fields such as infectious diseases, cancer biology, and genetic disorders. Droplet digital PCR (ddPCR) has emerged as a gold standard for achieving absolute quantification. While computational ddPCR technologies have advanced significantly, achieving automatic interpretation and consistent adaptability across diverse operational environments remains a challenge. To address these limitations, we introduce the intelligent interpretable droplet digital PCR (I2ddPCR) assay, a comprehensive framework integrating front-end predictive models (for droplet segmentation and classification) with GPT-4o multimodal large language model (MLLM, for context-aware explanations and recommendations) to automate and enhance ddPCR image analysis. This approach surpasses the state-of-the-art models, affording 99.05% accuracy in processing complex ddPCR images containing over 300 droplets per image with varying signal-to-noise ratios (SNRs). By combining specialized neural networks and large language models, the I2ddPCR assay offers a robust and adaptable solution for absolute molecular quantification, achieving a sensitivity capable of detecting low-abundance targets as low as 90.32 copies/{\mu}L. Furthermore, it improves model's transparency through detailed explanation and troubleshooting guidance, empowering users to make informed decisions. This innovative framework has the potential to benefit molecular diagnostics, disease research, and clinical applications, especially in resource-constrained settings.

Mapping land surface disturbances supports disaster response, resource and ecosystem management, and climate adaptation efforts. Synthetic aperture radar (SAR) is an invaluable tool for disturbance mapping, providing consistent time-series images of the ground regardless of weather or illumination conditions. Despite SAR's potential for disturbance mapping, processing SAR data to an analysis-ready format requires expertise and significant compute resources, particularly for large-scale global analysis. In October 2023, NASA's Observational Products for End-Users from Remote Sensing Analysis (OPERA) project released the near-global Radiometric Terrain Corrected SAR backscatter from Sentinel-1 (RTC-S1) dataset, providing publicly available, analysis-ready SAR imagery. In this work, we utilize this new dataset to systematically analyze land surface disturbances. As labeling SAR data is often prohibitively time-consuming, we train a self-supervised vision transformer - which requires no labels to train - on OPERA RTC-S1 data to estimate a per-pixel distribution from the set of baseline imagery and assess disturbances when there is significant deviation from the modeled distribution. To test our model's capability and generality, we evaluate three different natural disasters - which represent high-intensity, abrupt disturbances - from three different regions of the world. Across events, our approach yields high quality delineations: F1 scores exceeding 0.6 and Areas Under the Precision-Recall Curve exceeding 0.65, consistently outperforming existing SAR disturbance methods. Our findings suggest that a self-supervised vision transformer is well-suited for global disturbance mapping and can be a valuable tool for operational, near-global disturbance monitoring, particularly when labeled data does not exist.