Tootfinder

Random Forest Stratified K-Fold Cross Validation on SYN DoS Attack SD-IoV
Muhammad Arif Hakimi Zamrai, Kamaludin Mohd Yusof
https://arxiv.org/abs/2509.07016 https://

Random Forest Stratified K-Fold Cross Validation on SYN DoS Attack SD-IoV
In response to the prevalent concern of TCP SYN flood attacks within the context of Software-Defined Internet of Vehicles (SD-IoV), this study addresses the significant challenge of network security in rapidly evolving vehicular communication systems. This research focuses on optimizing a Random Forest Classifier model to achieve maximum accuracy and minimal detection time, thereby enhancing vehicular network security. The methodology involves preprocessing a dataset containing SYN attack insta…

Out-of-bag prediction balls for random forests in metric spaces
Diego Serrano, Eduardo Garc\'ia-Portugu\'es
https://arxiv.org/abs/2510.04299 https://

Out-of-bag prediction balls for random forests in metric spaces
Statistical methods for metric spaces provide a general and versatile framework for analyzing complex data types. We introduce a novel approach for constructing confidence regions around new predictions from any bagged regression algorithm with metric-space-valued responses. This includes the recent extensions of random forests for metric responses: Fréchet random forests (Capitaine et al., 2024), random forest weighted local constant Fréchet regression (Qiu et al., 2024), and metric random f…

Decade-long Emission Forecasting with an Ensemble Model in Taiwan
Gordon Hung, Salinna Abdullah
https://arxiv.org/abs/2510.05548 https://arxiv.org/pdf/2510…

Decade-long Emission Forecasting with an Ensemble Model in Taiwan
Taiwan's high population and heavy dependence on fossil fuels have led to severe air pollution, with the most prevalent greenhouse gas being carbon dioxide (CO2). There-fore, this study presents a reproducible and comprehensive case study comparing 21 of the most commonly employed time series models in forecasting emissions, analyzing both univariate and multivariate approaches. Among these, Feedforward Neural Network (FFNN), Support Vector Machine (SVM), and Random Forest Regressor (RFR) achie…

Artificial Intelligence for Cost-Aware Resource Prediction in Big Data Pipelines
Harshit Goyal
https://arxiv.org/abs/2510.05127 https://arxiv.org/pdf/2510.…

Artificial Intelligence for Cost-Aware Resource Prediction in Big Data Pipelines
Efficient resource allocation is a key challenge in modern cloud computing. Over-provisioning leads to unnecessary costs, while under-provisioning risks performance degradation and SLA violations. This work presents an artificial intelligence approach to predict resource utilization in big data pipelines using Random Forest regression. We preprocess the Google Borg cluster traces to clean, transform, and extract relevant features (CPU, memory, usage distributions). The model achieves high predi…

GS-BART: Bayesian Additive Regression Trees with Graph-split Decision Rules
Shuren He, Huiyan Sang, Quan Zhou
https://arxiv.org/abs/2509.07166 https://arxi…

GS-BART: Bayesian Additive Regression Trees with Graph-split Decision Rules
Ensemble decision tree methods such as XGBoost, Random Forest, and Bayesian Additive Regression Trees (BART) have gained enormous popularity in data science for their superior performance in machine learning regression and classification tasks. In this paper, we introduce a new Bayesian graph-split additive decision tree method, GS-BART, designed to enhance the performance of axis-parallel split-based BART for dependent data with graph structures. The proposed approach encodes input feature inf…

Low Recourse Arborescence Forests Under Uniformly Random Arcs
J Niklas Dahlmeier, D Ellis Hershkowitz
https://arxiv.org/abs/2510.02950 https://arxiv.org/pd…

Low Recourse Arborescence Forests Under Uniformly Random Arcs
In this work, we study how to maintain a forest of arborescences of maximum arc cardinality under arc insertions while minimizing recourse -- the total number of arcs changed in the maintained solution. This problem is the "arborescence version'' of max cardinality matching. On the impossibility side, we observe that even in this insertion-only model, it is possible for $m$ adversarial arc arrivals to necessarily incur $Ω(m \cdot n)$ recourse, matching a trivial upper bound of $O(m \cdot n)$…

Random forest-based out-of-distribution detection for robust lung cancer segmentation
Aneesh Rangnekar, Harini Veeraraghavan
https://arxiv.org/abs/2508.19112 https://

Random forest-based out-of-distribution detection for robust lung cancer segmentation
Accurate detection and segmentation of cancerous lesions from computed tomography (CT) scans is essential for automated treatment planning and cancer treatment response assessment. Transformer-based models with self-supervised pretraining can produce reliably accurate segmentation from in-distribution (ID) data but degrade when applied to out-of-distribution (OOD) datasets. We address this challenge with RF-Deep, a random forest classifier that utilizes deep features from a pretrained transform…

Astrophotometric search for massive stars in the Milky way. Confronting Random Forest predictions with available spectroscopy
N. Monsalves, A. Bayo, M. Jaque Arancibia, J. Bodensteiner, A. G. Caneppa, P. S\'anchez-S\'aez, R. Angeloni
https://arxiv.org/abs/2508.21573

Astrophotometric search for massive stars in the Milky way. Confronting Random Forest predictions with available spectroscopy
Massive stars play a significant role in different branches of astronomy, from shaping the processes of star and planet formation to influencing the evolution and chemical enrichment of the distant universe. Despite their high astrophysical significance, these objects are rare and difficult to detect. With Gaia's advent, we now possess extensive kinematic and photometric data for a significant portion of the Galaxy that can unveil, among others, new populations of massive star candidates. In or…

Comparison of Gaussian process regression, partial least squares, random forest and support vector machines for a near infrared calibration of paracetamol samples
Aminata Sow, Issiaka Traore, Tidiane Diallo, Mohamed Traore, Abdramane Ba
https://arxiv.org/abs/2510.01064

Comparison of Gaussian process regression, partial least squares, random forest and support vector machines for a near infrared calibration of paracetamol samples
In this article, we analyze the near-infrared (NIR) spectra of fifty-eight (58) commercial tablets of 500 mg of paracetamol from different origins (that is, with different batch numbers) in the local markets in Bamako. The NIR spectra were recorded in the spectral range 930 nm-1700 nm. The samples are divided into forty-eight (48) samples forming the set of calibration (training set) and ten (10) samples used as the validation or test set. To perform multivariate calibration, we apply-three non…

Solar and Wind Power Forecasting: A Comparative Review of LSTM, Random Forest, and XGBoost Models
Afsaneh Mollasalehi, Armin Farhadi
https://arxiv.org/abs/2509.24059 https://

Solar and Wind Power Forecasting: A Comparative Review of LSTM, Random Forest, and XGBoost Models
Rising global energy demand from population growth raises concerns about the sustainability of fossil fuels. Consequently, the energy sector has increasingly transitioned to renewable energy sources like solar and wind, which are naturally abundant. However, the periodic and unpredictable nature of these resources pose significant challenges for power system reliability. Accurate forecasting is essential to ensure grid stability and optimize energy management. But due to the high variability in…

Hybrid Physics-ML Framework for Pan-Arctic Permafrost Infrastructure Risk at Record 2.9-Million Observation Scale
Boris Kriuk
https://arxiv.org/abs/2510.02189 https://

Hybrid Physics-ML Framework for Pan-Arctic Permafrost Infrastructure Risk at Record 2.9-Million Observation Scale
Arctic warming threatens over 100 billion in permafrost-dependent infrastructure across Northern territories, yet existing risk assessment frameworks lack spatiotemporal validation, uncertainty quantification, and operational decision-support capabilities. We present a hybrid physics-machine learning framework integrating 2.9 million observations from 171,605 locations (2005-2021) combining permafrost fraction data with climate reanalysis. Our stacked ensemble model (Random Forest + Histogram G…

Adaptive Deception Framework with Behavioral Analysis for Enhanced Cybersecurity Defense
Basil Abdullah AL-Zahrani
https://arxiv.org/abs/2510.02424 https://

Adaptive Deception Framework with Behavioral Analysis for Enhanced Cybersecurity Defense
This paper presents CADL (Cognitive-Adaptive Deception Layer), an adaptive deception framework achieving 99.88% detection rate with 0.13% false positive rate on the CICIDS2017 dataset. The framework employs ensemble machine learning (Random Forest, XGBoost, Neural Networks) combined with behavioral profiling to identify and adapt responses to network intrusions. Through a coordinated signal bus architecture, security components share real-time intelligence, enabling collective decision-making. …

Limitations of Physics-Informed Neural Networks: a Study on Smart Grid Surrogation
Julen Cestero, Carmine Delle Femine, Kenji S. Muro, Marco Quartulli, Marcello Restelli
https://arxiv.org/abs/2508.21559

Limitations of Physics-Informed Neural Networks: a Study on Smart Grid Surrogation
Physics-Informed Neural Networks (PINNs) present a transformative approach for smart grid modeling by integrating physical laws directly into learning frameworks, addressing critical challenges of data scarcity and physical consistency in conventional data-driven methods. This paper evaluates PINNs' capabilities as surrogate models for smart grid dynamics, comparing their performance against XGBoost, Random Forest, and Linear Regression across three key experiments: interpolation, cross-validat…

Estimating volumetric water content from electrical resistivity using a random forest model
Constantin Schorling
https://arxiv.org/abs/2508.16593 https://a…

Estimating volumetric water content from electrical resistivity using a random forest model
Background: Accurately estimating volumetric water content (VWC) can greatly enhance the prediction of landslide risk. The standard approach involves using locally limited, invasive sensor measurements. Recently, however, electrical resistivity tomography (ERT) has emerged as a cost-effective, minimally invasive, real-time, indirect method of monitoring VWC. However, linking ER to VWC poses distinct challenges. Purpose: Random forest models were developed to estimate and predict VWC from ER. Sp…

A Hybrid Framework for Healing Semigroups with Machine Learning
Sarayu Sirikonda, Jasper van de Kreeke
https://arxiv.org/abs/2509.01763 https://arxiv.org/p…

A Hybrid Framework for Healing Semigroups with Machine Learning
In this paper, we propose a hybrid framework that heals corrupted finite semigroups, combining deterministic repair strategies with Machine Learning using a Random Forest Classifier. Corruption in these tables breaks associativity and invalidates the algebraic structure. Deterministic methods work for small cardinality n and low corruption but degrade rapidly. Our experiments, carried out on Mace4-generated data sets, demonstrate that our hybrid framework achieves higher healing rates than dete…

Crosslisted article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[2/2]:
- Random forest-based out-of-distribution detection for robust lung cancer segmentation
Aneesh Rangnekar, Harini Veeraraghavan

Random Forest Classification of MBTA Gravitational-Wave Triggers for Low-Latency Detection
Lorenzo Mobilia, Gianluca Maria Guidi
https://arxiv.org/abs/2509.12882 https://…

Random Forest Classification of MBTA Gravitational-Wave Triggers for Low-Latency Detection
In this work, we explore a possible application of a machine learning classifier for candidate events in a template-based search for gravitational-wave (GW) signals from various compact system sources. We analyze data from the O3a and O3b data acquisition campaign, during which the sensitivity of ground-based detectors is limited by real non-Gaussian noise transient. The state-of-the-art searches for such signals tipically rely on the signal-to-noise ratio (SNR) and a chi-square test to assess …

A Hybrid Approach for Unified Image Quality Assessment: Permutation Entropy-Based Features Fused with Random Forest for Natural-Scene and Screen-Content Images for Cross-Content Applications
Mohtashim Baqar, Sian Lun Lau, Mansoor Ebrahim
https://arxiv.org/abs/2508.17351

A Hybrid Approach for Unified Image Quality Assessment: Permutation Entropy-Based Features Fused with Random Forest for Natural-Scene and Screen-Content Images for Cross-Content Applications
Image Quality Assessment (IQA) plays a vital role in applications such as image compression, restoration, and multimedia streaming. However, existing metrics often struggle to generalize across diverse image types - particularly between natural-scene images (NSIs) and screen-content images (SCIs) - due to their differing structural and perceptual characteristics. To address this limitation, we propose a novel full-reference IQA framework: Permutation Entropy-based Features Fused with Random For…

Interpretable Network-assisted Random Forest
Tiffany M. Tang, Elizaveta Levina, Ji Zhu
https://arxiv.org/abs/2509.15611 https://arxiv.org/pdf/2509.15611…

Interpretable Network-assisted Random Forest+
Machine learning algorithms often assume that training samples are independent. When data points are connected by a network, the induced dependency between samples is both a challenge, reducing effective sample size, and an opportunity to improve prediction by leveraging information from network neighbors. Multiple methods taking advantage of this opportunity are now available, but many, including graph neural networks, are not easily interpretable, limiting their usefulness for understanding h…

2/2 I continued blogging Alberniweather and on FB and Twitter but I gradually removed my personal self from Facebook and eventually during the Pandemic, I decided the Facebook environment was just too toxic even for weather stuff and I shut down my page and left Facebook completely.
The impact on traffic to Alberniweather.ca and its prominence in the community was, and still is, significant.
I have diehard followers, many who have become friends over the years, I still get the odd call from media, or even the public about random weather things.
I have good connections with a few folks at Environment Canada (though their staff have become thinner and more transient :(
and major events still get spikes of local traffic but I since about 2022, and after I removed myself from Twitter that year, I don’t blog nearly as much. I would do a few posts in a week, and then go months without posting. I just got out of the habit I guess.
But I am still interested in the weather. I still feel like Alberniweather is a useful service for people in my community. I still feel a willing obligation to inform people about the weather and I believe I am trusted to do so by the public and local leaders. I’ve never made any money at it, I sold ad space on the website for a few years but it wasn’t worth the hassle and I didn’t feel comfortable taking the money when I was councillor. I have had some generous spontaneous donations at times.
But mainly I do it because it’s interesting, and I hope it is useful for people especially when people are looking for information during a major event.
The highest traffic I have ever had on Alberniweather pre-FB exit was the local Dog Mountain forest fire in 2015.
post-FB exit: the #underwoodfire
People want easy access to reliable local, trusted, information.
Large media orgs have mostly given up on this.
I am grateful we still have an active local newspaper and radio and that both trust me and I trust them.
@… @…

Kingman's coalescent on a random graph
Louigi Addario-Berry, Caelan Atamanchuk, Maxwell Kaye
https://arxiv.org/abs/2509.16181 https://arxiv.org/pdf/250…

Kingman's coalescent on a random graph
We introduce a generalization of Kingman's coalescent on $[n]$ that we call the Kingman coalescent on a graph $G = ([n],E)$. Specifically, we generalize a forest valued representation of the coalescent introduced in Addario-Berry and Eslava (2018). The difference between the Kingman coalescent on $G$ and the normal Kingman coalescent on $[n]$ is that two trees $T_1,T_2$ with roots $ρ_1,ρ_2$ can merge if and only if $\{ρ_1,ρ_2\} \in E$. When this process finishes (when there are no trees l…

A U-Statistic-based random forest approach for genetic interaction study
Ming Li, Ruo-Sin Peng, Changshuai Wei, Qing Lu
https://arxiv.org/abs/2508.14924 https://

A U-Statistic-based random forest approach for genetic interaction study
Variations in complex traits are influenced by multiple genetic variants, environmental risk factors, and their interactions. Though substantial progress has been made in identifying single genetic variants associated with complex traits, detecting the gene-gene and gene-environment interactions remains a great challenge. When a large number of genetic variants and environmental risk factors are involved, searching for interactions is limited to pair-wise interactions due to the exponentially i…

Team Westwood Solution for MIDOG 2025 Challenge
Tengyou Xu, Haochen Yang, Xiang 'Anthony' Chen, Hongyan Gu, Mohammad Haeri
https://arxiv.org/abs/2509.02600 https://

Team Westwood Solution for MIDOG 2025 Challenge
This abstract presents our solution (Team Westwood) for mitosis detection and atypical mitosis classification in the MItosis DOmain Generalization (MIDOG) 2025 challenge. For mitosis detection, we trained an nnUNetV2 for initial mitosis candidate screening with high sensitivity, followed by a random forest classifier ensembling predictions of three convolutional neural networks (CNNs): EfficientNet-b3, EfficientNet-b5, and EfficientNetV2-s. For the atypical mitosis classification, we trained an…

Both Stellar Mass and Gravitational Potential Shape the Gas-Phase Metallicity
Maria Koller, Roberto Maiolino, William M. Baker
https://arxiv.org/abs/2509.18961 https://

Both Stellar Mass and Gravitational Potential Shape the Gas-Phase Metallicity
The relation between metallicity and galaxy mass (the so-called mass-metallicity relation) is the strongest and most prominent among scaling relations between chemical enrichment and galactic properties. However, it is unclear whether this relation primarily traces metal retention or the integrated production of metals, as past studies have obtained contrasting results. We investigate this issue through an extensive Random Forest and Partial Correlations analysis of spectral cubes of 4,500 gala…

Multifractal features of multimodal cardiac signals: Nonlinear dynamics of exercise recovery
A. Maluckov, D. Stojanovic, M. Miletic, Lj. Hadzievski, J. Petrovic
https://arxiv.org/abs/2509.23317

Multifractal features of multimodal cardiac signals: Nonlinear dynamics of exercise recovery
We investigate the recovery dynamics of healthy cardiac activity after physical exertion using multimodal biosignals recorded with a polycardiograph. Multifractal features derived from the singularity spectrum capture the scale-invariant properties of cardiovascular regulation. Five supervised classification algorithms - Logistic Regression (LogReg), Suport Vector Machine with RBF kernel (SVM-RBF), k-Nearest Neighbors (kNN), Decision Tree (DT), and Random Forest (RF) - were evaluated to disting…

Enhanced Predictive Modeling for Hazardous Near-Earth Object Detection: A Comparative Analysis of Advanced Resampling Strategies and Machine Learning Algorithms in Planetary Risk Assessment
Sunkalp Chandra
https://arxiv.org/abs/2508.15106

Enhanced Predictive Modeling for Hazardous Near-Earth Object Detection: A Comparative Analysis of Advanced Resampling Strategies and Machine Learning Algorithms in Planetary Risk Assessment
This study evaluates the performance of several machine learning models for predicting hazardous near-Earth objects (NEOs) through a binary classification framework, including data scaling, power transformation, and cross-validation. Six classifiers were compared, namely Random Forest Classifier (RFC), Gradient Boosting Classifier (GBC), Support Vector Classifier (SVC), Linear Discriminant Analysis (LDA), Logistic Regression (LR), and K-Nearest Neighbors (KNN). RFC and GBC performed the best, b…

Generalized random forest for extreme quantile regression
Lucien M. Vidagbandji, Alexandre Berred, Cyrille Bertelle, Laurent Amanton
https://arxiv.org/abs/2508.15095 https://

Generalized random forest for extreme quantile regression
Quantile regression is a statistical method which, unlike classical regression, aims to predict the conditional quantiles. Classical quantile regression methods face difficulties, particularly when the quantile under consideration is extreme, due to the limited number of data available in the tail of the distribution, or when the quantile function is complex. We propose an extreme quantile regression method based on extreme value theory and statistical learning to overcome these difficulties. F…

Crosslisted article(s) found for cs.DB. https://arxiv.org/list/cs.DB/new
[1/1]:
- RFOD: Random Forest-based Outlier Detection for Tabular Data
Yihao Ang, Peicheng Yao, Yifan Bao, Yushuo Feng, Qiang Huang, Anthony K. H. Tung, Zhiyong Huang

Descriptor and Graph-based Molecular Representations in Prediction of Copolymer Properties Using Machine Learning
Elaheh Kazemi-Khasragh, Roc\'io Mercado, Carlos Gonzalez, Maciej Haranczyk
https://arxiv.org/abs/2509.11874

Descriptor and Graph-based Molecular Representations in Prediction of Copolymer Properties Using Machine Learning
Copolymers are highly versatile materials with a vast range of possible chemical compositions. By using computational methods for property prediction, the design of copolymers can be accelerated, allowing for the prioritization of candidates with favorable properties. In this study, we utilized two distinct representations of molecular ensembles to predict the seven different physical polymer properties copolymers using machine learning: we used a random forest (RF) model to predict polymer pro…

Automotive Sound Quality for EVs: Psychoacoustic Metrics with Reproducible AI/ML Baselines
Mandip Goswami
https://arxiv.org/abs/2509.16901 https://arxiv.or…

Automotive Sound Quality for EVs: Psychoacoustic Metrics with Reproducible AI/ML Baselines
We present an open, reproducible reference for automotive sound quality that connects standardized psychoacoustic metrics with lightweight AI/ML baselines, with a specific focus on electric vehicles (EVs). We implement loudness (ISO 532-1/2), tonality (DIN 45681), and modulation-based descriptors (roughness, fluctuation strength), and document assumptions and parameterizations for reliable reuse. For modeling, we provide simple, fully reproducible baselines (logistic regression, random forest, …

Asymptotic Consistency and Generalization in Hybrid Models of Regularized Selection and Nonlinear Learning
Luciano Ribeiro Galv\~ao, Rafael de Andrade Mora
https://arxiv.org/abs/2508.07754

Asymptotic Consistency and Generalization in Hybrid Models of Regularized Selection and Nonlinear Learning
This study explores how different types of supervised models perform in the task of predicting and selecting relevant variables in high-dimensional contexts, especially when the data is very noisy. We analyzed three approaches: regularized models (such as Lasso, Ridge, and Elastic Net), black-box models (such as Random Forest, XGBoost, LightGBM, CatBoost, and H2O GBM), and hybrid models that combine both approaches: regularization with nonlinear algorithms. Based on simulations inspired by the …

Machine Learning for Exoplanet Detection: A Comparative Analysis Using Kepler Data
Reihaneh Karimi, Mahdiyar Mousavi-Sadr, Mohammad H. Zhoolideh Haghighi, Fatemeh S. Tabatabaei
https://arxiv.org/abs/2508.09689

Machine Learning for Exoplanet Detection: A Comparative Analysis Using Kepler Data
The discovery of exoplanets has expanded our understanding of planetary systems and opened new avenues for astronomical research. In this study, we present a machine learning (ML) framework for exoplanet identification using a time-series photometric dataset from the Kepler Space Telescope, comprising 3,198 flux measurements across 5,074 stars. We investigate the performance of four supervised classification algorithms, namely Random Forest, k-Nearest Neighbors (KNN), Decision Tree, and Logisti…

Crosslisted article(s) found for stat.ME. https://arxiv.org/list/stat.ME/new
[1/1]:
- A U-Statistic-based random forest approach for genetic interaction study
Ming Li, Ruo-Sin Peng, Changshuai Wei, Qing Lu

Benchmarking Classical, Machine Learning, and Bayesian Survival Models for Clinical Prediction
Irving G\'omez-M\'endez, Sivakorn Phromsiri, Ittiphat Kijpaisansak, Settawut Chaithurdthum
https://arxiv.org/abs/2509.10073

Benchmarking Classical, Machine Learning, and Bayesian Survival Models for Clinical Prediction
Survival analysis is a statistical framework for modeling time-to-event data, particularly valuable in healthcare for predicting outcomes like patient discharge or recurrence. This study implements and compares several survival models - including Weibull, Weibull AFT, Weibull AFT with Gamma Frailty, Cox Proportional Hazards (CoxPH), Random Survival Forest (RSF), and DeepSurv - using a publicly available breast cancer dataset. This study aims to benchmark classical, machine learning, and Bayesia…