random forest
PulseAugur coverage of random forest — every cluster mentioning random forest across labs, papers, and developer communities, ranked by signal.
- 2026-05-19 research_milestone A new paper proposes a kernel-based smoothing mechanism to improve random forest regression. 来源
10 天有情绪数据
-
CNN-BiLSTM outperforms AutoML for Indonesian Twitter hate speech detection
This paper compares PyCaret AutoML and a CNN-BiLSTM model for detecting hate speech on Indonesian Twitter. The CNN-BiLSTM model achieved superior performance, with an accuracy of 83.8% and an F1-score of 81.2%, outperfo…
-
Manokhin 概率矩阵为分类器质量提供新框架
研究人员引入了 Manokhin 概率矩阵，这是一个旨在评估分类器概率预测质量的新诊断框架。该框架区分了可靠性和分辨率，将分类器分为四种原型：Eagle、Bull、Sloth 和 Mole。一项对 21 个分类器和 30 个任务进行的实证研究发现，像 CatBoost 和 Random Forest 这样的模型是 Eagles，而 XGBoost 和 LightGBM 是 Bulls，这对事后校准具有特定意义。
-
AI system detects moderate violence in public spaces using skeletal analysis
Researchers have developed a new system for detecting moderate physical violence, such as pushing, in real-time using surveillance footage. The system employs YOLO11 and YOLO11-Pose for human detection and keypoint extr…
-
Kisan AI integrates market price and disease detection for farmer profit optimization
Researchers have developed Kisan AI, a novel crop advisory system designed to enhance farmer profitability by integrating market price data alongside traditional agronomic factors. The system utilizes a Random Forest mo…
-
AI 预测汽车碰撞模拟中的数值分散度
研究人员开发了 CRADIPOR，一个旨在预测汽车碰撞模拟中数值分散度的新工具。该工具结合了秩约简自编码器 (RRAE) 和监督分类，以识别易受分散度影响的区域。由于复杂有限元碰撞模型固有的不可预测性，分散度可能会使工程决策复杂化。与随机森林基线相比，基于 RRAE 的方法表现出更优越的性能，其中基于斜率的输入表示在准确检测分散度方面显示出最大的潜力。
-
Machine learning corrects indentation size effect in steels with small datasets
Researchers have developed a data-efficient method for correcting the indentation size effect (ISE) in steels using machine learning and physics-guided augmentation. By augmenting a dataset of approximately 700 experime…
-
Indonesian students show positive sentiment towards AI in higher education
A new study analyzed Indonesian student sentiment regarding AI adoption in higher education, comparing traditional machine learning with Transformer-based deep learning models. The research utilized a dataset of 2,295 l…
-
AI enhances transport security as IoT data traffic explosion looms
A new research paper explores the use of machine learning models for intrusion detection in intelligent transport systems. The study proposes a federated hybrid intrusion detection framework that utilizes random forests…
-
Robotic fruit picking sensors analyzed for improved success rates
Researchers have developed a multimodal sensing suite for robotic fruit harvesting to improve pick success detection. The system analyzes which sensors are most informative during different stages of the picking process…
-
ABB Robotics study finds traditional ML outperforms transformers for bug localization
A new study explored using AI for fault localization in industrial software by analyzing natural-language bug reports. Researchers from ABB Robotics benchmarked traditional machine learning models against fine-tuned tra…
-
Machine learning models predict Alzheimer's drug candidates from natural compounds
Researchers have developed a machine learning approach to identify potential Alzheimer's disease treatments from natural compounds. The study utilized cheminformatics to extract molecular descriptors and trained various…
-
SeqShield uses API call sequences to detect elusive rootkits
Researchers have developed SeqShield, a novel approach for detecting rootkits on Windows systems by analyzing sequences of API calls. This behavior-based method moves beyond traditional signature detection, which strugg…
-
AI models predict at-risk students using digital learning traces
Researchers have investigated the generalizability of predictive models designed to identify at-risk students in higher education using digital learning traces. By analyzing data from undergraduate computer science cour…
-
Foundation models show promise in disease prediction and RF loss classification
Researchers have evaluated the Tabular Pre-Trained Foundation Network (TabPFN) for predicting the conversion of Mild Cognitive Impairment to Alzheimer's Disease, finding it outperforms traditional machine learning model…
-
Deep learning framework calibrates low-cost air quality sensors using LSTM
Researchers have developed a deep learning framework using Long Short-Term Memory (LSTM) networks to improve the calibration of low-cost air quality sensors. This method addresses challenges like sensor drift and enviro…