machine learning

790 items

RESEARCHarXiv CS.LG·4/23/2026

A Multi-Plant Machine Learning Framework for Emission Prediction, Forecasting, and Control in Cement Manufacturing

This study develops a machine learning framework to predict, forecast, and control NOx emissions in cement manufacturing, a major source of industrial air pollution. The framework utilizes large-scale operational data from multiple plants, significantly improving prediction accuracy and enabling proactive operational adjustments to mitigate pollution.

machine learning industrial automation Environmental monitoring Predictive Analytics

RESEARCHarXiv CS.CL·4/23/2026

Hybrid Multi-Phase Page Matching and Multi-Layer Diff Detection for Japanese Building Permit Document Review

This research presents a hybrid multi-phase page matching algorithm for automating the comparison of complex Japanese building permit document sets, which is currently a labor-intensive and error-prone manual process. The algorithm robustly pairs pages across revisions using structural alignment and dynamic programming, then applies a multi-layer diff engine to produce detailed difference reports with high accuracy.

machine learning computer vision document processing automation

RESEARCHarXiv CS.CL·20d ago

Long-Context Reasoning Through Proxy-Based Chain-of-Thought Tuning

Large language models struggle with complex long-context reasoning tasks despite supporting extensive inputs. ProxyCoT is a novel training framework designed to transfer reasoning capabilities from short proxy contexts to full long contexts, outperforming strong baselines.

machine learning Natural Language Processing Reasoning large language models

ARTICLEDEV.to AI·4/20/2026

Node.js vs Python for AI Backends in 2026: A Practical Decision Guide

This guide analyzes Node.js vs Python for AI backends in 2026, recommending Python for core AI/ML processing and Node.js for API orchestration. It emphasizes a hybrid approach and notes Python's significant ecosystem advantage in AI/ML tools.

machine learning backend development Node.js Python

RESEARCHarXiv CS.AI·28d ago

PIVOT: Bridging Planning and Execution in LLM Agents via Trajectory Refinement

PIVOT (Plan-Inspect-eVOlve Trajectories) addresses plan-execution misalignment in LLM agents using a self-supervised framework. It iteratively refines trajectories through environment interaction, demonstrating state-of-the-art performance in empirical evaluations.

LLM agents self-supervised learning Trajectory optimization machine learning

RESEARCHarXiv CS.LG·13d ago

Personalized Observation Normalization for Federated Reinforcement Learning in Simulation Environments with Heterogeneity

The paper introduces a personalized observation normalization (PON) method for federated reinforcement learning (FedRL) to address challenges in heterogeneous environments. PON allows each agent to locally normalize state inputs, ensuring consistent scaling and improving performance in heterogeneous MuJoCo tasks.

reinforcement learning machine learning federated learning Normalization

RESEARCHarXiv CS.LG·12d ago

TaxDistill: Improving Metagenomic Taxonomic Annotation via Distilled Genomic Foundation Models

TaxDistill introduces a knowledge distillation framework to improve metagenomic taxonomic annotation by addressing limitations of traditional methods. It utilizes GenomeOcean, a 500M-parameter genomic foundation model, as a teacher network to generate clean soft labels and enhance classification performance.

Genomics machine learning foundation models metagenomics

RESEARCHDEV.to AI·5d ago

Hierarchical Clustering Based on Mutual Information

This content describes a method for hierarchical clustering that leverages mutual information. It explores how mutual information can be applied to build a robust and effective clustering structure.

Mutual Information machine learning data science Hierarchical Clustering

ARTICLEDEV.to AI·5/9/2026

Vector Database Là Gì? Giải Mã "Trái Tim" Của Kỷ Nguyên AI

In the booming era of AI and Machine Learning, traditional relational databases struggle with unstructured data processing. Vector databases offer a specialized solution, designed to store and search vector embeddings that represent unstructured data in a multidimensional space, enabling highly efficient similarity searches.

vector database data management machine learning embeddings

RESEARCHDEV.to AI·4/17/2026

AiScientist Agent Uses 'File-as-Bus' to Score 81.82% on MLE-Bench Lite

Researchers introduced AiScientist, an autonomous ML research agent featuring a 'File-as-Bus' architecture for persistent state management, crucial for long-horizon tasks. This innovative approach helped it score 81.82% on MLE-Bench Lite, with the file system architecture contributing 31.82 points to its performance.

AI architecture research machine learning State Management

ARTICLEDEV.to AI·22d ago

AI Growth Hacks

This article introduces AI growth hacks, a concept combining AI and machine learning with traditional growth hacking to drive rapid business success. It defines growth hacking and highlights how AI elevates these strategies, especially for SaaS startups.

SaaS machine learning startups AI

CASEDEV.to AI·15d ago

A health plan in the southeast recently undertook an effort

A health plan utilized advanced analytics and machine learning to transform its member outreach from reactive to a more targeted, proactive approach. By analyzing member data, the plan identified individuals, like those with diabetes, most likely to benefit from specific interventions, optimizing its strategy.

Optimization member outreach Healthcare machine learning

ARTICLEDEV.to AI·27d ago

How Optimization Search Works — From Hill Climbing to Genetic Algorithms

Optimization is the process of finding a better solution than the current one by evaluating candidate solutions within a search space. It involves an objective function to define what "better" means and an update strategy to guide movement. The challenge lies in distinguishing between a locally good solution and the truly best overall solution, often using methods like exploring neighbors.

hill climbing Optimization machine learning Search

DOCDEV.to AI·5d ago

A surprisingly effective lightweight sentiment analysis approach for product reviews in Python

This content describes a surprisingly effective lightweight lexicon-based approach for sentiment analysis of e-commerce product reviews in Python. The simple technique proved useful for early-stage positive/negative detection, prototyping, and bulk filtering before moving to more advanced transformer-based models.

learning machine learning NLP sentiment analysis

RESEARCHarXiv CS.LG·4/15/2026

DBGL: Decay-aware Bipartite Graph Learning for Irregular Medical Time Series Classification

DBGL introduces a novel Decay-Aware Bipartite Graph Learning method to address the challenges of irregular medical time series classification. It utilizes a patient-variable bipartite graph to model irregular sampling patterns and variable relationships, alongside a node-specific temporal decay encoding for variable decay irregularity.

Graph Neural Networks machine learning medical AI irregular data

RESEARCHarXiv CS.CL·4/10/2026

Hybrid CNN-Transformer Architecture for Arabic Speech Emotion Recognition

Este artigo apresenta um sistema de Reconhecimento de Emoção da Fala (SER) em árabe, baseado em uma arquitetura híbrida CNN-Transformer. O modelo combina camadas convolucionais para extração de características espectrais e codificadores Transformer para capturar dependências temporais, alcançando 97,8% de precisão e 0,98 de F1-score macro.

CNN deep learning Transformer machine learning

RESEARCHDEV.to AI·4/16/2026

Generative Simulation Benchmarking for sustainable aquaculture monitoring systems for extreme data sparsity scenarios

This content addresses the challenge of building intelligent monitoring systems for aquaculture in scenarios of extreme data sparsity, as observed in a fish farm. The author proposes Generative Simulation Benchmarking to overcome the limitations of traditional machine learning in such conditions.

monitoring systems aquaculture machine learning Data Sparsity

DOCDEV.to AI·4/25/2026

Dirty Data: How to Find It and What to Do

This content discusses the systematic identification of dirty data in datasets, including missing values, duplicates, incorrect data types, and outliers, which can silently break AI models. It emphasizes that these problems are universal and must be found and addressed before model building.

machine learning Data Cleaning data preprocessing Data Quality

DOCDEV.to AI·6d ago

Cignara

Cignara is a cloud-based, AI-powered platform designed to streamline data analysis, automation, and decision-making using ML and NLP. Its architecture features cloud infrastructure, a data processing layer, and an AI/ML engine with NLP models and predictive analytics.

cloud computing machine learning AI platform Natural Language Processing

ARTICLEDEV.to AI·4/8/2026

The 5 Failure Modes of Federated Learning (And Why Outcome Routing Does It Differently)

Este artigo explora os cinco modos de falha da Aprendizagem Federada, uma técnica introduzida em 2016 para treinar modelos de IA em dados sensíveis e distribuídos. Ele detalha onde a Aprendizagem Federada falha e apresenta uma abordagem fundamentalmente diferente, o 'outcome routing'.

Outcome Routing machine learning federated learning distributed learning