AI Open

AI Open(英文缩写 AI OPEN),ISSN 2666-6510,eISSN 2666-6510 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。

2026 年数据 · 影响因子
8.900
JCR 分区
Q1
CAS 分区
-
近一年发文量
0
本站 PubMed 收录统计

发文量统计区间:2025-10-01 至 2026-09-30,按本站收录文献的发表日期统计。

ISSN: 2666-6510 · eISSN: 2666-6510 · 缩写: AI OPEN

期刊介绍

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期刊简介

AI Open 是一本聚焦人工智能开放科学与前沿进展的英文期刊,涵盖机器学习、自然语言处理、计算机视觉、知识推理及智能系统等方向。期刊强调开放获取与可复现研究,面向高校、科研机构及产业界的研究人员、工程师与研究生,致力于推动AI基础理论与应用技术的交流。

研究方向

主要发表人工智能领域的原创研究、综述与短篇通讯,主题包括深度学习、大模型、多模态学习、强化学习、智能决策、可信AI及跨学科应用。论文类型以实证研究和方法创新为主,也接受系统性综述与开源工具介绍。

期刊特色

研究取向偏重方法创新与实验验证,强调结果可复现和代码数据开放。论文通常要求清晰的问题定义、充分的基线对比和消融分析。适合有扎实实验基础、关注AI前沿且愿意公开研究细节的作者与读者。

投稿难度

投稿难度中等偏上,对方法新颖性、实验完整性和写作规范要求较高。建议在投稿前完善对比实验与统计分析,明确贡献边界,并针对审稿意见准备可复现材料。不能仅凭分区判断是否容易录用,需结合具体方向与稿件质量评估。

AI Open 最新收录文献

  1. JCR分区: Q1 CAS分区: N/A 影响因子: 8.9

    1. CellBoost: A pipeline for machine assisted annotation in neuroanatomy.

    作者:
    Kui Qian, Beth Friedman, Jun Takatoh, Alexander Groisman, Fan Wang, David Kleinfeld, Yoav Freund
    日期:
    2024-09-13

    One of the important yet labor intensive tasks in neuroanatomy is the identification of select populations of cells. Current high-throughput techniques enable marking cells with histochemical fluorescent molecules as well as through the genetic expression of fluorescent proteins. Modern scanning microscopes allow high resolution multi-channel imaging of the mechanically or optically sectioned brain with thousands of marked cells per square millimeter. Manual identification of all marked cells is prohibitively time consuming. At the same time, simple segmentation algorithms suffer from high error rates and sensitivity to variation in fluorescent intensity and spatial distribution. We present a methodology that combines human judgement and machine learning that serves to significantly reduce the labor of the anatomist while improving the consistency of the annotation. As a demonstration, we analyzed murine brains with marked premotor neurons in the brainstem. We compared the error rate of our method to the disagreement rate among human anatomists. This comparison shows that our method can reduce the time to annotate by as much as ten-fold without significantly increasing the rate of errors. We show that our method achieves significant reduction in labor while achieving an accuracy that is similar to the level of agreement between different anatomists.

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