刊·见 | 捕捉人工智能领域最新动态？收藏Applied Artificial Intelligence

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chatgpt的出现是ai研究中具有重大历史意义的突破性进展，看似无所不能的chatgpt对于人类社会产生了许多积极的影响，但同时也带来了发人深省的冲突。如何规避因人工智能的崛起而引发的风险和消极影响，是人工智能领域政策的制定者和领域内的专业人士需要深入思考和探讨的问题。

本期《刊·见》为您带来人工智能领域优质期刊Applied Artificial Intelligence。除了介绍期刊，我们还为您选出了近三年内高被引和2025年高阅读量的文章，供您阅读

• 基于迁移学习的框架，为番茄植株上的害虫分类
• 通用学习均衡优化器：一种新的用于生物数据分类的特征选择方法
• 综述：基于深度学习的2D图像语义分割体系架构的调查
• 综述：人工智能驱动的网络攻击的新威胁

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Applied Artificial Intelligence 是奥地利控制论研究学会官方刊物，旨在解决应用研究和人工智能应用方面的问题，同时为人工智能领域有影响力的研究提供交流观点和想法的平台。期刊关注如下领域，包括但不限于人工智能系统在解决管理、工业、工程、行政和教育工作方面的进展；对现有人工智能系统和工具的评估，侧重比较研究和用户体验；人工智能对经济、社会和文化的影响。

该期刊已被SCIE, Scopus, CSA, INSPECs, PsycINFO等数据库收录。

期刊主页：http://985.so/m1ug4

影响因子

根据JCR显示，Applied Artificial Intelligence 2025年影响因子为2.777，在

计算机：人工智能领域排名82/145

工程：电子与电气领域排名134/276

CiteScore

根据Scopus显示, Applied Artificial Intelligence 的

CiteScore（2025）为 3.0

CiteScoreTracker（2025）为 3.7

在计算机科学：人工智能领域排名 151/269

编辑团队

Applied Artificial Intelligence 的主编是来自奥地利人工智能研究所和维也纳大学的Robert Trappl教授。在副主编团队中，来自中国的是山东财经大学的刘培德教授。此外，编委团队由多国专家学者组成。

主编

Robert Trappl教授

Robert Trappl教授是奥地利人工智能研究所的负责人，他还是维也纳医科大学脑研究中心医学控制论和人工智能学科的名誉教授，曾担任维也纳大学医学控制论和人工智能系的全职教授和系主任长达30年。

来自中国的副主编

刘培德教授

刘培德教授是山东财经大学管理科学与工程学院院长、山东财经大学海洋经济与管理研究中心主任、中国优秀教师。

他的主要研究方向是：决策理论与优化方法；海洋经济与管理；大数据商务分析。

刊内新闻

目前，Applied Artificial Intelligence正在针对以下主题进行征稿。

主题一：Multiagent Systems in the Era of Trustworthy Artificial Intelligence

可信赖的人工智能时代的多智能体系统

投稿截止日期: 2025年8月23日

主题二：Artificial Intelligence Applications in Industry 4.0

工业4.0中的人工智能

投稿截止日期：2025年8月31日

主题三：Explainable Machine Learning Operational Applied Research and Applications for Improved Decision-Making

可解释的机器学习应用研究和提升决策的应用

Seede AI

AI 驱动的设计工具

713 查看详情

投稿截止日期：2025年10月30日

作者分布

根据JCR显示，近三年在Applied Artificial Intelligence 发文的国家中，排名前三的国家有：

• 印度
• 中国
• 伊朗

文章推荐可以前往【TandF学术】捷阅读：http://985.so/m1ug6

刊内高被引文章

Full article: Transfer Learning-Based Framework for Classification of Pest in Tomato Plants (tandfonline.com)

基于迁移学习的框架，为番茄植株上的害虫分类

作者：Gayatri Pattnaik et al.

The concept of transfer learning

文章摘要：

Pest in the plant is a major challenge in the agriculture sector. Hence, early and accurate detection and classification of pests could help in precautionary measures while substantially reducing economic losses. Recent developments in deep convolutional neural network (CNN) h*e drastically improved the accuracy of image recognition systems. In this paper, we h*e presented a transfer learning of pre-trained deep CNN-based framework for classification of pest in tomato plants. The dataset for this study has been collected from online sources that consist of 859 images categorized into 10 classes. This study is first of its kind where: (i) dataset with 10 classes of tomato pest are involved; (ii) an exhaustive comparison of the performance of 15 pre-trained deep CNN models has been presented on tomato pest classification. The experimental results show that the highest classification accuracy of 88.83% has been obtained using DenseNet169 model. Further, the encouraging results of transfer learning-based models demonstrate its effectiveness in pest detection and classification tasks.

Full article: General Learning Equilibrium Optimizer: A New Feature Selection Method for Biological Data Classification (tandfonline.com)

通用学习均衡优化器：一种新的用于生物数据分类的特征选择方法

作者：Jingwei Too & Seyedali Mirjalili

Basic concept of general learning strategy

文章摘要：

Finding relevant information from biological data is a critical issue for the study of disease diagnosis, especially when an enormous number of biological features are involved. Intentionally, the feature selection can be an imperative preprocessing step before the classification stage. Equilibrium optimizer (EO) is a recently established metaheuristic algorithm inspired by the principle of dynamic source and sink models when measuring the equilibrium states. In this research, a new variant of EO called general learning equilibrium optimizer (GLEO) is proposed as a wrapper feature selection method. This approach adopts a general learning strategy to help the particles to evade the local areas and improve the capability of finding promising regions. The proposed GLEO aims to identify a subset of informative biological features among a large number of attributes. The performance of the GLEO algorithm is validated on 16 biological datasets, where nine of them represent high dimensionality with a smaller number of instances. The results obtained show the excellent performance of GLEO in terms of fitness value, accuracy, and feature size in comparison with other metaheuristic algorithms.

刊内2025年高阅读量文章

Full article: A Survey on Deep Learning-based Architectures for Semantic Segmentation on 2D Images (tandfonline.com)

综述：基于深度学习的2D图像语义分割体系架构的调查

作者：Irem Ulku & Erdem Akagündüz

A sample image and its annotation for object, instance and parts segmentations separately, from left to right

文章摘要：

Semantic segmentation is the pixel-wise labeling of an image. Boosted by the extraordinary ability of convolutional neural networks (CNN) in creating semantic, high-level and hierarchical image features; several deep learning-based 2D semantic segmentation approaches h*e been proposed within the last decade. In this survey, we mainly focus on the recent scientific developments in semantic segmentation, specifically on deep learning-based methods using 2D images. We started with an analysis of the public image sets and leaderboards for 2D semantic segmentation, with an overview of the techniques employed in performance evaluation. In examining the evolution of the field, we chronologically categorized the approaches into three main periods, namely pre-and early deep learning era, the fully convolutional era, and the post-FCN era. We technically analyzed the solutions put forward in terms of solving the fundamental problems of the field, such as fine-grained localization and scale invariance. Before drawing our conclusions, we present a table of methods from all mentioned eras, with a summary of each approach that explains their contribution to the field. We conclude the survey by discussing the current challenges of the field and to what extent they h*e been solved.

Full article: The Emerging Threat of Ai-driven Cyber Attacks: A Review (tandfonline.com)

综述：人工智能驱动的网络攻击的新威胁

作者：Blessing Guembe et al.

PRISMA flowchart illustrating the systematic review process and article selection at various stages

文章摘要：

Cyberattacks are becoming more sophisticated and ubiquitous. Cybercriminals are inevitably adopting Artificial Intelligence (AI) techniques to evade the cyberspace and cause greater damages without being noticed. Researchers in cybersecurity domain h*e not researched the concept behind AI-powered cyberattacks enough to understand the level of sophistication this type of attack possesses. This paper aims to investigate the emerging threat of AI-powered cyberattacks and provide insights into malicious used of AI in cyberattacks. The study was performed through a three-step process by selecting only articles based on quality, exclusion, and inclusion criteria that focus on AI-driven cyberattacks. Searches in ACM, arXiv Blackhat, Scopus, Springer, MDPI, IEEE Xplore and other sources were executed to retrieve relevant articles. Out of the 936 papers that met our search criteria, a total of 46 articles were finally selected for this study. The result shows that 56% of the AI-Driven cyberattack technique identified was demonstrated in the access and penetration phase, 12% was demonstrated in exploitation, and command and control phase, respectively; 11% was demonstrated in the reconnaissance phase; 9% was demonstrated in the delivery phase of the cybersecurity kill chain. The findings in this study shows that existing cyber defence infrastructures will become inadequate to address the increasing speed, and complex decision logic of AI-driven attacks. Hence, organizations need to invest in AI cybersecurity infrastructures to combat these emerging threats.

审稿周期

• 从提交稿件到获取初审意见，平均需要61天
• 获取首个同行评审决定，平均需要62天
• 稿件一旦接受后，在线出版平均需要15天

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您可以通过我们的作者服务网站查询本期刊的标准文章出版费。

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