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刊・见｜Cybernetics and Systems期刊：人工智能、人机交互与复杂系统研究的前沿阵地 
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控制论与系统科学以反馈调控和信息交互为核心，为工程、生物、管理、人工智能等学科提供了跨学科分析框架。当前智能制造、智慧医疗、能源网络、人机协同等场景持续催生新理论需求，多主体自适应、非线性动态优化、复杂系统韧性介绍控制论与系统工程领域的期刊——Cybernetics and Systems。除了对关键指标进行详尽解读外，还将向您推荐近三年高被引文章及近一年高阅读量文章：
基于Yolo Tiny网络架构的变换方法在肺部疾病分类中的多模态融合应用
Online ISSN: 1087-6553 Print ISSN: 0196-9722
Cybernetics and Systems 聚焦控制论与系统工程领域的理论基础、基本原理及实践应用，重点关注以下领域：
应用人工智能与机器学习：探索人工智能和机器学习技术的发展及其在系统中的应用。
人机交互：研究人与机器之间的交互，以优化用户体验和系统效率。
智能系统与深度学习：深入探讨利用深度学习技术构建和实现智能系统。
决策与模糊系统：研究决策过程以及模糊系统在处理不确定性和不精确性中的作用。
环境系统中的控制论：应用控制论原理应对社会、经济和环境领域的挑战。
该期刊已经被 SCIE、BIOSIS Previews等核心数据库收录。
根据JCR显示，Cybernetics and Systems
在计算机科学、控制论排名 13/34
Scopus根据Scopus显示，Cybernetics and Systems
2025年CiteScore为 6.7
在计算机科学：
信息系统排名 142/519
软件排名 144/503
人工智能排名 167/570
所有来稿均须经主编和副主编进行初步评审；若被认定适合进一步审议，则将由两名或两名以上的独立、匿名专家审稿人进行双盲评审。
审稿周期
从提交稿件到获取初审意见，平均需要2天
稿件一旦接受后，在线出版平均需要24天
文章出版费（APC）
请访问期刊主页或Taylor & Francis Open Access APC Cost Finder查找适用于作者所在国家及不同文章类型的费用情况。若您所在的机构或相关资助者与我们签有开放获取出版协议，您可能有资格获得APC支持，请访问我们的作者服务网站以了解更多！
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编辑团队Cybernetics and Systems期刊由来自新加坡纽卡斯尔大学的Cheng Siong Chin 教授和来自中国西安理工大学的魏嵬教授共同担任联合主编，副主编团队汇聚了来自美国、中国、墨西哥的多位学者，另有来自澳大利亚、美国等地的研究人员组成编辑顾问委员会。
联合主编
Cheng Siong Chin
任职于新加坡纽卡斯尔大学（Newcastle University），主攻针对不确定环境下的复杂系统（海洋系统、储能系统及声学系统）开展智能系统建模与仿真研究，涉及预测分析（数据挖掘、预测建模和机器学习）。
魏嵬
任职于西安理工大学计算机科学与工程学院，一直从事物联网及AI大数据处理等相关方面的研究。
中国副主编
沈谋全
南京工业大学电气工程与控制科学学院教授。研究方向为智能学习优化、数据驱动、多自主体协同、故障诊断与容错、信号处理和信息物理系统等基础理论和方法及其在智能电网、无人驾驶、工业装备和智慧交通等领域的应用。
刘天元
东华大学机械工程学院副教授。研究方向为工业人工智能、计算视觉、焊接自动化、无损检测、数字孪生、知识图谱等。
方凯
浙江农林大学人工智能学院教授、学术副院长，长期从事物联网、人工智能、林业信息化等领域的研究。
周景春
大连海事大学信息科学技术学院副教授。研究方向为人工智能，深度学习，计算机视觉，图像增强与复原，图像质量评价，涉水光学，海洋信息智能感知计算，水下目标检测，水下大视角重建等。
作者分布根据JCR显示，近三年在Cybernetics and Systems发文的国家/地区中，发文数量较为活跃有：
印度
波兰
中国
近三年在Cybernetics and Systems发文的全球高校和科研机构中，发文数量较为活跃的是：
金奈 SRM 科学技术学院
法伦海特大学
法国国家科学研究中心（CNRS）
近三年内高被引文章
基于Yolo Tiny网络架构的变换方法在肺部疾病分类中的多模态融合应用
作者：Naresh Poloju & A. Rajaram
文章摘要：
This article focuses on the crucial task of identifying lung diseases through the analysis and processing of medical images, aiming to aid medical professionals in the diagnostic process. Image fusion technologies, employed in prospective remedies, enhance diagnostic precision and reliability by amalgamating radiological information from diverse sources. The study introduces a Yolo Tiny network-based medical imaging fusion technique, yielding fused images with enhanced clarity and diverse physical information. The proposed method involves the integration of an adaptive pre-processing module, YOLO Tiny for efficient classification, discrete wavelet transform for feature combination, and a novel adaptive image fusion technique based on a classifier, contributing to improved diagnostic accuracy. Addressing challenges like uneven illumination and poor contrast in medical images, the comprehensive workflow enhances image quality, facilitating effective classification in medical imaging. Experimental results showcase the efficacy of the proposed scheme in CT imaging signs retrieval and classification, specifically for lung nodule detection. The system exhibits high accuracy, specificity, sensitivity, and recall, outperforming existing systems in terms of SSIM, PSNR, MSE, and overall accuracy for both single-modal and multi-modal datasets. The incorporation of YOLO and YOLO Tiny networks underscores improved performance in image processing and classification tasks across diverse medical imaging modalities. The hybrid model, suggested in this study, effectively preserves rich specific information from source images, yielding esthetically pleasing visuals for medical practitioners and enhancing the diagnostic process.
作者：Edyta Gołąb-Andrzejak
文章摘要：
The use of AI-based solutions is currently discussed in relation to various industries. The proliferation of tools based on generative artificial intelligence (GAI), including the emergence of ChatGPT, has resulted in testing as a first step and implementations in further areas of business life, including marketing, as a second step. Still only a few studies have analyzed and evaluated specific solutions for different areas of marketing, including advert design. In order to fill this gap, areas where GAI and ChatGPT are used during the various stages of creating a digital advertising campaign have been identified. Therefore, the aim of this paper is to investigate the impact of GAI and ChatGPT on theory and practice on different stages of the digital advertising campaign building process. This objective is followed by a research question: how can marketers use GAI and ChatGPT to create an effective digital advertising campaign? The process of building a digital advertising campaign should be considered and analyzed in terms of the impact of using GAI and ChatGPT technology and, at the same time, the role played by marketers at each stage and their contribution to this process in collaboration with GAI-based tools. This article is intended as a preliminary exploration of the impact of using GAI and ChatGPT on the digital marketing campaign building process. Therefore, the methodology applied includes critical literature analysis, secondary data analysis and individual in depth interview (IDI) with an expert (CEO of a advertising agency). This represents a first step in the study, to be followed by in-depth empirical research (qualitative as well as quantitative) to verify and develop the conclusions drawn. The article analyses the impact of using generative AI and ChatGPT on the process of creating an advertising campaign in digital media from a theoretical and practical point of view. Theoretical and managerial implications are also presented.
近一年内高阅读量文章
作者：Adeel Ali et al.
文章摘要：
In today’s technologically advanced era, the banking sector still faces significant challenges in mitigating cyberattacks, despite the availability of innovative technologies such as cybersecurity expert systems (CSES). Barriers hinder the effective implementation of these technologies. The primary objective of SLR is to identify and map the barriers and mitigation strategies for the use of AI in cybersecurity. Attributed to recent developments in artificial intelligence, its scalability, and adaptability in cybersecurity, the current study limits its research span to the period between 2018 and 2024 (November). Based on the inclusion criteria, 65 articles were selected for the study. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach, literature from selected databases is extracted and synthesized to identify barriers to the use of AI in cybersecurity. This research identified 23 barriers and 12 mitigation strategies, and further categorized these findings within the Technological, Organizational, and Environmental (TOE) framework using thematic analysis. Hence, the current research makes a novel contribution by providing a framework for future studies, diverting attention toward adoption barriers to accelerate the use of AI for cybersecurity.
Figure 1. PRISMA flow diagram for selection of literature.
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以上内容可能更新，请以期刊官网主页为准。
https://blog.sciencenet.cn/blog-3574014-1559240.html
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