期刊信息

Engineering Applications of Artificial Intelligence (EAAI)

请登录查看期刊网址
免费注册：查看官网链接、跟踪截稿日期，并接收邮件提醒。
注册 登录
影响因子:
9.0
出版商:
Elsevier
ISSN:
0952-1976
浏览:
81174
关注:
76
这一页信息有误？告诉我们
用 API 获取这条数据
搜索与榜单列表完全无需凭证；本页的完整详情需要一把免费 API 密钥。详见开发者接入页。

征稿

Engineering Applications of Artificial Intelligence (EAAI) is an academic journal published by Elsevier. (ISSN 0952-1976, impact factor 9.0, CCF C).

A journal of IFAC, The International Federation of Automatic Control. Artificial Intelligence (AI) is playing a major role in the fourth industrial revolution, and we are seeing a lot of evolution in various machine learning methodologies. AI techniques are widely used by the practicing engineer to solve a whole range of hitherto intractable problems. Engineering Applications of Artificial Intelligence provides an international forum for rapid publication of work describing the practical application of AI methods in all branches of engineering. Submitted papers should report novel aspects of AI used for a real-world engineering application and also validated using public data sets for easy replicability of the research results. Focal points of the journal include, but are not limited to, innovative applications of: Internet–of–things and cyber-physical systems Intelligent transportation systems & smart vehicles Big data analytics, understanding complex networks Neural networks, fuzzy systems, neuro-fuzzy systems Deep learning and real-world applications Self-organizing, emerging or bio-inspired system Global optimization, Meta-heuristics and their applications: Evolutionary Algorithms, swarm intelligence, nature and biologically inspired meta-heuristics, etc. Architectures, algorithms and techniques for distributed AI systems, including multi-agent based control and holonic control Decision-support systems Aspects of reasoning: abductive, case-based, model-based, non-monotonic, incomplete, progressive and approximate reasoning Applications of chaos theory and fractals Real-time intelligent automation, and their associated supporting methodologies and techniques, including control theory and industrial informatics Knowledge processing, knowledge elicitation and acquisition, knowledge representation, knowledge compaction, knowledge bases, expert systems Perception, e.g. image processing, pattern recognition, vision systems, tactile systems, speech recognition and synthesis Aspects of software engineering, e.g. intelligent programming environments, verification and validation of AI-based software, software and hardware architectures for the real-time use of AI techniques, safety and reliability Intelligent fault detection, fault analysis, diagnostics and monitoring Industrial experiences in the application of the above techniques, e.g. case studies or benchmarking exercises Robotics Papers which do not respect the four following conditions will be desk-rejected without being sent for peer review: Papers on new metaphor-based metaheuristics are very rarely accepted by Engineering Applications of Artificial Intelligence (please see further details in the ‘Article types’ section of the Guide for Authors) The abstract should clearly specify which is the contribution in AI, and which is the application in engineering The use of undefined acronyms in the title and in the abstract is forbidden The papers must be formatted in single-column format
由 Admin Agent 最后更新于

特刊

特刊：Human-Centered and Trustworthy AI for Cybersecurity in Cyber-Physical Systems 截稿日期: 2027-01-31 Artificial Intelligence is transforming everyday applications and critical infrastructures, but this transformation brings systemic risks at both the model and socio-technical level. This Special Issue explores the intersection of AI, cybersecurity, human factors, and risk management, addressing adversarial threats to machine learning systems, e.g., adversarial examples, data poisoning, and backdoor attacks, alongside human-centered vulnerabilities exploited through phishing, social engineering, and disinformation. We welcome original contributions presenting AI-driven detection and mitigation methods, human-centered cybersecurity approaches, and real-world engineering applications and case studies, with a focus on reproducible, validated solutions for security-critical, human-in-the-loop, and cyber-physical systems.Artificial Intelligence systems are now deeply embedded in everyday applications, digital infrastructures, and security-critical environments, enabling unprecedented capabilities but also introducing complex and systemic risks at two interconnected levels. At the model level, AI systems are inherently vulnerable to adversarial manipulation, including adversarial examples, data poisoning, backdoor attacks, model extraction, and membership inference. As AI is increasingly deployed in safety-critical and cyber-physical systems, model robustness and trustworthiness under adversarial conditions become a foundational engineering concern. At the socio-technical level, cyber threats increasingly target the cognitive and social dimensions of digital ecosystems. Modern adversaries exploit human behavior and trust through social engineering and disinformation campaigns, exploiting cognitive biases and large-scale information manipulation to make human-in-the-loop systems particularly vulnerable. Addressing these risks has become a key challenge for researchers and practitioners as AI is rapidly integrated into critical engineering domains.This Special Issue addresses the intersection of AI, cybersecurity, human factors, and risk management. We invite submissions presenting advances in machine learning, knowledge discovery, cybersecurity, and human-centered computing, and that foster methodologies to identify, model, and mitigate risks in AI-based socio-technical systems operating in human-critical environments. AI-based models for detecting, preventing, and mitigating cyber threats: - Natural Language Processing and LLM-based methodology for identifying malicious, deceptive, and manipulative content - Data-driven and knowledge discovery approaches for cyber threat detection in large-scale heterogeneous environments - Graph learning and knowledge graph techniques for modeling cyber threat ecosystems, communication networks, and social interactions - Generative AI for both offensive and defensive cybersecurity purposes - AI-driven cyber threat intelligence, attack attribution, and predictive threat analytics Human-Centered AI approaches for cybersecurity: - Human-AI interaction, trust calibration, automation bias, overreliance, and decision support systems - Human-centered approaches to combating phishing, social engineering, and disinformation - Human-in-the-loop and hybrid intelligence systems for dynamic risk assessment and explainability - Behavioral, cognitive, and organizational factors influencing cybersecurity awareness and resilience Engineering Applications and Case Studies: - Real-world deployments of AI-based solutions - Industrial case studies, empirical evaluations, and benchmarking of AI-driven security approaches - Cross-domain applications in critical infrastructures, healthcare, smart cities, transportation, and Industry 5.0 - Lessons learned, best practices, and evidence-based assessments of trustworthy AI systems in real settings Guest editors: Dr. Stefano Cirillo Affiliation: Università degli Studi di Salerno, Salerno, Italy Dr. Idio Guarino Affiliation: Alma Mater Studiorum Università di Bologna, Bologna, Italy Dr. Pantaleone Nespoli Affiliation: Universidad de Murcia, Murcia, Spain Dr. Eliana Pastor Affiliation: Politecnico di Torino, Turin, Italy Dr. Giancarlo Sperlì Affiliation: Università degli Studi di Napoli Federico II, Naples, Italy Dr. Serena Tardelli Affiliation: Consiglio Nazionale delle Ricerche, Rome, Italy Manuscript submission information: All manuscripts will be peer-reviewed. Submissions will be evaluated based on originality, significance, technical quality, and clarity. Once accepted, articles will be posted online immediately and published in a journal regular issue within weeks. Articles will also be simultaneously collected in the online special issue. Open for Submission: from 01-Oct-2026 to 31-Jan-2027 Article Type Name: "AI for Cybersecurity" - please select this item when you submit manuscripts online For any inquiries about the appropriateness of contribution topics, please contact the Leading Guest Editor (Stefano Cirillo). Keywords: Adversarial machine learning; Cybersecurity; Human-centered AI; Trustworthy AI; Cyber-physical systems; Social engineering and disinformation https://www.sciencedirect.com/special-issue/335890/human-centered-and-trustworthy-ai-for-cybersecurity-in-cyber-physical-systems
由 Admin Agent 最后更新于
特刊：Intelligent Systems and Control for Applied Robotic Cases 截稿日期: 2027-01-31 This special issue aims to collect recent developments and results of the growing integration of intelligent systems and control, which are usually understood as systems based on decision-making according to data and control algorithms obtained from tools with strong mathematical rigor. Such an integration is not only crucial to improving the efficiency, flexibility, and overall productivity of robotic systems in various industrial processes; but also, their integration and combination will facilitate the development of significant advances in various research areas and even in the productivity and competitiveness of countries. This special issue welcomes contributions focused on the development of related applications and the integration of intelligent robotic systems, collaborative robotics, artificial intelligence, multi-agent systems, digital twins, the Internet of Things, the integration of advanced analytics for decision support systems in real time, and other topics related to mobile robotics, control theory, and applied mathematics to robotic systems. Topics of interest include, among others: AI-based optimization algorithms Applications of AI in aerospace systems Robust control schemes, adaptive control, and nonlinear control Industrial process control and automation Model predictive control (MPC) enhanced with machine learning Applications of Computational Intelligence Mathematical foundations for artificial intelligence and control Mobile robotics and intelligent systems applications Distributed, networked, and multi-agent control systems driven by AI Autonomous and robotic systems, energy, smart grids and infrastructure Industry 4.0 applications Biomedical and bioengineering applications in modeling, analysis, optimization, and control Transportation systems Deterministic, stochastic, and fractional-order models​ Guest editors: Prof. Esteban Tlelo-Cuautle Instituto Nacional de Astrofisica Optica y Electronica, Puebla, Mexico Dr. Yair Lozano-Hernández Instituto Politécnico Nacional, Mexico, Mexico Dr. Alejandro Rodríguez-Molina Universidad Autónoma de la Ciudad de México, Mexico, Mexico Dr. Jaime Gonzalez-Sierra Instituto Politécnico Nacional, Mexico, Mexico Manuscript submission information: Guest Editor Invitation Only Open for Submission: from 2026-09-15 to 2027-01-31 Article Type Name: "VSI: EAAI_Intelligent Systems" - please select this item when you submit manuscripts online All manuscripts will be peer-reviewed. Submissions will be evaluated based on originality, significance, technical quality, and clarity. Once accepted, articles will be posted online immediately and published in a journal regular issue within weeks. Articles will also be simultaneously collected in the online special issue. For any inquiries about the appropriateness of contribution topics, welcome to contact Leading Guest Editor (Prof. Esteban Tlelo-Cuautle). Keywords: Artificial Intelligence; Machine learning; Intelligent Systems; Control and Synchronization; Robotics​ https://www.sciencedirect.com/special-issue/336385/intelligent-systems-and-control-for-applied-robotic-cases
由 Admin Agent 最后更新于
特刊：Optimization, Control Theory, and Artificial Intelligence: Converging Methodologies for Engineering Applications 截稿日期: 2027-03-01 Optimization, control theory, and artificial intelligence (AI) have long served as foundational pillars of engineering research. Historically, these domains have progressed along separate trajectories: optimization has focused on detecting optimal solutions under tight constraints; control theory has addressed stability and robustness in dynamical systems; and AI has emphasized data-driven problem-solving, learning and decision-making. In modern engineering systems, characterized by nonlinear dynamics, high‑dimensionality, uncertainty, and incomplete information, the boundaries between these fields are rapidly dissolving. Contemporary applications such as cyber‑physical systems (CPS), autonomous systems, intelligent infrastructure, and biomedical engineering demand methodologies that are simultaneously optimal, adaptive, and intelligent. No single discipline is sufficient in isolation. This Special Issue aims to capture and articulate this growing methodological convergence, sometimes described as a form of “grand unification” between mathematical rigor and data‑driven intelligence. It highlights the role of advanced mathematical tools, including partial differential equations, graph theory, fractional calculus, and tensor methods, in enabling safe, interpretable, and robust AI‑driven engineering solutions. We welcome contributions that advance the theoretical foundations, computational methodologies, and engineering applications at the intersection of optimization, control theory, AI, and applied mathematics. Both continuous and discrete time systems, as well as deterministic, stochastic, and fractional order models, will be considered. Topics of interest include, but are not limited to: AI-based optimization algorithms, including metaheuristics, swarm intelligence, evolution computation, hybrid learning approaches for complex nonlinear systems. Learning-based and data-driven control frameworks integrating deep learning, reinforcement learning, robust control, adaptive control, and nonlinear control with formal guarantees in terms of stability, robustness, and performance. Machine‑learning‑enhanced Model Predictive Control (MPC), including including adaptive, stochastic, and data‑driven MPC. Mathematical foundations for AI and control using partial differential equations (PDEs), calculus of variations, fractional calculus, game theory, graph theory, and more, in the context of intelligent decision support and control. Optimization and control of large-scale, uncertain and non-linear systems. Distributed, networked, and multi‑agent control systems driven by hybrid AI, knowledge‑based approaches, or data‑driven methods, and their applications in cooperative and competitive environments. Autonomous and robotic systems, ranging from vehicles to drones and mobile manipulators, leveraging AI for perception, planning, learning, and dynamic control. Smart grids, energy, and smart infrastructure, including the integration of renewable energy, energy management, as well as intelligent transportation systems. Industry 4.0 applications: intelligent automation, predictive maintenance, fault diagnosis, and real‑time process control. Biomedical and biological engineering applications in modeling, analysis, optimization, and control. Transportation, traffic control, and mobility systems, including connected and autonomous vehicles. Communication networks and cyber-physical systems, focusing on optimization, control, and AI-driven decision-making. Guest editors: Prof. Dr. Necati Ozdemir Balikesir Üniversitesi , Balikesir, Turkey Dr. Ender E. Ozcan University of Nottingham, Nottingham, United Kingdom Assist. Prof. Anthony Siming Chen University of Nottingham, Nottingham, United Kingdom Manuscript submission information: Open for Submission: from 14-Sept-2026 to 01-Mar-2027 Submission Site: Editorial Manager® Article Type Name: "VSI: EAAI_Opti, Control&AI" - please select this item when you submit manuscripts online All manuscripts will be peer-reviewed. Submissions will be evaluated based on originality, significance, technical quality, and clarity. Once accepted, articles will be posted online immediately and published in a journal regular issue within weeks. Articles will also be simultaneously collected in the online special issue. For any inquiries about the appropriateness of contribution topics, welcome to contact Leading Guest Editor (Prof. Dr. Necati Ozdemir). Guide for Authors will be helpful for your future contributions, read more: Guide for authors - Engineering Applications of Artificial Intelligence - ISSN 0952-1976 | ScienceDirect.com by Elsevier For more information about our Journal, please visit our ScienceDirect Page: Engineering Applications of Artificial Intelligence | Journal | ScienceDirect.com by Elsevier Keywords: optimization; control; artificial intelligence; autonomous systems; cyber-physical systems https://www.sciencedirect.com/special-issue/333017/optimization-control-theory-and-artificial-intelligence-converging-methodologies-for-engineering-applications
由 Admin Agent 最后更新于
特刊：AI‑Enabled Systems Thinking for Advanced Construction Materials in Sustainable and Resilient Infrastructure: Knowledge-Guided Reasoning, Life Cycle Management, and Cross-Disciplinary Optimization 截稿日期: 2027-05-31 Sustainable and resilient infrastructure is a global priority, yet traditional material design and static life cycle assessment (LCA) cannot capture the complexity of long‑term durability, material interactions, and system‑wide impacts under climate change. This Special Issue of Engineering Applications of Artificial Intelligence advances a paradigm shift: from case‑level evaluation to system‑wide, data‑informed decision‑making. We invite contributions that integrate knowledge graphs, retrieval‑augmented generation (RAG), multi‑agent systems, digital twins, and explainable AI with civil engineering, materials science, and environmental assessment. Emphasis is placed on three pillars: knowledge‑guided reasoning for interpretable material design and discovery; data‑driven, digital‑twin‑enabled LCA for embodied carbon, energy, and durability; cross‑disciplinary optimization linking material choices to network resilience and lifecycle asset management. Topics include but are not limited to: knowledge graphs for construction materials, low‑carbon material design, RAG for knowledge integration, interpretable AI for strength‑sustainability trade‑offs, multi‑agent autonomous systems, digital twin‑based predictive LCA, multi‑scale aging/degradation modeling, uncertainty quantification, and service‑life modeling linked to environmental impact. All submissions must emphasize real‑world validation, explainability, and replicability (public datasets/code encouraged). The urgent demand for sustainable and resilient infrastructure has placed data-driven prediction and life cycle assessment (LCA) at the forefront of evaluating environmental and structural performance of construction materials. Traditional approaches, whether trial-and-error material design or static, inventory-based LCA, struggle to capture the complexity of material interactions, long-term durability, and system-wide impacts under climate change and decarbonization pressures. Addressing these challenges requires a paradigm shift from case-level evaluation to system-wide, data-informed decision-making. This Special Issue aims to advance AI‑enabled systems thinking for advanced construction materials (e.g., concrete, metals, composites) by integrating knowledge-guided reasoning, life cycle management, and cross-disciplinary optimization. We invite contributions that leverage digital twins, knowledge graphs, machine learning, and multi-agent systems to bridge civil engineering, materials science, computer science, and environmental assessment. The goal is to improve predictive reliability, explainability, resilience, and sustainability of construction materials in service of transportation, water, and energy infrastructure systems. Specifically, this collection emphasizes: Knowledge-Guided Reasoning for Materials Design and Discovery. Knowledge graphs, retrieval-augmented generation (RAG), and multi-agent systems for interpretable AI in mixture design, material discovery, and performance prediction. Integration of heterogeneous data and domain knowledge to enhance model reliability. Data-Driven Life Cycle Management. Advanced LCA powered by big data, field monitoring, and uncertainty analysis. Digital twin–enabled real-time or predictive LCA for embodied carbon, energy, and long-term durability. Cross-Disciplinary Optimization for Infrastructure Systems. System-level frameworks that connect material choices to network resilience, sustainability, and lifecycle asset management. Multi-scale modeling of aging, degradation, and uncertainty under competing objectives (e.g., strength vs. sustainability). Guest editors: Dr. Xiao Tan Affiliation: Department of Safety Engineering, Hohai University, Nanjing, China Dr. Dan Li Affiliation: Department of Civil Engineering, Southeast University, Nanjing, China Dr. Shulin Xiang Affiliation: College of Material Science and Engineering, Hohai University, Nanjing, China Dr. Soroush Mahjoubi Affiliation: Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States Dr. Pengwei Guo Affiliation: The Bartlett School of Sustainable Construction, University College London, London, United Kingdom Manuscript submission information: Open for Submission: from 01-Jun-2026 to 31-May-2027 Submission Site: Editorial Manager® Article Type Name: "VSI: EAAI_AI‑Enabled Systems" - please select this item when you submit manuscripts online All manuscripts will be peer-reviewed. Submissions will be evaluated based on originality, significance, technical quality, and clarity. Once accepted, articles will be posted online immediately and published in a journal regular issue within weeks. Articles will also be simultaneously collected in the online special issue. For any inquiries about the appropriateness of contribution topics, welcome to contact Leading Guest Editor (Dr. Xiao Tan). Guide for Authors will be helpful for your future contributions, read more: Guide for authors - Engineering Applications of Artificial Intelligence - ISSN 0952-1976 | ScienceDirect.com by Elsevier For more information about our Journal, please visit our ScienceDirect Page: Engineering Applications of Artificial Intelligence | Journal | ScienceDirect.com by Elsevier Keywords: knowledge graphs; explainable AI; sustainable concrete; environmental impact; embodied carbon; predictive maintenance; digital twins; multi-agent systems; infrastructure resilience; data-driven LCA https://www.sciencedirect.com/special-issue/333602/ai-enabled-systems-thinking-for-advanced-construction-materials-in-sustainable-and-resilient-infrastructure-knowledge-guided-reasoning-life-cycle-management-and-cross-disciplinary-optimization
由 Admin Agent 最后更新于
特刊：IoT in Disaster Management 截稿日期: 2027-06-15 The number of disasters is on rise year by year. The wild fires, rainfall, earthquakes, landslides, floods, volcano eruptions, cyclones, etc. are the common natural disasters. As the human population is growing, the effect is also worsening. The prediction and early warning of the disasters may help in saving lives and minimizing the economic losses and impact as well. In addition, the urban and critical infrastructure health monitoring and surveillance is becoming very significant. This includes bridges, power plants and distribution networks, canals, dams, fuel storage and supply units, etc. Even though the domain is being studied for decades, it still remains a challenge for the researchers. The Internet of Things (IoT) is a network of physical objects and devices such as computers, sensors, meters, actuators that enables the exchange of data over the internet enabling the problem to be solved collectively. The devices such as sensors and meters collect real time data and processed at the cloud servers, data centers, edge nodes, etc. The data could be multimodal and be in the form of numbers, photos, audio and videos, etc. The collected massive real time data can be utilized to simulate the physical world/scenario using the concept of Digital Twins. The CubeSats can collect the data over a wider geographical area and analyze it quickly. The ML algorithms, deterministic and AI based optimization algorithms, as well as metaheuristics play key role in classifying the data, making the precise predictions and making the systems learn continuously and progressively. The IoT and associated technologies are being considered as the means for the researchers to progress towards the precise prediction of the disasters, classifying the disaster warning messages, and routing them to corresponding rescue teams, develop early warning system, efficient deployment of the resources such as machinery, men, information, etc. The issue can accommodate the original and application-based contributions from within the below domains (not limited to): Digital Twin for the prediction of landslides Digital Twin for the prediction flood rises and gradient IoT for Structural Health Monitoring AI and ML Algorithms in IoT Big Data Analytics for Disaster Management CubeSats Network for Disaster Prediction Machine Learning enabling Early Warning IoT for Surveillance and Monitoring IoT in Early Warning IoT for Post Disaster Efficient Resource Management Guest editors: Dr. Anand Kulkarni MIT World Peace University, Pune, India Prof. Patrick Siarry University Paris-Est Créteil Val de Marne, Créteil, France Manuscript submission information: Open for Submission: from 2026-10-01 to 2027-06-15 ​Article Type Name: "VSI: EAAI_IoT Disaster Mgmt" - please select this item when you submit manuscripts online All manuscripts will be peer-reviewed. Submissions will be evaluated based on originality, significance, technical quality, and clarity. Once accepted, articles will be posted online immediately and published in a journal regular issue within weeks. Articles will also be simultaneously collected in the online special issue. For any inquiries about the appropriateness of contribution topics, welcome to contact Leading Guest Editor (Anand Kulkarni). Keywords: Disaster Management; IoT; Digital Twin; Early Warning; AI & ML Algorithms​ https://www.sciencedirect.com/special-issue/337512/iot-in-disaster-management
由 Admin Agent 最后更新于

看过这个的人也在看

CCF全称影响因子出版商ISSN
CFuture Generation Computer Systems5.9Elsevier0167-739X
CDiscover Computing1.9Springer2948-2992
BNeural Networks7.2Elsevier0893-6080
BPattern Recognition7.6Elsevier0031-3203
CNeurocomputing6.5Elsevier0925-2312
Applied Soft Computing7.8Elsevier1568-4946
BJournal of Systems and Software3.8Elsevier0164-1212
International Journal of Chaos, Control, Modelling and SimulationAIRCC2319-8990
Solid State Sciences3.3Elsevier1293-2558
BThe Journal of Systems Architecture: Embedded Software Design5.3Elsevier1383-7621

相关期刊

CCF全称影响因子出版商ISSN
Engineering11.6Elsevier2095-8099
CKnowledge-Based Systems7.2Elsevier0950-7051
CFuture Generation Computer Systems5.9Elsevier0167-739X
CNeurocomputing6.5Elsevier0925-2312
CPattern Recognition Letters3.5Elsevier0167-8655
CIEEE Transactions on Industrial Informatics11.7IEEE1551-3203
CIEEE Internet of Things Journal8.9IEEE2327-4662
CExpert Systems with Applications7.5Elsevier0957-4174
CIEEE Transactions on Big Data5.7IEEE2332-7790
CComputer Communications4.3Elsevier0140-3664

相关会议

评论 0

暂无评论。

请登录后发表评论