About the Journal

Journal Overview

Emerging Technologies in Intelligent Industry (ETII) is an international, peer-reviewed academic journal dedicated to advancing interdisciplinary research on emerging technologies and their applications in intelligent industrial systems, digital manufacturing, smart infrastructure, industrial automation, and next-generation engineering.

The journal provides a scholarly platform for researchers, engineers, industry practitioners, and policy professionals to disseminate original research, methodological advances, technological innovations, and practical applications that contribute to the transformation of conventional industries into intelligent, connected, adaptive, and sustainable systems.

ETII places particular emphasis on research that bridges the gap between technological innovation and real-world industrial implementation. The journal welcomes both fundamental studies and applied research that demonstrate clear relevance to industrial intelligence, digital transformation, intelligent decision-making, autonomous systems, and emerging engineering technologies.

The journal encourages interdisciplinary contributions integrating engineering, artificial intelligence, data science, automation, information systems, operations research, cybersecurity, and industrial management.

Aims and Mission

The mission of Emerging Technologies in Intelligent Industry is to promote high-quality research that advances the theory, methodology, technology, and practical implementation of intelligent industrial systems.

The journal aims to:

advance scientific understanding of emerging technologies for intelligent industry;
promote interdisciplinary integration between engineering, artificial intelligence, data science, automation, and industrial management;
encourage the development of intelligent, autonomous, resilient, and sustainable industrial systems;
support the translation of emerging technologies from research prototypes to industrial applications;
provide an international forum for discussing new technological paradigms, industrial architectures, engineering methods, and implementation challenges;
promote responsible, secure, reliable, and human-centered development of intelligent industrial technologies; and
facilitate knowledge exchange among academia, industry, government, and professional communities.

ETII particularly welcomes studies that combine methodological rigor with clear technological or industrial significance.

Scope

The scope of Emerging Technologies in Intelligent Industry covers, but is not limited to, the following areas.

Artificial Intelligence for Industry

Research on artificial intelligence and machine learning technologies designed for industrial environments, including:

industrial artificial intelligence;
machine learning and deep learning;
generative artificial intelligence;
foundation models and large language models for industrial applications;
explainable and trustworthy artificial intelligence;
reinforcement learning;
intelligent optimization;
knowledge graphs;
industrial knowledge representation;
multimodal industrial intelligence;
AI-based engineering design; and
intelligent decision-support systems.

Smart Manufacturing and Industry 4.0/5.0

Research related to digital and intelligent manufacturing, including:

smart manufacturing;
intelligent production systems;
Industry 4.0 and Industry 5.0;
flexible and reconfigurable manufacturing;
cyber-physical production systems;
intelligent factories;
autonomous manufacturing systems;
intelligent assembly;
production scheduling and optimization;
intelligent quality management;
additive manufacturing; and
human-centered manufacturing.

Industrial Internet of Things and Connected Systems

Research on connected industrial environments and machine-to-machine communication, including:

Industrial Internet of Things;
smart sensors and sensing systems;
industrial wireless communication;
edge and fog computing;
cloud-edge collaboration;
machine-to-machine communication;
industrial networking;
5G/6G-enabled industrial systems;
embedded intelligence; and
intelligent device ecosystems.

Digital Twins and Cyber-Physical Systems

Research on virtual-physical integration and intelligent system modeling, including:

digital twins;
cyber-physical systems;
virtual commissioning;
real-time simulation;
digital modeling of industrial systems;
system monitoring and synchronization;
predictive digital twins;
industrial metaverse applications;
physics-informed modeling; and
virtual-physical interaction.

Robotics and Autonomous Systems

Research addressing intelligent machines and autonomous industrial operations, including:

industrial robotics;
collaborative robots;
autonomous mobile robots;
unmanned industrial systems;
intelligent manipulation;
robot perception;
autonomous inspection;
human-robot collaboration;
multi-agent systems;
swarm intelligence; and
intelligent logistics robots.

Industrial Data Science and Analytics

Research focused on industrial data processing, modeling, and decision-making, including:

industrial big data analytics;
predictive analytics;
prescriptive analytics;
industrial data mining;
machine condition monitoring;
fault detection and diagnosis;
predictive maintenance;
remaining useful life prediction;
anomaly detection;
process mining;
industrial time-series analysis; and
data-driven optimization.

Intelligent Control and Automation

Research involving intelligent control architectures, optimization, and autonomous operation, including:

intelligent control;
adaptive control;
distributed control systems;
autonomous control;
model predictive control;
industrial automation;
process optimization;
self-organizing systems;
intelligent monitoring;
real-time industrial control; and
advanced control architectures.

Computer Vision and Intelligent Inspection

Research on machine vision and intelligent perception for industrial applications, including:

industrial computer vision;
visual inspection;
defect detection;
machine vision;
image-based quality control;
multimodal sensing;
visual recognition;
automated inspection;
intelligent monitoring; and
vision-guided robotics.

Industrial Cybersecurity and Trustworthy Systems

Research addressing security, reliability, privacy, and resilience in intelligent industrial environments, including:

industrial cybersecurity;
cyber-physical security;
Industrial IoT security;
secure industrial communication;
privacy-preserving industrial intelligence;
blockchain for industrial systems;
trusted computing;
intrusion detection;
resilient industrial systems;
AI security; and
secure digital infrastructure.

Intelligent Supply Chains and Industrial Operations

Research on intelligent coordination and optimization across industrial networks, including:

smart supply chains;
intelligent logistics;
digital supply chain management;
industrial operations research;
production planning;
inventory optimization;
industrial resource allocation;
intelligent transportation and warehousing;
resilient supply networks; and
AI-supported operational decision-making.

Sustainable and Green Intelligent Industry

Research connecting intelligent technologies with sustainability and resource efficiency, including:

sustainable manufacturing;
green industrial transformation;
energy-efficient production;
intelligent energy management;
low-carbon industrial systems;
circular manufacturing;
resource optimization;
environmental monitoring;
smart energy systems; and
AI-enabled sustainability.

Emerging Engineering Technologies

The journal also welcomes research on emerging engineering technologies with significant potential for intelligent industrial applications, including:

advanced sensing;
quantum technologies for industrial applications;
next-generation computing;
advanced materials with intelligent functions;
intelligent micro- and nano-systems;
digital engineering;
augmented and virtual reality;
industrial metaverse technologies;
edge intelligence;
neuromorphic computing; and
other emerging technologies relevant to the future of intelligent industry.

Interdisciplinary Research

ETII recognizes that the development of intelligent industry increasingly requires integration across traditional disciplinary boundaries.

The journal therefore encourages submissions combining multiple fields, such as:

artificial intelligence and mechanical engineering;
computer science and manufacturing;
automation and operations research;
data science and industrial engineering;
robotics and human factors;
cybersecurity and industrial systems;
communications engineering and smart manufacturing;
information systems and digital transformation; and
intelligent engineering and sustainable development.

Particular consideration is given to research that demonstrates meaningful integration between technological innovation, engineering implementation, and industrial value.

Types of Contributions

Emerging Technologies in Intelligent Industry considers high-quality manuscripts including:

Original Research Articles

Original theoretical, methodological, experimental, computational, or empirical studies presenting significant advances in intelligent industry and emerging technologies.

Review Articles

Critical and systematic reviews that synthesize current knowledge, identify research gaps, evaluate technological development, and propose future research directions.

Technical Articles

Articles presenting new engineering methods, system architectures, algorithms, platforms, frameworks, or technological implementations.

Application and Case Studies

Rigorous studies demonstrating the implementation, validation, or evaluation of emerging technologies in real industrial environments.

Data and Benchmark Studies

Research presenting valuable industrial datasets, benchmark frameworks, evaluation protocols, or reproducible experimental resources.

Perspectives and Research Frontiers

Scholarly discussions of emerging technological directions, interdisciplinary challenges, rapidly developing research areas, and future industrial paradigms.

Special Issues may also be organized on focused topics representing rapidly developing areas in intelligent industry.

Research Quality and Relevance

Manuscripts submitted to ETII are expected to demonstrate clear scientific, methodological, technological, or industrial contributions.

Relevant submissions should normally address one or more of the following:

development of a new theory, method, algorithm, architecture, or engineering framework;
meaningful improvement over existing approaches;
rigorous validation using experiments, simulations, industrial datasets, prototypes, or real-world deployments;
clear implications for intelligent industrial systems;
significant interdisciplinary integration;
reproducible or transferable technological contributions; or
new insights into emerging industrial technologies and their implementation.

Purely descriptive manuscripts, manuscripts without sufficient methodological contribution, and studies with limited connection to intelligent industry may fall outside the journal's scope.

Intended Readership

The journal serves an international readership including:

academic researchers;
engineers and scientists;
doctoral and postgraduate researchers;
industrial technology developers;
manufacturing and automation professionals;
artificial intelligence specialists;
data scientists;
industrial system architects;
technology managers;
policymakers; and
professionals involved in digital and intelligent industrial transformation.