Aims & Scope

JDDIEA publishes research that turns data-driven intelligence into measurable engineering impact, with clear methods and validation.

What we look for

A strong engineering problem statement, a clearly described intelligence method, and measurable validation (experiments, simulations, benchmarks, deployments, or real datasets).

Aims

Journal of Data-Driven Intelligence in Engineering Applications (JDDIEA) is an international peer-reviewed, open access journal focused on research that applies data-driven intelligence to solve real engineering problems. The journal encourages submissions that connect theory with implementation, including reproducible experiments, simulations, prototypes, and case studies.

All submissions undergo editorial screening followed by peer review (typically double-blind, unless a special issue specifies otherwise). JDDIEA is open access and publishes accepted articles under a Creative Commons license (CC BY) to support wide reuse with proper attribution. Authors are strongly encouraged to share datasets, code, and experimental details where feasible to improve reproducibility.

JDDIEA aims to offer a clean, author-friendly publication experience while maintaining academic rigor through structured editorial screening and peer review.

Peer review

Editorial screening + peer review (typically double-blind). Decisions are based on scope fit, novelty, technical soundness, and validation quality.

Open access & licensing

JDDIEA provides immediate open access to published articles. Articles are intended to be published under CC BY (reuse allowed with attribution).

Reproducibility expectation

Authors are encouraged to include reproducible details (data, code, parameters, and environment) or clearly explain any restrictions.

Engineering-first focus

We prioritize applied contributions with measurable engineering outcomes, realistic constraints, and meaningful baselines.

Scope focus

We publish work where data, learning, and intelligent decision methods are used to improve engineering systems, performance, reliability, safety, or efficiency.

Preferred contribution types

Original research, high-quality review papers, applied case studies, and reproducible methods with clear baselines and evaluation.

Scope and Topics

The journal welcomes interdisciplinary submissions across AI, machine learning, analytics, intelligent systems, IoT-enabled engineering, control, optimization, and applied computational methods. Example topic areas include, but are not limited to:

Industrial AI and Smart Manufacturing

Industrial AI, smart factories, adaptive automation
Quality analytics, anomaly detection, process control
Human-machine collaboration, safety, HMI

Predictive Maintenance and Reliability

Fault diagnosis, condition monitoring, prognostics
Remaining useful life (RUL) prediction
Digital twins for maintenance and lifecycle modeling

IoT, Edge Intelligence, CPS

IoT-enabled engineering, sensor fusion, edge AI
Cyber-physical systems and real-time inference
Security, robustness, privacy-aware intelligence

Control, Optimization, Planning

Optimization, scheduling, decision support systems
Control and automation with intelligent strategies
Resource allocation and constrained planning

Energy, Power, Transportation, Infrastructure

Smart grids, load forecasting, energy analytics
EV charging, traffic analytics, mobility intelligence
Smart infrastructure monitoring and resilience

Applied AI, ML, and Data Analytics

Machine learning, deep learning, explainable AI (XAI)
Time-series analytics, forecasting, and anomaly detection
Benchmarking, reproducibility, evaluation protocols

What we encourage

Strong engineering context

Clear definition of the engineering problem
Practical constraints and real-world relevance
Meaningful metrics tied to engineering outcomes

Solid validation

Experiments, simulations, prototypes, or deployments
Baselines and fair comparisons
Reproducible setup (datasets, parameters, environment)

Out of scope

To keep the journal focused on engineering applications, the following types of submissions are typically not considered:

Purely theoretical work without a clear engineering problem definition or validation
Papers with no measurable evaluation, baseline, or practical evidence
Low-effort surveys that do not provide a structured taxonomy, insights, and a clear research gap
Manuscripts that primarily repackage existing methods without novelty or meaningful application contribution

If your paper clearly connects intelligence methods with real engineering outcomes, JDDIEA is the right fit.

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