Author Guidelines and Manuscript Preparation

Introduction

The Journal of Integrated Engineering Innovation & Applications (JOIEIA) publishes leading research in artificial intelligence, machine learning, computer science, and the digital transformation of engineering systems. Manuscripts must present original contributions, clear methodologies, and significant findings for a multidisciplinary audience.

Manuscript Templates

Authors must prepare their submissions using the official JOIEIA manuscript template:

Microsoft Word Template: Download Docx

Important Notice: All sample text and instructional content included in the templates must be removed prior to submission. The template structure and formatting should remain unaltered.

Manuscript Specifications

Format Requirements

Article Length: Research articles should comprise 8–10 pages in the JOIEIA double-column format. Short communications and technical notes may range from 4–6 pages.
Typeface: Times New Roman, 10-point font for body text
Layout: Double-column format with single line spacing as specified in the template
Modifications: Authors must not alter the template's predefined formatting, margins, or style settings.

Manuscript Organization

All submissions to JOIEIA must adhere to the following organizational structure:

1. Title

The manuscript title should be concise yet informative, typically comprising 10–15 words. It must:

Clearly convey the central focus of the research
Avoid the use of abbreviations, acronyms, and mathematical formulae
Follow sentence case capitalization (capitalize only the first word and proper nouns)
Be sufficiently specific for effective indexing while remaining accessible to a broad readership

Example: "A deep learning framework for predictive maintenance in smart manufacturing systems"

2. Author Information

Complete information must be provided for all contributing authors, including:

Full Name: First name, middle initial(s), and surname
Institutional Affiliation: Department, institution, city, and country
Email Address: Institutional email addresses are strongly preferred
ORCID Identifier: Registration with ORCID (Open Researcher and Contributor ID) is strongly recommended for all authors

Corresponding Author Designation:

The corresponding author must be clearly identified using an asterisk (*) notation. This individual assumes responsibility for all communications throughout the submission, peer review, and publication processes. It should be noted that designation as corresponding author does not necessarily denote lead or senior authorship.

3. Author Roles and Contributions

Each author must explicitly declare their specific contributions to the research using the CRediT (Contributor Roles Taxonomy) framework. Authors should indicate their roles in one or more of the following categories:

Conceptualization: Ideas, formulation, or evolution of research goals and aims
Methodology: Development or design of methodology; creation of models
Software: Programming, software development; designing computer programs; implementation of algorithms
Validation: Verification of reproducibility of results/experiments
Formal Analysis: Application of statistical, mathematical, computational techniques to analyze data
Investigation: Conducting the research and investigation process, particularly performing experiments or data collection
Resources: Provision of study materials, reagents, materials, patients, laboratory samples, animals, instrumentation, computing resources
Data Curation: Management activities to annotate, scrub data, and maintain research data
Writing—Original Draft: Preparation, creation, and presentation of the published work
Writing—Review & Editing: Critical review, commentary, or revision
Visualization: Preparation, creation, and presentation of the published work in terms of data presentation or visualization
Supervision: Oversight and leadership responsibility for the research activity planning and execution
Project Administration: Management and coordination responsibility for the research activity
Funding Acquisition: Acquisition of financial support for the project

Example Format:

"A.K. Singh: Conceptualization, Methodology, Writing—Original Draft; P. Sharma: Software, Data Curation, Visualization; R. Gupta: Formal Analysis, Writing—Review & Editing; M. Kumar: Supervision, Funding Acquisition."

Authorship Responsibilities:

All listed authors must:

Have made substantial intellectual contributions to the research
Approved the final version of the manuscript for submission
Agree to be accountable for all aspects of the work
Be prepared to clarify questions related to the accuracy or integrity of any part of the work

Changes to Authorship:

Any changes to authorship (additions, deletions, or reordering) after initial submission require:

Written explanation of the reason for the change
Written confirmation from all authors (including those being added or removed) that they agree to the change

4. Funding Information

Complete and transparent disclosure of all funding sources is mandatory. Authors must:

Provide Full Details:

Name of funding organization(s)
Grant or award number(s)
Name(s) of principal investigator(s) or grant recipient(s)
Brief description of the role of funding source (if applicable)

Include All Support Types:

Direct research grants and contracts
Institutional funding
Fellowships and scholarships
Equipment grants or computational resource allocations
Industry partnerships and collaborative funding
Cloud computing credits (AWS, Google Cloud, Azure, etc.)

Statement Format:

If funding was received:

"This research was supported by the Department of Science & Technology, Government of India (Grant No. DST/INSPIRE/2024/001234) and the National Science Foundation, USA (Grant No. NSF-IIS-2024-56789). Computational resources were provided by the High Performance Computing Center at XYZ University."

If no external funding was received:

"The authors received no specific funding for this work."

Disclosure Requirements:

Authors must disclose:

Any financial relationships with entities that could be perceived as influencing the research
Whether funding agencies had any role in study design, data collection, analysis, interpretation, manuscript preparation, or decision to publish
Any constraints on data sharing imposed by funding agreements

5. Conflicts of Interest

Authors must disclose all potential conflicts of interest, including:

Financial interests in companies that might be affected by the research
Personal relationships that could influence judgment
Professional affiliations that could be perceived as affecting objectivity
Patents or intellectual property related to the research
Consulting relationships or honoraria

Statement Format:

If conflicts exist:

"Author A.K. Singh is a consultant for TechCorp Inc. Author M. Kumar holds stock options in AI Solutions Ltd. These relationships did not influence the research design or interpretation of results."

If no conflicts exist:

"The authors declare no conflicts of interest."

6. Abstract

The abstract serves as a concise summary of the manuscript and must adhere to the following specifications:

Length: 150–250 words
Format: Single paragraph without section headings
Content Structure:
- Purpose/Background: Research context, problem statement, and objectives
- Methodology: Brief description of approach, algorithms, models, datasets, or systems employed
- Principal Results: Key findings with specific metrics (accuracy, efficiency gains, error reduction, etc.)
- Conclusions/Implications: Significance of findings and contributions to the field

Restrictions:

No citations or reference numbers
No non-standard abbreviations
Must be self-contained and comprehensible independently

Example:

"Smart manufacturing systems require robust predictive maintenance strategies to minimize downtime and optimize production efficiency. This study presents a hybrid deep learning framework combining convolutional neural networks (CNN) and long short-term memory (LSTM) networks for early fault detection in industrial machinery. The model was trained and validated on sensor data from 120 manufacturing units over 18 months, comprising vibration, temperature, and acoustic signals. Results demonstrate 94.7% accuracy in predicting equipment failures 72 hours in advance, representing an 8.3% improvement over existing methods. The framework reduces false positive rates by 23% while maintaining computational efficiency suitable for edge deployment. These findings enable proactive maintenance scheduling and contribute to advancing Industry 4.0 implementation in smart factories."

7. Keywords

Authors must provide 3 to 6 keywords that:

Are arranged in alphabetical order
Reflect the specific focus and content of the research
Exclude general terms, plural forms, and concepts already represented in the title
Avoid abbreviations unless widely recognized

Examples:

convolutional neural network; digital twin; Industry 4.0; predictive maintenance; smart manufacturing
deep learning; energy optimization; neural network; renewable energy; smart grid

8. Introduction

The introduction should contextualize the research and typically spans 1–2 pages. It must:

Establish the significance and real-world relevance of the problem
Provide concise literature review highlighting recent advances and current limitations
Identify specific gaps in knowledge or technological challenges
Clearly articulate research objectives and contributions
Briefly outline the methodological approach

Writing Guidelines:

Write accessibly for multidisciplinary readers
Define specialized terminology upon first use
Emphasize practical applications and engineering impact

9. Methodology

This section must provide sufficient detail for replication and typically includes:

For AI/ML Research:

Dataset Description: Source, size, preprocessing, train/validation/test splits, data augmentation techniques
Model Architecture: Detailed description with layer specifications, activation functions, network depth
Training Procedure: Hyperparameters, optimization algorithms, learning rate schedules, batch sizes, epochs
Implementation Details: Frameworks (TensorFlow, PyTorch, etc.), hardware specifications, training time
Evaluation Metrics: Accuracy, precision, recall, F1-score, AUC-ROC, computational complexity

For Systems Research:

System Design: Architecture diagrams, component specifications, integration approaches
Implementation: Software frameworks, protocols, communication standards
Experimental Setup: Test environments, equipment, configurations
Validation Methods: Testing procedures, performance benchmarks

For Algorithm Development:

Clear algorithmic descriptions with pseudocode
Complexity analysis (time and space)
Comparison with baseline methods

10. Results and Discussion

Present findings systematically using:

Performance tables with statistical measures (mean ± standard deviation)
Confusion matrices and classification reports
Learning curves and convergence plots
Ablation studies demonstrating component contributions
Comparative analysis with state-of-the-art methods
Visualizations (attention maps, feature embeddings, system outputs)

Discussion should address:

Interpretation of results in context of existing literature
Practical implications for engineering applications
Computational efficiency and scalability
Limitations and failure cases
Generalization capabilities across different scenarios

11. Conclusions

This section (typically ½–1 page) should:

Summarize principal findings and key contributions
Highlight practical applications and engineering impact
Acknowledge study limitations (computational constraints, dataset biases, scope boundaries)
Propose specific future research directions
Emphasize significance for Industry 4.0, sustainability, or energy systems (as applicable)

12. Acknowledgments

Include recognition of:

Funding sources (with complete grant information)
Computational resources and facilities
Dataset providers or collaborators
Technical assistance from non-authors
Industry partners or institutional support

13. References

Minimum Requirement: At least 20 references from high-quality, peer-reviewed sources.

Citation Style: Numbered format,, ordered by appearance.

Reference Formats:

Journal Article:
Y. LeCun, Y. Bengio, G. Hinton, "Deep learning," Nature, vol. 521, no. 7553, pp. 436–444, 2015. DOI: 10.1038/nature14539

Conference Paper:
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, I. Polosukhin, "Attention is all you need," in Proc. Advances in Neural Information Processing Systems (NeurIPS), Long Beach, CA, USA, Dec. 2017, pp. 5998–6008.

Dataset/Repository:
MNIST Database, "Handwritten digit database," [Online]. Available: http://yann.lecun.com/exdb/mnist/ [Accessed: Nov. 10, 2025]

Software/Framework:
M. Abadi et al., "TensorFlow: Large-scale machine learning on heterogeneous systems," 2015. [Online]. Available: https://www.tensorflow.org

Requirements:

All authors listed in full (no "et al." in reference list)
Include DOIs when available
Recent publications from premier AI/ML/engineering venues preferred

14. Nomenclature (if applicable)

Define mathematical symbols, variables, and parameters:
Example:

n = number of training samples
d = input feature dimensionality
L = number of network layers
h = number of attention heads
θ = model parameters
η = learning rate
λ = regularization coefficient
O(n²) = computational complexity

15. Appendices (if applicable)

Include supplementary material such as:

Detailed mathematical derivations
Extended algorithm pseudocode
Additional experimental results
Hyperparameter sensitivity analyses
Supplementary visualizations

Technical Formatting

Equations

Use MathType or Microsoft Equation Editor.

Tables

Table 1. Performance comparison on benchmark datasets

Model	Accuracy (%)	F1-Score	Parameters	Inference Time (ms)
ResNet-50	93.5	0.921	25.5M	45
VGG-16	92.8	0.915	138M	78
Proposed	94.7	0.938	22.3M	38

Figures

High resolution (≥300 DPI)
Clear captions below figures
Include architecture diagrams, performance plots, confusion matrices, system flowcharts
Label axes with units

Code Availability

Authors are encouraged to provide:

Links to GitHub/GitLab repositories
Code documentation and README files
Pre-trained model weights (if applicable)
Docker containers for reproducibility