Call for Papers on Machine Learning
ENTECH’s Special Issue on Machine Learning 2026
This call for papers on machine learning invites researchers, educators, students, engineers, and AI/ML professionals to contribute original research papers, explainers, tutorials, and case studies for ENTECH Magazine’s Special Issue on Machine Learning 2026.
Google ScholarIndexed
Figures and indexing refer to ENTECH Magazine as a publication (ISSN 2584-2749).
Special Issue on Machine Learning 2026
ENTECH Magazine is publishing a Special Issue on Machine Learning 2026. This call for papers on machine learning aims to bring together accessible, technically accurate, and research-informed contributions that cover both the foundations and the emerging applications of Machine Learning.
ENTECH is an open-access magazine. Readers can access published content without a paywall. The magazine is identified by ISSN 2584-2749. Eligible published papers may receive a DOI through Zenodo and OpenAIRE after editorial curation, where applicable. ENTECH Magazine is indexed with Google Scholar, Zenodo, OpenAIRE, and Magzter.
This call for papers on machine learning welcomes:
Why Contribute to the Special Issue on Machine Learning 2026?
This call for papers zero APC is for authors who want visibility without a publication fee. Publication does not guarantee academic credit, employment, admission, promotion, or research impact.
Academic and professional visibility
Showcase your expertise in Machine Learning and Artificial Intelligence.
DOI and ISSN
Eligible publications can receive appropriate publication metadata, where applicable.
Global readership
Reach ENTECH’s growing audience across 100+ countries.
Professional recognition
Add your published contribution to your CV, LinkedIn, or academic portfolio.
Author visibility
Accepted contributions may be promoted through ENTECH’s website, newsletter, and social channels.
Open access and knowledge sharing
Published content remains freely accessible to readers.
Topics for This Call for Papers on Machine Learning
We welcome original work across the Machine Learning landscape. The list below is indicative, not exhaustive.
Machine Learning Fundamentals
- Introduction to Machine Learning
- Types of Machine Learning
- Machine Learning Algorithms
- Model Evaluation and Validation
- Feature Engineering
- Overfitting and Underfitting
- Bias-Variance Tradeoff
- Machine Learning Workflows
Optimization & Mathematical Foundations
- Loss Functions and Optimization
- Gradient Descent
- Optimization Algorithms
- Information Entropy
- Eigenvalues and Eigenvectors
- Taylor Series in Machine Learning
- Sensitivity Analysis
- AI-driven Optimization
Deep Learning & Advanced AI
- Neural Networks
- Deep Learning Architectures
- Convolutional Neural Networks
- Transformers
- Natural Language Processing
- Computer Vision
- Generative AI
Explainable & Responsible AI
- Explainable AI
- Model Interpretability
- SHAP and LIME
- AI Bias and Fairness
- Responsible AI
- Adversarial Machine Learning
- Transparency and Trustworthy AI
Machine Learning Applications
- Healthcare AI, Medical Imaging, Drug Discovery, Personalized Medicine
- Financial Machine Learning and Fraud Detection
- Robotics and Autonomous Systems
- Scientific Machine Learning
- Climate and Environmental Applications
Emerging Areas
- Large Language Models
- AI Agents
- Multimodal AI
- Machine Learning for Scientific Discovery
- AI Hardware and Edge AI
- Data-Centric AI
- Generative Optimization
Call for Papers on Machine Learning – Who Can Submit?
This call for papers on Machine Learning is open to:
Submissions are welcome regardless of country, institution, or career stage, provided the contribution meets the editorial and technical requirements.
Editorial & Peer Review Process
All submissions undergo editorial screening for relevance, originality, quality, and compliance with submission guidelines. Research papers and selected scholarly contributions may also undergo expert peer review. Authors may be asked to revise their work before the final publication decision. Not every submission is guaranteed peer review.
Submission Deadline
Early submissions are encouraged for this call for papers zero APC. The editorial team may review submissions on a rolling basis before the final deadline.
How to Submit?
Download the Article Template
Begin with the official ENTECH template so formatting stays consistent.
Prepare Your Manuscript
Follow the formatting, originality, citation, and image guidelines.
Submit Your Paper
Submit the completed manuscript through the official submission form, or email the editor.
Editorial Review
Your submission will be evaluated for relevance, quality, originality, and suitability for the Special Issue on Machine Learning 2026.
You can also contact us at [email protected].
What Happens after Acceptance?
Indexing of individual papers follows ENTECH’s standard workflow after publication and curation.
Editorial feedback
Revisions where required before the final version is prepared.
Special Issue publication
Inclusion in the Special Issue on Machine Learning 2026, scheduled for 30 November 2026.
Publication metadata
ISSN identification and DOI assignment where applicable after OpenAIRE/Zenodo curation.
Shareable link
A publication link you can share with colleagues and professional networks.
Promotion
Visibility through ENTECH’s digital channels where applicable.
Open access
Free availability to readers, with no paywall on published content.
FAQs: Call for papers on Machine Learning
Yes. ENTECH Magazine is inviting original research papers, review papers, explainers, tutorials, and case studies for its Special Issue on Machine Learning 2026.
No.
Yes. This is a call for papers with zero APC. There is no Article Processing Charge (APC) for eligible papers accepted for the Special Issue on Machine Learning 2026.
Yes. The Special Issue on Machine Learning 2026 welcomes original technical papers, research papers, review papers, explainers, tutorials and also case studies that fit the scope.
No. Academic affiliation is not mandatory. The submission’s relevance, originality, technical quality and clarity are more important.
APA style.
Researchers, educators, students, engineers, data scientists, AI/ML professionals, technology enthusiasts, and readers interested in Machine Learning.
Yes, after editorial curation.
Yes.
Yes. Students with relevant Machine Learning projects, research, experiments, or technical insights are encouraged to submit.
No. Submissions should be original and not previously published elsewhere.