Our Mission
YUKTAB Publisher exists to make rigorous, peer-reviewed research openly available to readers everywhere. Every article we publish is open access from the day it's released — free for anyone to read, cite, and build on.
5
Journals
388
Published Articles
53
Issues Published
100%
Open Access
Who We Are
About YUKTAB Publisher
Editorial Standards
Each journal under YUKTAB maintains an independent editorial board and a double-blind peer review process. We hold every title — spanning computing, artificial intelligence, multidisciplinary science, and social studies — to the same standard of rigor, regardless of field.
Our History
Since publishing our first issue in 2015, YUKTAB has grown into a home for five distinct, peer-reviewed journals, each with its own editorial identity and community of authors and reviewers.
Open Access Publishing
Readers never pay to access an article — every issue is free to read, download, and cite. Publishing is supported through author-side article processing charges, disclosed upfront with no hidden costs at acceptance.
Peer Review Process
All submissions go through double-blind peer review by at least two independent reviewers in the relevant field. Editorial decisions are made on scholarly merit alone, and reviewer identities are kept confidential throughout.
Indexing & Discoverability
Published articles are assigned persistent identifiers and made discoverable through standard academic indexing channels, so your work can be found, cited, and tracked well beyond our own website.
Publication Ethics
We follow established publication ethics guidelines on authorship, conflicts of interest, and data integrity. Any concerns raised about a published article are investigated formally by the relevant editorial board.
Plagiarism Policy
YUKTAB Publisher maintains a strict, zero-tolerance plagiarism policy. Every submission is screened through similarity-detection software before entering peer review, and confirmed cases of plagiarism — including self-plagiarism and improper citation — result in immediate rejection or retraction,...
Submit Your Work
For Authors
How to Submit
Choose the journal that best fits your research area, register an author account on that journal's site, and submit your manuscript through its online submission system. Full formatting and referencing guidelines are provided on each journal's Author Guidelines page.
What We Look For
Original, unpublished research with a clear contribution to the field, sound methodology, and honest reporting of results. Submissions currently under review elsewhere cannot be considered.
Review Timeline
Most submissions receive an initial editorial decision within 21 days. Manuscripts that proceed to full peer review typically receive reviewer feedback within 6–8 weeks.
Article Processing Charges
As an open-access publisher, we cover the cost of free public access through an author-side processing charge, disclosed upfront before submission — with no surprise fees at acceptance.
Latest Research
Recently Published Articles
The Metascience
2026-10-05
Advancing Interpretable AI for Clinical Decision Support and Image-Driven Cancer Detection: A Review of Reliability, Transparency, and Diagnostic Performance
Explainable AI has been studied separately for cancer classification and for other image-based clinical decision support tasks such as pneumonia detection, but a unified account of what these two literatures share, and where they diverge, is largely...
The Metascience
2026-10-05
Implementing Adaptive Machine Learning Frameworks for Financial Fraud Prevention
Adaptive machine-learning frameworks for fraud prevention combine several design choices — the learning algorithm, the treatment of class imbalance, the training window, the alert threshold and the policy for updating the model — yet these are usually...
International Journal of Modern Computing
2026-09-07
A Unified Explainable AI Framework for Reliable Clinical Decision Support and Medical Image-Based Cancer Classification
Artificial intelligence (AI) can match specialist accuracy in cancer detection, yet adoption in clinical practice remains limited because most high-performing models are opaque, poorly calibrated and trained on data that cannot be shared across...
International Journal of Modern Computing
2026-09-05
Federated and Drift-Resilient Machine Learning for Financial Fraud and Distress Detection
Federated learning allows multiple financial institutions to train a shared fraud-detection or financial-distress model collaboratively without any institution exposing its raw transaction or account data to the others, a privacy-preserving property that...
International Journal of Modern Computing
2026-09-05
Adaptive and Explainable Machine Learning for Temporal Drift-Aware Financial Fraud Detection and Risk Monitoring: A Review
Financial fraud detection is a moving target: fraud tactics, legitimate consumer behavior, and the statistical relationship between transaction features and fraud risk all shift over time, so a model that is accurate on the data it was trained on can...
International Journal of Modern Computing
2026-09-04
From Research to Practice: Operational Deployment of Temporal Drift Detection in Financial Fraud Prevention
Temporal drift detection is well established in research, but fraud teams that deploy it face practical questions that research benchmarks rarely answer: how often the tests will raise false alarms, how large a shift they can see, whether they detect...
