Information Sciences Submission Guide: How to Submit to INS (Elsevier)

Information Sciences publishes original research articles as its primary form, with a smaller number of tutorial and survey contributions appearing from time to time. The format you choose drives the expectations that apply.

Research articles are the main vehicle and carry no rigid page limit; length is governed by completeness rather than a hard cap. That means an over-long article is judged on whether every section earns its space. A typical full-length submission runs well beyond 3,000 words once methods, experiments, and analysis are complete, and figure-heavy computational work commonly carries 6 to 10 figures, so plan file sizes and figure quality for that range rather than a thin demonstration.

Survey and tutorial articles appear selectively and are held to a higher bar for field-level organization. A survey that only catalogs prior work without a synthesizing contribution reads as a reference list, not a durable review, and is the wrong format for incremental results.

For the submission package, prepare a cover letter, manuscript highlights, a data availability statement, a declaration of competing interest, author contributions in CRediT form, suggested reviewers, and ORCID iDs. Manuscripts that are unclear because of English-language quality can be returned for rewrite before review, so the language bar is enforced at the desk, not deferred.

Before the format and declarations are locked, an Information Sciences submission readiness check can confirm whether the rigor evidence and the theory-practice balance genuinely hold, or whether the work needs another revision round before it is upload-ready.

Information Sciences assigns submissions to an editor who handles them through Editorial Manager. Community-reported data on SciRev puts the first review round near 3.3 months, with about 2.8 reports per round and around two review rounds before a final decision. Treat the stages below as planning ranges, not commitments.

• Day 0: Submission and PDF build. Editorial Manager ingests your files and builds a submission PDF. You proof it, confirm the highlights and declarations, optionally opt into SSRN preprint posting, and submit.

• Days 1 to 7: Editorial desk screen. Editorial staff check scope fit, the general-contribution test, format compliance, language quality, and completeness. The fastest returns happen here: SciRev reports immediate rejections within roughly one day.

Narrow applications and one-sided manuscripts rarely pass this window.

• Days 7 to 28: Handling-editor assignment. A handling editor in the relevant computational-intelligence or informatics area decides whether to send the manuscript for external review or return it. Work without visible ablations, baselines, or reproducibility artifacts is commonly returned at this stage.

• Days 28 to 100: Peer review. Reviewers are invited and reports return, typically two to three reports, on a multi-week cadence.

SciRev rates reviewer comments as difficult, at about 4.0 of 5.0, so expect substantive methodological pushback rather than light edits.

• Days 100 to 150: Decision and revision. Reject, major revision, minor revision, or accept. A revised manuscript must include a response letter addressing each reviewer point.

Most papers that pass review go through at least one major-revision round.

• Days 150 to 200: Final decision and production. Total handling time for accepted manuscripts runs to roughly 6.7 months from submission, with faster outcomes for clean, well-scoped work and slower ones for multi-round revisions.

In our pre-submission review work with Information Sciences manuscripts, four patterns generate the most consistent early returns. None of them are about the method being wrong. They are about contribution shape and rigor packaging that this journal screens for before peer review begins.

In our review of computational-intelligence and informatics manuscripts, each of these is a named rejection pattern you can check your own draft against, and each reflects an editorial triage pattern specific to how handling editors at this journal read submissions. Editors consistently screen for these before sending a manuscript out for review.

Information Sciences guide-for-authors policies define the mechanics below; the patterns describe how manuscripts coming through pre-submission review for this journal most often fall short of them. SciRev community data on this journal, where authors report a first round near 3.3 months, about 2.8 reports per submission, and difficult reviewer comments, is consistent with what we see: most attrition happens at the desk and handling-editor screen, before reviewers ever weigh in, and these four patterns are why.

A narrow application with no general informatics or computational-intelligence contribution. The single most common stall we see is a manuscript that takes a known model, applies it to one dataset or one domain, reports an accuracy gain, and stops. The experiments are clean, but an editor reads the abstract and asks the scope question this journal exists to ask: what does information science learn from this beyond the single result?

When the contribution is "we applied method X to problem Y and it worked," with no new mechanism, no transferable insight, and no generalization beyond the one setting, the manuscript reads as an application paper. Information Sciences expects the advance to be general, and a competent one-domain demonstration is a leading reason computational-intelligence papers are returned before external review.

This is the inverse of an application-first venue: where Expert Systems with Applications rewards the application being real, Information Sciences rewards the contribution being general.

Check whether your Information Sciences manuscript carries a general informatics contribution

A manuscript that is all theory with no validation, or all validation with no theory. Information Sciences balances theory and practice, and the parallel failure is a one-sided paper. A purely theoretical manuscript that proves properties of an algorithm but never tests it on data, or runs only a toy example, reads as incomplete because the practice half is missing.

The mirror case is an empirical manuscript that reports benchmark numbers with no analysis of why the method works, no complexity discussion, and no theoretical framing, which reads as an engineering result without the science. Reviewers in this journal treat the theory-practice pairing as part of the contribution, so a manuscript strong on only one side is screened as half a paper.

The fix is to decide, before submitting, whether the draft genuinely carries both a methodological contribution and validation that speaks to it.

Check if your Information Sciences manuscript balances theory and validation

Missing methodology rigor: no ablation, weak or outdated baselines, no reproducibility. Because reviewer comments here are rated difficult, the rigor bar is enforced harder than at lighter venues.

The recurring gap is a results section that reports a headline number without an ablation showing which component drives the gain, a baseline set that omits recent competitive methods or compares only against weak or self-implemented alternatives, and no reproducibility artifacts such as released code, data, or random seeds. The figures look complete, but a reviewer cannot tell whether the improvement is real, which design choice causes it, or whether it replicates.

A manuscript that treats rigor as optional rather than shown is consistently returned with a major-revision or reject decision, and at this journal that costs a full multi-month cycle.

Check whether your Information Sciences methods show ablations and reproducible baselines

Scope drift to a data-engineering, soft-computing, or pure-application venue. Information Sciences sits in a crowded informatics cluster, and a recurring desk return is a manuscript whose real contribution belongs elsewhere.

The introduction frames the work as computational intelligence, but the novel result is a database, indexing, or data-management advance that fits IEEE Transactions on Knowledge and Data Engineering, or a soft-computing technique whose home is Applied Soft Computing, or a neural-architecture result that fits Neurocomputing, or an application study that fits Expert Systems with Applications. Editors at this journal identify quickly when the genuine advance would be evaluated more naturally by a different specialist community.

A manuscript routed to Information Sciences because it is high-profile, rather than because the contribution is a foundations-of-information-science advance, is consistently identified as a scope mismatch before review.

This guide tells you what Information Sciences editors look for; a Manusights review tells you whether YOUR paper passes that screen. A Manusights review checks the generality of the contribution, the theory-practice balance, the ablation and baseline evidence, and the scope routing against the editorial bar this journal applies before peer review. Paid Manusights reviews include a 60-day money-back guarantee, and we do not train models on submitted manuscripts.

Before submitting, an Information Sciences scope and rigor readiness check tests whether your contribution, balance, and rigor evidence clear the editorial bar this journal applies before peer review.

This guide was built from the Information Sciences guide for authors, Elsevier's Editorial Manager submission documentation, the journal's ScienceDirect scope page, and Manusights pre-submission review patterns from computational-intelligence and informatics manuscripts. We checked the submission system, the SSRN preprint option, the article types, and the declaration requirements against the journal's own pages, and we cross-checked review-timing ranges against SciRev community data and published metrics. The failure patterns describe what we see most often when computational-intelligence manuscripts come through pre-submission review for this journal.

Use this page before you upload, when the official instructions cannot answer the real question: whether your contribution is general enough, your theory-practice balance is genuine, your rigor evidence is visible, and your scope routing is correct. Source limitation: Elsevier updates article-type details, charges, and policies after this review date, so confirm administrative specifics against the journal's official pages before submission. To pressure-test the manuscript itself, run a manuscript readiness check.

• Expert Systems with Applications submission guide
• IEEE Transactions on Pattern Analysis and Machine Intelligence submission guide
• For the broader cluster, see the computer science journals overview.

Before you upload, run your manuscript through an Information Sciences submission package check to catch the scope, balance, and rigor issues editors filter for on first read. The check is free to run (/ai-review) and takes a single upload.