Expert Systems with Applications Review Time

The official ESWA journal page, journal insights, and guide for authors provide live scope and timing context. Elsevier reports seven days to first decision, 101 days to decision after review, 187 days to acceptance, and five days to online publication. They are workflow aggregates, not a deadline.

The exact ESWA SciRev page has N=19 reports. It currently reports 5.4 months for first review, 7.7 months accepted handling, 10 days for immediate rejection, 1.9 rounds, and 2.2 reports. This community record supports stage separation and uncertainty, not a guaranteed calendar.

ESWA's scope requires an original expert or intelligent system and a credible application contribution. A paper that simply applies a familiar model to a familiar dataset may be screened rapidly; a paper that reaches review can receive detailed scrutiny of baselines, data construction, validation, reproducibility, and decision value.

A renamed or recombined method lacks a formal advance. State what changes mathematically or computationally, which prior method is the real comparator, and why the change solves a real limitation.

The train-test split leaks information. Explain patient, customer, location, time, document, device, or source grouping; fit preprocessing only on training data; and prevent related records from crossing evaluation partitions.

The baseline comparison is weak. Use competitive, properly tuned baselines under the same data, budget, features, preprocessing, and evaluation protocol. A comparison against easy baselines cannot establish utility.

The application is decorative. Name the user, decision, cost of error, deployment constraint, and evidence that the model improves a relevant outcome rather than only a benchmark score.

The result does not survive uncertainty or shift. Show variance, confidence intervals, calibration, subgroup performance, error cases, robustness, drift, or external validation as appropriate to the claimed setting.

In our pre-submission review work with Expert Systems with Applications manuscripts, the strongest packages make both sides of the contribution testable. The intelligent-system change is defined against a real baseline, and the application change is tied to a decision, user, cost of error, and operational constraint. This avoids the common pattern where an impressive-looking architecture is evaluated on an arbitrary dataset without evidence that it improves a consequential applied task.

The ESWA paper describes a new architecture but not its necessary component. We ask authors to provide the closest baseline, a formal difference, an ablation, and the condition where the difference should matter. If the benefit disappears under a fair baseline, the contribution needs a narrower claim.

The ESWA dataset split lets related observations cross partitions. We inspect time, subject, source, site, document, and duplicate structure. Grouped or temporal validation can change the result substantially, but it gives a reviewer a credible estimate of deployment performance.

The ESWA application claim has no decision threshold. We check who will use the output, what action follows, what false positives and false negatives cost, and how calibration or uncertainty affects the choice. This turns application language into an evaluable operational argument.

The ESWA model outperforms weak or untuned baselines. We recommend reporting search space, compute budget, preprocessing, features, and tuning rules for every comparator. A fair result can be smaller and still more useful.

The ESWA revision response adds a result without explaining its effect. We advise a direct map from reviewer concern to added evidence, exact location, and revised conclusion. If a requested test is unavailable, explain the limitation and reduce the generalization claim.

The ESWA manuscript treats one benchmark as deployment proof. We ask authors to state population, data-generation process, time period, drift risk, access constraints, and monitoring needs. A bounded deployment story protects a promising method from an unsupported real-world promise.

The ESWA evaluation reports a single average while hiding where the system fails. We encourage authors to inspect error clusters, difficult subgroups, missing-data cases, confidence calibration, and the operational consequence of each failure mode. This does not weaken a strong model; it shows reviewers that its limits were considered before a real user depends on it.

The ESWA paper claims reproducibility but cannot recreate a reported run. We look for versioned data access, preprocessing scripts, environment details, random seeds, model checkpoints, and a minimal path from raw inputs to each central table. Where legal or privacy constraints prevent release, authors should state the restriction and provide the most inspectable alternative.

Before upload, an ESWA readiness review can test contribution novelty, evaluation integrity, application value, and revision risk.