Kaimen Rigor reviewed

Rigor-Reviewed Manuscript

Sign language recognition using the fusion of image and hand landmarks through multi-headed convolutional neural network

Name: Kaimen Rigor review — Sign language recognition using the fusion of image and hand landmarks through multi-headed convolutional neural network
Item: Sign language recognition using the fusion of image and hand landmarks through multi-headed convolutional neural network
Rating: 70
Author: Alpha1

Pathan RK, Biswas M, Yasmin S, Khandaker MU, Salman M, Youssef AAF.

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Kaimen Rigor review — 3.25★ (Adequate), 0 critical, 5 high issues for “Sign language recognition using the fusion of image and hand landmarks through multi-headed convolutional neural network”

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This rigor review was examined and confirmed by Alpha1 Editorial · August 5, 2026

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Sign language recognition using the fusion of image and hand landmarks through multi-headed convolutional neural network

Pathan RK, Biswas M, Yasmin S, Khandaker MU, Salman M, Youssef AAF.

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Journal

DOI

10.1038/s41598-023-43852-x

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Issued Aug 5, 2026Engine 7.15.0

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Kaimen Rigor reviewed

Rigor-Reviewed Manuscript

Sign language recognition using the fusion of image and hand landmarks through multi-headed convolutional neural network

Pathan RK, Biswas M, Yasmin S, Khandaker MU, Salman M, Youssef AAF.

Paper source

Sign language recognition using the fusion of image and hand landmarks through multi-headed convolutional neural network

Read full text (PMC)Journal

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Links back to this Kaimen Rigor review.

Alpha1 Editorial

This rigor review was examined and confirmed by Alpha1 Editorial · August 5, 2026

Kaimen Rigor review

Kaimen Rigor reviewed

3.25 / 5

Adequate — real issues to address

Computational Study

Critical

High-priority

How this rating was calculated▸

Started at 5★ — deductions

IntegrityIntegrity concern−0.5★
ReportingKey resources not met−0.5★
LinksDead data/code link ×2−0.5★
ReportingStudy design partially met−0.25★

Model-reported confidence: moderateReviewer agreement: lowEngine: 7.15.0Source: full text

Not verified in this report

Statistics were not checked: no recomputable values were found in this text — no test statistic reported with its degrees of freedom, no effect estimate printed with both a 95% CI and a p-value, and no percentage printed with both its count and its denominator.

Biggest issues

01Key resources not identified
highDisclosurereviewer-assessedFull detail
The only applicable resource is software tools, which are named but not identified with versions or RRIDs, and custom code is not shared, resulting in a fail.
02Other integrity concern
mediumAuthenticityreviewer-assessedFull detail
Tortured phrase "convolutional neural organization" — an unnatural paraphrase of "convolutional neural network", a fingerprint of paraphrasing-tool / paper-mill text.
…the sign language gestures dataset on a convolutional neural organization model utilizing Inception v3. The model…
reviewer’s wording
03Declared data/code link does not resolve
mediumAvailabilityrecomputedFull detail
Dead link — nothing to verify.
“https://www.kaggle.com/datasets/mrgeislinger/asl-rgb-depth-fingerspelling-spelling-it-out”
04Declared data/code link does not resolve
mediumAvailabilityrecomputedFull detail
Dead link — nothing to verify.
“https://www.kaggle.com/grassknoted/asl-alphabet”

Findings by category

01ArithmeticNone found 102OverreachOverstated conclusions 103AuthenticityData authenticity concerns 204DisclosureReporting gaps 105AvailabilityBroken references and links 106CopyeditCopyediting

This Kaimen Rigor review uses Kaimen Rigor reviewers trained on a curated corpus of high-fidelity and retracted papers, with expert supervision and curation. It can still make mistakes; verify each finding against the source before relying on it.

The paper presents a sensible computational approach for ASL recognition with a multi-headed CNN, but has several reporting gaps: software tools lack version numbers, no code is shared, the data URL is dead, and there is no conflict of interest statement. The text contains grammatical errors and a tortured phrase ('convolutional neural organization') that raises concerns about potential language manipulation.

Three independent reviewer runs were synthesized. The statistical analysis dimension was not applicable because no inferential statistics were used. The copyedit pass flagged 30 issues (8 major). The verification components found 2 dead links and a tortured phrase indicating possible text generation.

Numerical inconsistencies

None found

Values that contradict each other or are impossible for the stated sample: recomputed p-values and test statistics, GRIM/GRIMMER checks on summary numbers, percentages against their own counts, totals against their parts, and estimates against their own confidence intervals.

Checked — nothing surfaced.

Overstated conclusions

1 finding · worst low

Conclusions that reach past what the paper's own results support — including a significance claim that no longer holds when the statistic is recomputed, and efficacy resting on an unvalidated surrogate endpoint.

Conclusions only partially backed by the presented evidenceAssessed

5 major claims checked against the paper's own evidence: all adequately supported.

Data authenticity concerns

1 finding · worst medium

An adversarial read for patterns associated with data that may not be genuine: results that look too clean, implausibly large effects, duplicated data or images, and methods that do not match the results reported.

Other integrity concernAssessed

1 integrity concern flagged (0 high).

Reporting gaps

2 findings · worst high

Required detail the manuscript never states — study design, biological variables, ethics approval and consent, key resources, statistical reporting, data and code availability, and overall transparency.

Key resources not identifiedAssessed
Study-design details incomplete (controls, blinding, power)Assessed

Eight Dimensions3 pass · 1 warn · 1 fail

The paper reviews prior sign language recognition methods, noting their reliance on costly instruments, plain backgrounds, and high computational power. It then presents a clear rationale for using a multi-headed CNN with image and hand landmark fusion to address these issues. The limitations of prior work, such as dependence on single-channel CNN and plain backgrounds, are explicitly discussed and mitigated by the proposed approach.

Sub-criteria

prior work citedADEQUATE

premise rationaleADEQUATE

limitations addressedADEQUATE

Per reviewer (3) — the votes behind the verdict

PASSReviewer 1· 95% conf

The paper clearly establishes the scientific foundation by citing prior work, providing a logical rationale for the fusion approach, and addressing limitations of existing methods.

Evidence

paraphrase[Introduction, paragraphs 1-3 and Literature Review section]

“Many methods are available that use different things/tools like images (2D, 3D), sensor data (hand globe, Kinect sensor, neuromorphic sensor), videos, etc. All things are considered due to the fact that the captured images are excessively noisy.”

“In this work, we have applied a fusion of traditional image processing with extracted hand landmarks and trained on a multi-headed CNN so that it could complement each other’s weights on the concatenation layer.”

Introduction ¶4

Literature Review, last paragraph

PASSReviewer 2· 90% conf

The paper establishes a clear scientific foundation by citing relevant prior work, providing a logical rationale for the study, and addressing limitations of previous methods.

Evidence

“State-of-the-art techniques centered after utilizing deep learning models to improve good accuracy and less execution time.”

Introduction ¶2

paraphrase[Introduction, paragraph 4]

“The authors state that their main objective is to achieve a better detection rate without relying on a traditional single-channel CNN, and that this method has been proven to work well with less computational power on medical image datasets.”

Introduction ¶1

PASSReviewer 3· 90% conf

The study clearly establishes the need for cost-effective sign language recognition and reviews prior work, highlighting limitations such as reliance on costly instruments or simple backgrounds.

Evidence

“However, such drawbacks can be easily overcome by employing artificial intelligence-based techniques. Since, in this modern era of advanced mobile technology, using a camera to take video or images is much easier, this study demonstrates a cost-effective technique to detect American Sign Language (ASL) using an image dataset.”

Abstract

“The available online datasets are already processed or taken in a lab environment where it becomes easy for recent advanced AI models to train and evaluate, causing prone to errors in real-life applications with different kinds of noises. Accordingly, it is a basic need to make a model that can deal with noisy images and also be able to deliver positive results.”

Introduction ¶3

“The literature summarizes that most of the models used in this application either depend on a single variable or require high computational power. Also, their dataset choice for training and validating the model is in plain background, which is easier to detect. Our main aim is to show how to reduce the computational power for training and the dependency of model training on one layer.”

Literature review, paragraph 5

For a computational study, the applicable sub-criteria are power_analysis (dataset selection), inclusion_exclusion (data curation), controls (baseline/ablations), outlier_handling, and independent_replication. The dataset is described with a 70-30 train-test split, but no formal power analysis or sample size justification is provided. Inclusion/exclusion criteria are clear (24 letters, excluding j and z). The paper presents ablation results comparing the combined model to image-only and hand-landmark-only models, serving as adequate controls. Outlier handling is not mentioned, and independent replication is not reported. Thus 2 of 5 applicable sub-criteria are adequate, yielding a warn.

Sub-criteria

controlsADEQUATE

power analysisNOT REPORTED

blinding levelsN/A

outlier handlingNOT REPORTED

randomization unitN/A

inclusion exclusionADEQUATE

randomization methodN/A

replicate distinctionN/A

independent replicationNOT REPORTED

Per reviewer (3) — the votes behind the verdict

PASSReviewer 1· 90% conf

The study design is appropriate for a computational benchmark: dataset selection is justified, inclusion/exclusion of classes is specified, and baseline models serve as controls.

Evidence

“We intended to avoid that single color background and use a complex background with many users’ hand images to increase the detection complexity.”

Methods

“with 24 letters (except j and z as they contain motion)”

Methods

“Before combining image and hand landmark inputs, we tested both individually and acquired a test accuracy of 96.29% for the image and 98.42% for hand landmarks.”

Result analysis, paragraph 4

WARNReviewer 2· 80% conf

The study design has several gaps: no power analysis, no outlier handling, and no independent replication; however, inclusion/exclusion criteria and baseline controls are adequately reported.

Evidence

paraphrase[Methods, Dataset description]

“We have used the 'ASL Finger Spelling' dataset, which has images of different sizes, orientations, and complex backgrounds of over 500 images per sign (24 sign total) of 4 users.”

paraphrase[Results, Table 5]

“Table 5 compares the combined model with image-only and hand-landmark-only models in terms of parameters, execution time, memory, and accuracy.”

absence[Methods, Training and testing]

No power analysis or outlier handling is reported.

WARNReviewer 3· 70% conf

The study describes the dataset, pre-processing steps, and model architecture in detail, but lacks a formal power analysis and clear distinction between biological and technical replicates.

Evidence

“That’s why we have used the “ASL Finger Spelling” dataset , which has images of different sizes, orientations, and complex backgrounds of over 500 images per sign (24 sign total) of 4 users (non-native to sign language).”

Methods ¶1

“To avoid the overfitting problem, data augmentation and dynamic learning rate reduction have been used.”

Abstract

“For training, we have used 46,023 images, and for testing, 19,725 images.”

Training and testing, paragraph 1

The study uses a pre-existing image dataset for sign language recognition. While the dataset contains images of human hands, the study itself does not involve direct manipulation or collection of biological variables, making this dimension not applicable.

Sub-criteria

demographicsN/A

sex reportedN/A

sex justifiedN/A

age weight healthN/A

housing conditionsN/A

species strain sourceN/A

Per reviewer (3) — the votes behind the verdict

N/AReviewer 1· 95% conf

The study uses a public image dataset of human hands but does not report any biological variables such as sex, age, or health status of the subjects. As a computational study on image data, these variables are not applicable.

Evidence

“over 500 images per sign (24 sign total) of 4 users (non-native to sign language).”

Methods

N/AReviewer 2· 95% conf

The study uses only image data and does not involve biological subjects or variables, so this dimension is not applicable.

N/AReviewer 3· 100% conf

This study is computational, using an image dataset, so biological variables are not directly applicable.

Evidence

“this study demonstrates a cost-effective technique to detect American Sign Language (ASL) using an image dataset.”

Abstract

The paper explicitly states the use of a publicly available dataset from Kaggle. This dataset consists of images and does not involve the collection of new human or animal subject data, nor does it use non-public identifiable data.

Sub-criteria

iacuc statementN/A

informed consentN/A

irb ethics statementN/A

regulatory complianceN/A

Per reviewer (3) — the votes behind the verdict

N/AReviewer 1· 95% conf

The study uses a publicly available, de-identified dataset from Kaggle. No new human subject data was collected, so ethical approvals are not required.

Evidence

paraphrase[Data availability]

“The dataset used in this paper (ASL Fingerspelling Images (RGB & Depth)) is publicly available at Kaggle on this URL: https://www.kaggle.com/datasets/mrgeislinger/asl-rgb-depth-fingerspelling-spelling-it-out.”

N/AReviewer 2· 95% conf

The study uses a publicly available dataset from Kaggle with no new collection of human subject data, so ethics approval is not required.

Evidence

Data availability

N/AReviewer 3· 100% conf

The study uses a publicly available, de-identified image dataset and does not involve new human or animal subject data collection, making ethical approvals not applicable.

Evidence

Data availability

For a computational study, the applicable sub-criterion is software_tools_identified. The paper mentions Google's hand landmark model, ImageDataGenerator, Adam optimizer, and the Keras/TensorFlow framework, but does not provide version numbers, URLs, or RRIDs. No custom code is deposited in a public repository. Thus, the single applicable sub-criterion is reported_but_inadequate, leading to a fail.

Sub-criteria

mycoplasma testingN/A

reagents identifiedN/A

organisms identifiedN/A

antibodies identifiedN/A

cell line authenticationN/A

software tools identifiedINADEQUATE

Per reviewer (3) — the votes behind the verdict

WARNReviewer 1· 70% conf

The only applicable key resource is software tools, which are identified but not with version numbers or permanent identifiers, making the reporting inadequate.

Evidence

“Google’s hand landmark model has an input channel of RGB and an image size of (224 × 224 × 3).”

Methods

“This model has been compiled with Adam optimizer and MSE loss for 50 epochs.”

Methods

“Image Data Generator did image augmentation with 10° rotation, 0.1 zoom range, 0.1 widths and height shift range, and horizontal flip.”

Methods

FAILReviewer 2· 85% conf

The only applicable resource is software tools, which are named but not identified with versions or RRIDs, and custom code is not shared, resulting in a fail.

Evidence

paraphrase[Methods, Hand landmark detection]

“Google's hand landmark model has an input channel of RGB and an image size of (224 × 224 × 3).”

absence[Data availability]

No code repository is provided.

PASSReviewer 3· 90% conf

The study adequately identifies the dataset, pre-trained models, and software used, including specific versions and sources where applicable.

Evidence

Data availability

“Google’s hand landmark model has an input channel of RGB and an image size of (224 × 224 × 3).”

Hand landmark detection, paragraph 1

“This model has been compiled with Adam optimizer and MSE loss for 50 epochs.”

Model building, paragraph 2

The paper reports test accuracy, per-class precision, recall, and F1 scores from a confusion matrix. No hypothesis tests, p-values, or effect sizes with confidence intervals are reported. The data presentation is adequate (confusion matrix, per-class metrics). Since no inferential statistics are used, the entire dimension is not applicable.

Sub-criteria

tests namedN/A

exact p valuesN/A

effect sizes ciN/A

data presentationADEQUATE

software identifiedN/A

assumptions verifiedN/A

mathematical plausibilityN/A

Per reviewer (3) — the votes behind the verdict

N/AReviewer 1· 90% conf

The study reports classification metrics (accuracy, precision, recall, F1) but does not perform any inferential statistics (p-values, confidence intervals). Therefore, no statistical analysis criteria apply.

Evidence

paraphrase[Result analysis, Table 3 and Figure 7]

“The Precision, Recall, and F1 score for 24 classes are shown in Table 3.”

N/AReviewer 2· 90% conf

The paper reports classification metrics (accuracy, precision, recall, F1) and a confusion matrix, but no inferential statistical tests are performed, so this dimension is not applicable.

PASSReviewer 3· 85% conf

The study clearly names the metrics used (precision, recall, F1 score, accuracy) and provides detailed results in tables and figures, with per-class support counts.

Evidence

“For further evaluation, we have calculated the precision, recall, and F1 score of the proposed multi-headed CNN model, which shows excellent performance.”

Training and testing, paragraph 2

“To compute these values, we first calculated the confusion matrix (shown in Fig. ).”

Training and testing, paragraph 2

paraphrase[Table 3]

“Table 3 shows the Precision, Recall, and F1 score for 24 classes, along with the support for each class.”

The paper includes a dedicated 'Data availability' section that explicitly states the dataset is publicly available and provides a direct URL to the Kaggle repository. No custom code is explicitly mentioned as being shared, but the dataset is the primary data resource.

Sub-criteria

code sharingNOT REPORTED

accession numbersN/A

repository depositADEQUATE

data availability statementADEQUATE

Per reviewer (3) — the votes behind the verdict

PASSReviewer 1· 85% conf

The data availability statement is concrete, naming the public repository with a URL. Code is not shared, but the dataset is fully accessible.

Evidence

paraphrase[Data availability]

absence[Data availability and elsewhere]

No code sharing statement found.

WARNReviewer 2· 85% conf

The dataset URL is provided, but custom code is not shared, leading to a warn.

Evidence

Data availability

absence[Data availability]

No code is shared.

PASSReviewer 3· 95% conf

The study provides a clear data availability statement with a direct link to the public Kaggle dataset.

Evidence

Data availability

Methods are described in sufficient detail for replication, including dataset preprocessing, model architecture, training parameters, and augmentation. All outcomes (accuracy, precision, recall, F1) are reported. Limitations are discussed in the conclusion. Conclusions are proportional to the evidence, with no overclaiming. Funding source is stated, but a conflicts-of-interest statement is absent. Five of six applicable sub-criteria are adequate, yielding a pass.

Sub-criteria

funding coiINADEQUATE

trial registrationN/A

reporting guidelineN/A

methods completenessADEQUATE

all outcomes reportedADEQUATE

limitations discussedADEQUATE

conclusions proportionalADEQUATE

Per reviewer (3) — the votes behind the verdict

PASSReviewer 1· 85% conf

The paper provides detailed methods, reports all outcomes, discusses limitations, and states conclusions proportionally. A conflicts-of-interest statement is missing, but funding is reported.

Evidence

“Two-dimensional Convolutional layers with filter size 50, 25, kernel (3, 3) with Relu, strides 1; MaxPooling 2D with pool size (2, 2), batch normalization, and Dropout layer has been used in the hand landmarks training side.”

Methods

“Some limitations found in the proposed method compared with the literature. Some methods might work with low image dataset numbers, but as we use the simple CNN model, this method requires a good number of images for training.”

Conclusion

“Funding was provided by the American University of the Middle East, Egaila, Kuwait.”

Funding

PASSReviewer 2· 85% conf

The methods are detailed, outcomes are fully reported, limitations are discussed, and conclusions are proportional; however, a conflict of interest statement is missing, which is a minor gap.

Evidence

Methods

“Some methods might work with low image dataset numbers, but as we use the simple CNN model, this method requires a good number of images for training.”

Conclusion

paraphrase[Funding]

“The funding section states the source but does not include a conflict of interest declaration.”

WARNReviewer 3· 75% conf

The methods are generally well-described for replication, and limitations are discussed, but specific reporting guidelines are not referenced, and the statistical software used is not identified.

Evidence

“There are some limitations found in the proposed method compared with the literature. Some methods might work with low image dataset numbers, but as we use the simple CNN model, this method requires a good number of images for training. Also, the proposed method depends on the hand landmark extraction model. Other hand landmark model can cause different results.”

Conclusion ¶3

“Funding was provided by the American University of the Middle East, Egaila, Kuwait.”

Funding

paraphrase[Materials and methods]

“The 'Materials and methods' section provides detailed descriptions of the dataset, image pre-processing steps, and the multi-headed CNN model architecture, including layer types, filter sizes, and activation functions.”

Broken references and links

1 finding · worst medium

References checked against Crossref, OpenAlex and Retraction Watch for retractions and resolvability, plus declared data and code links probed for whether they resolve to content matching the paper.

Dead data/code linksRecomputed

Checked 32 references by DOI: 26 verified — 6 no DOI (shown, not verified).

2 data/code links checked; 0 live, 2 dead.

Copyediting

1 finding · worst low

Wording, consistency and formatting errors that need correcting before submission.

Wording or formatting errors that need correctingAssessed

30 copyedit issues flagged (8 major): mostly grammar, clarity, typo.

Kaimen Rigor Action ItemsFinal pass before journal submission

As a published paper, an informed reader should weigh the following: (1) the data availability link is dead, undermining reproducibility; (2) software versions are not reported, making the computational environment irreproducible; (3) the text contains a tortured phrase ('convolutional neural organization') that is a fingerprint of paraphrasing tools, warranting scrutiny of the manuscript's originality. These issues would warrant an erratum to fix the URL and add a COI statement, and ideally a public code repository for re-analysis.

1.
HIGHdata code
Fix the dead data availability link (currently https://www.kaggle.com/datasets/mrgeislinger/asl-rgb-depth-fingerspelling-spelling-it-out) or provide an alternative permanent repository (e.g., Zenodo) with a DOI.
The reproducibility verification found both links dead; readers cannot access the dataset without a working link.
2.
HIGHother
Investigate and replace the tortured phrase 'convolutional neural organization' (which appears in the text as a paraphrase of 'convolutional neural network') with the correct term, and review the entire manuscript for similar unnatural paraphrases that may indicate text generation or paper mill activity.
Such phrases are a known fingerprint of paraphrasing tools and undermine the integrity of the manuscript.
3.
HIGHcopyedit
Rewrite the Abstract and Introduction for clarity and correct grammar, including fixing the misuse of 'quiet' for 'muteness', 'handicapped' for 'disabled', and 'implacable' (should be 'applicable') in the Conclusion.
These errors significantly affect readability and professional presentation; a reader may question the rigor of the work if the writing is poor.
4.
HIGHreporting
Add a conflict of interest statement in the Declarations section (e.g., 'The authors declare no competing interests.') and ensure it is compliant with the journal's requirements.
This is a standard reporting requirement; its absence is a minor but noticeable gap.
5.
HIGHdata code
Deposit the custom code (multi-headed CNN model, training, and evaluation scripts) in a public repository (e.g., GitHub, Zenodo) and cite it in the Data Availability section.
Code sharing is essential for reproducibility of computational studies; its absence is a major gap.
6.
MEDIUMrigor
Add version numbers and source URLs for all software tools used (e.g., Python, TensorFlow/Keras, OpenCV, Google MediaPipe, scikit-learn) in the Methods section.
Without version information, the computational environment is not reproducible.
7.
MEDIUMrigor
Add a statement on whether any power analysis or sample size justification was performed, or justify why none is needed for this benchmark study.
A formal power analysis is not always required for computational benchmarks, but its absence should be acknowledged.
8.
MEDIUMreporting
Complete Table 1 by filling in the missing depth and intensity data for sessions B-E, or clarify that these values are only available for session A.
An incomplete table undermines the transparency of the dataset description.
9.
MEDIUMreporting
Add a statement on outlier handling (e.g., 'No outliers were excluded from the dataset') in the Methods section.
Readers need to know whether any data were excluded and why.
10.
MEDIUMcopyedit
Fix the awkward phrasing in the Abstract: 'it requires many costly instruments' should be 'they require many costly instruments' (subject-verb agreement with 'studies').
Grammatical errors reduce the professionalism of the paper.
11.
LOWreporting
Add a section on data curation detailing any inclusion/exclusion criteria for individual images (e.g., blurry images, unclear hands) to improve study design transparency.
This would strengthen the description of dataset preparation.
12.
LOWstatistics
If the exact test set accuracy is 98.98%, report the number of correctly classified images out of the total test set (e.g., '19,525 out of 19,725') to allow verification.
This provides a simple check for the reported accuracy.

Full text (34,528 chars)Analyzed: Aug 5, 2026

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Read alongside

Sign language recognition using the fusion of image and hand landmarks through multi-headed convolutional neural network

Pathan RK, Biswas M, Yasmin S, Khandaker MU, Salman M, Youssef AAF.

Read full text (PMC)

Journal

DOI

10.1038/s41598-023-43852-x

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Record fingerprint

fc71d5f6b867c3de5dc86d457a9b68759f8c198bd257eba33d25b7e1cdc9f03a

Verify via API

Issued Aug 5, 2026Engine 7.15.0

Alpha1 assesses methodological rigor, not the importance of the findings. See how we calibrate →

Kaimen Rigor review

Kaimen Rigor reviewed

3.25 / 5

Adequate — real issues to address

Computational Study

Critical

High-priority

How this rating was calculated▸

Started at 5★ — deductions

IntegrityIntegrity concern−0.5★
ReportingKey resources not met−0.5★
LinksDead data/code link ×2−0.5★
ReportingStudy design partially met−0.25★

Model-reported confidence: moderateReviewer agreement: lowEngine: 7.15.0Source: full text

Not verified in this report

Statistics were not checked: no recomputable values were found in this text — no test statistic reported with its degrees of freedom, no effect estimate printed with both a 95% CI and a p-value, and no percentage printed with both its count and its denominator.

Biggest issues

01Key resources not identified
highDisclosurereviewer-assessedFull detail
The only applicable resource is software tools, which are named but not identified with versions or RRIDs, and custom code is not shared, resulting in a fail.
02Other integrity concern
mediumAuthenticityreviewer-assessedFull detail
Tortured phrase "convolutional neural organization" — an unnatural paraphrase of "convolutional neural network", a fingerprint of paraphrasing-tool / paper-mill text.
…the sign language gestures dataset on a convolutional neural organization model utilizing Inception v3. The model…
reviewer’s wording
03Declared data/code link does not resolve
mediumAvailabilityrecomputedFull detail
Dead link — nothing to verify.
“https://www.kaggle.com/datasets/mrgeislinger/asl-rgb-depth-fingerspelling-spelling-it-out”
04Declared data/code link does not resolve
mediumAvailabilityrecomputedFull detail
Dead link — nothing to verify.
“https://www.kaggle.com/grassknoted/asl-alphabet”

Findings by category

01ArithmeticNone found 102OverreachOverstated conclusions 103AuthenticityData authenticity concerns 204DisclosureReporting gaps 105AvailabilityBroken references and links 106CopyeditCopyediting

Numerical inconsistencies

None found

Checked — nothing surfaced.

Overstated conclusions

1 finding · worst low

Conclusions only partially backed by the presented evidenceAssessed

5 major claims checked against the paper's own evidence: all adequately supported.

Data authenticity concerns

1 finding · worst medium

Other integrity concernAssessed

1 integrity concern flagged (0 high).

Reporting gaps

2 findings · worst high

Key resources not identifiedAssessed
Study-design details incomplete (controls, blinding, power)Assessed

Eight Dimensions3 pass · 1 warn · 1 fail

Sub-criteria

prior work citedADEQUATE

premise rationaleADEQUATE

limitations addressedADEQUATE

Per reviewer (3) — the votes behind the verdict

PASSReviewer 1· 95% conf

The paper clearly establishes the scientific foundation by citing prior work, providing a logical rationale for the fusion approach, and addressing limitations of existing methods.

Evidence

paraphrase[Introduction, paragraphs 1-3 and Literature Review section]

Introduction ¶4

Literature Review, last paragraph

PASSReviewer 2· 90% conf

The paper establishes a clear scientific foundation by citing relevant prior work, providing a logical rationale for the study, and addressing limitations of previous methods.

Evidence

“State-of-the-art techniques centered after utilizing deep learning models to improve good accuracy and less execution time.”

Introduction ¶2

paraphrase[Introduction, paragraph 4]

Introduction ¶1

PASSReviewer 3· 90% conf

The study clearly establishes the need for cost-effective sign language recognition and reviews prior work, highlighting limitations such as reliance on costly instruments or simple backgrounds.

Evidence

Abstract

Introduction ¶3

Literature review, paragraph 5

Sub-criteria

controlsADEQUATE

power analysisNOT REPORTED

blinding levelsN/A

outlier handlingNOT REPORTED

randomization unitN/A

inclusion exclusionADEQUATE

randomization methodN/A

replicate distinctionN/A

independent replicationNOT REPORTED

Per reviewer (3) — the votes behind the verdict

PASSReviewer 1· 90% conf

The study design is appropriate for a computational benchmark: dataset selection is justified, inclusion/exclusion of classes is specified, and baseline models serve as controls.

Evidence

“We intended to avoid that single color background and use a complex background with many users’ hand images to increase the detection complexity.”

Methods

“with 24 letters (except j and z as they contain motion)”

Methods

“Before combining image and hand landmark inputs, we tested both individually and acquired a test accuracy of 96.29% for the image and 98.42% for hand landmarks.”

Result analysis, paragraph 4

WARNReviewer 2· 80% conf

The study design has several gaps: no power analysis, no outlier handling, and no independent replication; however, inclusion/exclusion criteria and baseline controls are adequately reported.

Evidence

paraphrase[Methods, Dataset description]

“We have used the 'ASL Finger Spelling' dataset, which has images of different sizes, orientations, and complex backgrounds of over 500 images per sign (24 sign total) of 4 users.”

paraphrase[Results, Table 5]

“Table 5 compares the combined model with image-only and hand-landmark-only models in terms of parameters, execution time, memory, and accuracy.”

absence[Methods, Training and testing]

No power analysis or outlier handling is reported.

WARNReviewer 3· 70% conf

The study describes the dataset, pre-processing steps, and model architecture in detail, but lacks a formal power analysis and clear distinction between biological and technical replicates.

Evidence

Methods ¶1

“To avoid the overfitting problem, data augmentation and dynamic learning rate reduction have been used.”

Abstract

“For training, we have used 46,023 images, and for testing, 19,725 images.”

Training and testing, paragraph 1

Sub-criteria

demographicsN/A

sex reportedN/A

sex justifiedN/A

age weight healthN/A

housing conditionsN/A

species strain sourceN/A

Per reviewer (3) — the votes behind the verdict

N/AReviewer 1· 95% conf

Evidence

“over 500 images per sign (24 sign total) of 4 users (non-native to sign language).”

Methods

N/AReviewer 2· 95% conf

The study uses only image data and does not involve biological subjects or variables, so this dimension is not applicable.

N/AReviewer 3· 100% conf

This study is computational, using an image dataset, so biological variables are not directly applicable.

Evidence

“this study demonstrates a cost-effective technique to detect American Sign Language (ASL) using an image dataset.”

Abstract

Sub-criteria

iacuc statementN/A

informed consentN/A

irb ethics statementN/A

regulatory complianceN/A

Per reviewer (3) — the votes behind the verdict

N/AReviewer 1· 95% conf

The study uses a publicly available, de-identified dataset from Kaggle. No new human subject data was collected, so ethical approvals are not required.

Evidence

paraphrase[Data availability]

N/AReviewer 2· 95% conf

The study uses a publicly available dataset from Kaggle with no new collection of human subject data, so ethics approval is not required.

Evidence

Data availability

N/AReviewer 3· 100% conf

The study uses a publicly available, de-identified image dataset and does not involve new human or animal subject data collection, making ethical approvals not applicable.

Evidence

Data availability

Sub-criteria

mycoplasma testingN/A

reagents identifiedN/A

organisms identifiedN/A

antibodies identifiedN/A

cell line authenticationN/A

software tools identifiedINADEQUATE

Per reviewer (3) — the votes behind the verdict

WARNReviewer 1· 70% conf

The only applicable key resource is software tools, which are identified but not with version numbers or permanent identifiers, making the reporting inadequate.

Evidence

“Google’s hand landmark model has an input channel of RGB and an image size of (224 × 224 × 3).”

Methods

“This model has been compiled with Adam optimizer and MSE loss for 50 epochs.”

Methods

“Image Data Generator did image augmentation with 10° rotation, 0.1 zoom range, 0.1 widths and height shift range, and horizontal flip.”

Methods

FAILReviewer 2· 85% conf

The only applicable resource is software tools, which are named but not identified with versions or RRIDs, and custom code is not shared, resulting in a fail.

Evidence

paraphrase[Methods, Hand landmark detection]

“Google's hand landmark model has an input channel of RGB and an image size of (224 × 224 × 3).”

absence[Data availability]

No code repository is provided.

PASSReviewer 3· 90% conf

The study adequately identifies the dataset, pre-trained models, and software used, including specific versions and sources where applicable.

Evidence

Data availability

“Google’s hand landmark model has an input channel of RGB and an image size of (224 × 224 × 3).”

Hand landmark detection, paragraph 1

“This model has been compiled with Adam optimizer and MSE loss for 50 epochs.”

Model building, paragraph 2

Sub-criteria

tests namedN/A

exact p valuesN/A

effect sizes ciN/A

data presentationADEQUATE

software identifiedN/A

assumptions verifiedN/A

mathematical plausibilityN/A

Per reviewer (3) — the votes behind the verdict

N/AReviewer 1· 90% conf

Evidence

paraphrase[Result analysis, Table 3 and Figure 7]

“The Precision, Recall, and F1 score for 24 classes are shown in Table 3.”

N/AReviewer 2· 90% conf

The paper reports classification metrics (accuracy, precision, recall, F1) and a confusion matrix, but no inferential statistical tests are performed, so this dimension is not applicable.

PASSReviewer 3· 85% conf

The study clearly names the metrics used (precision, recall, F1 score, accuracy) and provides detailed results in tables and figures, with per-class support counts.

Evidence

“For further evaluation, we have calculated the precision, recall, and F1 score of the proposed multi-headed CNN model, which shows excellent performance.”

Training and testing, paragraph 2

“To compute these values, we first calculated the confusion matrix (shown in Fig. ).”

Training and testing, paragraph 2

paraphrase[Table 3]

“Table 3 shows the Precision, Recall, and F1 score for 24 classes, along with the support for each class.”

Sub-criteria

code sharingNOT REPORTED

accession numbersN/A

repository depositADEQUATE

data availability statementADEQUATE

Per reviewer (3) — the votes behind the verdict

PASSReviewer 1· 85% conf

The data availability statement is concrete, naming the public repository with a URL. Code is not shared, but the dataset is fully accessible.

Evidence

paraphrase[Data availability]

absence[Data availability and elsewhere]

No code sharing statement found.

WARNReviewer 2· 85% conf

The dataset URL is provided, but custom code is not shared, leading to a warn.

Evidence

Data availability

absence[Data availability]

No code is shared.

PASSReviewer 3· 95% conf

The study provides a clear data availability statement with a direct link to the public Kaggle dataset.

Evidence

Data availability

Sub-criteria

funding coiINADEQUATE

trial registrationN/A

reporting guidelineN/A

methods completenessADEQUATE

all outcomes reportedADEQUATE

limitations discussedADEQUATE

conclusions proportionalADEQUATE

Per reviewer (3) — the votes behind the verdict

PASSReviewer 1· 85% conf

The paper provides detailed methods, reports all outcomes, discusses limitations, and states conclusions proportionally. A conflicts-of-interest statement is missing, but funding is reported.

Evidence

Methods

Conclusion

“Funding was provided by the American University of the Middle East, Egaila, Kuwait.”

Funding

PASSReviewer 2· 85% conf

The methods are detailed, outcomes are fully reported, limitations are discussed, and conclusions are proportional; however, a conflict of interest statement is missing, which is a minor gap.

Evidence

Methods

“Some methods might work with low image dataset numbers, but as we use the simple CNN model, this method requires a good number of images for training.”

Conclusion

paraphrase[Funding]

“The funding section states the source but does not include a conflict of interest declaration.”

WARNReviewer 3· 75% conf

The methods are generally well-described for replication, and limitations are discussed, but specific reporting guidelines are not referenced, and the statistical software used is not identified.

Evidence

Conclusion ¶3

“Funding was provided by the American University of the Middle East, Egaila, Kuwait.”

Funding

paraphrase[Materials and methods]

Broken references and links

1 finding · worst medium

References checked against Crossref, OpenAlex and Retraction Watch for retractions and resolvability, plus declared data and code links probed for whether they resolve to content matching the paper.

Dead data/code linksRecomputed

Checked 32 references by DOI: 26 verified — 6 no DOI (shown, not verified).

2 data/code links checked; 0 live, 2 dead.

Copyediting

1 finding · worst low

Wording, consistency and formatting errors that need correcting before submission.

Wording or formatting errors that need correctingAssessed

30 copyedit issues flagged (8 major): mostly grammar, clarity, typo.

Kaimen Rigor Action ItemsFinal pass before journal submission

1.
HIGHdata code
Fix the dead data availability link (currently https://www.kaggle.com/datasets/mrgeislinger/asl-rgb-depth-fingerspelling-spelling-it-out) or provide an alternative permanent repository (e.g., Zenodo) with a DOI.
The reproducibility verification found both links dead; readers cannot access the dataset without a working link.
2.
HIGHother
Investigate and replace the tortured phrase 'convolutional neural organization' (which appears in the text as a paraphrase of 'convolutional neural network') with the correct term, and review the entire manuscript for similar unnatural paraphrases that may indicate text generation or paper mill activity.
Such phrases are a known fingerprint of paraphrasing tools and undermine the integrity of the manuscript.
3.
HIGHcopyedit
Rewrite the Abstract and Introduction for clarity and correct grammar, including fixing the misuse of 'quiet' for 'muteness', 'handicapped' for 'disabled', and 'implacable' (should be 'applicable') in the Conclusion.
These errors significantly affect readability and professional presentation; a reader may question the rigor of the work if the writing is poor.
4.
HIGHreporting
Add a conflict of interest statement in the Declarations section (e.g., 'The authors declare no competing interests.') and ensure it is compliant with the journal's requirements.
This is a standard reporting requirement; its absence is a minor but noticeable gap.
5.
HIGHdata code
Deposit the custom code (multi-headed CNN model, training, and evaluation scripts) in a public repository (e.g., GitHub, Zenodo) and cite it in the Data Availability section.
Code sharing is essential for reproducibility of computational studies; its absence is a major gap.
6.
MEDIUMrigor
Add version numbers and source URLs for all software tools used (e.g., Python, TensorFlow/Keras, OpenCV, Google MediaPipe, scikit-learn) in the Methods section.
Without version information, the computational environment is not reproducible.
7.
MEDIUMrigor
Add a statement on whether any power analysis or sample size justification was performed, or justify why none is needed for this benchmark study.
A formal power analysis is not always required for computational benchmarks, but its absence should be acknowledged.
8.
MEDIUMreporting
Complete Table 1 by filling in the missing depth and intensity data for sessions B-E, or clarify that these values are only available for session A.
An incomplete table undermines the transparency of the dataset description.
9.
MEDIUMreporting
Add a statement on outlier handling (e.g., 'No outliers were excluded from the dataset') in the Methods section.
Readers need to know whether any data were excluded and why.
10.
MEDIUMcopyedit
Fix the awkward phrasing in the Abstract: 'it requires many costly instruments' should be 'they require many costly instruments' (subject-verb agreement with 'studies').
Grammatical errors reduce the professionalism of the paper.
11.
LOWreporting
Add a section on data curation detailing any inclusion/exclusion criteria for individual images (e.g., blurry images, unclear hands) to improve study design transparency.
This would strengthen the description of dataset preparation.
12.
LOWstatistics
If the exact test set accuracy is 98.98%, report the number of correctly classified images out of the total test set (e.g., '19,525 out of 19,725') to allow verification.
This provides a simple check for the reported accuracy.

Full text (34,528 chars)Analyzed: Aug 5, 2026