Evidence map›Paper›PMID 41307629›Full record

ArticleTropical animal health and production2025

End-to-end deep SAE-DNN model for predicting Egyptian buffalo calf sex, weight, and daily milk yield.

Sali Issa, Montaser Elsayed Ali, Qi Wang, Fatimah A Al-Saeed, Mohamed Abdelrahman

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Article in Tropical animal health and production, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Sali IssaDepartment of Electrical Information of Science and Technology, Hubei University of Education, Wuhan, 430205, China.
Montaser Elsayed AliDepartment of Animal Productions, Faculty of Agriculture, Al-Azhar University, Assiut, 71524, Egypt.
Qi WangDepartment of Electrical Information of Science and Technology, Hubei University of Education, Wuhan, 430205, China.
Fatimah A Al-SaeedDepartment of Biology, College of Science, King Khalid University, Abha, 61413, Saudi Arabia.
Mohamed AbdelrahmanDepartment of Animal Production, Faculty of Agriculture, Assuit University, Asyut, 71515, Egypt. mohamed.asad@agr.au.edu.eg.ORCID http://orcid.org/0000-0001-6860-3543

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the present study, a novel stacked Sparse Autoencoder-Deep Neural Network (SAE-DNN) learning prediction model was applied to predict calf sex, weight, and daily milk yield for dairy buffalo. First, SAE stage extracts the unique statistical features of calf records (protocol type, sire, gestation length, lactation season, calving interval, parturition season, open days, and dry period). Next, the DNN stage utilizes the extracted statistical sparse features to predict calf sex, weight, and daily milk yield production. The results showed that the proposed SAE-DNN model introduces a robust model with 80% accuracy, an R-squared value of 0.9149, a Mean Absolute Error (MAE) of 0.2097, and a Root Mean Squared Error (RMSE) of 0.4579 for calf sex. An accuracy of 87%, an R² value of 0.7122, a MAE of 3.9394, and an RMSE of 2.7635 for calf weight. As well as 86% accuracy, R2 value of 0.7964, a MAE of 2.0475, and an RMSE of 2.7798 for daily milk yield.

Indexed as

Body WeightBuffaloesDairyingLactationMilkNeural Networks, ComputerSex Determination AnalysisAnimalsEgyptFemaleMaleCalf sexCalf weightDaily milk yieldDeep neural networkPredictionSmart farmSparse autoencoder

Identifiers

PMID41307629

What OpenQuestion holds

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Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.