Evidence map›Paper›PMID 41908140›Full record

ArticleFrontiers in artificial intelligence2026

HCA-DBN: a hill climbing optimized Deep Belief Network for crop yield classification based on kernel weight threshold.

Prakash Sandhya, B Venkataramana

Abstract read
In one paragraph

Article in Frontiers in artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Prakash SandhyaDepartment of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Vellore, India.
B VenkataramanaDepartment of Mathematics, School of Advanced Sciences, Vellore Institute of Technology, Vellore, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate classification of maize yield potential is essential for food security and effective agricultural planning, particularly in regions characterized by environmental variability and socio-economic constraints. This study explores the binary classification of maize kernel weight into low (<25 g) and high (≥25 g) categories, utilizing plant and ear traits collected from an organic maize field experiment in Vellore district, Tamil Nadu (

Indexed as

Binary classificationDeep Belief Networkfield experimentHill Climbing Algorithmmaize

Identifiers

PMID41908140
PMCPMC13018166

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.