ArticleScientific reports2025
Deep learning based optimal fish species identification to maximize production in fish ponds.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Aquaculture is one of the most important activity in India that decides economic growth of the country. Currently, India ranked second in the world in terms of total fish production. In fish farming identifying the best fish species can grow in the fish pond is required that leads increase in production. In this based Deep Neural Networks (DNN) one of the deep learning models is used to identify the best fish species based on fish ponds water parameters like pH, temperature and turbidity. A dataset with 196 samples is developed by collecting real time data through pH sensor, temperature sensor and turbidity sensor in various ponds located at Warangal, Telangana State, India. The proposed DNN model is validated by comparing with other machine learning models like decision tree, random forest, SVC, KNN and Naive Bayes classifiers. Based on the simulation results it is observed that the proposed DNN model is performing better the other models with 100% accuracy in training and testing.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.