Evidence map›Paper›PMID 39543212›Full record

ArticleScientific reports2024

Artificial intelligence neural network and fuzzy modelling of unsteady Sisko trihybrid nanofluids for cancer therapy with entropy insights.

A Divya, Thandra Jithendra, Muhammad Jawad, Taoufik Saidani, Qasem M Al-Mdallal, Abeer A Shaaban

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Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

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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

2 citing papers in PubMed.

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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

6 authors.

A DivyaSchool of Technology, The Apollo University, Chittoor, Andhra Pradesh, 517127, India.
Thandra JithendraDepartment of Mathematics, Koneru Lakshmaiah Education Foundation, Hyderabad, Telangana, 500075, India.
Muhammad JawadDepartment of Mathematics, The University of Faisalabad, Faisalabad, 38000, Pakistan.
Taoufik SaidaniDepartment of Computer Sciences Faculty of Computing and Information Technology, Northern Border University, Rafha, 91911, Saudi Arabia.
Qasem M Al-MdallalDepartment of Mathematical Sciences, UAE University, P.O. Box 15551, Al-Ain, United Arab Emirates. q.almdallal@uaeu.ac.ae.
Abeer A ShaabanDepartment of Management Information Systems, College of Business and Economics, Qassim University, Buraydah, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The main objective of the current endeavor is to monitor hypothetical processes utilizing a Sisko tri-hybrid fluid over a rotating disk with entropy generation suspended in Darcy-Forchheimer porous medium. Electro Magneto Hydro Dynamics (EMHD), non-linear thermal radiation and exponential and thermal- space dependent heat source/sink coefficients are considered with the intent of conceiving an Runge-Kutta-Fehlberg method with shooting procedures integrated with a combination of an Adaptive Neuro-Fuzzy Inference System (ANFIS) and Reptile Search Algorithm (RSA). Then, ANFIS-RSA, is used to predict the Nusselt number, skin friction co-efficient in radial and tangential velocities. Reliable self-similarity variables have reduced a non-linear partial differential set of equations into an ordinary differential equation. According to the empirical evidence, Sisko fluid parameter rises the radial velocity whereas for magnetic field and Darcy-Forchheimer the azimuthal and axial velocities visualizations decreasing trend, respectively. The entropy generation and Bejan number rises for electric and radiation effects. Also, ANFIS-RSA indicates that the model attained a high level of precision in terms of radial velocity (98.13%), tangential velocity (98.18%) and Nusselt number (98.91%). Thus, the longer rendering of the nanoparticles used here might, makes them potentially helpful for regulating the therapeutic impact in the management and treatment of cancer.

Indexed as

AlgorithmsEntropyFuzzy LogicNeoplasmsNeural Networks, ComputerArtificial IntelligenceHumansHydrodynamicsANFIS-RSAEMHDEntropy generationExponential and thermal space dependent heat source/SinkSisko tri-hybrid nanofluid

Identifiers

PMID39543212
PMCPMC11564712

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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.