Medical Defense Nanorobots (MDNRs): a new evaluation and selection of controller criteria for improved disease diagnosis and patient safety using NARMA(L2)-FOP + D(ANFIS)µ – Iλ-based Archimedes Optimization Algorithm

This article addresses the complexity of optimizing movements in Medical Defense Nanorobots (MDNRs) by proposing a novel integration approach. The challenge lies in selecting the Archimedes Optimization Algorithm (AOA) for MDNR movements, considering specific criteria for fractional-order proportion...

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Vydáno v:International journal of information technology (Singapore. Online) Ročník 17; číslo 7; s. 3935 - 3945
Hlavní autoři: Marhoon, Hamzah M., Basil, Noorulden, Mohammed, Abdullah Fadhil
Médium: Journal Article
Jazyk:angličtina
Vydáno: Singapore Springer Nature Singapore 01.09.2025
Springer Nature B.V
Témata:
Algorithms
Artificial Intelligence
Biocompatibility
Communication
Computer Imaging
Computer Science
Controllers
Criteria
Drug delivery systems
Dynamical systems
Image Processing and Computer Vision
L-2 control
Machine Learning
Mathematical models
Medical diagnosis
Optimization
Optimization techniques
Original Research
Patient safety
Pattern Recognition and Graphics
Proportional integral derivative
Sensors
Software Engineering
Systems stability
Vision
NARMA-L2 controller
Medical Defense Nanorobots
ANFIS
Fractional order proportional integral derivative controller
Archimedes Optimization Algorithm
ISSN:2511-2104, 2511-2112
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Shrnutí:This article addresses the complexity of optimizing movements in Medical Defense Nanorobots (MDNRs) by proposing a novel integration approach. The challenge lies in selecting the Archimedes Optimization Algorithm (AOA) for MDNR movements, considering specific criteria for fractional-order proportional-integral-derivative (FOPID) controller gains. To overcome this, the study introduces a three-phase approach: MDNR-based NARMA-L2 controller Pre-process and Identification, Enhancement of NARMA-L2 controller-based NARMA(L2)- F O P + D ( A N F I S ) μ - I λ , and Evaluation of FOPID criteria-based AOA. This approach integrates NARMA-L2 for criterion weighting and ANFIS for AOA selection, validated through NARMA(L2)- F O P + D ( A N F I S ) μ - I λ evaluation, showcasing the efficacy of the proposed methodology.
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ISSN:2511-2104
2511-2112
DOI:10.1007/s41870-023-01724-7