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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Abstract 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.
AbstractList 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)-FOP+D(ANFIS)μ-Iλ, and Evaluation of FOPID criteria-based AOA. This approach integrates NARMA-L2 for criterion weighting and ANFIS for AOA selection, validated through NARMA(L2)-FOP+D(ANFIS)μ-Iλ evaluation, showcasing the efficacy of the proposed methodology.
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.
Author Marhoon, Hamzah M.
Mohammed, Abdullah Fadhil
Basil, Noorulden
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The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.
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Medical Defense Nanorobots
ANFIS
Fractional order proportional integral derivative controller
Archimedes Optimization Algorithm
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Snippet This article addresses the complexity of optimizing movements in Medical Defense Nanorobots (MDNRs) by proposing a novel integration approach. The challenge...
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SubjectTerms 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
Title 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
URI https://link.springer.com/article/10.1007/s41870-023-01724-7
https://www.proquest.com/docview/3255829861
Volume 17
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