Comfort evaluation of ZnO coated fabrics by artificial neural network assisted with golden eagle optimizer model

Amor, Nahla Ben; Noman, Muhammad; Petrů, Michal; Sebastian, Neethu

doi:10.1038/s41598-022-10406-6

Cited by 16 publications

(4 citation statements)

References 53 publications

(30 reference statements)

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“…These include the count of hidden layers, the number of nodes within each hidden layer, the selection of the activation function for the hidden layer, the training function type, learning rate, and the number of training epochs 50 . In accordance with previous research findings, it has been demonstrated that the feedforward backpropagation ANN model attains optimal performance when configured with a singular hidden layer, a sigmoid tangent function for said hidden layer (Equation 9), and a pure linear activation function (Equation 10) [42][43][44][45][46]51 . This configuration was adopted in the present study.…”

Section: Modeling and Optimizationsupporting

confidence: 89%

Tuning Drafting Zone Parameters for Polyester Yarn within a Ring Spinning System: Modeling and Optimization

Savadroodbari,

Razbin,

Hasani

et al. 2023

Preprint

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Most textile products incorporate yarn as a fundamental element in the production process. Among various yarn manufacturing methods, the ring spinning system stands out as a crucially employed technology due to its advantages including yarn quality, evenness, low hairiness, and ease of handling. The parameters of drafting zone in this technology have a great impact on the quality of yarns. Typically, tuning this drafting zone parameters is time-consuming and costly through trial and error method. This study introduces an algorithmic procedure based on response surface methodology (RSM), experimental modeling, and multi-objective optimization to reduce unevenness percentage (U%) and imperfection index (IPI). Input parameters, including cots hardness of front and back top rollers, spacer size, and break draft, are optimized. Results indicate superior prediction performance of the artificial neural network (ANN) (average vaule of TGF = 1.9996) compared to RSM (average value of TGF = 1.8668). Consequently, ANN is selected for optimization. Furthermore, coupling the genetic algorithm with two ANN-based models reduced IPI from 39 to 33.67 and a reduction from 9.73–9.67% occurred in terms of U%. The final setting of Input parameters were cots hardness of front roller of 70 shore and cots hardness of back roller of 76 shore, spacer size 2.8 mm, and break draft of 1.26. This method efficiently optimizes the drafting zone parameter, enhancing yarn quality.

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Section: Modeling and Optimizationsupporting

confidence: 89%

Tuning Drafting Zone Parameters for Polyester Yarn within a Ring Spinning System: Modeling and Optimization

Savadroodbari,

Razbin,

Hasani

et al. 2023

Preprint

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“…While MOGEO exhibits the capability to broaden the search range of the population through abrupt movements in the initial stages of iteration (Amor et al, 2022; Mohammadi‐Balani et al, 2021), continuously search for better solutions, and converges towards the global optimum during the iteration, practical evidence suggests that MOGEO still suffers from problems, such as inadequate population diversity and the susceptibility to local optima (Hu et al, 2022). To address these issues, an IMOGEO is proposed in this work.…”

Section: Trajectory Optimization Based On Multiobjective Golden Eagle...mentioning

confidence: 99%

“…While MOGEO exhibits the capability to broaden the search range of the population through abrupt movements in the initial stages of iteration (Amor et al, 2022;Mohammadi-Balani et al, 2021),…”

Section: Proposed Imogeomentioning

confidence: 99%

Time–jerk optimal trajectory planning for industrial robots with coupled interpolation function selection

Wang,

Zhang,

Zhou

et al. 2024

Journal of Field Robotics

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In the contemporary field of optimal trajectory planning for industrial robots, it is customary to construct trajectories through the manual predefinition of interpolation functions. Unfortunately, this method frequently overlooks the influence of the interpolation function itself on the optimization objectives, resulting in suboptimal outcomes. To remedy this limitation, an optimal trajectory planning method with coupled interpolation function selection is proposed, in which the total task time and the integral squared jerk are defined as optimization objectives. This method minimizes the optimization objectives while also factoring in the optimal interpolation function, and avoiding subjective interference. To address the aforementioned biobjective optimization problem better, an Improved MultiObjective Golden Eagle Optimizer is introduced. Population diversity and the ability to escape local optima are enhanced through the incorporation of Chaotic Mapping, Opposition‐Based Learning, Differential Evolution, and adaptive inertia weight strategy into the algorithm. The superiority of the algorithm is validated through a series of simulations on 17 benchmark functions. In the context of the robotic stirring operation within the automated block cast charging process, the proposed method is utilized to derive the time–jerk optimal trajectory. The results demonstrate the effectiveness of the proposed method.

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“…The GEO algorithm 33,34 is introduced by keeping the intelligent hunting behavior of the golden eagle, which adjusts its velocity of the spiral path on foraging. The golden eagle has a greater affinity for wandering around and monitoring its prey before it attacks the selected prey.…”

Section: Geomentioning

confidence: 99%

HECC‐ABE: A novel blockchain‐based IoT healthcare data storage using hybrid cryptography schemes with key optimization by hybrid meta‐heuristic algorithm

Dubey,

Ramanjaneyulu,

Saraswat

et al. 2023

Trans Emerging Tel Tech

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The most contemporary development in information technology is the Internet of Things (IoT), which integrates the digital world with the real world. It makes it possible for things and people to communicate via the Internet. Blockchain is one of the popular topics of interest right now, and it can be used in most IoT applications. The blockchain's salient characteristics, like decentralization, data integrity, confidentiality, protection, and openness, are the main reasons for implementing it in healthcare services. Hence, this paper introduces an effective approach for storing IoT‐based healthcare data storage in the blockchain. The procedures in this suggested paradigm are as follows: user authentication, user trust verification, and optimal key storage in the blockchain. Wearable sensors are inserted in or adhered to a patient's body and used by IoT networks to gather healthcare data. These details are supplied into the verification structure constructed by adaptive dilated long short term memory with attention network (AD‐LSTM‐AN). Once the user's authentication is confirmed, then the user's trust level is computed. The same AD‐LSTM‐AN‐based verification structure is used to verify the historical data that has been stored and retrieved by the user, together with their previous transactions, in this phase. The healthcare data are then sent to the hybrid elliptic curve cryptography with attribute‐based encryption (HECC‐ABE) cryptography schemes for data encryption once the authentication of the person who attempts to store the data is confirmed. An adaptive final position‐based golden eagle‐Harris hawks optimization (AFP‐GEHHO) algorithm is used for optimizing the keys of the encrypted data. The blockchain platform stores the encrypted data of the authorized user with a high level of trust. The same process is done in reverse order whenever a user needs to recover information that is saved in the blockchain to retrieve the initially stored healthcare data. The effectiveness of secure data storage in blockchain is tested through experimental simulations. Throughout the analysis, the designed model achieves 96% in terms of accuracy. Thus, the developed model shows more secure and effective, has less error and reduces the time and memory requirements compared to existing approaches.

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Comfort evaluation of ZnO coated fabrics by artificial neural network assisted with golden eagle optimizer model

Cited by 16 publications

References 53 publications

Tuning Drafting Zone Parameters for Polyester Yarn within a Ring Spinning System: Modeling and Optimization

Tuning Drafting Zone Parameters for Polyester Yarn within a Ring Spinning System: Modeling and Optimization

Time–jerk optimal trajectory planning for industrial robots with coupled interpolation function selection

HECC‐ABE: A novel blockchain‐based IoT healthcare data storage using hybrid cryptography schemes with key optimization by hybrid meta‐heuristic algorithm

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