Decision-making and optimal design of green energy system based on statistical methods and artificial neural network approaches

Samy, Mohamed; Almamlook, Rabia; Elkhouly, Heba I.; Barakat, Shimaa

doi:10.1016/j.scs.2022.104015

Cited by 67 publications

(13 citation statements)

References 65 publications

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“…In Figure 5, GD and CG denote the gradient descent and conjugate gradient algorithms, respectively. Considering the maximum and minimum values for each solution, problem ( 21) is rewritten as (23) based on the proposed method. e results obtained in both cases (exact and imprecise data) have been obtained and compared with some other methods (method of [39] and method of [37]).…”

Section: Simulationsmentioning

confidence: 99%

“…So far, several approaches based on NNs have been proposed to solve MOP problems [21,22]. For example, in [23,24], DM is designed using NNs, and the particle swarm optimization is used to learn the suggested NN. In [25], the stakeholder theory is used to construct a DM system, and the concept of NNs is used to improve the accuracy versus uncertainties.…”

Section: Introductionmentioning

confidence: 99%

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A New Decision-Making GMDH Neural Network: Effective for Limited and Fuzzy Data

Hong

Zhao

Kausar

et al. 2022

Computational Intelligence and Neuroscience

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This paper presents a new approach to solve multi-objective decision-making (DM) problems based on neural networks (NN). The utility evaluation function is estimated using the proposed group method of data handling (GMDH) NN. A series of training data is obtained based on a limited number of initial solutions to train the NN. The NN parameters are adjusted based on the error propagation training method and unscented Kalman filter (UKF). The designed DM is used in solving the practical problem, showing that the proposed method is very effective and gives favorable results, under limited fuzzy data. Also, the results of the proposed method are compared with some similar methods.

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Section: Simulationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A New Decision-Making GMDH Neural Network: Effective for Limited and Fuzzy Data

Hong

Zhao

Kausar

et al. 2022

Computational Intelligence and Neuroscience

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show abstract

“…The concept of ANN is able to carry out the learning process based on a model that is built with mathematical calculations [40]. The learning is applied in a step that is presented in the algorithm based on the network architecture [41], [42]. In more detail, the concept of ANN can produce outputs to be taken into consideration in decision making [43].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Optimization artificial neural network classification analysis model diagnosis Gingivitis disease

Ariandi

Yanto²,

Jamhur³

et al. 2023

IJEECS

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<span lang="EN-US">Gingivitis is a disease that can be caused by the buildup of bacteria and plaque caused by leftover food. This disease can attack anyone, especially children who are not aware of maintaining dental and oral health. This study aims to build and optimize the classification analysis model for the diagnosis of Gingivitis. The classification analysis model was built using the artificial neural network (ANN) method which was optimized using fuzzy logic and the multiple linear regression (MRL) method. Optimization with fuzzy aims to develop a pattern of rules in the detection. The MRL method is also used as a process of measuring analysis patterns to ensure the analytical model presents maximum results. The results study indicate that the optimization of fuzzy and MRL methods provides excellent output. These results are based on the fuzzy output which can provide a pattern of 40 rules. The MRL method is can present the level of correlation of each analysis variable with a significant output having an average value of 94.2%. Based on the results of this study, the analysis model that is optimized with the fuzzy logic method and MRL contributes to maximizing the process of diagnosing Gingivitis.</span>

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“…Neural networks are a collection of algorithms which are weakly modelled after the human brain and are intended to recognize patterns. They perceive sensory data by labelling or grouping raw inputs using a machine perspective [25][26][27]. They recognize numerical patterns enclosed in vectors, into which all real-world information, whether images, sound, message, or time series, should be transformed.…”

Section: Neural Network (Nn)mentioning

confidence: 99%

Selection of an Efficient Classification Algorithm for Ambient Assisted Living: Supportive Care for Elderly People

et al. 2023

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Ambient Assisted Living (AAL) is a medical surveillance system comprised of connected devices, healthcare sensor systems, wireless communications, computer hardware, and software implementations. AAL could be used for an extensive variety of purposes, comprising preventing, healing, as well as improving the health and wellness of elderly individuals. AAL intends to ensure the wellbeing of elderly persons while also spanning the number of years seniors can remain independent in their preferred surroundings. It also decreases the quantity of family caregivers by giving patients control over their health situations. To avert huge costs as well as possible adverse effects on standard of living, classifiers must be used to distinguish between adopters as well as nonadopters of such innovations. With the development of numerous classification algorithms, selecting the best classifier became a vital and challenging step in technology acceptance. Decision makers must consider several criteria from different domains when selecting the best classifier. Furthermore, it is critical to define the best multicriteria decision-making strategy for modelling technology acceptance. Considering the foregoing, this research reports the incorporation of the multicriteria decision-making (MCDM) method which is founded on the fuzzy method for order of preference by similarity to ideal solution (TOPSIS) to identify the top classifier for continuing toward supporting AAL implementation research. The results indicate that the classification algorithm KNN is the preferred technique among the collection of different classification algorithms for the ambient assisted living system.

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Decision-making and optimal design of green energy system based on statistical methods and artificial neural network approaches

Cited by 67 publications

References 65 publications

A New Decision-Making GMDH Neural Network: Effective for Limited and Fuzzy Data

A New Decision-Making GMDH Neural Network: Effective for Limited and Fuzzy Data

Optimization artificial neural network classification analysis model diagnosis Gingivitis disease

Selection of an Efficient Classification Algorithm for Ambient Assisted Living: Supportive Care for Elderly People

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