A fuzzy-based prediction approach for blood delivery using machine learning and genetic algorithm

Midaoui, Marouane El; Qbadou, Mohammed; Mansouri, Khalifa

doi:10.11591/ijece.v12i1.pp1056-1068

Cited by 6 publications

(5 citation statements)

References 19 publications

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“…The defuzzifier stage is important, since it can build a defuzzifying system that incorporates defuzzification, for example, a randomised alpha-cut method, into the whole setup of the system components. Several researchers have stated the benefits that could be achieved if a match between defuzzifying and the other components of the fuzzy system is improved [13]- [15]. In addition, the researchers have developed defuzzification strategies to aid in optimising or improving system performance.…”

Section: Randomised Alpha-cut Fuzzy Logicmentioning

confidence: 99%

Randomised Alpha-Cut Fuzzy Logic Hybrid Model in Solving 3-Satisfiability Hopfield Neural Network

Azizan

Sathasivam²

2023

Mal. J. Fund. Appl. Sci.

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This paper proposes an innovative approach to improve the performance of 3SAT logic programming in the Hopfield neural network. The merged structures of the 3SAT and Hopfield network have specific weaknesses, one of which is that, at times, the system attained local minimum solutions rather than global minimum solutions. A new model of integration randomised alpha-cut fuzzy logic with 3SAT in the Hopfield network is built to convey information more effectively. 3SAT and fuzzy logic can work together to solve Hopfield networks' combinatorial optimisation issues. Procedures of fuzzifying and defuzzifying the neurons might ease the computational burden of determining the correct neuron states. Until the proper neuron state is established, unsatisfied neuron clauses will be modified using a randomised alpha-cut approach in the defuzzifier step. An incorporated design built a random approach to select the alpha-cut values of 0.1, 0.25, and 0.5. At this point, a fuzzy value switches into a crisp output back through the defuzzifier process. Based on the results obtained, the proposed hybrid strategy effectively improves the indicators of RMSE, SSE, MAE, MAPE, global minima and total computational time. A computer-generated data set was used to measure how well the hybridised techniques performed. The performance of the proposed network was trained and validated using Matlab 2020b. The results are significant because this model significantly affects how successfully Hopfield networks merged with fuzzy logic can tackle the 3SAT challenges. The obtained data and ideas will help to create novel approaches to data collection for upcoming logic programming exploration.

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Section: Randomised Alpha-cut Fuzzy Logicmentioning

confidence: 99%

Randomised Alpha-Cut Fuzzy Logic Hybrid Model in Solving 3-Satisfiability Hopfield Neural Network

Azizan

Sathasivam²

2023

Mal. J. Fund. Appl. Sci.

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“…203 number of possible models to choose from is 2 44 -1>10 13 . Once a model structure is selected, the parameters are estimated using the least squares method.…”

Section: Simulation Setupmentioning

confidence: 99%

“…Evolutionary computation, more specifically genetic algorithm (GA), has proven its strength and endurance, and able to reduce computational burden. GA has become an interesting area of investigation among researchers for many applications such as wireless sensor network energy optimization [9], control of vehicles [10], [11], modelling of disease severity [12], scheduling in medical field [13], aeronautics and robotics [14].…”

Section: Introductionmentioning

confidence: 99%

Single parent mating in genetic algorithm for real robotic system identification

Samad

Zainuddin²

2023

IJ-AI

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<span lang="EN-US">System identification (SI) is a method of determining a mathematical model for a system given a set of input-output data. A representation is made using a mathematical model based on certain specified assumptions. In SI, model structure selection is a step where a model structure perceived as an adequate system representation is selected. A typical rule is that the final model must have a good balance between parsimony and accuracy. As a popular search method, genetic algorithm (GA) is used for selecting a model structure. However, the optimality of the final model depends much on the effectiveness of GA operators. This paper presents a mating technique named single parent mating (SPM) in GA for use in a real robotic SI. This technique is based on the chromosome structure of the parents such that a single parent is sufficient in achieving mating that eases the search for the optimal model. The results show that using three different objective functions (Akaike information criterion, Bayesian information criterion and parameter magnitude–based information criterion 2) respectively, GA with the mating technique is able to find more optimal models than without the mating technique. Validations show that the selected models using the mating technique are acceptable.</span>

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“…It can simulate human experience and decision-making. Fuzzy logic has been used in numerous scientific fields, including neural networks, machine learning, artificial intelligence, and data mining [8][9][10][11]. This method can handle insufficient, inaccurate, inconsistent, or uncertain data.…”

Section: Introductionmentioning

confidence: 99%

Hybridised Network of Fuzzy Logic and a Genetic Algorithm in Solving 3-Satisfiability Hopfield Neural Networks

Azizan

Sathasivam

Ali

et al. 2023

Axioms

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This work proposed a new hybridised network of 3-Satisfiability structures that widens the search space and improves the effectiveness of the Hopfield network by utilising fuzzy logic and a metaheuristic algorithm. The proposed method effectively overcomes the downside of the current 3-Satisfiability structure, which uses Boolean logic by creating diversity in the search space. First, we included fuzzy logic into the system to make the bipolar structure change to continuous while keeping its logic structure. Then, a Genetic Algorithm is employed to optimise the solution. Finally, we return the answer to its initial bipolar form by casting it into the framework of the hybrid function between the two procedures. The suggested network’s performance was trained and validated using Matlab 2020b. The hybrid techniques significantly obtain better results in terms of error analysis, efficiency evaluation, energy analysis, similarity index, and computational time. The outcomes validate the significance of the results, and this comes from the fact that the proposed model has a positive impact. The information and concepts will be used to develop an efficient method of information gathering for the subsequent investigation. This new development of the Hopfield network with the 3-Satisfiability logic presents a viable strategy for logic mining applications in future.

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A fuzzy-based prediction approach for blood delivery using machine learning and genetic algorithm

Cited by 6 publications

References 19 publications

Randomised Alpha-Cut Fuzzy Logic Hybrid Model in Solving 3-Satisfiability Hopfield Neural Network

Randomised Alpha-Cut Fuzzy Logic Hybrid Model in Solving 3-Satisfiability Hopfield Neural Network

Single parent mating in genetic algorithm for real robotic system identification

Hybridised Network of Fuzzy Logic and a Genetic Algorithm in Solving 3-Satisfiability Hopfield Neural Networks

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