A New Recurrent Neural Network Fuzzy Mean Square Clustering Method

Moutaouakil, Karim El; Touhafi, Abdellah

doi:10.1109/cloudtech49835.2020.9365873

Cited by 18 publications

(11 citation statements)

References 16 publications

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“…Given a universe of discourse X and E a subset of X, we denote by µ A (x) the membership grade of the element x in the set A which is a real number form the interval [0, 1] (see [22,23]).…”

Section: Integral Fuzzy Ranking Function To Triangular Fuzzy Numbersmentioning

confidence: 99%

Hybrid firefly genetic algorithm and integral fuzzy quadratic programming to an optimal Moroccan diet

Moutaouakil¹,

Ahourag²,

Chakir³

et al. 2023

Math. Model. Comput.

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In this paper, we solve the Moroccan daily diet problem based on 6 optimization programming (P) taking into account dietary guidelines of US department of health, human services, and department of agriculture. The objective function controls the fuzzy glycemic load, the favorable nutrients gap, and unfavorable nutrient excess. To transform the proposed program into a line equation, we use the integral fuzzy ranking function. To solve the obtained model, we use the Hybrid Firefly Genetic Algorithm (HFGA) that combines some advantages of the Firefly Algorithm (FA) and the Genetic Algorithm (GA). The proposed model produces the best and generic diets with reasonable glycemic loads and acceptable core nutrient deficiencies. In addition, the proposed model showed remarkable consistency with the uniform distribution of glycemic load of different foods.

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Section: Integral Fuzzy Ranking Function To Triangular Fuzzy Numbersmentioning

confidence: 99%

Hybrid firefly genetic algorithm and integral fuzzy quadratic programming to an optimal Moroccan diet

Moutaouakil¹,

Ahourag²,

Chakir³

et al. 2023

Math. Model. Comput.

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“…To minimize the cost function we can utilize a recurrent network [18][19][20]. We can also use evolutionary heuristic methods such as: firefly optimization [21], genetic algorithm [23], particle swarm algorithm, stochastic fractal search with specific operators [2], etc.…”

Section: Nutrients Crisp Representation Using Optimal Auto-encodermentioning

confidence: 99%

Optimal fuzzy deep daily nutrients requirements representation: Application to optimal Morocco diet problem

Moutaouakil¹,

Chellak²,

Hicham³

2022

Math. Model. Comput.

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Solving the optimal diet problem necessarily involves estimating the daily requirements in positive and negative nutrients. Most approaches proposed in the literature are based on standard nominal estimates, which may cause shortages in some nutrients and overdoses in others. The approach proposed in this paper consists in personalizing these needs based on an intelligent system. In the beginning, we present the needs derived from the recommendations of experts in the field of nutrition in trapezoidal numbers. Based on this model, we generate a vast database. The latter is used to educate a deep learning neural network, the architecture of which we optimize by the fuzzy genetic algorithm method in the way of adopting a customized regulation term. Our system estimates nutrient requirements based only on gender and age. These estimations are integrated into a mathematical model obtained in our previous work. Then we again use the fuzzy genetic algorithm to draw up personalized diets. The proposed system has demonstrated a very high capacity to predict the needs of different individuals and has allowed the drawing up of very high-quality diets.

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“…The main aim of the data is to discriminate healthy people from those with PD, according to "status" column which is set to 0 for healthy and 1 for PD. Number of examples (197) and number of features (23).…”

Section: Number Of Examples (336) and Number Of Features (8)mentioning

confidence: 99%

“…Unfortunately, these methods inert the sensitivity of the kmeans hardness. In this study, we use RNN-fuzzy-C-Means Smote to overcome these shortcomings and to handle the border data [17]. These approach process in three steps to know: grouping, filtering, and interpolation [12]; see section 3.…”

Section: Introductionmentioning

confidence: 99%

RNN-Fuzzy-c-means-smote and fuzzy classifier to early predict hidden disease

Roudani

Moutaouakil

2022

Preprint

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Unbalanced dataset classification issues have been prevalent in medical domain. Lately, clustering-based oversampling techniques were introduced to address this issue. Regrettably, they suffer from the vulnerability of hard grouping approaches. This paper introduces Recurrent Neural Network Fuzzy-C-Means SMOTE (RNNFCM-SMOTE) that balances data based on Recurrent Neural Network Fuzzy Fuzzy-C-Means in filtering phase. First of all, to manage the sensitiveness of the hard clustering, RNNFCM is used to determine safe regions. Second, take benefit of the capacity of neural networks to comprehend the features of data and dynamic systems to recover from past clusters, recurrent neural network is implemented to determine the membership function of different instances. To generate artificial data, classical smote is used. As the methods implementing fuzzy logic have proven to be very competent when it comes to the edge problem, RNNFCM-SMOTE is combined to nine fuzzy classifier methods to predict the existence of hidden diseases represented by five unbalanced medical data sets. The proposed method is compared to 12 oversampling methods using three performance measures. RNNFCM-SMOTE has been shown to consistently exceed many other popular oversampling techniques

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A New Recurrent Neural Network Fuzzy Mean Square Clustering Method

Cited by 18 publications

References 16 publications

Hybrid firefly genetic algorithm and integral fuzzy quadratic programming to an optimal Moroccan diet

Hybrid firefly genetic algorithm and integral fuzzy quadratic programming to an optimal Moroccan diet

Optimal fuzzy deep daily nutrients requirements representation: Application to optimal Morocco diet problem

RNN-Fuzzy-c-means-smote and fuzzy classifier to early predict hidden disease

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