Multi-Objective Optimization for Controlling the Dynamics of the Diabetic Population

Moutaouakil, Karim El; Ouissari, Abdellatif El; Palade, Vasile; Charroud, Anas; Olaru, Adrian; Balkhi, H.; Chellak, Saliha; Cheggour, Mouna

doi:10.3390/math11132957

Cited by 4 publications

(2 citation statements)

References 49 publications

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“…The control u is nothing but the time step of the BP algorithm. We can use Pontryagin's Minimum Principle [59] or a local search method [60] to solve the problem (P).…”

Section: Optimal Control Problem To Train Deep Neural Networkmentioning

confidence: 99%

INT-FUP: Intuitionistic Fuzzy Pooling

Rajafillah,

El Moutaouakil,

Patriciu

et al. 2024

Mathematics

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Convolutional Neural Networks (CNNs) are a kind of artificial neural network designed to extract features and find out patterns for tasks such as segmentation, recognizing objects, and drawing up classification. Within a CNNs architecture, pooling operations are used until the number of parameters and the computational complexity are reduced. Numerous papers have focused on investigating the impact of pooling on the performance of Convolutional Neural Networks (CNNs), leading to the development of various pooling models. Recently, a fuzzy pooling operation based on type-1 fuzzy sets was introduced to cope with the local imprecision of the feature maps. However, in fuzzy set theory, it is not always accurate to assume that the degree of non-membership of an element in a fuzzy set is simply the complement of the degree of membership. This is due to the potential existence of a hesitation degree, which implies a certain level of uncertainty. To overcome this limitation, intuitionistic fuzzy sets (IFS) were introduced to incorporate the concept of a degree of hesitation. In this paper, we introduce a novel pooling operation based on intuitionistic fuzzy sets to incorporate the degree of hesitation heretofore neglected by a fuzzy pooling operation based on classical fuzzy sets, and we investigate its performance in the context of image classification. Intuitionistic pooling is performed in four steps: bifuzzification (by the transformation of data through the use of membership and non-membership maps), first aggregation (through the transformation of the IFS into a standard fuzzy set, second aggregation (through the transformation and use of a sum operator), and the defuzzification of feature map neighborhoods by using a max operator. IFS pooling is used for the construction of an intuitionistic pooling layer that can be applied as a drop-in replacement for the current, fuzzy (type-1) and crisp, pooling layers of CNN architectures. Various experiments involving multiple datasets demonstrate that an IFS-based pooling can enhance the classification performance of a CNN. A benchmarking study reveals that this significantly outperforms even the most recent pooling models, especially in stochastic environments.

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“…The control u is nothing but the time step of the BP algorithm. We can use Pontryagin's Minimum Principle [59] or a local search method [60] to solve the problem (P).…”

Section: Optimal Control Problem To Train Deep Neural Networkmentioning

confidence: 99%

INT-FUP: Intuitionistic Fuzzy Pooling

Rajafillah,

El Moutaouakil,

Patriciu

et al. 2024

Mathematics

Self Cite

View full text Add to dashboard Cite

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“…These algorithms can be broadly categorized into evolutionary algorithms (e.g., Genetic Algorithms, Particle Swarm Optimization), swarm intelligence algorithms, mathematical programming-based approaches (e.g., linear programming, nonlinear programming), and decomposition-based methods (e.g., weighted sum, εconstraint). These algorithms differ in their search strategies, exploration-exploitation balance, and handling of constraints, but they all aim to identify diverse and high-quality solutions on the Pareto front (Briones-Baez et al, 2022;El Moutaouakil et al, 2023).…”

Section: Multi-objective Optimization Approachesmentioning

confidence: 99%

Renewable Carbohydrates: Advancements in Sustainable Glucose Production and Optimization

Mperiju,

Silas,

Aji

et al. 2023

GSSR

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This study explores and optimizes glucose production through various biochemical processes and assesses the potential of diverse feedstock sources to meet the growing demand for renewable carbohydrates. It focuses on glucose production's significance in biological systems and industrial applications, analyzing pathways like enzymatic hydrolysis of polysaccharides and acid hydrolysis of biomass. The kinetics of glucose production are examined, encompassing kinetic models for enzymatic hydrolysis, acid hydrolysis, and fermentation processes. Factors influencing reaction kinetics are explored, and experimental techniques for kinetic parameter estimation are discussed. To address sustainability and resource utilization challenges, the study investigates locally sourced materials like agricultural residues, forest biomass, algal biomass, and food waste as renewable feedstock sources. Optimization strategies for glucose production are presented, using statistical design of experiments and response surface methodology. Techno-economic analysis and life cycle assessments provide a holistic evaluation of environmental and economic aspects associated with glucose production processes. The study's comprehensive approach to glucose production, encompassing both technological advancements and sustainability considerations, offers insights into enzymatic, acid hydrolysis, and fermentation processes, as well as comparing diverse feedstock sources. This knowledge can foster further advancements in the field, benefit industries, and encourage policymakers to promote the integration of renewable carbohydrates in the broader bioeconomy. The research contributes to the global shift towards a greener and more sustainable future, where glucose production plays a key role in building a resilient and eco-conscious society.

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