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The growing energy demand and population raising require alternative, clean, and sustainable energy systems. During the last few years, hydrogen energy has proven to be a crucial factor under the current conditions. Although the energy conversion process in polymer electrolyte fuel cells (PEFCs) is clean and noiseless since the only by-products are heat and water, the inside phenomena are not simple. As a result, correct monitoring of the health situation of the device is required to perform efficiently. This paper aims to explore and evaluate the machine learning (ML) and deep learning (DL) models for predicting classification fault detection in PEFCs. It represents a support for decision-making by the fuel cell operator or user. Seven ML and DL model classifiers are considered. A database comprising 182,156 records and 20 variables arising from the fuel cell's energy conversion process and operating conditions is considered. This dataset is unbalanced; therefore, techniques to balance are applied and analyzed in the training and testing of several models. The results showed that the logistic regression (LR), k-nearest neighbor (KNN), decision tree (DT), random forest (RF), and Naive Bayes (NB) models present similar and optimal trends in terms of performance indicators and computational cost; unlike support vector machine (SMV) and multi-layer perceptron (MLP) whose performance is affected when the data is balanced and even presents a higher computational cost. Therefore, it is a novel approach for fault detection analysis in PEFC that combines the interpretability of different ML and DL algorithms while addressing data imbalance, so common in the real world, using resampling techniques. This methodology provides clear information for the model decision-making process, improving confidence and facilitating further optimization; in contrast to traditional physics-based models, paving the way for data-driven control strategies. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new technique for handling a class of sparse supervised learning problems, specifically focusing on regression, binary, and multi-class classification problems. Sparse regression problems are associated with datasets including both arithmetic and categorical features and by encoding the dataset leads to an underdetermined sparse linear system. Furthermore, logistic regression can be used for both sparse binary and multi-class classification problems, solving an underdetermined sparse linear system. A new technique called Sparse Approximate Pseudoinverse Preconditioning (SAPP), namely the Explicit Preconditioned Conjugate Gradient for Normal Equations (EPCGNE) method based on Generic Approximate Sparse Pseudoinverse matrices is introduced for solving underdetermined sparse least square problems. Numerical experiments were carried out demonstrating a significant improvement of the performance metrics for the proposed SAPP scheme compared to other learners. [ABSTRACT FROM AUTHOR]

In recent years, classification problems have been widely studied as one of the critical research directions in artificial intelligence. Efficiently simulating the human brain's ability to solve deterministic classification problems has become a prominent research branch in the field of classification problems. Numerical spiking neural P systems (NSN P systems) with the characteristics of the highly flexible spiking neural P (SNP)systems architecture and addressing numerical variants have special advantages for solving classification problems in the real world. This paper proposes, a novel NSN P system, namely, a learning numerical spiking neural P system (LNSN P system), is proposed to solve classification problems. In LNSN P systems, each neuron contains not only discrete spikes and rules that must be described by regular expressions but also numerical variables and production functions that can be in any form, overcoming the discontinuity of traditional SN P systems. In addition, an ascending structure without an outer encoder has been designed in this study to encode information from low-dimensional indivisible space to high-dimensional divisible space. At the same time, an adaptive learning mechanism has been introduced to improve algorithm convergence and precision. Finally, the experimental results obtained using eight benchmark datasets from the machine learning repository demonstrate the feasibility and effectiveness of the proposed LNSN P system. Moreover, the proposed LNSN P system exhibits lower space complexity and better generalisation ability than the existing SN P systems with the learning ability to solve different classification problems. • An LNSN P system is proposed to solve classification problems. • An ascending structure without an outer encoder is designed to encode information. • An adaptive learning mechanism is introduced to improve algorithm performance. [ABSTRACT FROM AUTHOR]

In this research, we study convex bi-level optimization problems for which the inner level consists of the sum of two proper, convex, and lower semi-continuous functions. We propose and analyze a new accelerated forward-backward algorithm using linesearch and inertial techniques for solving a solution of convex bi-level optimization. We then establish a strong convergence theorem of the proposed method under some suitable conditions. As an application, we apply our algorithm to solving data classifications of some non-communicable diseases. We conduct a comparative analysis with existing algorithms to show the effectiveness of our algorithm. Our numerical experiments confirm that our proposed algorithm outperforms other methods in the literature. [ABSTRACT FROM AUTHOR]

Abstract The growing energy demand and population raising require alternative, clean, and sustainable energy systems. During the last few years, hydrogen energy has proven to be a crucial factor under the current conditions. Although the energy conversion process in polymer electrolyte fuel cells (PEFCs) is clean and noiseless since the only by-products are heat and water, the inside phenomena are not simple. As a result, correct monitoring of the health situation of the device is required to perform efficiently. This paper aims to explore and evaluate the machine learning (ML) and deep learning (DL) models for predicting classification fault detection in PEFCs. It represents a support for decision-making by the fuel cell operator or user. Seven ML and DL model classifiers are considered. A database comprising 182,156 records and 20 variables arising from the fuel cell's energy conversion process and operating conditions is considered. This dataset is unbalanced; therefore, techniques to balance are applied and analyzed in the training and testing of several models. The results showed that the logistic regression (LR), k-nearest neighbor (KNN), decision tree (DT), random forest (RF), and Naive Bayes (NB) models present similar and optimal trends in terms of performance indicators and computational cost; unlike support vector machine (SMV) and multi-layer perceptron (MLP) whose performance is affected when the data is balanced and even presents a higher computational cost. Therefore, it is a novel approach for fault detection analysis in PEFC that combines the interpretability of different ML and DL algorithms while addressing data imbalance, so common in the real world, using resampling techniques. This methodology provides clear information for the model decision-making process, improving confidence and facilitating further optimization; in contrast to traditional physics-based models, paving the way for data-driven control strategies.