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Copyright of Retos: Nuevas Perspectivas de Educación Física, Deporte y Recreación is the property of Federacion Espanola de Asociaciones de Docentes de Educacion Fisica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Retos: Nuevas Perspectivas de Educación Física, Deporte y Recreación is the property of Federacion Espanola de Asociaciones de Docentes de Educacion Fisica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Retos: Nuevas Perspectivas de Educación Física, Deporte y Recreación is the property of Federacion Espanola de Asociaciones de Docentes de Educacion Fisica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Accurate and efficient COVID-19 diagnosis is crucial in clinical settings. However, the limited availability of labeled data poses a challenge for traditional machine learning algorithms. To address this issue, we propose Turning Point (TP), a few-shot learning (FSL) approach that leverages high-level turning point mappings to build sophisticated representations across previously labeled data. Unlike existing FSL models, TP learns using quasi-configured topological spaces and efficiently combines the outputs of diverse TP learners. We evaluated TPFSL using three COVID-19 datasets and compared it with seven different benchmarks. Results show that TPFSL outperformed the top-performing benchmark models in both one-shot and five-shot tasks, with an average improvement of 4.50% and 4.43%, respectively. Additionally, TPFSL significantly outperformed the ProtoNet benchmark by 12.966% and 11.033% in one-shot and five-shot classification problems across all datasets. Ablation experiments were also conducted to analyze the impact of variables such as TP density, network topology, distance measure, and TP placement. Overall, TPFSL has the potential to improve the accuracy and speed of diagnoses for COVID-19 in clinical settings and can be a valuable tool for medical professionals. [ABSTRACT FROM AUTHOR]

The COVID19 pandemic globally and significantly has affected the life and health of many communities. The early detection of infected patients is effective in fighting COVID19. Using radiology (X-Ray) images is, perhaps, the fastest way to diagnose the patients. Thereby, deep Convolutional Neural Networks (CNNs) can be considered as applicable tools to diagnose COVID19 positive cases. Due to the complicated architecture of a deep CNN, its real-time training and testing become a challenging problem. This paper proposes using the Extreme Learning Machine (ELM) instead of the last fully connected layer to address this deficiency. However, the parameters' stochastic tuning of ELM's supervised section causes the final model unreliability. Therefore, to cope with this problem and maintain network reliability, the sine–cosine algorithm was utilized to tune the ELM's parameters. The designed network is then benchmarked on the COVID-Xray-5k dataset, and the results are verified by a comparative study with canonical deep CNN, ELM optimized by cuckoo search, ELM optimized by genetic algorithm, and ELM optimized by whale optimization algorithm. The proposed approach outperforms comparative benchmarks with a final accuracy of 98.83% on the COVID-Xray-5k dataset, leading to a relative error reduction of 2.33% compared to a canonical deep CNN. Even more critical, the designed network's training time is only 0.9421 ms and the overall detection test time for 3100 images is 2.721 s. [ABSTRACT FROM AUTHOR]

In this paper, a radial basis function neural network (RBF-NN) automatic sonar target recognition system is proposed. For the RBF-NN training phase, a whale optimisation algorithm (WOA) developed with a fuzzy system has been used (which is called FWOA). The reason for using the fuzzy system is the lack of correct identification of the boundary between the two stages of exploration and exploitation. Thus, the tuning of the effective parameters of the WOA is left to the fuzzy system of the Mamdani type. RBF-NN was trained by chimp optimisation algorithm (ChOA), genetic algorithm (GA), Evolution Strategy (ES), league championship algorithm (LCA), grey wolf algorithms (GWO), gravitational search algorithm (GSA), and WOA to compare the proposed algorithm. The measured criteria are convergence speed, ability to avoid local optimisation, and classification rate. The simulation results showed that FWOA with 97.49% classification accuracy rate in sonar data performed better than the other seven benchmark algorithms. [ABSTRACT FROM AUTHOR]

In this paper, a new Multi-Layer Perceptron Neural Network (MLP NN) classifier is proposed for classifying sonar targets and non-targets from the acoustic backscattered signals. Besides the capabilities of MLP NNs, it uses Back Propagation (BP) and Gradient Descent (GD) for training; therefore, MLP NNs face with not only impertinent classification accuracy but also getting stuck in local minima as well as lowconvergence speed. To lift defections, this study uses Adaptive Best Mass Gravitational Search Algorithm (ABGSA) to train MLP NN. This algorithm develops marginal disadvantage of the GSA using the bestcollected masses within iterations and expediting exploitation phase. To test the proposed classifier, this algorithm along with the GSA, GD, GA, PSO and compound method (PSOGSA) via three datasets in various dimensions will be assessed. Assessed metrics include convergence speed, fail probability in local minimum and classification accuracy. Finally, as a practical application assumed network classifies sonar dataset. This dataset consists of the backscattered echoes from six different objects: four targets and two non-targets. Results indicate that the new classifier proposes better output in terms of aforementioned criteria than whole proposed benchmarks. [ABSTRACT FROM AUTHOR]

Correction to: Archives of Computational Methods in Engineering https://doi.org/10.1007/s1183... This correction is published as Fig. The original article can be found online at https://doi.org/10.1007/s11831-021-09707-2. [Extracted from the article]