Recent Trends in Computational Intelligence

Sadollah, Ali; Sinha, Tilendra Shishir

doi:10.5772/intechopen.78839

Cited by 5 publications

References 7 publications

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A new boundary element formulation for modeling and simulation of three‐temperature distributions in carbon nanotube fiber reinforced composites with inclusions

Fahmy

2021

Math Methods in App Sciences

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A new boundary element method (BEM) formulation is developed in this paper for modeling and simulation of nonlinear three‐temperature (3T) distributions in carbon nanotube (CNT) fiber‐reinforced composites embedded with many rigid‐line inclusions that can be treated as having constant temperature distributions. The fast multipole method (FMM) has been applied to accelerate the proposed BEM solution of the considered problem of a large number of nano‐inclusions, and therefore, the computational complexity and computational time are reduced. The CPU time and memory requirement are also reduced by implementing generalized modified shift‐splitting (GMSS) iteration method which does not require the entire matrix to be stored in the memory. Then, we introduced a new formula which can play an important role in solving thermal problems arising in CNT nanotechnology. Effective thermal conductivity of fiber‐reinforced composites can be evaluated using the computed temperature and heat flux fields. The fast multipole BEM results are depicted graphically to show the effects of CNT manufacturing methods and thermal conductivities of different CNT composites on the temperature and heat flux distributions. The computational performance of the proposed methodology has been demonstrated. The fast multipole BEM results are compared graphically with the finite element method (FEM) results and with the experimental results reported in the literature. Excellent agreements are found in both cases, which clearly demonstrate the validity, accuracy, and efficiency of our proposed algorithm and modeling and simulation technique. The findings and solutions of this study contribute to the further development of CNT nanotechnology applications and devices.

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A new boundary element formulation for modeling and simulation of three‐temperature distributions in carbon nanotube fiber reinforced composites with inclusions

Fahmy

2021

Math Methods in App Sciences

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Investigating The Impact of Sampling Techniques on an Imbalanced Classification Problem

Shehada,

Muammar,

Amour

et al. 2023

2023 6th International Conference on Signal Processing and Information Security (ICSPIS)

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Semantic Point Cloud Segmentation with Deep-Learning-Based Approaches for the Construction Industry: A Survey

Rauch

Braml

2023

Applied Sciences

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Point cloud learning has recently gained strong attention due to its applications in various fields, like computer vision, robotics, and autonomous driving. Point cloud semantic segmentation (PCSS) enables the automatic extraction of semantic information from 3D point cloud data, which makes it a desirable task for construction-related applications as well. Yet, only a limited number of publications have applied deep-learning-based methods to address point cloud understanding for civil engineering problems, and there is still a lack of comprehensive reviews and evaluations of PCSS methods tailored to such use cases. This paper aims to address this gap by providing a survey of recent advances in deep-learning-based PCSS methods and relating them to the challenges of the construction industry. We introduce its significance for the industry and provide a comprehensive look-up table of publicly available datasets for point cloud understanding, with evaluations based on data scene type, sensors, and point features. We address the problem of class imbalance in 3D data for machine learning, provide a compendium of commonly used evaluation metrics for PCSS, and summarize the most significant deep learning methods developed for PCSS. Finally, we discuss the advantages and disadvantages of the methods for specific industry challenges. Our contribution, to the best of our knowledge, is the first survey paper that comprehensively covers deep-learning-based methods for semantic segmentation tasks tailored to construction applications. This paper serves as a useful reference for prospective research and practitioners seeking to develop more accurate and efficient PCSS methods.

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Recent Trends in Computational Intelligence

Abstract: His research interests include algorithm development, optimization and metaheuristics, applications of soft computing methods in engineering, artificial neural networks, and computational solid mechanics.

Cited by 5 publications

References 7 publications

A new boundary element formulation for modeling and simulation of three‐temperature distributions in carbon nanotube fiber reinforced composites with inclusions

A new boundary element formulation for modeling and simulation of three‐temperature distributions in carbon nanotube fiber reinforced composites with inclusions

Investigating The Impact of Sampling Techniques on an Imbalanced Classification Problem

Semantic Point Cloud Segmentation with Deep-Learning-Based Approaches for the Construction Industry: A Survey

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