Series Type Solution of Fuzzy Fractional Order Swift–Hohenberg Equation by Fuzzy Hybrid Sumudu Transform

Ullah, Abd; Ullah, Aman; Ahmad, Shabir; Haq, Mahmood Ul; Shah, Kamal; Mlaiki, Nabil

doi:10.1155/2022/3864053

Cited by 4 publications

(1 citation statement)

References 30 publications

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“…Abu Arqub et al [28,29] extended the reproducing kernel Hilbert space method in seeking the numerical solutions of FFDEs and fuzzy fractional Volterra and Fredholm integrodifferential equations in presence of the AtanganaBaleanuCaputo derivative. Ullah et al [30,31] used the fuzzy Laplace transform along with Adomian decomposition method to find the general numerical solutions for population dynamical model and SwiftHohenberg equations in the fuzzy environment. Besides, the modified Adomian decomposition method was also constructed in [45] to study the solution of the fuzzy fractional Volterra-Fredholm integro-differential equation in the Caputo sense.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Yang transform for second order fuzzy differential equations of integer and fractional order

Ullah

Ahmad

et al. 2023

Phys. Scr.

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In this work, a Laplace-like transform in a fuzzy environment called Yang transform is introduced to solve fuzzy differential equations (FDEs) with the order θ ∈ (1, 2] involving the Caputo fractional derivative in the sense of gH-differentiability. Some basic properties of Yang transform for integer and fractional derivatives are also provided. Furthermore, by utilizing the combination between the Adomian decomposition method (ADM) and the Yang transform method, a general algorithm called the hybrid Yang transform method (HYTM) to solve the solutions of FDEs in the nonlinear form is proposed. For the validity and accuracy of this novel method, some examples and their simulations are given.

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Section: Introductionmentioning

confidence: 99%

Fuzzy Yang transform for second order fuzzy differential equations of integer and fractional order

Ullah

Ahmad

et al. 2023

Phys. Scr.

Self Cite

View full text Add to dashboard Cite

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Deep Strategy of Object Detection in Remote Sensing Images

Ahmad,

El Affendi,

Alluhaidan

et al. 2024

Deep Cognitive Modelling in Remote Sensing Image Processing

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Recently, the demand for satellite image analysis increased with the recent advancement in various research areas (e.g., improvements in remote sensing image resolution, object recognition ideas, and deep learning techniques). Articles report new trends in object detection using remote sensing images, such as multi-temporal scene classifications, semantic segmentation, multiresolution, and large-scale optical image analysis. However, this study observed a black box of synchronization among the advancements in various technologies (i.e., new ideas in satellite image analysis) and advancements in deep learning techniques. Without investigating this black box, the optimization of object detection will be out of track. So, to keep the deep learning innovation on track, the primary goal of the current work is to explore the aforementioned black box to advance object detection in remote sensing images. In contrast, focusing on a specific technique, this study explores the black box while reviewing 150 articles from 2019 to 2023. First, the study highlights research methodologies and novel features of literature to achieve object detection in remote sensing images. Second, it evaluates effective deep learning models and assesses various featured studies to draw a clear picture of limitations and recent object detection trends to provide in-depth recommendations and future directions.

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The Development, Applications, Challenges, and Analysis of a Cricket Player Face Recognition Dataset

Haq,

Sethi,

Ullah

et al. 2024

Deep Cognitive Modelling in Remote Sensing Image Processing

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Over the last decade, face recognition technology has played a critical role in various circumstances, such as airport boarding, security applications, biometric verification, and smart homes. Along with the major role of face recognition in the areas above, we must recognize the important role of face recognition in various sports (i.e., cricket and football). The importance of proper player surveillance and identification in sports, particularly cricket, cannot be overstated. Articles are saturated with many deep-face evaluation systems; however, they are not up to mark due to the lack of significant face posture data. To address the black box in facial expression datasets, this chapter presents a comprehensive cricket player facial recognition dataset. The authors have a wide selection of cricket player images from various teams, playing styles, and backgrounds. It includes images taken during games, practices, and official team photos, providing a diverse range of facial changes and challenges for facial recognition systems. Furthermore, they evaluate the efficacy of cutting-edge facial recognition algorithms on our dataset, providing insights into the effectiveness of current methodologies as well as potential areas for development. Eventually, the extensive experimental analyses demonstrate that the current work is significant in addressing the black box in facial expression datasets.

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Series Type Solution of Fuzzy Fractional Order Swift–Hohenberg Equation by Fuzzy Hybrid Sumudu Transform

Cited by 4 publications

References 30 publications

Fuzzy Yang transform for second order fuzzy differential equations of integer and fractional order

Fuzzy Yang transform for second order fuzzy differential equations of integer and fractional order

Deep Strategy of Object Detection in Remote Sensing Images

The Development, Applications, Challenges, and Analysis of a Cricket Player Face Recognition Dataset

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