Analysis of Brain Imaging Data for the Detection of Early Age Autism Spectrum Disorder Using Transfer Learning Approaches for Internet of Things

Ashraf, Adnan; Qingjie, Zhao; Bangyal, Waqas Haider Khan; Iqbal, Muddesar

doi:10.1109/tce.2023.3328479

Cited by 18 publications

(1 citation statement)

References 72 publications

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“…In this case, fresh, dependable, and structure-preserving techniques are needed. Researchers are adopting the evolutionary computational [26][27][28][29][30] paradigm as one of the expanding contemporary problem-solving approaches for medical predictions [31,32] and solving highly nonlinear physical phenomena modeled by initial and boundary value ordinary differential equations and partial differential equations with applications in applied sciences and epidemiology [33][34][35].…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Safe Padé Approximation Scheme for Dynamical Study of Nonlinear Cervical Human Papilloma Virus Infection Model

Ali,

Ciancio,

Khan

et al. 2024

CMES

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This study proposes a structure-preserving evolutionary framework to find a semi-analytical approximate solution for a nonlinear cervical cancer epidemic (CCE) model. The underlying CCE model lacks a closed-form exact solution. Numerical solutions obtained through traditional finite difference schemes do not ensure the preservation of the model's necessary properties, such as positivity, boundedness, and feasibility. Therefore, the development of structure-preserving semi-analytical approaches is always necessary. This research introduces an intelligently supervised computational paradigm to solve the underlying CCE model's physical properties by formulating an equivalent unconstrained optimization problem. Singularity-free safe Padé rational functions approximate the mathematical shape of state variables, while the model's physical requirements are treated as problem constraints. The primary model of the governing differential equations is imposed to minimize the error between approximate solutions. An evolutionary algorithm, the Genetic Algorithm with Multi-Parent Crossover (GA-MPC), executes the optimization task. The resulting method is the Evolutionary Safe Padé Approximation (ESPA) scheme. The proof of unconditional convergence of the ESPA scheme on the CCE model is supported by numerical simulations. The performance of the ESPA scheme on the CCE model is thoroughly investigated by considering various orders of non-singular Padé approximants.

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Section: Introductionmentioning

confidence: 99%

Evolutionary Safe Padé Approximation Scheme for Dynamical Study of Nonlinear Cervical Human Papilloma Virus Infection Model

Ali,

Ciancio,

Khan

et al. 2024

CMES

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A Hyper-Heuristic Algorithm with Q-Learning for Distributed Flow Shop-Vehicle Transport-U-Assembly Integrated Scheduling Problem

Yang,

Qian,

Zhang

et al. 2024

Lecture Notes in Computer Science

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An efficient claim management assurance system using EPC contract based on improved monarch butterfly optimization models

Mukilan,

Rameshbabu,

Baranitharan

et al. 2024

Neural Comput & Applic

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The Engineering Procurement Construction (EPC) contract systems are widely employed in the construction industry. Among the prevalent issues in this sector, cash flow problems frequently lead to decreased productivity and efficiency. To address these challenges, a claim management system is developed based on the Improved Monarch Butterfly Optimization Algorithm (IMBOA) and the principles of EPC. Conventional construction models typically optimize only a single objective, such as time, cost, or delay, which may not effectively enhance overall performance. This study aims to develop a claim management system based on IMBOA and EPC principles to optimize multiple objectives, focusing on minimizing project costs and time delays while ensuring high-quality results. The basic methodology of this research involves integrating EPC and claim management principles with the IMBOA algorithm to create an efficient, high-quality system. This process starts with a comprehensive literature review on EPC, claims, MBOA, and related algorithms. Common disputes and claims in the construction industry are examined, and critical factors influencing these claims are identified. The Monarch Butterfly Optimization Algorithm (MBOA) and its improved version (IMBOA) are explored for their application in optimizing project performance. A case study in China's coal mining industry evaluates the effectiveness of the EPC approach, demonstrating that it minimizes time delays and costs. The IMBOA approach proposed in this study has the potential to mitigate 23% of risks and avoid 32% of risks associated with the action plan of China's coal mining industry. Furthermore, comparative analysis with other optimization models indicates that the developed IMBOA model yields superior results, reducing overall project time by 15% and cost by 18%.

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Analysis of Brain Imaging Data for the Detection of Early Age Autism Spectrum Disorder Using Transfer Learning Approaches for Internet of Things

Cited by 18 publications

References 72 publications

Evolutionary Safe Padé Approximation Scheme for Dynamical Study of Nonlinear Cervical Human Papilloma Virus Infection Model

Evolutionary Safe Padé Approximation Scheme for Dynamical Study of Nonlinear Cervical Human Papilloma Virus Infection Model

A Hyper-Heuristic Algorithm with Q-Learning for Distributed Flow Shop-Vehicle Transport-U-Assembly Integrated Scheduling Problem

An efficient claim management assurance system using EPC contract based on improved monarch butterfly optimization models

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