标题： 基于3D视频数据集的自闭症谱系障碍检测的仿生算法实证分析 显示英文标题

标题： Empirical Analysis of Nature-Inspired Algorithms for Autism Spectrum Disorder Detection Using 3D Video Dataset

摘要： 自闭症谱系障碍（ASD）是一种慢性神经发育障碍，其症状包括重复行为以及缺乏社交和沟通技能。 尽管这些症状在社会中可以非常明显，但仍有大量ASD患者未被诊断。 在本文中，我们研究了一种从三维行走视频数据集中检测ASD的方法，利用监督机器学习（ML）分类算法以及自然启发优化算法从数据集中提取特征。 所提出的方法涉及使用监督ML分类算法对ASD进行分类，并利用自然启发优化算法从数据集中提取重要且相关的特征。 我们还包含了排名系数以找到初始主导粒子。 这种粒子的选择显著减少了计算时间，从而提高了ASD检测的总体效率和准确性。 为了评估所提出方法的效率，我们部署了各种分类算法和自然启发算法的组合，结果使用随机森林分类算法和重力搜索算法进行特征选择，达到了$100\%$的出色分类准确率。 将所提出的方法应用于不同的数据集将增强所提出方法的鲁棒性和泛化能力。 由于高准确性和较少的总计算时间，所提出的方法将在医学和学术领域做出重要贡献，为ASD诊断的未来研究和进展提供基础。 显示英文摘要

摘要： Autism Spectrum Disorder (ASD) is a chronic neurodevelopmental disorder symptoms of which includes repetitive behaviour and lack of social and communication skills. Even though these symptoms can be seen very clearly in social but a large number of individuals with ASD remain undiagnosed. In this paper, we worked on a methodology for the detection of ASD from a 3-dimensional walking video dataset, utilizing supervised machine learning (ML) classification algorithms and nature-inspired optimization algorithms for feature extraction from the dataset. The proposed methodology involves the classification of ASD using a supervised ML classification algorithm and extracting important and relevant features from the dataset using nature-inspired optimization algorithms. We also included the ranking coefficients to find the initial leading particle. This selection of particle significantly reduces the computation time and hence, improves the total efficiency and accuracy for ASD detection. To evaluate the efficiency of the proposed methodology, we deployed various combinationsalgorithms of classification algorithm and nature-inspired algorithms resulting in an outstanding classification accuracy of $100\%$ using the random forest classification algorithm and gravitational search algorithm for feature selection. The application of the proposed methodology with different datasets would enhance the robustness and generalizability of the proposed methodology. Due to high accuracy and less total computation time, the proposed methodology will offer a significant contribution to the medical and academic fields, providing a foundation for future research and advancements in ASD diagnosis.