Autism Spectrum Disorder(ASD)is a neurodevelopmental condition characterized by significant challenges in social interaction,communication,and repetitive behaviors.Timely and precise ASD detection is crucial,particula...Autism Spectrum Disorder(ASD)is a neurodevelopmental condition characterized by significant challenges in social interaction,communication,and repetitive behaviors.Timely and precise ASD detection is crucial,particularly in regions with limited diagnostic resources like Pakistan.This study aims to conduct an extensive comparative analysis of various machine learning classifiers for ASD detection using facial images to identify an accurate and cost-effective solution tailored to the local context.The research involves experimentation with VGG16 and MobileNet models,exploring different batch sizes,optimizers,and learning rate schedulers.In addition,the“Orange”machine learning tool is employed to evaluate classifier performance and automated image processing capabilities are utilized within the tool.The findings unequivocally establish VGG16 as the most effective classifier with a 5-fold cross-validation approach.Specifically,VGG16,with a batch size of 2 and the Adam optimizer,trained for 100 epochs,achieves a remarkable validation accuracy of 99% and a testing accuracy of 87%.Furthermore,the model achieves an F1 score of 88%,precision of 85%,and recall of 90% on test images.To validate the practical applicability of the VGG16 model with 5-fold cross-validation,the study conducts further testing on a dataset sourced fromautism centers in Pakistan,resulting in an accuracy rate of 85%.This reaffirms the model’s suitability for real-world ASD detection.This research offers valuable insights into classifier performance,emphasizing the potential of machine learning to deliver precise and accessible ASD diagnoses via facial image analysis.展开更多
Children with autism spectrum disorder(ASD)often encounter difficulties in language learning and utilization,a concern that has gained significant academic attention,particularly given the widespread occurrence of ASD...Children with autism spectrum disorder(ASD)often encounter difficulties in language learning and utilization,a concern that has gained significant academic attention,particularly given the widespread occurrence of ASD globally.Previous reviews,however,have relied on empirical observations rather than a more rigorous selection criterion.This preliminary study seeks to systematize the scientific knowledge base regarding language development in autistic children by utilizing the analysis tool Citespace 6.2.R5.We visualized and analyzed research patterns and trends regarding autism by drawing data from the Web of Science.Through document citation and emerging trend analyses,seven key research clusters and their chronological associations are identified,along with research hotspots such as language disorder diagnosis and intervention,social communication,language acquisition,and multilingual and multicultural influences.Research findings show that there exist some issues with the current research,including small sample sizes,the need for further investigation into receptive language development,and a lack of cross-cultural comparative studies.Meanwhile,the scope and depth of interdisciplinary research on language development in autistic children also need to be further enhanced.The research contributes to the extant literature by providing valuable references for autism researchers and practitioners.展开更多
文摘Autism Spectrum Disorder(ASD)is a neurodevelopmental condition characterized by significant challenges in social interaction,communication,and repetitive behaviors.Timely and precise ASD detection is crucial,particularly in regions with limited diagnostic resources like Pakistan.This study aims to conduct an extensive comparative analysis of various machine learning classifiers for ASD detection using facial images to identify an accurate and cost-effective solution tailored to the local context.The research involves experimentation with VGG16 and MobileNet models,exploring different batch sizes,optimizers,and learning rate schedulers.In addition,the“Orange”machine learning tool is employed to evaluate classifier performance and automated image processing capabilities are utilized within the tool.The findings unequivocally establish VGG16 as the most effective classifier with a 5-fold cross-validation approach.Specifically,VGG16,with a batch size of 2 and the Adam optimizer,trained for 100 epochs,achieves a remarkable validation accuracy of 99% and a testing accuracy of 87%.Furthermore,the model achieves an F1 score of 88%,precision of 85%,and recall of 90% on test images.To validate the practical applicability of the VGG16 model with 5-fold cross-validation,the study conducts further testing on a dataset sourced fromautism centers in Pakistan,resulting in an accuracy rate of 85%.This reaffirms the model’s suitability for real-world ASD detection.This research offers valuable insights into classifier performance,emphasizing the potential of machine learning to deliver precise and accessible ASD diagnoses via facial image analysis.
文摘Children with autism spectrum disorder(ASD)often encounter difficulties in language learning and utilization,a concern that has gained significant academic attention,particularly given the widespread occurrence of ASD globally.Previous reviews,however,have relied on empirical observations rather than a more rigorous selection criterion.This preliminary study seeks to systematize the scientific knowledge base regarding language development in autistic children by utilizing the analysis tool Citespace 6.2.R5.We visualized and analyzed research patterns and trends regarding autism by drawing data from the Web of Science.Through document citation and emerging trend analyses,seven key research clusters and their chronological associations are identified,along with research hotspots such as language disorder diagnosis and intervention,social communication,language acquisition,and multilingual and multicultural influences.Research findings show that there exist some issues with the current research,including small sample sizes,the need for further investigation into receptive language development,and a lack of cross-cultural comparative studies.Meanwhile,the scope and depth of interdisciplinary research on language development in autistic children also need to be further enhanced.The research contributes to the extant literature by providing valuable references for autism researchers and practitioners.
