Relevant bibliographies by topics / Autism in children Classification

Academic literature on the topic 'Autism in children Classification'

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Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Autism in children Classification"

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Hassan, Masoud Muhammed, and Sulav Adil Taher. "Analysis and Classification of Autism Data Using Machine Learning Algorithms." Science Journal of University of Zakho 10, no. 4 (November 7, 2022): 206–12. http://dx.doi.org/10.25271/sjuoz.2022.10.4.1036.

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Autism is a neurodevelopmental disorder that affects children worldwide between the ages of 2 and 8 years. Children with autism have communication and social difficulties, and the current standardized clinical diagnosis of autism still relies on behaviour-based tests. The rapidly growing number of autistic patients in the Kurdistan Region of Iraq necessitates. However, such data are scarce, making extensive evaluations of autism screening procedures more difficult. For this purpose, the use of machine learning algorithms for this disease to assist health practitioners if formal clinical diagnosis should be pursued was investigated. Data from 515 patients were collected in Dohuk city related to autism screening for young children. Three classification algorithms, namely (DT, KNN, and ANN) were applied to diagnose and predict autism using various rating scales. Before applying the above classifiers, the newly obtained data set was in different ways undergo data reprocessing. Since our data is unbalanced with high dimensionality, we suggest combining SMOTE (Synthetic Minority Hyper sampling Technique) and PCA (Primary Component Analysis) to improve the performance of classification models. Experimental results showed that the combination of PCA and SMOTE methods improved classification performance. Moreover, ANN exceeded the other models in terms of accuracy and F1 score, suggesting that these classification methods could be used to diagnose autism in the future.
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Blacher, Jan, Katherine Stavropoulos, and Yasamine Bolourian. "Anglo-Latino differences in parental concerns and service inequities for children at risk of autism spectrum disorder." Autism 23, no. 6 (January 7, 2019): 1554–62. http://dx.doi.org/10.1177/1362361318818327.

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In an evaluation of Anglo and Latina mothers and their children at risk of autism, this study compared mother-reported child behavioral concerns to staff-observed symptoms of autism. Within Latina mothers, the impact of primary language (English/Spanish), mothers’ education, and child age on ratings of developmental concerns was examined. Participants were 218 mothers (Anglo = 85; Latina = 133) of children referred to a no-cost autism screening clinic. Mothers reported on behavioral concerns, autism symptomology, and services received; children were administered the Autism Diagnostic Observation Schedule by certified staff. Results revealed that Anglo and Latino children did not differ by autism symptoms or classification. However, Anglo mothers reported significantly more concerns than Latina mothers. Within the Latina group, analyses revealed significant interaction effects of language and child age; Spanish-speaking mothers of preschoolers endorsed fewer concerns, while Spanish-speaking mothers of school-aged children endorsed more concerns. Despite these reports, Anglo children with a classification of autism spectrum disorder were receiving significantly more services than Latino children with autism spectrum disorder, suggesting early beginnings of a service divide as well as the need for improved parent education on child development and advocacy for Latino families.
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Kuznetsova, L., M. Brychuk, L. Pogasiy, and K. Zhizhkun. "Features of the influence of playing activities on preschool children with a spectrum of autistic disorders in the process of adaptive physical education." Scientific Journal of National Pedagogical Dragomanov University. Series 15. Scientific and pedagogical problems of physical culture (physical culture and sports), no. 1(121) (January 29, 2020): 53–59. http://dx.doi.org/10.31392/npu-nc.series15.2019.1(121)20.10.

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The article deals with the peculiarities of the mental development of children with autism spectrum disorders, their psychophysical abilities, the formation of cognitive functions, the means of communication, the development of the emotional-volitional sphere, behavior in society. Features of correctional and pedagogical work with autistic children at the present stage are considered. A detailed definition of the definition of "autism", a modern classification of autism, the main features of autistic disorders in all its clinical variants are presented. Statistics on the incidence of autism in the world are provided. The characteristics and peculiarities of psychomotor development in preschool children with autism spectrum disorders and the logic of psychomotor development, the features of psychomotor development, the offered educational and correction tasks are presented. Importance and place of mobile games as the main means of adaptive physical education of preschool children with this nosology have been determined. Mobility games are distributed in the focus on the development of motor skills of preschool children with autism spectrum disorders. A modified classification of mobile games, entertainment, and entertainment that can be used in adaptive physical education and extracurricular forms of preschool-age children with autism spectrum disorders is presented.
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Shihab, Ammar I., Faten A. Dawood, and Ali H. Kashmar. "Data Analysis and Classification of Autism Spectrum Disorder Using Principal Component Analysis." Advances in Bioinformatics 2020 (January 7, 2020): 1–8. http://dx.doi.org/10.1155/2020/3407907.

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Autism spectrum disorder (ASD) is an early developmental disorder characterized by mutation of enculturation associated with attention deficit disorder in the visual perception of emotional expressions. An estimated one in more than 100 people has autism. Autism affects almost four times as many boys than girls. Data analysis and classification of ASD is still challenging due to unsolved issues arising from many severity levels and range of signs and symptoms. To understanding the functions which involved in autism, neuroscience technology analyzed responses to stimuli of autistic audio and video. The study focuses on analyzing the data set of adults and children with ASD using practical component analysis method. To satisfy this aim, the proposed method consists of three main stages including: (1) data set preparation, (2) Data analysis, and (3) Unsupervised Classification. The experimental results were performed to classify adults and children with ASD. The classification results in adults give a sensitivity of 78.6% and specificity of 82.47%, while the classification results in children give a sensitivity of 87.5% and specificity of 95.7%.
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Pradhan, Ashirbad, Victoria Chester, and Karansinh Padhiar. "Classification of Autism and Control Gait in Children Using Multisegment Foot Kinematic Features." Bioengineering 9, no. 10 (October 14, 2022): 552. http://dx.doi.org/10.3390/bioengineering9100552.

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Previous research has demonstrated that children with autism walk with atypical ankle kinematics and kinetics. Although these studies have utilized single-segment foot (SSF) data, multisegment foot (MSF) kinematics can provide further information on foot mechanics. Machine learning (ML) tools allow the combination of MSF kinematic features for classifying autism gait patterns. In this study, multiple ML models are investigated, and the most contributing features are determined. This study involved 19 children with autism and 21 age-matched controls performing walking trials. A 34-marker system and a 12-camera motion capture system were used to compute SSF and MSF angles during walking. Features extracted from these foot angles and their combinations were used to develop support vector machine (SVM) models. Additional techniques-S Hapley Additive exPlanations (SHAP) and the Shapley Additive Global importancE (SAGE) are used for local and global importance of the black-box ML models. The results suggest that models based on combinations of MSF kinematic features classify autism patterns with an accuracy of 96.3%, which is higher than using SSF kinematic features (83.8%). The relative angle between the metatarsal and midfoot segments had the highest contribution to the classification of autism gait patterns. The study demonstrated that kinematic features from MSF angles, supported by ML models, can provide an accurate and interpretable classification of autism and control patterns in children.
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Chiappedi, Matteo, Giorgio Rossi, Maura Rossi, Maurizio Bejor, and Umberto Balottin. "Autism and classification systems: a study of 84 children." Italian Journal of Pediatrics 36, no. 1 (2010): 10. http://dx.doi.org/10.1186/1824-7288-36-10.

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Li, Genyuan, Olivia Lee, and Herschel Rabitz. "High efficiency classification of children with autism spectrum disorder." PLOS ONE 13, no. 2 (February 15, 2018): e0192867. http://dx.doi.org/10.1371/journal.pone.0192867.

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Bazyma, Nataliia, Yevheniia Lyndina, Olha Rasskazova, Ganna Kavylina, Olga Litovchenko, and Iryna Hrynyk. "Research of the Problem of Autism and Autistic Disorders: Theoretical Aspect." Revista Romaneasca pentru Educatie Multidimensionala 14, no. 2 (May 9, 2022): 301–17. http://dx.doi.org/10.18662/rrem/14.2/582.

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The causes of autism remain insufficiently differentiated. It is unlikely that any single disorder can be considered as the only cause of various symptoms and severity of autistic disorders. Although the specific causes of autism remain unclear, significant progress has been made in understanding the possible mechanisms of the disease. Turning to historical sources, we find that the origin and origin of the term "autism" are associated with forming a system of knowledge on the problem of diagnosis and further therapeutic work with children who need unique approaches to learn and educate. Analysis of the classifications of autism reveals the ambiguity of approaches to them. The first attempts at differentiation in the middle of childhood autism syndrome were clinical classifications based on the syndrome's etiology. They play a significant role in developing adequate approaches to providing medical care to children with autism. Psychological and pedagogical tasks required other approaches to determine, depending on the specific situation, the specialization, strategy, and tactics of correctional work. First of all, there was a search for prognostic signs that would assess the possibilities of mental and social development of children in this category. To this end, some scholars have put forward criteria for assessing speech and intellectual development. The analysis of difficulties of the unanimous possibility of classification on separate indicators of mental development of the child (intelligence, speech, behavior, self-regulation, etc.) can be explained by parallel existence of classifications operating today in world practice.
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Ahmed, Zeyad A. T., Theyazn H. H. Aldhyani, Mukti E. Jadhav, Mohammed Y. Alzahrani, Mohammad Eid Alzahrani, Maha M. Althobaiti, Fawaz Alassery, Ahmed Alshaflut, Nouf Matar Alzahrani, and Ali Mansour Al-madani. "Facial Features Detection System To Identify Children With Autism Spectrum Disorder: Deep Learning Models." Computational and Mathematical Methods in Medicine 2022 (April 4, 2022): 1–9. http://dx.doi.org/10.1155/2022/3941049.

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Autism spectrum disorder (ASD) is a neurodevelopmental disorder associated with brain development that subsequently affects the physical appearance of the face. Autistic children have different patterns of facial features, which set them distinctively apart from typically developed (TD) children. This study is aimed at helping families and psychiatrists diagnose autism using an easy technique, viz., a deep learning-based web application for detecting autism based on experimentally tested facial features using a convolutional neural network with transfer learning and a flask framework. MobileNet, Xception, and InceptionV3 were the pretrained models used for classification. The facial images were taken from a publicly available dataset on Kaggle, which consists of 3,014 facial images of a heterogeneous group of children, i.e., 1,507 autistic children and 1,507 nonautistic children. Given the accuracy of the classification results for the validation data, MobileNet reached 95% accuracy, Xception achieved 94%, and InceptionV3 attained 0.89%.
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Mayes, Susan Dickerson, Susan L. Calhoun, Michael J. Murray, Jill D. Morrow, Shiyoko Cothren, Heather Purichia, Kirsten K. L. Yurich, and James N. Bouder. "Use of Gilliam Asperger's Disorder Scale in Differentiating High and Low Functioning Autism and ADHD." Psychological Reports 108, no. 1 (February 2011): 3–13. http://dx.doi.org/10.2466/04.10.15.pr0.108.1.3-13.

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Little is known about the validity of Gilliam Asperger's Disorder Scale (GADS), although it is widely used. This study of 199 children with high functioning autism or Asperger's Disorder, 195 with low functioning autism, and 83 with Attention Deficit Hyperactivity Disorder (ADHD) showed high classification accuracy (autism vs ADHD) for clinicians' GADS Quotients (92%), and somewhat lower accuracy (77%) for parents' Quotients. Both children with high and low functioning autism had clinicians' Quotients ( M = 99 and 101, respectively) similar to the Asperger's Disorder mean of 100 for the GADS normative sample. Children with high functioning autism scored significantly higher on the Cognitive Patterns subscale than children with low functioning autism, and the later had higher scores on the remaining subscales: Social Interaction, Restricted Patterns of Behavior, and Pragmatic Skills. Using the clinicians' Quotient and Cognitive Patterns score, 70% of children were correctly identified as having high or low functioning autism or ADHD.
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Dissertations / Theses on the topic "Autism in children Classification"

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Fung, Kar-yan Cecilia, and 馮嘉欣. "Use of dysmorphology for subgroup classification on autism spectrum disorder in Chinese Children." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B45160697.

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Wong, Tsz-yan Polly, and 黃芷欣. "Pilot study for subgroup classification for autism spectrum disorder based on dysmorphology and physical measurements in Chinese children." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2012. http://hub.hku.hk/bib/B4786932X.

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Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder affecting individuals along a continuum of severity in communication, social interaction and behaviour. The impact of ASD significantly varies amongst individuals, and the cause of ASD can originate broadly between genetic and environmental factors. Previous ASD researches indicate that early identification combined with a targeted treatment plan involving behavioural interventions and multidisciplinary therapies can provide substantial improvement for ASD patients. Currently there is no cure for ASD, and the clinical variability and uncertainty of the disorder still remains. Hence, the search to unravel heterogeneity within ASD by subgroup classification may provide clinicians with a better understanding of ASD and to work towards a more definitive course of action. In this study, a norm of physical measurements including height, weight, head circumference, ear length, outer and inner canthi, interpupillary distance, philtrum, hand and foot length was collected from 658 Typical Developing (TD) Chinese children aged 1 to 7 years (mean age of 4.19 years). The norm collected was compared against 80 ASD Chinese children aged 1 to 12 years (mean age of 4.36 years). We then further attempted to find subgroups within ASD based on identifying physical abnormalities; individuals were classified as (non)dysmorphic with the Autism Dysmorphology Measure (ADM) from physical examinations of 12 body regions. Our results show that there were significant differences between ASD and TD children for measurements in: head circumference (p=0.009), outer (p=0.021) and inner (p=0.021) canthus, philtrum length (p=0.003), right (p=0.023) and left (p=0.20) foot length. Within the 80 ASD patients, 37(46%) were classified as dysmorphic (p=0.00). This study attempts to identify subgroups within ASD based on physical measurements and dysmorphology examinations. The information from this study seeks to benefit ASD community by identifying possible subtypes of ASD in Chinese population; in seek for a more definitive diagnosis, referral and treatment plan.
published_or_final_version
Paediatrics and Adolescent Medicine
Master
Master of Philosophy
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Garside, Kristine Dianne Cantin. "Behavioral Monitoring to Identify Self-Injurious Behavior among Children with Autism Spectrum Disorder." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/88533.

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Self-injurious behavior (SIB) is one of the most dangerous behavioral responses among individuals with autism spectrum disorder (ASD), often leading to injury and hospitalization. There is an ongoing need to measure the triggers of SIB to inform management and prevention. These triggers are determined traditionally through clinical observations of the child with SIB, often involving a functional assessment (FA), which is methodologically documenting responses to stimuli (e.g., environmental or social) and recording episodes of SIB. While FA has been a "gold standard" for many years, it is costly, tedious, and often artificial (e.g., in controlled environments). If performed in a naturalistic environment, such as the school or home, caregivers are responsible for tracking behaviors. FA in naturalistic environments relies on caregiver and patient compliance, such as responding to prompts or recalling past events. Recent technological developments paired with classification methods may help decrease the required tracking efforts and support management plans. However, the needs of caregivers and individuals with ASD and SIB should be considered before integrating technology into daily routines, particularly to encourage technology acceptance and adoption. To address this, the perspectives of SIB management and technology were first collected to support future technology design considerations (Chapter 2). Accelerometers were then selected as a specific technology, based on caregiver preferences and reported preferences of individuals with ASD, and were used to collect movement data for classification (Chapter 3). Machine learning algorithms with featureless data were explored, resulting in individual-level models that demonstrated high accuracy (up to 99%) in detecting and classifying SIB. Group-level classifiers could provide more generalizable models for efficient SIB monitoring, though the highly variable nature of both ASD and SIB can preclude accurate detection. A multi-level regression model (MLR) was implemented to consider such individual variability (Chapter 4). Both linear and nonlinear measures of motor variability were assessed as potential predictors in the model. Diverse classification methods were used (as in Chapter 3), and MLR outperformed other group level classifiers (accuracy ~75%). Findings from this research provide groundwork for a future smart SIB monitoring system. There are clear implications for such monitoring methods in prevention and treatment, though additional research is required to expand the developed models. Such models can contribute to the goal of alerting caregivers and children before SIB occurs, and teaching children to perform another behavior when alerted.
Doctor of Philosophy
Autism spectrum disorder (ASD) is a prevalent developmental disorder that adversely affects communication, social skills, and behavioral responses. Roughly half of individuals diagnosed with ASD show self-injurious behavior (SIB), including self-hitting or head banging), which can lead to injury and hospitalization. Clinicians or trained caregivers traditionally observe and record events before/after SIB to determine possible causes (“triggers”) of this behavior. Clinicians can then develop management plans to redirect, replace, or extinguish SIB at the first sign of a known trigger. Tracking SIB in this way, though, requires substantial experience, time, and effort from caregivers. Observations may suffer from subjectivity and inconsistency if tracked across caregivers, or may not generalize to different contexts if SIB is only tracked in the home or school. Recent technological innovations, though, could objectively and continuously monitor SIB to address the described limitations of traditional tracking methods. Yet, “smart” SIB tracking will not be adopted into management plans unless first accepted by potential users. Before a monitoring system is developed, caregiver needs related to SIB, management, and technology should be evaluated. Thus, as an initial step towards developing an accepted SIB monitoring system, caregiver perspectives of SIB management and technology were collected here to support future technology design considerations (Chapter 2). Sensors capable of collecting the acceleration of movement (accelerometers) were then selected as a specific technology, based on the reported preferences of caregivers and individuals with ASD, and were used to capture SIB movements from individuals with ASD (Chapter 3). These movements were automatically classified as “SIB” or “non-SIB” events using machine learning algorithms. When separately applying these methods to each individual, up to 99% accuracy in detecting and classifying SIB was achieved. Classifiers that predict SIB for diverse individuals could provide more generalizable and efficient methods for SIB monitoring. ASD and SIB presentations, however, range across individuals, which impose challenges for SIB detection. A multi-level regression model (MLR) was implemented to consider individual differences, such as those that may occur from diagnosis or behavior (Chapter 4). Model inputs included measures capturing changes of movement over time, and these were found to enhance SIB identification. Diverse classification models were also developed (as in Chapter 3), though MLR outperformed these (yielding accuracy of ~75%). Findings from this research provide groundwork for a smart SIB monitoring system. There are clear implications for monitoring methods in prevention, though additional research is required to expand the developed models. Such models can contribute to the goal of alerting caregivers and children before SIB occurs, and teaching children to perform another behavior when alerted.
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Borden, Michael Christopher. "Social subtypes in autism : an examination of their validity and relations to measures of social cognition /." Diss., This resource online, 1991. http://scholar.lib.vt.edu/theses/available/etd-07282008-135801/.

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Farrant, Annette. "Metamemory in children with autism." Thesis, University of Sheffield, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.267107.

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Adams, Nena Capitola. "Inhibition in children with autism." Thesis, University of Bristol, 2010. http://hdl.handle.net/1983/9c2f4c7b-fed2-4f79-a4b2-e214d9805a18.

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This thesis aimed to provide an insight into, and account for, the varying levels and patterns of ability across different tests of inhibition in autism. In order to address the effects of the meaningful word stimuli of the classic Stroop task on inhibitory performance of children with autism, Experiment I explored reasons for the unique inhibitory strengths of children with autism on the classic Stroop task. The remainder of the thesis focused on tests of inhibition which do not use meaningful word stimuli and explored potential reasons why these tasks still give rise to conflicting results. A comprehensive investigation of motor versus cognitive inhibition in children with autism, the impact of presentation style and working memory load on inhibitory performance, and the impact of same and different response sets was conducted in Experiments 2-4. Furthermore, the possibility of differential impairment in prepotent response inhibition and resistance to distractor inhibition, including the role of a possible bias to weak central coherence in children with autism on the flanker task, was investigated in the final Experiments 5 and 6. The exploration of the impact on inhibitory performance of motor versus cognitive responses, presentation style, and response set showed that although these factors do not seem to particularly affect children with autism in comparison to controls, they do play a large role in determining the inhibitory performance of all participants. Finally, it was concluded that there is evidence for a differential impairment of prepotent response and resistance to distractor inhibition in children with autism, with children with autism being impaired in resistance to distractor inhibition while maintaining intact prepotent response inhibition.
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Abdun-Nur, Roy D. "School for Autism - Responding to Autism." VCU Scholars Compass, 2015. http://scholarscompass.vcu.edu/etd/3836.

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Schools can often be overstimulating environments for children with autism. Creating a space where these children can thrive is what this project explored. The site used for this exploration was the Lewis Ginter Recreation Association on the north end of Richmond, VA. Children with autism have very sensitive needs, so exploring these needs within the context of an educational setting provided for an in-depth journey into the lives of those affected by autism.
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Sayers, Nicola Louise. "Stereotyped behaviours in children with autism." Thesis, University of Birmingham, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.408832.

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Richardson, Cathryn. "Dream conceptualisation in children with autism." Thesis, University of Warwick, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.275295.

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Hauck, Joy Alison. "Hand preference in children with autism." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/mq24668.pdf.

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Books on the topic "Autism in children Classification"

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Constantino, J. N. Social responsiveness scale: SRS-2. Torrane, California: Western Psychological Services, 2012.

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1937-, Reichler Robert Jay, and Renner Barbara Rochen, eds. The childhood autism rating scale (CARS): For diagnostic screening and classification of autism. New York: Irvington, 1986.

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Whiteley, Paul. Guidelines for the implementation of a gluten and/or casein free diet with people with autism or associated spectrum disorders. Sunderland: Autism Research Unit, 1997.

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Children With Autism. 2nd ed. Bethesda, MD: Woodbine House, Inc., 2000.

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Shattock, Paul. Autism as a metabolic disorder. Sunderland: Autism Research Unit, 1997.

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Peacock, Geraldine. Autism: The invisible children? London: National Autistic Society, 1996.

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Scovell, LaZebnik Claire, ed. Overcoming autism. New York: Viking, 2004.

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1952-, Paris Betty A., ed. Autism interventions: Exploring the spectrum of autism. Austin, Texas: Hammill Institute on Disabilities, 2014.

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Communication in autism. Amsterdam: John Benjamins Publishing Company, 2014.

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Chilman-Blair, Kim. Medikidz explain autism. New York: Rosen Central, 2011.

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Book chapters on the topic "Autism in children Classification"

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Magboo, Ma Sheila A., and Vincent Peter C. Magboo. "Explainable AI for Autism Classification in Children." In Agents and Multi-Agent Systems: Technologies and Applications 2022, 195–205. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3359-2_17.

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Noruzman, Ainie Hayati Binti, Ngahzaifa Ab Ghani, and Nor Saradatul Akmar Zulkifli. "A Comparative Study on Autism Among Children Using Machine Learning Classification." In Proceedings of International Conference on Emerging Technologies and Intelligent Systems, 131–40. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85990-9_12.

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Abirami, S. P., G. Kousalya, and P. Balakrishnan. "A Meta-Heuristic Model Based Computational Intelligence in Exploration and Classification of Autism in Children." In Intelligent Systems, Technologies and Applications, 61–77. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3914-5_6.

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Wilson, Derek. "Autism." In Handicapping Conditions in Children, 100–114. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003261964-8.

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Timimi, Sami, Neil Gardner, and Brian McCabe. "Classification." In The Myth of Autism, 141–71. London: Macmillan Education UK, 2011. http://dx.doi.org/10.1007/978-1-137-05773-0_6.

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Russell, Ginny. "Children." In The Rise of Autism, 45–56. Milton Park, Abingdon, Oxon; New York, NY: Routledge, 2021. | Series: Routledge studies in the sociology of health and illness: Routledge, 2020. http://dx.doi.org/10.4324/9780429285912-5.

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Goldstein, Mark L., and Stephen Morewitz. "Autism." In Chronic Disorders in Children and Adolescents, 59–80. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-9764-7_3.

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Valtellina, Enrico. "A.S.: Classification, Interpellation." In Autism in Translation, 207–29. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93293-4_10.

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Charlop-Christy, Marjorie H., and Susan E. Kelso. "Autism." In Handbook of Psychosocial Characteristics of Exceptional Children, 247–73. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4757-5375-2_10.

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Ward, Tracey, Raphael Bernier, Cora Mukerji, Danielle Perszyk, James C. McPartland, Ellen Johnson, Susan Faja, et al. "Feral Children." In Encyclopedia of Autism Spectrum Disorders, 1266–73. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-1698-3_447.

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Conference papers on the topic "Autism in children Classification"

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Budarapu, Amrita, Nara Kalyani, and Seetha Maddala. "Early Screening of Autism among Children Using Ensemble Classification Method." In 2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N). IEEE, 2021. http://dx.doi.org/10.1109/icac3n53548.2021.9725586.

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Barik, Kasturi, Katsumi Watanabe, Joydeep Bhattacharya, and Goutam Saha. "Classification of Autism in Young Children by Phase Angle Clustering in Magnetoencephalogram Signals." In 2020 National Conference on Communications (NCC). IEEE, 2020. http://dx.doi.org/10.1109/ncc48643.2020.9056022.

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Ilias, Suryani, Nooritawati Md Tahir, Rozita Jailani, and Che Zawiyah Che Hasan. "Classification of autism children gait patterns using Neural Network and Support Vector Machine." In 2016 IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE). IEEE, 2016. http://dx.doi.org/10.1109/iscaie.2016.7575036.

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Kakihara, Yasuhiro, Tetsuya Takiguchi, Yasuo Ariki, Yasushi Nakai, and Satoshi Takada. "Acoustic feature selection utilizing multiple kernel learning for classification of children with autism spectrum and typically developing children." In 2013 IEEE/SICE International Symposium on System Integration (SII). IEEE, 2013. http://dx.doi.org/10.1109/sii.2013.6776604.

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Praveena, T. Lakshmi, and N. V. Muthu Lakshmi. "Multi Label Classification for Emotion Analysis of Autism Spectrum Disorder Children using Deep Neural Networks." In 2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA). IEEE, 2021. http://dx.doi.org/10.1109/icirca51532.2021.9545073.

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Feil-Seifer, David, and Maja Mataric. "Automated detection and classification of positive vs. negative robot interactions with children with autism using distance-based features." In the 6th international conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/1957656.1957785.

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Millen, Laura, Tessa Hawkins, Sue Cobb, Massimo Zancanaro, Tony Glover, Patrice L. Weiss, and Eynat Gal. "Collaborative technologies for children with autism." In the 10th International Conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/1999030.1999073.

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Capetillo, Guadalupe, Reyna Esparza, Evelyn Torres, Silvia Georgina Flores, Clara Luz Parra, Fabiola Leyva, Teresita Mendez, Ignacio Ortiz Betancourt, and Beatriz Torres. "DENTAL CARE FOR CHILDREN WITH AUTISM." In International Conference on Education and New Learning Technologies. IATED, 2016. http://dx.doi.org/10.21125/edulearn.2016.1997.

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CHujkin, S. V., R. V. Galeev, and R. R. Galeeva. "Dental status of children with autism." In SCIENCE OF RUSSIA: TARGETS AND GOALS. "Science of Russia", 2019. http://dx.doi.org/10.18411/sr-10-10-2019-17.

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Bai, Zhen. "Augmenting imagination for children with autism." In the 11th International Conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2307096.2307159.

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Reports on the topic "Autism in children Classification"

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Anderson, Kristy A., Jessica E. Rast, Anne M. Roux, Tamara Garfield, and Paul T. Shattuck. National Autism Indicators Report: Children on the autism spectrum and family financial hardship. A.J. Drexel Autism Institute, June 2020. http://dx.doi.org/10.17918/nairfinancialhardship2020.

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Families of children with ASD face significant financial challenges due to their child's complex service needs and frequent out-of-pocket expenditures for community services and health care. This report describes financial hardship among families of children with ASD (ages 3-17 years) and their participation in safety net programs.
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Siri Ming, Siri Ming. Can children with autism learn more flexible language patterns? Experiment, July 2014. http://dx.doi.org/10.18258/2920.

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Kushak, Rafail. Analysis of Small Intestinal Microbiome in Children with Autism. Fort Belvoir, VA: Defense Technical Information Center, January 2013. http://dx.doi.org/10.21236/ada575715.

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Nylund, Cade, Gregory H. Gorman, Matthew D. Eberly, Elizabeth Hisle-Gorman, Anthony Goudie, Adam Huillet, Matilda Eide, Stephen L. Nelson, Christine Erdie-Lalena, and Luis E. Lozada. Risk Factors, Comorbid Conditions, and Epidemiology of Autism in Children. Fort Belvoir, VA: Defense Technical Information Center, October 2014. http://dx.doi.org/10.21236/ada613604.

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Williamson, M.D., Edwin, Nila A. Sathe, M.A., M.L.I.S., and Jeffrey C. Andrews, M.D. Medical Therapies for Children With Autism Spectrum Disorder—An Update. Agency for Healthcare Research and Quality (AHRQ), 2017. http://dx.doi.org/10.23970/ahrqepccer189.

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Kushak, Rafail. Analysis of the Small Intestinal Microbiome of Children With Autism. Fort Belvoir, VA: Defense Technical Information Center, May 2013. http://dx.doi.org/10.21236/ada584963.

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Anderson, Kristy A., Anne M. Roux, Hillary Steinberg, Tamara Garfield, Jessica E. Rast, Paul T. Shattuck, and Lindsay L. Shea. The Intersection of National Autism Indicators Report: Autism, Health, Poverty and Racial Inequity. A.J. Drexel Autism Institute, April 2022. http://dx.doi.org/10.17918/nairintersection2022.

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This report examines the following two questions: 1) do income-based differences in health and health care outcomes look the same for children with and without autism? and 2) do income-based differences in health and health care outcomes look the same for BIPOC (Black, Indigenous, and People of Color) children with autism and white children with autism? Examining the health and healthcare outcomes of children with autism in combination with other social characteristics offers several advantages. First, we can illuminate how demographics alone, and in combination with other social characteristics of children, are associated with differences in the rates of health and healthcare outcomes they experience. Second, it increases our understanding of the health-related experiences of social groups who are often neglected in research. Third, it provides current and comprehensive evidence on how children with autism experience relative disadvantages related to social determinants of health, which are aspects of the environment that affect health, functioning, and quality-of-life outcomes and risks.
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Shin, Su-Jeong Hwang, Brianna Smith, and Kristi Gaines. Investigation of Therapy Clothing Products for Children with Autism Spectrum Disorders. Ames: Iowa State University, Digital Repository, November 2015. http://dx.doi.org/10.31274/itaa_proceedings-180814-1151.

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Weitlauf, Ph.D., Amy S., Nila A. Sathe, M.A., M.L.I.S., and Melissa L. McPheeters, Ph.D., M.P.H. Interventions Targeting Sensory Challenges in Children With Autism Spectrum Disorder—An Update. Agency for Healthcare Research and Quality (AHRQ), 2017. http://dx.doi.org/10.23970/ahrqepccer186.

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Andrew M. Colombo-Dougovito, Andrew M. Colombo-Dougovito. Building guidelines when assessing motor skills in children with autism spectrum disorder. Experiment, July 2014. http://dx.doi.org/10.18258/3080.

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