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Relevant bibliographies by topics / Autism spectrum disorders autoencoder

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Academic literature on the topic 'Autism spectrum disorders autoencoder'

Author: Grafiati

Published: 7 June 2025

Last updated: 16 July 2025

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Journal articles on the topic "Autism spectrum disorders autoencoder"

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Han, Wenhao, Wenzhu Shao, and Yaluo Wang. "Classifying autism spectrum disorder using machine learning through ABIDE dataset." Applied and Computational Engineering 2, no. 1 (2023): 809–22. http://dx.doi.org/10.54254/2755-2721/2/20220528.

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A neurodevelopmental disorder named autism spectrum disorder (ASD) is challenging to diagnose. The prevailing diagnostic manner is based merely on the behavioral measure-ment with a high tendency of misdiagnosis. People require an advanced method to make more quantitative diagnosis. In this paper, two deep learning architectures were explored with the machine learning methods. The Mixup method was used to augment the original functional Magnetic Resonance Imaging data. Features of the data extracted by two dif-ferent kinds of autoencoders which are Sparse Autoencoder and Variational Autoencoder were used as inputs of two deep neural networks functioning as classifiers respectively. The models can classify patients with ASD from typical control subjects with the accura-cy of 75.5% and 75.2% respectively, which outperformed the other state-of-the-art meth-od by 4.7% and 4.4%. The further significance of this project is to help develop our per-ception of the neurobiological foundation of the ASD.

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Amirbay, Aizat, Ayagoz Mukhanova, Nurlan Baigabylov, et al. "Development of an algorithm for identifying the autism spectrum based on features using deep learning methods." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 5 (2024): 5513. http://dx.doi.org/10.11591/ijece.v14i5.pp5513-5523.

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The presented scientific work describes the results of the development and evaluation of two deep learning algorithms: long short-term memory with a convolutional neural network (LSTM+CNN) and long short-term memory with an autoencoder (LSTM+AE), designed for the diagnosis of autism spectrum disorders. The study focuses on the use of eye tracking technology to collect data on participants' eye movements while interacting with animated objects. These data were saved in NumPy array format (.npy) for ease of later analysis. The algorithms were evaluated in terms of their accuracy, generalization ability, and training time, which was confirmed by experts. The main goal of the study is to improve the diagnosis of autism, making it more accurate and effective. The convolutional neural network long short-term memory and autoencoder-long short-term memory models have shown promise as tools for achieving this goal, with the autoencoder model standing out for its ability to identify internal relationships in data. The article also discusses potential clinical applications of these algorithms and directions for future research.

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Khan, Naseer Ahmed, Samer Abdulateef Waheeb, Atif Riaz, and Xuequn Shang. "A Three-Stage Teacher, Student Neural Networks and Sequential Feed Forward Selection-Based Feature Selection Approach for the Classification of Autism Spectrum Disorder." Brain Sciences 10, no. 10 (2020): 754. http://dx.doi.org/10.3390/brainsci10100754.

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Autism disorder, generally known as Autism Spectrum Disorder (ASD) is a brain disorder characterized by lack of communication skills, social aloofness and repetitions in the actions in the patients, which is affecting millions of the people across the globe. Accurate identification of autistic patients is considered a challenging task in the domain of brain disorder science. To address this problem, we have proposed a three-stage feature selection approach for the classification of ASD on the preprocessed Autism Brain Imaging Data Exchange (ABIDE) rs-fMRI Dataset. In the first stage, a large neural network which we call a “Teacher ” was trained on the correlation-based connectivity matrix to learn the latent representation of the input. In the second stage an autoencoder which we call a “Student” autoencoder was given the task to learn those trained “Teacher” embeddings using the connectivity matrix input. Lastly, an SFFS-based algorithm was employed to select the subset of most discriminating features between the autistic and healthy controls. On the combined site data across 17 sites, we achieved the maximum 10-fold accuracy of 82% and for the individual site-wise data, based on 5-fold accuracy, our results outperformed other state of the art methods in 13 out of the total 17 site-wise comparisons.

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Lee, Jung Hyuk, Geon Woo Lee, Guiyoung Bong, Hee Jeong Yoo, and Hong Kook Kim. "End-to-End Model-Based Detection of Infants with Autism Spectrum Disorder Using a Pretrained Model." Sensors 23, no. 1 (2022): 202. http://dx.doi.org/10.3390/s23010202.

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In this paper, we propose an end-to-end (E2E) neural network model to detect autism spectrum disorder (ASD) from children’s voices without explicitly extracting the deterministic features. In order to obtain the decisions for discriminating between the voices of children with ASD and those with typical development (TD), we combined two different feature-extraction models and a bidirectional long short-term memory (BLSTM)-based classifier to obtain the ASD/TD classification in the form of probability. We realized one of the feature extractors as the bottleneck feature from an autoencoder using the extended version of the Geneva minimalistic acoustic parameter set (eGeMAPS) input. The other feature extractor is the context vector from a pretrained wav2vec2.0-based model directly applied to the waveform input. In addition, we optimized the E2E models in two different ways: (1) fine-tuning and (2) joint optimization. To evaluate the performance of the proposed E2E models, we prepared two datasets from video recordings of ASD diagnoses collected between 2016 and 2018 at Seoul National University Bundang Hospital (SNUBH), and between 2019 and 2021 at a Living Lab. According to the experimental results, the proposed wav2vec2.0-based E2E model with joint optimization achieved significant improvements in the accuracy and unweighted average recall, from 64.74% to 71.66% and from 65.04% to 70.81%, respectively, compared with a conventional model using autoencoder-based BLSTM and the deterministic features of the eGeMAPS.

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Ma, Ruimin, Ruitao Xie, Yanlin Wang, et al. "Autism Spectrum Disorder Classification with Interpretability in Children Based on Structural MRI Features Extracted Using Contrastive Variational Autoencoder." Big Data Mining and Analytics 7, no. 3 (2024): 781–93. http://dx.doi.org/10.26599/bdma.2024.9020004.

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Sidulova, Mariia, and Chung Hyuk Park. "Conditional Variational Autoencoder for Functional Connectivity Analysis of Autism Spectrum Disorder Functional Magnetic Resonance Imaging Data: A Comparative Study." Bioengineering 10, no. 10 (2023): 1209. http://dx.doi.org/10.3390/bioengineering10101209.

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Generative models, such as Variational Autoencoders (VAEs), are increasingly employed for atypical pattern detection in brain imaging. During training, these models learn to capture the underlying patterns within “normal” brain images and generate new samples from those patterns. Neurodivergent states can be observed by measuring the dissimilarity between the generated/reconstructed images and the input images. This paper leverages VAEs to conduct Functional Connectivity (FC) analysis from functional Magnetic Resonance Imaging (fMRI) scans of individuals with Autism Spectrum Disorder (ASD), aiming to uncover atypical interconnectivity between brain regions. In the first part of our study, we compare multiple VAE architectures—Conditional VAE, Recurrent VAE, and a hybrid of CNN parallel with RNN VAE—aiming to establish the effectiveness of VAEs in application FC analysis. Given the nature of the disorder, ASD exhibits a higher prevalence among males than females. Therefore, in the second part of this paper, we investigate if introducing phenotypic data could improve the performance of VAEs and, consequently, FC analysis. We compare our results with the findings from previous studies in the literature. The results showed that CNN-based VAE architecture is more effective for this application than the other models.

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Wang, Mingzhi, Zhiqiang Ma, Yongjie Wang, Jing Liu, and Jifeng Guo. "A multi-view convolutional neural network method combining attention mechanism for diagnosing autism spectrum disorder." PLOS ONE 18, no. 12 (2023): e0295621. http://dx.doi.org/10.1371/journal.pone.0295621.

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Autism Spectrum Disorder (ASD) is a neurodevelopmental condition whose current psychiatric diagnostic process is subjective and behavior-based. In contrast, functional magnetic resonance imaging (fMRI) can objectively measure brain activity and is useful for identifying brain disorders. However, the ASD diagnostic models employed to date have not reached satisfactory levels of accuracy. This study proposes the use of MAACNN, a method that utilizes multi-view convolutional neural networks (CNNs) in conjunction with attention mechanisms for identifying ASD in multi-scale fMRI. The proposed algorithm effectively combines unsupervised and supervised learning. In the initial stage, we employ stacked denoising autoencoders, an unsupervised learning method for feature extraction, which provides different nodes to adapt to multi-scale data. In the subsequent stage, we perform supervised learning by employing multi-view CNNs for classification and obtain the final results. Finally, multi-scale data fusion is achieved by using the attention fusion mechanism. The ABIDE dataset is used to evaluate the model we proposed., and the experimental results show that MAACNN achieves superior performance with 75.12% accuracy and 0.79 AUC on ABIDE-I, and 72.88% accuracy and 0.76 AUC on ABIDE-II. The proposed method significantly contributes to the clinical diagnosis of ASD.

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Tyutyunnikova, Nina Borisovna. "AUTISM SPECTRUM DISORDERS." Nauka v sovremennom mire 6, no. 11 (2020): 33–34. http://dx.doi.org/10.31618/2524-0935-2019-44-11-4.

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Fitzgerald, Kara, Mark Hyman, Mark Hyman, and Kathie Swift. "Autism Spectrum Disorders." Global Advances in Health and Medicine 1, no. 4 (2012): 62–74. http://dx.doi.org/10.7453/gahmj.2012.1.4.010.

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Dudová, Iva. "Autism spectrum disorders." Česko-slovenská pediatrie 77, no. 3 (2022): 143–47. http://dx.doi.org/10.55095/cspediatrie2022/018.

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Dissertations / Theses on the topic "Autism spectrum disorders autoencoder"

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Williams, Joanna Gwendolyn. "Screening for autism spectrum disorders." Thesis, University of Cambridge, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.615931.

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Ing, Natalia. "Wayfinding in autism spectrum disorders." Master's thesis, University of Cape Town, 2011. http://hdl.handle.net/11427/11122.

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Burnett, Hollie. "(Re-)conceptualisation in autism spectrum disorders." Thesis, University of Hull, 2012. http://hydra.hull.ac.uk/resources/hull:6865.

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Background: Individuals with Autism Spectrum Disorder (ASD) have been shown to be compromised in cognitive flexibility and attentional switching. However, most studies that examine these executive functions did not distinguish between the ability to form new concepts and the ability to switch between concepts. Very few attempts have been made to disassociate them as separate abilities, or investigate whether the animate or inanimate nature of the concepts/objects affects these abilities. Further, very few switching tasks have investigated the autistic spectrum as a whole, with most studies focusing on severely autistic individuals. Aims: The aim of this thesis was to explore individual limitations in the perceptual-cognitive abilities of forming concepts (conceptualisation) and of switching between concepts (reconceptualisation) in individuals with varying degrees of ASD and in typically-developed (TD) individuals. Further aims were: (i) Examine whether the animate or inanimate nature of the concepts affect the (re-)conceptualisation abilities, and whether this effect varies along the autism spectrum. (ii) Examine the impact of the ‘salience of physical reality’ on the (re-)conceptualisation abilities. (iii) Examine whether there is a continuum in concept forming and/or switching underlying the entire autism spectrum, extending into the TD population. Methods: The basic experimental paradigm involved recognition of ambiguous and impoverished objects. Distinct animate and/or inanimate objects were morphed into each other, resulting in a sequence of interpolations with decreasing proportions of one object and increasing proportions of the other object. Participants had to identify the newly emerging object. There were two distinct versions: the Conceptualisation Task, in which participants had to form a new concept from ‘scratch’, and the Reconceptualisation Task, in which an existing concept had to be traded in for a new concept. Participants: Three different clinical groups were tested: adults with Asperger’s Syndrome (AS), children with AS, and children with autism. Each group and their control group, did not differ significantly in terms of age, sex or cognitive ability. In addition, on the basis of their score on the Autism Quotient (AQ), approximately the top and bottom 20% of the TD individuals were allocated to either a low or high AQ group. Experiments: Four new experimental paradigms were employed: (Re-)Conceptualisation Silhouette Task (see Chapters 2 and 3), (Re-)conceptualisation Gabor Task (see Chapter 4), Delis-Kaplan Executive Functioning System (D-KEFS) Sorting Task with a unique added ‘No Shuffle’ condition, where the cards were not shuffled after each correct sort (see Chapter 5) and an Object-Ratio Task (see Chapter 7). In addition, the performance of the participant groups on these new tasks was compared with their performance on existing concept-switching tasks that are part of the D-KEFS: the Trail Making Task and the Twenty Questions Task (see Chapter 6). Results: In both the Silhouette and Gabor tasks, the ASD groups were significantly impaired in identifying concepts compared to TD groups, in both the conceptualisation and the reconceptualisation conditions. However, the deficit was largest when they first had to disengage attention (reconceptualisation), and when the object was animate. The autism group performed worse than the AS group, but only with respect to animate objects. Furthermore, when the start-object remained physically present (Gabor Tasks), or when the correctly made sort was not shuffled, but remained physically present until a new sort was made (Card Sorting Task), the ASD groups were even more impaired. Quite strikingly, this impairment specifically pertained to animate objects. In the TD population, differences were found between those with low and those with high AQ scores. In terms of performance on the (Re-)Conceptualisation Tasks, the high AQ group occupied a position in between the low AQ and AS groups. Conclusions: Overall, the studies suggest that individuals with ASD are impaired in forming new concepts, especially when they first have to disengage their attention from a previously identified concept, and when the concept is animate. This deficit also extends to the TD population (to those TD individuals with high AQ scores). The findings therefore support the notion of a concept forming and concept switching continuum, that is present not only in ASD, but also in the general population. The findings further suggest that individuals with ASD possess a processing deficit specifically for animate concepts/objects, which becomes worse with increasing ASD severity.

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Koh, Hwan Cui. "Visual perception in autism spectrum disorders." Thesis, University of Sheffield, 2009. http://etheses.whiterose.ac.uk/10348/.

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This thesis presents research conducted at the cognitive level, the neurophysiological level and the psycho-physical level, for examining vision in ASD. The psycho-physical findings suggest that atypical visual perception in ASD is unlikely to have a sub-cortical origin as sub-cortical magnocellular and parvocellular pathway functioning, and low/high spatial frequency detection in adolescents with ASD were found to be no different from typically-developing controls. There was, however, evidence indicating local motion direction perception deficits in the same adolescents with ASD suggesting that atypical motion perception in ASD may have a cortical origin. Electrophysiological investigation of low level visual perception in ASD revealed findings concurring with this latter interpretation. More specifically, whereas visual evoked potentials demonstrated visuo-integrative processes associated with perception of second order and hyperbolic gratings were not atypical in children with ASD, there was increased activity of the visual cortical region. A further gamma power analysis then demonstrated that there may be increased neuro-connectivity within primary visual area V1 in the children with ASD. Atypical low level visual cortical processes may result in locally-biased perceptual style previously observed in individuals with ASD. However, a cross-cultural comparison of perceptual style in children with ASD and TD children from Singapore and England, found evidence suggesting that locally-biased perceptual style in ASD may not be culturally universal. In sum, lower level visual cortical processes may be atypical in ASD, and whether these atypicalities manifest at the higher perceptual level can be determined by cultural variability in attention and response processes.

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Ludlow, Amanda Katherine. "Colour processing in autism spectrum disorders." Thesis, Goldsmiths College (University of London), 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.428989.

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Homs, Raubert Aïda 1983. "Epigenetic alterations in autism spectrum disorders (ASD)." Doctoral thesis, Universitat Pompeu Fabra, 2015. http://hdl.handle.net/10803/403885.

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The aetiology of autism spectrum disorders (ASD), a group of neurodevelopmental conditions with early onset, characterized by social and communication impairment and restricted interests, is unknown in about a third of the patients. The intense research done over the past decade has revealed a genetic contribution, while the epigenetic contribution barely begins to show. The epigenetic marks can exert an effect in gene expression without altering the underlying genetic sequence. In turn, these marks can be impaired by genetic mutations in their target sequence. Therefore, research in genomic, epigenomic and transcriptomic fields will provide convergent information to unravel the causes of ASD, necessary to establish improved diagnostic protocols and therapeutic strategies, allowing an earlier diagnosis and personalized treatment crucial for a better prognosis. Our data reveal variants associated to the phenotype which shows genetic-epigenetic interplay along with gene expression consequences. It also reveals region epigenetic variants, which follow a polygenic or complex model. Finally, we found ASD genotype-specific epigenetic marks. In the future, the progress in cost-efficiency technologies assessing epigenomics, and the availability of a reference epigenome in various tissues and cell types will provide the background to set a step-forward in establishing the developmental stage, cell types and tissues involved in the epigenetic mechanisms of the disorder.<br>L'etiologia dels trastorns de l'espectre autista (TEA), un grup de malalties del neurodesenvolupament d’aparició primerenca caracteritzades per problemes de comunicació, relació social, i per la presencia d’interessos restringits, és desconeguda per un terç dels individus afectats. La intensa investigació feta durant l'última dècada ha revelat una gran contribució genètica en aquesta malaltia, mentre que de l’epigenètica tot just es comença a evidenciar. Les marques epigenètiques, sense alterar la seqüència genètica subjacent, tenen un efecte en l'expressió dels gens. A la vegada, aquestes marques epigenètiques es poden veure afectades per mutacions genètiques a la seqüència. Així doncs, la recerca en genòmica, epigenòmica i transcriptòmica proporcionarà informació convergent per determinar les causes dels TEA, indispensable per establir millores en els protocols de diagnòstic i en estratègies terapèutiques, facilitant el diagnòstic precoç i el tractament personalitzat, crucial per a un millor pronòstic. Les nostres dades mostren que hi ha alteracions genètiques i epigenètiques associades al fenotip, que interactuen i tenen conseqüències sobre l’expressió gènica. També hem trobat regions amb alteracions epigenètiques, que sembla que contribueixen de manera additiva i seguint un model complex. Finalment, trobem marques epigenètiques específiques de grups de genotips TEA. En el futur, la millora de les tecnologies disponibles per avaluar l’epigenòmica, i la disponibilitat d'un epigenoma de referència en diversos teixits i tipus cel•lulars, serviran com a base per fer un pas cap endavant en l'establiment de l’etapa del desenvolupament, dels tipus cel•lulars i els teixits involucrats en els mecanismes epigenètics del trastorn.

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Tomchek, Scott David. "CHARACTERIZING SENSORY PROCESSING IN AUTISM SPECTRUM DISORDERS." UKnowledge, 2005. http://uknowledge.uky.edu/gradschool_diss/455.

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Rationale: Autism is a neurodevelopmental disorder with onset prior to the age of three years characterized by qualitative impairments in social interaction and communication skill, along with a restricted repetitive and stereotyped pattern of behavior, interests, and activities. In addition to these core diagnostic features, aberrant sensory responding has also been widely reported in the literature describing children and adolescents with autism spectrum disorders (ASD). Aberrant sensory processing has, however, been infrequently studied compared to communication and cognition in autism and existing studies have had multiple methodological deficiencies, especially with sampling procedures. Purpose. The purpose of this study is to describe patterns of sensory processing found in children with an ASD to test the relationship(s) of these patterns to diagnostic and developmental variables. Method. Retrospective data collection was used to collect developmental and sensory processing variables of 400 children with an ASD. Sensory processing abilities were measured by the SSP. Results. The majority of the sample (80.5%) had a diagnosis of autism. The average age of the sample was 49.58 months. The adaptive, social, language, and motor developmental variables were consistent with diagnostic patterns in that the children with Asperger Disorder demonstrated higher developmental levels than the children with autism and PDD-NOS. Eighty-nine percent of the sample demonstrated some degree of sensory processing dysfunction on the SSP Total Score with the greatest difficulties reported on the Underresponsive/Seeks Sensation, Auditory Filtering, and Tactile Sensitivity sections. Exploratory factor analysis identified 6 parsimonious factors: Low Energy/Weak, Tactile and Movement Sensitivity, Taste/Smell Sensitivity, Auditory and Visual Sensitivity, Sensory Seeking/Distractibility, and Hypo-responsivity. These factor variables contributed to explaining the differences in five of six developmental variables of the sample that are associated with the diagnosis of autism. Receptive language, adaptive and expressive language performance were significantly correlated with sensory processing factor scores. Conclusions. Together, the sensory processing findings noted in this study describe a pattern of dysfunctional sensory modulation. These findings have significant implications for intervention programs involving individuals with an ASD, given the potential impact of these findings on a childs ability to maintain active engagement.

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StrÃ¸mgren, BÃ¸rge. "Aggression replacement training and autism spectrum disorders." Thesis, University of Ulster, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.445228.

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Witwer, Andrea N. "Psychopathology in Youngsters with Autism Spectrum Disorders." The Ohio State University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=osu1243561855.

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Hitt, Sara Beth, and false. "Autism Spectrum Disorder." Digital Commons @ East Tennessee State University, 2016. https://dc.etsu.edu/etsu-works/4068.

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Books on the topic "Autism spectrum disorders autoencoder"

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Zager, Dianne. Autism Spectrum Disorders. Routledge, 2016. http://dx.doi.org/10.4324/9781315794181.

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Zager, Dianne, David F. Cihak, and Angi Stone-MacDonald. Autism Spectrum Disorders. 5th ed. Routledge, 2022. http://dx.doi.org/10.4324/9781003255147.

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Geier, Richard, Glenis Benson, and Sue Geier. Autism spectrum disorders. Attainment Company, 2002.

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Sicile-Kira, Chantal. Autism Spectrum Disorders. Penguin Group USA, Inc., 2008.

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Bowler, Dermot. Autism Spectrum Disorders. John Wiley & Sons, Ltd., 2006.

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1957-, Hollander Eric, ed. Autism spectrum disorders. Dekker, 2003.

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Goldstein, Sam, and Jack A. Naglieri, eds. Interventions for Autism Spectrum Disorders. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-5301-7.

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Barahona Corrêa, Bernardo, and Rutger-Jan van der Gaag, eds. Autism Spectrum Disorders in Adults. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-42713-3.

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Volkmar, Fred R., ed. Encyclopedia of Autism Spectrum Disorders. Springer New York, 2020. http://dx.doi.org/10.1007/978-1-4614-6435-8.

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Goldstein, Sam, Jack A. Naglieri, and Sally Ozonoff, eds. Assessment of autism spectrum disorders. Guilford Press, 2008.

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Book chapters on the topic "Autism spectrum disorders autoencoder"

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Mostafa, Sakib, Wutao Yin, and Fang-Xiang Wu. "Autoencoder Based Methods for Diagnosis of Autism Spectrum Disorder." In Computational Advances in Bio and Medical Sciences. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46165-2_4.

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Angkustsiri, Kathleen, and Robin L. Hansen. "Autism Spectrum Disorders." In Textbook of Clinical Pediatrics. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-02202-9_47.

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Coman, Drew C. "Autism Spectrum Disorders." In The Massachusetts General Hospital Guide to Learning Disabilities. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98643-2_9.

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Griffioen, Trudy. "Autism Spectrum Disorders." In Psychiatry and Sexual Medicine. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-52298-8_23.

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Nazeer, Ahsan. "Autism Spectrum Disorders." In Neurodevelopmental Disabilities. Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-0627-9_7.

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Muncie, Herbert L., Emilio Russo, and David Mohr. "Autism Spectrum Disorders." In Family Medicine. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-04414-9_37.

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Turner, Kylan S., Jonathan R. Pletcher, and Cynthia R. Johnson. "Autism Spectrum Disorders." In Encyclopedia of Adolescence. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-1695-2_402.

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Abrams, David B., J. Rick Turner, Linda C. Baumann, et al. "Autism Spectrum Disorders." In Encyclopedia of Behavioral Medicine. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-1005-9_100133.

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Dawson, Geraldine, and Karen Toth. "Autism Spectrum Disorders." In Developmental Psychopathology. John Wiley & Sons, Inc., 2015. http://dx.doi.org/10.1002/9780470939406.ch8.

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Charman, Tony. "Autism Spectrum Disorders." In Child Psychology and Psychiatry. John Wiley & Sons, Ltd, 2011. http://dx.doi.org/10.1002/9781119993971.ch23.

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Conference papers on the topic "Autism spectrum disorders autoencoder"

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Preethi, C., and S. Gomathi. "Enhancing Autism Spectrum Disorder Detection and Classification with GAN-Driven Autoencoder Models." In 2025 8th International Conference on Trends in Electronics and Informatics (ICOEI). IEEE, 2025. https://doi.org/10.1109/icoei65986.2025.11013383.

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Mei, Jie, and Yukie Nagai. "Understanding Context Dependence in Time Perception in Autism Spectrum Disorders: A Computational Study." In 2024 IEEE International Conference on Development and Learning (ICDL). IEEE, 2024. http://dx.doi.org/10.1109/icdl61372.2024.10645025.

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Noman, Fuad, Sin-Yee Yap, Raphael C. W. Phan, Hernando Ombao, and Chee-Ming Ting. "Graph Autoencoder-Based Embedded Learning in Dynamic Brain Networks for Autism Spectrum Disorder Identification." In 2022 IEEE International Conference on Image Processing (ICIP). IEEE, 2022. http://dx.doi.org/10.1109/icip46576.2022.9898034.

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Elbattah, Mahmoud, Romuald Carette, Gilles Dequen, Jean-Luc Guerin, and Federica Cilia. "Learning Clusters in Autism Spectrum Disorder: Image-Based Clustering of Eye-Tracking Scanpaths with Deep Autoencoder." In 2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2019. http://dx.doi.org/10.1109/embc.2019.8856904.

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Dubynin, V. A., N. Yu Sarycheva, Ya V. Krushinskaya, and V. R. Gedzun. "EXPERIMENTAL MODELS FOR AUTISM SPECTRUM DISORDERS." In MODERN PROBLEMS IN SYSTEMIC REGULATION OF PHYSIOLOGICAL FUNCTIONS. NPG Publishing, 2019. http://dx.doi.org/10.24108/5-2019-confnf-26.

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Santos, Maria Isabel, Ana Breda, and Ana Margarida Almeida. "Learning Environment for Autism Spectrum Disorders." In DSAI 2016: 7th International Conference on Software Development and Technologies for Enhancing Accessibility and Fighting Info-exclusion. ACM, 2016. http://dx.doi.org/10.1145/3019943.3019967.

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Gorbachevskaya, Natalia. "NEUROPHYSIOLOGICAL STUDIES OF AUTISM SPECTRUM DISORDERS." In XVI International interdisciplinary congress "Neuroscience for Medicine and Psychology". LLC MAKS Press, 2020. http://dx.doi.org/10.29003/m1001.sudak.ns2020-16/155-156.

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Parish-Morris, Julia, Mark Liberman, Neville Ryant, et al. "Exploring Autism Spectrum Disorders Using HLT." In Proceedings of the Third Workshop on Computational Lingusitics and Clinical Psychology. Association for Computational Linguistics, 2016. http://dx.doi.org/10.18653/v1/w16-0308.

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Fernandez I, George, Himanshu, Uday Narayan Gautam, Fahad Ur Rahman, and Ayush Kumar. "Early-Stage Detection of Autism Spectrum Disorders." In 2024 Ninth International Conference on Science Technology Engineering and Mathematics (ICONSTEM). IEEE, 2024. http://dx.doi.org/10.1109/iconstem60960.2024.10568723.

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Bugnariu, Nicoleta, Coen de Weerd, Carolyn Young, et al. "Motor function in children with Autism spectrum disorders." In 2013 International Conference on Virtual Rehabilitation (ICVR). IEEE, 2013. http://dx.doi.org/10.1109/icvr.2013.6662080.

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Reports on the topic "Autism spectrum disorders autoencoder"

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Wang, Xiaoxi. A Meta-Analysis of Acupuncture for Autism Spectrum Disorders. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2020. http://dx.doi.org/10.37766/inplasy2020.4.0087.

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Mong, Jessica. Etiology of Sleep Disorders in ASD (Autism Spectrum Disorders): Role for Inflammatory Cytokines. Defense Technical Information Center, 2011. http://dx.doi.org/10.21236/ada581407.

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Manoach, Dara. Neural Correlates of Restricted, Repetitive Behaviors in Autism Spectrum Disorders. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada612865.

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Manoach, Dara. Neural Correlates of Restricted, Repetitive Behaviors in Autism Spectrum Disorders. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada614050.

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Manoach, Dara, and Susan Santangelo. Neural Correlates of Restricted, Repetitive Behaviors in Autism Spectrum Disorders. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada575709.

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Santangelo, Susan L., and Dara Manoach. Neural Correlates of Restricted, Repetitive Behaviors in Autism Spectrum Disorders. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada583969.

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Sweeney, John A. Family Studies of Sensorimotor and Neurocognitive Heterogeneity in Autism Spectrum Disorders. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada613859.

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Shin, Su-Jeong Hwang, Brianna Smith, and Kristi Gaines. Investigation of Therapy Clothing Products for Children with Autism Spectrum Disorders. Iowa State University, Digital Repository, 2015. http://dx.doi.org/10.31274/itaa_proceedings-180814-1151.

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Platt, Michael L. Neural Basis of Empathy and Its Dysfunction in Autism Spectrum Disorders (ASD). Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada612863.

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Costa-Mattioli, Mauro. The Role of the New mTOR Complex, MTORC2, in Autism Spectrum Disorders. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada613836.

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