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Journal articles on the topic 'Autism spectrum disorder detection'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Gyanchandani, Priya, and Gourav SHrivastava. "Autism Spectrum Disorder Detection Using Machine Learning." International Journal on Advances in Engineering, Technology and Science (IJAETS) 5, no. 1 (2024): 95–98. https://doi.org/10.5281/zenodo.10719317.

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<em>Abstract</em>— autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by a variety of behavioral and social problems that can be effectively managed through intervention and support if diagnosed early. However, early diagnosis of autism spectrum disorders is still very difficult. Current diagnostic methods often involve lengthy and expensive tests, including clinical examinations and interviews, making them impractical for large-scale screening. The aim of this study is to use a noninvasive and cost-effective method to solve important problems in identifying au

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Arvapalli, Surya Teja, Sai Abhay A, Mounika D, and Vani Pujitha M. "Autism Spectrum Disorder Detection Using MobileNet." International Journal of Online and Biomedical Engineering (iJOE) 18, no. 10 (2022): 129–42. http://dx.doi.org/10.3991/ijoe.v18i10.31415.

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Autism Spectrum Illness (ASD), a evolution of the brain disorder, is commonly related with sensory difficulties, such as excessive or insufficient sensitivity to sounds, scents, or touch. Autism Spectrum Disorder (ASD) is evolving at a faster rate than ever before. By screening tests autism detection is very expensive and time consuming. With the advancement of Deep Learning (DL),autism can be predicted from a young age.In this paper we are using Convolutional Neural Network (CNN) with Transfer Learning (TL) models to classify the disease and we will suggest the precautions if it is detected a

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Charita Ganipineni, Dr. U Chaitanya, and Jhade Sharanya. "Detection of Autism Spectrum Disorder." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 06 (2025): 2862–68. https://doi.org/10.47392/irjaeh.2025.0422.

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Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder with complex characteristics of social interaction, communication, and behavior difficulties. Early identification of ASD is important for early intervention and successful treatment. The purpose of this project is to create a multimodal system for early detection and classification of ASD using both textual information from behavioral screening questionnaires and visual information from facial images. The system makes use of a machine learning-driven decision tree model for analyzing text responses and a deep learning-driven Conv

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McCue, Lena M., Louise H. Flick, Kimberly A. Twyman, and Hong Xian. "Gastrointestinal dysfunctions as a risk factor for sleep disorders in children with idiopathic autism spectrum disorder: A retrospective cohort study." Autism 21, no. 8 (2017): 1010–20. http://dx.doi.org/10.1177/1362361316667061.

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Sleep disorders often co-occur with autism spectrum disorder. They further exacerbate autism spectrum disorder symptoms and interfere with children’s and parental quality of life. This study examines whether gastrointestinal dysfunctions increase the odds of having sleep disorders in 610 children with idiopathic autism spectrum disorder, aged 2–18 years, from the Autism Genetic Resource Exchange research program. The adjusted odds ratio for sleep disorder among those with gastrointestinal dysfunctions compared to those without was 1.74 (95% confidence interval: 1.22–2.48). In addition, the odd

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Chambers, Nola J., Amy M. Wetherby, Sheri T. Stronach, Nonyameko Njongwe, Shuaib Kauchali, and Richard R. Grinker. "Early detection of autism spectrum disorder in young isiZulu-speaking children in South Africa." Autism 21, no. 5 (2016): 518–26. http://dx.doi.org/10.1177/1362361316651196.

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Culturally appropriate tools are needed for detecting symptoms of autism spectrum disorder in young South African children. The objectives of this study were to (1) adapt and translate into isiZulu existing measures for detecting early signs of autism spectrum disorder, (2) use the measures to characterize and compare behavioural profiles of young isiZulu-speaking children with and without autism spectrum disorder and (3) compare symptom profiles across sampling procedures. Measures were translated and adapted into isiZulu and used to evaluate 26 isiZulu-speaking children, 15 children with no

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Snijder, Michelle IJ, Shireen PT Kaijadoe, Maarten van ‘t Hof, Wietske A. Ester, Jan K. Buitelaar, and Iris J. Oosterling. "Early detection of young children at risk of autism spectrum disorder at well-baby clinics in the Netherlands: Perspectives of preventive care physicians." Autism 25, no. 7 (2021): 2012–24. http://dx.doi.org/10.1177/13623613211009345.

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To improve early detection of autism spectrum disorder in preventive care, a Dutch guideline was developed 5 years ago. The guideline provides preventive care physicians at well-baby clinics action-oriented advice and describes a step-by-step approach for children identified at an increased risk for autism spectrum disorder during general healthcare surveillance. This qualitative study evaluated the guideline adherence and studied barriers regarding early detection of autism spectrum disorder at well-baby clinics. Semi-structured interviews were undertaken with 12 preventive care physicians (o

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Kavitha, M., and M. Kasthuri. "Enco – Standardization Data Pre-Processing Technique in Autism Spectrum Disorder Detection." Indian Journal Of Science And Technology 16, no. 45 (2023): 4156–63. http://dx.doi.org/10.17485/ijst/v16i45.1781.

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H. L, Priyanka. "NeuroSpectra : Revolutionizing Autism detection through AI." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem46677.

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Abstract: Identifying and Assessing Autism Spectrum Disorder is usually dependent on the behavioral Developments in the human life and the condition known as Autism Spectrum Disorder is an neurological and developmental disorder which occurs during the initial stages of the child life i.e the initial two years of child birth. As per the recent census this a neurodevelopmental condition characterized by challenges in social interaction and communication affects 1% across the entire world’s population. While its primary origin lies in genetics, early detection is crucial, and leveraging machine

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Wang, Ji, Darren Hedley, Simon M. Bury, and Josephine Barbaro. "A systematic review of screening tools for the detection of autism spectrum disorder in mainland China and surrounding regions." Autism 24, no. 2 (2019): 285–96. http://dx.doi.org/10.1177/1362361319871174.

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Screening for autism spectrum disorder is the first step toward early detection and diagnosis, thereby impacting the likelihood of children accessing early intervention and, importantly, improving long-term outcomes. This systematic review aimed to (a) establish a clear baseline of autism spectrum disorder screening tools currently used throughout mainland China and surrounding regions, (b) identify the strengths and limitations of these instruments, and (c) develop specific recommendations regarding screening for autism spectrum disorder throughout Chinese-speaking countries. Databases were s

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Pijl, Mirjam KJ, Jan K. Buitelaar, Manon WP de Korte, Nanda NJ Rommelse, and Iris J. Oosterling. "Sustainability of an early detection program for autism spectrum disorder over the course of 8 years." Autism 22, no. 8 (2017): 1018–24. http://dx.doi.org/10.1177/1362361317717977.

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The importance of early detection of autism spectrum disorder followed by early intervention is increasingly recognized. This quasi-experimental study evaluated the long-term effects of a program for the early detection of autism spectrum disorder (consisting of training of professionals and use of a referral protocol and screening instrument), to determine whether the positive effects on the age at referral were sustained after the program ended while controlling for overall changes in the number of referrals. Before, during, and after the program, the proportion of children referred before 3

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Ravishankar T and P.Sujatha. "A Survey: Approaches for Detecting the Autism Spectrum Disorder." International Journal of Data Informatics and Intelligent Computing 2, no. 2 (2023): 39–46. http://dx.doi.org/10.59461/ijdiic.v2i2.60.

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A brain disease mean autism spectrum disorder affects a person's ability to connect, communicate, and remember. Though autism is capable of being diagnosed regardless of age, most of the disorder's signs begin to appear around its initial two years of life and increase as time goes on. People with autism suffer from a wide range of difficulties, such sensory problems, action impairments, intellectual disabilities, and psychological disorders including depression and anxiety. Autism has been rising at an unacceptably rapid pace surrounding around the globe. Autism detection involves an enormous

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Forgeot d’Arc, Baudouin, Richard Delorme, Tiziana Zalla, et al. "Gaze direction detection in autism spectrum disorder." Autism 21, no. 1 (2016): 100–107. http://dx.doi.org/10.1177/1362361316630880.

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Veeramalla Shirisha, Vinnakota Poojitha, B.Akhil Reddy, B.Lohith Kumar, Mr B.Mahesh, and Dr.M.Ramesh. "Early Stage Detection of Autism Spectrum Disorder." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 05 (2025): 2688–96. https://doi.org/10.47392/irjaeh.2025.0399.

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Autism Spectrum Disorder (ASD) is a developmental condition impacting communication, behavior, and social interaction, and it affects approximately 2% of children in the U.S. Early identification and intervention are critical to improving long-term outcomes for children with ASD, yet delays in diagnosis remain a significant barrier. Current screening tools, such as the Modified Checklist for Autism in Toddlers, revised with Follow-Up (M-CHAT-R/F), are widely used and effective in identifying potential ASD cases. However, these tools often exhibit limitations in specificity, leading to a high r

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Hadi, Mahdi Saleh. "Kindergartens Teachers Early Detection Knowledge about Autism Spectrum Disorder in Babylon Province." International Journal of Psychosocial Rehabilitation 24, no. 4 (2020): 7258–66. http://dx.doi.org/10.37200/ijpr/v24i4/pr2020540.

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Bodhke, Prof B. K., Devesh Sandeep Singh, Shaikh Umme Hanni Aiyaz Hussain, Sakshee Vijay Phadtare, and Mohammed Aslaan. "Autism Spectrum Disorder DetectionUsingMachine Learning." IOSR Journal of Computer Engineering 26, no. 5 (2024): 41–47. http://dx.doi.org/10.9790/0661-2605024147.

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Convolutional Neural Networks (CNNs) have made significant strides in various domains such as image classifica- tion, speech recognition, automotive software engineering and neuroscience. In this study we leverage CNNs for the automated detection of Autism Spectrum Disorder using brain imaging data,specificallywe utilize resting-state functional Magnetic Resonance Imaging (f-MRI) data from the Autism Brain Imaging Exchange (ABIDE) dataset. To identify ASD patients, our approach relies on analyzing patterns of functional connectivity to classify ASD and control subjects. Experimental results de

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Nah, Yong-Hwee, Robyn L. Young, and Neil Brewer. "Development of a brief version of the Autism Detection in Early Childhood." Autism 23, no. 2 (2018): 494–502. http://dx.doi.org/10.1177/1362361318757563.

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While autism spectrum disorder screening tools provide a useful resource for practitioners, the reality is they are underused. The justifications often provided include the time required for administration and the training involved. A brief tool with good psychometric properties that require minimal training is required. This study examined the development and the psychometric properties of a brief version of the Autism Detection in Early Childhood. The data showed the potential of the brief version of Autism Detection in Early Childhood for screening children age 12–36 months. Our dataset com

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Sheetal, DN, and Math Shrishail. "Early detection of autism spectrum disorder based on parental input." Global Journal of Engineering and Technology Advances 20, no. 1 (2024): 206–15. https://doi.org/10.5281/zenodo.13694640.

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Nowadays, the Autism Spectrum Disorder (ASD) movement is moving at a breakneck pace. Screening for autism characteristics is a laborious and costly process. It is now possible to detect autism in its early stages thanks to developments in AI and ML. Although several research have been conducted using various methods, no conclusive results have been drawn regarding the prediction of autism features by age group. Consequently, the purpose of this paper is to establish a mobile app that may predict ASD in individuals of any age using a model that is based on ML techniques. The study's results inc

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Thabtah, Fadi, and David Peebles. "A new machine learning model based on induction of rules for autism detection." Health Informatics Journal 26, no. 1 (2019): 264–86. http://dx.doi.org/10.1177/1460458218824711.

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Autism spectrum disorder is a developmental disorder that describes certain challenges associated with communication (verbal and non-verbal), social skills, and repetitive behaviors. Typically, autism spectrum disorder is diagnosed in a clinical environment by licensed specialists using procedures which can be lengthy and cost-ineffective. Therefore, scholars in the medical, psychology, and applied behavioral science fields have in recent decades developed screening methods such as the Autism Spectrum Quotient and Modified Checklist for Autism in Toddlers for diagnosing autism and other pervas

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Alkoor, Mariam. "Nurturing Gifted Students Who Have Autism Spectrum Disorder." International Journal of Educational Sciences and Arts 3, no. 12 (2024): 241–88. https://doi.org/10.59992/ijesa.2024.v3n12p9.

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The objectives of the Kingdom of Saudi Arabia's Vision 2030 provide great and continuous support for discovering and nurturing talents in all priority development disciplines, which believe in the importance of investing in nurturing talents and innovators as they are the most important tributary to the prosperity of nations and are capable of shaping new future horizons to serve society. This research paper aims to identify the concept of talents with autism spectrum disorder, and to know the distinctive characteristics, methods and ways of detecting talents with autism spectrum disorder, and

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Kamuk, C., C. Cantio, and N. Bilenberg. "Early Screening for Autism Spectrum Disorder." European Psychiatry 41, S1 (2017): s131—s132. http://dx.doi.org/10.1016/j.eurpsy.2017.01.1948.

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IntroductionMost parents to children with Autism Spectrum Disorder (ASD) can report concerns about their child's development within the first year of life. In spite of this, children with ASD are rarely diagnosed before the age of 3–4 years. Early identification allows early intervention, which seems to be substantial for improvement of core behavioural symptoms in children with ASD. The Child Behaviour Checklist for ages 1½ to 5 (CBCL/1½–5) have shown promising utility for early detection of children with ASD.ObjectivesThis study will estimate the positive predictive value of CBCL/1½–5 Pervas

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Kadi, Imane, Messaoud Abbas, and Meftah Mohammed Charaf Eddine. "Intelligent models for early Autism detection from MRI images." STUDIES IN ENGINEERING AND EXACT SCIENCES 5, no. 2 (2024): e8464. http://dx.doi.org/10.54021/seesv5n2-273.

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Autism Spectrum Disorders (ASDs) are neurodevelopmental conditions that usually manifest during childhood. It is a multi-symptom disorder, and its symptoms overlap with several other disorders. The early detection of these disorders is conducive to more successful treatment outcomes, as treatment is more effective before the disorder becomes severe. However, the conventional diagnostic procedures are quite time-consuming, with a typical confirmation period of several months with different specialists in speech and neurology. Recent advances in artificial intelligence (AI), specifically machine

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Sheetal DN and Shrishail Math. "Early detection of autism spectrum disorder based on parental input." Global Journal of Engineering and Technology Advances 20, no. 1 (2024): 206–15. http://dx.doi.org/10.30574/gjeta.2024.20.1.0137.

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Nowadays, the Autism Spectrum Disorder (ASD) movement is moving at a breakneck pace. Screening for autism characteristics is a laborious and costly process. It is now possible to detect autism in its early stages thanks to developments in AI and ML. Although several research have been conducted using various methods, no conclusive results have been drawn regarding the prediction of autism features by age group. Consequently, the purpose of this paper is to establish a mobile app that may predict ASD in individuals of any age using a model that is based on ML techniques. The study's results inc

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Khowaja, Meena, Diana L. Robins, and Lauren B. Adamson. "Utilizing two-tiered screening for early detection of autism spectrum disorder." Autism 22, no. 7 (2017): 881–90. http://dx.doi.org/10.1177/1362361317712649.

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Despite advances in autism screening practices, challenges persist, including barriers to implementing universal screening in primary care and difficulty accessing services. The high false positive rate of Level 1 screening methods presents especially daunting difficulties because it increases the need for comprehensive autism evaluations. This study explored whether two-tiered screening—combining Level 1 (Modified Checklist for Autism in Toddlers, Revised with Follow-Up) and Level 2 (Screening Tool for Autism in Toddlers and Young Children) measures—improves the early detection of autism. Thi

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MacDuffie, Katherine E., Annette M. Estes, Lucas T. Harrington, et al. "Presymptomatic Detection and Intervention for Autism Spectrum Disorder." Pediatrics 147, no. 5 (2021): e2020032250. http://dx.doi.org/10.1542/peds.2020-032250.

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Erkan, Uğur, and Dang N. H. Thanh. "Autism Spectrum Disorder Detection with Machine Learning Methods." Current Psychiatry Research and Reviews 15, no. 4 (2020): 297–308. http://dx.doi.org/10.2174/2666082215666191111121115.

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Background: Autistic Spectrum Disorder (ASD) is a disorder associated with genetic and neurological components leading to difficulties in social interaction and communication. According to statistics of WHO, the number of patients diagnosed with ASD is gradually increasing. Most of the current studies focus on clinical diagnosis, data collection and brain images analysis, but do not focus on the diagnosis of ASD based on machine learning. Objective: This study aims to classify ASD data to provide a quick, accessible and easy way to support early diagnosis of ASD. Methods: Three ASD datasets ar

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Yoo, Heejeong. "Early Detection and Intervention of Autism Spectrum Disorder." Hanyang Medical Reviews 36, no. 1 (2016): 4. http://dx.doi.org/10.7599/hmr.2016.36.1.4.

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Erkan, Uğur. "Autism Spectrum Disorder Detection with Machine Learning Methods." Current Psychiatry Research and Reviews 15, no. 4 (2020): 297–308. http://dx.doi.org/10.2174/26660830mtayqmjimw.

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Ridouh, Abdelhakim, Fayçal Imedjdouben, and Sarra Mahi. "Autism spectrum disorder detection using machine learning techniques." International Journal of Bioinformatics Research and Applications 20, no. 5 (2024): 495–516. http://dx.doi.org/10.1504/ijbra.2024.141770.

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Al-Qazzaz, Noor, Sumaya Jaffer, Israa Abdulazez, and Teba Yousif. "Data Mining for Autism Spectrum Disorder detection among Adults." Al-Nahrain Journal for Engineering Sciences 25, no. 4 (2022): 142–51. http://dx.doi.org/10.29194/njes.25040142.

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Autism Spectrum Disorder (ASD) is one of the most common children's neurodevelopmental disorders (NDD) with an estimated global incidence of 1% to 2%. There are two aims for this research, first, to propose a data mining architecture that combines behavioural and clinical characteristics with demographic data. Second, to provide a quick, acceptable and easy way to support the ASD diagnosis. this can be performed by conducting a comparison study to determine the efficacy of four possible classifiers: logistic regression (LR), sequential minimum optimization (SMO), naïve Bayes, and instance-base

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Rajani, shree, N. Harshitha, C. S. Prabha, N. Swathi, and S. Apoorva. "AN EFFICIENT METHOD FOR PREDICTION OF AUTISM SPECTRUM DISORDER." International Research Journal of Computer Science VII, no. V (2020): 123–26. https://doi.org/10.26562/irjcs.2020.v0705.006.

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Autism Spectrum Disorder (ASD) is a neurodevelopment abnormality that affects the behavior and communication of an individual. In present- day Autism Spectrum Disorder (ASD) is gaining its momentum faster than ever, which limits the social and cognitive skills whereas its symptoms may vary from person to person. Though much research have been carried out on ASD using different techniques, these studies didn’t provide sufficient improvement in accuracy and performance. Also, detecting autism traits through screening tests is expensive and time-consuming. The main aim is to propose an effe

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Vedhakshari, T., A. Bhargav Kumar, R. Sneha Latha Reddy, J. Narendra Shiva Sai, and V. L. Sowjanya. "Screening For Childhood Autism Spectrum Disorder Using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (2025): 1–8. https://doi.org/10.55041/ijsrem41649.

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Autism Spectrum Disorder (ASD) affects communication, behavior, and social interactions, and early detection is crucial for intervention. Current diagnostic method sare subjective and prone to delays or misdiagnosis. This project explores machine learning (ML) to automate ASD screening using behavioral data, diagnostic questionnaires, and developmental milestones. Supervised learning algorithms classify children into ASD or non-ASD categories with high accuracy. The approach aims to assist clinicians in early identification and intervention. It offers a scalable, efficient, and objective scree

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Dantas, Adilmar Coelho, and Marcelo Zanchetta do Nascimento. "Recognition of Emotions for People with Autism: An Approach to Improve Skills." International Journal of Computer Games Technology 2022 (January 15, 2022): 1–21. http://dx.doi.org/10.1155/2022/6738068.

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Autism spectrum disorder refers to a neurodevelopmental disorders characterized by repetitive behavior patterns, impaired social interaction, and impaired verbal and nonverbal communication. The ability to recognize mental states from facial expressions plays an important role in both social interaction and interpersonal communication. Thus, in recent years, several proposals have been presented, aiming to contribute to the improvement of emotional skills in order to improve social interaction. In this paper, a game is presented to support the development of emotional skills in people with aut

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Chaudhuri, S., and N. Chatterjee. "Autism Spectrum Disorder: the Present Perspective." Journal of College of Medical Sciences-Nepal 10, no. 3 (2015): 37–47. http://dx.doi.org/10.3126/jcmsn.v10i3.12775.

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The last decade has witnessed a surge of awareness about autism among the public and professionals. Much revealing research is being done on this issue and the knowledge base has improved substantially and a set of professionals are specializing on the subject, focusing on its causative factors and management. Autism being a disorder stemming from early childhood and the prevalence rate rising alarmingly over the years, Pediatricians are expected to play a vital role in early detection and early intervention in management of the problem. But, unfortunately, autism is not yet considered to be u

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Salum Tomé, José Manuel. "Autism Spectrum Disorder (ASD) Law in Chile." Advances in Social Sciences Research Journal 12, no. 07 (2025): 49–52. https://doi.org/10.14738/assrj.1207.19048.

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The TEA Law guarantees the right to equal opportunities and safeguards the social inclusion of children, adolescents, and adults with ASD, eliminating all forms of discrimination. It promotes a comprehensive approach in the social, health, and educational spheres and raises awareness about this issue. The law is based on fundamental principles such as dignified treatment, progressive autonomy, a gender perspective, intersectionality, participation and social dialogue, early detection, ongoing monitoring, and neurodiversity.

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Saeed Mian, Tariq. "Efficient Net-based Transfer Learning Technique for Facial Autism Detection." Scalable Computing: Practice and Experience 24, no. 3 (2023): 551–60. http://dx.doi.org/10.12694/scpe.v24i3.2233.

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Autism Spectrum Disorder is a neurological disorder in which an individual faces life-long effects in communication and interaction with others. Nowadays, the Autism Spectrum disorder ratio is increasing drastically more than ever before. Autism can be identified at all developmental levels as a ”behavioural condition,” and its symptoms often arise between the ages of two and four. The ASD issue starts during puberty and persists through adolescence and adulthood. Children with ASD use both nonverbal and verbal behaviour to communicate, and they struggle with joint attention and social recipro

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Tomé, José Manuel Salum. "Autism Spectrum Disorder (ASD) Law in Chile." Advances in Social Sciences Research Journal 11, no. 10 (2024): 163–66. http://dx.doi.org/10.14738/assrj.1110.17717.

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The TEA Law ensures the right to equal opportunities and safeguards the social inclusion of children, adolescents and adults with ASD, eliminating any form of discrimination. It promotes a comprehensive approach in the social, health and education areas, and raises awareness in society on this issue. The law is based on fundamental principles such as dignified treatment, progressive autonomy, gender perspective, intersectorality, participation and social dialogue, early detection, continuous monitoring, and neurodiversity.

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Tomé, Jose Manuel Salum. "Autism Spectrum Disorder (ASD) Law in Chile." Advances in Social Sciences Research Journal 12, no. 02 (2025): 79–82. https://doi.org/10.14738/assrj.1202.18296.

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The TEA Law ensures the right to equal opportunities and safeguards the social inclusion of children, adolescents and adults with ASD, eliminating any form of discrimination. It promotes a comprehensive approach in the social, health and education areas, and raises awareness in society on this issue. The law is based on fundamental principles such as dignified treatment, progressive autonomy, gender perspective, intersectorality, participation and social dialogue, early detection, continuous monitoring, and neurodiversity.

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Salum Tome, PhD, Jose Manuel. "Autism Spectrum Disorder (ASD) Law in Chile." Frontiers in Education Technology 8, no. 1 (2025): p51. https://doi.org/10.22158/fet.v8n1p51.

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The TEA Law ensures the right to equal opportunities and safeguards the social inclusion of children, adolescents and adults with ASD, eliminating any form of discrimination. It promotes a comprehensive approach in the social, health and education areas, and raises awareness in society on this issue. The law is based on fundamental principles such as dignified treatment, progressive autonomy, gender perspective, intersectorality, participation and social dialogue, early detection, continuous monitoring, and neurodiversity.

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Glotov, O. S., A. N. Chernov, P. A. Suchko, Yu A. Eismont, and L. A. Mayorova. "Formation of cognitive processes in children with autism. Part II. Genetic mechanisms." Rossiyskiy Vestnik Perinatologii i Pediatrii (Russian Bulletin of Perinatology and Pediatrics) 69, no. 2 (2024): 26–33. http://dx.doi.org/10.21508/1027-4065-2024-69-2-26-33.

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Autism and autism spectrum disorders are neuropsychiatric diseases that begin to appear in children under 3 years. Over the past decade, the number of children with autism spectrum disorders has increased more than in 10-fold and continues to grow, accounting for 1–2% of the world’s population. Currently, the diagnosis of autism spectrum disorders is based only on clinical and behavioral tests, and there are no biological and genetic markers that could contribute to the early detection of this disorder. The review, based on the analysis of modern literature data about symptoms, genetic etiolog

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Ehsan, Khafsa, Kashif Sultan, Abreen Fatima, Muhammad Sheraz, and Teong Chee Chuah. "Early Detection of Autism Spectrum Disorder Through Automated Machine Learning." Diagnostics 15, no. 15 (2025): 1859. https://doi.org/10.3390/diagnostics15151859.

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Background/Objectives: Autism spectrum disorder (ASD) is a neurodevelopmental disorder distinguished by an extensive range of symptoms, including reduced social interaction, communication difficulties and tiresome behaviors. Early detection of ASD is important because it allows for timely intervention, which significantly improves developmental, behavioral, and communicative outcomes in children. However, traditional diagnostic procedures for identifying autism spectrum disorder (ASD) typically involve lengthy clinical examinations, which can be both time-consuming and costly. This research pr

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Vanmarcke, Steven, Ilse Noens, Jean Steyaert, and Johan Wagemans. "Change detection of meaningful objects in real-world scenes in adolescents with and without autism spectrum disorder." Autism 22, no. 6 (2017): 728–39. http://dx.doi.org/10.1177/1362361317702559.

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Previous research suggested that adolescents with autism spectrum disorder are better than typically developing children in detecting local, non-social details within complex visual scenes. To better understand these differences, we used the image database by Sareen et al., containing the size and on-screen location information of all changes in the images, in a change blindness paradigm. In this task, an original and a modified real-world scene, separated by a gray blank, alternate repeatedly until observers detect the change. Our results indicated that participants with and without autism sp

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GAMIROVA, RIMMA G., AISILU R. SAFINA, ELENA A. GOROBETS, and DIANA R. SAFINA. "AUTISM SPECTRUM DISORDER IN CHILDREN: DIAGNOSTIC SIGNIFICANCE OF ELECTROENCEPHALOGRAPHY." Bulletin of Contemporary Clinical Medicine 16, no. 2 (2023): 80–88. http://dx.doi.org/10.20969/vskm.2023.16(2).80-88.

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Introduction. Early diagnosis of autism spectrum disorder in children is significant clinical problem due to the ever-increasing incidence of this condition in children. Aim. The aim of the study is a comprehensive analysis of data on the features of the bioelectrical activity of the brain in children with autism spectrum disorders, identification of the electroencephalography significance in the diagnosis of these disorders, including the ability to differentiate their subtypes. Material and Methods. The materials for the review were scientific articles indexed in Russian and international da

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Noreen Fatima, Ayesha Ikram, Wajeeha Abdulahad, and Faiza Yousaf. "Types of Repetitive Behaviors in Autism Spectrum Disorder." Healer Journal of Physiotherapy and Rehabilitation Sciences 2, no. 3 (2022): 238–44. http://dx.doi.org/10.55735/hjprs.v2i3.95.

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Background: Autism spectrum disorder is a neurodevelopmental disorder occurring in children. Each child on the spectrum has different behaviors. Objective: To identify different types of repetitive behaviors in patients with autism spectrum disorder. Methods: This study was carried out at the Children's hospital, The Institute of Child Health Centre and Rising Sun Institute DHA Lahore, Department of Developmental Pediatrics, Lahore. Purposive sampling was used to collect data through three months’ period. It was a cross-sectional study including 32 children with autism spectrum disorder betwee

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Ida Seraphim, B., Lavi Samuel Rao, and Shiwani Joshi. "Survey on Early Detection of Autism Using Data Mining Techniques." International Journal of Engineering & Technology 7, no. 2.24 (2018): 79. http://dx.doi.org/10.14419/ijet.v7i2.24.12003.

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The children of today are the future of the nation and there are many hurdles in their development like ASD.ASD (Autism Spectrum Disorder) is a neurological disorder which has a lifetime impact on the basic skills and talents of a human being. The foremost goal of the paper is to know the number of people suffering from autism and the various symptoms of autism.

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Puts, Nicolaas A. J., Ericka L. Wodka, Mark Tommerdahl, Stewart H. Mostofsky, and Richard A. E. Edden. "Impaired tactile processing in children with autism spectrum disorder." Journal of Neurophysiology 111, no. 9 (2014): 1803–11. http://dx.doi.org/10.1152/jn.00890.2013.

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Impaired responses to tactile stimulation are a commonly reported symptom among children with autism spectrum disorder (ASD). Furthermore, impairments in filtering or habituation to tactile input have been described in ASD. This study measured different aspects of tactile processing to investigate atypical touch sensitivity in children with ASD, methodology that has not been previously used in this population. Sixty-seven typically developing children (TDC) and 32 children with ASD (ages 8–12) completed vibrotactile tasks assessing: reaction time (RT); static and dynamic detection threshold (D

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Sandhya, A. Kulkarni, Vasudevaraju Dhanush, Ramesh Chandra C, M. Dinesh, and Sai Ranganath C. "Machine Learning Based Model for Prediction of Autism Spectrum Disorder." International Journal of Computer Science and Information Technology Research 10, no. 4 (2022): 46–52. https://doi.org/10.5281/zenodo.7432991.

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<strong>Abstract:</strong> Autism spectrum disorder is a neurodevelopmental disorder that affects a person's interaction, communication and learning skills. Although diagnosis of autism can be done at any age, its symptoms generally appear in the first two years of life and develop through time. Autism patients face different types of challenges such as difficulties with concentration, learning disabilities, mental health problems such as anxiety, depression, motor difficulties, sensory problems, and many others. Diagnosis of autism requires significant amount of time and cost. Earlier det

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Unnisa,, Wajeeha, M. Yamini,, J. Yashwanth,, Yasmeen, Yasmeen,, G. Yugandhar Yugandhar, and Md Syes Shafi. "Detecting Autism Spectrum Disorder Using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42803.

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This project explores the use of eye-tracking technology and machine learning to detect Autism Spectrum Disorder (ASD) in children, a condition that affects social communication and behavior. Early diagnosis of ASD is critical as it enables timely interventions, improving the quality of life for affected children and their families. The developed system analyzes eye movement patterns using Python-based algorithms and machine-learning models, with a focus on vector machine classifiers and computer vision techniques like the Viola-Jones algorithm. The primary outcome of this project is an effici

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Nagashree, Nagesh, Patil Premjyoti, Patil Shantakumar, and Kokatanur Mallikarjun. "An architectural framework for automatic detection of autism using deep convolution networks and genetic algorithm." International Journal of Electrical and Computer Engineering (IJECE) 12, no. 2 (2022): 1768–75. https://doi.org/10.11591/ijece.v12i2.pp1768-1775.

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The brainchild in any medical image processing lied in how accurately the diseases are diagnosed. Especially in the case of neural disorders such as autism spectrum disorder (ASD), accurate detection was still a challenge. Several noninvasive neuroimaging techniques provided experts information about the functionality and anatomical structure of the brain. As autism is a neural disorder, magnetic resonance imaging (MRI) of the brain gave a complex structure and functionality. Many machine learning techniques were proposed to improve the classification and detection accuracy of autism in MRI im

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Lawysen, Anggara Nelsen, and Anggara Nelsen. "Autism detection based on autism spectrum quotient using weighted average ensemble method." International Journal of Informatics and Communication Technology 13, no. 2 (2024): 188–96. https://doi.org/10.11591/ijict.v13i2.pp188-196.

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Autism spectrum disorder (ASD) is a condition that occurs in an individual, wherein it is accompanied by various symptoms such as difficulties in socializing with others. Early detection of ASD patients can assist in preventing various symptoms caused by ASD. The focus of this research is to automate the diagnosis of ASD in an individual based on the results of the autism spectrum quotient (AQ) using weighted average ensemble method. Initially, preprocessing is carried out on the dataset to ensure optimal performance of the resulting model. In the preprocessing step, the filling of missing val

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Kishore, Dr G. Krishna. "Early Detection of Autism Spectrum Disorder Using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 13, no. 3 (2025): 1202–6. https://doi.org/10.22214/ijraset.2025.67418.

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Autism Spectrum Disorder is a neuro-developmental condition that affects multiple aspects of an individual's daily life. Early detection and intervention can significantly reduce its impact. In this paper, we propose a framework to evaluate the performance of key machine learning algorithms for early ASD detection. We apply four feature scaling techniques : Quantile Transformer , Power Transformer , Normalizer, and Max Abs Scaler , to standardize input features. These scaled datasets are then classified using four ML algorithms: AdaBoost , Random Forest , Support Vector Machine, and Linear Dis

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