2023-09-28 14:46:10 +00:00
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abstract: 'Background : Socio-behavioral disorders(SBD), a subtype of
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neurodevelopmental disorders (NDDs) characterized by social and
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behavioral abnormalities, is a significant mental health concern
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requiring immediate attention. Phenotypic knowledge, biological
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understanding and the tools developed are all from western countries.
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Numerous researches have been conducted that have scrutinized the
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performance accuracy of traditional-based SBD tools developed in western
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culture. However, very little information is available for low or
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middle-income countries. Objective: In middle-income countries like
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India, there is a shortage of resources, trained professionals and a
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lack of knowledge regarding which tools are effective for a particular
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target group owing to which most of the cases go undetected and
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undiagnosed until adolescence. Motivated by the earlier discussion, this
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study''s objective is to consider all the pathways from traditional to
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Artificial Intelligence (AI) tools developed for diagnosing SBD in the
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Indian population. This research work expounds on the systematic study
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and analysis of various conventional and fuzzy-based expert systems
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introduced between 1925-2021. Methods: PRISMA guidelines were used to
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select the articles published on the web of science, SCOPUS, and EMBASE
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to identify relevant Indian studies. A total of 148 papers are
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considered impactful for SBD prediction using traditional or fuzzy-based
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techniques. This survey deliberated the work done by the different
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researchers, highlighting the limitations in the existing literature and
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the performance comparison of tools based on various parameters such as
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accuracy, sensitivity, specificity, target audience, along with their
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pros and cons. Some investigations have been designed, and the solutions
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to those were explored. Results : Results of this study indicated that
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most validated SBD tools present many barriers to use in the Indian
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population. Thus, to overcome these implications, an Artificial
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Intelligence(AI) framework, MRIMMTL, based on MRI multimodality transfer
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learning techniques(TL), is proposed to be implemented for the early
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detection of SBD subjects. (c) 2022 Elsevier B.V. All rights reserved.'
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affiliation: 'Mengi, M (Corresponding Author), Cent Univ, Dept Comp Sci \& Informat
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Technol, Jammu 181143, India.
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Mengi, Mehak; Malhotra, Deepti, Cent Univ, Dept Comp Sci \& Informat Technol, Jammu
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181143, India.'
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article-number: '109633'
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author: Mengi, Mehak and Malhotra, Deepti
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author-email: '0550519.csit@cujammu.ac.in
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deepti.csit@cujammu.ac.in'
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author_list:
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- family: Mengi
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given: Mehak
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- family: Malhotra
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given: Deepti
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da: '2023-09-28'
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doi: 10.1016/j.asoc.2022.109633
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earlyaccessdate: SEP 2022
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eissn: 1872-9681
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files: []
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issn: 1568-4946
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journal: APPLIED SOFT COMPUTING
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keywords: 'Socio-behavioral disorders; Neurodevelopmental disorders; Autism
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spectrum disorder; Attention deficit hyperactivity disorder; ASD; ADHD;
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Artificial intelligence; Fuzzy tools; Soft computing; Transfer learning;
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Domain adaptation; Screening tools; Diagnostic tools; Biomarkers'
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keywords-plus: 'AUTISM SPECTRUM DISORDER; CHILD-BEHAVIOR-CHECKLIST; HIGH-FUNCTIONING
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AUTISM; FUZZY COGNITIVE MAPS; ADHD RATING-SCALE; SCREENING TOOL;
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ASPERGERS-DISORDER; 2-YEAR-OLDS STAT; YOUNG-CHILDREN; PRIMARY-CARE'
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language: English
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month: NOV
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number-of-cited-references: '152'
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papis_id: c826edb51ec99c93bdbb8d3aa5b9f6c8
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ref: Mengi2022systematicliterature
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tags:
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- review
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times-cited: '1'
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title: 'A systematic literature review on traditional to artificial intelligence based
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socio-behavioral disorders diagnosis in India: Challenges and future perspectives'
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2023-10-01 08:15:07 +00:00
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type: article
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2023-09-28 14:46:10 +00:00
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unique-id: WOS:000914071400001
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usage-count-last-180-days: '4'
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usage-count-since-2013: '5'
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volume: '129'
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web-of-science-categories: 'Computer Science, Artificial Intelligence; Computer Science,
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Interdisciplinary Applications'
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year: '2022'
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