TransLearning ASD: Detection of Autism Spectrum Disorder Using Domain Adaptation and Transfer Learning-Based Approach on RS-FMRI Data Samreen Singh, Deepti Malhotra, Mehak Mengi Central University of Jammu, Jammu and Kashmir Corresponding author: Samreen Singh, Email: samreensingh61@yahoo.in), Autism Spectrum Disorder abbreviated as ASD, is a complex neuro-developmental disease specifcally linked to nervous system that infuences patients’ communica- tionand social behavior. Traditional clinical techniques used for the discovery of ASD fall short of defnite and early ASD diagnosis. Consequently, biomarkers have been introduced in the feld of ASD diagnosis and particularly, resting-state func- tional Magnetic Resonance Imaging (rs-fMRI) has posed as a valuable biomarker. Researchers have focused on utilizing the vast span of Artifcial Intelligence tech- niques in combination with rs-fMRI, to build an efective framework for ASD de- tection. However, these systems have not been able to generalize to a larger set of patients, because of theheterogeneity in the available f-MRI dataset for ASD. Mo- tivated from the aforementioned discussion, this study performs a comprehensive literature review of the existing systems covering a period of 2019-2021, thereby identifying several research gaps. To overcome the efect of existing implications, this paper expounds a TransLearning ASD framework which will achieve normal- ization of the heterogeneous fMRI data using domain adaptation followed by trans- fer learning technique for efective ASD prediction and to overcome the model generalization problem Keywords: Machine Learning, Deep learning, ABIDE, ASD, Functional MRI, Do- main Adaptation, Transfer Learning etc. 2023. In Saroj Hiranwal & Garima Mathur (eds.), Artifcial Intelligence and Communication Technologies, 863–871. Computing & Intelligent Systems, SCRS, India. https://doi.org/10.52458/978-81-955020-5-9-81