deep learning (dl) based improved probabilistic dense model (IPD) for autism spectrum disorders (asd) classification analysis

Samuel Sandeep; Amritpal Singh; Aditya Khamparia

Author(s)

Samuel Sandeep School of Computer Science and Engineering, Lovely Professional University,Punjab,India

Amritpal Singh School of Computer Science and Engineering, Lovely Professional University,Punjab,India

Aditya Khamparia Department of Computer Science, Babasaheb Bhimrao Ambedkar University,Amethi,India

Abstract

Among other symptoms, people with autism spectrum disorder (ASD) suffer from communication, social interaction, and repetitive habits. Even while complete eradication is not common, early interventions may make the illness more manageable. Here, we propose a practical framework for contrasting various Machine Learning (ML) methods for diagnosing ASD at an early age. The proposed framework uses a number of Feature Scaling (FS) methodologies, such as min-max scalar, principal component analysis, and intuitive visual representation analysis, to incorporate an efficient prediction method into the overall architecture. Our recommended design, which utilizes the IPD model, suggests that the categorization of adult autism spectrum condition, as an entity, is reflected in the overall verification of the features, and varied functional qualities. To do the mathematical analysis in our IPD model, we use chi-squared and probability density functional methods. The structure's purpose is to recognize and classify patients based on their palpable traits. We can observe that the 95.9% accuracy is far greater than the Machine Learning approach in the end.

Publication Details

Page(s) 2332-2337

DOI DOI not available

Published Journal: ARPN Journal of Engineering and Applied Sciences, Volume: 18, Issue: 20, Year: 2023

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Keywords

support vector machine (SVM), Artificial Neural Network (ANN), Random Forest Classifier, confusion matrix (CM), improved probabilistic dense model (IPD), ensemble approach (EA)

References

Ranjana K. N. R and P. .2023 .Fuzzy Logic Based Deep Learning Approach (FRNN) for Autism Spectrum Disorder Detection. , : .

Atyabietal A.,NV A. .2023 .Large-scale Investigations of AAC Usage Patterns: Trends, Autism, and Stacked Auto encoders. Las Vegas, : 851-859.

Shilaskar S.,Bhatlawande S.,Deshukh S.,Dande H. .2023 .Prediction and autism and Dylexia using machine learning and clinical Data Balancing. 2023 international conferences on advance in intelligent computing and applications. , 111(doi) : .

Pandya S.,Jain S.,Verma J. P. .2022 .AI based Classification for Autism Spectrum Disorder Detection using Video Analysis. 2022 IEEE Bombay Section Signature Conference (IBSSC), 10037438 : 1-6.

Siva V. Kavithaand R. .2022 .Review of Machine Learning Algorithms for Autism Spectrum Disorder Prediction. 2022 International Conference on Automation, Computing and Renewable Systems (ICACRS), 10029201(doi) : .

Kavitha V.,Siva R. .2022 .Review of Machine Learning Algorithms for Autism Spectrum Disorder Prediction. 2022 International Conference on 2022International Conference on Automation, Computing and Renewable Systems (ICACRS), 10029201(doi) : .

Yang T.,Al-Duailij M. A.,Bozdag S.,Saeed F. .2022 .Classification of Autism Spectrum Disorder Using rs-fMRI data and Graph Convolutional Networks. 2022 IEEE International Conference on, : .

Kaur N.,Gupta G.,Hafiz A. .2022 .Accurate Estimate of Autism Spectrum Disorder in Children Utilizing Several Machine Learning Techniques. 2022 14th International Conference on Computational Intelligence and Communication Networks (CICN), 406411(doi) : .

Villanuevaetal A. .2022 .Design of a Behavior Classification System for Monitoring Autism Spectrum Disorder using Deep Learning. doi: 10.1109/IISEC56263, 9998273 : .

Ramanjot D.,Singh M.,Rakhra S.,Aggarwal S. .2022 .Autism Spectrum Disorder Detection using the Deep Learning Approaches. 20222nd International Conference on Technological Advancements in Computational Sciences (ICTACS), 9988442(doi) : .

Zhu H.,Wang J.,Zhao Y. -P.,Lu M.,Shi J. .2022 .Contrastive Multi-view Composite Graph Convolutional Networks Based on Contribution Learning for Autism Spectrum Disorder Classification. in IEEE Transactions on Biomedical Engineering, 3232104 : .

B. J ,Singh T.,Duraisamy P. .2022 .Skull Stripping and Classification of Autism Spectrum Disorder Using Deep Learning. 2022 13thInternational Conference on Computing Communication and Networking Technologies (ICCCNT), 9984484 : 1-6.