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Stream/Area:
-
Subject:
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Paper ID:
IJSREM64255
Volume:
10 |
5 |
May 2026
Pages:
1-8
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

Department of Computer Science And Engineering
ASHOKRAO MANE GROUP OF INSTITUTION

Department of Computer Science And Engineering
ASHOKRAO MANE GROUP OF INSTITUTION

Department of Computer Science And Engineering
ASHOKRAO MANE GROUP OF INSTITUTION

Department of Computer Science And Engineering
ASHOKRAO MANE GROUP OF INSTITUTION

Department of Computer Science And Engineering
ASHOKRAO MANE GROUP OF INSTITUTION
Department of Computer Science And Engineering
ASHOKRAO MANE GROUP OF INSTITUTION
In present day autism spectrum disorder (ASD) is gaining its momentum faster than ever. Applying Image processing technique Detecting autism traits through screening tests is very expensive and time consuming. With the advancement of artificial intelligence and machine learning (ML), autism can be predicted at quite an early stage. Though a number of studies have been carried out using different techniques, these studies didn’t provide any definitive conclusion about predicting autism traits in terms of different age groups. Therefore, this paper aims to propose an effective prediction model based on ML technique and to develop a user interface forpredicting ASD for people of any age. As outcomes of this research, an autism prediction model was developed by merging Random Forest-CART (Classification and Regression Trees) and Random Forest-ID3 (Iterative Dichotomiser 3) and cnn algorithm also a user interface was developed based on the proposed prediction model. The proposed model was evaluated with AQ10 dataset and 250 real datasets collected from people with and without autistic traits.city, sensitivity, precision and false positive rate (FPR) for thThe evaluation results showed that the proposed prediction model provides better results in terms of accuracy, specifie data.
KEYWORDS
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that affects social interaction, communication, and behavior. It is a spectrum disorder, which means that the severity of symptoms and challenges can vary greatly among individuals with ASD.Research has shown that there is no single cause of ASD, but rather a combination of genetic and environmental factors. While there is no known cure for ASD, early intervention and treatment can help individuals with ASD lead fulfilling and productive lives.Individuals with ASD may face challenges in social interaction, communication, and behavior, but they also have unique strengths and abilities. It is important to recognize and appreciate these strengths, and to work to create a more inclusive and supportive society for individuals with ASD.Overall, ASD is a complex and varied disorder that requires individualized support and understanding. With the right resources and support, individuals with ASD can thrive and make valuable contributions to their communities. The integration of multimodal learning and decision fusion significantly improved overall system robustness and reduced false positive rates. The proposed framework demonstrates its effectiveness in detecting sophisticated multimedia forgeries and can be effectively applied in digital forensics, cybersecurity, social media verification, and online content authentication systems.
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Publication Date
5/29/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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