Autism Spectrum Disorder Identification Using Polynomial Distribution based Convolutional Neural Network

2021 ◽  
Vol 19 (2) ◽  
pp. 19-30
Author(s):  
G. Nagarajan ◽  
Dr.A. Mahabub Basha ◽  
R. Poornima
Keyword(s):  
Neural Network ◽  
Prediction Models ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Machine Learning Techniques ◽  
Hybrid Technique ◽  
Learning Techniques ◽  
Chicken Swarm Optimization ◽  
Sensitivity Specificity ◽  
Polynomial Distribution

One main psychiatric disorder found in humans is ASD (Autistic Spectrum Disorder). The disease manifests in a mental disorder that restricts humans from communications, language, speech in terms of their individual abilities. Even though its cure is complex and literally impossible, its early detection is required for mitigating its intensity. ASD does not have a pre-defined age for affecting humans. A system for effectively predicting ASD based on MLTs (Machine Learning Techniques) is proposed in this work. Hybrid APMs (Autism Prediction Models) combining multiple techniques like RF (Random Forest), CART (Classification and Regression Trees), RF-ID3 (RF-Iterative Dichotomiser 3) perform well, but face issues in memory usage, execution times and inadequate feature selections. Taking these issues into account, this work overcomes these hurdles in this proposed work with a hybrid technique that combines MCSO (Modified Chicken Swarm Optimization) and PDCNN (Polynomial Distribution based Convolution Neural Network) algorithms for its objective. The proposed scheme’s experimental results prove its higher levels of accuracy, precision, sensitivity, specificity, FPRs (False Positive Rates) and lowered time complexity when compared to other methods.

Machine Learning Techniques for Analysing and Identifying Autism Spectrum Disorder

2021 ◽  
pp. 69-81
Author(s):  
Jyoti Bhola ◽  
Rubal Jeet ◽  
Malik Mustafa Mohammad Jawarneh ◽  
Shadab Adam Pattekari
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Prediction Models ◽  
Medical Diagnostics ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Machine Learning Techniques ◽  
Autistic Spectrum ◽  
Learning Techniques ◽  
Behavioural Condition

Autism spectrum disorder (ASD) is a neuro disorder in which a person's contact and connection with others has a lifetime impact. In all levels of development, autism can be diagnosed as a “behavioural condition,” since signs generally occur within the first two years of life. The ASD problem begins with puberty and goes on in adolescence and adulthood. In this chapter, an effort is being made to use the supporting vector machine (SVM) and the convolutionary neural network (CNN) for prediction and interpretation of children's ASD problems based on the increased use of machine learning methodology in the research dimension of medical diagnostics. On freely accessible autistic spectrum disorder screening dates in children's datasets, the suggested approaches are tested. Using different techniques of machine learning, the findings clearly conclude that CNN-based prediction models perform more precisely on the dataset for autistic spectrum disorders.

Detecting and Classifying Self-injurious Behavior in Autism Spectrum Disorder Using Machine Learning Techniques

2020 ◽  
Vol 50 (11) ◽  
pp. 4039-4052 ◽  
Author(s):  
Kristine D. Cantin-Garside ◽  
Zhenyu Kong ◽  
Susan W. White ◽  
Ligia Antezana ◽  
Sunwook Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals Predicting Academic Performance of Deaf Students Using Feed Forward Neural Network and An Improved PSO Algorithm

Webology ◽  
2021 ◽  
Vol 18 (Special Issue 01) ◽  
pp. 112-126
Author(s):  
V. Sathya Durga ◽  
Thangakumar Jeyaprakash
Keyword(s):  
Neural Network ◽  
Prediction Models ◽  
Pso Algorithm ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Feed Forward Neural Network ◽  
Deaf Students ◽  
Literacy Rate ◽  
The Neural Network ◽  
Learning Techniques

Literacy rate of deaf students is very less in India. So there is a need to build an effective academic prediction model for identifying weak deaf students. Many machine learning techniques like Decision tree, Support Vector Machine, Neural Network are used to build prediction models. But the most preferred technique is neural network. It is found out that regression model build with neural networks takes more time to converge and the error rate is quite high. To solve the problems of neural network, we use Particle Swarm Optimization (PSO) for weight adjustment in the neural network. But, one of the main drawback of PSO lies in setting the initial parameters. So, a new PSO algorithm which determines the initial weight of the neural network using regression equation is proposed. The results show that neural network build with the proposed PSO algorithm performs well than neural network build with basic PSO algorithm. The Mean Square Error (MSE) achieved in this work is 0.0998, which is comparatively less than many existing models.

An Enhanced Deep Recurrent Neural Network for Autism Spectrum Disorder Diagnosis

2021 ◽  
Vol 11 (12) ◽  
pp. 3028-3037
Author(s):  
D. Pavithra ◽  
A. N. Jayanthi
Keyword(s):  
Neural Network ◽  
Autism Spectrum Disorder ◽  
Deep Learning ◽  
Recurrent Neural Network ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Domain Identification ◽  
Investigation Area ◽  
Deep Recurrent Neural Network ◽  
Sensitivity Specificity

Autism Spectrum Disorder is one of the major investigation area in current era. There are many research works introduced earlier for handling the Autism Spectrum Disorders. However those research works doesn’t achieve the expected accuracy level. The accuracy and prediction efficiency can be increased by building a better classification system using Deep Learning. This paper focuses on the deep learning technique for Autism Diagnosis and the domain identification. In the proposed work, an Enhanced Deep Recurrent Neural Network has been developed for the detection of ASD at all ages. It attempts to predict the autism spectrum in the children along with prediction of areas which can predict the autism in the prior level. The main advantage of EDRNN is to provide higher accuracy in classification and domain identification. Here Artificial Algal Algorithm is used for identifying the most relevant features from the existing feature set. This model was evaluated for the data that followed Indian Scale for Assessment of Autism. The results obtained for the proposed EDRNN has better accuracy, sensitivity, specificity, recall and precision.

scholarly journals Detecting autism spectrum disorder using machine learning techniques

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Md Delowar Hossain ◽  
Muhammad Ashad Kabir ◽  
Adnan Anwar ◽  
Md Zahidul Islam
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals Optimization of Machine Learning Techniques on Autism Spectrum Disorder with Swarm Intelligence Based Feature Selection

2019 ◽  
Vol 8 (2) ◽  
pp. 6248-6251
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Feature Selection ◽  
Swarm Intelligence ◽  
Autism Spectrum ◽  
Machine Learning Algorithms ◽  
Spectrum Disorder ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Dsm V

This paper is a study on the various machine learning algorithms in order to perform ASD (Autism spectrum Disorder) as per the DSM-V standards. ASD occurs more frequently among children and in order to diagnose this with better accuracy, the study on binary firefly algorithm, a swarm intelligence based wrapper feature selection algorithm is used to obtain best results with optimum feature subsets. This paper will provide overall result after applying it to all types of machine learning models on supervised learning.

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