BrainOS: A Novel Artificial Brain-Alike Automatic Machine Learning Framework

Howard, Newton; Chouikhi, Naima; Adeel, Ahsan; Dial, Katelyn; Howard, Adam; Hussain, Amir

doi:10.3389/fncom.2020.00016

Cited by 6 publications

(2 citation statements)

References 21 publications

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“…Orange components are called widgets and range from basic visualization of data, selection of subsets, and preprocessing, to realistic evaluation of learning algorithms and predictive modelling. Machine learning is a data analytics tool which automates the building of analytical models [31]. Machine Learning method is way easier to use, low costs and can analyze more specific data samples.…”

Section: Machine Learningmentioning

confidence: 99%

The Role of Gross Motor Performance Towards Hand-Eye Coordination Among Girls Aged 7 Years Old in Malaysia Using Machine Learning

Puspitasari¹,

Abdullah²,

Kamarudin³

et al. 2021

Proceedings of the First International Conference on Science, Technology, Engineering and Industrial Revolution (ICSTEIR 2020)

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Range of motor performance is a prerequisite for children to participate in daily activities and has significant consequences for different aspects of children and adolescent's growth. The objective of the study is to clusterize the coordination performance, to validate the role of gross motor performance towards classification of hand-eyes coordination and to develop best fit model among girls aged 7 years old in Malaysia. The sample size of this study was calculated using G power software. The statistical power was set at 95% with an effect size of 0.50%. According to [1], an effect size of +0.20 was considered as small, +0.50 as medium and +0.80 as strong. Population for this analysis were student of 7-year-old girls in primary schools in peninsular Malaysia and Borneo. In this study anthropometric measurement and motor performance data was collected from 23133 girls from all districts in Malaysia. Analysis that used in this research was univariate clustering, discriminant analysis and machine learning. Univariate clustering helps in grouping the coordination. Next, discriminant analysis helps in validating the group and shows the prediction that correctly assigned to the group. Lastly, machine learning help in developing best fit model. In conclusion, this research can help many parties in an effort to improve coordination among girl aged 7 years old in Malaysia.

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Section: Machine Learningmentioning

confidence: 99%

The Role of Gross Motor Performance Towards Hand-Eye Coordination Among Girls Aged 7 Years Old in Malaysia Using Machine Learning

Puspitasari¹,

Abdullah²,

Kamarudin³

et al. 2021

Proceedings of the First International Conference on Science, Technology, Engineering and Industrial Revolution (ICSTEIR 2020)

View full text Add to dashboard Cite

show abstract

“…Artificial intelligence is inspired by biological processes, especially input processing approaches used by the human brain. Artificial intelligence can learn, process inputs, identify patterns, predict, and make decisions ( 4 ). Artificial intelligence techniques are mainly divided into supervised and unsupervised learning.…”

Section: Introductionmentioning

confidence: 99%

An Augmented Artificial Intelligence Approach for Chronic Diseases Prediction

Rashid

Batool

Kim

et al. 2022

Front. Public Health

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Chronic diseases are increasing in prevalence and mortality worldwide. Early diagnosis has therefore become an important research area to enhance patient survival rates. Several research studies have reported classification approaches for specific disease prediction. In this paper, we propose a novel augmented artificial intelligence approach using an artificial neural network (ANN) with particle swarm optimization (PSO) to predict five prevalent chronic diseases including breast cancer, diabetes, heart attack, hepatitis, and kidney disease. Seven classification algorithms are compared to evaluate the proposed model's prediction performance. The ANN prediction model constructed with a PSO based feature extraction approach outperforms other state-of-the-art classification approaches when evaluated with accuracy. Our proposed approach gave the highest accuracy of 99.67%, with the PSO. However, the classification model's performance is found to depend on the attributes of data used for classification. Our results are compared with various chronic disease datasets and shown to outperform other benchmark approaches. In addition, our optimized ANN processing is shown to require less time compared to random forest (RF), deep learning and support vector machine (SVM) based methods. Our study could play a role for early diagnosis of chronic diseases in hospitals, including through development of online diagnosis systems.

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