Automatic classification of hyperactive children: Comparing multiple artificial intelligence approaches

Delavarian, Mona; Towhidkhah, Farzad; Gharibzadeh, Shahriar; Dibajnia, Parvin

doi:10.1016/j.neulet.2011.03.012

Cited by 4 publications

(3 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Machine learning is the development of discovering algorithms that permit machines to learn relations without human intervention from the existing datasets. To find appropriate classifiers, Delavarian et al (2011) compare 16 linear and non-linear classifiers to distinguish and diagnose children with many similar symptoms and different behavioural disorders such as ADHD, depression, and anxiety. The results show that the nearest mean classifier was selected as a relevant classifier by categorizing 96.92% of the samples correctly.…”

Section: Tablementioning

confidence: 99%

See 1 more Smart Citation

The key artificial intelligence technologies in early childhood education: a review

Yi,

Liu,

Lan

2024

Artif Intell Rev

View full text Add to dashboard Cite

Artificial Intelligence (AI) technologies have been applied in various domains, including early childhood education (ECE). Integration of AI educational technology is a recent significant trend in ECE. Currently, there are more and more studies of AI in ECE. To date, there is a lack of survey articles that discuss the studies of AI in ECE. In this paper, we provide an up-to-date and in-depth overview of the key AI technologies in ECE that provides a historical perspective, summarizes the representative works, outlines open questions, discuss the trends and challenges through a detailed bibliometric analysis, and provides insightful recommendations for future research. We mainly discuss the studies that apply AI-based robots and AI technologies to ECE, including improving the social interaction of children with an autism spectrum disorder. This paper significantly contributes to provide an up-to-date and in-depth survey that is suitable as introductory material for beginners to AI in ECE, as well as supplementary material for advanced users.

show abstract

Section: Tablementioning

confidence: 99%

“…Current deep learning models are hard to be explained how they generate specific results. Interpretable techniques such as rule-based approaches, linear regression and tree-based algorithms could be fit to the interpretability in ECE (Delavarian et al 2011;Su et al 2020).…”

Section: Challenges For Deep Learning In Ecementioning

confidence: 99%

The key artificial intelligence technologies in early childhood education: a review

Yi,

Liu,

Lan

2024

Artif Intell Rev

View full text Add to dashboard Cite

show abstract

“…Star Trek fans will remember EMH, the Emergency Medical Holographic program that had the appearance of a reliable, middle aged family doctor. Even if we are miles away from developing an artificial healthcare practitioner, in recent years, significant advancements have been made in computer-aided diagnosis, digital technology and artificial-intelligence support to clinical practice 1 – 8 .…”

Section: Introductionmentioning

confidence: 99%

An emotional modulation model as signature for the identification of children developmental disorders

Mencattini

Mosciano

Comes

et al. 2018

Sci Rep

View full text Add to dashboard Cite

In recent years, applications like Apple’s Siri or Microsoft’s Cortana have created the illusion that one can actually “chat” with a machine. However, a perfectly natural human-machine interaction is far from real as none of these tools can empathize. This issue has raised an increasing interest in speech emotion recognition systems, as the possibility to detect the emotional state of the speaker. This possibility seems relevant to a broad number of domains, ranging from man-machine interfaces to those of diagnostics. With this in mind, in the present work, we explored the possibility of applying a precision approach to the development of a statistical learning algorithm aimed at classifying samples of speech produced by children with developmental disorders(DD) and typically developing(TD) children. Under the assumption that acoustic features of vocal production could not be efficiently used as a direct marker of DD, we propose to apply the Emotional Modulation function(EMF) concept, rather than running analyses on acoustic features per se to identify the different classes. The novel paradigm was applied to the French Child Pathological & Emotional Speech Database obtaining a final accuracy of 0.79, with maximum performance reached in recognizing language impairment (0.92) and autism disorder (0.82).

show abstract

Disruptive Behavior Rating Scale-2nd Edition-Parent Version (DBRS-II-P): A Study of Reliability and Validity

Erford

Bardhoshi

Johnson

et al. 2017

Counseling Outcome Research and Evaluation

View full text Add to dashboard Cite

Automatic classification of hyperactive children: Comparing multiple artificial intelligence approaches

Cited by 4 publications

References 15 publications

The key artificial intelligence technologies in early childhood education: a review

The key artificial intelligence technologies in early childhood education: a review

An emotional modulation model as signature for the identification of children developmental disorders

Disruptive Behavior Rating Scale-2nd Edition-Parent Version (DBRS-II-P): A Study of Reliability and Validity

Contact Info

Product

Resources

About