Comparison of Bayes and Meta Algorithms in Diagnosing Autism
Anahtar Kelimeler:
Data Mining, Decision Tree, AutismÖzet
Today, with the advancement of technologies, especially technologies related to information and communication, a massive amount of data is being produced in communication and information networks. One of the necessities for the success of businesses, even on a small scale, is the ability to use from this information and data; managers of different enterprises know that collecting information and data from customers and audiences of a company is one of the factors needed for the growth and development of these companies. But this is only halfway; If the data is only collected and remains unused, the primary purpose of collecting this information has yet to be fulfilled. An important step that must be taken after collecting data and information is extracting knowledge from the news and making the data more tangible. Assembled, it helps derive rules and practical results. In this article, an attempt has been made to compare two widely used algorithms in the field of data mining, Bayes and Meta, which are in Weka Explorer data mining software, and to conclude which algorithm can have better results and performance in data mining.
Referanslar
Praveena TL, Lakshmi NM (2020) Detection of autism spectrum disorder effectively using modified regression algorithm, in emerging research in data engineering systems and computer communications.
Rahman MA, Bhuiyan M (2015) IOT enabled sensor in multimodal intelligent applications for children with special needs. In: 2015 Internet Technologies and Applications (ITA).
Tang TY, Winoto P (2018) An interactive picture exchange communication system (PECS) embedded with augmented aids enabled by IoT and sensing technologies for Chinese individuals with autism. In: Proceedings of the 2018 ACM international joint conference and 2018 international symposium on pervasive and ubiquitous computing and wearable computers-UbiComp’18.
Liu X et al (2016) An interactive training system of motor learning by imitation and speech instructions for children with autism. In: 2016 9th International Conference on Human System Interactions (HSI). IEEE, New York
Izadi M, Jabari M, Izadi N, Jabari M, Ghaffari A. Adaptive Control based on the Lyapunov Reference Model Method of Humanoid Robot Arms using EFK. In2021 13th Iranian Conference on Electrical Engineering and Computer Science (ICEESC) 2021.
So WC et al (2019) Who is a better teacher for children with autism? Comparison of learning outcomes between robot-based and human-based interventions in gestural production and recognition.
Ismail LI et al (2012) Estimation of concentration by eye contact measurement in robot–based intervention program with autistic children.
Kitchenham B et al (2009) Systematic literature reviews in software engineering—a systematic literature review.
Pellicano, E. & Burr, D. When the world becomes 'too real': a Bayesian explanation of autistic perception.
Izadi N, Jabari M, Izadi M, Jabari M, Ghaffari A. Optimal Path Design for a Flexible Rigid Two-Bar Robot in Point-to-Point Motion. In2022 14th Iranian Conference on Electrical Engineering and Computer Science (ICEESC) 2022.
Lawson, R.P., Rees, G. & Friston, K.J. An aberrant precision account of autism. Front. Hum (2014).
Behrens, T.E.J., Hunt, L.T., Woolrich, M.W. & Rushworth, M.F.S. Associative learning of social value (2008).
Amorosa, H. (1992). Disorders of vocal signaling in children. In H. Papousek, U. Jürgens, & M. Papoušek (1992). Nonverbal vocal communication: comparative and developmental approaches. New York (NY): Cambridge University Press.
Asgari, M., Bayestehtashk, A., & Shafran, I. (2013). Robust and accurate features for detecting and diagnosing autism spectrum disorders. In F. Bimbot (Ed.), Proceedings of Interspeech 2013. Lyon: International Speech Communication Association.
Goudarzi B, Savabkar A, Serajchi M, Haghighatseresht M. Identification of Electrohydrodynamic Printer and designing ADRC Control with Extended Observer. In2022 14th Iranian Conference on Electrical Engineering and Computer Science (ICEESC) 2022.
Bone, D., Chaspari, T., Audhkhasi, K., Gibson, J., Tsiartas, A., Van Segbroeck, M., … Narayanan, S.S. (2013). Classifying language-related developmental disorders from speech cues: The promise and the potential confounds. In F. Bimbot (Ed.), Proceedings of Interspeech 2013. Lyon: International Speech Communication Association.
Boucher, M.J., Andrianopoulos, M.V., Velleman, S.L., & Pecora, L. (2009). Voice characteristics of autism. Paper presented at the Annual Convention of the American Speech-Language-Hearing Association, New Orleans, LA.
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