Bruxism management in individuals with autism spectrum disorder and down syndrome - A systematic review.

BACKGROUND: In dentistry, association between bruxism and individuals with autism spectrum disorders (ASD) and Down Syndrome (DS) is high. Bruxism is one of the most common oral and psychophysiological disorders, that is still an unsolved issue with limited data. OBJECTIVES: The purpose of this systematic review (SR) was to evaluate evidence about bruxism and its management in individuals with ASD and DS. MATERIALS AND METHODS: The researchers performed an electronic search using keywords on three databases, reference lists and complemented by manual searching from January 2000 to February 2023 to find out the relevant documents. An extensive literature review using the "Preferred Reporting Items for Systematic Review and Meta Analysis" method was carried out. PICO parameters were formulated, and studies risk of bias was evaluated using the JBI critical appraisal checklist tool for case reports. RESULTS: Out of 527 documents, 8 case studies and one review paper were identified as final articles for data synthesis. The findings showed, bruxism was reduced for all the participants with ASD and DS after implementation of functional analysis or dental treatment. CONCLUSION: The current SR found that despite the positive results of all the studies, there was a lack of evidence due to a limited number of studies and only case studies were conducted through functional analysis and dental treatment. NOVELTY: This SR is the first study on bruxism treatments in individuals with ASD and DS that included all the available studies (n = 9) since last 23 years and the first study that specifically addresses the incorporation of case reports in a systemic review.

Bagging and boosting negatively correlated neural networks.

In this paper, we propose two cooperative ensemble learning algorithms, i.e., NegBagg and NegBoost, for designing neural network (NN) ensembles. The proposed algorithms incrementally train different individual NNs in an ensemble using the negative correlation learning algorithm. Bagging and boosting algorithms are used in NegBagg and NegBoost, respectively, to create different training sets for different NNs in the ensemble. The idea behind using negative correlation learning in conjunction with the bagging/boosting algorithm is to facilitate interaction and cooperation among NNs during their training. Both NegBagg and NegBoost use a constructive approach to automatically determine the number of hidden neurons for NNs. NegBoost also uses the constructive approach to automatically determine the number of NNs for the ensemble. The two algorithms have been tested on a number of benchmark problems in machine learning and NNs, including Australian credit card assessment, breast cancer, diabetes, glass, heart disease, letter recognition, satellite, soybean, and waveform problems. The experimental results show that NegBagg and NegBoost require a small number of training epochs to produce compact NN ensembles with good generalization.