OBJECTIVES: In this manuscript, the Robotic Facial Recognition System using the Compound Classifier (RERS-CC) is introduced to improve the recognition rate of human faces. The process is differentiated into classification, detection, and recognition phases that employ principal component analysis based learning. In this learning process, the errors in image processing based on the extracted different features are used for error classification and accuracy improvements.

RESULTS: The performance of the proposed RERS-CC is validated experimentally using the input image dataset in MATLAB tool. The performance results show that the proposed method improves detection and recognition accuracy with fewer errors and processing time.

METHODS: This study is a pragmatic, cluster-randomised, parallel-group, matched pair, controlled trial with blinded outcome assessment. Randomisation is performed using a computer-generated table with a 1:1 allocation comparing the SIMSP and the POHP involving 28 preschools in the Kampar district, Perak, Malaysia. The intervention consists of preschool visits by a group of dental therapists, in-class oral health lessons and daily toothbrushing conducted by class teacher, child home toothbrushing supervised by parents, and infographic oral health messages to parents. The control consists of the existing POHP that involves preschool visits by a group of dental therapists only. The trial lasts for 6 months. Primary outcome variable is the mean plaque score change after 6 months. To determine the feasibility of the SIMSP, a process evaluation will be conducted using the perspectives of dental therapists, teachers, and parents on the appropriateness, effectiveness, facilitators, and barriers to the SIMSP implementation as well as an audit trail to assess the trial intervention.

DISCUSSION: Cluster randomisation may lead to a random effect and cluster selection bias. These factors will be accounted for when analysing the data and interpreting the outcomes. The effectiveness of the SIMSP will be evaluated by comparing the results with those of the POHP.

Methods: The accuracy of Webgazer.js for software-based gaze tracking is tested under different lighting conditions. Predefined time delays of a prototype diagnosis task automation script are contrasted against with manual delays based on human time estimation to understand how automation influences diagnosis accuracy. SLI diagnosis binary classifier was built and tested based on randomised parameters. The obtained results were cross-compared to Singlims_ES.exe for equality.

Results: Webgazer.js achieved an average accuracy of 88.755% under global lighting conditions, 61.379% under low lighting conditions and 52.7% under face-focused lighting conditions. The diagnosis task automation script found to execute with actual time delays with a deviation percentage no more than 0.04%, while manually executing time delays based on human time estimation resulted in a deviation percentage of not more than 3.37%. One-tailed test probability value produced by both the newly built classifier and Singlims_ES were observed to be similar up to three decimal places.

PURPOSE: The purpose of this simulation study was to establish a reference percentage value that can be used to effectively reduce the size and polygons of the 3D mesh without drastically affecting the dimensions of the prosthesis itself.

MATERIAL AND METHODS: Fifteen different maxillary palatal defects were simulated on a dental cast and scanned to create 3D casts. Digital bulbs were fabricated from the casts. Conventional bulbs for the defects were fabricated, scanned, and compared with the digital bulb to serve as a control. The polygon parameters of digital bulbs were then reduced by different percentages (75%, 50%, 25%, 10%, 5%, and 1% of the original mesh) which created a total of 105 meshes across 7 mesh groups. The reduced mesh files were compared individually with the original design in an open-source point cloud comparison software program. The parameters of comparison used in this study were Hausdorff distance (HD), Dice similarity coefficient (DSC), and volume.