ABSTRACT
INTRODUCTION: When planning esthetic dental treatments, understanding smile preferences is important for dental professionals. This study aimed to evaluate the impact of selected smile characteristics on the attractiveness of young Saudis as assessed by Saudi laypersons and explore gender-preferred changes in smile attractiveness. METHODOLOGY: This observational study assessed the dynamic smile attractiveness of 168 Saudi individuals (84 males and 84 females), selected through non-probability convenience sampling. Dynamic smiles were elicited by viewing comedic content and captured with a camera standardized for consistent positioning. Videos were edited and adjusted to images, and the frames with the most pronounced smiles were chosen. The intra-rater reliability was assessed using intra-class correlation coefficients (ICCs) and Cohen's kappa tests (κ). The highest and lowest 25th percentiles were categorized as attractive and unattractive smiles, respectively, on the visual analog scale (VAS) by laypersons. Six smile characteristics - anterior smile line, smile arc, upper lip curvature, posterior teeth displayed, smile index, and smile symmetry - were quantitatively evaluated from these images for each participant and classified into attractive and unattractive groups based on laypersons' VAS evaluations. Continuous variables were tested with the Mann-Whitney U test, and for the categorical variables, the Chi-square test was applied. The significance was set at 5%. RESULTS: The four randomly selected out of the 22 raters had good VAS reliability; ICCs varied from 0.661 to 0.94, with an average of 0.737, and Cohen's kappa tests for smile characteristics showed values from 0.617 to 0.89. Good agreement was also found with the smile index, with ICCs of 0.775, and dynamic smile symmetry, with ICCs of 0.872. Laypersons rated female smiles as more attractive compared to male smiles (P = 0.004). Low or average anterior smile lines (P = 0.001 for males; P = 0.03 for females), parallel smile arcs (P = 0.001 for males; P = 0.02 for females), and higher smile indexes (P = 0.001 for males; P = 0.004 for females) were significantly attractive, showing no significant gender differences. Conclusions: Laypersons reliably rated the young Saudis' dynamic smiles as attractive. Of the rated smile characteristics, those with a low or average anterior smile line, parallel smile arcs, and a larger smile index were deemed more attractive. This study's findings show no significant gender differences in the impact of the studied smile characteristics on attractiveness. This study's findings can help dental professionals customize treatment plans that meet patients' expectations.
ABSTRACT
A large number of countries worldwide depend on the agriculture, as agriculture can assist in reducing poverty, raising the country's income, and improving the food security. However, the plan diseases usually affect food crops and hence play a significant role in the annual yield and economic losses in the agricultural sector. In general, plant diseases have historically been identified by humans using their eyes, where this approach is often inexact, time-consuming, and exhausting. Recently, the employment of machine learning and deep learning approaches have significantly improved the classification and recognition accuracy for several applications. Despite the CNN models offer high accuracy for plant disease detection and classification, however, the limited available data for training the CNN model affects seriously the classification accuracy. Therefore, in this paper, we designed a Cycle Generative Adversarial Network (CycleGAN) to overcome the limitations of over-fitting and the limited size of the available datasets. In addition, we developed an efficient plant disease classification approach, where we adopt the CycleGAN architecture in order to enhance the classification accuracy. The obtained results showed an average enhancement of 7% in the classification accuracy.
