Detection and 3-D Visualization of Human Tooth Surface Cracks Using Line Structured Light

Chen, Lizhi; He, Jiayan; Wu, Yuyan; Tang, Yadong; Ge, Guanghua; Wang, Wenlong

doi:10.1109/jsen.2024.3375864

Cited by 1 publication

(1 citation statement)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Machine learning (ML) and deep learning (DL) have received interest in the scientific community as well as industry [1][2][3][4][5][6][7]. Many applications of artificial intelligence methods can be found in many research areas, notably in engineering by monitoring the condition of structures in construction [8][9][10]; in robotics through autonomous robots equipped with artificial intelligence and vision systems, enabling objective assessment, reduced report generation time, and improved maintenance planning [11][12][13]; in medicine by performing diagnostic imaging, remote surgeries, surgical subtasks, and whole surgical procedures [14][15][16][17][18]; in finance for risk analysis, prediction, and maintenance planning of financial infrastructure [19][20][21]; and also in the agri-food industry [22][23][24]. Concentrating on the food sector, the important steps are production processes, quality control, optimization, and even the forecasting of profits and losses resulting from management when obtaining the final product.…”

Section: Introductionmentioning

confidence: 99%

Explainable AI: Machine Learning Interpretation in Blackcurrant Powders

Przybył

2024

Sensors

View full text Add to dashboard Cite

Recently, explainability in machine and deep learning has become an important area in the field of research as well as interest, both due to the increasing use of artificial intelligence (AI) methods and understanding of the decisions made by models. The explainability of artificial intelligence (XAI) is due to the increasing consciousness in, among other things, data mining, error elimination, and learning performance by various AI algorithms. Moreover, XAI will allow the decisions made by models in problems to be more transparent as well as effective. In this study, models from the ‘glass box’ group of Decision Tree, among others, and the ‘black box’ group of Random Forest, among others, were proposed to understand the identification of selected types of currant powders. The learning process of these models was carried out to determine accuracy indicators such as accuracy, precision, recall, and F1-score. It was visualized using Local Interpretable Model Agnostic Explanations (LIMEs) to predict the effectiveness of identifying specific types of blackcurrant powders based on texture descriptors such as entropy, contrast, correlation, dissimilarity, and homogeneity. Bagging (Bagging_100), Decision Tree (DT0), and Random Forest (RF7_gini) proved to be the most effective models in the framework of currant powder interpretability. The measures of classifier performance in terms of accuracy, precision, recall, and F1-score for Bagging_100, respectively, reached values of approximately 0.979. In comparison, DT0 reached values of 0.968, 0.972, 0.968, and 0.969, and RF7_gini reached values of 0.963, 0.964, 0.963, and 0.963. These models achieved classifier performance measures of greater than 96%. In the future, XAI using agnostic models can be an additional important tool to help analyze data, including food products, even online.

show abstract

Section: Introductionmentioning

confidence: 99%

Explainable AI: Machine Learning Interpretation in Blackcurrant Powders

Przybył

2024

Sensors

View full text Add to dashboard Cite

show abstract

Detection and 3-D Visualization of Human Tooth Surface Cracks Using Line Structured Light

Cited by 1 publication

References 47 publications

Explainable AI: Machine Learning Interpretation in Blackcurrant Powders

Explainable AI: Machine Learning Interpretation in Blackcurrant Powders

Contact Info

Product

Resources

About