Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Artificial intelligence (AI) is a technology that utilizes machines to mimic intelligent human behavior. To appreciate human-technology interaction in the clinical setting, augmented intelligence has been proposed as a cognitive extension of AI in health care, emphasizing its assistive and supplementary role to medical professionals. While truly autonomous medical robotic systems are still beyond reach, the virtual component of AI, known as software-type algorithms, is the main component used in dentistry. Because of their powerful capabilities in data analysis, these virtual algorithms are expected to improve the accuracy and efficacy of dental diagnosis, provide visualized anatomic guidance for treatment, simulate and evaluate prospective results, and project the occurrence and prognosis of oral diseases. Potential obstacles in contemporary algorithms that prevent routine implementation of AI include the lack of data curation, sharing, and readability; the inability to illustrate the inner decision-making process; the insufficient power of classical computing; and the neglect of ethical principles in the design of AI frameworks. It is necessary to maintain a proactive attitude toward AI to ensure its affirmative development and promote human-technology rapport to revolutionize dental practice. The present review outlines the progress and potential dental applications of AI in medical-aided diagnosis, treatment, and disease prediction and discusses their data limitations, interpretability, computing power, and ethical considerations, as well as their impact on dentists, with the objective of creating a backdrop for future research in this rapidly expanding arena.

Application of Artificial Intelligence in Dentistry.

Alternative fuel is one of the widely used fuel substitutions for both petrol and diesel in the field of internal combustion engine. The increase in the demand for alternative fuel is currently driven by the requirement of decreasing engine fuel consumption and fulfilling the stringent engine exhaust emissions pollutant regulations. In order to effectively tackle the aforementioned concerns, it appears that through engine experimental analysis alone for both engine performance and exhaust emissions is insufficient. Recently, the need for engine modelling based on statistical and machine learning methodologies through response surface and artificial neural network technique, respectively, are non-trivial to provide a better decision support analysis. Therefore, the present study reviews the extent to which the application of these methods in various alternative fuel in both spark and compression ignition engine to investigate their viability. The paper also describes herein the ways to determine the accuracy and the significance of model fitting for both methodologies. It was demonstrated from the review that most of the research yield favourable results of engine modelling prediction for both of the methods. It can be concluded the comparison between predicted and experimental results provided a high degree of determination coefficient indicating that the model could predict the model efficiency with reasonable accuracy.

A review on the application of response surface method and artificial neural network in engine performance and exhaust emissions characteristics in alternative fuel

Dentronics: Towards robotics and artificial intelligence in dentistry

(1) Background: The rapid pace of digital development in everyday life is also reflected in dentistry, including the emergence of the first systems based on artificial intelligence (AI). This systematic review focused on the recent scientific literature and provides an overview of the application of AI in the dental discipline of prosthodontics. (2) Method: According to a modified PICO-strategy, an electronic (MEDLINE, EMBASE, CENTRAL) and manual search up to 30 June 2021 was carried out for the literature published in the last five years reporting the use of AI in the field of prosthodontics. (3) Results: 560 titles were screened, of which 30 abstracts and 16 full texts were selected for further review. Seven studies met the inclusion criteria and were analyzed. Most of the identified studies reported the training and application of an AI system (n = 6) or explored the function of an intrinsic AI system in a CAD software (n = 1). (4) Conclusions: While the number of included studies reporting the use of AI was relatively low, the summary of the obtained findings by the included studies represents the latest AI developments in prosthodontics demonstrating its application for automated diagnostics, as a predictive measure, and as a classification or identification tool. In the future, AI technologies will likely be used for collecting, processing, and organizing patient-related datasets to provide patient-centered, individualized dental treatment.

The Use and Performance of Artificial Intelligence in Prosthodontics: A Systematic Review

Artificial Neural Networks as a powerful numerical tool to classify specific features of a tooth based on 3D scan data

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Artificial Neural Networks as a powerful numerical tool to classify specific features of a tooth based on 3D scan data