Within the ever-growing healthcare industry, dental informatics is a burgeoning field of study. One of the major obstacles to the health care system’s transformation is obtaining knowledge and insightful data from complex, high-dimensional, and diverse sources. Modern biomedical research, for instance, has seen an increase in the use of complex, heterogeneous, poorly documented, and generally unstructured electronic health records, imaging, sensor data, and text. There were still certain restrictions even after many current techniques were used to extract more robust and useful elements from the data for analysis. New effective paradigms for building end-to-end learning models from complex data are provided by the most recent deep learning technology breakthroughs. Therefore, the current study aims to examine the most recent research on the use of deep learning techniques for dental informatics problems and recommend creating comprehensive and meaningful interpretable structures that might benefit the healthcare industry. We also draw attention to some drawbacks and the need for better technique development and provide new perspectives about this exciting new development in the field.

Agent-based social simulations are typically described in imperative form. While this facilitates implementation as computer programs, it makes implicit the different assumptions made, both about the functional form and the causal ordering involved. As a solution to the problem, a probabilistic graphical model, Action Network (AN), is proposed in this paper for social simulation. Simulation variables are represented by nodes, and causal links by edges. An Action Table is associated with each node, describing incremental probabilistic actions to be performed in response to fuzzy parental states. AN offers a graphical causal model that captures the dynamics of a social process. Details of the formalism are presented along with illustrative examples. Software that implements the formalism is available at http://actionnetwork.epizy.com.

This study aimed to develop Bayesian Network model integrated with Deep Learning to help doctors diagnose Obstructive Sleep Apnoea Syndrome (OSAS) more holistically and clearly. The results of this research will produce a useful and beneficial clinical workflow for future support in health care. The model will be developed based on the methods of analysis and the quantitative data used to compromise the developing of Hybrid Bayesian Network in Neural Network using Deep Learning Algorithm. The aim of this study was to apply a hybrid model of convolutional neural network (CNN) that could be used during sleep consultation to determine the need for electrocardiography (ECG) signals stimuli for Polysomnography (PSG).

Asynchronous Non-Blocking Algorithm to Handle Straggler Reduce Tasks in Hadoop System