...Qiong Jia; Ying Zhu; Rui Xu; Yubin Zhang; Yihua Zhao Purpose Abundant studies of outpatient visits apply traditional recurrent neural network (RNN) approaches; more recent methods, such as the deep long short-term memory (DLSTM) model, have yet to be implemented in efforts to forecast key hospital...

...Yi-Cheng Chen Purpose Recently, more and more attention has been put forth on the application and deep learning, due to the widespread practicability of neural network computation. The purpose of this paper is developing an effective algorithm to automatically discover the optimal neural network...

... and reinforcement of safe learning. Among AI techniques, the most widely used are Bayesian networks (BNs) and deep neural networks. In addition, given the huge potential in this field, four future research directions were also proposed. Practical implications This study is of vital interest to industry...

... of the battery, and provide effective information to the user to avoid the risk of battery accidents. Design/methodology/approach The particle filter (PF) algorithm is taken as the core, and the double-exponential model is used as the state equation and the artificial neural network is used...

.../methodology/approach This study examined the churn prediction through words presented the previous studies and additionally identified words what churn generate using data mining technology in combination with logistic regression, decision tree graphing, neural network models, and a partial least square (PLS...

... – The system makes use of cloud technology to collect and manage data among the franchisees. An integrated approach of association rule mining and the neural network technique is adopted to investigate customer behavioral patterns and to forecast sales demand, respectively. Findings – The significance...