Locating good design solutions within a sequential process environment is necessary to improve the quality and overall productivity of the processes. Multi-objective, multi-stage sequential process design is a complex problem involving large number of design variables and sequential relationship between any two stages. The aim of this paper is to propose a novel framework to handle real-life se...

Multi-behavioral sequential recommendation has recently attracted increasing attention. However, existing methods suffer from two major limitations. Firstly, user preferences and intents can be described in fine-grained detail multiple perspectives; yet, these fail to capture their multi-aspect nature. Secondly, behaviors may contain noises, most could not effectively deal with noises. In this ...

Sequential learning is the discipline of machine learning that deals with dependent data such that neighboring labels exhibit some kind of relationship. The paper main contribution is two-fold: first, we generalize the stacked sequential learning, highlighting the key role of neighboring interactions modeling. Second, we propose an effective and efficient way of capturing and exploiting sequent...

Discriminative learning techniques for sequential data have proven to be more effective than generative models for named entity recognition, information extraction, and other tasks of discrimination. However, semi-supervised learning mechanisms that utilize inexpensive unlabeled sequences in addition to few labeled sequences – such as the Baum-Welch algorithm – are available only for generative...

the main purpose of this research is designing model of factors that affect utilization of policy research with concept mapping technique and verifying it. for this in three stage, sequential-exploratory mixed methods design is used. in first stage, literature review, interview and focus group is used for identifying factors. the analysis is used for analyzing data. in second stage for designin...