A systematic review on sequence-to-sequence learning with neural network and its models

Abstract

We develop a precise writing survey on sequence-to-sequence learning with neural network and its models. The primary aim of this report is to enhance the knowledge of the sequence-to-sequence neural network and to locate the best way to deal with executing it. Three models are mostly used in sequence-to-sequence neural network applications, namely: recurrent neural networks (RNN), connectionist temporal classification (CTC), and attention model. The evidence we adopted in conducting this survey included utilizing the examination inquiries or research questions to determine keywords, which were used to search for bits of peer-reviewed papers, articles, or books at scholastic directories. Through introductory hunts, 790 papers, and scholarly works were found, and with the assistance of choice criteria and PRISMA methodology, the number of papers reviewed decreased to 16. Every one of the 16 articles was categorized by their contribution to each examination question, and they were broken down. At last, the examination papers experienced a quality appraisal where the subsequent range was from 83.3% to 100%. The proposed systematic review enabled us to collect, evaluate, analyze, and explore different approaches of implementing sequence-to-sequence neural network models and pointed out the most common use in machine learning. We followed a methodology that shows the potential of applying these models to real-world applications.

A systematic review of structural equation modeling in augmented reality applications

“…It is an evidence-based minimum set of elements for systematic review reports that are intended to assist systematic reviewers in clearly explaining why the review was conducted and what the authors performed. It has previously been used to target comparable research objectives [21], [22].…”

“…References Not consider other factors (21) [25], [26], [30], [31], [32], [38], [43], [44], [45], [46], [47], [48], [49], [50], [51], [52], [53], [54], [55], [56], [57] Convenience sampling (19) [32], [35], [37], [40], [43], [45], [46], [50], [52], [53], [55], [57], [58], [59], [60], [61], [62], [63],…”

Faster Capacitated Arc Routing: A Sequence-to-Sequence Approach

“…Additionally, it is worth noting that the purpose of this work is not to propose a new sequence-to-sequence learning model, but to use a sequenceto-sequence learning model to assist CARP solving and to verify that the use of sequence-to-sequence learning can help speed up the solving. There are many well-established models that are available to address the resultant task [32]- [34] and it is believed that the performance may be further improved with more advanced methods.…”

A Review on Intention-aware and Interaction-aware Trajectory Prediction for Autonomous Vehicles

“…Recurrent Neural Networks) [15], [59], [72]- [74], and static (e.g., Convolutional Neural Network) models. It was first introduced in sequenceto-sequence deep learning models, and widely applied for machine translation task, speech recognition, and time series analysis [75].…”