DSpace-CRIS

Now showing 1 - 4 of 4

Some of the metrics are blocked by your
consent settings
A Multi-Strategy Computer-Assisted EFL Writing Learning System With Deep Learning Incorporated and Its Effects on Learning: A Writing Feedback Perspective
(2024-01-01)
Chen, Binbin
;
Bao, Lina
;
Zhang, Rui
;
Zhang, Jingyu
;
Liu, Feng
Language learning has increasingly benefited from Computer-Assisted Language Learning (CALL) technologies, especially with Artificial Intelligence involved in recent years. CALL in writing learning acknowledged as the core of language learning is being realized by technologies like Automated Writing Evaluation (AWE), and Automated Essay Scoring (AES), which have developed considerably in both computer and language education fields. AWE has effectively enhanced EFL students’ writing performance to some extent, but such technology can only provide an evaluation in the form of scores, the majority of which are based on holistic scoring, resulting in the inability to provide comprehensive and detailed content-based feedback. In order to provide not only the writing multiple trait-specific evaluation scores, but also detailed writing feedback, we proposed a computer-assisted EFL writing learning system incorporating the neural network models and a couple of semantic-based NLP techniques, MsCAEWL, which fully meets the requirements of writing feedback theory, i.e., multiple, continuous, timely, clear, and multi-aspect guidance interactive feedback. The results of comparison experiments with the AWE baseline models and human raters demonstrated the superiority and the high correlation contained by the proposed system. The independent-sample t-test and paired-sample t-test results of the experiments on MsCAEWL effect validation suggested the significant impact of our proposed system in enhancing students’ EFL writing proficiency.
Some of the metrics are blocked by your
consent settings
An Improved Neural Manufacturing Corporate Credit Rating Model Based on LSTM
(2024-01-01)
Zhang, Rui
;
Chen, Binbin
;
Sakdanuphab, Rachsak
This paper proposes an improved neural manufacturing corporate credit rating model based on Multi-head Self-attention (MSA) mechanism and Long Short-Term Memory (LSTM) network. The proposed model leverages MSA to simulate the marketdynamics and generate dynamic weights for each indicator based on the financial dataof all manufacturing companies. Meanwhile, LSTM is utilized to extract sequential features from long-term financial and operational data to capture the long-term financial status and reduce the risk of deviation. The experimental results show that the proposedmodel provides more objective and reliable credit ratings for manufacturing companies.In the comparison experiment with the baseline model, it was proven that the model proposed in this paper outperforms other baseline models. In the comparison experiment with SMAGRU, it was proven that the proposed model has better predictionability than SMAGRU on both datasets, and it also demonstrates that the GRU simplifies the internal computation of LSTM. The ablation experiment verified the feasibility of the two modules of the proposed model separately, which further proved the effectiveness of the proposed model.
1
Some of the metrics are blocked by your
consent settings
A Multi-Objective Multi-Period Low-Carbon Location-Routing Problem: Improved NSGA-II Approach
(2024-01-01)
Chen, Binbin
;
Zhang, Rui
;
Long, Shengjie
;
Sakdanuphab, Rachsak
In light of the escalating global concerns surrounding climate change, the significance of sustainable development in the realm of logistics cannot be overstated. This study undertakes the imperative task of devising strategies aimed at mitigating carbon emissions, reducing logistics costs, minimizing transportation time, and enhancing customer satisfaction. The research delves into the intricacies of an optimization model tailored for a specific iteration of the Location-Routing Problem (LRP), namely the Multi-Objective Multi-Period Low-Carbon Location-Routing Problem (MMLCLRP). This variant of the LRP takes into meticulous consideration several crucial parameters, such as the overall logistics cost, the arrival times of demand points, and carbon emissions. These factors are pivotal in determining both the optimal location for depots and the programming of routes within a multi-period planning horizon. The proposed model guarantees the long-term sustainability of logistics operations while flexibly adapting location routing decisions for each period in response to evolving market demands. To tackle the inherent complexity of this problem, an improved version of the Non-dominated Sorting Genetic Algorithm (NSGA-II) was employed. This approach integrates a pioneering similarity distance metric to quantify the resemblance between potential solutions. Additionally, a crowding clustering strategy was implemented to enhance the diversity within the NSGA-II. Empirical results illustrate the capability of the proposed optimization model in effectively harmonizing various objectives, encompassing economic, efficiency, and environmental aspects within the logistics domain. Additionally, the enhanced algorithm exhibits notable advantages in addressing the complexities inherent in the optimization model.
1
Some of the metrics are blocked by your
consent settings
Novel neural network model for predicting susceptibility of facial post-inflammatory hyperpigmentation
(2022-12-01)
Sun, Nana
;
Chen, Binbin
;
Zhang, Rui
;
Wen, Yang
Background

KMITL

Browse

Filters

Settings

Sort By

Results per page

Search Results