3 research outputs found
A Novel Kernel for Text Classification Based on Semantic and Statistical Information
In text categorization, a document is usually represented by a vector space model which can accomplish the classification task, but the model cannot deal with Chinese synonyms and polysemy phenomenon. This paper presents a novel approach which takes into account both the semantic and statistical information to improve the accuracy of text classification. The proposed approach computes semantic information based on HowNet and statistical information based on a kernel function with class-based weighting. According to our experimental results, the proposed approach could achieve state-of-the-art or competitive results as compared with traditional approaches such as the k-Nearest Neighbor (KNN), the Naive Bayes and deep learning models like convolutional networks
Machine Learning Applications for Sustainable Manufacturing: A Bibliometric-based Review for Future Research
The role of data analytics is significantly important in manufacturing industries as it
holds the key to address sustainability challenges and handle the large amount of data generated
from different types of manufacturing operations. The present study, therefore, aims to conduct a
systematic and bibliometric-based review in the applications of machine learning (ML) techniques
for sustainable manufacturing (SM).
In the present study, we use a bibliometric review approach that
is focused on the statistical analysis of published scientific documents with an unbiased objective
of the current status and future research potential of ML applications in sustainable manufacturing.
The present study highlights how manufacturing industries can benefit from ML
techniques when applied to address SM issues. Based on the findings, a ML-SM framework is
proposed. The framework will be helpful to researchers, policymakers and practitioners to provide
guidelines on the successful management of SM practices.
A comprehensive and bibliometric review of opportunities for ML techniques in SM
with a framework is still limited in the available literature. This study addresses the bibliometric
analysis of ML applications in SM, which further adds to the originalityN/