Impact of word embedding models on text analytics in deep learning environment: a review
The selection of word embedding and deep learning models for better outcomes is vital.
Word embeddings are an n-dimensional distributed representation of a text that attempts to …
Word embeddings are an n-dimensional distributed representation of a text that attempts to …
Securing critical infrastructures: deep-learning-based threat detection in IIoT
K Yu, L Tan, S Mumtaz, S Al-Rubaye… - IEEE …, 2021 - ieeexplore.ieee.org
The Industrial Internet of Things (IIoT) is a physical information system developed based on
traditional industrial control networks. As one of the most critical infrastructure systems, IIoT …
traditional industrial control networks. As one of the most critical infrastructure systems, IIoT …
The geometry of culture: Analyzing the meanings of class through word embeddings
AC Kozlowski, M Taddy… - American Sociological …, 2019 - journals.sagepub.com
We argue word embedding models are a useful tool for the study of culture using a historical
analysis of shared understandings of social class as an empirical case. Word embeddings …
analysis of shared understandings of social class as an empirical case. Word embeddings …
A comprehensive survey on process-oriented automatic text summarization with exploration of llm-based methods
H Jin, Y Zhang, D Meng, J Wang, J Tan - arXiv preprint arXiv:2403.02901, 2024 - arxiv.org
Automatic Text Summarization (ATS), utilizing Natural Language Processing (NLP)
algorithms, aims to create concise and accurate summaries, thereby significantly reducing …
algorithms, aims to create concise and accurate summaries, thereby significantly reducing …
Netsmf: Large-scale network embedding as sparse matrix factorization
We study the problem of large-scale network embedding, which aims to learn latent
representations for network mining applications. Previous research shows that 1) popular …
representations for network mining applications. Previous research shows that 1) popular …
On sampling strategies for neural network-based collaborative filtering
Recent advances in neural networks have inspired people to design hybrid
recommendation algorithms that can incorporate both (1) user-item interaction information …
recommendation algorithms that can incorporate both (1) user-item interaction information …
COVID-19 fake news detection: A hybrid CNN-BiLSTM-AM model
With the rapid development of technology, social media as a communication platform has
caused a significant increase in the dissemination of false information and fake news. We …
caused a significant increase in the dissemination of false information and fake news. We …
A smart conflict resolution model using multi-layer knowledge graph for conceptual design
Reducing the impact of conflicts on requirement-function-structure mapping in the early
stage of product design is an important measure to achieve conceptual innovation, which …
stage of product design is an important measure to achieve conceptual innovation, which …
Explicit and implicit oriented Aspect-Based Sentiment Analysis with optimal feature selection and deep learning for demonetization in India
K Ananthajothi, K Karthikayani, R Prabha - Data & Knowledge Engineering, 2022 - Elsevier
Abstract Aspect-Based Sentiment Analysis (ABSA) is a popular scheme that looks for the
prediction of the sentiment of positive characteristics in text. The sentiment of text sequences …
prediction of the sentiment of positive characteristics in text. The sentiment of text sequences …
A survey of deep learning on cpus: opportunities and co-optimizations
CPU is a powerful, pervasive, and indispensable platform for running deep learning (DL)
workloads in systems ranging from mobile to extreme-end servers. In this article, we present …
workloads in systems ranging from mobile to extreme-end servers. In this article, we present …