Data Science and Engineering
ESCI-ISI SCOPUS (2016-2025)
2364-1541
2364-1185
Cơ quản chủ quản: SPRINGER , Springer Nature
Lĩnh vực:
Computer Science ApplicationsComputational Mechanics
Phân tích ảnh hưởng
Các bài báo tiêu biểu
Graph Learning for Combinatorial Optimization: A Survey of State-of-the-Art
Tập 6 - Trang 119-141 - 2021
Graphs have been widely used to represent complex data in many applications,
such as e-commerce, social networks, and bioinformatics. Efficient and effective
analysis of graph data is important for graph-based applications. However, most
graph analysis tasks are combinatorial optimization (CO) problems, which are
NP-hard. Recent studies have focused a lot on the potential of using machine
learning...
Big Data Privacy: Challenges to Privacy Principles and Models
- 2016
Abstract This paper explores the challenges raised by big data in
privacy-preserving data management. First, we examine the conflicts raised by
big data with respect to preexisting concepts of private data management, such
as consent, purpose limitation, transparency and individual rights of access,
rectification and erasure. Anonymization appears as the best tool to mitigate
such conflicts, and i...
Image Preprocessing in Classification and Identification of Diabetic Eye Diseases
Tập 6 Số 4 - Trang 455-471 - 2021
AbstractDiabetic eye disease (DED) is a cluster of eye problem that affects
diabetic patients. Identifying DED is a crucial activity in retinal fundus
images because early diagnosis and treatment can eventually minimize the risk of
visual impairment. The retinal fundus image plays a significant role in early
DED classification and identification. An accurate diagnostic model’s
development using a ...
Exploring the Semantic Content of Unsupervised Graph Embeddings: An Empirical Study
Tập 4 Số 3 - Trang 269-289 - 2019
An Interactive Network for End-to-End Review Helpfulness Modeling
Tập 5 Số 3 - Trang 261-279 - 2020
AbstractReview helpfulness prediction aims to prioritize online reviews by
quality. Existing methods largely combine review texts and star ratings for
helpfulness prediction. However, star ratings are used in a way that has either
little representation capacity or limited interaction with review texts. As a
result, rating information has yet to be fully exploited during the combination.
This paper...