Scientific Programming

Scientific Programming for Multimodal Big Data


Publishing date
01 Sep 2021
Status
Closed
Submission deadline
07 May 2021

Lead Editor

1Dalian University of Technology, Dalian, China

2Montclair State University, Montclair, USA

3St. Francis Xavier University, Antigonish, Canada

4China University of Mining and Technology, Xuzhou, China

This issue is now closed for submissions.
More articles will be published in the near future.

Scientific Programming for Multimodal Big Data

This issue is now closed for submissions.
More articles will be published in the near future.

Description

In this era of big data, with the enrichment of data collection and description measures, a wide array of data in various formats or modalities, called multimodal data, can be collected far more easily than ever before. It is important to discover the features and knowledge hidden in the data through comprehensive understanding and scientific programming, which can provide benefits across many different applications, from scientific and engineering computing to intelligent decisions and predictive services.

Regarding multimodal data, different modalities represent data samples from different perspectives, which usually provide complementary information to each other, and exploiting complementary characteristics can lead to a more comprehensive description of data samples. However, integrating knowledge across modalities and thereby unlocking the huge value of the data is still a significant problem in big data research, and this is the major difference between learning tasks in big data and traditional data. Scientific programming will play a significant role in providing solutions and mechanisms to issues surrounding multimodal big data, such as optimising the processing of large volumes or high dimensions of low-quality multimodal data and accelerating the analysis of real-time multimodal data, as well as integrating and fusing the features of heterogeneous multimodal data.

Therefore, the aim of this Special Issue is to attract high-quality papers from academics and industry researchers in big data and data fusion, and to present the most recent advanced methods and applications for realising the effective fusion of multimodal big data through scientific programming. We welcome both original research and review articles.

Potential topics include but are not limited to the following:

  • Scientific programming methods and tools for multimodal data
  • Multimodal data fusion and analysis
  • Multimodal feature learning
  • Low-quality multimodal data fusion
  • Incremental/online multimodal data fusion
  • Domain adaption for multimodal data
  • Multimodal data programming and industry applications
  • Multimodal data programming in other fields

Articles

  • Special Issue
  • - Volume 2021
  • - Article ID 3225933
  • - Review Article

Exploration of Cross-Modal Text Generation Methods in Smart Justice

Yangqianhui Zhang
  • Special Issue
  • - Volume 2021
  • - Article ID 9921831
  • - Research Article

Saliency Detection in Weak Light Images via Optimal Feature Selection-Guided Seed Propagation

Nan Mu | Hongyu Wang | ... | Jun Yang
  • Special Issue
  • - Volume 2021
  • - Article ID 2292703
  • - Research Article

Similarity Network Fusion Based on Random Walk and Relative Entropy for Cancer Subtype Prediction of Multigenomic Data

Jian Liu | Wenfeng Liu | ... | Xuesong Wang
  • Special Issue
  • - Volume 2021
  • - Article ID 9937061
  • - Research Article

Improved Deep Hashing with Scalable Interblock for Tourist Image Retrieval

Jiangfan Feng | Wenzheng Sun
  • Special Issue
  • - Volume 2021
  • - Article ID 5576978
  • - Research Article

Research Based on Multimodal Deep Feature Fusion for the Auxiliary Diagnosis Model of Infectious Respiratory Diseases

Jingyuan Zhao | Liyan Yu | Zhuo Liu
  • Special Issue
  • - Volume 2021
  • - Article ID 6668496
  • - Research Article

A Comparison of Analgesic Effect between Preoperative and Postoperative Transversus Abdominis Plane (TAP) Blocks for Different Durations of Laparoscopic Gynecological Surgery

Meiyu Wei | Ming Liu | ... | Haitao Yang
  • Special Issue
  • - Volume 2021
  • - Article ID 5573751
  • - Research Article

CPGAN :  An Efficient Architecture Designing for Text-to-Image Generative Adversarial Networks Based on Canonical Polyadic Decomposition

Ruixin Ma | Junying Lou
  • Special Issue
  • - Volume 2021
  • - Article ID 6692975
  • - Research Article

A Clustering Algorithm via Density Perception and Hierarchical Aggregation Based on Urban Multimodal Big Data for Identifying and Analyzing Categories of Poverty-Stricken Households in China

Hui Liu | Yang Liu | ... | Xia Wu
  • Special Issue
  • - Volume 2021
  • - Article ID 6699313
  • - Research Article

Sensors Anomaly Detection of Industrial Internet of Things Based on Isolated Forest Algorithm and Data Compression

Desheng Liu | Hang Zhen | ... | Hui Wang
  • Special Issue
  • - Volume 2021
  • - Article ID 6613452
  • - Research Article

Semisupervised Deep Embedded Clustering with Adaptive Labels

Zhikui Chen | Chaojie Li | ... | Peng Li
Scientific Programming
 Journal metrics
Acceptance rate27%
Submission to final decision66 days
Acceptance to publication33 days
CiteScore2.000
Journal Citation Indicator0.300
Impact Factor1.025
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