scholarly journals Artificial intelligence models for suspended river sediment prediction: state-of-the art, modeling framework appraisal, and proposed future research directions

2021 ◽  
Vol 15 (1) ◽  
pp. 1585-1612
Author(s):  
Hai Tao ◽  
Zainab S. Al-Khafaji ◽  
Chongchong Qi ◽  
Mohammad Zounemat-Kermani ◽  
Ozgur Kisi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
River Sediment ◽  
State Of The Art ◽  
Future Research ◽  
Modeling Framework ◽  
Research Directions ◽  
Intelligence Models ◽  
Artificial Intelligence Models ◽  
Future Research Directions

scholarly journals Artificial Intelligence Techniques for Cognitive Sensing in Future IoT: State-of-the-Art, Potentials, and Challenges

2020 ◽  
Vol 9 (2) ◽  
pp. 21 ◽  
Author(s):  
Martins O. Osifeko ◽  
Gerhard P. Hancke ◽  
Adnan M. Abu-Mahfouz
Keyword(s):  
Artificial Intelligence ◽  
Data Collection ◽  
Energy Efficient ◽  
State Of The Art ◽  
Future Internet ◽  
Future Research ◽  
Research Directions ◽  
Efficient Data ◽  
Future Research Directions ◽  
Efficient Data Collection

Smart, secure and energy-efficient data collection (DC) processes are key to the realization of the full potentials of future Internet of Things (FIoT)-based systems. Currently, challenges in this domain have motivated research efforts towards providing cognitive solutions for IoT usage. One such solution, termed cognitive sensing (CS) describes the use of smart sensors to intelligently perceive inputs from the environment. Further, CS has been proposed for use in FIoT in order to facilitate smart, secure and energy-efficient data collection processes. In this article, we provide a survey of different Artificial Intelligence (AI)-based techniques used over the last decade to provide cognitive sensing solutions for different FIoT applications. We present some state-of-the-art approaches, potentials, and challenges of AI techniques for the identified solutions. This survey contributes to a better understanding of AI techniques deployed for cognitive sensing in FIoT as well as future research directions in this regard.

scholarly journals Social commerce—state-of-the-art and future research directions

2016 ◽  
Vol 26 (3) ◽  
pp. 269-290 ◽  
Author(s):  
Catherine Baethge ◽  
Julia Klier ◽  
Mathias Klier
Keyword(s):  
State Of The Art ◽  
Future Research ◽  
Social Commerce ◽  
Research Directions ◽  
Future Research Directions

scholarly journals Beyond Intra-modality: A Survey of Heterogeneous Person Re-identification

2020 ◽  
Author(s):  
Zheng Wang ◽  
Zhixiang Wang ◽  
Yinqiang Zheng ◽  
Yang Wu ◽  
Wenjun Zeng ◽  
...  
Keyword(s):  
Video Analysis ◽  
State Of The Art ◽  
Future Research ◽  
Low Resolution ◽  
Research Directions ◽  
Comprehensive Review ◽  
Practical Applications ◽  
Future Research Directions

An efficient and effective person re-identification (ReID) system relieves the users from painful and boring video watching and accelerates the process of video analysis. Recently, with the explosive demands of practical applications, a lot of research efforts have been dedicated to heterogeneous person re-identification (Hetero-ReID). In this paper, we provide a comprehensive review of state-of-the-art Hetero-ReID methods that address the challenge of inter-modality discrepancies. According to the application scenario, we classify the methods into four categories --- low-resolution, infrared, sketch, and text. We begin with an introduction of ReID, and make a comparison between Homogeneous ReID (Homo-ReID) and Hetero-ReID tasks. Then, we describe and compare existing datasets for performing evaluations, and survey the models that have been widely employed in Hetero-ReID. We also summarize and compare the representative approaches from two perspectives, i.e., the application scenario and the learning pipeline. We conclude by a discussion of some future research directions. Follow-up updates are available at https://github.com/lightChaserX/Awesome-Hetero-reID

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