【SciencePub学术】
本期推荐
部分学者论文完成后，选刊上犯难，面对纷繁复杂的期刊信息及流程，很难有时间和精力一一调研查看，小编在也经常收到此类信息，希望我们帮助查询期刊信息。为此，小编开设此专栏【选刊CFP】，定期为大家遴选解析各领域期刊及专题征集信息，以期对您有所帮助。
本期，小编给大家推荐的是一本Elsevier旗下、计算机领域、影响因子高达18.6的中科院1区TOP刊，期刊详情及专题信息如下：
Information Fusion
（专题：Multi-source data fusion for sustainable cities）
ISSN：1566-2535
E-ISSN：1872-6305
IF（2022）：18.6
自引率：4.30%
出版商：Elsevier
检索数据库：SCIE&EI
【简介】该杂志旨在在一个单一的论坛中展示多传感器，多源，多过程信息融合领域的所有发展，从而促进促进其发展的许多学科之间的协同作用。
期刊指标
01 影响因子
2022-2023最新影响因子：18.6
来源：LetPub
02 期刊分区
JCR分区：COMPUTER SCIENCEQ1
来源：WOS数据库
中科院分区：计算机科学1区TOP
来源：LetPub
03 自引率
2022-2023自引率：4.30%
来源：LetPub
04 预警情况
中科院《国际期刊预警名单（试行）》名单：无预警记录
来源：LetPub
05 年发文量
近三年年发文量：150-250篇（年发文量稳定）
来源：WOS数据库
06 国人占比
国人占比总计：第一，76.99%（对国人非常友好）
来源：WOS数据库
07 录用周期
录用周期：预计3个月左右录用（以下为期刊官网最新案例）
来源：期刊官网
案例一：提交到录用3个月17天！录用后4天见刊！
来源：期刊官网
案例二：提交到录用3个月18天！录用后13天见刊！
专
题
信
息
28 September 2023
Multi-source data fusion for sustainable cities
With the rapid growth of urban areas and the increasing challenges posed by climate change and resource scarcity, the concept of sustainable cities has gained significant attention. Sustainable cities are those that prioritize the efficient use of resources, minimize environmental impact, and enhance the quality of life for their residents. To effectively address these challenges and achieve sustainable urban development, it is essential to harness the power of multi-source data fusion techniques. By integrating data from various sources such as sensors, satellites, social media, and citizen-generated data, we can gain valuable insights into urban systems and implement effective strategies for sustainable urban planning and management. Multi-source data fusion plays a crucial role in sustainable cities by providing a comprehensive and holistic understanding of urban environments. However, existing approaches often fall short in building sustainable cities due to their limitations in their scope and coverage. Furthermore, different data sources often have varying formats, resolutions, and quality, making it challenging to combine them effectively. Lastly, sustainable urban development requires a multidisciplinary approach, considering social, economic, and environmental factors. To overcome these limitations and advance sustainable urban development, multi-source data fusion techniques provide a promising solution.
The proposed special issue aims to explore the latest advancements, methodologies, and case studies in multi-source data fusion for sustainable cities, facilitating the development of innovative solutions and practical applications to address the pressing challenges. We welcome the submission of original research articles, case studies, and review articles that provide unique perspectives, empirical evidence, or theoretical advancements in the field of data fusion techniques for sustainable cities. All submissions to this special issue will undergo a thorough peer-review process to ensure the publication of exceptional and influential work. The main objective of this issue is to emphasize the practical application of data fusion techniques, aiming to foster the creation of intelligent and sustainable cities that benefit both society and the environment.
Topics include (but are not restricted to):
▪Multi-modal data fusion for sustainable transportation planning and management.
▪Integration of data from heterogeneous sources, such as GPS, cameras, and crowdsourced data.
▪Data quality assessment and data preprocessing techniques in multi-source data fusion.
▪Applications of multi-source data fusion in traffic flow modeling, prediction, and control.
▪Fusion of socio-economic data for understanding social sustainability.
▪Data fusion for optimizing energy generation, delivery, consumption and demand in cities.
▪Multi-source data fusion for autonomous vehicles, transportation network optimization and routing.
▪Privacy and security issues in the fusion of sensitive urban data.
▪Integration of spatial and temporal data for urban sustainability analysis.
Guest editors:
Dr. P.N. Suganthan, PhD
KINDI Center for Computing Research, Qatar University, Doha, Qatar
(randomization-based learning methods, swarm, and
evolutionary algorithms, pattern recognition, deep learning and applications of the swarm, evolutionary & machine learning algorithms)
Dr. Ruobin Gao, PhD
School of Civil and Environmental Engineering, Nanyang Technological University, Singapore
(machine learning, randomized neural networks, time series forecasting, and artificial intelligence for the maritime industry.)
Dr. Ryan Wen Liu, PhD
Hubei Key Laboratory of Inland Shipping Technology, School of Navigation, Wuhan University of Technology, Wuhan, China
(intelligent transportation system, intelligent vehicle, multi-sensor data fusion, computer vision, trajectory data mining)
Manuscript submission information:
The journal's submission platform (Editorial Manager®) will be available for receiving submissions to this Special Issue from October 9th, 2023. Please refer to the Guide for Authors to prepare your manuscript and select the article type of “VSI: MDF for SC” when submitting your manuscript online. Both the Guide for Authors and the submission portal could be found on the Journal Homepage: Information Fusion | Journal | ScienceDirect.com by Elsevier.
Timeline:
Submission Open Date *09/10/2023
Final Manuscript Submission Deadline *03/06/2024
Editorial Acceptance Deadline *01/07/2024
Keywords:
Multimodal data fusion, Smart Cities, Intelligent Transportation, Smart Building, Privacy and Security, Energy Efficiency
以上信息仅供参考，如有意向，可于官网自行投稿！
