![]() ![]() 175+ Highly Indexed Journals to Choose From (Gold, Platinum, & Retrospective OA Opportunities).Institution Level OA Agreements Available (Recommend or Contact Your Librarian for Details).Publications Adhere to All Current OA Mandates and Compliances.Affordable APCs (Often 50% Lower Than the Industry Average) Including Robust Editorial Service Provisions.Double-Blind Peer Review by Notable Editorial Boards ( Committee on Publication Ethics (COPE) Certified).Easily Track Your Work in Our Advanced Manuscript Submission System with Rapid Turnaround Times.Join a Diverse Community of 150,000+ Researchers Worldwide Publishing with IGI Global.When you publish under the OA model with IGI Global, you enable your work to be viewed by millions of readers worldwide immediately after publication and you are able to experience our personal support and commitment to editorial service. Web database and Web-based information systems.Unified modeling language and unified process.Service-oriented architecture/service-oriented computing.Object-oriented methods and methodologies.Intelligent agents and agent-based applications.Human-computer interaction and user experience.Enterprise systems and supply chain integration.E-business, m-commerce, and social-commerce models and architectures. ![]() Topics of interest to the journal include, but are not limited to, the following areas: Authors are welcome to submit manuscripts that qualify for any of the three categories. Research reviews are insightful and carefully crafted articles that conceptualize research areas, synthesize previous innovative findings, advance the understanding of the field, and identify and develop future research directions. Research notes are novel and complete but not as comprehensive as full research articles they include exploratory studies and methodological articles. ![]() Research articles are full innovative findings that make substantial theoretical and empirical contributions to knowledge in the field by using various theoretical and methodological approaches. The Journal of Database Management (JDM) publishes three types of rigorous and high quality articles: research articles, research notes, and research reviews. Experiments on AIS data show that our method is effective in classifying vessel encounter situations to provide decision support for collision avoidance. All motion features and sailing segment labels are combined as input to one trajectory similarity matching method based on convolutional neural network to recognize crossing, overtaking or head-on situations for each potential encountering vessel pair, which may lead to collision if false actions are adopted. With statistical analysis of vessel trajectories, sailing segment labels will be added to the input feature. To ensure consistent features extracted from the trajectories in the same time period, time alignment is also adopted. Potential encountering trajectory pairs will be recorded based on the candidate meeting vessel searching algorithm. Here, the AIS database is created based on the raw AIS data after parsing, noise reduction and dynamic Ramer-Douglas-Peucker compression. How to extract a collection of trajectories for different vessels from the raw AIS data to discover vessel meeting knowledge is a heavily studied focus. It performs well to solve vehicle routing problems. The performance test shows that it overcomes the defect of slow convergence compared with other five algorithms. The negative value of the maximum entropy and the shortest total path length of the vehicle are selected as the fitness. The frog individuals gather near the origin with the maximum probability and in the area circle, with the frog leaping radius or frog-oriented radius, as the neighborhood. A novel framework of algorithm is proposed to solve capacity-limited vehicle routing problem, including three modules such as origin oriented shuffled frog leaping algorithm strategy, origin oriented shuffled frog leaping vehicle routing multiobjective optimization algorithm strategy, and output module. However, the optimization performance is limited with improvement strategies in major of the improvement algorithm. Shuffled frog leaping algorithm is a biological swarm intelligent optimization algorithm and improved into capacity-limited vehicle routing problem. ![]()
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