Open Special Issues
Computer Vision and Data Science for Engineering Applications
Guest Editor: Dr. B. Santhi and Dr. G. R. Brindha
Manuscript Due: June 15, 2019
Publication Date: September 15, 2019
View Call for Papers | Submit your Article
Machine learning (ML) and Deep Learning (DP) are having significant impact in the research area. As the need for real time automatic learning increases, the methods are being finetuned and the research is growing vastly. The applications range from Finance to Webpage, Networking to Biological Analysis and Marketing to IoT. Self learning and automating the processes are the strength of the ML and DP. The analytical learning model offer constant learning by adaptive techniques for the given data. The objective of ML and DP is to generate reliable information that can be executed with less human intervention.
The generalization concept of ML includes successful real time applications such as sales data analysis, prediction of customer behaviour, Spam identification, credit card fraud detection, optimizing robot navigation, extraction of information from biomedical samples, Image, video, signal processing and the list goes on long which includes almost all domains.
The main scope of this special issue is data reasoning and inference, ML and DP techniques to employ self learning and automate engineering applications. In turn this special issue focus to encourage discussion and analysis of research and learning activities in the models and design of engineering applications such as Image processing, Sensor based learning, Biomedical analysis, electrical, mechanical and civil. We specially promote innovative and enhanced ML & DP techniques to tackle the real time applications which are complex in both data & process.
|Dr. B. Santhi||Associate Dean, SASTRA Deemed University, India|
|Dr. G. R. Brindha||Associate Professor, SASTRA Deemed University, India|
|Manuscript Submission Deadline||June 15, 2019|
|First Review Round Complete By||July 25, 2019|
|Possible Publication Date||September 15, 2019|
Application of nature-inspired algorithms to solve Vehicle Routing Problems (VRPs)
Guest Editor: Gerhard-Wilhelm Weber, Erfan Babaee Tirkolaee and Alireza Goli
Manuscript Due: July 31, 2019
Publication Date: November 30, 2019
View Call for Papers | Submit your Article
Vehicle Routing Issue (VRP) is a popular optimization problem with vast practical aspects in the real-life such as industrial transportations, urban waste collection, milk distribution, etc. The main aim of VRP is to determine a set of covering routes to serve a given number of customers where the total cost is minimized. Nowadays, many variants of VRP have been proposed and investigated by researchers according to different real-world applications and limitations. Since it belongs to the category of NP-Hard problems, the role of computer science including heuristic/metaheuristic algorithms development becomes is highlighted.
In recent years, one of the most successful approximation methods has been "nature-inspired metaheuristic algorithms" through studying the social or natural behavior of creatures. Their applications are based on continuous inventive techniques to yield appropriate and near-optimal results.
For example, Genetic Algorithm (GA) is inspired by the reproduction and evolution of human being. Particle Swarm Optimization (PSO) algorithm is inspired by the collective movement of birds as well as Ant Colony Optimization (ACO) algorithm which was designed according to the collective behavior of ants. Among the recently proposed metaheuristics, Grey Wolf Optimization (GWO), Invasive Weed Optimization (IWO), and Runner-Root (RR) algorithms can be mentioned.
In this special issue, we plan to investigate the applicability of the novel nature-inspired metaheuristic algorithms to generate high-quality solutions for VRPs.
All accepted papers of this special issue will be published free of charge.
Topics of interest include, but are not limited to:
- Novel VRP models
- Sustainable transportation with VRP models
- Internet of things in transportation and logistics based on VRPs
- Application of computer science to solve VRPS
- Single-objective and multi-objective nature-based heuristics/metaheuristics
- Exact solution methods such as Benders decomposition and Lagrangian relaxation
- Goal Programming, ε-constraint and lexicographic methods
- Multiple Criteria Decision Aiding
- Uncertainty approaches including fuzzy theory, robust optimization, grey systems, etc.
|Gerhard-Wilhelm Weber||Professor, Poznan University of Technology, Poznan, Poland|
|Erfan Babaee Tirkolaee||Researcher, Mazandaran University of Science and Technology, Iran|
|Alireza Goli||Researcher, Yazd University, Iran|
|Manuscript Submission Deadline||July 31, 2019|
|First Round of Review||September 30, 2019|
|Possible Publication Date||November 30, 2019|