Open Special Issues
Advanced and Sustainable Solutions in Communications, Networking, Computing, and Engineering Systems
Guest Editor: Dr. Osamah Ibrahim Khalaf, Dr. Kingsley A. Ogudo, Dr. Vitaliy Mezhuyev, Dr. Gautam Srivastava, Dr. Meirambek Kazimovich Zhaparov, Dr. Hamed Daei Kasmaei and Dr. Hasan Bulut
Manuscript Due: November 15, 2019
Publication Date: February 15, 2020
View Call for Papers | Submit your Article
With the rapid advent of various expanding research on wireless mobile networks, such as MANETs, VANETs, CCNs, WSNs, and IoT etc., more and more smart mobile devices (e.g. smart phones, smart-watches, smart-glasses, personal wearable communication devices and wearable healthcare devices, etc.) have been designed to adapt to the development of wireless mobile networks, which indicates that we are entering an era of smart society. By leveraging the wireless mobile networks, smart mobile devices can communicate with each other and exchange information to perform the optimal control or display necessary information anytime and anywhere. Although there have been large numbers of research efforts relevant to this area, it is still necessary to address many challenges of wireless mobile networks by utilizing ongoing works. On one hand, the efficient resource utilization in terms of energy consumption, radio resource allocation, routing selection etc., is still a big challenge in wireless mobile networks. On the other hand, the deployment of exiting wireless mobile networks lacks appropriate monitoring, response mechanisms and cognitive ability etc. Therefore, building "advanced" wireless mobile networks with the features of intelligent and cooperative communication, cross-layer optimization, and security-guarantee etc. has become an utmost important and urgent task to be solved. The aim of this special issue is to motivate researchers to publish their latest research, up-to-date issues, and challenges in the field of wireless mobile networks. Proposed submissions should be original and unpublished. Potential topics include, but are not limited to:
- Artificial Intelligence
- Computer and Information Science
- Content centric networking
- Context and location-aware wireless mobile services & applications
- Cross-layer design and optimization for wireless mobile networks
- Cryptography and Information Security
- Data storage, data centers and cloud computing in wireless mobile networks
- Earth Sciences, Electrical and Electronic Engineering
- Energy-saving and QoE-oriented applications
- Engineering and Environmental Sciences
- High performance network virtualization
- Image Processing and Computer Vision
- Internet of Things
- Materials Science
- Mobility management and modeling in wireless mobile networks
- Multipath communication over wireless mobile networks
- Numerical Analysis Applications in Engineering
- Optimization and Computational Aspects of High Performance Electrical Engineering Systems
- Production and Industrial Engineering
- Robotic ,system engineering and control
- Sustainable development in engineering
- Trust, security, and privacy in wireless mobile networks
- Wireless & mobile network management and data services
- Wireless applications, mobile e-commerce, multimedia
- Wireless network architectures
- Wireless Sensor and Actor Networks, Ad hoc and Opportunistic Networks, Vehicular Networks
To deal with these challenges, we need to develop strong communities that work across industries and with multiple stakeholders to address these issues. Hence, having an understanding of energy issues in community and regional levels would pave the way to a more comprehensive solution globally.
|Dr. Osamah Ibrahim Khalaf||Senior Lecturer, Al-Nahrain University, Iraq|
|Dr. Kingsley A. Ogudo||Senior Lecturer, University of Johannesburg, South Africa|
|Dr. Vitaliy Mezhuyev||Professor, University Malaysia Pahang, Malaysia|
|Dr. Gautam Srivastava||Associate Professor, Brandon University, Canada|
|Dr. Meirambek Kazimovich Zhaparov||Assistant Professor, Suleyman Demirel University, Kazakhstan|
|Dr. Hamed Daei Kasmaei||Researcher, Islamic Azad University, Iran|
|Dr. Hasan Bulut||Professor, Firat University, Turkey|
|Manuscript Submission Deadline||November 15, 2019|
|Review Completed by||January 15, 2020|
|Possible Publication Date||February 15, 2020|
Computer Vision and Data Science for Engineering Applications
Guest Editor: Dr. B. Santhi and Dr. G. R. Brindha
Manuscript Due: July 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||July 15, 2019|
|First Review Round Complete By||August 25, 2019|
|Possible Publication Date||September 15, 2019|
Application of Optimization Algorithms in Engineering Manufacturing
Guest Editor: Gerhard-Wilhelm Weber, Erfan Babaee Tirkolaee and Alireza Goli
Manuscript Due: August 31, 2019
Publication Date: November 30, 2019
View Call for Papers | Submit your Article
Optimization algorithms, especially metaheuristics, are one of the most important emerging technologies of recent times for optimization in engineering manufacturing. Over the last years, there has been exponential growth of research activity in this field. Despite the fact that metaheuristics itself has not been precisely defined, it has become a standard term that encompasses several stochastic, population-based, and system-inspired approaches.
Metaheuristic methods use as inspiration our scientific understanding of biological, natural, or social systems, which at some level of abstraction can be represented as optimization processes. They intend to serve as general-purpose easy-to-use optimization techniques capable of reaching globally optimal or at least nearly optimal solutions. 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.
All accepted papers of this special issue will be published free of charge.
In this special issue, we plan to investigate the applicability of the novel nature-inspired metaheuristic algorithms to generate high-quality solutions for optimization problems.
Topics of interest include, but are not limited to:
- Novel models in the field of engineering manufacturing
- Application of optimization algorithms in supply chain design
- Neural networks and deep learning
- Internet of things and sustainability issues in engineering problems
- 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 (MCDA) algorithms
- 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||August 31, 2019|
|First Round of Review||September 30, 2019|
|Possible Publication Date||November 30, 2019|