Calls for papers

 

International Journal of Swarm Intelligence

 

ICEODS-2019: Special Issue on: "Recent Advances in Engineering Optimisation and Data Science for Sustainable Future Development"

Guest Editors:
Dr. Akash Saxena, Swami Keshvanand Institute of Technology, Management & Gramothan, India
Dr. Rajesh Kumar, Malaviya National Institute of Technology, India, India
Dr. Ameena Al Sumaiti, Khalifa University, UAE

Artificial Intelligence (AI) is a prominent area of research. A wide effect of AI based paradigms is visible in finance, marketing, medicine, service sector, power sector, human resource management, etc. For example, AI driven web-browsing system learns your web-browsing pattern and suggests you the items of your choice.

Today, major decision-making tools are data driven. One can think of decisions during a complex medical investigation, early stage detection of tumours, detection of cancerous cells to decide coarse of action during treatment as data driven examples. Another field is forecasting that can be market driven as in the case of market forecasting of stock prices towards profit-making decisions from volatile market conditions and forecasting is also essential in the power sector (e.g. renewable energy f generation and consumer demand) for strategic decisions on resources scheduling and management.

Sometimes, data driven paradigms are based on optimization algorithms. Optimization methods and development of robust optimization paradigms with reference to special applications of machine learning and some real world challenging problems are potential areas of research. With this outset, this issue will emerge as a platform to share ideas, views experimental findings and arguments to make the society sustainable and hassle free.

This special issue is a place to exhibit recent advancements and research findings in the field of data science, methods of data accumulation, classification, text analysis, segregation, segmentation, filtration and application of data for high computing and intelligent decision-making. Along with this opportunity, this call allows submission of articles on data based applications incorporating several optimization methods for selection of optimal features for classification, forecasting and other supervised architectures purposes. More specifically, the following application areas will be included in the issues:

• Foundations, algorithms, models and theory of data science, including big data mining
• Machine learning and statistical methods for big data
• Machine learning algorithms and models of neural networks and learning Systems
• Convolutional neural networks and applications
• Unsupervised, semi-supervised, and supervised learning
• Applications in social sciences, physical sciences, engineering, life sciences, web, marketing, finance, precision medicine, health informatics, medicine and other domains
• Optimization algorithms for real world applications, optimization for big data applications
• Knowledge discovery, and representation learning for planning and reinforcement learning
• Metric learning and kernel learning, sparse coding and dimensionality expansion, hierarchical models
• Multi-objective optimization, optimization and game theory, surrogate-assisted optimization, and derivative-free optimization
• Big data Mining from heterogeneous data sources, including text, semi- structured, spatio-temporal, streaming, graph, web, and multimedia data
• Big Data mining systems and platforms, and their efficiency, scalability, security and privacy
• Optimization for representation learning. Optimization under Uncertainty

Subject Coverage
Suitable topics include, but are not limited, to the following:

• Foundations, algorithms, models and theory of data science, including big data mining.
• Memetic algorithms and applications
• Machine Learning algorithms and models. Neural Networks and Learning Systems. Convolutional neural networks
• Unsupervised, semi-supervised, and supervised Learning
• Applications in social sciences, physical sciences, engineering, life sciences, web, marketing, finance, precision medicine, health informatics, medicine and other domains
• Optimization algorithms for Real World Applications. Optimization for Big Data. Optimization and applications
• Knowledge Discovery. Learning Representations. Representation learning for planning and reinforcement learning
• Metric learning and kernel learning. Sparse coding and dimensionality expansion. Hierarchical models. Learning representations of outputs or states
• Multi-objective optimization. Optimization and Game Theory. Surrogate-assisted Optimization. Derivative-free Optimization
• Big data Mining from heterogeneous data sources, including text, semi-structured, spatio-temporal, streaming, graph, web, and multimedia data
• Big Data mining systems and platforms, and their efficiency, scalability, security and privacy

Notes for Prospective Authors

Submitted papers should not have been previously published nor be currently under consideration for publication elsewhere. (N.B. Conference papers may only be submitted if the paper has been completely re-written and if appropriate written permissions have been obtained from any copyright holders of the original paper).

All papers are refereed through a peer review process.

All papers must be submitted online. To submit a paper, please read our Submitting articles page.

Important Dates

Manuscripts due by: 5 November, 2019

Notification to authors: 10 January, 2020

Final versions due by: 11 March, 2020