Scope & Topics

by admin last modified May 11, 2017 05:48 PM
Topics of interest include, but are not limited to the following:

Computational Science (CS)

  • Big Data and Data Analytic

Software and hardware architectures; Big Data visualization; Services; Data analytics; toolkits; open platforms; business processes; Managing,analyzing, and using large volumes of structured and/or unstructured data; Simulation and modeling; Consumerization of Big Data; Big Data in social media; Big Data and decision sciences and analytics; Data and text mining; Crowdsourcing; Case studies; and Applications.

  • High Performance Computing and Communication Systems

Cluster computing; Supercomputing; Cloud computing; Autonomic computing; P2P computing; Mobile computing; Grid computing; Parallel/distributed architectures and algorithms; Networks and nterconnection networks; Reliability and fault-tolerance; The use of building block processors; Real-time and embedded systems; Multimedia communications, systems, and applications; Software tools and environments for computational science; Performance analysis, evaluation and monitoring; Wireless networks and distributed systems; FPGA, multicore, GPU, SOC and applications; Nanotechnology in HPC; High-performance mobile computation and communication; Petri Nets; Web-based simulation and computing; Emerging technologies.

  • Image Processing and Computer Vision
Multi-resolution vision techniques; Machine learning technologies for  vision; Active and robot vision; Cognitive and biologically inspired vision; Dimensionality reduction methods in pattern recognition; Classification and clustering techniques; Statistical pattern recognition; Image-based modeling and algorithms; Illumination and reflectance modeling; Motion and tracking algorithms; Biometric authentication; Medical image processing and analysis; Segmentation techniques; Geometric modeling and fractals; Image data structures and databases; Image compression, coding, and encryption; Image feature extraction; Novel document image understanding techniques; Enhancement techniques; Novel noise reduction algorithms; Mathematical morphology; 3D imaging; Watermarking methods and protection; Wavelet methods; Image restoration; Shape representation; Video analysis; Indexing and retrieval of images; Object recognition; and Case studies and applications.
  • Modeling, Simulation and Visualization Methods

Computational modeling and simulation in science and engineering; Molecular modeling and simulation; Simulation languages and tools; Performance modeling; Information and scientific visualization; Modeling methodologies; Visual interactive simulation and modeling; Visualization tools and systems for simulation and modeling; Process, device, circuit simulation and modeling Multi-level modeling; CAD/CAE/CAM; Agent based simulation; Analytical and stochastic modeling techniques and applications; Chaos modeling, control and signal transmission; Simulation of complex systems; Simulation of intelligent systems; Vision and visualization; Prototyping and simulation; Biomedical visualization and applications; Discrete and numeric simulation; Internet, web and security visualization; Virtual reality and simulation; Object oriented and knowledge-based simulation.

  • Information and Knowledge Engineering

Information retrieval systems and databases; Information and knowledge structures; Knowledge management and cyber-learning; Information reliability and security; Knowledge mining; Knowledge classification tools; Knowledge representation and acquisition; Large-scale information processing methods; Intelligent knowledge-based systems; Aspect-oriented programming; Formal and visual specification languages; Decision support and expert systems; Ontology engineering, sharing and reuse; Ontology matching and alignment; Agent-based techniques and systems; Workflow management; Large-scale information processing methods and systems; Database engineering and systems; Data-web models and systems; Data warehousing and datacenters; Data security and privacy issues; Quantum information theory; Natural language processing; Information integration; Domain analysis and modeling.

  • Algorithms and Methods

Monte Carlo methods and applications; Numerical methods and simulation; Quantum computing; Computational number theory; Optimization and approximation methods; Probabilistic and randomized methodologies; Computational geometry; Computational biology; Computational chemistry; Computational fluid dynamics; Computational physics; Computational mechanics; Computational electromagnetics and computational electrodynamics; computational sociology; Splines and wavelets; Inversion problems; Cellular automata; Ordinary and partial differential equations; Stochastic differential equations; Finite element methods; Multi-level and Multi-grid methods; Operational research; Dynamical systems; Nonsymmetric solvers; Engineering problems and emerging applications.


Computational Intelligence (CI)

  • Fuzzy Logic Systems

Fuzzy logic and fuzzy set theory; Computing with words; Neural-fuzzy systems; Fuzzy and rough data analysis; Fuzzy optimization and design; Fuzzy decision making; Systems modeling and identification; Systems architect2ures and hardware; Control and systems; Fuzzy logic applications.

  • Big Data and Data Analytic

Big Data Services; Optimization and data analytics; Machine learning technologies; Knowledge extraction and business processes; Big Data to knowledge mapping; Information engineering; Big Data and decision sciences; Data and information mining; Case studies; and Applications.

    • Neural Networks

Neural network theory and models; Evolutionary neural systems; Collective intelligence; Computational neuroscience; Cognitive models; Neurodynamics; Neuroinformatics; Neuroengineering; Neural hardware; Mathematical modeling of neural systems; Hybrid systems; Self-aware systems; Agent-based systems; Artificial life; and Neural network applications.

    • Evolutionary Computations

Metaheuristic optimization algorithms; Evolutionary algorithms; Genetic algorithms; Evolutionary programming; Evolution strategy; Particle swarm optimization; Ant colony optimization; Artificial immune systems; Differential evolution; Learning classifier systems; Learnable evolution models; Self-organizing maps and competitive learning; Multi-objective evolutionary algorithms; Reinforcement learning; Parallel simulated annealing; Cultural algorithms; Intelligent, bio-inspired and autonomic computing.

  • Artificial Intelligence (AI) 

All aspects of AI as they relate to Computational Intelligence, including: Brain models and cognitive science; Natural language processing; Fuzzy logic and soft computing; Software tools for AI; Expert systems; Decision support systems; Automated problem solving; Knowledge discovery; Knowledge-intensive problem solving techniques; Knowledge networks and management; Intelligent information systems; Intelligent data mining and farming; Intelligent web-based business; Intelligent agents; Intelligent user interface; Intelligent tutoring systems; Reasoning strategies; Distributed AI algorithms and techniques; Heuristic search methods; Languages and programming techniques for AI; Constraint-based reasoning and constraint programming; Intelligent information fusion; Search and meta-heuristics; Multisensor data fusion using neural and fuzzy techniques; Integration of AI with other technologies; Evaluation of AI tools; Social intelligence (markets and computational societies); Social impact of AI; and Satisfiability methods.



Software engineering; Student recruitment and retention methods; Promoting  multi-disciplinary initiatives; curriculum; Capstone research projects; Preparing graduates for academia and industry; Undergraduate research experiences; The balance between course-work and research; Transition to  graduate studies; Debugging tools and learning; Evaluation methods; Advising methods; Learning models and learning from mistakes; Distance learning; Active learning tools; Funding opportunities for curriculum development and studies; Partnerships with industry and government; Collaborative learning; STEM (Science, Technology, Engineering & Mathematics) promising initiatives; Student observation and mentoring strategies; Team projects and case studies; The role of visualization and  animation in education; Academic dishonesty in a high-tech environment; Innovative uses of technology in the classroom; Computer and web-based software for instruction; e-Learning design and methodologies; e-Learning portals; Audio and video technologies for e-Learning; Content management and development; Policy issues in e-Learning; e-Learning standards; Virtual learning environments; Authoring tools; On-demand e-Learning; On-line education; e-Universities; and Case studies.


Applications of Computational Science and Computational Intelligence

Pattern recognition applications; Machine vision; Brain-machine interface; Embodied robotics; Biometrics; Computational biology; Bioinformatics; Image and signal processing; Information mining and forecasting; Sensor networks; Information processing; Internet and multimedia; DNA computing; Machine learning applications; Multi-agent systems applications; Telecommunications; Transportation systems; Intrusion detection and fault diagnosis; Game technologies; Material sciences; Space, weather, climate systems and global changes; Computational ocean and earth sciences; Combustion system simulation; Computational chemistry and biochemistry; Computational physics; Medical applications; Transportation systems and simulations; Structural engineering; Computational electro-magnetic; Computer graphics and multimedia; Face recognition; Semiconductor technology, and electronic circuits and system design; Dynamic systems; Computational finance; Information mining and applications; Astrophysics; Biometric modeling; Geology and geophysics; Nuclear physics; Computational journalism; Computational sociology; Geographical Information Systems (GIS) and remote sensing; Military and defense related applications; Ubiquitous computing; and Emerging applications.