Call for Papers
IACC, 2018 invites original contributions on all topics related to advance computing. Specific tracks of the conference are given below:
Track 1: High Performance Computing

Algorithms: 

  1. Algorithmic Techniques to Improve Energy and Power Efficiency 
  2. Quantum and Bio-Inspired Algorithms
  3. Resilient and Fault Tolerant Algorithms 
  4. Parallel Algorithms for Numerical Linear Algebra
  5. Concurrent Algorithms and Data Structures
  6. Load Balancing, Scheduling and Resource Management
  7. Large Scale Graph Analytics
  8. Streaming Algorithms

Architectures:

  1. Interconnection Networks and Architectures
  2. Cache/Memory Architecture for High Performance Computing
  3. High Performance/Scalable Storage Systems
  4. Power-Efficient and Reconfigurable Architectures
  5. Quantum and Bio-Inspired Architectures
  6. Software Support and Advanced Micro-architecture Techniques
  7. Resilient and Fault Tolerant Architectures

  Applications:

  1. Big Data Computing and Applications
  2. Cross-Cutting Methods such as Co-Design of Parallel Algorithms, Software, and Architectures
  3. Emerging Applications such as Biotechnology, IoT, and Nanotechnology
  4. Hardware Acceleration for Parallel Applications
  5. Parallelism in Scientific Data Visualization and Visual Analytics
  6. Scientific/Engineering/Industrial Applications and Workloads
  7. Scalable Graph and other Irregular Applications 
  8. Design of Simulation Applications and Peta- and Exascale Applications

Systems Software:

  1. Big Data Analytics Systems and Software Architectures
  2. Compiler Technologies for High-Performance Computing
  3. Exascale Computing, Cloud Platforms, Data Center Architectures and Services
  4. Parallel Languages, Programming Environments and Performance Assessment
  5. Operating Systems for Scalable High Performance Computing
  6. Hybrid Parallel Programming with GPUs and Accelerators
  7. Dealing with Uncertainties, Resilient/Fault-Tolerant Systems
Track 2: Machine Learning

Model Selection:

  1. Learning using Ensemble and Boosting Strategies
  2. Active Machine Learning
  3. Manifold Learning
  4. Fuzzy Learning
  5. Kernel Based Learning
  6. Genetic Learning
  7. Hybrid Models

Evolutionary Parameter Estimation:

  1. Fuzzy Approaches to Parameter Estimation
  2. Genetic Optimization
  3. Bayesian Estimation Approaches
  4. Boosting Approaches to Transfer Learning
  5. Heterogeneous Information Networks
  6. Recurrent Neural Networks
  7. Influence Maximization
  8. Co-evolution of time sequences

Graphs and Social Networks:

  1. Social Group Evolution - Dynamic Modelling
  2. Adaptive and Dynamic Shrinking
  3. Pattern Summarization
  4. Graph Embeddings
  5. Graph Mining Methods
  6. Structure Preserving Embedding
  7. Non-Parametric
  8. Models for Sparse Networks
  9. Forecasting
  10. Nested Multi-Instance Learning

Large Scale Machine Learning:

  1. Large Scale Item Categorization
  2. Machine Learning over the Cloud
  3. Anomaly Detection in Streaming Heterogeneous Datasets
  4. Signal Analysis
  5. Learning Paradigms
  6. Clustering, Classification and Regression Methods
  7. Supervised, Semi-Supervised and Unsupervised Learning
  8. Algebra, Calculus, Matrix and Tensor Methods in Context of Machine Learning
  9. Reinforcement Learning
  10. Optimization Methods
  11. Parallel and Distributed Learning
Track 3: Deep Learning
  1. Inference Dependencies on Multi-Layered Networks
  2. Recurrent Neural Networks and its Applications
  3. Tensor Learning
  4. Higher Order Tensors
  5. Graph Wavelets
  6. Spectral Graph Theory
  7. Self Organizing Networks
  8. Multi-Scale Learning
  9. Unsupervised Feature Learning
  10. Recommender Systems
  11. Automated Response
  12. Conversational Recommender Systems
  13. Collaborative Deep Learning
  14. Trust Aware Collaborative Learning
  15. Cold-Start Recommendation Systems
  16. Multi-Contextual Behaviours of Users
  17. Applications
  18. Bioinformatics and Biomedical Informatics
  19. Healthcare and Clinical Decision Support
  20. Collaborative Filtering
  21. Computer Vision
  22. Human Activity Recognition
  23. Information Retrieval
  24. Cybersecurity
  25. Natural Language Processing
  26. Web Search
  27. Evaluation of Learning Systems
  28. Computational Learning Theory
  29. Experimental Evaluation
  30. Knowledge Refinement and Feedback Control
  31. Scalability Analysis
  32. Statistical Learning Theory
  33. Computational Metrics
Track 4: Data Science

Foundations:

  1. Mathematical, Probabilistic and Statistical Models and Theories
  2. Machine Learning Theories, Models and Systems
  3. Knowledge Discovery Theories, Models and Systems
  4. Manifold and Metric Learning
  5. Deep Learning and Deep Analytics
  6. Scalable Analysis and Learning
  7. Heterogeneous Data/Information Integration
  8. Data Pre-Processing, Sampling and Reduction
  9. Dimensionality Reduction
  10. Feature Selection, Transformation and Construction
  11. Large Scale Optimization
  12. High Performance Computing for Data Analytics
  13. Architecture, Management and Process for Data Science
  14. Data Analytics, Machine Learning and Knowledge Discovery

Learning for Streaming Data

  1. Learning for Structured and Relational Data
  2. Latent Semantics and Insight Learning
  3. Mining Multi-Source and Mixed-Source Information
  4. Mixed-Type and Structure Data Analytics
  5. Cross-Media Data Analytics
  6. Big Data Visualization, Modeling and Analytics
  7. Multimedia/Stream/Text/Visual Analytics
  8. Relation, Coupling, Link and Graph Mining
  9. Personalization Analytics and Learning
  10. Web/Online/Social/Network Mining and Learning
  11. Structure/Group/Community/Network Mining
  12. Cloud Computing and Service Data Analysis

Management, Storage, Retrieval and Search

  1. Cloud Architectures and Cloud Computing
  2. Data Warehouses and Large-Scale Databases
  3. Memory, Disk and Cloud-based Storage and Analytics
  4. Distributed Computing and Parallel Processing
  5. High Performance Computing and Processing
  6. Information and Knowledge Retrieval, and Semantic Search
  7. Web/Social/Databases Query and Search
  8. Personalized Search and Recommendation
  9. Human-Machine Interaction and Interfaces
  10. Crowdsourcing and Collective Intelligence

Social Issues

  1. Data Science Meets Social Science
  2. Security, Trust and Risk in Big Data
  3. Data Integrity, Matching and Sharing
  4. Privacy and Protection Standards and Policies
  5. Privacy Preserving Big Data Access/Analytics
  6. Social Impact and Social Good
Track 5: Algorithms

Sequential, Parallel and Distributed Algorithms and Data Structures

  1. Approximation and Randomized Algorithms
  2. Graph Algorithms and Graph Drawing
  3. On-Line and Streaming Algorithms
  4. Analysis of Algorithms and Computational Complexity
  5. Algorithm Engineering
  6. Web Algorithms
  7. Exact and Parameterized Computation
  8. Algorithmic Game Theory
  9. Computational Biology
  10. Foundations of Communication Networks
  11. Computational Geometry
  12. Discrete Optimization
Track 6: Advances in Computing

Advances in Communications and Networking

  1. Adhoc Networks
  2. Network Security
  3. Social Media and Networking
  4. Wireless communications
  5. Sensor Networks
  6. Internet of things
  7. Smart sensors and MEMS
  8. RF and Microwave Communications

Circuits and Systems in Computing

  1. Embedded Computing
  2. Micro and Nano Electronics
  3. Mixed-Signal SoC Applications
  4. Distribution System Planning
  5. Reliability for System Planning, Operation, Control and protection
  6. Electrical System Modeling and Simulation
  7. Transients, Propagation, Measurement and Modeling
  8. Smart Grid
  9. Advanced Control Systems
  10. Intelligent Instrumentation
  11. Process Control
  12. System Analytics
  13. Virtual Instrumentation
  14. Micro Grid

Signal, Image and Multimedia Processing

  1. Statistical Learning and Pattern Recognition
  2. Advanced Signal Processing
  3. Multimedia Signal Processing
  4. Multi-Core Processing
  5. Image and Video Processing
  6. Audio and Speech Processing
  7. Biomedical Signal Processing
  8. Signal Processing of Applications of Power Electronics and Drives
  9. Power Quality

Databases and Data Management

  1. Clustering
  2. Databases and Data Mining Applications
  3. Database Tuning
  4. Distributed Databases
  5. Feature Selection and Feature Extraction
  6. High Performance Data Mining Algorithms
  7. Information Retrieval
  8. Knowledge Discovery in Database
  9. Knowledge Management
  10. Query Optimization
  11. Search Engine Optimization
  12. Data Mining

Software Engineering

  1. Verification and Validation
  2. Software Construction
  3. Testing Techniques
  4. Process Models
  5. Software Reuse
  6. Software Repositories
  7. Software Metrics
  8. Software Project Management
  9. Component/Aspect Based Software Engineering
  10. Knowledge Based Software Engineering
  11. Other Advance topics of Software Engineering

This is a blind peer-reviewed conference. Authors are cordially invited to submit papers through on line paper submission process (Easy Chair submission system) before 28 May, 2018.

Instructions For Authors

Authors are requested to submit their file in the format specified in the IEEE Paper Template.
Prospective authors are invited to submit original technical papers for publication in the IACC 2018.

Important: IEEE Policy Announcement The IEEE reserves the right to exclude a paper from distribution after the conference (including its removal from IEEE Xplore) if the paper is not presented at the conference.

Papers are reviewed on the basis that they do not contain plagiarized material and have not been submitted to any other conference at the same time (double submission). These matters are taken very seriously and the IEEE will take action against any author who engages in either practice.

Follow these links to learn more:

An author of an accepted paper is required to register for the conference and present the paper at the conference. All accepted papers will be submitted in IEEE Xplore for consideration. Non-refundable registration fees must be paid prior to the due date of registration. For authors with multiple accepted papers, one registration for each paper is required.

Paper Submission :

Prospective authors are invited to submit papers of five (5) to eight (8) A4 pages (including tables, figures and references) in standard IEEE double-column format (it is absolutely necessary to respect the Styleguide for Papers). A blind peer-review process will be used to evaluate all submitted papers. Each full registration for the conference will cover a maximum of one paper; each student registration will cover a single paper only. Extra paper, 2nd paper and onwards, must be registered separately.

The format instructions in the template must be followed, it is notably important to use the right paper format: A4 to have the right margins not to use page numbering (page footer must be empty)

The IEEE Citation Reference may help you with the references in your paper. Get the list of IEEE recommended keywords from https://www.ieee.org/documents/taxonomy_v101.pdf or send an empty e-mail to keywords@ieee.org with "IEEE Keywords" in the subject line.

All submissions should be written in English with a maximum paper length of eight (8) printed pages including figures, without incurring additional page charges. One (1) additional page is allowed with a charge of USD 20, if accepted.

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