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Journal of Artificial Intelligence Practice

ISSN 2371-8315(Print)

ISSN 2371-8412(Online)

Aims & Scope

The journal promotes and expedites the dissemination of new research results.There is an exciting and large volume of research activity in the field worldwide.The goal of this journal is to provide a platform for academicians and scientists all over the world to share, promote, and discuss various new issues and developments in different areas of artificial Intelligence.

All manuscripts must be prepared in English and are subject to a rigorous and fair peer-review process. Generally, accepted papers will appear online within 3 weeks followed by printed hard copy. The journal publishes original papers including but not limited to the following fields:

1. AI and Decision Systems

Decision Guidance and Support Systems
Optimization-based Recommender Systems
Group, Distributed and Collaborative Decisions
Crowd-Sourcing and Collective Intelligence Decision Making
Strategic, Tactical and Operational Level Decisions
Decision Making in Social and Mobile Networks

2. Multimodal Communicative Signals: Behavioural and Algorithmic Issues

Verbal and Nonverbal Social Communicative Signals
Trustful Human-Human and Human-Machine Exchanges
Emotionally and Socially believable robots and ICT Interfaces
Recognition, Synthesis, and Understanding of Human Interactions
Context and Cultural Effects on Perception, Memory, and Cognition
Multimodal Signal Analysis and Processing
Affective Systems and Related Applications
Learning Algorithms in Social Robotics and ICT Interfaces
Analysis and Processing of Human Daily Cognitive Activities

3. Distributed Intelligence

Multi-Agent Systems
Data Fusion
Robust Decision Making

4. AI in Data Analytics and Big Data

Visualization Analytics for Big Data
Computational Modeling for Big Data
Large-Scale Recommendation and Social Media Systems
Cloud/Grid/Stream Data Mining for Big Velocity Data
Semantic-based Big Data Mining

5. Uncertainty in AI

Uncertainty and Fuzzineess Representation and Reasoning
Approximate/Exact Probabilistic Inference
Knowledge Discovery and Data Mining for Uncertain Data

6. Machine Learning and Data Mining

Data Pre-processing, Reduction, and Feature Selection
Learning Graphical Models and Complex Networks
Active, Cost-Sensitive, Semi-Supervised, Multi-Instance, Multi-Label, and Multi-Task Learning
Transfer/Adaptive, Rational and Structured Learning
Preference/Ranking, Ensemble, and Reinforcement Learning

7. Knowledge Representation, Reasoning and Cognitive Modeling

Knowledge Representation and Reasoning
Knowledge Extraction, Management and Sharing
Case-based Reasoning and Knowledge-based Systems
Cognitive Modeling and Semantic Web

8. AI Foundations

Evolutionary Computing, Bayesian and Neural Networks
Decision/Utiliity Theory and Decision Optimization
Search, SAT and CSP
Description Logic and Ontologies

9. AI in Domain Specific Applications

AI in Natural Langauage Processing and Understanding
AI in Computational Biology, Medicine and Biomedical Applications
AI in WWW, Communication, Social Networking, Recommender Systems, Games and E-Commerce
AI in Finance and Risk Management

10. AI in Computer Systems

AI in Robotics, Computer Vision and Games
AI in Software Engineering, Real-Time and Embedded Applications and Sensor Networks
AI in Coud Computing, Data-Intensive Applications, and Online/Streaming and Multimedia Systems
AI in Web Search and Information Retrieval
AI in Computer Security, Data Privacy, and Information Assurance

11. Applied Artificial Intelligence

Intelligent Signal Processing
Intelligent Control Applications
Application to Inverse Problems
Application to Smart Grids and Smart Cities

12. The applications of Meta-heuristic algorithms for optimization problems in electrical engineering, power system, energy, etc.

13. Improvement of efficiency of new energy such as wind, solar, etc.

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