Cognitive Big Data Intelligence with a Metaheuristic Approach

A. Foundations and Architectural Models of Cognitive Big Data and Meta heuristics
1. Cognitive Computing fundamentals like perception, memory, reasoning, emotion, and problem solving
2. Cognitive Computing techniques using artificial intelligence, pattern and speech recognition, and natural language processing
3. Cognitive approaches within data mining and machine learning techniques
4. Big Data Infrastructure for Cognition and Distributed Data Centers for Cognition
5. Meta heuristics in classification, clustering and frequent pattern mining problems
6. Nature-inspired computing and Optimization algorithms
7. Meta heuristics and swarm intelligence approach
8. Use of Computational intelligence and Intelligent computing approaches in engineering domains
9. Big Data, Clouds and Internet of Things (IoT)
10. Dimensionality reduction models with Meta heuristics
11. Neuro-evolutionary and fuzzy models in big data and cognitive analytics
12. Innovative methods for cognitive business big data analytics
13. Cognitive techniques for mining unstructured, spatial-temporal, streaming and multimedia data
14. Data-driven large scale optimization architectures
15. Ensemble learning with Meta heuristics optimization

B. Application Domains and use of Cognitive Big data with Meta heuristics
16. Applications in Logistics, Transportation and Supply Chain Management
17. Cognitive Sensor-Networks applications
18. Algorithm development for big data analysis in E-health and Telemedicine
19. Biomedical Image Processing and Big Data Applications
20. Data Applications of Cognitive Communication
21. Intelligent distributed applications in e-commerce
22. Applications in Economics and Finance
23. Applications in Aeronautics
24. Applications in financial analysis
25. Applications in Cyber security and Intelligence
26. Applications in Traffic Optimization
27. Applications in routing of energy efficient communication networks
28. Other Miscellaneous applications

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• Inteligência artificial
• Aprendizagem mecânica

Accuracy rate; Adaptive metaheuristics; Artificial neural network (ANN)Collaborative filtering(CF)K-nearest neighbor (KNN)Multicriteria recommender system (MCRS)Support vector machine (SVM)Artificial neural network (ANN)Feature extrication and classification; Autoencoders; Big data; Cardiac data; Chess; Classification; Client assignment; Clustering; Cognitive big data; Cognitive computing; Cognitive; Combinatorial optimization; Convolutional neural networks; Crime scene; Cryptography; Data security; Deep learning; Deep reinforcement learning; E-Commerce; E-health; e-health; Encryption; Energy-efficient routing; Evolutionary algorithms; Firefly algorithm; Frequent pattern mining; Greedy algorithm; Healthcare sector; Heuristics; Inertia weight; Internet of Things (IoT)Livestock; IoT; K-means clustering; Liquid flow process; Load balancing; Long-term monitoring; Machine learning (ML)Spiking neural network; Makespan; Metaheuristic; Metaheuristics; Modeling; Multiagent system; Neural network; Nonadaptive metaheuristics; Optimization; Particle swarm optimization; PSO; Routing; Self-driving cars; Semantic segmentation; SVM; Swarm intelligence; Task scheduling; Telemedicine; Virtual reality; Wearable device; Wearables; Wireless body area networks