<p>Section 1: Cognitive Technology for processing of Healthcare data <br>1. Cognitive technology in personalized Medicine/healthcare solutions<br>2. Cognitive technology for blend of personalized healthcare information with scientific data for better clinical risk analysis and healthcare innovation<br>3. Healthcare data encryption, data processing for the data acquired from smart sensors and approaches</p> <p>Section 2: Artificial Intelligence Approaches for Healthcare Industry<br>4. Artificial Neural Networks based approaches for computer-aided disease diagnosis and treatment<br>5. AI and Deep Learning for processing the huge amount of patient centric data that assists in clinical decisions<br>6. Pattern Recognition and Computer vision approaches for handling healthcare data<br>7. Applications of Recurrent Neural Networks, Generative Neural Networks, Ensemble methods, Weakly Trained Approaches towards Data associated with healthcare solutions</p> <p>Section 3: Evolutionary Algorithms for Healthcare Data Analysis<br>8. Optimization inspired by biological evolution for high dimensional data for forecasting of illness in advance like Cancer, Heart disease, Brain tumors<br>9. Swarm Intelligence and Evolutionary Algorithms in processing the Healthcare Data<br>10. Recent advancements in evolutionary algorithms for handling the information related to healthcare industry</p> <p>Section4: Computational Intelligence and soft computing models in processing the data related to healthcare industry<br>11. Natural computing and Unsupervised Learning Methods in healthcare data-centric operations<br>12. Soft Computing and Machine Learning Techniques for healthcare data analytics<br>13. Probabilistic approaches for minimizing the healthcare diagnosis cost through data-centric operations<br>14. Computational Intelligence in Human-machine interface (HMI) e.g. ECG, EEG, EMG, PCG and predictive data analysis</p>
