Ning Xiong, Professor


Ning Xiong is Professor of  Artificial Intelligence. He obtained the Ph.D with Excellent distinction from the University of Kaiserslautern (Germany) in 2000. His research addresses various aspects of computational intelligence techniques, incuding machine learning and big data analytics, evolutionary computing, fuzzy systems, uncertainty management, as well as multi-sensor data fusion, for building self-learning and adaptive systems in industrial and medical domains. He is serving as editorial board members for three international journals. He also has been invited keynote speakers, program committee chairs for internastional conferences and guest editors of special issues for leading  journals.

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Latest publications:

Smart Case Mining based on Membrane Clustering (Dec 2019)
Johan Holmberg, Ning Xiong
IEEE Symposium Series on Computational Intelligence 2019 (ISSCI19)

A Novel Memetic Framework for Enhancing Differential Evolution Algorithms via Combination with Alopex Local Search (Aug 2019)
Miguel Leon Ortiz, Ning Xiong, Francisco Herrera, Daniel Molina
International Journal of Computational Intelligence Systems (IJCIS19)

Online feature selection via deep reconstruction network (Jul 2019)
Johan Holmberg, Ning Xiong
International Conference on Harmony Search, Soft Computing and Applications 2019 (ICHSA19)

A Deep Neural Network Model for Music Genre Recognition (Jul 2019)
Martin Suero, Carsten Paul Gassen, Dimitrije Mitic, Ning Xiong, Miguel Leon Ortiz
International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD19)

Road Boundary Detection Using Ant Colony Optimization Algorithm (Jul 2019)
Tim Andersson , August Kihiberg, Anton Sundström , Ning Xiong
International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD19)

Feature Selection of EEG Oscillatory Activity Related to Motor Imagery Using a Hierarchical Genetic Algorithm (Jun 2019)
Miguel Leon Ortiz, Joaquin Ballesteros, Jonatan Tidare, Ning Xiong, Elaine Åstrand
IEEE Congress on Evolutionary Computation (IEEE CEC'19)

PhD students supervised as main supervisor:

Johan Holmberg
Jonatan Tidare
Miguel Leon Ortiz

PhD students supervised as assistant supervisor:

Ella Olsson (former)
Per Hellström
Shahina Begum (former)
Tomas Olsson (former)

MSc theses supervised (or examined):
Thesis TitleStatus
available
Data Stream Mining with the PRAAG change detection algorithm available
Developing Simulation Models of Data Center Infrastructure available
Dynamic modelling of ship behavior using recurrent neural networks available
Fitness approximation in expensive optimization problems available
Similarity learning in case-based reasoning available
USING DOMAIN KNOWLEDGE FUNCTIONS TO ACCOUNT FOR HETEROGENEOUS CONTEXT FOR TASKS IN DECISION SUPPORT SYSTEM FOR PLANNING selected
Autonomous robot collecting waste bins in an office environment in progress
Design of an Active Boom Suspension System in a Hybrid Wheel Loader in progress
Evolutionary computation in continuous optimisation and machine learning in progress
INTELLIGENT MATCHING FOR CLINICAL DECISION SUPPORT SYSTEM FOR CEREBRAL PALSY USING DOMAIN KNOWLEDGE in progress
Intelligent orange-picking robot in progress
The Influence of Bitcoin on Ethereum Price Predictions in progress
Using ant colony optimization as pathfinding in a changing environment in progress
Using AI and Statistics on Structured Electronic Patient Records for Clinical Decision Support Systems finished
Case-based approach for process modeling finished
Clinical Decision Support System for Cerebral Palsy finished
Combining different feature weighting methods for case-based reasoning finished
Enhancing the human-team awareness of a robot finished
Evaluation of grasp-and-extend hand dynamics and intelligent modeling of grasp hand dynamics finished
Generating Fuzzy Rules from Case Base for Classification Problems finished
Monitoring system for free form modeling machines at Digital Mechanics. finished
NOISY BIG DATA CLASSIFICATION USING MAPREDUCE DISTRIBUTED FUZZY RANDOM FOREST finished
OBJECT RECOGNITION THROUGH DEEP CONVOLUTIONAL LEARNING FOR FPGA finished
Ocean Waves Estimation finished
Real-time Process Modelling Based on Big Data Stream Learning finished