High performance hybrid cognitive framework for bio-facial signal fusion processing for the disease diagnosis

Abstract In today’s world, every human’s life is affected by the several numbers of diseases which increases day-by-day due to the unpredicted growth of the pathogens. This leads to the increase in the death rate of the humans in which 70% take place without the proper knowledge of the diagnosis of diseases and care-taking mechanism. Numerous methods have been proposed for the diagnosis of the diseases or predetermination of the diseases. We propose a new method for diagnosing the disease through the fusion of bio-signal and the facial expression codes. The new algorithm which is based on the Cognitive Extreme Learning Machines (CELM) has been implemented for the classification of different facial expressions in accordance with the symptoms of the diseases and relates the results for their diagnosis. Again, the Cognitive Rule Engine has been used for the incorporation for the predetermination and diagnosis. The proposed method has been compared with the existing intelligent learning algorithms and the results are proved to be more accurate in terms of the recognition rate, and training speed.

[1]  R. Meuli,et al.  An evaluation of volume-based morphometry for prediction of mild cognitive impairment and Alzheimer's disease , 2014, NeuroImage: Clinical.

[2]  Rahim Rahmani,et al.  Automatic Emotion Recognition through Facial Expression Analysis in Merged Images Based on an Artificial Neural Network , 2013, 2013 12th Mexican International Conference on Artificial Intelligence.

[3]  Daoqiang Zhang,et al.  Multimodal classification of Alzheimer's disease and mild cognitive impairment , 2011, NeuroImage.

[4]  V. Jaiganesh,et al.  Kernelized Extreme Learning Machine with Levenberg-Marquardt Learning Approach towards Intrusion Detection , 2012 .

[5]  Tongsheng Zhang,et al.  Extreme Learning Machine-Based Classification of ADHD Using Brain Structural MRI Data , 2013, PloS one.

[6]  Chandra Prasetyo Utomo,et al.  Heart disease diagnosis using extreme learning based neural networks , 2014, 2014 International Conference of Advanced Informatics: Concept, Theory and Application (ICAICTA).

[7]  Jun Zhang,et al.  Fault detection and diagnosis method for batch process based on ELM-based fault feature phase identification , 2014, Neural Computing and Applications.

[8]  S. N. Deepa,et al.  Medical Dataset Classification: A Machine Learning Paradigm Integrating Particle Swarm Optimization with Extreme Learning Machine Classifier , 2015, TheScientificWorldJournal.

[9]  Ali Miri,et al.  Using the Extreme Learning Machine (ELM) technique for heart disease diagnosis , 2015, 2015 IEEE Canada International Humanitarian Technology Conference (IHTC2015).

[10]  Gunnar Blohm,et al.  A New Method for EEG-Based Concealed Information Test , 2013, IEEE Transactions on Information Forensics and Security.

[11]  Damilola A. Okuboyejo,et al.  Automating Skin Disease Diagnosis Using Image Classification , 2013 .

[12]  Gang Wang,et al.  An efficient hybrid kernel extreme learning machine approach for early diagnosis of Parkinson's disease , 2016, Neurocomputing.

[13]  Qingyu Yang,et al.  ELM-based adaptive neural estimation for actuator faults detection and diagnosis of nonlinear uncertain systems , 2017, 2017 29th Chinese Control And Decision Conference (CCDC).

[14]  Alejandro Ribeiro,et al.  A Graph Signal Processing Perspective on Functional Brain Imaging , 2018, Proceedings of the IEEE.