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

• Published in: Measurement : journal of the International Measurement Confederation Vol. 140; pp. 89 - 99
• Main Authors: Buvaneswari, B., Kalpa Latha Reddy, T.
• Format: Journal Article
• Language: English
• Published: London Elsevier Ltd 01.07.2019; Elsevier Science Ltd
• Subjects: Algorithms; Artificial neural networks; CELM; Cognitive Rule Engine; Diagnosis; Disease; Diseases; Facial expressions; Intelligent; Machine learning; Medical diagnosis; Pathogens; Signal processing; Signs and symptoms; Cognitive Rule Engine; Diagnosis; Facial expressions; CELM; Intelligent
• ISSN: 0263-2241; 1873-412X
• DOI: 10.1016/j.measurement.2019.02.041

•Algorithm which is based on CELM has been implemented for the classification of different facial expressions.•(SRPSO) algorithm with the extreme learning machine (ELM) for classification issues.•FISH algorithm has been designed for self diagnosing the two important diseases, Parkinson and the Epilepsy.•The extreme learning machine (ELM) is the most important and emerging learning technique. 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.