Cascaded PFLANN Model for Intelligent Health Informatics in Detection of Respiratory Diseases from Speech Using Bio-inspired Computation

作     者：Jagannath Dayal Pradhan L.V.Narasimha Prasad Tusar Kanti Dash Manisha Guduri Ganapati Panda 

作者机构：Electronics and Communications EngineeringC V Raman Global UniversityBhubaneswarIndia Department of Computer Science and EngineeringInstitute of Aeronautical EngineeringHyderabadIndia School of Computing and InformaticsUniversity of LouisianaLafayetteLAUSA 

出 版 物：《Journal of Artificial Intelligence and Technology》 (人工智能技术学报（英文）)

年 卷 期：2024年第4卷第2期

页      面：124-131页

学科分类：1002[医学-临床医学] 100201[医学-内科学(含：心血管病、血液病、呼吸系病、消化系病、内分泌与代谢病、肾病、风湿病、传染病)] 10[医学] 

主　　题：AI-ML ASR FLANN health informatics neural network PFLANN 

摘      要：Due to the recent developments in communications technology,cognitive computations have been used in smart healthcare techniques that can combine massive medical data,artificial intelligence,federated learning,bio-inspired computation,and the Internet of Medical *** has helped in knowledge sharing and scaling ability between patients,doctors,and clinics for effective treatment of ***-based respiratory disease detection and monitoring are crucial in this direction and have shown several promising *** the subject’s speech can be remotely recorded and submitted for further examination,it offers a quick,economical,dependable,and noninvasive prospective alternative detection ***,the two main requirements of this are higher accuracy and lower computational complexity and,in many cases,these two requirements do not correlate with each *** problem has been taken up in this paper to develop a low computational complexity-based neural network with higher accuracy.A cascaded perceptual functional link artificial neural network(PFLANN)is used to capture the nonlinearity in the data for better classification performance with low computational *** proposed model is being tested for multiple respiratory diseases,and the analysis of various performance matrices demonstrates the superior performance of the proposed model both in terms of accuracy and complexity.