Metaheuristic-Based Kernel Extreme Learning Machine Model for Disease Diagnosis in Industrial Internet of Things Sensor Networks

• Published in: Artificial Intelligence Techniques in IoT Sensor Networks Vol. 1; pp. 83 - 98
• Main Authors: Dhanasekaran, S., Punithavathi, I. S. Hephzi, Duraipandy, P., Kumar, A. Sivanesh, Vijayakarthik, P., Rajasekaran, S., Murugan, B. S.
• Format: Book Chapter
• Language: English
• Published: United Kingdom CRC Press 2021; CRC Press LLC
• Edition: 1
• Subjects: Equivalent Initiate; SSIM; Hd Diagnosis; Original File; RFID; SVM; Dt Approach; BPNN; Cloud Server; NB Method; Proposed Clustering Algorithm; Kernel Function Parameter; ECG Signal; Dt Method; Dt Model; Huffman Code; Attribute Based Encryption; EMC; Local Search Phase; Spider Monkey; KNN Method; IoT Device
• ISBN: 9780367681456; 9780367439255; 0367681455; 0367439255
• DOI: 10.1201/9781003007265-6

Nowadays, enhanced utilization of intelligent tools and communication applications in medical observance and the control on actions of medical employees such as doctors, nurses, and hospital managers, patients, and healthcare manufacturers have been witnessed. Based on Gartner and Forbes, it is estimated that the Internet of Things (IoT) has contributed maximum financial cost for the global economy and minimum amount for IoT-based healthcare production [1]. According to these estimates, it can be observed that the Health IIoT is mostly an important player in the Industrial IoTs (IIoTs)-motivated healthcare sector. IIoT has had a remarkable control over several massive and tiny healthcare manufacturing domains. Thus, an enhanced amount of carrying IoT tools, machines, and apps are being utilized to monitor several health-related details such as glucose meter, ECG screens, and blood pressure (BP), etc.