TY - JOUR N2 - Workflow Scheduling is the major problem in Cloud Computing consists of a set of interdependent tasks which is used to solve the various scientific and healthcare issues. In this research work, the cloud based workflow scheduling between different tasks in medical imaging datasets using Machine Learning (ML) and Deep Learning (DL) methods (hybrid classification approach) is proposed for healthcare applications. The main objective of this research work is to develop a system which is used for both workflow computing and scheduling in order to minimize the makespan, execution cost and to segment the cancer region in the classified abnormal images. The workflow computing is performed using different Machine Learning classifiers and the workflow scheduling is carried out using Deep Learning algorithm. The conventional AlexNet Convolutional Neural Networks (CNN) architecture is modified and used for workflow scheduling between different tasks in order to improve the accuracy level. The AlexNet architecture is analyzed and tested on different cloud services Amazon Elastic Compute Cloud- EC2 and Amazon Lightsail with respect to Makespan (MS) and Execution Cost (EC). L1 - http://journals.pan.pl/Content/119989/PDF/19_02071_Bpast.No.69(4)_27.08.21_druk.pdf L2 - http://journals.pan.pl/Content/119989 PY - 2021 IS - 4 EP - e137728 DO - 10.24425/bpasts.2021.137728 KW - cloud KW - workflow scheduling KW - machine learning KW - CNN KW - AlexNet A1 - Tharani, P. A1 - Kalpana, A.M. VL - 69 DA - 26.06.2021 T1 - An enhanced performance evaluation of workflow computing and scheduling using hybrid classification approach in the cloud environment SP - e137728 UR - http://journals.pan.pl/dlibra/publication/edition/119989 T2 - Bulletin of the Polish Academy of Sciences Technical Sciences ER -