Osteoporosis disease is caused by hormonal changes, vitamin D, and calcium deficiency. With current technologies, the identification of osteoporosis requires many tests with the support of medications. Bone mineral density is a typical measure implemented using a DEXA scan which can be very costly. Such high technology equipment is usually not accessible for remote people, and thus a low-cost screening system is very appealing. This article proposes an osteoporosis prediction system that effectively determines its possibility of occurrence based on essential factors such as smoking habits and calcium level so that the people at high risk can be referred to access the DEXA scanner. Our proposed system is implemented by an improved version of the artificial immune system, enabling care providers to take precautionary measures at the right time to avoid the early development of osteoporosis. The experiments demonstrated a promising result of 94% prediction accuracy that proved its usefulness in identifying people with potential osteoporosis in the future.

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Heterogeneous multi-cloud environments make use of a collection of varied performance rich cloud resources, linked with huge-speed, performs varied applications which are of computational nature. Applications require distinct computational features for processing. Heterogeneous multi-cloud domain well suits to satisfy the computational need of very big diverse nature of collection of tasks. Mapping problem provides an optimal solution in scheduling tasks to distributed heterogeneous clouds is termed NP-complete, which leads to the ultimate establishment of heuristic problem solving technique. Identifying the heuristic which is appropriate and best still exists as a complicated problem. In this paper, to address scheduling collection of 'n' tasks in two groups among a set of 'm' clouds, we propose three heuristics PTL (Pair-Task Threshold Limit), PTMax-Min, and PTMin-Max. Firstly to determine the tasks scheduling order, proposed heuristics based on the tasks attributes calculate tasks threshold value. Tasks sorted in descending value of threshold. Group G1 comprises tasks ordered in descending value of threshold. Group G2 comprises remaining tasks ordered in ascending value of threshold. Secondly, tasks form Group 1 are scheduled rst based on minimum completion time, and then tasks in Group 2 are scheduled. The proposed heuristicsare compared with existing heuristics, namely MCT, MET, Min-Min using benchmark dataset. Heuristics PTL, PTMax-Min, and PTMin-Max bring out reduced makespan compared to MCT, MET, and Min-min.

Heterogeneous multi-cloud environments make use of a collection of varied performance rich cloud resources, linked with huge-speed, performs varied applications which are of computational nature. Applications require distinct computational features for processing. Heterogeneous multi-cloud domain well suits to satisfy the computational need of very big diverse nature of collection of tasks. Mapping problem provides an optimal solution in scheduling tasks to distributed heterogeneous clouds is termed NP-complete, which leads to the ultimate establishment of heuristic problem solving technique. Identifying the heuristic which is appropriate and best still exists as a complicated problem. In this paper, to address scheduling collection of ‘n’ tasks in two groups among a set of 'm' clouds, we propose three heuristics PTL (Pair-Task Threshold Limit), PTMax-Min, and PTMin-Max. Firstly to determine the tasks scheduling order, proposed heuristics based on the tasks attributes calculate tasks threshold value. Tasks sorted in descending value of threshold. Group G1 comprises tasks ordered in descending value of threshold. Group G2 comprises remaining tasks ordered in ascending value of threshold. Secondly, tasks form Group 1 are scheduled first based on minimum completion time, and then tasks in Group 2 are scheduled. The proposed heuristicsare compared with existing heuristics, namely MCT, MET, Min-Min using benchmark dataset. Heuristics PTL, PTMax-Min, and PTMin-Max bring out reduced makespan compared to MCT, MET, and Min-min.