Optimizing Resource Allocation Framework for Multi-Cloud Environment

Cloud computingmakes dynamic resource provisioning more accessible.Monitoring a functioning service is crucial,and changes are made when particular criteria are surpassed.This research explores the decentralized multi-cloud environment for allocating resources and ensuring the Quality of Service(QoS),estimating the required resources,and modifying allotted resources depending on workload and parallelism due to resources.Resource allocation is a complex challenge due to the versatile service providers and resource providers.The engagement of different service and resource providers needs a cooperation strategy for a sustainable quality of service.The objective of a coherent and rational resource allocation is to attain the quality of service.It also includes identifying critical parameters to develop a resource allocation mechanism.A framework is proposed based on the specified parameters to formulate a resource allocation process in a decentralized multi-cloud environment.The three main parameters of the proposed framework are data accessibility,optimization,and collaboration.Using an optimization technique,these three segments are further divided into subsets for resource allocation and long-term service quality.The CloudSim simulator has been used to validate the suggested framework.Several experiments have been conducted to find the best configurations suited for enhancing collaboration and resource allocation to achieve sustained QoS.The results support the suggested structure for a decentralized multi-cloud environment and the parameters that have been determined.

IoMT-Based Healthcare Framework for Ambient Assisted Living Using a Convolutional Neural Network

In the age of universal computing,human life is becoming smarter owing to the recent developments in the Internet of Medical Things(IoMT),wearable sensors,and telecommunication innovations,which provide more effective and smarter healthcare facilities.IoMT has the potential to shape the future of clinical research in the healthcare sector.Wearable sensors,patients,healthcare providers,and caregivers can connect through an IoMT network using software,information,and communication technology.Ambient assisted living(AAL)allows the incorporation of emerging innovations into the routine life events of patients.Machine learning(ML)teaches machines to learn from human experiences and to use computer algorithms to“learn”information directly instead of relying on a model.As the sample size accessible for learning increases,the performance of the algorithms improves.This paper proposes a novel IoMT-enabled smart healthcare framework for AAL to monitor the physical actions of patients using a convolutional neural network(CNN)algorithm for fast analysis,improved decision-making,and enhanced treatment support.The simulation results showed that the prediction accuracy of the proposed framework is higher than those of previously published approaches.

Alzheimer Disease Detection Empowered with Transfer Learning

Alzheimer’s disease is a severe neuron disease that damages brain cells which leads to permanent loss of memory also called dementia.Many people die due to this disease every year because this is not curable but early detection of this disease can help restrain the spread.Alzheimer’s ismost common in elderly people in the age bracket of 65 and above.An automated system is required for early detection of disease that can detect and classify the disease into multiple Alzheimer classes.Deep learning and machine learning techniques are used to solvemanymedical problems like this.The proposed system Alzheimer Disease detection utilizes transfer learning on Multi-class classification using brain Medical resonance imagining(MRI)working to classify the images in four stages,Mild demented(MD),Moderate demented(MOD),Non-demented(ND),Very mild demented(VMD).Simulation results have shown that the proposed systemmodel gives 91.70%accuracy.It also observed that the proposed system gives more accurate results as compared to previous approaches.