Cloud computing enables businesses to improve their market competitiveness, enabling instant and easy access to a pool of virtualized and distributed resources such as virtual machines (VM) and containers for executin...
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Cloud computing enables businesses to improve their market competitiveness, enabling instant and easy access to a pool of virtualized and distributed resources such as virtual machines (VM) and containers for executing their business operations efficiently. Though the cloud enables the deployment and management of business processes (BPs), it is challenging to deal with the enormous fluctuating resource demands and ensure smooth execution of business operations in containerized multi-cloud. Therefore, there is a need to ensure elastic provisioning of resources to tackle the over and under-provisioning problems and satisfy the objectives of cloud providers and end-users considering the quality of service (QoS) and service level agreement (SLA) constraints. In this article, an efficient multi-agent autonomic resource provisioning framework is proposed to ensure the effective execution of BPs in a containerized multi-cloud environment with guaranteed QoS. To improve the performance and ensure elastic resource provisioning, autonomic computing is utilized to monitor the resource usage and predict the future resource demands, then resources are scaled based on demand. Initially, the required resources for executing the incoming workloads are identified by clustering the workloads into CPU and I/O intensive, and the local agent achieves this with the help of an initialization algorithm and K-means clustering. Then, the analysis phase predicts the workload demand using the proposed enhanced deep stacked auto-encoder (EDSAE), further, the containers are scaled based on the prediction outcomes, finally, the multi-objective termite colony optimization (MOTCO) algorithm is used by the global agent to find suitable containers for executing the clustered workloads. The proposed framework has been implemented in the Container Cloudsim platform and evaluated using the business workload traces. The overall simulation results proved the effectiveness of the proposed approach compare
Anaemia, a condition characterised by reduced haemoglobin levels, exerts a significant global impact, affecting billions of individuals worldwide. According to data from the World Health Organisation (WHO), India exhi...
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Restricted Boltzmann Machines (RBMs) are a system getting-to-know algorithm that may be used to predict the overall performance of inventory markets. This painting aims to analyze the overall performance of inventory ...
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ISBN:
(纸本)9798350383348
Restricted Boltzmann Machines (RBMs) are a system getting-to-know algorithm that may be used to predict the overall performance of inventory markets. This painting aims to analyze the overall performance of inventory markets via growing an RBM version. The RBM version will benefit from the temporal correlation of stock marketplace information and put it to use to make more correct predictions. The version is educated using historical facts, and the model parameters are adjusted to optimize the prediction accuracy. This painting demonstrates the use of an RBM model for predicting the returns of stocks and analyzing the stock marketplace's overall performance. The outcomes from these paintings show that the proposed RBM version effectively predicts the overall performance of inventory markets and provides an alternative version for forecasting stock markets. Confined Boltzmann Machines (RBMs) are a form of synthetic neural network which can be used for unsupervised studying tasks consisting of dimensionality discount, function mastering, and collaborative filtering. RBMs have been correctly used in numerous applications, including sentiment evaluation, natural language processing, and computer imagination and prescient. In current years, there was a surge of hobby in using RBMs to expect inventory market overall performance. On the way to expand a predictive model, RBMs extract separable functions from the uncooked market data, which can be pattern inputs. The extracted capabilities are used because of the input to a supervised mastering version to predict stock marketplace overall performance based on previous statistics. A vital advantage of using RBMs for feature extraction is that they can identify latent components of the facts, which would, in any other case, remain hidden from the consumer. By combining RBMs with supervised learning, it is viable to develop models which can be more powerful than those using either technique for my part. Because of this reality,
Nowadays,the COVID-19 virus disease is spreading *** are some testing tools and kits available for diagnosing the virus,but it is in a lim-ited *** diagnose the presence of disease from radiological images,auto-mated ...
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Nowadays,the COVID-19 virus disease is spreading *** are some testing tools and kits available for diagnosing the virus,but it is in a lim-ited *** diagnose the presence of disease from radiological images,auto-mated COVID-19 diagnosis techniques are *** enhancement of AI(Artificial Intelligence)has been focused in previous research,which uses X-ray images for detecting *** most common symptoms of COVID-19 are fever,dry cough and sore *** symptoms may lead to an increase in the rigorous type of pneumonia with a severe *** medical imaging is not suggested recently in Canada for critical COVID-19 diagnosis,computer-aided systems are implemented for the early identification of COVID-19,which aids in noticing the disease progression and thus decreases the death ***,a deep learning-based automated method for the extraction of features and classi-fication is enhanced for the detection of COVID-19 from the images of computer tomography(CT).The suggested method functions on the basis of three main pro-cesses:data preprocessing,the extraction of features and *** approach integrates the union of deep features with the help of Inception 14 and VGG-16 *** last,a classifier of Multi-scale Improved ResNet(MSI-ResNet)is developed to detect and classify the CT images into unique labels of *** the support of available open-source COVID-CT datasets that consists of 760 CT pictures,the investigational validation of the suggested method is *** experimental results reveal that the proposed approach offers greater performance with high specificity,accuracy and sensitivity.
Quality degradation due to the compression and the transmission of images is a significant threat to multimedia applications. Blind image quality assessment (BIQA) is a principal technique to measure the distortion an...
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In power distribution systems, common issues such as voltage fluctuations, voltage instability, current harmonics, and power imbalances often arise, negatively impacting the stability and power quality (PQ) of the pow...
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Multiple Sclerosis (MS) is an immunological disorder that causes tumors in the central nervous system. Brain Magnetic Resonance Images (MRI) were considered for the visualization of MS. In the past, neural approaches ...
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Three-dimensional(3D)reconstruction of human organs has gained attention in recent years due to advances in the Internet and graphics processing *** the coming years,most patient care will shift toward this new ***,de...
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Three-dimensional(3D)reconstruction of human organs has gained attention in recent years due to advances in the Internet and graphics processing *** the coming years,most patient care will shift toward this new ***,development of fast and accurate 3D models from medical images or a set of medical scans remains a daunting task due to the number of pre-processing steps involved,most of which are dependent on human *** this review,a survey of pre-processing steps was conducted,and reconstruction techniques for several organs in medical diagnosis were *** methods and principles related to 3D reconstruction were *** usefulness of 3D reconstruction of organs in medical diagnosis was also highlighted.
In large-scale information systems, storage device performance continues to improve while workloads expand in size and access characteristics. This growth puts tremendous pressure on caches and storage hierarchy in te...
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Enhancing the coverage area of the sensing range with the limiting resource is a critical problem in the wireless sensor network (WSN). Mobile sensors are patched coverage holes and they also have limited energy to mo...
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