相关论文: Subjective Metrics-based Cloud Market Performance …

Analytical performance models are very effective in ensuring the quality of service and cost of service deployment remain desirable under different conditions and workloads. While various analytical performance models have been proposed for…

Predicting the stock market trend has always been challenging since its movement is affected by many factors. Here, we approach the future trend prediction problem as a machine learning classification problem by creating tomorrow_trend…

Workloads in modern cloud data centers are becoming increasingly complex. The number of workloads running in cloud data centers has been growing exponentially for the last few years, and cloud service providers (CSP) have been supporting…

Cloud computing is the backbone of the digital society. Digital banking, media, communication, gaming, and many others depend on cloud services. Unfortunately, cloud services may fail, leading to damaged services, unhappy users, and perhaps…

This paper presents a meta-analysis evaluating ML performance in sentiment analysis for Twitter data. The study aims to estimate the average performance, assess heterogeneity between and within studies, and analyze how study characteristics…

Cloud platforms are increasingly relied upon to host diverse, resource-intensive workloads due to their scalability, flexibility, and cost-efficiency. In multi-tenant cloud environments, virtual machines are consolidated on shared physical…

Benchmarking the performance of public cloud providers is a common research topic. Previous research has already extensively evaluated the performance of different cloud platforms for different use cases, and under different constraints and…

While machine learning (ML) technology affects diverse stakeholders, there is no one-size-fits-all metric to evaluate the quality of outputs, including performance and fairness. Using predetermined metrics without soliciting stakeholder…

Load data from power network clusters indicates economic development in each area, crucial for predicting regional trends and guiding power enterprise decisions. The Transformer model, a leading method for load prediction, faces challenges…

Demand forecasting is extremely important in revenue management. After all, it is one of the inputs to an optimisation method which aim is to maximize revenue. Most, if not all, forecasting methods use historical data to forecast the…

Generating models from large data sets -- and determining which subsets of data to mine -- is becoming increasingly automated. However choosing what data to collect in the first place requires human intuition or experience, usually supplied…

Being able to predict stock prices might be the unspoken wish of stock investors. Although stock prices are complicated to predict, there are many theories about what affects their movements, including interest rates, news and social media.…

In point cloud analysis, point-based methods have rapidly developed in recent years. These methods have recently focused on concise MLP structures, such as PointNeXt, which have demonstrated competitiveness with Convolutional and…

Nowadays, an important topic that is considered a lot is how to integrate Machine Learning(ML) to cloud resources management. In this study, our goal is to explore the most important cloud resources management issues that have been combined…

In Amazon EC2, cloud resources are sold through a combination of an on-demand market, in which customers buy resources at a fixed price, and a spot market, in which customers bid for an uncertain supply of excess resources. Standard market…

Credit ratings are one of the primary keys that reflect the level of riskiness and reliability of corporations to meet their financial obligations. Rating agencies tend to take extended periods of time to provide new ratings and update…

Cloud computing is a paradigm that has the potential to transform and revolutionalize the next generation IT industry by making software available to end-users as a service. A cloud, also commonly known as a cloud network, typically…

In the era of rapid technological advancement, social media platforms such as Twitter (X) have emerged as indispensable tools for gathering consumer insights, capturing diverse opinions, and understanding public attitudes. This research…

Sentiment analysis can be used for stock market prediction. However, existing research has not studied the impact of a user's financial background on sentiment-based forecasting of the stock market using artificial neural networks. In this…

The workload prediction and resource allocation significantly play an inevitable role in production of an efficient cloud environment. The proactive estimation of future workload followed by decision of resource allocation have become a…