Computer Science Homework Help

Answer 1

The approach uses a machine-learning-based framework to classify cloud consumers based on their behavioral characteristics, including time, location, and computing power. The cloud consumers can be categorized based on their data access requirements by considering the following types of cloud consumers: consumers who access cloud services in bulk, those who access cloud services using a subscription model, those who access cloud services in the cloud, and consumers who access cloud services through a per-service model. The service consumers can be classified based on the types of services they access in the cloud. The service consumers for the cloud consumer that access cloud services through a subscription model. Summary, A variety of cloud applications and services can be accessed and managed in the cloud system through the cloud consumer. The primary advantage of using an access scheme for cloud service usage is that the cloud consumer does not incur additional provisioning and management costs for data center infrastructure. It is essential to understand the differences between cloud storage and network. With the network, the cloud consumer uses the underlying network infrastructure and the underlying data center infrastructure (Thirugnanam et al., 2020). Cloud storage is designed to be the most flexible and cost-effective solution because it can leverage the existing infrastructure in many different situations. Network architecture is a significant concern for cloud consumers because it is essential to realize that cloud services and virtualization are not the same. Cloud providers have the responsibility to build a virtual network that provides high-speed service to their customers. Network topologies can only be used if the network structure for the customers is optimized. Therefore, the provider needs to understand the customers’ requirements to ensure that the network architecture for the customers can meet those requirements. In order to optimize the network architecture, network management and the service delivery team work together to optimize the best possible network architecture for the cloud providers.

This method also facilitates the use of nonparametric measures in future decision-making processes and enhances our ability to detect unexpected statistical events. We propose a machine learning-based approach to analyze the responses to weather models, and specifically, we utilize the model performance measures for prediction accuracy. This approach allows for the use of improved nonparametric statistics and is scalable for large datasets. The results presented in this show that our approach can be applied to many existing data-mining frameworks. The previous presents an example of using ANN to mine eCommerce data using sentiment analysis to understand purchasing behavior. In practice, ANNs are applied to real-world data in order to support real-world business and social problems (Melati et al., 2019).

Reference:

Melati, D., Grinberg, Y., Kamandar Dezfouli, M., Janz, S., Cheben, P., Schmid, J. H., Sánchez-Postigo, A., & Xu, D.-X. (2019). Mapping the global design space of nanophotonic components using machine learning pattern recognition. Nature Communications, 10(1), 4775–4775. https://doi.org/10.1038/s41467-019-12698-1

Thirugnanam, H., Ramesh, M. V., & Rangan, V. P. (2020). Enhancing the reliability of landslide early warning systems by machine learning. Landslides, 17(9), 2231–2246. https://doi.org/10.1007/s10346-020-01453-z

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Answer 2

The huge amount of data requires powerful methods, algorithms, and techniques that are hard to find, describe, and implement affordably. In the field of big data analytics, one such approach is called big-data processing. Big data processing attempts to make large amounts of existing data available to efficient analytics tools by analyzing it in a scalable way that is suitable..handling large data sets. The big data paradigm encompasses a variety of different technologies that we explore later in this. We begin with the challenges and opportunities people face when adopting the distributed, scalable database model, and we continue with key technologies that are driving these changes. The emergence of distributed, scalable database applications has had profound effects on the world of information. We can now create new and richer services that span the globe without deploying servers and data centers across the globe (Terol et al., 2020).

Moreover, this capability has resulted in some interesting developments in this field. For example, some techniques to achieve high mobility have been proposed, including the Transport of magnetic materials over non-volatile media. For example, in In, the authors proposed a novel mechanism to support magnetic and thermal media transmission over non-conductive surfaces such as those of a radio system over a non-conductive carrier medium. This was achieved by employing a hybrid electronic waveguide. As already stated, such a transmission network would not only be more reliable but, more importantly, more efficient. This transmission network would not only save energy by reducing the amount of power required to transmit a packet, but it would also lead to fewer errors being made in transmission. It is important to point out that this mechanism is, at the same time, more complex and more expensive than sending a message by direct radio or electricity exchange (Xu & Jackson, 2019).

The big data transformation will be performed as a parallel process, and once done, all the data will be stored in a “cloud” of data warehouses, on which each of the data warehouse operations is built. The data warehouse operations are executed in a parallel manner. This is the new way for companies. The big data revolution has provided us with the means to change the way people live. People live without the necessary tools or knowledge to act. When tools and knowledge are insufficient, people behave and exploit others. These are the types of relationships that we want to eliminate. The goal of this book is to help you move beyond the self-serving, ego-driven, selfish, short-sighted, and ineffective approach to security management (Xu & Jackson, 2019).

Reference:

Terol, R. M., Reina, A. R., Ziaei, S., & Gil, D. (2020). A Machine Learning Approach to Reduce Dimensional Space in Large Datasets. IEEE Access, 8, 148181–148192. https://doi.org/10.1109/ACCESS.2020.3012836

Xu, C., & Jackson, S. A. (2019). Machine learning and complex biological data. Genome Biology, 20(1), 76–76. https://doi.org/10.1186/s13059-019-1689-0