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Accuracy is one of the characteristics of data. Information is supposed to be accurate and correct. If one wants to determine if the information, they are using is correct or not, one needs to ask themselves if that particular data reflects on the real-world position. Inaccurate data in any business or organization will cause problems that will hinder the organization from meeting its goals and objectives. Data should also be relevant. There must be a valid reason why the data is being collected. One should contemplate if they need that information. Data should also be up to date. This is a crucial characteristic because untimely details will lead to making wrong decisions (Hamel et al, 2017).

In the prototype-based cluster, observations carried out are assigned to the centroids and medoids. This is unlike density-based clustering, whereby the unsupervised learning methods recognize different groups in the information or data. On the other hand, graph-based clustering consists of unsupervised algorithms mainly designed to group the graph edges and vertices.

The scalable clustering algorithm is defined as identifying the same descriptions in different groups of data on a profile basis. Salable clustering algorithm involves distance metrics whereby the data points resemble various partition points (Vishwasrao & Sangaiah, 2017).

For one to choose the correct algorithm, one needs to consider the number of features. In addition to this, the accuracy of the output should be deemed when choosing the correct algorithm. One should also collect enough amount of information if they want to get reliable predictions. Linearity is also essential to consider when choosing a good algorithm. The number of parameters helps in selecting the right algorithm. Algorithms that have large numbers of parameters need many trials to get the right combination.

I need a 3D Scene created from the attached picture. The code must be in C++ language and run in OpenGL in Visual Studio 2019. The solution must be a .zip file containing all the necessary directories and libraries. I will upload the picture I need to recreate the 3D scene from. Please don’t use glut. Here are the requirements from the course:

Directions

Using the image you selected in a previous milestone, you will be creating 3D objects that represent the components and layout of that image. Although you have already begun to complete some of this work in your other milestones, during this project you will be refining and adding to your earlier submissions before bringing everything together. Note that you will be working on your 3D scene in Visual Studio but will also submit a written design decisions document discussing your approach throughout the process.

3D OBJECTS

1. Create low-polygon 3D representations of real-world objects. Make sure you have at least four completed objects in your 3D scene. At least one of the objects you create should be made using two or more primitive shapes. Note that the object you completed in a previous milestone can count as one of your four. Utilize organized geometry and ensure that polygons (triangles) on each 3D model are well spaced and connected. To minimize complexity and save 3D modeling time, the polygon count for your objects should not exceed 1,000 triangles. As you work, remember to think in terms of simple shapes and ask yourself what primitive 3D shapes go into making up each object in your scene. Four of the following primitive shapes must appear at least once in your creation:
   • Cube
   • Cylinder
   • Plane
   • Pyramid
   • Sphere
   • Torus

1. Apply accurately projected textures to a 3D model. You must select two objects to texture. Note that you should have already textured one object in a previous milestone. If you use that object here, it will count as one of your two. As you work, the textures you select should be royalty-free images with resolutions of 1024 x 1024 pixels or higher. Please refer to the Sourcing Textures Tutorial, linked in the Supporting Materials section, for guidance on how to locate images that can be used for textures.

1. Apply lighting to create a polished visualization of 3D models. You must include a minimum of two light sources, and at least one of them should be colored. Note that the light you worked on in a previous milestone counts as one of your two lights. The light sources you create will need to capture all of the objects in the 3D world you are building, meaning they should be positioned at locations that do not cause parts of the objects to appear dark when moving the camera around them. While we recommend that you include a point light for one of your two lights, you may implement a directional light or spotlight if you choose. As you generate lighting, make sure that any lights are designed in a way that helps curate a final polished presentation. You will need to properly implement all components of the Phong shading model, including the following:
   • Ambient
   • Diffuse
   • Specular

1. Place objects appropriately, using the X, Y, and Z coordinates, relative to one another in the 3D world. As you work, be sure to match the photograph you selected as closely as possible by placing the objects in their proper locations. Note that when you first import code for the objects you created in previous weeks, the objects may overlap, as it is likely that they were all initially placed at 0, 0, 0.

NAVIGATION

1. Apply horizontal, vertical, and depth camera navigation around the 3D scene. The camera will be traversing the X, Y, and Z axes, and you should ensure it can capture all of the objects in your 3D scene. In a previous milestone, you already created some of this code. It is recommended that you use the code you have already created and then increase the radius of the camera’s orbit so it will correctly encompass all of the objects in the world you are building. You may find it easiest to add each object separately and then adjust the orbit radius or position of the camera each time. As you work, we recommend you use the following input devices:
   • WASD keys: These keys should be used to control the forward, backward, left, and right motion.
   • QE keys: These keys should be used to control the upward and downward movement.

1. Apply nuanced camera controls to effectively view the 3D objects in the application. This should allow the orientation of the camera to change even though its location has not moved. You should focus first on pitch and yaw, but careful changes can be made to roll, keeping in mind that you may want the upward direction to stay in the same location. As you work, you will also want to code for adjustments in the speed of the movement so a user will have more control over how they explore the objects in the scene. We recommend you use the following input devices:
   • Mouse cursor: This should be used to change the orientation of the camera so it can look up and down or right and left.
   • Mouse scroll: This should be used to adjust the speed of the movement, or the speed the camera travels around the scene.

1. Create perspective and orthographic displays of the 3D world. Use the tap of a keyboard key to allow a user to change the viewport display of all objects in the scene between orthographic (2D) and perspective (3D) views at will. To accomplish this, you will be switching the function call to retrieve either the perspective or orthographic projection matrix. Note that you will be keeping the camera in the same orientation that you already developed in previous criteria.

You will write a 2 page review/abstract/summary on an article from a peer- reviewed scholarly journal. This is to assess your ability to select and summarize the research of others, analyze and apply the research of others, and communicate professionally and effectively to their regard. However, the most important rationale for this assignment is for you to see how statistical analysis is presented.Instructions:

1. Use the library system (https://www.ucumberlands.edu/library) or online catalog to locate a journal article that pertains to your research, thesis, or area of interest. The article you chose should have performed some statistical analysis of gathered data and made an inference using something other than just the average (mean). That is, they can’t just talk about averages; they must have used one of these tests: t-test, chi-square, F-test, Fischer test, ANOVA, MANOVA, ANCOVA, Mann-Whitney, correlation, regression.
2. Readthearticlethoroughly.
3. Write a 2-page summary about the article following the given guidelines.

Guidelines:

1. Your review should include:
   1. Thequestion/problembeingresearchedbytheauthor
   2. The experiment that will answer the question
   3. How they collected data
   4. Analysis of the data (Must identify the statistical test used)
   5. Theirconclusionorfindings

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