1)What is an artificial neural network and for what types of problems can it be used? 2)Compare artificial and biological neural networks. What aspects of biological networks are not mimicked by artificial ones? What aspects are similar? 3)What are the most common ANN architectures? For what types of problems can they be used? 4)ANN can be used for both supervised and unsupervised learning. Explain how they learn in a supervised mode and in an unsupervised mode. 5) Go to Google Scholar (scholar.google.com). Conduct a search to find two papers written in the last five years that compare and contrast multiple machine-learning methods for a given problem domain. Observe commonalities and differences among their findings and prepare a report to summarize your understanding. 6) Go to neuroshell.com. Comment on the feasibility of achieving the results claimed by the developers of this neural network model. When submitting work, be sure to include an APA cover page and include at least two APA formatted references (and APA in-text citations) to support the work this week. All work must be original (not copied from any source). These 6 Questions I need in 2 Pages ( THats excluding first cover page and reference page) Textbook is attached below

Artificial neural networks (ANNs) are computational models inspired by the structure and function of biological neural networks, which are found in the brains of living organisms. ANNs are composed of interconnected artificial neurons that can receive, process, and transmit information. These networks use a system of mathematical algorithms and functions to learn from input data and make predictions or perform tasks.

ANNs can be used for a wide range of problem-solving tasks, including pattern recognition, classification, regression, and optimization. They have been successfully applied in various fields such as image and speech recognition, natural language processing, financial analysis, and robotics, among others. ANNs are particularly effective in tasks where the input data is complex and contains nonlinear relationships.

When comparing artificial and biological neural networks, it is important to note that while ANNs attempt to mimic the behavior of biological networks, they are still very different. Artificial networks are simplified mathematical models, whereas biological networks are highly complex and dynamic systems.

One aspect of biological networks that is not mimicked by artificial ones is the ability to learn from a single or very few examples. Biological networks can exhibit rapid learning and adaptability, whereas in ANNs, learning typically requires a large dataset and multiple iterations. Another aspect not replicated in ANNs is the ability of biological networks to self-organize and rewire their connections based on changing environmental conditions.

However, there are also several similarities between artificial and biological networks. Both types of networks have interconnected units or neurons that process and transmit information. Both can exhibit emergent behavior, meaning that complex patterns or behaviors may arise from the interactions of simpler elements. Additionally, both types of networks can learn from experience and adjust their responses accordingly.

The most common ANN architectures can be classified into several categories, including feedforward networks, recurrent networks, and self-organizing networks. Feedforward networks, such as the multilayer perceptron (MLP), are the most widely used and consist of multiple layers of artificial neurons, where information flows in one direction, from input to output. Recurrent networks, such as the long short-term memory (LSTM) networks, have feedback connections that enable them to store and process sequential information. Self-organizing networks, such as the Kohonen self-organizing map (SOM), can create a topological representation of the input data, allowing for clustering and visualization.

The choice of ANN architecture depends on the nature of the problem and the type of data being processed. Feedforward networks are commonly used for pattern recognition and classification tasks, while recurrent networks are suitable for tasks involving sequential data, such as time series analysis or natural language processing. Self-organizing networks are often used for data clustering and visualization.

ANNs can learn in both supervised and unsupervised modes. In supervised learning, the network is trained using labeled data, where the desired output is known. The network adjusts its internal parameters to minimize the difference between its predicted output and the desired output. In unsupervised learning, the network learns patterns or structures in the input data without any explicit supervision. It clusters similar data points or discovers hidden relationships in the data.

To conduct the search on Google Scholar to find two papers written in the last five years that compare and contrast multiple machine-learning methods for a given problem domain, one can enter relevant keywords related to the problem domain and the type of machine learning methods being compared. For example, a search query could include terms such as “machine learning,” “comparison,” “multiple methods,” and the specific problem domain. It is important to critically analyze the commonalities and differences among the findings of the papers and provide a summary that highlights the strengths and limitations of each method.

Regarding the feasibility of achieving the results claimed by the developers of the neural network model found on neuroshell.com, it is necessary to thoroughly review the provided information, including any technical documentation or research papers, as well as user experiences and reviews. Evaluating the validity and reliability of the claims requires a comprehensive understanding of the underlying algorithms, the specific problem domain, and the performance metrics used to assess the results. A critical analysis of the model’s strengths and weaknesses should be conducted to determine its feasibility in achieving the claimed outcomes.

In conclusion, artificial neural networks are computational models inspired by biological neural networks that can be used for a variety of problem-solving tasks. While they aim to replicate certain aspects of biological networks, they are distinct in terms of their complexity and capabilities. Different ANN architectures are suited for different problem domains and data types. ANNs can learn through supervised or unsupervised modes, depending on the availability of labeled data. It is important to critically analyze and compare multiple machine learning methods in order to determine their effectiveness in a given problem domain. Evaluating the feasibility of a neural network model requires a comprehensive examination of the available information, including technical documentation and user experiences.

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