CSC792 Web Intelligence Assignment UITM Example Malaysia
In this course we will explore the different aspects of designing intelligent web technology. We’ll cover everything from artificial intelligence and machine learning to carbohydrates, chemicals or even vitamin supplements!
We’ll touch on each of these topics and more over the course of four weeks. By the end you will be able to understand how machine learning works and also know a little bit about artificial intelligence. You can then use this knowledge to create your own applications that harness the power of neural networks and deep learning!
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Assignment Brief 1: Identify problems within web intelligence
Some of the problems within web intelligence include the lack of context, large amounts of false or irrelevant information, and information overload.
Lack of context can make it difficult to determine the relevance of information. Large amounts of false or irrelevant information can make it difficult to find accurate information.
Web intelligence is reliant on the context in which it is being used. Because web intelligence allows people to incorporate information from other sources, it becomes difficult to determine whether or not that information should be included in the analysis.
There are a few problems with web intelligence. For one, the algorithms used to scrape and analyze data from the web are often inaccurate or incomplete. Additionally, the data that is scraped and analyzed is often biased or manipulated, meaning that it’s not an accurate representation of reality. Finally, the way in which data is collected and analyzed can be easily gamed by entities with malicious intent.
Assignment Brief 2: Formulate problems within web intelligence
There are a few problems with web intelligence. One is that it’s often difficult to determine the validity of information sources on the internet. Another problem is that the internet can be used to spread disinformation or misleading information. Additionally, there can be bias in search results based on the personal biases of those who design and conduct searches. Finally, there is the issue of “filter bubbles” – whereby people only see information that agrees with their own personal biases, and thus they are not exposed to other viewpoints.
One proposed solution to these problems is the creation of “algorithmic editors” – software that will select information for you so you don’t have to rely on your own judgment. That seems like a bad idea, because it means avoiding making decisions and taking responsibility for your own actions. Fortunately, this is unlikely to ever happen. Here’s why.
First, it’s not possible to have a fully-automated editor that selects information for you because the only way to find information is by searching through a large amount of data, and that can’t be automated because there are too many possibilities to consider in one search.
Even if there were only modest amounts of data, it would take years for a computer to check through all of it, and even then there are no guarantees that you would find the information you were looking for.
Second, some people may want an editor because they don’t understand how information on the internet works, but I suspect that these cases will be rare.
For example, anyone who uses Google knows that the search results are determined by algorithms, but most people don’t care. They just want to find the information they’re looking for quickly and easily.
Assignment Brief 3: Distinguish artificial intelligence techniques
There are a few different types of artificial intelligence (AI) techniques: rule-based systems, decision trees, genetic algorithms, artificial neural networks, and fuzzy logic systems.
Rule-based systems are the simplest form of AI. They work by applying a set of hard-coded rules to a problem. For example, if you want to create a system that can determine whether or not an email is spam, you would first need to write down all the rules that define spam.
Decision trees are similar to rule-based systems, but they allow for more flexibility in terms of how rules can be applied. Decision trees are also data-driven, meaning that they can be trained to recognize certain patterns in data.
Genetic algorithms are inspired by biological evolution and natural selection. They use a series of ‘breeding’ iterations based on the evaluation of individuals within a population to evolve solutions for problems involving multiple variables.
Genetic algorithms have shown promise across many problem types including software testing, planning, scheduling, and classification.
Artificial neural networks are inspired by the layout of neurons in the human brain. They are made up of thousands or even millions of simple computing elements that are interconnected in complex ways. These elements can be organized into layers including input, hidden, and output layers.
Neural networks have been one of the most successful types of AI, being used in areas including computer vision, speech recognition, and text mining. Fuzzy logic systems are a type of AI that can handle uncertainty within data by assigning degrees of truth to rules. This is often done through the use of linguistic variables to represent different states such as ‘cold’, ‘warm’ or ‘hot’.
Assignment Brief 4: Evaluate intelligence methods in web management tasks
There are a number of different intelligence methods that can be used in web management tasks. Some of the most common include artificial intelligence, machine learning, natural language processing, and predictive analytics.
Each of these methods has its own strengths and weaknesses, so it’s important to evaluate which one will work best for your specific needs. For example, if you need to make decisions quickly, then predictive analytics may be a better option than machine learning. On the other hand, if you need to process a large amount of data, then artificial intelligence may be more appropriate.
In this article we’ll discuss the basics of each method and how they can be used in web management. It’s important to note that these methods are not mutually exclusive, and many of today’s software solutions for web management combine multiple methods together to achieve better results.
Artificial intelligence is great for tasks that require complex decision-making, while machine learning is better suited for tasks that require pattern recognition. Both methods can be used to improve website performance by automatically adjusting settings based on user behavior or traffic patterns. However, it’s important to note that these methods are still in their early stages and may not be perfect for every situation.
Assignment Brief 5: Apply intelligence methods in web management tasks
There are a variety of intelligence methods that can be applied to web management tasks. For example, data mining can be used to extract information from web logs in order to improve website performance and optimize user experience. Additionally, artificial intelligence can be used to process large amounts of data in order to identify patterns and trends that would otherwise be difficult to detect.
Finally, machine learning can be utilized to automatically learn how users interact with a website in order to make better design decisions. altogether, these methods allow web managers to make more informed decisions and achieve better results.
Data mining is a process that allows users to extract information from web logs in order to gain a better understanding of a website’s visitors.
In general, data mining can be used for two main purposes: performance optimization and user behavior analysis. When it comes to performance optimization, data mining allows web engineers to find common problems such as pages taking too long to load or visitors being directed to the wrong page.
Additionally, engineers can use data mining to conduct A/B tests that help determine what design options are most effective for users. Finally, when it comes to user behavior analysis, web managers can use data mining to improve search results and identify problems within various sections of a website.
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