Artificial Intelligence (AI) is the study that includes computational procedures to perform actions that human beings do and require a certain amount of intelligence. Communication and learning represent the ways that humans show their intelligence. Both ways require certain mental skills like synthesis, analysis, decision making, judgment, organizing, reasoning, hypothesis, interpretation etc. Artificial intelligence (AI) involves knowledge representation, search, perception and inference. To generate results, artificial intelligence (AI) must define and read the data in forms that will allow representation and processing. The way to succeed that is search and inference. Referring to search there must sophisticated search algorithms to find the optimal solutions. Then inferences are drawn for each situation. The way through artificial intelligence (AI) leads to decision making start from data selection process, continues pre-processing data, then data transformation, data mining plays a key role to entire process, finally results evaluation will lead to decision making.

Machine learning algorithms have proven to be useful in (a) data mining problems where large databases may contain valuable implicit regularities that can be discovered automatically (e.g., to analyze outcomes of medical treatments from patient databases or to learn general rules for credit worthiness from financial databases); (b) poorly understood domains where humans might not have the knowledge needed to develop effective algorithms (e.g., human face recognition from images); and (c) domains where the program must dynamically adapt to changing conditions (e.g., controlling manufacturing processes under changing supply stocks or adapting to the changing reading interests of individuals).
Machine learning draws on ideas from a diverse set of disciplines, including artificial intelligence, probability and statistics, computational complexity, information theory, psychology and neurobiology, control theory, and philosophy. A well-defined learning problem requires a well-specified task, performance metric, and source of training experience. Designing a machine learning approach involves a number of design choices, including choosing the type of training experience, the target function to be learned, a representation for this target function, and an algorithm for learning the target function from training examples.

The Internet of Behaviors (IoB) refers to gathering, analyzing, combining, processing data user in terms of behavioral psychology to predict audience behavior and needs. Data usually comes from consumers online activity, e-government services, health monitors, social media platforms, mobile phone use, facial recognition from public CCTV, web searches, GPS use etc. IoB it is commonly used along with the Internet of Things (IoT) devices and applications aiming to provide better user experience, increase user satisfaction and search experience optimization through better user profiling, segmentation, and prediction to utterly increase the product or service conversions performance.

User behavior encompasses all the actions visitors take on a website: where and what they click on, how they scroll down a page, where they stumble, and where they eventually drop off the page and leave. Tracking user behavior gives you an inside look at how people interact with your site and what obstacles or hooks they experience in their journey as your customers. User behavior analysis refers to collecting, combining, and analyzing quantitative and qualitative user data to understand how users interact with a product or website, and why.

User experience (UX) focuses on having a deep understanding of users, what they need, what they value, their abilities, and also their limitations.  It also takes into account the business goals and objectives of the group managing the project. UX best practices promote improving the quality of the user’s interaction with and perceptions of your product and any related services.

User behavior encompasses all the actions visitors take on a website: where and what they click on, how they scroll down a page, where they stumble, and where they eventually drop off the page and leave. Tracking user behavior gives you an inside look at how people interact with your site and what obstacles or hooks they experience in their journey as your customers. User behavior analysis refers to collecting, combining, and analyzing quantitative and qualitative user data to understand how users interact with a product or website, and why.

Search engine optimization (SEO) is often about making small modifications to parts of a website. When viewed individually, these changes might seem like incremental improvements, but when combined with other optimizations, they could have a noticeable impact on a website's user experience and performance in organic search results.