Saif Jasim - Artificial Intelligence

Agenda

What is Artificial Intelligence?

What is Top-Down Approach?

What is Bottom-Up Approach?

Develop an Intelligent System Using Top-Down Approach.

Develop an Intelligent System Using Bottom-Up Approach.

Analysis & Possible Improvements.

Artificial Intelligence

At its simplest form, artificial intelligence is a field, which combines computer science and robust datasets, to enable problem-solving. It also encompasses sub-fields of machine learning and deep learning, which are frequently mentioned in conjunction with artificial intelligence. These disciplines are comprised of AI algorithms which seek to create expert systems which make predictions or classifications based on input data.

Top-Down Approach

In the top-down model, an overview of the system is formulated without going into detail for any part of it. Each part of it then refined into more details, defining it in yet more details until the entire specification is detailed enough to validate the model. if we glance at a haul as a full, it’s going to appear not possible as a result of it’s so complicated For example: Writing a University system program, writing a word processor. Complicated issues may be resolved victimization high down style, conjointly referred to as Stepwise refinement where,

Bottom-Up Approach

The bottom-up approach of artificial intelligence (AI) involves starting with a set of specific observations or data and using them to build up a more general understanding of a problem. This approach is often used in machine learning, where a model is trained on a large dataset and then used to make predictions or decisions based on new data.

Movie Recommendation SysteM

The recent rapid growth of the Internet content has led to building recommendation systems that guide users to their needs through an information retrieving process. An expert recommendation system is an emerging area that attempts to detect the most knowledgeable people in some specific topics. This detection is based on both the extracted information from peoples’ activities and the content of the documents concerned with them. Moreover, an expert recommendation system takes a user topic or query and then provides a list of people sorted by the degree of their relevant expertise with the given topic or query. These systems can be modeled by information retrieval approaches, along with search engines or a combination of natural language processing systems.

Analysis & Possible Improvements

In order to achieve more accuracy than collaborative filtering methods; the maximal clique method used in social network analysis is the first time that used in a movie recommendation system and the output of this method is very effective. To achieve more accurate results; the k-clique method, which is very effective in social networks, can be introduced in experiments and the output showed this method was more effective than maximal clique method.

For performance evaluation, we can evaluate the collaborative filtering method using a k nearest neighbor, maximal clique method, k-clique method and improved k-clique methods. The results showed that the improved k-clique method enhanced the precision of the movie recommendation system more than the other methods used.

Digit Recognition Neural Network

A neural network is a model inspired by how the brain works. It consists of multiple layers having many activations, this activation resembles neurons of our brain. A neural network tries to learn a set of parameters in a set of data which could help to recognize the underlying relationships. Neural networks can adapt to changing input; so the network generates the best possible result without needing to redesign the output criteria.

The difficulty of visual pattern recognition becomes apparent if you attempt to write a computer program to recognize digits like those you see everyday. Neural networks approach the problem in a different way. The idea is to take a large number of handwritten digits, known as training examples, and then develop a system which can learn from those training examples.

Analysis and Possible Improvements

Research in the handwriting recognition field is focused around deep learning techniques and has achieved breakthrough performance in the last few years. Still, the rapid growth in the amount of handwritten data and the availability of massive processing power demands improvement in recognition accuracy and deserves further investigation. Convolutional neural networks (CNNs) are very effective in perceiving the structure of handwritten characters/words in ways that help in automatic extraction of distinct features and make CNN the most suitable approach for solving handwriting recognition problems.