Preface

**Chapter 9 153**

**Chapter 10 165**

**Chapter 11 177**

Data Mining for Student Performance Prediction in Education

Tracer Transport in a Homogeneous Porous Medium: Experimental Study and Acquisition Data with LabVIEW

Data Mining and Fuzzy Data Mining Using MapReduce

*by Sana Dardouri and Jalila Sghaier*

*by Poli Venkata Subba Reddy*

*by Ferda Ünal*

Algorithms

**II**

Data mining is a branch of computer science that is used to automatically extract meaningful, useful knowledge and previously unknown, hidden, interesting patterns from a large amount of data to support the decision-making process.

This book presents recent theoretical and practical advances in the field of data mining. It reports on a number of data mining methods, including *classification*, *clustering*, and *association rule mining*.

This book brings together many different successful data mining studies in various areas such as health, banking, education, software engineering, animal science, and the environment. The goal is to help data miners, researchers, academics, and scientists who wish to apply data mining techniques in their studies. The references collected in this book may be used as further reading lists.

The main strength of the book is the wealth of the case studies contained within. Chapters cover a number of innovative and recently developed data mining applications. Another important feature of the book is the clear introduction and background information provided at the beginning of each chapter.

The authors of this book have been actively working in the data mining field for years and thus have a lot of experience. They have the skills, knowledge, and expertise needed to share with us about real-world data mining applications. They have aimed at providing readers with a comprehensive understanding of data mining methods and thus present research results in various domains from different points of view. They explain the fundamental data mining techniques for extracting information from a large dataset.

It was not possible for me to accomplish this book without the outstanding contributions of many people. I would like to thank the contributing authors for their excellent works. Much appreciation goes to them for the time and effort they put in. I would also like to thank my husband for his love and support during the editing of this book. I also extend many thanks to Lada Bozic and Marijana Francetic for facilitating administrative matters. Finally, I express my gratitude to the publisher, IntechOpen, for giving me the opportunity to complete this book.

I hope you enjoy reading this book as much as I enjoyed editing it.

**Dr. Derya Birant** Associate Professor, Dokuz Eylül University, Department of Computer Engineering, Turkey

**Chapter 1**

**Abstract**

provided.

**1. Introduction**

visual data.

**1**

computer vision, transfer learning

Deep Learning: Exemplar Studies

Deep learning has become the most popular approach in machine learning in recent years. The reason lies in considerably high accuracies obtained by deep learning methods in many tasks especially with textual and visual data. In fact, natural language processing (NLP) and computer vision are the two research areas that deep learning has demonstrated its impact at utmost level. This chapter will firstly summarize the historical evolution of deep neural networks and their funda-

mental working principles. After briefly introducing the natural language processing and computer vision research areas, it will explain how exactly deep learning is used to solve the problems in these two areas. Several examples regarding the common tasks of these research areas and some discussion are also

**Keywords:** deep learning, machine learning, natural language processing,

Early approaches of artificial intelligence (AI) have sought solutions through formal representation of knowledge and applying logical inference rules. Later on, with having more data available, machine learning approaches prevailed which have the capability of learning from data. Many successful examples today, such as language translation, are results of this data-driven approach. When compared to other machine learning approaches, deep learning (deep artificial neural networks) has two advantages. It benefits well from vast amount of data—more and more of what we do is recorded every day, and it does not require defining the features to be learned beforehand. As a consequence, in the last decade, we have seen numerous success stories achieved with deep learning approaches especially with textual and

In this chapter, first a relatively short history of neural networks will be provided, and their main principles will be explained. Then, the chapter will proceed to two parallel paths. The first path treats text data and explains the use of deep learning in the area of natural language processing (NLP). Neural network methods first transformed the core task of language modeling. Neural language models have been introduced, and they superseded n-gram language models. Thus, initially the task of language modeling will be covered. The primary focus of this part will be

in Natural Language Processing

and Computer Vision

*Selma Tekir and Yalin Bastanlar*

#### **Chapter 1**
