Hybridization of Biogeography-Based Optimization and Gravitational Search Algorithm for Efficient Face Recognition

This chapter aims to apply a novel hybridized evolutionary algorithm to the application of face recognition. Biogeography-based optimization (BBO) has some element of randomness to it that apart from improving the feasibility of a solution could reduce it as well. In order to overcome this drawback, this chapter proposes a hybridization of BBO with gravitational search algorithm (GSA), another nature-inspired algorithm, by incorporating certain knowledge into BBO instead of the randomness. The migration procedure of BBO that migrates SIVs between solutions is done between solutions only if the migration would lead to the betterment of a solution. BBO-GSA algorithm is applied to face recognition with the LFW (labelled faces in the wild) and ORL datasets in order to test its efficiency. Experimental results show that the proposed BBO-GSA algorithm outperforms or is on par with some of the nature-inspired techniques that have been applied to face recognition so far by achieving a recognition rate of 80% with the LFW dataset and 99.75% with the ORL dataset.

Big Data and Privacy State of the Art

The phenomenon of big data (massive data mining) refers to the exponential growth of the volume of data available on the web. This new concept has become widely used in recent years, enabling scalable, efficient, and fast access to data anytime, anywhere, helping the scientific community and companies identify the most subtle behaviors of users. However, big data has its share of the limits of ethical issues and risks that cannot be ignored. Indeed, new risks in terms of privacy are just beginning to be perceived. Sometimes simply annoying, these risks can be really harmful. In the medium term, the issue of privacy could become one of the biggest obstacles to the growth of big data solutions. It is in this context that a great deal of research is under way to enhance security and develop mechanisms for the protection of privacy of users. Although this area is still in its infancy, the list of possibilities continues to grow.

Spam Filtering and Detection

In today's world of globalization and technology without borders, the emergence of the internet and the rapid development of telecommunications have made the world a global village. Recently, the email service has become immensely used, and the main means of communication because it is cheap, reliable, fast, and easy to access. In addition, it allows users with a mailbox (BAL) and email address to exchange messages (images, files, and text documents) from anywhere in the world via the internet. Unfortunately, this technology has become undeniably the original source of malicious activity, in particular the problem of unwanted emails (spam), which has increased dramatically over the past decade. According to the latest report from Radicati Group, which provides quantitative and qualitative research with details of the e-mail, security, and social networks, published in 2012, 70-80% of email traffic consists of spam. The goal of the chapter is to give a state of the art on spam and spam techniques and the disadvantages of this phenomenon.

Cardiovascular Disease Prediction Using Data Mining Techniques

Cardiovascular diseases (CVDs) have turned out to be one of the life-threatening diseases in recent times. The key to effectively managing this is to analyze a huge amount of datasets and effectively mine it to predict and further prevent heart-related diseases. The primary objective of this chapter is to understand and survey various information mining strategies to efficiently determine occurrence of CVDs and also propose a big data architecture for the same. The authors make use of Apache Spark for the implementation.

Context of the Person in Social Networks

In this chapter, the authors define the context of the person in social networks. Subsequently they introduce modeling and context of the development process of a person. Then they work on analyzing and feeling defined analysis technique, the sour feelings tweets and their characteristics, and method of how to recuperate data from Twitter by using API extraction.

Ecological Data Exploration

Computer modeling of ecological systems is the activity of implementing computer solutions to analyze data related to the fields of remote sensing, earth science, biology, and oceans. The ecologists analyze the data to identify the relationships between a response and a set of predictors, using statistical models that do not accurately describe the main sources of variation in the response variable. Knowledge discovery techniques are often more powerful, flexible, and effective for exploratory analysis than statistical techniques. This chapter aims to test the use of data mining in ecology. It will discuss the exploration of ecological data by defining at first data mining, its advantages, and its different types. Then the authors detail the field of bio-inspiration and meta-heuristics. And finally, they give case studies from where they applied these two areas to explore ecological data.

Clear and Private Ad Hoc Retrieval Models on Web Data

During the 1980s, and despite its maturity, the search information (RI) was only intended for librarians and experts in the field of information. Such tendentious vision prevailed for many years. Since the mid-90s, the web has become an increasingly crucial source of information , which has a renewed interest in IR. In the last decade, the popularization of computers, the terrible explosion in the amount of unstructured data, internal documents, and corporate collections, and the huge and growing number of internet document sources have deeply shaken the relationship between man and information. Today, a great change has taken place, and the RI is often used by billions of people around the world. Simply, the need for automated methods for efficient access to this huge amount of digital information has become more important, and appears as a necessity.

Enrichment Ontology via Linked Data

The automatic construction of ontologies from texts is usually based on the text itself, and the domain described is limited to the content of the text. In order to design semantically richer ontologies, the authors propose to extend the classical methods of ontology construction (1) by taking into account the text from the point of view of its structure and its content to build a first nucleus ontology and (2) enriching the ontology obtained by exploiting external resources (general texts and controlled vocabularies of the same domain). This chapter describes how these different resources are analyzed and exploited using linked data properties.

Study on the Different Forms of Plagiarism in Textual Data and Image

We live in a world of information. It is everywhere, but it is sometimes difficult to find and know that data first. In today's digital society, it's easy to find texts to plagiarize. These texts may come from the internet, publishers, or other content providers. Plagiarism is considered a serious fault. Throughout the world, universities are making significant efforts to educate students and teachers, offering guides and tutorials to explain the types of plagiarism, to avoid plagiarism. Internet contains easy to get texts the people can use in their newsrooms simply using copy and paste. This chapter shows the various types of plagiarism and the different techniques of automatic plagiarism detection and related work that addresses the topic.

Classification and Machine Learning

The manual classification of a large amount of textual materials are very costly in time and personnel. For this reason, a lot of research has been devoted to the problem of automatic classification and work on the subject dates from 1960. A lot of text classification software has appeared. For some tasks, automatic classifiers perform almost as well as humans, but for others, the gap is still large. These systems are directly related to machine learning. It aims to achieve tasks normally affordable only by humans. There are generally two types of learning: learning “by heart,” which consists of storing information as is, and learning generalization, where we learn from examples. In this chapter, the authors address the classification concept in detail and how to solve different classification problems using different machine learning techniques.