Showing papers by "Lakhmi C. Jain published in 2019"

Applications of Learning and Analytics in Intelligent Systems

Abstract: The book at hand constitutes the inaugural volume in the new Springer series on Learning and Analytics in Intelligent Systems. The series aims at making available a publication of books in hardcopy and soft-copy form on all aspects of learning, analytics and advanced intelligent systems and related technologies. These disciplines are strongly related and complement one another significantly. Thus, the new series encourages a unified/integrated approach to themes and topics in these disciplines which will result in significant cross-fertilization, research advancement and new knowledge creation. To maximize dissemination of research results and knowledge, the series will publish edited books, monographs, handbooks, textbooks and conference proceedings. The book at hand is directed towards professors, researchers, scientists, engineers and students. An extensive list of references at the end of each chapter guides readers to probe further into application areas of interest to them.

Co-authorship Networks: An Introduction

Abstract: In this chapter we introduce and formally define co-authorship networks. Formal definitions of co-authorship networks as undirected graphs, directed graphs and hypergraphs are given. Different schemes to assign weights to co-authorship links are also discussed. Then, we give a classification of co-authorship networks according to the type of research collaboration they represent. Finally, the main applications of co-authorship networks are outlined.

Machine Learning Paradigms: Advances in Data Analytics

Abstract: At the dawn of the 4th Industrial Revolution, data analytics is emerging as a force that drives towards dramatic changes in our daily lives, the workplace and human relationships. Synergies between physical, digital, biological and energy sciences and technologies, brought together by non-traditional data collection and analysis, drive the digital economy at all levels and offer new, previously-unavailable opportunities. The need for data analytics arises in most modern scientific disciplines, including engineering; natural-, computer- and information sciences; economics; business; commerce; environment; healthcare; and life sciences. Coming as the third volume under the general title MACHINE LEARNING PARADIGMS, the book includes an editorial note (Chapter 1) and an additional 12 chapters, and is divided into five parts: (1) Data Analytics in the Medical, Biological and Signal Sciences, (2) Data Analytics in Social Studies and Social Interactions, (3) Data Analytics in Traffic, Computer and Power Networks, (4) Data Analytics for Digital Forensics, and (5) Theoretical Advances and Tools for Data Analytics. This research book is intended for both experts/researchers in the field of data analytics, and readers working in the fields of artificial and computational intelligence as well as computer science in general who wish to learn more about the field of data analytics and its applications. An extensive list of bibliographic references at the end of each chapter guides readers to probe further into the application areas of interest to them.

Analysis of Co-authorship Networks

Abstract: Scientific collaboration can be quantitatively studied by analyzing the structure and evolution of co-authorship networks. In this chapter we present a comprehensive overview of research studies focused on empirical analysis of co-authorship networks. Typical structural and evolutionary characteristics of co-authorship networks are identified by an aggregate analysis of examined studies.

Analysis of Enriched Co-authorship Networks: Methodology and a Case Study

Abstract: The nodes of an enriched co-authorship network are annotated with various types of nominal and numeric attributes that provide additional information about researchers present in the network. In this chapter we propose a novel methodology to study the structure and evolution of enriched co-authorship networks. The proposed methodology is illustrated on an enriched co-authorship network encompassing researchers employed at one large faculty of sciences.

Extraction of Co-authorship Networks

Abstract: The extraction of a co-authorship network from a set of bibliographic records in which articles and authors are uniquely identified is an easily solvable problem. However, in a vast majority of bibliographic databases authors are identified by their names. This causes the problem of correct identification of nodes in co-authorship networks due to ambiguous author names. In this chapter we present an overview of initial-based, heuristic and machine learning approaches to the name disambiguation problem. Then, we study the performance of various string similarity measures for detecting name synonyms in bibliographic records. After that, we propose a novel method for disambiguating author names that is based on reference similarity networks and community detection techniques. Finally, we present a case study investigating the impact of name disambiguation on the structure of co-authorship networks.

Fundamentals of Complex Network Analysis

Abstract: Complex network analysis is a collection of quantitative methods for studying the structure and dynamics of complex networked systems. This chapter presents the fundamentals of complex network analysis. We start out by presenting the basic concepts of complex networks and graph theory. Then, we focus on fundamental network analysis measures and algorithms related to node connectivity, distance, centrality, similarity and clustering. Finally, we discuss fundamental complex network models and their characteristics.

Introduction to Complex Networks

Abstract: Complex networks are graphs describing complex natural, conceptual and engineered systems. In this chapter we present an introduction to complex networks by giving several examples of technological, social, information and biological networks. Then, we discuss complex networks that are in the focus of this monograph (software, ontology and co-authorship networks). Finally, we briefly outline our main research contributions presented in the monograph.

More Than Data Mining

Abstract: Data Mining has received a great momentum of interest due to the automatic processes transforming big amount of data into novel, valid, useful, and structured knowledge by detecting concealed patterns and relationships in data. However, embedded knowledge has not been thoroughly considered in data mining. The chapters reported in this book discuss on several facets of embedded knowledge and propose solutions for data mining.