Ajith Abraham Livres

Cluster analysis organizes an unlabeled collection of objects into distinct groups based on similarity. This process has been explored through various domains, including graph theory, multivariate analysis, neural networks, and fuzzy set theory. While clustering is often labeled as an unsupervised learning method, many traditional algorithms require prior specification of the number of clusters, which limits its unsupervised nature. Modern data mining tools necessitate rapid and fully automatic clustering of large datasets with minimal user intervention to predict future trends and behaviors for informed decision-making. This volume presents clustering as an optimization problem, aiming to find the best partitioning of a dataset by minimizing or maximizing one or more objective functions. It showcases real-world applications and demonstrates the effectiveness of several metaheuristics, particularly the Differential Evolution algorithm, in both single and multi-objective clustering scenarios where the number of clusters is not predetermined. Comprising seven chapters, the book starts with fundamental definitions and concludes with significant research challenges. It offers valuable insights for academics, scientists, and engineers involved in optimization techniques and data mining research and applications.

SVMs have become highly effective pattern recognition methods, demonstrating superior performance in applications like text categorization, face detection, content-based image retrieval, and learning image similarity. The focus here is on detecting suspicious bags in security scenarios. Traditionally, bomb personnel resort to detonating suspicious bags, which can be catastrophic if the bags contain chemical, biological, or radiological materials. This method also destroys crucial evidence such as fingerprints and explosive signatures needed for forensic analysis. Utilizing telerobotics for extraction of bag contents mitigates these risks. In a telerobotic system, automating bag classification is beneficial, especially when combined with robotic tactics like shaking out contents. In cases where autonomous capabilities falter, human intervention may be necessary to identify bag types. This discussion emphasizes the autonomous operation of the telerobotic system, where SVMs classify bags to identify initial manipulator grasp points. Proper grasping requires the gripper to slide under the bag, but slippage can lead to failure. Therefore, understanding the bag type and the location of its opening is critical before assessing grasp points.

Swarm Intelligence is an innovative distributed intelligent paradigm for solving optimization problems that originally took its inspiration from the biological examples by swarming, flocking and herding phenomena in vertebrates. Data Mining is an analytic process designed to explore large amounts of data in search of consistent patterns and/or systematic relationships between variables, and then to validate the findings by applying the detected patterns to new subsets of data. This book deals with the application of swarm intelligence in data mining. Addressing the various issues of swarm intelligence and data mining using different intelligent approaches is the novelty of this edited volume. This volume comprises of 11 chapters including an introductory chapter giving the fundamental definitions and some important research challenges. Important features include the detailed overview of the various swarm intelligence and data mining paradigms, excellent coverage of timely, advanced data mining topics, state-of-the-art theoretical research and application developments and chapters authored by pioneers in the field. Academics, scientists as well as engineers engaged in research, development and application of optimization techniques and data mining will find the comprehensive coverage of this book invaluable.

This comprehensive text is the first of three volumes on computational social networks (CSNs), exploring the intersection of social behavior and computer systems. It features contributions from global experts, focusing on practical tools, applications, and research opportunities. Ideal for researchers and students, it covers design, analysis, and simulations of social networks.

This volume is the first part of a four-volume set (CCIS 190, CCIS 191, CCIS 192, CCIS 193), which constitutes the refereed proceedings of the First International Conference on Computing and Communications, ACC 2011, held in Kochi, India, in July 2011. The 68 revised full papers presented in this volume were carefully reviewed and selected from a large number of submissions. The papers are organized in topical sections on ad hoc networks; advanced micro architecture techniques; autonomic and context-aware computing; bioinformatics and bio-computing; cloud, cluster, grid and P2P computing; cognitive radio and cognitive networks; cyber forensics; database and information systems.

This volume is the third part of a four-volume set (CCIS 190, CCIS 191, CCIS 192, CCIS 193), which constitutes the refereed proceedings of the First International Conference on Computing and Communications, ACC 2011, held in Kochi, India, in July 2011. The 70 revised full papers presented in this volume were carefullyreviewed and selected from a large number of submissions. The papers are organized in topical sections on security, trust and privacy; sensor networks; signal and image processing; soft computing techniques; system software; vehicular communications networks.

This book presents the proceedings of the Fourth International Workshop on Soft Computing as Transdisciplinary Science and Technology (WSTST '05), May 25-27, 2005, Muroran, Japan. It brings together the original work of international soft computing/computational intelligence researchers, developers, practitioners, and users. This proceedings provide contributions to all areas of soft computing including intelligent hybrid systems, agent-based systems, intelligent data mining, decision support systems, cognitive and reactive distributed artificial intelligence (AI), internet modelling, human interface, and applications in science and technology.

The International Symposium on Distributed Computing and Artificial Intelligence 2011 (DCAI 2011) serves as a dynamic forum for the scientific community to foster collaboration in the fields of Distributed Computing and Artificial Intelligence. This conference highlights innovative techniques applied to complex problems, showcasing the transformative impact of artificial intelligence on society. Its applications in distributed environments—such as the internet, e-commerce, environmental monitoring, mobile communications, and wireless devices—are rapidly expanding, offering significant social and economic benefits across industry, quality of life, and research. The continuous evolution of these technologies is driven by extensive research efforts in both academic and industrial settings. Facilitating the exchange of ideas among scientists and technicians is crucial for developing systems that address contemporary societal needs. This edition of DCAI consolidates past experiences, current research, and promising trends in distributed computing and artificial intelligence, aiming to provide efficient solutions to real-world challenges. Organized by the Bioinformatics, Intelligent System and Educational Technology Research Group at the University of Salamanca, the symposium took place in Salamanca, Spain, from April 6 to 8, 2011.

This book constitutes the thoroughly refereed post-conference proceedings of the first International Symposium on Intelligent Informatics (ISI'12) held in Chennai, India during August 4-5, 2012. The 54 revised papers presented were carefully reviewed and selected from 165 initial submissions. The papers are organized in topical sections on data mining, clustering and intelligent information systems, multi agent systems, pattern recognition, signal and image processing and, computer networks and distributed systems. The book is directed to the researchers and scientists engaged in various fields of intelligent informatics.

Digital systems that bring together the computing capacity for processing large bodies of information with the human cognitive capability are called intelligent systems.