Data visualization is currently a very active and vital area of research, teaching and development. The term unites the established field of scientific visualization and the more recent field of information visualization. The success of data visualization is due to the soundness of the basic idea behind it: the use of computer-generated images to gain insight and knowledge from data and its inherent patterns and relationships. A second premise is the utilization of the broad bandwidth of the human sensory system in steering and interpreting complex processes, and simulations involving data sets from diverse scientific disciplines and large collections of abstract data from many sources.

This book is the third volume of the Handbook of Computational Statistics and cov- ers the field of data visualization. In line with the companion volumes, it contains a collection of chapters by experts in the field to present readers with an up-to-date andcomprehensiveoverviewofthestateoftheart.Datavisualizationisanactivearea of application and research, and this is a good time to gather together a summary of current knowledge.

Aims and Scope This book is both an introductory textbook and a research monograph on modeling the statistical structure of natural images. In very simple terms, “natural images” are photographs of the typical environment where we live. In this book, their statistical structure is described using a number of statistical models whose parameters are estimated from image samples. Our main motivation for exploring natural image statistics is computational m- eling of biological visual systems. A theoretical framework which is gaining more and more support considers the properties of the visual system to be re?ections of the statistical structure of natural images because of evolutionary adaptation processes. Another motivation for natural image statistics research is in computer science and engineering, where it helps in development of better image processing and computer vision methods. While research on natural image statistics has been growing rapidly since the mid-1990s, no attempt has been made to cover the ?eld in a single book, providing a uni?ed view of the different models and approaches. This book attempts to do just that. Furthermore, our aim is to provide an accessible introduction to the ?eld for students in related disciplines.

This proposed text appears to be a good introduction to evolutionary computation for use in applied statistics research. The authors draw from a vast base of knowledge about the current literature in both the design of evolutionary algorithms and statistical techniques. Modern statistical research is on the threshold of solving increasingly complex problems in high dimensions, and the generalization of its methodology to parameters whose estimators do not follow mathematically simple distributions is underway. Many of these challenges involve optimizing functions for which analytic solutions are infeasible. Evolutionary algorithms represent a powerful and easily understood means of approximating the optimum value in a variety of settings. The proposed text seeks to guide readers through the crucial issues of optimization problems in statistical settings and the implementation of tailored methods (including both stand-alone evolutionary algorithms and hybrid crosses of these procedures with standard statistical algorithms like Metropolis-Hastings) in a variety of applications. This book would serve as an excellent reference work for statistical researchers at an advanced graduate level or beyond, particularly those with a strong background in computer science.

This book constitutes the proceedings of the LISA 2025 Challenge, held in conjunction with MICCAI 2025 in Daejeon, South Korea, during September 27, 2025.

Computer vision is one of the most complex and computationally intensive problem. Like any other computationally intensive problems, parallel pro­ cessing has been suggested as an approach to solving the problems in com­ puter vision. Computer vision employs algorithms from a wide range of areas such as image and signal processing, advanced mathematics, graph theory, databases and artificial intelligence. Hence, not only are the comput­ ing requirements for solving vision problems tremendous but they also demand computers that are efficient to solve problems exhibiting vastly dif­ ferent characteristics. With recent advances in VLSI design technology, Single Instruction Multiple Data (SIMD) massively parallel computers have been proposed and built. However, such architectures have been shown to be useful for solving a very limited subset of the problems in vision. Specifically, algorithms from low level vision that involve computations closely mimicking the architec­ ture and require simple control and computations are suitable for massively parallel SIMD computers. An Integrated Vision System (IVS) involves com­ putations from low to high level vision to be executed in a systematic fashion and repeatedly. The interaction between computations and information dependent nature of the computations suggests that architectural require­ ments for computer vision systems can not be satisfied by massively parallel SIMD computers.

This book presents best selected research papers presented at the 4th International Conference on Cognitive Informatics and Soft Computing (CISC 2021), held at Balasore College of Engineering & Technology, Balasore, Odisha, India, from 21–22 August 2021. It highlights, in particular, innovative research in the fields of cognitive informatics, cognitive computing, computational intelligence, advanced computing, and hybrid intelligent models and applications. New algorithms and methods in a variety of fields are presented, together with solution-based approaches. The topics addressed include various theoretical aspects and applications of computer science, artificial intelligence, cybernetics, automation control theory, and software engineering.

The three volume set LNAI 10462, LNAI 10463, and LNAI 10464 constitutes the refereed proceedings of the 10th International Conference on Intelligent Robotics and Applications, ICIRA 2017, held in Wuhan, China, in August 2017.
The 235 papers presented in the three volumes were carefully reviewed and selected from 310 submissions. The papers in this second volume of the set are organized in topical sections on industrial robot and robot manufacturing; mechanism and parallel robotics; machine and robot vision; robot grasping and control.

The three volume set LNAI 10462, LNAI 10463, and LNAI 10464 constitutes the refereed proceedings of the 10th International Conference on Intelligent Robotics and Applications, ICIRA 2017, held in Wuhan, China, in August 2017.
The 235 papers presented in the three volumes were carefully reviewed and selected from 310 submissions. The papers in this first volume of the set are organized in topical sections on soft, micro-nano, bio-inspired robotics; human-machine interaction; swarm robotics; underwater robotics.

The three volume set LNAI 10462, LNAI 10463, and LNAI 10464 constitutes the refereed proceedings of the 10th International Conference on Intelligent Robotics and Applications, ICIRA 2017, held in Wuhan, China, in August 2017.
The 235 papers presented in the three volumes were carefully reviewed and selected from 310 submissions. The papers in this first volume of the set are organized in topical sections on soft, micro-nano, bio-inspired robotics; human-machine interaction; swarm robotics; underwater robotics.

This book presents best selected research papers presented at the 4th International Conference on Cognitive Informatics and Soft Computing (CISC 2021), held at Balasore College of Engineering & Technology, Balasore, Odisha, India, from 21–22 August 2021. It highlights, in particular, innovative research in the fields of cognitive informatics, cognitive computing, computational intelligence, advanced computing, and hybrid intelligent models and applications. New algorithms and methods in a variety of fields are presented, together with solution-based approaches. The topics addressed include various theoretical aspects and applications of computer science, artificial intelligence, cybernetics, automation control theory, and software engineering.

After 20 years of pursuing rough set theory and its applications a look on its present state and further prospects is badly needed. The monograph Rough Set Theory and Granular Computing edited by Masahiro Inuiguchi, Shoji Hirano and Shusaku Tsumoto meets this demand. It presents the newest developments in this area and gives fair picture of the state of the art in this domain. Firstly, in the keynote papers by Zdzislaw Pawlak, Andrzej Skowron and Sankar K. Pal the relationship of rough sets with other important methods of data analysis -Bayes theorem, neuro computing and pattern recognitio- is thoroughly examined. Next, several interesting generalizations of the the­ ory and new directions of research are presented. Furthermore application of rough sets in data mining, in particular, rule induction methods based on rough set theory is presented and discussed. Further important issue dis­ cussed in the monograph is rough set based data analysis, including study of decisions making in conflict situations. Last but not least, some recent engi­ neering applications of rough set theory are given. They include a proposal of rough set processor architecture organization for fast implementation of ba­ sic rough set operations and discussion of results concerning advanced image processing for unmanned aerial vehicle. Thus the monograph beside presenting wide spectrum of ongoing research in this area also points out new emerging areas of study and applications, which makes it a valuable source of information to all interested in this do­ main.

In 1979, the Nobel Prize for Medicine and Physiology was awarded jointly to Allan McLeod Cormack and Godfrey Newbold Houns eld, the two pioneering scienti- engineers primarily responsible for the development, in the 1960s and early 1970s, of computerized axial tomography, popularly known as the CAT or CT scan. In his papers [13], Cormack, then a Professor at Tufts University, in Massachusetts, dev- oped certain mathematical algorithms that, he envisioned, could be used to create an image from X-ray data. Working completely independently of Cormack and at about the same time, Houns eld, a research scientist at EMI Central Research Laboratories in the United Kingdom, designed the rst operational CT scanner as well as the rst commercially available model. (See [22] and [23]. ) Since 1980, the number of CT scans performed each year in the United States has risen from about 3 million to over 67 million. What few people who have had CT scans probably realize is that the fundamental problem behind this procedure is essentially mathematical: If we know the values of the integral of a two- or three-dimensional fu- tion along all possible cross-sections, then how can we reconstruct the function itself? This particular example of what is known as an inverse problem was studied by Johann Radon, an Austrian mathematician, in the early part of the twentieth century.

The three volume set LNAI 10462, LNAI 10463, and LNAI 10464 constitutes the refereed proceedings of the 10th International Conference on Intelligent Robotics and Applications, ICIRA 2017, held in Wuhan, China, in August 2017.
The 235 papers presented in the three volumes were carefully reviewed and selected from 310 submissions. The papers in this first volume of the set are organized in topical sections on soft, micro-nano, bio-inspired robotics; human-machine interaction; swarm robotics; underwater robotics.

This book constitutes the refereed post-conference proceedings of the 5th International Conference on Cognitive Systems and Signal Processing, ICCSIP 2020, held in Zhuhai, China, in December 2020.
The 59 revised papers presented were carefully reviewed and selected from 120 submissions. The papers are organized in topical sections on algorithm; application; manipulation; bioinformatics; vision; and autonomous vehicles.