Sanghamitra Bandyopadhyay – författare

An invaluable tool in Bioinformatics, this unique volume provides both theoretical and experimental results, and describes basic principles of computational intelligence and pattern analysis while deepening the reader's understanding of the ways in which these principles can be used for analyzing biological data in an efficient manner. This book synthesizes current research in the integration of computational intelligence and pattern analysis techniques, either individually or in a hybridized manner. The purpose is to analyze biological data and enable extraction of more meaningful information and insight from it. Biological data for analysis include sequence data, secondary and tertiary structure data, and microarray data. These data types are complex and advanced methods are required, including the use of domain-specific knowledge for reducing search space, dealing with uncertainty, partial truth and imprecision, efficient linear and/or sub-linear scalability, incremental approaches to knowledge discovery, and increased level and intelligence of interactivity with human experts and decision makers Chapters authored by leading researchers in CI in biology informatics.Covers highly relevant topics: rational drug design; analysis of microRNAs and their involvement in human diseases.Supplementary material included: program code and relevant data sets correspond to chapters.

Inequality and its evolution over time are increasingly important subjects within the social sciences, particularly in the field of political economy. This Element identifies for the first time which inequality measures are best suited to capture the dynamics of inequality. The author generates a dataset of twelve types of inequality measures for 108 years across 34 countries using mortality distributions. When modelling inequality as a fractionally integrated process and using a Vector Autoregression approach, they find that mean-independent inequality measures are more suited to dynamic studies. In contrast, however, mean-dependent measures are unsuitable for dynamic studies. They suggest that no inequality measure should be used for dynamic purposes without rigorously testing its suitability. Tests of temporal normality and volatility serve as excellent "marker" tests of whether a chosen inequality measure is suitable for dynamic contexts. This title is also available as Open Access on Cambridge Core.

Inequality and its evolution over time are increasingly important subjects within the social sciences, particularly in the field of political economy. This Element identifies for the first time which inequality measures are best suited to capture the dynamics of inequality. The author generates a dataset of twelve types of inequality measures for 108 years across 34 countries using mortality distributions. When modelling inequality as a fractionally integrated process and using a Vector Autoregression approach, they find that mean-independent inequality measures are more suited to dynamic studies. In contrast, however, mean-dependent measures are unsuitable for dynamic studies. They suggest that no inequality measure should be used for dynamic purposes without rigorously testing its suitability. Tests of temporal normality and volatility serve as excellent "marker" tests of whether a chosen inequality measure is suitable for dynamic contexts. This title is also available as Open Access on Cambridge Core.

This volume contains research on how we measure poverty, inequality and welfare and how we use such measurements to devise policies to deliver social mobility. It contains ten papers, some of which were presented at the third meeting of The Theory and Empirics of Poverty, Inequality and Mobility at Queen Mary University of London, London, October 2016. The volume begins with theoretical issues at the frontier of the literature. Three papers discuss the impact of social welfare policies on poverty measurement, and with innovations on the measurement of relative bipolarisation. Two papers address the conceptualisation of multidimensional poverty by incorporating inequality within the poor, and that of chronic poverty for time dependent analyses, with applications to India and Haiti, and Ethiopia respectively. The second half of the volume consists of empirical contributions, using novel techniques and datasets to investigate the dynamics of poverty and welfare. These studies track the dynamics of poverty using unique datasets for China, the Caucasus and Italy. The volume concludes with investigations about within-household inequalities between siblings due to the unequal effects of conditional cash transfers in Cambodia and a cross-country study on the effect of historical income inequality on entrepreneurship in developing countries.

This volume of Research on Economic Inequality contains research on how we measure poverty, inequality and welfare and how these measurements contribute towards policies for social mobility. The volume contains eleven papers, some of which focus on the uneven impact of the Covid-19 pandemic on poverty and welfare.Opening with debates on theoretical issues that lie at the forefront of the measurement of inequality and poverty literature, the first two chapters go on to propose new methods for measuring wellbeing and inequality in multidimensional categorical environments, and for measuring pro-poor growth in a Bayesian setting. The following three papers present theoretical innovations for measuring poverty and inequality, namely, in estimating the dynamic probability of being poor using a Bayesian approach, and when presented with ordinal variables.The next three chapters are contributions on empirical methods in the measurement of poverty, inclusive economic growth and mobility, with a focus on India, Israel and a unique longitudinal dataset for Chile. The volume concludes with three chapters exploring the impact of the Covid-19 pandemic as an economic shock on income and wealth poverty in EU countries and in an Argentinian city slum.

Research on Economic Inequality is a well-established publication of quality research. This 30th volume features insightful and original papers from the 9th Society for the Study of Economic Inequality (ECINEQ) meeting.Mobility and Inequality Trends begins by illustrating the trajectory of income inequality in the world over the course of recent decades before the second paper makes a crucial distinction between ‘bad’ inequality, which is detrimental to society, and ‘good’ inequality, which is beneficial. Focus then shifts to bad inequality, one paper covering the relationship between intergenerational elasticity and inequality of opportunity, and the second studying the relationship between intergenerational mobility and life satisfaction in Spain. The volume then progresses to defend the use of intermediate views of inequality when constructing indicators of social welfare obtained through the use of average income and the Gini coefficient before investigating the advantage of using a multifaceted approach to income mobility measurement. To conclude Mobility and Inequality Trends presents an intensive exploration of income inequality in China and then studies the effects of the policy measure “Minimum Living Income. Finally, the last paper studies the impact of the COVID-19 pandemic on economic stimulus policies.

Research on Economic Inequality is a well-established publication of quality research. Volume 31 provides original research on intergenerational earnings mobility, the factors determining adult health inequality, the evolution of wealth inequality in different regions of the world, inequality of employment opportunities, and the effects of the type of labour agreement on wage inequality and on an extended measure (income plus wealth) of well-being.Opportunity, Mobility and Inequality presents newly published, rich datasets and deepens our understanding of these issues, uncovering novel innovations around how we conceive and measure such economic entities.

This book constitutes the proceedings of the 6th International Conference on Pattern Recognition and Machine Intelligence, PReMI 2015, held in Warsaw, Poland, in June/July 2015. image processing; image retrieval; image tracking; data mining techniques for large scale data;

This Edited Volumecontains a selection of refereed and revised papers originally presented at thesecond International Symposium on Signal Processing and Intelligent RecognitionSystems (SIRS-2015), December 16-19, 2015, Trivandrum, India.

This book provides a unified framework that describes how genetic learning can be used to design pattern recognition and learning systems. It examines how a search technique, the genetic algorithm, can be used for pattern classification mainly through approximating decision boundaries. Coverage also demonstrates the effectiveness of the genetic classifiers vis-a-vis several widely used classifiers, including neural networks.

This book provides a unified framework that describes how genetic learning can be used to design pattern recognition and learning systems. It examines how a search technique, the genetic algorithm, can be used for pattern classification mainly through approximating decision boundaries. Coverage also demonstrates the effectiveness of the genetic classifiers vis-a-vis several widely used classifiers, including neural networks.

This is the first book primarily dedicated to clustering using multiobjective genetic algorithms with extensive real-life applications in data mining and bioinformatics. The authors first offer detailed introductions to the relevant techniques – genetic algorithms, multiobjective optimization, soft computing, data mining and bioinformatics. They then demonstrate systematic applications of these techniques to real-world problems in the areas of data mining, bioinformatics and geoscience. The authors offer detailed theoretical and statistical notes, guides to future research, and chapter summaries.The book can be used as a textbook and as a reference book by graduate students and academic and industrial researchers in the areas of soft computing, data mining, bioinformatics and geoscience.

This volume constitutes the refereed proceedings of the Fourth International Conference on Contemporary Computing, IC3 2010, held in Noida, India, in August 2011. The 58 revised full papers presented were carefully reviewed and selected from 175 submissions.

Clustering is an important unsupervised classification technique where data points are grouped such that points that are similar in some sense belong to the same cluster. Cluster analysis is a complex problem as a variety of similarity and dissimilarity measures exist in the literature.This is the first book focused on clustering with a particular emphasis on symmetry-based measures of similarity and metaheuristic approaches. The aim is to find a suitable grouping of the input data set so that some criteria are optimized, and using this the authors frame the clustering problem as an optimization one where the objectives to be optimized may represent different characteristics such as compactness, symmetrical compactness, separation between clusters, or connectivity within a cluster. They explain the techniques in detail and outline many detailed applications in data mining, remote sensing and brain imaging, gene expression data analysis, and face detection.The book will be useful to graduate students and researchers in computer science, electrical engineering, system science, and information technology, both as a text and as a reference book. It will also be useful to researchers and practitioners in industry working on pattern recognition, data mining, soft computing, metaheuristics, bioinformatics, remote sensing, and brain imaging.

Clustering is an important unsupervised classification technique where data points are grouped such that points that are similar in some sense belong to the same cluster. Cluster analysis is a complex problem as a variety of similarity and dissimilarity measures exist in the literature.This is the first book focused on clustering with a particular emphasis on symmetry-based measures of similarity and metaheuristic approaches. The aim is to find a suitable grouping of the input data set so that some criteria are optimized, and using this the authors frame the clustering problem as an optimization one where the objectives to be optimized may represent different characteristics such as compactness, symmetrical compactness, separation between clusters, or connectivity within a cluster. They explain the techniques in detail and outline many detailed applications in data mining, remote sensing and brain imaging, gene expression data analysis, and face detection.The book will be useful to graduate students and researchers in computer science, electrical engineering, system science, and information technology, both as a text and as a reference book. It will also be useful to researchers and practitioners in industry working on pattern recognition, data mining, soft computing, metaheuristics, bioinformatics, remote sensing, and brain imaging.

This is the first book primarily dedicated to clustering using multiobjective genetic algorithms with extensive real-life applications in data mining and bioinformatics. The authors first offer detailed introductions to the relevant techniques – genetic algorithms, multiobjective optimization, soft computing, data mining and bioinformatics. They then demonstrate systematic applications of these techniques to real-world problems in the areas of data mining, bioinformatics and geoscience. The authors offer detailed theoretical and statistical notes, guides to future research, and chapter summaries.The book can be used as a textbook and as a reference book by graduate students and academic and industrial researchers in the areas of soft computing, data mining, bioinformatics and geoscience.

Bioinformatics, a field devoted to the interpretation and analysis of biological data using computational techniques, has evolved tremendously in recent years due to the explosive growth of biological information generated by the scientific community. Soft computing is a consortium of methodologies that work synergistically and provides, in one form or another, flexible information processing capabilities for handling real-life ambiguous situations. Several research articles dealing with the application of soft computing tools to bioinformatics have been published in the recent past; however, they are scattered in different journals, conference proceedings and technical reports, thus causing inconvenience to readers, students and researchers.This book, unique in its nature, is aimed at providing a treatise in a unified framework, with both theoretical and experimental results, describing the basic principles of soft computing and demonstrating the various ways in which they can be used for analyzing biological data in an efficient manner. Interesting research articles from eminent scientists around the world are brought together in a systematic way such that the reader will be able to understand the issues and challenges in this domain, the existing ways of tackling them, recent trends, and future directions. This book is the first of its kind to bring together two important research areas, soft computing and bioinformatics, in order to demonstrate how the tools and techniques in the former can be used for efficiently solving several problems in the latter.

This book provides an updated and in-depth introduction to the application of multiobjective optimization techniques in bioinformatics.