Paramartha Dutta

This book discusses human emotion recognition from face images using different modalities, highlighting key topics in facial expression recognition, such as the grid formation, distance signature, shape signature, texture signature, feature selection, classifier design, and the combination of signatures to improve emotion recognition. The book explains how six basic human emotions can be recognized in various face images of the same person, as well as those available from benchmark face image databases like CK+, JAFFE, MMI, and MUG. The authors present the concept of signatures for different characteristics such as distance and shape texture, and describe the use of associated stability indices as features, supplementing the feature set with statistical parameters such as range, skewedness, kurtosis, and entropy. In addition, they demonstrate that experiments with such feature choices offer impressive results, and that performance can be further improved by combining thesignatures rather than using them individually.There is an increasing demand for emotion recognition in diverse fields, including psychotherapy, biomedicine, and security in government, public and private agencies. This book offers a valuable resource for researchers working in these areas.

This book discusses human emotion recognition from face images using different modalities, highlighting key topics in facial expression recognition, such as the grid formation, distance signature, shape signature, texture signature, feature selection, classifier design, and the combination of signatures to improve emotion recognition. The book explains how six basic human emotions can be recognized in various face images of the same person, as well as those available from benchmark face image databases like CK+, JAFFE, MMI, and MUG. The authors present the concept of signatures for different characteristics such as distance and shape texture, and describe the use of associated stability indices as features, supplementing the feature set with statistical parameters such as range, skewedness, kurtosis, and entropy. In addition, they demonstrate that experiments with such feature choices offer impressive results, and that performance can be further improved by combining thesignatures rather than using them individually.There is an increasing demand for emotion recognition in diverse fields, including psychotherapy, biomedicine, and security in government, public and private agencies. This book offers a valuable resource for researchers working in these areas.

This book identifies the time-dependent network parameters: neighbour count, link load, path length, cluster count and delay, and presents a first-of-its-kind discussion on temporal parameters in mobile ad hoc networks. Frequent topology changes and multiple link failures occur in mobile ad hoc network due to arbitrary and random movement of nodes. This dynamic environment challenges the delivery of data and makes it essential to find better models for network parameters that are shifting with time. The parameters identified are put into the framework of time series because of their temporal characteristic, and when they are modelled using time series framework they exhibit a sound fit with Autoregressive AR(p) models of order p. The order p is evaluated for each fitted model and found to lie between one and three. The book also analyses the dependence of end-to-end delay of ad hoc network on various external factors such as the number of nodes, routing protocol, mobility models and path length and develops two prediction models. The book will be useful for researchers and professionals alike.

This book explains efficient solutions for segmenting the intensity levels of different types of multilevel images. The authors present hybrid soft computing techniques, which have advantages over conventional soft computing solutions as they incorporate data heterogeneity into the clustering/segmentation procedures.This is a useful introduction and reference for researchers and graduate students of computer science and electronics engineering, particularly in the domains of image processing and computational intelligence.

This book identifies the time-dependent network parameters: neighbour count, link load, path length, cluster count and delay, and presents a first-of-its-kind discussion on temporal parameters in mobile ad hoc networks. Frequent topology changes and multiple link failures occur in mobile ad hoc network due to arbitrary and random movement of nodes. This dynamic environment challenges the delivery of data and makes it essential to find better models for network parameters that are shifting with time. The parameters identified are put into the framework of time series because of their temporal characteristic, and when they are modelled using time series framework they exhibit a sound fit with Autoregressive AR(p) models of order p. The order p is evaluated for each fitted model and found to lie between one and three. The book also analyses the dependence of end-to-end delay of ad hoc network on various external factors such as the number of nodes, routing protocol, mobility models and path length and develops two prediction models. The book will be useful for researchers and professionals alike.

This book explains efficient solutions for segmenting the intensity levels of different types of multilevel images. The authors present hybrid soft computing techniques, which have advantages over conventional soft computing solutions as they incorporate data heterogeneity into the clustering/segmentation procedures.This is a useful introduction and reference for researchers and graduate students of computer science and electronics engineering, particularly in the domains of image processing and computational intelligence.

Quantum Inspired Computational Intelligence: Research and Applications explores the latest quantum computational intelligence approaches, initiatives, and applications in computing, engineering, science, and business. The book explores this emerging field of research that applies principles of quantum mechanics to develop more efficient and robust intelligent systems. Conventional computational intelligence—or soft computing—is conjoined with quantum computing to achieve this objective. The models covered can be applied to any endeavor which handles complex and meaningful information.