Sriparna Saha – författare

The technological advancements made in recent decades have not only helped us better comprehend the morphology and physiology of the organs of the human body, but they have also advanced the diagnosis and, therefore, the treatment of a number of diseases in a variety of medical specialties from very early stages. Artificial Intelligence (AI) and Computer Vision (CV) enable us to collect, process, interpret, and analyze a limitless quantity of static and dynamic medical data in real time, which improve the way each disease is characterized and the patients are chosen. Many potentially fatal illnesses, such as COVID-19, pneumonia, and cancer, can be cured if diagnosed in initial stages very early on. Computer-based medical imaging techniques, such as CT scan and X-rays are useful in detecting all of these illnesses. On the other hand, various brain anomalies and heart diseases can also be anticipated using biological signals, like electroencephalography (EEG), electrocardiogram (ECG) etc. The application of machine learning makes the predictions more accurate and help the clinician to detect appropriate one. This helps in faster recognition of disease as well as with the intervention of the technology, makes it feasible to spread to the remote places. The goal of the book is to create machine learning algorithms that aids in the analysis of diverse medical data and the prediction of diseases based on the characteristics of the data.

The technological advancements made in recent decades have not only helped us better comprehend the morphology and physiology of the organs of the human body, but they have also advanced the diagnosis and, therefore, the treatment of a number of diseases in a variety of medical specialties from very early stages. Artificial Intelligence (AI) and Computer Vision (CV) enable us to collect, process, interpret, and analyze a limitless quantity of static and dynamic medical data in real time, which improve the way each disease is characterized and the patients are chosen. Many potentially fatal illnesses, such as COVID-19, pneumonia, and cancer, can be cured if diagnosed in initial stages very early on. Computer-based medical imaging techniques, such as CT scan and X-rays are useful in detecting all of these illnesses. On the other hand, various brain anomalies and heart diseases can also be anticipated using biological signals, like electroencephalography (EEG), electrocardiogram (ECG) etc. The application of machine learning makes the predictions more accurate and help the clinician to detect appropriate one. This helps in faster recognition of disease as well as with the intervention of the technology, makes it feasible to spread to the remote places. The goal of the book is to create machine learning algorithms that aids in the analysis of diverse medical data and the prediction of diseases based on the characteristics of the data.

This book presents a thorough analysis of gestural data extracted from raw images and/or range data with an aim to recognize the gestures conveyed by the data. It covers image morphological analysis, type-2 fuzzy logic, neural networks and evolutionary computation for classification of gestural data. The application areas include the recognition of primitive postures in ballet/classical Indian dances, detection of pathological disorders from gestural data of elderly people, controlling motion of cars in gesture-driven gaming and gesture-commanded robot control for people with neuro-motor disability.

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.