Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Dimensionality reduction, also known as manifold learning, is an area of machine learning used for extracting informative features from data for better representation of data or separation between classes. This book presents a cohesive review of linear and nonlinear dimensionality reduction and manifold learning. Three main aspects of dimensionality reduction are covered: spectral dimensionality reduction, probabilistic dimensionality reduction, and neural network-based dimensionality reduction, which have geometric, probabilistic, and information-theoretic points of view to dimensionality reduction, respectively. The necessary background and preliminaries on linear algebra, optimization, and kernels are also explained to ensure a comprehensive understanding of the algorithms. The tools introduced in this book can be applied to various applications involving feature extraction, image processing, computer vision, and signal processing. This book is applicable to a wide audience who would like to acquire a deep understanding of the various ways to extract, transform, and understand the structure of data. The intended audiences are academics, students, and industry professionals. Academic researchers and students can use this book as a textbook for machine learning and dimensionality reduction. Data scientists, machine learning scientists, computer vision scientists, and computer scientists can use this book as a reference. It can also be helpful to statisticians in the field of statistical learning and applied mathematicians in the fields of manifolds and subspace analysis. Industry professionals, including applied engineers, data engineers, and engineers in various fields of science dealing with machine learning, can use this as a guidebook for feature extraction from their data, as the raw data in industry often require preprocessing. The book is grounded in theory but provides thorough explanations and diverse examples to improve the reader’s comprehension of the advanced topics. Advanced methods are explained in a step-by-step manner so that readers of all levels can follow the reasoning and come to a deep understanding of the concepts. This book does not assume advanced theoretical background in machine learning and provides necessary background, although an undergraduate-level background in linear algebra and calculus is recommended.

Dimension reduction (Statistics) --- Machine learning. --- Learning, Machine --- Artificial intelligence --- Machine theory --- Dimensionality reduction (Statistics) --- Reduction, Dimension (Statistics) --- Reduction, Dimensionality (Statistics) --- Statistics

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Biosensors are analytical devices used for the detection of a chemical substance, or analyte, which combines a biological component with a physicochemical detector. Detection and quantification are based on the measurement of the biological interactions. The biological element of a biosensor may consist of tissues, microorganisms, organelles, cell receptors, enzymes, antibodies and nucleic acids. These devices have been shown to have a wide range of applications in a vast array of fields of research, including environmental monitoring, food analysis, drug detection and health and clinical assessment, and even security and safety. The current Special Issue, “Biosensors: 10th Anniversary Feature Papers”, addresses the existing knowledge gaps and aids the advancement of biosensing applications, in the form of six peer-reviewed research and review papers, detailing the most recent and innovative developments of biosensors.

Technology: general issues --- History of engineering & technology --- Materials science --- antioxidants --- biosensor --- nanomaterials --- vasodilator activity --- breast cancer screening --- imaging biomarker --- deep sparse autoencoder --- dimensionality reduction --- deep-learning features --- Bioassays --- high performance thin layer chromatography --- endocrine disrupting compounds --- fluorescent proteins --- wastewater --- electromagnetic piezoelectric acoustic sensor --- quartz --- adsorption --- diagnostics --- extracellular vesicle --- dithiocarbamate fungicides --- chromatography --- Raman spectroscopy --- sensors --- enzyme inhibition --- voltammetry --- biosensors --- tobacco mosaic virus (TMV) --- capacitive field-effect sensor --- bi-enzyme biosensor --- enzyme-logic gate --- urease --- penicillinase

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This book is published open access under a CC BY 4.0 license. It covers aspects of unsupervised machine learning used for knowledge discovery in data science and introduces a data-driven approach to cluster analysis, the Databionic swarm(DBS). DBS consists of the 3D landscape visualization and clustering of data. The 3D landscape enables 3D printing of high-dimensional data structures.The clustering and number of clusters or an absence of cluster structure are verified by the 3D landscape at a glance. DBS is the first swarm-based technique that shows emergent properties while exploiting concepts of swarm intelligence, self-organization and the Nash equilibrium concept from game theory. It results in the elimination of a global objective function and the setting of parameters. By downloading the R package DBS can be applied to data drawn from diverse research fields and used even by non-professionals in the field of data mining. Contents Approaches to Unsupervised Machine Learning Methods of Visualization of High-Dimensional Data Quality Assessments of Visualizations Behavior-Based Systems in Data Science Databionic Swarm (DBS) Target Groups Lecturers, students as well as non-professional users of data science, statistics, computer science, business mathematics, medicine, biology The Author Michael C. Thrun, Dipl.-Phys., successfully defended his Ph.D. in 2017 at the Philipps University of Marburg. Thrun’s advisor was the Chair of Neuroinformatics, Prof. Dr. rer. nat. Alfred G. H. Ultsch.

Computer science. --- Data structures (Computer science). --- Pattern recognition. --- Computer Science. --- Pattern Recognition. --- Data Structures. --- Design perception --- Pattern recognition --- Form perception --- Perception --- Figure-ground perception --- Information structures (Computer science) --- Structures, Data (Computer science) --- Structures, Information (Computer science) --- Electronic data processing --- File organization (Computer science) --- Abstract data types (Computer science) --- Informatics --- Science --- Optical pattern recognition. --- Data structures (Computer scienc. --- Optical data processing --- Pattern perception --- Perceptrons --- Visual discrimination --- Data structures (Computer science) --- Cluster Analysis --- Dimensionality Reduction --- Swarm Intelligence --- Visualization --- Unsupervised Machine Learning --- Data Science --- Knowledge Discovery --- 3D Printing --- Self-Organization --- Emergence --- Game Theory --- Advanced Analytics --- High-Dimensional Data --- Multivariate Data --- Analysis of Structured Data

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This book aims to provide a new vision of how algorithms are the core of decision support systems (DSSs), which are increasingly important information systems that help to make decisions related to unstructured and semi-unstructured decision problems that do not have a simple solution from a human point of view. It begins with a discussion of how DSSs will be vital to improving the health of the population. The following article deals with how DSSs can be applied to improve the performance of people doing a specific task, like playing tennis. It continues with a work in which authors apply DSSs to insect pest management, together with an interactive platform for fitting data and carrying out spatial visualization. The next article improves how to reschedule trains whenever disturbances occur, together with an evaluation framework. The final works focus on different relevant areas of DSSs: 1) a comparison of ensemble and dimensionality reduction models based on an entropy criterion; 2) a radar emitter identification method based on semi-supervised and transfer learning; 3) design limitations, errors, and hazards in creating very large-scale DSSs; and 4) efficient rule generation for associative classification. We hope you enjoy all the contents in the book.

History of engineering & technology --- semi-supervised learning --- transfer learning --- radar emitter --- decision support systems --- population health management --- big data --- machine learning --- deep learning --- personalized patient care --- Nonlinear regression --- interactive platform --- component-based approach --- software architecture --- Eclipse-RCP (Rich Client Platform) --- spatial prediction --- rule-based expert systems --- tennis hitting technique --- computer algebra systems --- Groebner bases --- Boolean logic --- data envelopment analysis --- dimensionality reduction --- ensembles --- exhaustive state space search --- entropy --- associative classification --- class association rule --- vertical data representation --- classification --- algorithm evaluation --- parallel algorithms --- multi-objective optimization --- train rescheduling --- very large-scale decision support systems --- very large-scale data and program cores of information systems --- meta-database --- teleological meta-database --- thematic list --- indicators list --- computational methods list --- geographically dispersed systems --- external sources --- semi-supervised learning --- transfer learning --- radar emitter --- decision support systems --- population health management --- big data --- machine learning --- deep learning --- personalized patient care --- Nonlinear regression --- interactive platform --- component-based approach --- software architecture --- Eclipse-RCP (Rich Client Platform) --- spatial prediction --- rule-based expert systems --- tennis hitting technique --- computer algebra systems --- Groebner bases --- Boolean logic --- data envelopment analysis --- dimensionality reduction --- ensembles --- exhaustive state space search --- entropy --- associative classification --- class association rule --- vertical data representation --- classification --- algorithm evaluation --- parallel algorithms --- multi-objective optimization --- train rescheduling --- very large-scale decision support systems --- very large-scale data and program cores of information systems --- meta-database --- teleological meta-database --- thematic list --- indicators list --- computational methods list --- geographically dispersed systems --- external sources

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Emotions play a very important role in how we think and behave. As such, the emotions we feel every day can compel us to act and influence the decisions and plans we make about our lives. Being able to measure, analyze, and better comprehend how or why our emotions may change is thus of much relevance to understand human behavior and its consequences. Despite the great efforts made in the past in the study of human emotions, it is only now, with the advent of wearable, mobile, and ubiquitous technologies, that we can aim to sense and recognize emotions, continuously and in real time. This book brings together the latest experiences, findings, and developments regarding ubiquitous sensing, modeling, and the recognition of human emotions.

Information technology industries --- self-management interview application --- emotion analysis --- facial recognition --- image-mining --- deep convolutional neural network --- emotion recognition --- pattern recognition --- texture descriptors --- mobile tool --- neuromarketing --- brain computer interface (BCI) --- consumer preferences --- EEG signal --- deep learning --- deep neural network (DNN) --- electroencephalogram (EEG) --- logistic regression --- Gaussian kernel --- Laplacian prior --- affective computing --- human–robot interaction --- thermal IR imaging --- social robots --- facial expression analysis --- line segment feature analysis --- dimensionality reduction --- convolutional recurrent neural network --- driver health risk --- intelligent speech signal processing --- human computer interaction --- supervised learning --- computer vision --- optical flow --- micro facial expressions --- real-time processing --- driver stress state --- IR imaging --- machine learning --- support vector machine (SVR) --- advanced driver-assistance systems (ADAS) --- artificial intelligence --- image processing --- video processing --- self-management interview application --- emotion analysis --- facial recognition --- image-mining --- deep convolutional neural network --- emotion recognition --- pattern recognition --- texture descriptors --- mobile tool --- neuromarketing --- brain computer interface (BCI) --- consumer preferences --- EEG signal --- deep learning --- deep neural network (DNN) --- electroencephalogram (EEG) --- logistic regression --- Gaussian kernel --- Laplacian prior --- affective computing --- human–robot interaction --- thermal IR imaging --- social robots --- facial expression analysis --- line segment feature analysis --- dimensionality reduction --- convolutional recurrent neural network --- driver health risk --- intelligent speech signal processing --- human computer interaction --- supervised learning --- computer vision --- optical flow --- micro facial expressions --- real-time processing --- driver stress state --- IR imaging --- machine learning --- support vector machine (SVR) --- advanced driver-assistance systems (ADAS) --- artificial intelligence --- image processing --- video processing