Data Mining - Concepts and Techniques.

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

This ebook is the first authorized digital version of Kernighan and Ritchie's 1988 classic, The C Programming Language (2nd Ed.). One of the best-selling programming books published in the last fifty years, "K&R" has been called everything from the "bible" to "a landmark in computer science" and it has influenced generations of programmers. Available now for all leading ebook platforms, this concise and beautifully written text is a "must-have" reference for every serious programmers digital library.

As modestly described by the authors in the Preface to the First Edition, this "is not an introductory programming manual; it assumes some familiarity with basic programming concepts like variables, assignment statements, loops, and functions. Nonetheless, a novice programmer should be able to read along and pick up the language, although access to a more knowledgeable colleague will help."

The C programming language

Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

Fiber-based structures are highly desirable for wearable electronics that are expected to be light-weight, long-lasting, flexible, and conformable Many fibrous structures have been manufactured by well-established lost-effective textile processing technologies, normally at ambient conditions The advancement of nanotechnology has made it feasible to build electronic devices directly on the surface or inside of single fibers, which have typical thickness of several to tens microns However, imparting electronic functions to porous, highly deformable and three-dimensional fiber assemblies and maintaining them during wear represent great challenges from both views of fundamental understanding and practical implementation This article attempts to critically review the current state-of-arts with respect to materials, fabrication techniques, and structural design of devices as well as applications of the fiber-based wearable electronic products In addition, this review elaborates the performance requirements of the fiber-based wearable electronic products, especially regarding the correlation among materials, fiber/textile structures and electronic as well as mechanical functionalities of fiber-based electronic devices Finally, discussions will be presented regarding to limitations of current materials, fabrication techniques, devices concerning manufacturability and performance as well as scientific understanding that must be improved prior to their wide adoption

Fiber‐Based Wearable Electronics: A Review of Materials, Fabrication, Devices, and Applications

Epilepsy is a common neurological disorder that is characterized by the recurrence of seizures. Electroencephalogram (EEG) signals are widely used to diagnose seizures. Because of the non-linear and dynamic nature of the EEG signals, it is difficult to effectively decipher the subtle changes in these signals by visual inspection and by using linear techniques. Therefore, non-linear methods are being researched to analyze the EEG signals. In this work, we use the recorded EEG signals in Recurrence Plots (RP), and extract Recurrence Quantification Analysis (RQA) parameters from the RP in order to classify the EEG signals into normal, ictal, and interictal classes. Recurrence Plot (RP) is a graph that shows all the times at which a state of the dynamical system recurs. Studies have reported significantly different RQA parameters for the three classes. However, more studies are needed to develop classifiers that use these promising features and present good classification accuracy in differentiating the three types of EEG segments. Therefore, in this work, we have used ten RQA parameters to quantify the important features in the EEG signals.These features were fed to seven different classifiers: Support vector machine (SVM), Gaussian Mixture Model (GMM), Fuzzy Sugeno Classifier, K-Nearest Neighbor (KNN), Naive Bayes Classifier (NBC), Decision Tree (DT), and Radial Basis Probabilistic Neural Network (RBPNN). Our results show that the SVM classifier was able to identify the EEG class with an average efficiency of 95.6%, sensitivity and specificity of 98.9% and 97.8%, respectively.

Application of recurrence quantification analysis for the automated identification of epileptic EEG signals.

Diabetic retinopathy (DR) is a condition where the retina is damaged due to fluid leaking from the blood vessels into the retina. In extreme cases, the patient will become blind. Therefore, early detection of diabetic retinopathy is crucial to prevent blindness. Various image processing techniques have been used to identify the different stages of diabetes retinopathy. The application of non-linear features of the higher-order spectra (HOS) was found to be efficient as it is more suitable for the detection of shapes. The aim of this work is to automatically identify the normal, mild DR, moderate DR, severe DR and prolific DR. The parameters are extracted from the raw images using the HOS techniques and fed to the support vector machine (SVM) classifier. This paper presents classification of five kinds of eye classes using SVM classifier. Our protocol uses, 300 subjects consisting of five different kinds of eye disease conditions. We demonstrate a sensitivity of 82% for the classifier with the specificity of 88%.

Application of Higher Order Spectra for the Identification of Diabetes Retinopathy Stages

Automated identification of normal and diabetes heart rate signals using nonlinear measures

This paper reports the prosthetic hand controller discriminating ten forearm motions from two channels of EMG signals. The controller uses the real-time learning method that is defined as simultaneously controlling a prosthetic hand and learning to adapt to the operator's characteristics. In this method, the controller is divided into three units. The analysis unit extracts useful information for discriminating motions from EMG. The adaptation unit learns the relation between EMG and control command and adapts to the operator's characteristics. The trainer unit generates training data and makes the adaptation unit learn in real-time. In experiments, the proposed controller performs discriminating a maximum of ten forearm motions including four wrist motions and six hand motions. In an eight forearm motions experiment, the five subjects' average discriminating rate, which serves an index of accurate controlling, was 85.1%. Two groups occur from this result, one marks a high performance (91.7%) and another does not (75.2%). The paper discusses the factors of this difference in performance from both phases of training and reasons that the low proficiency leads to undesirable results in the latter groups. Besides, in the ten forearm motions experiment the average discriminating rate of three subjects who achieve high performance in the previous experiment was 91.5%. This paper concludes that the effectiveness of the real-time learning method is confirmed by these experiments.

Wenwei Yu

Papers

Application of recurrence quantification analysis for the automated identification of epileptic EEG signals.

Application of Higher Order Spectra for the Identification of Diabetes Retinopathy Stages

Automated identification of normal and diabetes heart rate signals using nonlinear measures

EMG prosthetic hand controller using real-time learning method

Application of infrared thermography in computer aided diagnosis.