50th Anniversary Celebration: Technology Meets Science Fiction Display

Technology meets Science Fiction in our November book display. Works on teleportation, time travel, the history of the science fiction genre, science fiction in film, AI, and the multiverse theory are included along with sci-fi films and recent Nebula, Hugo, and Andre Norton Award nominees and winners

Domain Specific Deep Neural Network Model for Classification of Abnormalities on Chest Radiographs

This study collected pre-processed dataset of chest radiographs formulated a deep neural network model for detecting abnormalities It also evaluated the performance of the formulated model and implemented a prototype of the formulated model This was with the view to develop a deep neural network model to automatically classify abnormalities in chest radiographs In order to achieve the overall purpose of this research a large set of chest x-ray images were sourced for and collected from the CheXpert dataset which is an online repository of annotated chest radiographs compiled by the Machine Learning Research group Stanford University The chest radiographs were preprocessed into a format that can be fed into a deep neural network The preprocessing techniques used were standardization and normalization The classification problem was formulated as a multi-label binary classification model which used convolutional neural network architecture for making decision on whether an abnormality was present or not in the chest radiographs The classification model was evaluated using specificity sensitivity and Area Under Curve AUC score as parameter A prototype of the classification model was implemented using Keras Open source deep learning framework in Python Programming Language The AUC ROC curve of the model was able to classify Atelestasis Support devices Pleural effusion Pneumonia A normal CXR no finding Pneumothorax and Consolidation However Lung opacity and Cardiomegaly had probability out of less than 0 5 and thus were classified as absent Precision recall and F1 score values were 0 78 this imply that the number of False Positive and False Negative are the same revealing some measure of label imbalance in the dataset The study concluded that the developed model is sufficient to classify abnormalities present in chest radiographs into present or absen

Possible application of neural networks in concrete production

У цій статті розглядається можливість застосування нейронних мереж у процесі виробництві бетону. Розглядаються перспективи використання відновлювальних матеріалів таких як летючий попіл та подрібнений вапняк з точки зору розробки більш надійної моделі використання ресурсів.This paper present overview of possible application of neural networks in concrete production. Describe potential usage of recycled materials such as fly ash and limestone powder in order to develop more sustainable pattern of resource use.В этой статье рассматривается возможность применения нейронных сетей в процессе производстве бетона. Рассматриваются перспективы использования возобновляемых материалов таких как летучий пепел и измельченный известняк, с точки зрения разработки более надежной модели использования ресурсов

METODOLOGÍA PARA LA IMPLEMENTACIÓN DE UN OPERADOR INTELIGENTE SOBRE TARJETA DE DESARROLLO QUE PERMITA LA CLASIFICACIÓN DE INDIVIDUOS SANOS Y ENFERMOS CON FENOTIPOS METABÓLICOS SOBREPESO Y OBESOS.

La obesidad y el sobrepeso son unas patología que ha estado aumentando a un ritmo acelerado en las últimas décadas y ahora ha alcanzado proporciones epidémicas. Datos recientes sugieren que el 65% de los adultos tienen sobrepeso (índice de masa corporal > 25 kg m -2 ) y el 30% son obesos (índice de masa corporal > 30 kg m -2 ). Estas patologías se asocia con numerosas complicaciones metabólicas como Diabetes tipo 2, Dislipidemia, Hipertensión, enfermedades cardiovasculares y varias formas de cáncer. Se postula actualmente que no todos los individuos que muestran fenotipos metabólicos de obesos y con sobrepeso tienen que considerarse como individuos patológicos. Un alto porcentaje de los individuos estudiados que presentan estos fenotipos no muestran las complicaciones metabólicas habituales. El objetivo general de esta investigación es desarrollar una metodología para la implementación de un operador inteligente en tarjeta de desarrollo que permita hacer una clasificación de individuos sanos y enfermos considerando los fenotipos metabólicos sobrepeso y obesos. El resultado de esta investigación es un protocolo de desarrollo de una aplicación que permita diagnosticar con un porcentaje aceptable de error los individuos sanos y enfermos en los fenotipos mencionados y además sirva como apoyo a los especialistas de la salud en el diagnóstico de este tipo de patologías

Feature Selection For The Fuzzy Artmap Neural Network Using A Hybrid Genetic Algorithm And Tabu Search [QA76.87. T164 2007 f rb].

Prestasi pengelas rangkaian neural amat bergantung kepada set data yang digunakan dalam process pembelajaran. Secara praktik, set data berkemungkinan mengandungi maklumat yang tidak diperlukan. Dengan itu, pencarian ciri merupakan suatu langkah yang penting dalam pembinaan suatu pengelas berdasarkan rangkaian neural yang efektif. The performance of Neural-Network (NN)-based classifiers is strongly dependent on the data set used for learning. In practice, a data set may contain noisy or redundant data items. Thus, feature selection is an important step in building an effective and efficient NN-based classifier

Pengubahsuaian Senibina Rangkaian Neural Secara Berhirarki Dalam Penentuan Jenis Dan Kualiti Air Sungai [QA76.87. F177 2008 f rb].

Penggunaan rangkaian neural dalam pengecaman corak jelas memberikan implikasi yang besar kepada bidang pengkelasan dan pengelompokan data. The use of neural network in pattern recognition has given a big impact in the classification and clustering of the data

High dimentional neural fuzzy controller for nonlinear systems

De nos jours, la théorie de contrôle joue un rôle significatif dans presque tous les domaine de la science et de l'ingénierie. Les contrôleurs linéaires PID sont les applications principales de la théorie de contrôle, et ils se basent sur les systèmes de contrôle simples. Mais beaucoup de vrais systèmes possèdent des caractéristiques non-linéaires. Dans la pratique, il est nécessaire de faire beaucoup de linéarisations. Quand nous employons le contrôleur classique dans un système non-linéaire fortement complexe, les difficultés augmentent exponentiellement. Pour éviter les imperfections, on peut employer des contrôleurs flous. Le contrôleurs flous se basent sur le système de connaisance. Ce sont des outils importants dans le domaine de l'automatique. Ils possèdent beaucoup plus d'avantages que les contrôleurs classiques"PID", mais ils ont besoin d'experts pour concevoir les règles de base. La limite principale des contrôleurs flous est la difficulté d'établir les règles de base. Maintenant, beaucoup de recherches sont consacrées à la fusion des réseaux de neurones et de systèmes flous dans une nouvelle structure (les réseaux de neuro-floue). Cette approche combine les avantages de deux paradigmes puissants dans une capsule simple, et fournit un cadre puissant pour extraire des règles floues des données numériques. Cependant, cette technologie n'est pas parfaite. Il reste quelques difficultés: beaucoup de règles floues sont nécessaires, les algorithmes sont complexes et la fiabilité est basse (Par exemple, pour un même modèle ou fonction, les résultats dépendent des ensembles d'apprentissage). Pour éviter les difficultés, ce mémoire présente une nouvelle méthode, appelée"inférence neuro-floue de haute-dimension". L'idée fondamentale de cette méthode proposé est de considérer chaque donné dans ce système comme point avec la haute dimension. Chaque dimension d'entrée sera traitée en même temps dans les mêmes sous-ensembles de haute dimension. L'algorithme proposé a été examiné sur différentes applications, et les résultats ont été comparés aux données éditées sur trois problèmes de repère. Cet algorithme est simple à employer, et les résultats expérimentaux prouvent que le nombre de faisceaux exigés est inférieur à ceux rapportés dans la littérature. L'exactitude de rendement est bonne dans beaucoup d'applications

Real Time Pattern Recognition using Matrox Imaging System

A primary goal of pattern recognition is to be able to classify data into a set of related elements. Many applications today take advantage of pattern recognition; among them are data mining, face recognition, web searching, robotics and a lot more. Pattern recognition concerning Artificial Intelligence has been in research and development for approximately 50 years. The ability to quickly locate one or more instances of a model in a grey scale image is of importance to industry. The recognition/ localization must be fast and accurate. Two algorithms are employed in this work to achieve fast recognition and localization. Both the algorithm relies on a pyramid representation of both the model image and the search image. Specifically these algorithms are suitable for implementation on a personal computer equipped with an image acquisition board and a camera. Finally, results are given for searches on real images with perspective distortion and the addition of Gaussian noise

Glucose-powered neuroelectronics

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 157-164).A holy grail of bioelectronics is to engineer biologically implantable systems that can be embedded without disturbing their local environments, while harvesting from their surroundings all of the power they require. As implantable electronic devices become increasingly prevalent in scientific research and in the diagnosis, management, and treatment of human disease, there is correspondingly increasing demand for devices with unlimited functional lifetimes that integrate seamlessly with their hosts in these two ways. This thesis presents significant progress toward establishing the feasibility of one such system: A brain-machine interface powered by a bioimplantable fuel cell that harvests energy from extracellular glucose in the cerebrospinal fluid surrounding the brain. The first part of this thesis describes a set of biomimetic algorithms and low-power circuit architectures for decoding electrical signals from ensembles of neurons in the brain. The decoders are intended for use in the context of neural rehabilitation, to provide paralyzed or otherwise disabled patients with instantaneous, natural, thought-based control of robotic prosthetic limbs and other external devices. This thesis presents a detailed discussion of the decoding algorithms, descriptions of the low-power analog and digital circuit architectures used to implement the decoders, and results validating their performance when applied to decode real neural data. A major constraint on brain-implanted electronic devices is the requirement that they consume and dissipate very little power, so as not to damage surrounding brain tissue. The systems described here address that constraint, computing in the style of biological neural networks, and using arithmetic-free, purely logical primitives to establish universal computing architectures for neural decoding. The second part of this thesis describes the development of an implantable fuel cell powered by extracellular glucose at concentrations such as those found in the cerebrospinal fluid surrounding the brain. The theoretical foundations, details of design and fabrication, mechanical and electrochemical characterization, as well as in vitro performance data for the fuel cell are presented.by Benjamin Isaac Rapoport.Ph.D