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Articles 1 - 5 of 5
Full-Text Articles in Physical Sciences and Mathematics
Analysis Of Artificial Neural Networks In The Diagnosing Of Breast Cancer Using Fine Needle Aspirates, Janette Vazquez
Analysis Of Artificial Neural Networks In The Diagnosing Of Breast Cancer Using Fine Needle Aspirates, Janette Vazquez
Theses and Dissertations
This thesis examines how Artificial Neural Networks can be used to classify a set of samples from a fine needle aspirate dataset. The dataset is composed of various different attributes, each of which are used to come to the conclusion as to whether a sample is benign or malignant. To automate the process of analyzing the various attributes and coming to a correct prediction, a neural network was implemented. First, a Feedforward Neural Network was trained with the dataset using a Backpropagation training method and an activation sigmoid function with one hidden layer in the architecture of the network. After …
Using Perceptually Grounded Semantic Models To Autonomously Convey Meaning Through Visual Art, Derrall L. Heath
Using Perceptually Grounded Semantic Models To Autonomously Convey Meaning Through Visual Art, Derrall L. Heath
Theses and Dissertations
Developing advanced semantic models is important in building computational systems that can not only understand language but also convey ideas and concepts to others. Semantic models can allow a creative image-producing-agent to autonomously produce artifacts that communicate an intended meaning. This notion of communicating meaning through art is often considered a necessary part of eliciting an aesthetic experience in the viewer and can thus enhance the (perceived) creativity of the agent. Computational creativity, a subfield of artificial intelligence, deals with designing computational systems and algorithms that either automatically create original and functional products, or that augment the ability of humans …
An Approach Based On Neural Computation To Simulate Transition Metals Using Tight Binding Measurements, Adel Belayadi, Boualem Bourahla, Leila Ait-Gougam, Fawzia Mekideche-Chafa
An Approach Based On Neural Computation To Simulate Transition Metals Using Tight Binding Measurements, Adel Belayadi, Boualem Bourahla, Leila Ait-Gougam, Fawzia Mekideche-Chafa
Turkish Journal of Physics
A theoretical study of neural networks modeling, based on the tight binding approach, is proposed in this study. The aim of the present contribution is to establish a network topology to compute the binding energy parameters of transition metals. However, because of the different types of crystallization fcc, bcc, hcp, and sc of transition metals, neural network topology determination cannot be easily established, i.e. it would not be able to collect the data to feed the neurocomputing model. Hence, in order to overcome this problem, it would be helpful to distinguish one common structure from fcc, bcc, hcp, and sc. …
Online Monitoring And Accident Diagnosis Aid System For The Nur Nuclear Research Reactor, Amina Nasrine Allalou, Mohamed Tadjine, Mohamed Seghir Boucherit
Online Monitoring And Accident Diagnosis Aid System For The Nur Nuclear Research Reactor, Amina Nasrine Allalou, Mohamed Tadjine, Mohamed Seghir Boucherit
Turkish Journal of Electrical Engineering and Computer Sciences
This paper deals with the design of a computerized monitoring and diagnosis aid system (CMDAS) for the Nur Nuclear Research Reactor based on real-time plant-specific safety parameters. The CMDAS carries out early detection and identification of accidents that might affect this reactor using supervised neural networks. The graphical programming language LabVIEW is used for creating a human--operator interface, networking, embedding the diagnosis procedure, and handling and storing the data. The methodology presented in this paper can be adapted for any nuclear research reactor.
Eeg Interictal Spike Detection Using Artificial Neural Networks, Howard J. Carey Iii
Eeg Interictal Spike Detection Using Artificial Neural Networks, Howard J. Carey Iii
Theses and Dissertations
Epilepsy is a neurological disease causing seizures in its victims and affects approximately 50 million people worldwide. Successful treatment is dependent upon correct identification of the origin of the seizures within the brain. To achieve this, electroencephalograms (EEGs) are used to measure a patient’s brainwaves. This EEG data must be manually analyzed to identify interictal spikes that emanate from the afflicted region of the brain. This process can take a neurologist more than a week and a half per patient. This thesis presents a method to extract and process the interictal spikes in a patient, and use them to reduce …