Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 23 for “"Neural Network Algorithm"”.
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Deep learning for face detection using matlab
… presents face detection using Convolutional Neural Network algorithm and Deep Learning combination (DCT / DL) throughout MATLAB simulation and modeling. It reveals that the research project has successfully managed to establish an accurate accurate human face detection and crystal-clear human …
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Classification of underwater pipeline events using deep convolutional neural networks
… In this work, we present a deep convolutional neural network algorithm for the classification of underwater pipeline events. The neural network architecture and parameters that result in optimal classifier performance are selected. The convolutional neural network technique outperforms the …
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Design of optoelectronic activation, local memory and weighting circuits for Compact Integrated Optoelectronic Neural (COIN) Co-processor
The Compact Integrated Optoelectronic Neural (COIN) Co-processor, a prototype of artificial neural network implemented in hybrid optics and optoelectronic hardware, aims to implement a multi-layer neural network algorithm by performing parallel and efficient neural computations. In this thesis, we …
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Position estimation of an outer rotor permanent magnet synchronous machine using linear hall-effect sensors and neural networks
… which is fed into a machine-learning based neural network algorithm to interpret the signals. Due to the use of machine-learning, the algorithm will first need to be trained to properly correlate the sensor signals to the rotor angle. Data sets of training signals are acquired with …
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Feed-Forward Neural Network (FFNN) Based Optimization Of Air Handling Units: A State-Of-The-Art Data-Driven Demand-Controlled Ventilation Strategy
… estimation accuracy. In this study, feed-forward neural network algorithm (FFNN) was proposed to estimate the zone occupancy using CO2 concentrations, observed occupancy data and the zone schedule. The occupancy prediction result was then utilized to optimize supply fan operation of the air …
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A graphical, self-organizing approach to classifying electronic meeting output.
… the classification process using a Hopfield Neural Network. Evaluation of the Kohonen output in comparison with the Hopfield and human expert output over the same set of data found that the Kohonen SOM performed as well as a human expert in the recollection of associated term pairs and …
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Cyber security risk analysis framework : network traffic anomaly detection
… (IoT), mobile devices, cloud computing, 5G network, and artificial intelligence, the need for cybersecurity is more critical than ever before. These technologies drive the need for tighter cybersecurity implementations, while at the same time act as enablers to provide more advanced security …
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Fractal based speech recognition and synthesis
… a Least Square Method as well as a novel Neural Network algorithm is employed to derive the recognition performance of the speech data. The second part of this work studies the synthesis of speech words, which is based mainly on the fractal dimension to create natural sounding speech. The …
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Non-silicon Microfabricated Nanostructured Chemical Sensors For Electric Nose Application
… The feasibility of using TSK Fuzzy neural network algorithm for Electric Nose has been exploited during the research. A training process of using TSK Fuzzy neural network with input/output pairs from individual gas sensor cell has been developed. This will make electric nose smart …
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Computer-Assisted Analysis of Arterial Narrowing in Whole-Body Magnetic Resonance Angiography
… for WBMRA data, comparing five different algorithms using 3 ground truth vessel maps annotated manually following a clear protocol. We find that a U-Net convolutional neural network algorithm outperforms previous, well established algorithms despite the limited amount of training …
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Planar waveguide enzyme sensors coated with nanocomposite membranes for water pollution monitoring.
… was analysed by the implementation of artificial neural network algorithm. Despite a rather small amount of experimental data, the trained neural networks were able to classify and quantify the pollutants with an acceptable average error of 6.24 %.
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Data mining, fraud detection and mobile telecommunications: call pattern analysis with unsupervised neural networks
… fraudulent usage. An unsupervised learning algorithm can analyse and cluster call patterns for each subscriber in order to facilitate the fraud detection process. This research investigates the unsupervised learning potentials of two neural networks for the profiling of calls made by users …
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Optimization of Quarry Operations and Maintenance Schedules
… similarly in the surface coal mine data set. The Neural Network algorithm created a model that predicted the loader from the performance metrics with 90.26% accuracy using the CAT Productivity data set, while the Random Forest algorithm achieved a 79.82% accuracy using the CAT MineStar Edge data …
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Size-Adaptive Convolutional Neural Network with Parameterized-Swish Activation for Enhanced Object Detection
… introduces a size-adaptive Convolutional Neural Network (CNN) framework to enhance detection performance across different object sizes. By dynamically adjusting the CNN’s configuration based on the observed distribution of object sizes, the framework employs statistical analysis and …
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Application of Machine Learning Techniques for the Classification of Lower Back Pain in Human Body
… as normal and abnormal. Naïve Bayes, Artificial Neural Networks, Logistic Regression, Deep Learning, Fast Large Margin, Random Forest, Gradient Boosted Trees, Multi-Layer Perceptron, K-Nearest Neighbour, Decision Tree and Support Vector Machine methods are most suitable machine learning …
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Approaches to analyse and interpret biological profile data
… shows the potentials of component extraction algorithms to identify the major factors which influenced the observed data. This can be the expected experimental factors such as the time or temperature as well as unexpected factors such as technical artefacts or even unknown biological …
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Optimum Actuator Grouping in Feedforward Active Control Applications
… compensator coefficients. The backpropagation neural network algorithm provides the proper procedure to determine the minimum of this cost function. The main disadvantage of using a stochastic gradient technique, while searching the prescribed control surface, is convergence to local minima. In …
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IMPROVED SEGMENTATION FOR AUTOMATED SEIZURE DETECTION USING CHANNEL-DEPENDENT POSTERIORS
… tool used for the diagnosis of a varietyof neural pathologies such as epilepsy. Identification of a critical event, such as an epileptic seizure, is difficult because the signals are collected by transducing extremely low voltages, and as a result, are corrupted by noise. Also, EEG signals …
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Neural networks applied to ocean colour remote sensing for environmental monitoring
… leaving signal can be isolated and different algorithms exist to retrieve chlorophyll a. In open waters, blue-green ratios perform well (O’Reilly et al., 1998). In coastal waters, other water constituents (dissolved matter and sediments) make both the atmospheric correction process and the …
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The structure of global invasive assemblages and their relationship to regional habitat variables: converting scientifically relevant data into decision relevant information
… map (SOM), which is an artificial neural network algorithm. Two other clustering methods that have also been used for PPA are hierarchical clustering (HC) and k-means. The main aim of this thesis was to perform a thorough validation test of the PPA approach. To do so, I first …
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