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- Title
- ACCURATE DETECTION OF SELECTIVE SWEEPS WITH TRANSFER LEARNING.
- Creator
- Sigler, Priya, DeGiorgio, Michael, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
- Abstract/Description
-
Positive natural selection leaves detectable, distinctive patterns in the genome in the form of a selective sweep. Identifying areas of the genome that have undergone selective sweeps is an area of high interest as it enables understanding of species and population evolution. Previous work has accomplished this by evaluating patterns within summary statistics computed across the genome and through application of machine learning techniques to raw population genomic data. When using raw...
Show morePositive natural selection leaves detectable, distinctive patterns in the genome in the form of a selective sweep. Identifying areas of the genome that have undergone selective sweeps is an area of high interest as it enables understanding of species and population evolution. Previous work has accomplished this by evaluating patterns within summary statistics computed across the genome and through application of machine learning techniques to raw population genomic data. When using raw population genomic data, convolutional neural networks have most recently been employed as they can handle large input arrays and maintain correlations among elements. Yet, such models often require massive amounts of training data and can be computationally expensive to train for a given problem. Instead, transfer learning has recently been used in the image analysis literature to improve machine learning models by learning the important features of images from large unrelated datasets beforehand, and then refining these models through subsequent application on smaller and more relevant datasets. We combine transfer learning with convolutional neural networks to improve classification of selective sweeps from raw population genomic data. We show that the combination of transfer learning with convolutional neural networks allows for accurate classification of selective sweeps.
Show less - Date Issued
- 2021
- PURL
- http://purl.flvc.org/fau/fd/FA00013785
- Subject Headings
- Transfer learning (Machine learning), Neural networks (Computer science), Natural selection, Genomes
- Format
- Document (PDF)
- Title
- Activity analysis and detection of falling and repetitive motion.
- Creator
- Carryl, Clyde, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
This thesis examines the use of motion detection and analysis systems to detect falls and repetitive motion patterns of at-risk individuals. Three classes of motion are examined: Activities of daily living (ADL), falls, and repetitive motion. This research exposes a simple relationship between ADL and non-ADL movement, and shows how to use Principal Component Analysis and a kNN classifier to tell the 2 classes of motion apart with 100% sensitivity and specificity. It also identifies a more...
Show moreThis thesis examines the use of motion detection and analysis systems to detect falls and repetitive motion patterns of at-risk individuals. Three classes of motion are examined: Activities of daily living (ADL), falls, and repetitive motion. This research exposes a simple relationship between ADL and non-ADL movement, and shows how to use Principal Component Analysis and a kNN classifier to tell the 2 classes of motion apart with 100% sensitivity and specificity. It also identifies a more complex relationship between falls and repetitive motion, which both produce bodily accelerations exceeding 3G but differ with regard to their periodicity. This simplifies the classification problem of falls versus repetitive motion when taking into account that their data representations are similar except that repetitive motion displays a high degree of periodicity as compared to falls.
Show less - Date Issued
- 2013
- PURL
- http://purl.flvc.org/FAU/3360774
- Subject Headings
- Perpetual-motion processes, Human locomotion, Neural networks (Computer science), Artificial intelligence
- Format
- Document (PDF)
- Title
- An Exploration into Synthetic Data and Generative Aversarial Networks.
- Creator
- Shorten, Connor M., Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
This Thesis surveys the landscape of Data Augmentation for image datasets. Completing this survey inspired further study into a method of generative modeling known as Generative Adversarial Networks (GANs). A survey on GANs was conducted to understood recent developments and the problems related to training them. Following this survey, four experiments were proposed to test the application of GANs for data augmentation and to contribute to the quality improvement in GAN-generated data....
Show moreThis Thesis surveys the landscape of Data Augmentation for image datasets. Completing this survey inspired further study into a method of generative modeling known as Generative Adversarial Networks (GANs). A survey on GANs was conducted to understood recent developments and the problems related to training them. Following this survey, four experiments were proposed to test the application of GANs for data augmentation and to contribute to the quality improvement in GAN-generated data. Experimental results demonstrate the effectiveness of GAN-generated data as a pre-training metric. The other experiments discuss important characteristics of GAN models such as the refining of prior information, transferring generative models from large datasets to small data, and automating the design of Deep Neural Networks within the context of the GAN framework. This Thesis will provide readers with a complete introduction to Data Augmentation and Generative Adversarial Networks, as well as insights into the future of these techniques.
Show less - Date Issued
- 2019
- PURL
- http://purl.flvc.org/fau/fd/FA00013263
- Subject Headings
- Neural networks (Computer science), Computer vision, Images, Generative adversarial networks, Data sets
- Format
- Document (PDF)
- Title
- An artificial neural network architecture for interpolation, function approximation, time series modeling and control applications.
- Creator
- Luebbers, Paul Glenn., Florida Atlantic University, Pandya, Abhijit S., Sudhakar, Raghavan, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
A new artificial neural network architecture called Power Net (PWRNET) and Orthogonal Power Net (OPWRNET) has been developed. Based on the Taylor series expansion of the hyperbolic tangent function, this novel architecture can approximate multi-input multi-layer artificial networks, while requiring only a single layer of hidden nodes. This allows a compact network representation with only one layer of hidden layer weights. The resulting trained network can be expressed as a polynomial...
Show moreA new artificial neural network architecture called Power Net (PWRNET) and Orthogonal Power Net (OPWRNET) has been developed. Based on the Taylor series expansion of the hyperbolic tangent function, this novel architecture can approximate multi-input multi-layer artificial networks, while requiring only a single layer of hidden nodes. This allows a compact network representation with only one layer of hidden layer weights. The resulting trained network can be expressed as a polynomial function of the input nodes. Applications which cannot be implemented with conventional artificial neural networks, due to their intractable nature, can be developed with these network architectures. The degree of nonlinearity of the network can be directly controlled by adjusting the number of hidden layer nodes, thus avoiding problems of over-fitting which restrict generalization. The learning algorithm used for adapting the network is the familiar error back propagation training algorithm. Other learning algorithms may be applied and since only one hidden layer is to be trained, the training performance of the network is expected to be comparable to or better than conventional multi-layer feed forward networks. The new architecture is explored by applying OPWRNET to classification, function approximation and interpolation problems. These applications show that the OPWRNET has comparable performance to multi-layer perceptrons. The OPWRNET was also applied to the prediction of noisy time series and the identification of nonlinear systems. The resulting trained networks, for system identification tasks, can be expressed directly as discrete nonlinear recursive polynomials. This characteristic was exploited in the development of two new neural network based nonlinear control algorithms, the Linearized Self-Tuning Controller (LSTC) and a variation of a Neural Adaptive Controller (NAC). These control algorithms are compared to a linear self-tuning controller and an artificial neural network based Inverse Model Controller. The advantages of these new controllers are discussed.
Show less - Date Issued
- 1994
- PURL
- http://purl.flvc.org/fcla/dt/12357
- Subject Headings
- Neural networks (Computer science)
- Format
- Document (PDF)
- Title
- A BCU scalable sensory acquisition system for EEG embedded applications.
- Creator
- Fathalla, Sherif S., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
Electroencephalogram (EEG) Recording has been through a lot of changes and modification since it was first introduced in 1929 due to rising technologies and signal processing advancements. The EEG Data acquisition stage is the first and most valuable component in any EEG recording System, it has the role of gathering and conditioning its input and outputting reliable data to be effectively analyzed and studied by digital signal processors using sophisticated and advanced algorithms which help...
Show moreElectroencephalogram (EEG) Recording has been through a lot of changes and modification since it was first introduced in 1929 due to rising technologies and signal processing advancements. The EEG Data acquisition stage is the first and most valuable component in any EEG recording System, it has the role of gathering and conditioning its input and outputting reliable data to be effectively analyzed and studied by digital signal processors using sophisticated and advanced algorithms which help in numerous medical and consumer applications. We have designed a low noise low power EEG data acquisition system that can be set to act as a standalone mobile EEG data processing unit providing data preprocessing functions; it can also be a very reliable high speed data acquisition interface to an EEG processing unit.
Show less - Date Issued
- 2010
- PURL
- http://purl.flvc.org/FAU/3164095
- Subject Headings
- Brain-computer interfaces, Computational neuroscience, Neural networks (Computer science)
- Format
- Document (PDF)
- Title
- A case study: Performance enhancement of nonlinear combinational optimization problem by neural networks.
- Creator
- Soni, Saurabh., Florida Atlantic University, Pandya, Abhijit S., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
Artificial Neural Networks have been widely used for obtaining solutions for combinational optimization problems. Traveling Salesman problem is a well known nonlinear combinational optimization problem. In Traveling Salesman problem, a fixed number of cities is given. An optimal tour of all these cities is required such that each city is visited only once and the total tour distance to be covered has to be minimized. Hopfield Networks have been applied for generating an optimal solution....
Show moreArtificial Neural Networks have been widely used for obtaining solutions for combinational optimization problems. Traveling Salesman problem is a well known nonlinear combinational optimization problem. In Traveling Salesman problem, a fixed number of cities is given. An optimal tour of all these cities is required such that each city is visited only once and the total tour distance to be covered has to be minimized. Hopfield Networks have been applied for generating an optimal solution. However there are certain factors which result in instability and local optimization of Hopfield Networks. In such cases the solutions obtained may not be optimal and feasible. In this thesis, the application of the K-Means algorithm is combined with the Hopfield Networks to generate more stable and optimum solutions to traveling salesperson problem.
Show less - Date Issued
- 2004
- PURL
- http://purl.flvc.org/fcla/dt/13108
- Subject Headings
- Neural networks (Computer science), Traveling-salesman problem
- Format
- Document (PDF)
- Title
- COMPARISON OF PRE-TRAINED CONVOLUTIONAL NEURAL NETWORK PERFORMANCE ON GLIOMA CLASSIFICATION.
- Creator
- Andrews, Whitney Angelica Johanna, Furht, Borko, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
- Abstract/Description
-
Gliomas are an aggressive class of brain tumors that are associated with a better prognosis at a lower grade level. Effective differentiation and classification are imperative for early treatment. MRI scans are a popular medical imaging modality to detect and diagnosis brain tumors due to its capability to non-invasively highlight the tumor region. With the rise of deep learning, researchers have used convolution neural networks for classification purposes in this domain, specifically pre...
Show moreGliomas are an aggressive class of brain tumors that are associated with a better prognosis at a lower grade level. Effective differentiation and classification are imperative for early treatment. MRI scans are a popular medical imaging modality to detect and diagnosis brain tumors due to its capability to non-invasively highlight the tumor region. With the rise of deep learning, researchers have used convolution neural networks for classification purposes in this domain, specifically pre-trained networks to reduce computational costs. However, with various MRI modalities, MRI machines, and poor image scan quality cause different network structures to have different performance metrics. Each pre-trained network is designed with a different structure that allows robust results given specific problem conditions. This thesis aims to cover the gap in the literature to compare the performance of popular pre-trained networks on a controlled dataset that is different than the network trained domain.
Show less - Date Issued
- 2020
- PURL
- http://purl.flvc.org/fau/fd/FA00013450
- Subject Headings
- Gliomas, Neural networks (Computer science), Deep Learning, Convolutional neural networks
- Format
- Document (PDF)
- Title
- A connectionist approach to adaptive reasoning: An expert system to predict skid numbers.
- Creator
- Reddy, Mohan S., Florida Atlantic University, Pandya, Abhijit S., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
This project illustrates the neural network approach to constructing a fuzzy logic decision system. This technique employs an artificial neural network (ANN) to recognize the relationships that exit between the various inputs and outputs. An ANN is constructed based on the variables present in the application. The network is trained and tested. Various training methods are explored, some of which include auxiliary input and output columns. After successful testing, the ANN is exposed to new...
Show moreThis project illustrates the neural network approach to constructing a fuzzy logic decision system. This technique employs an artificial neural network (ANN) to recognize the relationships that exit between the various inputs and outputs. An ANN is constructed based on the variables present in the application. The network is trained and tested. Various training methods are explored, some of which include auxiliary input and output columns. After successful testing, the ANN is exposed to new data and the results are grouped into fuzzy membership sets based membership evaluation rules. This data grouping forms the basis of a new ANN. The network is now trained and tested with the fuzzy membership data. New data is presented to the trained network and the results form the fuzzy implications. This approach is used to compute skid resistance values from G-analyst accelerometer readings on open grid bridge decks.
Show less - Date Issued
- 1996
- PURL
- http://purl.flvc.org/fcla/dt/15239
- Subject Headings
- Artificial intelligence, Fuzzy logic, Neural networks (Computer science), Pavements--Skid resistance
- Format
- Document (PDF)
- Title
- DATA-DRIVEN IDENTIFICATION AND CONTROL OF TURBULENT STRUCTURES USING DEEP NEURAL NETWORKS.
- Creator
- Jagodinski, Eric, Verma, Siddhartha, Florida Atlantic University, Department of Ocean and Mechanical Engineering, College of Engineering and Computer Science
- Abstract/Description
-
Wall-bounded turbulent flows are pervasive in numerous physics and engineering applications. Such flows tend to have a strong impact on the design of ships, airplanes and rockets, industrial chemical mixing, wind and hydrokinetic energy, utility infrastructure and innumerable other fields. Understanding and controlling wall bounded turbulence has been a long-pursued endeavor yielding plentiful scientific and engineering discoveries, but there is much that remains unexplained from a...
Show moreWall-bounded turbulent flows are pervasive in numerous physics and engineering applications. Such flows tend to have a strong impact on the design of ships, airplanes and rockets, industrial chemical mixing, wind and hydrokinetic energy, utility infrastructure and innumerable other fields. Understanding and controlling wall bounded turbulence has been a long-pursued endeavor yielding plentiful scientific and engineering discoveries, but there is much that remains unexplained from a fundamental viewpoint. One unexplained phenomenon is the formation and impact of coherent structures like the ejections of slow near-wall fluid into faster moving ow which have been shown to correlate with increases in friction drag. This thesis focuses on recognizing and regulating organized structures within wall-bounded turbulent flows using a variety of machine learning techniques to overcome the nonlinear nature of this phenomenon. Deep Learning has provided new avenues of analyzing large amounts of data by applying techniques modeled after biological neurons. These techniques allow for the discovery of nonlinear relationships in massive, complex systems like the data found frequently in fluid dynamics simulation. Using a neural network architecture called Convolutional Neural Networks that specializes in uncovering spatial relationships, a network was trained to estimate the relative intensity of ejection structures within turbulent flow simulation without any a priori knowledge of the underlying flow dynamics. To explore the underlying physics that the trained network might reveal, an interpretation technique called Gradient-based Class Activation Mapping was modified to identify salient regions in the flow field which most influenced the trained network to make an accurate estimation of these organized structures. Using various statistical techniques, these salient regions were found to have a high correlation to ejection structures, and to high positive kinetic energy production, low negative production, and low energy dissipation regions within the flow. Additionally, these techniques present a general framework for identifying nonlinear causal structures in general three-dimensional data in any scientific domain where the underlying physics may be unknown.
Show less - Date Issued
- 2022
- PURL
- http://purl.flvc.org/fau/fd/FA00014119
- Subject Headings
- Turbulent flow, Turbulence, Neural networks (Computer science), Deep learning (Machine learning)
- Format
- Document (PDF)
- Title
- DEEP MAXOUT NETWORKS FOR CLASSIFICATION PROBLEMS ACROSS MULTIPLE DOMAINS.
- Creator
- Castaneda, Gabriel, Khoshgoftaar, Taghi M., Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
- Abstract/Description
-
Machine learning techniques such as deep neural networks have become an indispensable tool for a wide range of applications such as image classification, speech recognition, and sentiment analysis in text. An activation function is a mathematical equation that determines the output of each neuron in the neural network. In deep learning architectures the choice of activation functions is very important to the network’s performance. Activation functions determine the output of the model, its...
Show moreMachine learning techniques such as deep neural networks have become an indispensable tool for a wide range of applications such as image classification, speech recognition, and sentiment analysis in text. An activation function is a mathematical equation that determines the output of each neuron in the neural network. In deep learning architectures the choice of activation functions is very important to the network’s performance. Activation functions determine the output of the model, its computational efficiency, and its ability to train and converge after multiple iterations of training epochs. The selection of an activation function is critical to building and training an effective and efficient neural network. In real-world applications of deep neural networks, the activation function is a hyperparameter. We have observed a lack of consensus on how to select a good activation function for a deep neural network, and that a specific function may not be suitable for all domain-specific applications.
Show less - Date Issued
- 2019
- PURL
- http://purl.flvc.org/fau/fd/FA00013362
- Subject Headings
- Classification, Machine learning--Technique, Neural networks (Computer science)
- Format
- Document (PDF)
- Title
- Design of analog building blocks useful for artificial neural networks.
- Creator
- Renavikar, Ajit Anand., Florida Atlantic University, Shankar, Ravi, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
Software simulations of a scaleable VLSI implementable architecture and algorithm for character recognition by a research group at Florida Atlantic University (FAU) have shown encouraging results. We address here hardware implementation issues pertinent to the classification phase of character recognition. Using the digit classification techniques developed at FAU as a foundation, we have designed and simulated general purpose building blocks useful for a possible implementation of a Digital ...
Show moreSoftware simulations of a scaleable VLSI implementable architecture and algorithm for character recognition by a research group at Florida Atlantic University (FAU) have shown encouraging results. We address here hardware implementation issues pertinent to the classification phase of character recognition. Using the digit classification techniques developed at FAU as a foundation, we have designed and simulated general purpose building blocks useful for a possible implementation of a Digital & Analog CMOS VLSI chip that is suitable for a variety of artificial neural network (ANN) architectures. HSPICE was used to perform circuit-level simulations of the building blocks. We present here the details of implementation of the recognition chip including the architecture, circuit design and the simulation results.
Show less - Date Issued
- 1996
- PURL
- http://purl.flvc.org/fcla/dt/15328
- Subject Headings
- Neural networks (Computer science), Artificial intelligence, Optical character recognition devices, Pattern recognition systems
- Format
- Document (PDF)
- Title
- Detection of change-prone telecommunications software modules.
- Creator
- Weir, Ronald Eugene., Florida Atlantic University, Khoshgoftaar, Taghi M., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
Accurately classifying the quality of software is a major problem in any software development project. Software engineers develop models that provide early estimates of quality metrics which allow them to take actions against emerging quality problems. The use of a neural network as a tool to classify programs as a low, medium, or high risk for errors or change is explored using multiple software metrics as input. It is demonstrated that a neural network, trained using the back-propagation...
Show moreAccurately classifying the quality of software is a major problem in any software development project. Software engineers develop models that provide early estimates of quality metrics which allow them to take actions against emerging quality problems. The use of a neural network as a tool to classify programs as a low, medium, or high risk for errors or change is explored using multiple software metrics as input. It is demonstrated that a neural network, trained using the back-propagation supervised learning strategy, produced the desired mapping between the static software metrics and the software quality classes. The neural network classification methodology is compared to the discriminant analysis classification methodology in this experiment. The comparison is based on two and three class predictive models developed using variables resulting from principal component analysis of software metrics.
Show less - Date Issued
- 1995
- PURL
- http://purl.flvc.org/fcla/dt/15183
- Subject Headings
- Computer software--Evaluation, Software engineering, Neural networks (Computer science)
- Format
- Document (PDF)
- Title
- Determination of probability density from statistical moments by neural network approach.
- Creator
- Zheng, Zhiyin., Florida Atlantic University, Cai, Guo-Qiang, College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
- Abstract/Description
-
It is known that response probability densities, although important in failure analysis, are seldom achievable for stochastically excited systems except for linear systems under additive excitations of Gaussian processes. Most often, statistical moments are obtainable analytically or experimentally. It is proposed in this thesis to determine the probability density from the known statistical moments using artificial neural networks. A multi-layered feed-forward type of neural networks with...
Show moreIt is known that response probability densities, although important in failure analysis, are seldom achievable for stochastically excited systems except for linear systems under additive excitations of Gaussian processes. Most often, statistical moments are obtainable analytically or experimentally. It is proposed in this thesis to determine the probability density from the known statistical moments using artificial neural networks. A multi-layered feed-forward type of neural networks with error back-propagation training algorithm is proposed for the purpose and the parametric method is adopted for identifying the probability density function. Three examples are given to illustrate the applicability of the approach. All three examples show that the neural network approach gives quite accurate results in comparison with either the exact or simulation ones.
Show less - Date Issued
- 1996
- PURL
- http://purl.flvc.org/fcla/dt/15330
- Subject Headings
- Distribution (Probability theory), Moments method (Statistics), Estimation theory, Structural failures--Investigation, Neural networks (Computer science)
- Format
- Document (PDF)
- Title
- Determination of receptive fields in neural networks using Alopex.
- Creator
- Shah, Gaurang G., Florida Atlantic University, Pandya, Abhijit S., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
This research aims at proposing a model for visual pattern recognition inspired by the neural circuitry in the brain. Our attempt is to propose few modifications in the Alopex algorithm and try to use it for the calculations of the receptive fields of neurons in the trained network. We have developed a small-scale, four-layered neural network model for simple character recognition as well as complex image patterns, which can recognize the patterns transformed by affine conversion. Here Alopex...
Show moreThis research aims at proposing a model for visual pattern recognition inspired by the neural circuitry in the brain. Our attempt is to propose few modifications in the Alopex algorithm and try to use it for the calculations of the receptive fields of neurons in the trained network. We have developed a small-scale, four-layered neural network model for simple character recognition as well as complex image patterns, which can recognize the patterns transformed by affine conversion. Here Alopex algorithm is presented as an iterative and stochastic processing method, which was proposed for optimization of a given cost function over hundreds or thousands of iterations. In this case the receptive fields of the neurons in the output layers are obtained using the Alopex algorithm.
Show less - Date Issued
- 2005
- PURL
- http://purl.flvc.org/fcla/dt/13298
- Subject Headings
- Pattern recognition systems, Neural networks (Computer science), Computer algorithms, Neuroanatomy, Image processing
- Format
- Document (PDF)
- Title
- Determining the Effectiveness of Human Interaction in Human-in-the-Loop Systems by Using Mental States.
- Creator
- Lloyd, Eric, Huang, Shihong, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
A self-adaptive software is developed to predict the stock market. It’s Stock Prediction Engine functions autonomously when its skill-set suffices to achieve its goal, and it includes human-in-the-loop when it recognizes conditions benefiting from more complex, expert human intervention. Key to the system is a module that decides of human participation. It works by monitoring three mental states unobtrusively and in real time with Electroencephalography (EEG). The mental states are drawn from...
Show moreA self-adaptive software is developed to predict the stock market. It’s Stock Prediction Engine functions autonomously when its skill-set suffices to achieve its goal, and it includes human-in-the-loop when it recognizes conditions benefiting from more complex, expert human intervention. Key to the system is a module that decides of human participation. It works by monitoring three mental states unobtrusively and in real time with Electroencephalography (EEG). The mental states are drawn from the Opportunity-Willingness-Capability (OWC) model. This research demonstrates that the three mental states are predictive of whether the Human Computer Interaction System functions better autonomously (human with low scores on opportunity and/or willingness, capability) or with the human-in-the-loop, with willingness carrying the largest predictive power. This transdisciplinary software engineering research exemplifies the next step of self-adaptive systems in which human and computer benefit from optimized autonomous and cooperative interactions, and in which neural inputs allow for unobtrusive pre-interactions.
Show less - Date Issued
- 2016
- PURL
- http://purl.flvc.org/fau/fd/FA00004764, http://purl.flvc.org/fau/fd/FA00004764
- Subject Headings
- Cognitive neuroscience., Neural networks (Computer science), Pattern recognition systems., Artificial intelligence., Self-organizing systems., Human-computer interaction., Human information processing.
- Format
- Document (PDF)
- Title
- Dynamic simulation and control of an autonomous surface vehicle.
- Creator
- VanZwieten, Tannen S., Florida Atlantic University, Leonessa, Alexander, College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
- Abstract/Description
-
Autonomous Surface Vehicle (ASV) research and development is inspired by the navigating and communicatiog challenges of Autonomous Underwater Vehicles (AUVs). The development objective is to provide real time positioning of and communication with AUVs through the air-sea interface. Despite extensive research on AUVs, the ASV has had limited research. The NAVY's desire to make AUV's defense capabilities realizable adds to the project's appeal. Guidance and control play an integral part in the...
Show moreAutonomous Surface Vehicle (ASV) research and development is inspired by the navigating and communicatiog challenges of Autonomous Underwater Vehicles (AUVs). The development objective is to provide real time positioning of and communication with AUVs through the air-sea interface. Despite extensive research on AUVs, the ASV has had limited research. The NAVY's desire to make AUV's defense capabilities realizable adds to the project's appeal. Guidance and control play an integral part in the ASV's success, motivating this thesis work. The overall vehicle dynamics were modeled and numerically simulated for 3 DOF lateral motion. These are development tools for the testing and tuning of PID and adaptive control algorithms. The results show the adaptive controller to be advantageous in terms of tuning, robustness and tracking performances. It uses a single layer neural network that bypasses the need for information about the system's dynamic structure and characteristics and provides portability.
Show less - Date Issued
- 2003
- PURL
- http://purl.flvc.org/fcla/dt/13081
- Subject Headings
- Hydrodynamics, Adaptive control systems--Computer simulation, PID controllers--Computer simulation, Neural networks (Computer science)
- Format
- Document (PDF)
- Title
- EMBEDDING LEARNING FOR COMPLEX DYNAMIC INFORMATION NETWORKS.
- Creator
- Wu, Man, Zhu, Xingquan, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
- Abstract/Description
-
With the rapid development of networking platforms and data intensive applications, networks (or graphs) are becoming convenient and fundamental tools to model the complex inter-dependence among big scale data. As a result, networks (or graphs) are being widely used in many applications, including citation networks [40], social media networks [71], and so on. However, the high complexity (containing many important information) as well as the dynamic nature of the network makes the graph...
Show moreWith the rapid development of networking platforms and data intensive applications, networks (or graphs) are becoming convenient and fundamental tools to model the complex inter-dependence among big scale data. As a result, networks (or graphs) are being widely used in many applications, including citation networks [40], social media networks [71], and so on. However, the high complexity (containing many important information) as well as the dynamic nature of the network makes the graph learning task more difficult. To have better graph representations (capture both node content and graph structure), many research efforts have been made to develop reliable and efficient algorithms. Therefore, the good graph representation learning is the key factor in performing well on downstream tasks. The dissertation mainly focuses on the graph representation learning, which aims to embed both structure and node content information of graphs into a compact and low dimensional space for a new representation learning. More specifically, in order to achieve an efficient and robust graph representation, the following four problems will be studied from different perspectives: 1) We study the problem of positive unlabeled graph learning for network node classification, and present a new deep learning model as a solution; 2) We formulate a new open-world learning problem for graph data, and propose an uncertain node representation learning approach and sampling strategy to solve the problem; 3) For cross-domain graph learning, we present a novel unsupervised graph domain adaptation problem, and propose an effective graph convolutional network algorithm to solve it; 4) We consider a dynamic graph as a network with changing nodes and edges in temporal order and propose a temporal adaptive aggregation network (TAAN) for dynamic graph learning. Finally, the proposed models are verified and evaluated on various real-world datasets.
Show less - Date Issued
- 2022
- PURL
- http://purl.flvc.org/fau/fd/FA00014066
- Subject Headings
- Neural networks (Computer science), Machine learning, Graphs, Embeddings (Mathematics)
- Format
- Document (PDF)
- Title
- Enhancement of Deep Neural Networks and Their Application to Text Mining.
- Creator
- Prusa, Joseph Daniel, Khoshgoftaar, Taghi M., Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
Many current application domains of machine learning and arti cial intelligence involve knowledge discovery from text, such as sentiment analysis, document ontology, and spam detection. Humans have years of experience and training with language, enabling them to understand complicated, nuanced text passages with relative ease. A text classi er attempts to emulate or replicate this knowledge so that computers can discriminate between concepts encountered in text; however, learning high-level...
Show moreMany current application domains of machine learning and arti cial intelligence involve knowledge discovery from text, such as sentiment analysis, document ontology, and spam detection. Humans have years of experience and training with language, enabling them to understand complicated, nuanced text passages with relative ease. A text classi er attempts to emulate or replicate this knowledge so that computers can discriminate between concepts encountered in text; however, learning high-level concepts from text, such as those found in many applications of text classi- cation, is a challenging task due to the many challenges associated with text mining and classi cation. Recently, classi ers trained using arti cial neural networks have been shown to be e ective for a variety of text mining tasks. Convolutional neural networks have been trained to classify text from character-level input, automatically learn high-level abstract representations and avoiding the need for human engineered features. This dissertation proposes two new techniques for character-level learning, log(m) character embedding and convolutional window classi cation. Log(m) embedding is a new character-vector representation for text data that is more compact and memory e cient than previous embedding vectors. Convolutional window classi cation is a technique for classifying long documents, i.e. documents with lengths exceeding the input dimension of the neural network. Additionally, we investigate the performance of convolutional neural networks combined with long short-term memory networks, explore how document length impacts classi cation performance and compare performance of neural networks against non-neural network-based learners in text classi cation tasks.
Show less - Date Issued
- 2018
- PURL
- http://purl.flvc.org/fau/fd/FA00005959
- Subject Headings
- Text Mining, Neural networks (Computer science), Machine learning
- Format
- Document (PDF)
- Title
- Financial prediction using time series.
- Creator
- Srinivasan, Arunkumar., Florida Atlantic University, Pandya, Abhijit S., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
This thesis discusses the implementation of a feed forward NN using time series model to predict the sudden rise or sudden crash of a company's stock prices. The theory behind this prediction system is Pattern recognition. Pattern recognition techniques for time-series prediction are based on structural matching of the current state of the time-series with previously occurring states in historical data for making predictions. This study reports the result of attempts to predict the Motorola...
Show moreThis thesis discusses the implementation of a feed forward NN using time series model to predict the sudden rise or sudden crash of a company's stock prices. The theory behind this prediction system is Pattern recognition. Pattern recognition techniques for time-series prediction are based on structural matching of the current state of the time-series with previously occurring states in historical data for making predictions. This study reports the result of attempts to predict the Motorola stock price index using artificial neural networks (ANN). Daily data from January 1999 to December 2001 were taken from the NYSE. These data are classified based on criteria of an n% fall or rise of price corresponding to the previous day close price. A novel method using Hurst exponent is used in selecting the data set. These data are fed into a Back Propagated Neural Network. The number of hidden layers and number of neurons are systematically selected to implement a better predicting machine. The implemented model is tested using both interpolated and extrapolated data. Fundamental limitations and inherent difficulties when using neural networks for processing of high noise, small sample size signals are also discussed. Results of the prediction are presented and an elaborate discussion is made comparing the results.
Show less - Date Issued
- 2003
- PURL
- http://purl.flvc.org/fcla/dt/13045
- Subject Headings
- Pattern recognition systems, Neural networks (Computer science), Stock exchanges
- Format
- Document (PDF)
- Title
- Forecasting foreign exchange rates using neural networks.
- Creator
- Talati, Amit H., Florida Atlantic University, Pandya, Abhijit S., College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
- Abstract/Description
-
Time series is a phenomena which appears in the financial world in various forms. One of the objectives of time series is to forecast the future based on the past. The goal of this thesis is to use foreign exchange time series, and predict its future values and trends using neural networks. The thesis covers background work in this area and discusses the results obtained by other researchers. A neural network is then developed to predict the future values of the USD/GBP and USD/DEM exchange...
Show moreTime series is a phenomena which appears in the financial world in various forms. One of the objectives of time series is to forecast the future based on the past. The goal of this thesis is to use foreign exchange time series, and predict its future values and trends using neural networks. The thesis covers background work in this area and discusses the results obtained by other researchers. A neural network is then developed to predict the future values of the USD/GBP and USD/DEM exchange rates. Both single-step and iterated multi-step predictions are considered. The performance of neural networks strongly depends on the inputs supplied. The effect of the changes in the number of inputs is also considered, and a method suggested for deciding on the optimum number. The forecasting of foreign exchange rates is a challenge because of the dynamic nature of the FOREX market and its dependencies on world events. The tool used for building the neural network and validating the approach is "Brainmaker".
Show less - Date Issued
- 2000
- PURL
- http://purl.flvc.org/fcla/dt/12699
- Subject Headings
- Foreign exchange rates--Mathmematical models, Foreign exchange--Forecasting--Mathematical models, Neural networks (Computer science)
- Format
- Document (PDF)