Engineering Commons^™

Effects Of Training Datasets On Both The Extreme Learning Machine And Support Vector Machine For Target Audience Identification On Twitter, Siaw Ling Lo, David Cornforth, Raymond Chiong

Research Collection School Of Computing and Information Systems

The ability to identify or predict a target audience from the increasingly crowded social space will provide a company some competitive advantage over other companies. In this paper, we analyze various training datasets, which include Twitter contents of an account owner and its list of followers, using features generated in different ways for two machine learning approaches - the Extreme Learning Machine (ELM) and Support Vector Machine (SVM). Various configurations of the ELM and SVM have been evaluated. The results indicate that training datasets using features generated from the owner tweets achieve the best performance, relative to other feature sets. …

Avatar Captcha : Telling Computers And Humans Apart Via Face Classification And Mouse Dynamics., Darryl Felix D’Souza

Electronic Theses and Dissertations

Bots are malicious, automated computer programs that execute malicious scripts and predefined functions on an affected computer. They pose cybersecurity threats and are one of the most sophisticated and common types of cybercrime tools today. They spread viruses, generate spam, steal personal sensitive information, rig online polls and commit other types of online crime and fraud. They sneak into unprotected systems through the Internet by seeking vulnerable entry points. They access the system’s resources like a human user does. Now the question arises how do we counter this? How do we prevent bots and on the other hand allow human …

Temporal Contextual Descriptors And Applications To Emotion Analysis., Haythem Balti

Electronic Theses and Dissertations

The current trends in technology suggest that the next generation of services and devices allows smarter customization and automatic context recognition. Computers learn the behavior of the users and can offer them customized services depending on the context, location, and preferences. One of the most important challenges in human-machine interaction is the proper understanding of human emotions by machines and automated systems. In the recent years, the progress made in machine learning and pattern recognition led to the development of algorithms that are able to learn the detection and identification of human emotions from experience. These algorithms use different modalities …

Identifying The High-Value Social Audience From Twitter Through Text-Mining Methods, Siaw Ling Lo, David Cornforth, Raymond Chiong

Research Collection School Of Computing and Information Systems

Doing business on social media has become a common practice for many companies these days. While the contents shared on Twitter and Facebook offer plenty of opportunities to uncover business insights, it remains a challenge to sift through the huge amount of social media data and identify the potential social audience who is highly likely to be interested in a particular company. In this paper, we analyze the Twitter content of an account owner and its list of followers through various text mining methods, which include fuzzy keyword matching, statistical topic modeling and machine learning approaches. We use tweets of …

Training Set Design For Test Removal Classication In Ic Test, Nagarjun Hassan Ranganath

Dissertations and Theses

This thesis reports the performance of a simple classifier as a function of its training data set. The classifier is used to remove analog tests and is named the Test Removal Classifier (TRC).

The thesis proposes seven different training data set designs that vary by the number of wafers in the data set, the source of the wafers and the replacement scheme of the wafers. The training data set size ranges from a single wafer to a maximum of five wafers. Three of the training data sets include wafers from the Lot Under Test (LUT). The training wafers in the …

Model-Free Method Of Reinforcement Learning For Visual Tasks, Jeff S. Soldate, Jonghoon Jin, Eugenio Culurciello

The Summer Undergraduate Research Fellowship (SURF) Symposium

There has been success in recent years for neural networks in applications requiring high level intelligence such as categorization and assessment. In this work, we present a neural network model to learn control policies using reinforcement learning. It takes a raw pixel representation of the current state and outputs an approximation of a Q value function made with a neural network that represents the expected reward for each possible state-action pair. The action is chosen an \epsilon-greedy policy, choosing the highest expected reward with a small chance of random action. We used gradient descent to update the weights and biases …

Energy-Efficient Stdp-Based Learning Circuits With Memristor Synapses, Xinyu Wu, Vishal Saxena, Kristy A. Campbell

Electrical and Computer Engineering Faculty Publications and Presentations

It is now accepted that the traditional von Neumann architecture, with processor and memory separation, is ill suited to process parallel data streams which a mammalian brain can efficiently handle. Moreover, researchers now envision computing architectures which enable cognitive processing of massive amounts of data by identifying spatio-temporal relationships in real-time and solving complex pattern recognition problems. Memristor cross-point arrays, integrated with standard CMOS technology, are expected to result in massively parallel and low-power Neuromorphic computing architectures. Recently, significant progress has been made in spiking neural networks (SNN) which emulate data processing in the cortical brain. These architectures comprise of …

Using The K-Means Clustering Algorithm To Classify Features For Choropleth Maps, Mark Polczynski, Michael Polczynski

Electrical and Computer Engineering Faculty Research and Publications

Common methods for classifying choropleth map features typically form classes based on a single feature attribute. This technical note reviews the use of the k-means clustering algorithm to perform feature classification using multiple feature attributes. The k-means clustering algorithm is described and compared to other common classification methods, and two examples of choropleth maps prepared using k-means clustering are provided.

Differentiating Schizophrenic Patients From Healthy Control; Application Of Machine Learning To Resting State Fmri, Hossein Ebrahimi Nezhad

Theses

In recent years, one analysis approach that has grown in popularity is the use of machine learning algorithms to train classifiers to decode stimuli, mental states, behaviors and other variables of interest from fMRI data. Most of these studies focus on fMRI low frequency oscillations. This study focuses on the amplitude of low-frequency fluctuations (ALFF) and fractional amplitude of low-frequency fluctuations (fALFF). A Voxel-wise analysis is performed on the whole brain for two groups of subjects. A machine learning algorithm is applied to two independent groups of subjects (a total of 160 healthy control and schizophrenic subjects) to classify Schizophrenia …

On Kernel-Base Multi-Task Learning, Cong Li

Electronic Theses and Dissertations

Multi-Task Learning (MTL) has been an active research area in machine learning for two decades. By training multiple relevant tasks simultaneously with information shared across tasks, it is possible to improve the generalization performance of each task, compared to training each individual task independently. During the past decade, most MTL research has been based on the Regularization-Loss framework due to its flexibility in specifying various types of information sharing strategies, the opportunity it offers to yield a kernel-based methods and its capability in promoting sparse feature representations. However, certain limitations exist in both theoretical and practical aspects of Regularization-Loss-based MTL. …

An Unsupervised Consensus Control Chart Pattern Recognition Framework, Siavash Haghtalab

Electronic Theses and Dissertations

Early identification and detection of abnormal time series patterns is vital for a number of manufacturing. Slide shifts and alterations of time series patterns might be indicative of some anomaly in the production process, such as machinery malfunction. Usually due to the continuous flow of data monitoring of manufacturing processes requires automated Control Chart Pattern Recognition(CCPR) algorithms. The majority of CCPR literature consists of supervised classification algorithms. Less studies consider unsupervised versions of the problem. Despite the profound advantage of unsupervised methodology for less manual data labeling their use is limited due to the fact that their performance is not …

An Urgent Precaution System To Detect Students At Risk Of Substance Abuse Through Classification Algorithms, Faruk Bulut, İhsan Ömür Bucak

Turkish Journal of Electrical Engineering and Computer Sciences

In recent years, the use of addictive drugs and substances has turned out to be a challenging social problem worldwide. The illicit use of these types of drugs and substances appears to be increasing among elementary and high school students. After becoming addicted to drugs, life becomes unbearable and gets even worse for their users. Scientific studies show that it becomes extremely difficult for an individual to break this habit after being a user. Hence, preventing teenagers from addiction becomes an important issue. This study focuses on an urgent precaution system that helps families and educators prevent teenagers from developing …

Human-Robot Interaction For Multi-Robot Systems, Bennie Lewis

Electronic Theses and Dissertations

Designing an effective human-robot interaction paradigm is particularly important for complex tasks such as multi-robot manipulation that require the human and robot to work together in a tightly coupled fashion. Although increasing the number of robots can expand the area that the robots can cover within a bounded period of time, a poor human-robot interface will ultimately compromise the performance of the team of robots. However, introducing a human operator to the team of robots, does not automatically improve performance due to the difficulty of teleoperating mobile robots with manipulators. The human operator’s concentration is divided not only among multiple …

Remote Sensing With Computational Intelligence Modelling For Monitoring The Ecosystem State And Hydraulic Pattern In A Constructed Wetland, Golam Mohiuddin

Electronic Theses and Dissertations

Monitoring the heterogeneous aquatic environment such as the Stormwater Treatment Areas (STAs) located at the northeast of the Everglades is extremely important in understanding the land processes of the constructed wetland in its capacity to remove nutrient. Direct monitoring and measurements of ecosystem evolution and changing velocities at every single part of the STA are not always feasible. Integrated remote sensing, monitoring, and modeling technique can be a state-of-the-art tool to estimate the spatial and temporal distributions of flow velocity regimes and ecological functioning in such dynamic aquatic environments. In this presentation, comparison between four computational intelligence models including Extreme …

Sketchart: A Pen-Based Tool For Chart Generation And Interaction., Andres Vargas Gonzalez

Electronic Theses and Dissertations

It has been shown that representing data with the right visualization increases the understanding of qualitative and quantitative information encoded in documents. However, current tools for generating such visualizations involve the use of traditional WIMP techniques, which perhaps makes free interaction and direct manipulation of the content harder. In this thesis, we present a pen-based prototype for data visualization using 10 different types of bar based charts. The prototype lets users sketch a chart and interact with the information once the drawing is identified. The prototype's user interface consists of an area to sketch and touch based elements that will …

Robustness And Prediction Accuracy Of Machine Learning For Objective Visual Quality Assessment, Andrew Hines, Paul Kendrick, Adriaan Barri, Manish Narwaria, Judith A. Redi

Conference papers

Machine Learning (ML) is a powerful tool to support the development of objective visual quality assessment metrics, serving as a substitute model for the perceptual mechanisms acting in visual quality appreciation. Nevertheless, the reliability of ML-based techniques within objective quality assessment metrics is often questioned. In this study, the robustness of ML in supporting objective quality assessment is investigated, specifically when the feature set adopted for prediction is suboptimal. A Principal Component Regression based algorithm and a Feed Forward Neural Network are compared when pooling the Structural Similarity Index (SSIM) features perturbed with noise. The neural network adapts better with …

Complementary Layered Learning, Sean Mondesire

Electronic Theses and Dissertations

Layered learning is a machine learning paradigm used to develop autonomous robotic-based agents by decomposing a complex task into simpler subtasks and learns each sequentially. Although the paradigm continues to have success in multiple domains, performance can be unexpectedly unsatisfactory. Using Boolean-logic problems and autonomous agent navigation, we show poor performance is due to the learner forgetting how to perform earlier learned subtasks too quickly (favoring plasticity) or having difficulty learning new things (favoring stability). We demonstrate that this imbalance can hinder learning so that task performance is no better than that of a suboptimal learning technique, monolithic learning, which …

Cooperative Target Tracking Enhanced With The Sequence Memoizer, Everett A. Bryan

Theses and Dissertations

Target tracking is an important part of video surveillance from a UAV. Tracking a target in an urban environment can be difficult because of the number of occlusions present in the environment. If multiple UAVs are used to track a target and the target behavior is learned autonomously by the UAV then the task may become easier. This thesis explores the hypothesis that an existing cooperative control algorithm can be enhanced by a language modeling algorithm to improve over time the target tracking performance of one or more ground targets in a dense urban environment. Observations of target behavior are …

Geometric Modeling Of Non-Rigid 3d Shapes : Theory And Application To Object Recognition., Mostafa Abdelrahman

Electronic Theses and Dissertations

One of the major goals of computer vision is the development of flexible and efficient methods for shape representation. This is true, especially for non-rigid 3D shapes where a great variety of shapes are produced as a result of deformations of a non-rigid object. Modeling these non-rigid shapes is a very challenging problem. Being able to analyze the properties of such shapes and describe their behavior is the key issue in research. Also, considering photometric features can play an important role in many shape analysis applications, such as shape matching and correspondence because it contains rich information about the visual …

New Covariance-Based Feature Extraction Methods For Classification And Prediction Of High-Dimensional Data, Mopelola Adediwura Sofolahan

Open Access Dissertations

When analyzing high dimensional data sets, it is often necessary to implement feature extraction methods in order to capture relevant discriminating information useful for the purposes of classification and prediction. The relevant information can typically be represented in lower-dimensional feature spaces, and a widely used approach for this is the principal component analysis (PCA) method. PCA efficiently compresses information into lower dimensions; however, studies indicate that it is not optimal for feature extraction especially when dealing with classification problems. Furthermore, for high-dimensional data having limited observations, as is typically the case with remote sensing data and nonstationary data such as …

Application Of Machine Learning And Parametric Nurbs Geometry To Mode Shape Identification, Robert Mceuen Porter

Theses and Dissertations

In any design, the dynamic characteristics of a part are dependent on its geometric and material properties. Identifying vibrational mode shapes within an iterative design process becomes difficult and time consuming due to frequently changing part definition. Although research has been done to improve the process, visual inspection of analysis results is still the current means of identifying each vibrational mode determined by a modal analysis. This research investigates the automation of the mode shape identification process through the use of parametric geometry and machine learning.In the developed method, displacement results from finite element modal analysis are used to create …

User Modeling Via Machine Learning And Rule-Based Reasoning To Understand And Predict Errors In Survey Systems, Leonard Cleve Stuart

School of Computing: Dissertations, Theses, and Student Research

User modeling is traditionally applied to systems were users have a large degree of control over their goals, the content they view, and the manner in which they navigate through the system. These systems aim to both recommend useful goals to users and to assist them in achieving perceived goals. Systems such as online or telephone surveys are different in that users have only a singular goal of survey completion, extremely limited control over navigation, and content is restricted to prescribed set of survey tasks; changing the user modeling problem to one in which the best means of assisting users …

Segmentation And Model Generation For Large-Scale Cyber Attacks, Steven E. Strapp

Theses

Raw Cyber attack traffic can present more questions than answers to security analysts. Especially with large-scale observables it is difficult to identify which packets are relevant and what attack behaviors are present. Many existing works in Host or Flow Clustering attempt to group similar behaviors to expedite analysis; these works often phrase the problem directly as offline unsupervised machine learning. This work proposes online processing to simultaneously model coordinating actors and segment traffic that is relevant to a target of interest, all while it is being received. The goal is not just to aggregate similar attack behaviors, but to provide …

A Memristor-Based Neuromorphic Computing Application, Adrian Rothenbuhler

Boise State University Theses and Dissertations

Artificial neural networks have recently received renewed interest because of the discovery of the memristor. The memristor is the fourth basic circuit element, hypothesized to exist by Leon Chua in 1971 and physically realized in 2008. The two-terminal device acts like a resistor with memory and is therefore of great interest for use as a synapse in hardware ANNs. Recent advances in memristor technology allowed these devices to migrate from the experimental stage to the application stage.

This Master's thesis presents the development of a threshold logic gate (TLG), which is a special case of an ANN, implemented with discrete …

Reward-Driven Training Of Random Boolean Network Reservoirs For Model-Free Environments, Padmashri Gargesa

Dissertations and Theses

Reservoir Computing (RC) is an emerging machine learning paradigm where a fixed kernel, built from a randomly connected "reservoir" with sufficiently rich dynamics, is capable of expanding the problem space in a non-linear fashion to a higher dimensional feature space. These features can then be interpreted by a linear readout layer that is trained by a gradient descent method. In comparison to traditional neural networks, only the output layer needs to be trained, which leads to a significant computational advantage. In addition, the short term memory of the reservoir dynamics has the ability to transform a complex temporal input state …

Life Long Learning In Sparse Learning Environments, John Reeder

Electronic Theses and Dissertations

Life long learning is a machine learning technique that deals with learning sequential tasks over time. It seeks to transfer knowledge from previous learning tasks to new learning tasks in order to increase generalization performance and learning speed. Real-time learning environments in which many agents are participating may provide learning opportunities but they are spread out in time and space outside of the geographical scope of a single learning agent. This research seeks to provide an algorithm and framework for life long learning among a network of agents in a sparse real-time learning environment. This work will utilize the robust …

Human Action Localization And Recognition In Unconstrained Videos, Hakan Boyraz

Electronic Theses and Dissertations

As imaging systems become ubiquitous, the ability to recognize human actions is becoming increasingly important. Just as in the object detection and recognition literature, action recognition can be roughly divided into classification tasks, where the goal is to classify a video according to the action depicted in the video, and detection tasks, where the goal is to detect and localize a human performing a particular action. A growing literature is demonstrating the benefits of localizing discriminative sub-regions of images and videos when performing recognition tasks. In this thesis, we address the action detection and recognition problems. Action detection in video …

Resolving Pronominal Anaphora Using Commonsense Knowledge, Seyedeh Leili Javadpour

LSU Doctoral Dissertations

Coreference resolution is the task of resolving all expressions in a text that refer to the same entity. Such expressions are often used in writing and speech as shortcuts to avoid repetition. The most frequent form of coreference is the anaphor. To resolve anaphora not only grammatical and syntactical strategies are required, but also semantic approaches should be taken into consideration. This dissertation presents a framework for automatically resolving pronominal anaphora by integrating recent findings from the field of linguistics with new semantic features. Commonsense knowledge is the routine knowledge people have of the everyday world. Because such knowledge is …

Integrated Data Fusion And Mining (Idfm) Technique For Monitoring Water Quality In Large And Small Lakes, Benjamin Vannah

Electronic Theses and Dissertations

Monitoring water quality on a near-real-time basis to address water resources management and public health concerns in coupled natural systems and the built environment is by no means an easy task. Furthermore, this emerging societal challenge will continue to grow, due to the ever-increasing anthropogenic impacts upon surface waters. For example, urban growth and agricultural operations have led to an influx of nutrients into surface waters stimulating harmful algal bloom formation, and stormwater runoff from urban areas contributes to the accumulation of total organic carbon (TOC) in surface waters. TOC in surface waters is a known precursor of disinfection byproducts …

Visuomotor Learning Enhanced By Augmenting Instantaneous Trajectory Error Feedback During Reaching, James L. Patton, John Wei Yejun, Preeti Bajaj, Robert A. Scheidt

Biomedical Engineering Faculty Research and Publications

We studied reach adaptation to a 30u visuomotor rotation to determine whether augmented error feedback can promote faster and more complete motor learning. Four groups of healthy adults reached with their unseen arm to visual targets surrounding a central starting point. A manipulandum tracked hand motion and projected a cursor onto a display immediately above the horizontal plane of movement. For one group, deviations from the ideal movement were amplified with a gain of 2 whereas another group experienced a gain of 3.1. The third group experienced an offset equal to the average error seen in the initial perturbations, while …