Open Access. Powered by Scholars. Published by Universities.®

Computer Engineering Commons™

Open Access. Powered by Scholars. Published by Universities.®

Machine learning

Discipline
Institution
Publication Year
Publication
Publication Type
File Type

Articles 421 - 439 of 439

Full-Text Articles in Computer Engineering

Using The K-Means Clustering Algorithm To Classify Features For Choropleth Maps, Mark Polczynski, Michael Polczynski Apr 2014

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.

Go to article

An Urgent Precaution System To Detect Students At Risk Of Substance Abuse Through Classification Algorithms, Faruk Bulut, İhsan Ömür Bucak Jan 2014

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 …

Go to article

Human-Robot Interaction For Multi-Robot Systems, Bennie Lewis Jan 2014

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 …

Go to article

Robustness And Prediction Accuracy Of Machine Learning For Objective Visual Quality Assessment, Andrew Hines, Paul Kendrick, Adriaan Barri, Manish Narwaria, Judith A. Redi Jan 2014

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 …

Go to article

User Modeling Via Machine Learning And Rule-Based Reasoning To Understand And Predict Errors In Survey Systems, Leonard Cleve Stuart Aug 2013

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 …

Go to article

Segmentation And Model Generation For Large-Scale Cyber Attacks, Steven E. Strapp Aug 2013

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 …

Go to article

Life Long Learning In Sparse Learning Environments, John Reeder Jan 2013

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 …

Go to article

An Automated Prognosis System For Estrogen Hormone Status Assessment In Breast Cancer Tissue Samples, Fati̇h Sarikoç, Adem Kalinli, Hülya Akgün, Fi̇gen Öztürk Jan 2013

An Automated Prognosis System For Estrogen Hormone Status Assessment In Breast Cancer Tissue Samples, Fati̇h Sarikoç, Adem Kalinli, Hülya Akgün, Fi̇gen Öztürk

Turkish Journal of Electrical Engineering and Computer Sciences

Estrogen receptor (ER) status evaluation is a widely applied method in the prognosis of breast cancer. However, testing for the existence of the ER biomarker in a patient's tumor sample mainly depends on the subjective decisions of the doctors. The aim of this paper is to introduce the usage of a machine learning tool, functional trees (FTs), to attain an ER prognosis of the disease via an objective decision model. For this aim, 27 image files, each of which came from a biopsy sample of an invasive ductal carcinoma patient, were scanned and captured by a light microscope. From these …

Go to article

Anticipating The Friction Coefficient Of Friction Materials Used In Automobiles By Means Of Machine Learning Without Using A Test Instrument, Mustafa Ti̇mur, Fati̇h Aydin Jan 2013

Anticipating The Friction Coefficient Of Friction Materials Used In Automobiles By Means Of Machine Learning Without Using A Test Instrument, Mustafa Ti̇mur, Fati̇h Aydin

Turkish Journal of Electrical Engineering and Computer Sciences

The most important factor for designs in which friction materials are used is the coefficient of friction. The coefficient of friction has been determined taking such variants as velocity, temperature, and pressure into account, which arise from various factors in friction materials, and by analyzing the effects of these variants on friction materials. Many test instruments have been produced in order to determine the coefficient of friction. In this article, a study about the use of machine learning algorithms instead of test instruments in order to determine the coefficient of friction is presented. Isotonic regression was selected as the machine …

Go to article

Human Intention Recognition Based Assisted Telerobotic Grasping Of Objects In An Unstructured Environment, Karan Hariharan Khokar Jan 2013

Human Intention Recognition Based Assisted Telerobotic Grasping Of Objects In An Unstructured Environment, Karan Hariharan Khokar

USF Tampa Graduate Theses and Dissertations

In this dissertation work, a methodology is proposed to enable a robot to identify an object to be grasped and its intended grasp configuration while a human is teleoperating a robot towards the desired object. Based on the detected object and grasp configuration, the human is assisted in the teleoperation task. The environment is unstructured and consists of a number of objects, each with various possible grasp configurations. The identification of the object and the grasp configuration is carried out in real time, by recognizing the intention of the human motion. Simultaneously, the human user is assisted to preshape over …

Go to article

A Review Of Situation Identification Techniques In Pervasive Computing, Juan Ye, Simon Dobson, Susan Mckeever Feb 2012

A Review Of Situation Identification Techniques In Pervasive Computing, Juan Ye, Simon Dobson, Susan Mckeever

Articles

Pervasive systems must offer an open, extensible, and evolving portfolio of services which integrate sensor data from a diverse range of sources. The core challenge is to provide appropriate and consistent adaptive behaviours for these services in the face of huge volumes of sensor data exhibiting varying degrees of precision, accuracy and dynamism. Situation identification is an enabling technology that resolves noisy sensor data and abstracts it into higher-level concepts that are interesting to applications. We provide a comprehensive analysis of the nature and characteristics of situations, discuss the complexities of situation identification, and review the techniques that are most …

Go to article

Sports Data Mining Technology Used In Basketball Outcome Prediction, Chenjie Cao Jan 2012

Sports Data Mining Technology Used In Basketball Outcome Prediction, Chenjie Cao

Dissertations

Driven by the increasing comprehensive data in sports datasets and data mining technique successfully used in different area, sports data mining technique emerges and enables us to find hidden knowledge to impact the sport industry. In many instances, predicting the outcomes of sporting events has always been a challenging and attractive work and is therefore drawing a wide concern to conduct research in this field. This project focuses on using machine learning algorithms to build a model for predicting the NBA game outcomes and the algorithms involve Simple Logistics Classifier, Artificial Neural Networks, SVM and Naïve Bayes. In order to …

Go to article

Data Mining Based Learning Algorithms For Semi-Supervised Object Identification And Tracking, Michael P. Dessauer Jan 2011

Data Mining Based Learning Algorithms For Semi-Supervised Object Identification And Tracking, Michael P. Dessauer

Doctoral Dissertations

Sensor exploitation (SE) is the crucial step in surveillance applications such as airport security and search and rescue operations. It allows localization and identification of movement in urban settings and can significantly boost knowledge gathering, interpretation and action. Data mining techniques offer the promise of precise and accurate knowledge acquisition techniques in high-dimensional data domains (and diminishing the “curse of dimensionality” prevalent in such datasets), coupled by algorithmic design in feature extraction, discriminative ranking, feature fusion and supervised learning (classification). Consequently, data mining techniques and algorithms can be used to refine and process captured data and to detect, recognize, classify, …

Go to article

Algorithms For Training Large-Scale Linear Programming Support Vector Regression And Classification, Pablo Rivas Perea Jan 2011

Algorithms For Training Large-Scale Linear Programming Support Vector Regression And Classification, Pablo Rivas Perea

Open Access Theses & Dissertations

The main contribution of this dissertation is the development of a method to train a Support Vector Regression (SVR) model for the large-scale case where the number of training samples supersedes the computational resources. The proposed scheme consists of posing the SVR problem entirely as a Linear Programming (LP) problem and on the development of a sequential optimization method based on variables decomposition, constraints decomposition, and the use of primal-dual interior point methods. Experimental results demonstrate that the proposed approach has comparable performance with other SV-based classifiers. Particularly, experiments demonstrate that as the problem size increases, the sparser the solution …

Go to article

A Neural Network Approach To Border Gateway Protocol Peer Failure Detection And Prediction, Cory B. White Dec 2009

A Neural Network Approach To Border Gateway Protocol Peer Failure Detection And Prediction, Cory B. White

Master's Theses

The size and speed of computer networks continue to expand at a rapid pace, as do the corresponding errors, failures, and faults inherent within such extensive networks. This thesis introduces a novel approach to interface Border Gateway Protocol (BGP) computer networks with neural networks to learn the precursor connectivity patterns that emerge prior to a node failure. Details of the design and construction of a framework that utilizes neural networks to learn and monitor BGP connection states as a means of detecting and predicting BGP peer node failure are presented. Moreover, this framework is used to monitor a BGP network …

Go to article

Constructive Induction Machines For Data Mining, Marek Perkowski, Stanislaw Grygiel, Qihong Chen, Dave Mattson Mar 1999

Constructive Induction Machines For Data Mining, Marek Perkowski, Stanislaw Grygiel, Qihong Chen, Dave Mattson

Electrical and Computer Engineering Faculty Publications and Presentations

"Learning Hardware" approach involves creating a computational network based on feedback from the environment (for instance, positive and negative examples from the trainer), and realizing this network in an array of Field Programmable Gate Arrays (FPGAs). Computational networks can be built based on incremental supervised learning (Neural Net training) or global construction (Decision Tree design). Here we advocate the approach to Learning Hardware based on Constructive Induction methods of Machine Learning (ML) using multivalued functions. This is contrasted with the Evolvable Hardware (EHW) approach in which learning/evolution is based on the genetic algorithm only.

Go to article

Data-Driven Process Discovery: A Discrete Time Algebra For Relational Signal Analysis, David M. Conrad Dec 1996

Data-Driven Process Discovery: A Discrete Time Algebra For Relational Signal Analysis, David M. Conrad

Theses and Dissertations

This research presents an autonomous and computationally tractable method for scientific process analysis, combining an iterative algorithmic search and a recognition technique to discover multivariate linear and non-linear relations within experimental data series. These resultant data-driven relations provide researchers with a potentially real-time insight into experimental process phenomena and behavior. This method enables the efficient search of a potentially infinite space of relations within large data series to identify relations that accurately represent process phenomena. Proposed is a time series transformation that encodes and compresses real-valued data into a well-defined, discrete-space of 13 primitive elements where comparative evaluation between variables …

Go to article

Convergence Properties Of Perceptrons, Ratnasri Krishna Adharapurapu Jan 1995

Convergence Properties Of Perceptrons, Ratnasri Krishna Adharapurapu

Theses Digitization Project

No abstract provided.

Go to article

Using Discovery-Based Learning To Prove The Behavior Of An Autonomous Agent, David N. Mezera Dec 1993

Using Discovery-Based Learning To Prove The Behavior Of An Autonomous Agent, David N. Mezera

Theses and Dissertations

Computer-generated autonomous agents in simulation often behave predictably and unrealistically. These characteristics make them easy to spot and exploit by human participants in the simulation, when we would prefer the behavior of the agent to be indistinguishable from human behavior. An improvement in behavior might be possible by enlarging the library of responses, giving the agent a richer assortment of tactics to employ during a combat scenario. Machine learning offers an exciting alternative to constructing additional responses by hand by instead allowing the system to improve its own performance with experience. This thesis presents NOSTRUM, a discovery-based learning DBL system …

Go to article

First Prev