Artificial Intelligence and Robotics Commons^™

Automatic Multi-Model Fitting For Blood Vessel Extraction, Xuefeng Chang

Electronic Thesis and Dissertation Repository

Blood vessel extraction and visualization in 2D images or 3D volumes is an essential clinical task. A blood vessel system is an example of a tubular tree like structure, and fully automated reconstruction of tubular tree like structures remains an open computer vision problem. Most vessel extraction methods are based on the vesselness measure. A vesselness measure, usually based on the eigenvalues of the Hessian matrix, assigns a high value to a voxel that is likely to be a part of a blood vessel. After the vesselness measure is computed, most methods extract vessels based on the shortest paths connecting …

Scene-Dependent Human Intention Recognition For An Assistive Robotic System, Kester Duncan

USF Tampa Graduate Theses and Dissertations

In order for assistive robots to collaborate effectively with humans for completing everyday tasks, they must be endowed with the ability to effectively perceive scenes and more importantly, recognize human intentions. As a result, we present in this dissertation a novel scene-dependent human-robot collaborative system capable of recognizing and learning human intentions based on scene objects, the actions that can be performed on them, and human interaction history. The aim of this system is to reduce the amount of human interactions necessary for communicating tasks to a robot. Accordingly, the system is partitioned into scene understanding and intention recognition modules. …

Realistic Dialogue Engine For Video Games, Caroline M. Rose

Electronic Thesis and Dissertation Repository

The concept of believable agent has a long history in Artificial Intelligence. It has applicability in multiple fields, particularly video games. Video games have shown tremendous technological advancement in several areas such as graphics and music; however, techniques used to simulate dialogue are still quite outdated. In this thesis, a method is proposed to allow a human player to interact with non-player characters using natural-language input. By using various techniques of modern Artificial Intelligence such as information retrieval and sentiment analysis, non-player characters have the capability of engaging in dynamic dialogue: they can answer questions, ask questions, remember events, and …

A Hybrid Prognostic Model For Oral Cancer Based On Clinicopathologic And Genomic Markers, Sameem Abdul Kareem

Sameem Abdul Kareem

There are very few prognostic studies that combine both clinicopathologic and genomic data. Most of the studies use only clinicopathologic factors without taking into consideration the tumour biology and molecular information, while some studies use genomic markers or microarray information only without the clinicopathologic parameters. Thus, these studies may not be able to prognoses a patient effectively. Previous studies have shown that prognosis results are more accurate when using both clinicopathologic and genomic data. The objectives of this research were to apply hybrid artificial intelligent techniques in the prognosis of oral cancer based on the correlation of clinicopathologic and genomic …

Faults Identification In Three-Phase Induction Motors Using Support Vector Machines, Rama Hammo

Master of Technology Management Plan II Graduate Projects

Induction motor is one of the most important motors used in industrial applications. The operating conditions may sometime lead the machine into different fault situations. The main types of external faults experienced by these motors are over loading, single phasing, unbalanced supply voltage, locked rotor, phase reversal, ground fault, under voltage and over voltage. The machine should be shut down when a fault is experienced to avoid damage and for the safety of the workers. Computer based relays monitor the machine and disconnect it during the faults. The relay logic used to identify these faults requires sophisticated signal processing techniques …

Monocular Pose Estimation And Shape Reconstruction Of Quasi-Articulated Objects With Consumer Depth Camera, Mao Ye

Theses and Dissertations--Computer Science

Quasi-articulated objects, such as human beings, are among the most commonly seen objects in our daily lives. Extensive research have been dedicated to 3D shape reconstruction and motion analysis for this type of objects for decades. A major motivation is their wide applications, such as in entertainment, surveillance and health care. Most of existing studies relied on one or more regular video cameras. In recent years, commodity depth sensors have become more and more widely available. The geometric measurements delivered by the depth sensors provide significantly valuable information for these tasks. In this dissertation, we propose three algorithms for monocular …

Vulnerability Analysis Of Cyber-Behavioral Biometric Authentication, Abdul Serwadda

Doctoral Dissertations

Research on cyber-behavioral biometric authentication has traditionally assumed naïve (or zero-effort) impostors who make no attempt to generate sophisticated forgeries of biometric samples. Given the plethora of adversarial technologies on the Internet, it is questionable as to whether the zero-effort threat model provides a realistic estimate of how these authentication systems would perform in the wake of adversity. To better evaluate the efficiency of these authentication systems, there is need for research on algorithmic attacks which simulate the state-of-the-art threats.

To tackle this problem, we took the case of keystroke and touch-based authentication and developed a new family of algorithmic …

Depth-Assisted Semantic Segmentation, Image Enhancement And Parametric Modeling, Chenxi Zhang

Theses and Dissertations--Computer Science

This dissertation addresses the problem of employing 3D depth information on solving a number of traditional challenging computer vision/graphics problems. Humans have the abilities of perceiving the depth information in 3D world, which enable humans to reconstruct layouts, recognize objects and understand the geometric space and semantic meanings of the visual world. Therefore it is significant to explore how the 3D depth information can be utilized by computer vision systems to mimic such abilities of humans. This dissertation aims at employing 3D depth information to solve vision/graphics problems in the following aspects: scene understanding, image enhancements and 3D reconstruction and …

Exploring Customer Specific Kpi Selection Strategies For An Adaptive Time Critical User Interface, Ingo Keck, Robert J. Ross

Conference papers

Rapid growth in the number of measures available to describe customer-organization relationships has presented a serious challenge for Business Intelligence (BI) interface developers as they attempt to provide business users with key customer information without requiring users to painstakingly sift through many interface windows and layers. In this paper we introduce a prototype Intelligent User Interface that we have deployed to partially address this issue. The interface builds on machine learning techniques to construct a ranking model of Key Performance Indicators (KPIs) that are used to select and present the most important customer metrics that can be made available to …

A Decision Making Tool For Sustainable Design In Construction, Enda Collins

Theses

This report defines sustainability and sustainable development before any research is carried out. This is necessary so that the research carried out further in the report makes sense and there is a reason for including such items in this report. The need for this project and what the project aims to achieve is also highlighted in the introduction of this report.

Sustainability is made up of three equal parts; social, economic and environmental. This report goes through each item and gives examples of each type of development. Having discussed sustainability in a broad sense the report then focuses on Legislation …

A Network That Really Works - The Application Of Artificial Neural Networks To Improve Yield Predictions And Nitrogen Management In Western Australia, Jinsong Leng, Andreas Neuhaus, Leisa Armstrong

Research outputs 2014 to 2021

Yield predictions are notorious for being difficult due to many interdependent factors such as rainfall, soil properties, plant health, plant density etc. This study is based upon the author’s previously published work and extends its findings by further investigating the best mathematical solution to this dilemma. Artificial intelligence (AI) techniques have been applied to a large set of soil, plant, rainfall, and yield data from CSBP’s field research trial program. Here we further differentiate by investigate two ANN techniques, a genetic algorithm with back propagation neural networks (GA-BP-NN) and a particle swarm optimization with back propagation neural networks (PSO-BP-NN). Results …

An Artificial Neural Network For Predicting Crops Yield In Nepal, Tirtha Ranjeet, Leisa Armstrong

Research outputs 2014 to 2021

This paper examines the application of artificial neural networks (ANNs) for predicting crop yields for an agricultural region in Nepal. The neural network algorithm has become an effective data mining tool and the outcome produced by this algorithm is considered to be less error prone than other computer science techniques. The backpropagation algorithm which iteratively finds a suitable weight value is considered for computing the error derivative. Agricultural data was collected from thirteen years from paddy field cultivation in the Siraha district, an eastern region in Nepal, and used for this investigation of neural networks. Additionally, climatic parameters including rainfall, …

Integrating Soil And Plant Tissue Tests And Using An Artificial Intelligence Method For Data Modelling Is Likely To Improve Decisions For In-Season Nitrogen Management, Andreas Neuhaus, Leisa Armstrong, Jinsong Leng, Dean Diepeveen, Geoff Anderson

Research outputs 2014 to 2021

This paper hypothesizes that there is value in combining soil, climate and plant tissue data to give more reliable advice on nitrogen top-ups in-season when compared with models that are currently available. The benefit of soil and climate data is to factor in N mineralisation and potential yield while plant test data is a more direct approach of yield estimates when considering firstly plant N uptake from the whole soil profile and secondly biomass (important yield component). Plant test data are closer to yield in time and space than soil test data, shortening the time period for any yield prognosis …

Hybrid Intelligent Model For Software Maintenance Prediction, Abdulrahman Ahmed Bobakr Baqais, Mohammad Alshayeb, Zubair A. Baig

Research outputs 2014 to 2021

Maintenance is an important activity in the software life cycle. No software product can do without undergoing the process of maintenance. Estimating a software’s maintainability effort and cost is not an easy task considering the various factors that influence the proposed measurement. Hence, Artificial Intelligence (AI) techniques have been used extensively to find optimized and more accurate maintenance estimations. In this paper, we propose an Evolutionary Neural Network (NN) model to predict software maintainability. The proposed model is based on a hybrid intelligent technique wherein a neural network is trained for prediction and a genetic algorithm (GA) implementation is used …

Evaluating Heuristics And Crowding On Center Selection In K-Means Genetic Algorithms, William Mcgarvey

CCE Theses and Dissertations

Data clustering involves partitioning data points into clusters where data points within the same cluster have high similarity, but are dissimilar to the data points in other clusters. The k-means algorithm is among the most extensively used clustering techniques. Genetic algorithms (GA) have been successfully used to evolve successive generations of cluster centers. The primary goal of this research was to develop improved GA-based methods for center selection in k-means by using heuristic methods to improve the overall fitness of the initial population of chromosomes along with crowding techniques to avoid premature convergence. Prior to this research, no rigorous systematic …

Towards A Computational Analysis Of Probabilistic Argumentation Frameworks, Pierpaolo Dondio

Articles

In this paper we analyze probabilistic argumentation frameworks (PAFs), defined as an extension of Dung abstract argumentation frameworks in which each argument n is asserted with a probability p(n). The debate around PAFs has so far centered on their theoretical definition and basic properties. This work contributes to their computational analysis by proposing a first recursive algorithm to compute the probability of acceptance of each argument under grounded and preferred semantics, and by studying the behavior of PAFs with respect to reinstatement, cycles and changes in argument structure. The computational tools proposed may provide strategic information for agents selecting the …

Risk Minimization Of Disjunctive Temporal Problem With Uncertainty, Hoong Chuin Lau, Tuan Anh Hoang

Research Collection School Of Computing and Information Systems

The Disjunctive Temporal Problem with Uncertainty (DTPU) is a fundamental problem that expresses temporal reasoning with both disjunctive constraints and contingency. A recent work (Peintner et al, 2007) develops a complete algorithm for determining Strong Controlla- bility of a DTPU. Such a notion that guarantees 100% confidence of execution may be too conservative in practice. In this paper, following the idea of (Tsamardinos 2002), we are interested to find a schedule that minimizes the risk (i.e. probability of failure) of executing a DTPU. We present a problem decomposition scheme that enables us to compute the probability of failure efficiently, followed …

A Mathematical Framework For Unmanned Aerial Vehicle Obstacle Avoidance, Sorathan Chaturapruek

HMC Senior Theses

The obstacle avoidance navigation problem for Unmanned Aerial Vehicles (UAVs) is a very challenging problem. It lies at the intersection of many fields such as probability, differential geometry, optimal control, and robotics. We build a mathematical framework to solve this problem for quadrotors using both a theoretical approach through a Hamiltonian system and a machine learning approach that learns from human sub-experts' multiple demonstrations in obstacle avoidance. Prior research on the machine learning approach uses an algorithm that does not incorporate geometry. We have developed tools to solve and test the obstacle avoidance problem through mathematics.

Scalable Collaborative Filtering Recommendation Algorithms On Apache Spark, Walker Evan Casey

CMC Senior Theses

Collaborative filtering based recommender systems use information about a user's preferences to make personalized predictions about content, such as topics, people, or products, that they might find relevant. As the volume of accessible information and active users on the Internet continues to grow, it becomes increasingly difficult to compute recommendations quickly and accurately over a large dataset. In this study, we will introduce an algorithmic framework built on top of Apache Spark for parallel computation of the neighborhood-based collaborative filtering problem, which allows the algorithm to scale linearly with a growing number of users. We also investigate several different variants …

Strategic Decision Support System Using Heuristic Algorithm For Practical Outlet Zones Allocation To Dealers In A Beer Supply Distribution Network, Michelle Lee Fong Cheong

Research Collection School Of Computing and Information Systems

We consider a two-echelon beer supply distribution network with the brewer replenishing the dealers and the dealers serving the outlet zones directly, for multiple product types. The allocation of the outlet zones to the dealers will determine the quantity of products the brewer replenishes each dealer, which will in turn impact the total warehousing and transportation costs. The non-linear optimization model formulated is difficult to solve to optimality, and the model itself does not include practical business considerations in the distribution business. A heuristics algorithm is designed and easily implemented using spreadsheets with Visual Basic programming to effectively and efficiently …