Digital Commons Network^™

Values Of Artificial Intelligence In Marketing, Yingrui Xi, Keng Siau

Research Collection School Of Computing and Information Systems

Artificial Intelligence (AI) is causing radical changes in marketing and emerging as a competent assistant supporting all areas of the marketing field. The influences and impacts AI has created in various marketing segments have aroused much interest among marketing professionals and academic scholars. Comprehensive and systematic studies on the values of AI in marketing, however, are still lacking and the existing literature fragmented. This research provides a comprehensive review of the existing literature in the relevant fields as well as a series of systematic interviews using the Value-Focused Thinking approach to understand the values of AI in marketing. This research …

Do You Feel Me?: Learning Language From Humans With Robot Emotional Displays, David Mcneill

Boise State University Theses and Dissertations

In working towards accomplishing a human-level acquisition and understanding of language, a robot must meet two requirements: the ability to learn words from interactions with its physical environment, and the ability to learn language from people in settings for language use, such as spoken dialogue. The second requirement poses a problem: If a robot is capable of asking a human teacher well-formed questions, it will lead the teacher to provide responses that are too advanced for a robot, which requires simple inputs and feedback to build word-level comprehension.

In a live interactive study, we tested the hypothesis that emotional displays …

Content Based Image Retrieval (Cbir) For Brand Logos, Enjal Parajuli

Boise State University Theses and Dissertations

This thesis explores the problem of automatically detecting the presence of logos in general images. Brand logos carry the goodwill of a company and are considered to be of high value in the corporate world, and thus automatically determining whether or not a logo is present in an image can be of interest for companies that wish to protect their brand. The problem of automated logo detection is inherently complex, but is further complicated through intentional obfuscation of logo images, for example by color shifting or other slight image modifications that leave the logo intact and easily recognizable by a …

Development Of Fully Balanced Ssfp And Computer Vision Applications For Mri-Assisted Radiosurgery (Mars), Jeremiah Sanders

Dissertations and Theses (Open Access)

Prostate cancer is the second most common cancer in men and the second-leading cause of cancer death in men. Brachytherapy is a highly effective treatment option for prostate cancer, and is the most cost-effective initial treatment among all other therapeutic options for low to intermediate risk patients of prostate cancer. In low-dose-rate (LDR) brachytherapy, verifying the location of the radioactive seeds within the prostate and in relation to critical normal structures after seed implantation is essential to ensuring positive treatment outcomes.

One current gap in knowledge is how to simultaneously image the prostate, surrounding anatomy, and radioactive seeds within the …

Knot Flow Classification And Its Applications In Vehicular Ad-Hoc Networks (Vanet), David Schmidt

Electronic Theses and Dissertations

Intrusion detection systems (IDSs) play a crucial role in the identification and mitigation for attacks on host systems. Of these systems, vehicular ad hoc networks (VANETs) are difficult to protect due to the dynamic nature of their clients and their necessity for constant interaction with their respective cyber-physical systems. Currently, there is a need for a VANET-specific IDS that meets this criterion. To this end, a spline-based intrusion detection system has been pioneered as a solution. By combining clustering with spline-based general linear model classification, this knot flow classification method (KFC) allows for robust intrusion detection to occur. Due its …

Non-Autoregressive Dialog State Tracking, Hung Le, Richard Socher, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Recent efforts in Dialogue State Tracking (DST) for task-oriented dialogues have progressed toward open-vocabulary or generation-based approaches where the models can generate slot value candidates from the dialogue history itself. These approaches have shown good performance gain, especially in complicated dialogue domains with dynamic slot values. However, they fall short in two aspects: (1) they do not allow models to explicitly learn signals across domains and slots to detect potential dependencies among (domain, slot) pairs; and (2) existing models follow auto-regressive approaches which incur high time cost when the dialogue evolves over multiple domains and multiple turns. In this paper, …

Predicting Disease Progression Using Deep Recurrent Neural Networks And Longitudinal Electronic Health Record Data, Seunghwan Kim

McKelvey School of Engineering Graduate Student Theses & Dissertations

Electronic Health Records (EHR) are widely adopted and used throughout healthcare systems and are able to collect and store longitudinal information data that can be used to describe patient phenotypes. From the underlying data structures used in the EHR, discrete data can be extracted and analyzed to improve patient care and outcomes via tasks such as risk stratification and prospective disease management. Temporality in EHR is innately present given the nature of these data, however, and traditional classification models are limited in this context by the cross-sectional nature of training and prediction processes. Finding temporal patterns in EHR is especially …

Predicting Disease Progression Using Deep Recurrent Neural Networks And Longitudinal Electronic Health Record Data, Seunghwan Kim

McKelvey School of Engineering Graduate Student Theses & Dissertations

Electronic Health Records (EHR) are widely adopted and used throughout healthcare systems and are able to collect and store longitudinal information data that can be used to describe patient phenotypes. From the underlying data structures used in the EHR, discrete data can be extracted and analyzed to improve patient care and outcomes via tasks such as risk stratification and prospective disease management. Temporality in EHR is innately present given the nature of these data, however, and traditional classification models are limited in this context by the cross- sectional nature of training and prediction processes. Finding temporal patterns in EHR is …

A Framework For Vector-Weighted Deep Neural Networks, Carter Chiu

UNLV Theses, Dissertations, Professional Papers, and Capstones

The vast majority of advances in deep neural network research operate on the basis of a real-valued weight space. Recent work in alternative spaces have challenged and complemented this idea; for instance, the use of complex- or binary-valued weights have yielded promising and fascinating results. We propose a framework for a novel weight space consisting of vector values which we christen VectorNet. We first develop the theoretical foundations of our proposed approach, including formalizing the requisite theory for forward and backpropagating values in a vector-weighted layer. We also introduce the concept of expansion and aggregation functions for conversion between real …

Achieving Causal Fairness In Machine Learning, Yongkai Wu

Graduate Theses and Dissertations

Fairness is a social norm and a legal requirement in today's society. Many laws and regulations (e.g., the Equal Credit Opportunity Act of 1974) have been established to prohibit discrimination and enforce fairness on several grounds, such as gender, age, sexual orientation, race, and religion, referred to as sensitive attributes. Nowadays machine learning algorithms are extensively applied to make important decisions in many real-world applications, e.g., employment, admission, and loans. Traditional machine learning algorithms aim to maximize predictive performance, e.g., accuracy. Consequently, certain groups may get unfairly treated when those algorithms are applied for decision-making. Therefore, it is an imperative …

A Matheuristic Algorithm For Solving The Vehicle Routing Problem With Cross-Docking, Aldy Gunawan, Audrey Tedja Widjaja, Pieter Vansteenwegen, Vincent F. Yu

Research Collection School Of Computing and Information Systems

This paper studies the integration of the vehicle routing problem with cross-docking, namely VRPCD. The aim is to find a set of routes to deliver single products from a set of suppliers to a set of customers through a cross-dock facility, such that the operational and transportation costs are minimized, without violating the vehicle capacity and time horizon constraints. A two-phase matheuristic approach that uses the routes of the local optima of an adaptive large neighborhood search (ALNS) as columns in a set-partitioning formulation of the VRPCD is designed. This matheuristic outperforms the state-of-the-art algorithms in solving a subset of …

Learning Expensive Coordination: An Event-Based Deep Rl Approach, Runsheng Yu, Xinrun Wang, Rundong Wang, Youzhi Zhang, Bo An, Zhen Yu Shi, Hanjiang Lai

Research Collection School Of Computing and Information Systems

Existing works in deep Multi-Agent Reinforcement Learning (MARL) mainly focus on coordinating cooperative agents to complete certain tasks jointly. However, in many cases of the real world, agents are self-interested such as employees in a company and clubs in a league. Therefore, the leader, i.e., the manager of the company or the league, needs to provide bonuses to followers for efficient coordination, which we call expensive coordination. The main difficulties of expensive coordination are that i) the leader has to consider the long-term effect and predict the followers’ behaviors when assigning bonuses, and ii) the complex interactions between followers make …

Partial Discharge Classification For Xlpe Cable Joints Using K Nearest Neighbors Algorithm, Mohd Salleh Muhammad Shairazi

Student Works (2020-2029)

Due to excellent mechanical and electrical properties, cross-linked polyethylene (XLPE) cables are commonly used in the power industry. However, cable joints are the weakest part of XLPE cables and are susceptible to insulation failures. Cable joint insulation breakup can cause large losses for power companies. It is therefore necessary to evaluate the consistency of the insulation for early detection of insulation failure. It is known that there is a link between the partial discharge (PD) and the quality of the insulation. PD analysis is an important tool for assessing the quality of insulation in cable joints. In this study, XLPE …

Classification Of Labour Pain Using Electroencephalogram Signal Based On Wavelet Method, Chong Yeh Sai

Student Works (2020-2029)

Electroencephalogram (EEG) is the recording of electrical activity of the cerebral cortex through electrodes placed on the scalp. EEG is used to acquire neurophysiological signals for application in clinical diagnosis and brain computer interface (BCI). However, in practical settings the EEG signals are often contaminated by signal artifacts known as the biological and environmental artifacts. These artifacts degrade EEG signals, thereby obstructing clinical diagnosis or BCI applications by distorting the observed power spectrum. Procedures for automated removal of EEG artifacts are frequently sought after in pre-processing and filtering of the EEG signals. In recent years, a combination of independent component …

Classification Of Dividend News Based On The Movement Of The Share Market Prices Of Public Listed Companies In Bursa Malaysia, Vijaya Kumar Shubana

Student Works (2020-2029)

Stock market is naturally complex and plays a major role in towards the nation’s growth. However, the performance of a company in stock market varies due to many influences but not limited to economics, political and financial related news. This study attempts to classify the share market dividend news announcement in Bursa Malaysia based on the pattern of share market price. Samples including five hundred (500) observations of dividend news from forty-seven (47) listed companies in Bursa Malaysia during the period of 2000 to 2018 are used in this study. There are three (3) main objectives in this study which …

Energy-Efficient Data Transmission With Clustering And Compressive Sensing In Wireless Sensor Networks, Alagirisamy Mukil

Student Works (2020-2029)

One of the most important application of wireless sensor network is environmental monitoring. The application involves lifetime of sensor nodes for longer duration associating its energy module. Wireless sensor nodes deployed in sensing field aggregate enormous amount of sensed data and transfer them to the sink. The inherent limitation of energy carried within the battery of sensor nodes fetches extreme difficulty to acquire adequate network lifetime, becoming a bottleneck in forwarding data to sink. Hence the motivation is to reduce the amount of data transfer and attain energy efficiency. This is achieved by clustering and compressive sensing techniques. First objective …

Supervised Optimal Decision Machine Learning Approach To Class- And Method-Level Data Preprocessing Towards Effective Software Defect Prediction, Felix Ebubeogu Amarachukwu

Student Works (2020-2029)

Software defect prediction provides actionable outputs to software teams while contributing to industrial success. Therefore, predicting the number of defects in a new version of software at both the class and method levels is an important goal of defect prediction studies to assist software teams in optimizing their test efforts towards improving software quality. However, despite remarkable achievements in defect prediction, the quality of the data applied in defect prediction studies has been a major concern, with related quality issues leading to numerous contradictory findings in machine learning research. In addition, a demonstrated approach for predicting the number of defects …

A Machine Learning Method For Predicting Liver Transplant Survival Outcomes, Brandon C. Revels

Honors Theses

For years, doctors have utilized the Model for End-stage Liver Disease (MELD) score to aid in the allocation of organs for liver transplants (LT). A major issue with using the MELD score to allocate organs for transplantation is that the MELD score does not accurately predict post-transplant survival. This research project aims to investigate the use of machine learning (ML) methods to predict LT survival using the newer Scientific Registry of Transplant Recipients (SRTR) dataset. For this project, death and nonfatal graft failure were treated equally as both cases result in a loss of a donated organ. The ML algorithms …

Improving Syntactic Relationships Between Language And Objects, Benjamin Wilke, Tej Tenmattam, Anand Rajan, Andrew Pollock, Joel Lindsey

SMU Data Science Review

This paper presents the integration of natural language processing and computer vision to improve the syntax of the language generated when describing objects in images. The goal was to not only understand the objects in an image, but the interactions and activities occurring between the objects. We implemented a multi-modal neural network combining convolutional and recurrent neural network architectures to create a model that can maximize the likelihood of word combinations given a training image. The outcome was an image captioning model that leveraged transfer learning techniques for architecture components. Our novelty was to quantify the effectiveness of transfer learning …

Multi-Objective Signal Timing Optimal Model For Rural-Urban Fringe Area Intersection, Xiaoyu Zhang, Chunfu Shao

Journal of System Simulation

Abstract: With the acceleration of the urbanization process in our country, the road traffic problems in the urban-rural fringe area are becoming serious. In order to improve the traffic efficiency, environmental benefits and traffic safety, a multi-objective optimal model for signal timing is established under the comprehensive consideration of several factors, such as the delay, capacity, number of stops and vehicle emissions. The genetic algorithm is used to solve the problem. A typical intersection is taken as the example for the analysis and the improved design schemes can be achieved and evaluated by the road traffic simulation. The …

Lightmap-Based Gi Collaborative Rendering System For Web3d Application, Shao Wei, Liu Chang, Jinyuan Jia

Journal of System Simulation

Abstract: For the real-time dynamic lighting rendering in low-end web environment, a view-independent collaborative real-time rendering system is presented. The whole system consists of a cloud renderer which produces the global illumination, a web renderer which computes the local illumination, and a scalable collaboration solution which takes the user behavior into account to adjust the range and frequency of the lighting computation. The cloud renderer computes the lighting data into the Lightmap, streams the Lightmap to the web client. After computing the direct light, the web renderer achieves the global illumination information by Sampling the Lightmap, and shows the …

Thinking On Digital Twin For Weapon System, Junhua Zhou, Junjie Xue, Heyu Li, Ji Hang, Tingyu Lin, Guoqiang Shi

Journal of System Simulation

Abstract: The main development process of the digital twin technology is reviewed and its necessity for the weapon system is put forward. The definition and composition of the digital twin for the weapon system is given and its application architecture and key technologies, such as the construction of the digital thread, bi-directional collaborative simulation and control, intelligent autonomous evolution, digital twin operation support based on the container-simulation-cloud, et al, are analyzed. The preliminary research and practice of some key technologies is expounded, and the future research directions are summarized and looked forward to.

Modeling And Propeller Location Optimization For Diving Propeller Vehicle, Jianjun Zhang, Yang Bing, Jinhui Wu, Yan Qiang

Journal of System Simulation

Abstract: Focus on the rapid physical exertion and the poor flexibility of diving propeller vehicle, a wearable diving propeller equipped with buoyancy control devices is proposed, and the position of the propeller in the device is optimized. The human body structure is regarded as a rigid body to establish a spatial 6-degree-of-freedom motion equation, and its surrounding hydrodynamic performance is analyzed. The hydrodynamic parameters in the model are obtained based on the FLUENT. The propeller position optimization model based on the minimum external force of rectilinear diving and slewing motions is established. The optimization results demonstrate that the optimized …

Research On Image Description Method Based On Neural Network, Kong Rui, Xie Wei, Lei Tai

Journal of System Simulation

Abstract: The automatic recognition and automatically describing image content is an important research direction to the artificial intelligence to connect the computer vision and the natural language processing. A method of describing the image content is proposed to generate the natural language by using the deep neural network model. The model consists of a convolutional neural network (CNN) and a recurrent neural network (RNN). The CNN is used to extract features of the input image to generate a fixed-length feature vector, which initializes the RNN to generate the sentences. Experimental results on the MSCOCO image description dataset show the syntactic …

Multi-Objective Dynamic Programming Algorithm Of Energy-Efficient Scheduling For Tow-Train, Xinyan Zhang, Yuqing Zhou

Journal of System Simulation

Abstract: To balance the performance and energy consumption of the mixed-model assembly lines effectively, a multi-objective energy-saving scheduling method for the tow-train is proposed. The energy-saving objective is introduced into the traditional material handling scheduling model for the tow-train and a multi-objective mixed integer programming model is constructed with two objective functions of minimizing the maximum line-side inventory and the total energy consumption. A forwards multi-objective dynamic programming based on the time window and dominance rules is presented to obtain the Pareto solutions: the definition for new states is given to obtain the Markov property, the time window and dominance …

Design Of Space-Based Surveillance Distributed Simulation System For Space Targets, Zhang Yang, Qingbo Gan, Yuan Hong, Jiangliang Fu, Chengming Sun

Journal of System Simulation

Abstract: Following the wide application of the HLA in the aerospace system simulation, the space-based surveillance distributed simulation system for the space targets based on the HLA is designed. In the infrastructure design level, the hierarchical structure including data layer, model layer and mission layer are designed. In the system design level, the simulation control members, space-based surveillance member, space target member, optical payload member, and visual simulation member are divided and confirmed. The orbit dynamics and maneuvering model, attitude dynamics and control model, payload pointing and optical imaging model are established. In system implement level, the …

One Design Mode Of Highly Configurable Modular Simulation System, Chunsheng Hu, Chengdong Xu, Penfei Zhang

Journal of System Simulation

Abstract: The computer simulation technology is an essential verification way to the design, construction and application optimization of the modern complex system. Following the continuous growing complexity of the system, the higher requirement on the configurability of the simulation system is needed to face the rapidly change. Based on the analysis on the configurability and some coupling factors with the adverse effect, a design mode of highly configurable and open and easy to expend modular simulation system is proposed. The results show that, despite the cost of some real-time performance, the design mode can realize the high configurability of the …

Modeling And Simulation Of Locating Water Resources For Forest Fire Extinguishing, Guoxiong Zhou, Li Tong, Aibin Chen, Mingfang He

Journal of System Simulation

Abstract: At the time of the forest fire, the effective water resource position is a good assistance to the firefighters to put out the fire. Taking a state-owned tree-farm, Huangfeng Bridge as an example in You Xian, an approach is proposed to optimally locate and determine the water resources to extinguish the forest fires, which uses the barycenter of a Weighted Voronoi Diagram and is generated dynamically via Cellular Automata and locally optimized by Alternative Location and Allocation algorithm. In order to reduce the low ratio impact of the water resource to the high fire risk areas, a barycenter model …

Cost-Efficiency Analysis Of Weapon System Portfolios For Effect Based Operation, Zhong Lin, Mingan Tong, Zhang Jian, Jianhai Liang

Journal of System Simulation

Abstract: In considertion of the cost-efficiency analysis of weapon system mainly for the target based operation, the uncertainties about preferences and possible interactions of different weapon systems, a cost-efficiency analysis method weapon system portfolios for effect based operation is presented. The framework of weapon system assignment for effect based operation is presented, the Bayesian network is used to build up the connection between effects and targets, and the cost-efficiency analysis model based on robust portfolio optimization is carried out. In view of the combinatorial explosion, the approximation algorithm based on the Tchebycheff norm and the linear integer programming is proposed …

System Dynamics And Multi-Agent Simulation Research On Ground Combatpersonnel Attrition, Xiaoxiong Hao, Jianwei Ma, Zhaohui Huang, Yungui Wang

Journal of System Simulation

Abstract: In order to study the law of the combat personnel attrition and guide the military medical logistics support, the relevant researches are systematically sorted out and the key questions are clarified. Based on Lanchester equation theory, a system dynamics model of the combat attrition process is established, and the consistency between the model conclusion and the historical experience is analyzed. A multi-agent model of the combat attrition process is also constructed, and the advantages and disadvantages of the two models are analyzed. The main conclusion is that the theoretical model’s outcomes of combat attrition are relatively consistent with the …