Computer Sciences Commons^™

A Comparative Study Of Text Summarization On E-Mail Data Using Unsupervised Learning Approaches, Tijo Thomas

Dissertations

Over the last few years, email has met with enormous popularity. People send and receive a lot of messages every day, connect with colleagues and friends, share files and information. Unfortunately, the email overload outbreak has developed into a personal trouble for users as well as a financial concerns for businesses. Accessing an ever-increasing number of lengthy emails in the present generation has become a major concern for many users. Email text summarization is a promising approach to resolve this challenge. Email messages are general domain text, unstructured and not always well developed syntactically. Such elements introduce challenges for study …

Customer Churn Prediction, Deepshikha Wadikar

Dissertations

Churned customers identification plays an essential role for the functioning and growth of any business. Identification of churned customers can help the business to know the reasons for the churn and they can plan their market strategies accordingly to enhance the growth of a business. This research is aimed at developing a machine learning model that can precisely predict the churned customers from the total customers of a Credit Union financial institution. A quantitative and deductive research strategies are employed to build a supervised machine learning model that addresses the class imbalance problem handled feature selection and efficiently predict the …

Experimenting With A Biologically Plausible Neural Network, Dmitri Murphy

University Honors Theses

We present research on an implementation of a biologically inspired Bayesian Confidence Propagation Neural Network (BCPNN). Based on previous work by Christopher Johansson and Anders Lansner, our implementation seeks to test and understand the various properties of this model. The floating-point implementation we built uses discrete time and bit-vectors as input/output. We found that the column based BCPNN model is able to memorize a decent number of input vectors and is able to restore noisy versions of these vectors with relatively high accuracy. We examine the model’s capacity, noise recovery ability and cross-column connection influence, among other attributes. The clearest …

Identifying And Disentangling Interleaved Activities Of Daily Living From Sensor Data, Eoin Rogers

Doctoral

Activity discovery (AD) refers to the unsupervised extraction of structured activity data from a stream of sensor readings in a real-world or virtual environment. Activity discovery is part of the broader topic of activity recognition, which has potential uses in fields as varied as social work and elder care, psychology and intrusion detection. Since activity recognition datasets are both hard to come by, and very time consuming to label, the development of reliable activity discovery systems could be of significant utility to the researchers and developers working in the field, as well as to the wider machine learning community.

This …

Self-Reported Data For Mental Workload Modelling In Human-Computer Interaction And Third-Level Education, Lucas Rizzo, Luca Longo

Articles

Mental workload (MWL) is an imprecise construct, with distinct definitions and no predominant measurement technique. It can be intuitively seen as the amount of mental activity devoted to a certain task over time. Several approaches have been proposed in the literature for the modelling and assessment of MWL. In this paper, data related to two sets of tasks performed by participants under different conditions is reported. This data was gathered from different sets of questionnaires answered by these participants. These questionnaires were aimed at assessing the features believed by domain experts to influence overall mental workload. In total, 872 records …

An Empirical Evaluation Of The Inferential Capacity Of Defeasible Argumentation, Non-Monotonic Fuzzy Reasoning And Expert Systems, Lucas Rizzo, Luca Longo

Articles

Several non-monotonic formalisms exist in the field of Artificial Intelligence for reasoning under uncertainty. Many of these are deductive and knowledge-driven, and also employ procedural and semi-declarative techniques for inferential purposes. Nonetheless, limited work exist for the comparison across distinct techniques and in particular the examination of their inferential capacity. Thus, this paper focuses on a comparison of three knowledge-driven approaches employed for non-monotonic reasoning, namely expert systems, fuzzy reasoning and defeasible argumentation. A knowledge-representation and reasoning problem has been selected: modelling and assessing mental workload. This is an ill-defined construct, and its formalisation can be seen as a reasoning …

การพัฒนาตัวแบบรู้จำบุคลิกภาพจากเสียงสนทนาในบริบทของศูนย์ให้บริการข้อมูลลูกค้า, นคร ศรีณรงค์

Chulalongkorn University Theses and Dissertations (Chula ETD)

ศูนย์บริการข้อมูลลูกค้าเป็นช่องทางสำคัญในการติดต่อระหว่างภาคธุรกิจและลูกค้า ตัวชี้วัดที่สำคัญของการทำงานของศูนย์บริการข้อมูลลูกค้าคือความพึงพอใจของลูกค้า ข้อมูลหลักที่ได้จากศูนย์บริการข้อมูลลูกค้าคือเสียงสนทนา ทำให้ผู้วิจัยสนใจที่จะศึกษาแนวทางในการสร้างตัวแบบรู้จำบุคลิกภาพจากเสียง ข้อมูลเสียงและเพศถูกเก็บจากหน่วยตัวอย่าง 92 คน พร้อมกับข้อมูลบุคลิกภาพโดยใช้แบบวัด MPI (Maudsley Personality Inventory) แบบวัดดังกล่าวแบ่งบุคลิกภาพออกเป็น 2 ด้าน คือด้าน E-scale (Extraversion และ Introversion) และ N-scale (Neuroticism และ Stability) ซึ่งนำมาใช้ในการพัฒนาตัวแบบจำแนกบุคลิกภาพทั้งสองด้าน เทคนิคที่ใช้ในการพัฒนาตัวแบบประกอบด้วย Logistic regression, SVM, Random forest และ Artificial neural network โดยพบว่าตัวแบบที่พัฒนาด้วยเทคนิค Artificial neural network มีประสิทธิภาพสูงสุดในการรู้จำ E-scale โดยมีค่า Positive predictive value (ค่าวัดประสิทธิภาพของ Introversion) เท่ากับ 0.71 และค่า Negative predictive value (ประสิทธิภาพของ Extraversion) เท่ากับ 0.75 ในส่วนของ N-scale ไม่พบตัวแบบที่พัฒนาด้วยเทคนิคใดมีประสิทธิภาพเพียงพอ ในการศึกษาครั้งนี้พบว่าบุคลิกภาพ Extraversion และ Introversion ซึ่งสามารถนำมาประยุกต์กับงานภาคธุรกิจ สามารถรู้จำจากเสียงสนทนาในบริบทศูนย์บริการข้อมูลลูกค้า โดยสามารถนำไปใช้มอบหมายพนักงานที่มีบุคลิกภาพเหมือนกับลูกค้าเพื่อเพิ่มความพึงพอใจของลูกค้าในการติดต่อสื่อสาร ภาคธุรกิจยังสามารถนำข้อมูลบุคลิกภาพเหล่านี้ไปใช้ต่อยอดในการแนะนำผลิตภัณฑ์ หรือโฆษณา ให้เหมาะสมกับลูกค้าแต่ละคน

Recommendation System For Horticultural Commodities, Lukman Adlin Harahap

Chulalongkorn University Theses and Dissertations (Chula ETD)

Horticultural commodities commonly have fluctuating prices due to their nature. Seasonality and climate are the main factors that cause their prices to fluctuate. Price instability causes a planning on horticultural cultivation to become difficult. Local farmers would intuitively know the planning by their experience, but this might be too complicated for those farmers who are new or have no experience. Thus, the proposed recommendation system would be able to help the new farmers to set a schedule for horticultural cultivation. The proposed recommendation system consists of three phases: price prediction, commodity recommendation, and cultivation scheduling. A hybrid of Long Short-Term …

Remark On Artificial Intelligence, Humanoid And Terminator Scenario: A Neutrosophic Way To Futurology, Victor Christianto, Florentin Smarandache

Branch Mathematics and Statistics Faculty and Staff Publications

This article is an update of our previous article in this SGJ journal, titled: On Gödel's Incompleteness Theorem, Artificial Intelligence & Human Mind [7]. We provide some commentary on the latest developments around AI, humanoid robotics, and future scenario. Basically, we argue that a more thoughtful approach to the future is "technorealism."

Exploring The Relationship Between Iot Security And Standardization, James Jenness Clapp

Walden Dissertations and Doctoral Studies

The adoption of the Internet of Things (IoT) technology across society presents new and unique challenges for security experts in maintaining uninterrupted services across the technology spectrum. A botnet implemented over 490,000 IoT connected devices to cripple the Internet services for major companies in one recent IoT attack. Grounded in Roger’s diffusion of innovations theory, the purpose of this qualitative exploratory multiple-case study was to explore implementation strategies used by some local campus IT managers in educational institutions in the United States to secure the IoT environment. The participants were 10 IT local campus IT managers within educational institutions across …

Relationship Between Risk Identification, Risk Response, And Project Success, Marsha Marinich

Walden Dissertations and Doctoral Studies

AbstractProjects are used to implement the organization's strategic goals, but high failure rates reduce projects' effectiveness in successfully achieving goals. High failure rates reduce project managers’ effectiveness of projects in successfully achieving goals. Senior leaders and project managers are unable to deliver successful projects due to unmanaged risks. Grounded in expected utility theory, the purpose of this quantitative correlational study was to examine the relationship between risk identification, risk responses, and project success. A survey was created in SurveyMonkey® and distributed on LinkedIn. Survey responses were analyzed from 71 project managers with at least five years of experience in Washington, …

Key Strategies For Successful Information Technology Projects, David Morris

Walden Dissertations and Doctoral Studies

AbstractThe successful implementation of information technology (IT) projects globally depends on the increasing reliance of project managers on IT innovations. Some project managers and business leaders do not possess the strategies to effectively manage and successfully implement IT projects. Grounded in coordination theory, the purpose of this qualitative multiple case study was to explore strategies used to successfully implement IT projects. Data were collected using semistructured interviews and from company documents. The participants comprised 6 IT project managers in Northeast Ohio who successfully implemented IT projects. Thematic analysis was used to analyze the data. Four themes emerged: communication amongst all …

Factors Affecting Electronic Banking Adoption In Barbados, Jacqueline Delores Bend

Walden Dissertations and Doctoral Studies

The low rate of customers' adoption of electronic banking services affects retail banks' profitability. The operating cost for a financial transaction performed by bank tellers averages US$1.07 compared to US$0.01 using electronic banking channels. It is paramount for retail banking leaders to understand the factors influencing customer adoption of electronic banking to sustain competitive advantage. Grounded in the technology acceptance model framework, the purpose of this quantitative correlational study was to examine the relationship between perceived usefulness (PU), perceived ease of use (PEOU), and customer adoption of electronic banking in Barbados. The validated technology acceptance model survey instrument was used …

Youth Leadership Development Program Evaluation, Loreley Lyn Smith

Walden Dissertations and Doctoral Studies

In competitive youth sports organizations, few organizational leaders focus on leadership development to prepare youth for life situations. Sports organizational leaders are concerned about youth leadership development, as development allows youth to build skills necessary to understand their strengths and weaknesses and recognize ways to overcome fears. Grounded in social learning theory, the purpose of this summative program evaluation was to determine the extent to which a competitive youth sports leadership development program aligned with the organization’s primary objective to provide skilled athletic training while providing lessons to prepare youth for adulthood. The participants comprised 40 key stakeholders, including participants …

Estimating Refactoring Efforts For Architecture Technical Debt, Samir Deeb

Graduate Theses, Dissertations, and Problem Reports (ETD)

Paying-off the Architectural Technical Debt by refactoring the flawed code is important to control the debt and to keep it as low as possible. Project Managers tend to delay paying off this debt because they face difficulties in comparing the cost of the refactoring against the benefits they gain. For these managers to decide whether to refactor or to postpone, they need to estimate the cost and the efforts required to conduct these refactoring activities as well as to decide which flaws have higher priority to be refactored among others.

Our research is based on a dataset used by other …

Searching For Needles In The Cosmic Haystack, Thomas Ryan Devine

Graduate Theses, Dissertations, and Problem Reports (ETD)

Searching for pulsar signals in radio astronomy data sets is a difficult task. The data sets are extremely large, approaching the petabyte scale, and are growing larger as instruments become more advanced. Big Data brings with it big challenges. Processing the data to identify candidate pulsar signals is computationally expensive and must utilize parallelism to be scalable. Labeling benchmarks for supervised classification is costly. To compound the problem, pulsar signals are very rare, e.g., only 0.05% of the instances in one data set represent pulsars. Furthermore, there are many different approaches to candidate classification with no consensus on a best …

Cloud Adoption Decision-Making Processes By Small Businesses: A Multiple Case Study, Francis Blay

Walden Dissertations and Doctoral Studies

Cloud services have emerged as a compelling technology with immense benefits, but many companies still have concerns about cloud services adoption because of several failures that have occurred, including mistakes by service providers, exploitation of security flaws by hackers, and immature policies and procedures. The specific problem is how small businesses often lack the understanding of the ramifications of their respective decision-making processes to adopt cloud services. There is little understanding of the decision-making processes managers use in cloud services adoption. The purpose of this qualitative multiple case study was to explore the decision-making processes of 3 small businesses in …

A Machine Learning Approach To Estimate The Annihilation Photon Interactions Inside The Scintillator Of A Pet Scanner, Sai Akhil Bharthavarapu

Graduate Theses, Dissertations, and Problem Reports (ETD)

Biochemical processes are chemical processes that occur in living organisms. They can be studied with nuclear medicine through the help of radioactive tracers. Based on the radioisotope used, the photons that are emitted from the body tissue are either detected by single-photon emission computed tomography (SPECT) or by positron emission tomography (PET) scanners. SPECT uses gamma rays as tracer but gives a weaker contrast and spatial resolution compared to a PET scanner which uses positrons as tracer. PET scans show the metabolic changes occurring at the cellular level in an organ or a tissue. This detection is important because diseases …

Enterprise Resource Planning Implementation In Higher Education: Cost Containment Strategies, Tysha K. Tolefree

Walden Dissertations and Doctoral Studies

Lack of effective cost containment strategies to support enterprise resource planning (ERP) system implementations within higher education institutions (HEIs) result in budget overruns 50% of the time. Grounded in Gartner’s IT cost containment techniques, the purpose of this qualitative multiple case study was to explore strategies HEI project directors use to support a successful ERP implementation on time and within budget. The participants comprised 5 project directors and managers from HEIs and another public organization in the state of Washington. Data were collected from semistructured interviews, archival data, and organization documents. Thematic analysis was used to analyze the data, and …

Elucidating The Properties And Mechanism For Cellulose Dissolution In Tetrabutylphosphonium-Based Ionic Liquids Using High Concentrations Of Water, Brad Crawford

Graduate Theses, Dissertations, and Problem Reports (ETD)

The structural, transport, and thermodynamic properties related to cellulose dissolution by tetrabutylphosphonium chloride (TBPCl) and tetrabutylphosphonium hydroxide (TBPH)-water mixtures have been calculated via molecular dynamics simulations. For both ionic liquid (IL)-water solutions, water veins begin to form between the TBPs interlocking arms at 80 mol % water, opening a pathway for the diffusion of the anions, cations, and water. The water veins allow for a diffusion regime shift in the concentration region from 80 to 92.5 mol % water, providing a higher probability of solvent interaction with the dissolving cellulose strand. The hydrogen bonding was compared between small and large …

Automated And Standardized Tools For Realistic, Generic Musculoskeletal Model Development, Trevor Rees Moon

Graduate Theses, Dissertations, and Problem Reports (ETD)

Human movement is an instinctive yet challenging task that involves complex interactions between the neuromusculoskeletal system and its interaction with the surrounding environment. One key obstacle in the understanding of human locomotion is the availability and validity of experimental data or computational models. Corresponding measurements describing the relationships of the nervous and musculoskeletal systems and their dynamics are highly variable. Likewise, computational models and musculoskeletal models in particular are vitally dependent on these measurements to define model behavior and mechanics. These measurements are often sparse and disparate due to unsystematic data collection containing variable methodologies and reporting conventions. To date, …

Performances Of The Lbp Based Algorithm Over Cnn Models For Detecting Crops And Weeds With Similar Morphologies, Vi Nguyen Thanh Le, Selam Ahderom, Kamal Alameh

Research outputs 2014 to 2021

Weed invasions pose a threat to agricultural productivity. Weed recognition and detection play an important role in controlling weeds. The challenging problem of weed detection is how to discriminate between crops and weeds with a similar morphology under natural field conditions such as occlusion, varying lighting conditions, and different growth stages. In this paper, we evaluate a novel algorithm, filtered Local Binary Patterns with contour masks and coefficient k (k-FLBPCM), for discriminating between morphologically similar crops and weeds, which shows significant advantages, in both model size and accuracy, over state-of-the-art deep convolutional neural network (CNN) models such as VGG-16, VGG-19, …

Geoaware - A Simulation-Based Framework For Synthetic Trajectory Generation From Mobility Patterns, Jameson D. Morgan

Browse all Theses and Dissertations

Recent advances in location acquisition services have resulted in vast amounts of trajectory data; providing valuable insight into human mobility. The field of trajectory data mining has exploded as a result, with literature detailing algorithms for (pre)processing, map matching, pattern mining, and the like. Unfortunately, obtaining trajectory data for the design and evaluation of such algorithms is problematic due to privacy, ethical, dataset size, researcher access, and sampling frequency concerns. Synthetic trajectories provide a solution to such a problem as they are cheap to produce and are derived from a fully controllable generation procedure. Citing deficiencies in modern synthetic trajectory …

Extracting Information From Subroutines Using Static Analysis Semantics, Luke A. Burnett

Browse all Theses and Dissertations

Understanding how a system component can interact with other services can take an immeasurable amount of time. Reverse engineering embedded and large systems can rely on understanding how components interact with one another. This process is time consuming and can sometimes be generalized through certain behavior.We will be explaining two such complicated systems and highlighting similarities between them. We will show that through static analysis you can capture compiler behavior and apply it to the understanding of a function, reducing the total time required to understand a component of whichever system you are learning.

Predicting Subjective Sleep Quality Using Objective Measurements In Older Adults, Reza Sadeghi

Browse all Theses and Dissertations

Humans spend almost a third of their lives asleep. Sleep has a pivotal effect on job performance, memory, fatigue recovery, and both mental and physical health. Sleep quality (SQ) is a subjective experience and reported via patients’ self-reports. Predicting subjective SQ based on objective measurements can enhance diagnosis and treatment of SQ defects, especially in older adults who are subject to poor SQ. In this dissertation, we assessed enhancement of subjective SQ prediction using an easy-to-use E4 wearable device, machine learning techniques and identifying disease-specific risk factors of abnormal SQ in older adults. First, we designed a clinical decision support …

Identifying Knowledge Gaps Using A Graph-Based Knowledge Representation, Daniel P. Schmidt

Browse all Theses and Dissertations

Knowledge integration and knowledge bases are becoming more and more prevalent in the systems we use every day. When developing these knowledge bases, it is important to ensure the correctness of the information upon entry, as well as allow queries of all sorts; for this, understanding where the gaps in knowledge can arise is critical. This thesis proposes a descriptive taxonomy of knowledge gaps, along with a framework for automated detection and resolution of some of those gaps. Additionally, the effectiveness of this framework is evaluated in terms of successful responses to queries on a knowledge base constructed from a …

Android Game, Ryan Weston

Williams Honors College, Honors Research Projects

The purpose of this project was to create an endless runner game for Android coded in Java and XML and developed in Android Studio. In the game, the player controls a frog that jumps from lily pad to lily pad to avoid logs moving toward the player. The player must also maneuver the lily pads as they can randomly disappear. There are three difficulties in the game that vary the disappearance rate of lily pads as well as the frequency and acceleration rate of the log obstacles. The game also has a scoring system and saves the high score locally …

Catgame: A Tool For Problem Solving In Complex Dynamic Systems Using Game Theoretic Knowledge Distribution In Cultural Algorithms, And Its Application (Catneuro) To The Deep Learning Of Game Controller, Faisal Waris

Wayne State University Dissertations

Cultural Algorithms (CA) are knowledge-intensive, population-based stochastic optimization methods that are modeled after human cultures and are suited to solving problems in complex environments. The CA Belief Space stores knowledge harvested from prior generations and re-distributes it to future generations via a knowledge distribution (KD) mechanism. Each of the population individuals is then guided through the search space via the associated knowledge. Previously, CA implementations have used only competitive KD mechanisms that have performed well for problems embedded in static environments. Relatively recently, CA research has evolved to encompass dynamic problem environments. Given increasing environmental complexity, a natural question arises …

Exploring Virtual Worlds With Cultural Algorithms: Ancient Alpena-Amberley Land Bridge, Thomas Joseph Palazzolo

Wayne State University Dissertations

In this thesis the Land Bridge system (DEEPDIVE) is described. The goal of the project is to use Artificial Intelligence technology to aid Archaeologists in the discovery of ancient prehistoric sites, now underwater. The example used here is the Alpena-Amberley Land Bridge that stretched across Lake Huron from Alpena in Michigan to Amberley in Ontario. During the Ice Age (around 10,000 years ago) it was above water for several thousand years. It was postulated that during that time it was used as a migration pathway for caribou, a major food source then. AI techniques were used to create a virtual …

Improving Predictive Modeling Via Effective Feature Selection And Representation Learning, Xiangrui Li

Wayne State University Dissertations

Predictive modeling (a.k.a. supervised learning) is a machine learning paradigm that has enormous important applications for real-world problems. With the recent surge of data in volume and complexity, effectively capturing the information in input features that is relevant to targets is critical to the success of predictive modeling. Tackling this challenge requires different techniques depending on the specific applications. In this thesis, we develop several methods to improve the performance of predictive models.

Specifically, in the case of small $n$, large $p$ problem, we propose two sparsity-inducing regularization methods for multi-class logistic regression and finite mixture of linear regression, respectively. …