Databases and Information Systems Commons^™

Fuzzy Decision Tree-Based Inference System For Liver Disease Diagnosis, Himaja Sivaraju

Theses and Dissertations

Medical diagnosis can be challenging because of a number of factors. Uncertainty in the diagnosis process arises from inaccuracy in the measurement of patient attributes, missing attribute data and limitation in the medical expert’s ability to define cause and effect relationships when there are multiple interrelated variables. Given this situation, a decision support system, which can help doctors come up with a more reliable diagnosis, can have a lot of potential.

Decision trees are used in data mining for classification and regression. They are simple to understand and interpret as they can be visualized. But, one of the disadvantages of …

Data Persistence In Eiffel, Jimmy J. Johnson

Theses and Dissertations--Computer Science

This dissertation describes an extension to the Eiffel programming language that provides automatic object persistence (the ability of programs to store objects and later recreate those objects in a subsequent execution of a program). The mechanism is orthogonal to other aspects of the Eiffel language. The mechanism serves four main purposes: 1) it gives Eiffel programmers a needed service, filling a gap between serialization, which provides limited persistence functions and database-mapping, which is cumbersome to use; 2) it greatly reduces the coding burden incurred by the programmer when objects must persist, allowing the programmer to focus instead on the business …

Cloud Practical Exam Invigilation Control Systems With The Objective Of Enhancing Quality Control For Online Exams, Gordon O'Reilly

Theses

Online cloud computing courses are becoming more common place facilitating a student to study from home from anywhere in the world. Coupled with this trend is a need for educational institutions to facilitate students to take tests or exams online from home. It reduces the remote student’s time and cost of travel to an exam invigilation centre as well as reducing the educational institution’s test administration overhead. It does however introduce potential quality control concerns. A new industry called online proctoring has emerged that attempts to provide quality control in these online test scenarios.

This research provides an insight into …

Towards Developing An Intelligent Agent To Assist In Patient Diagnosis Using Neural Networks On Unstructured Patient Clinical Notes: Initial Analysis And Models, Ma. Regina Justina E. Estuar, Christian E. Pulmano

Department of Information Systems & Computer Science Faculty Publications

Technological advances in information-communication technologies in the health ecosystem have allowed for the recording and consumption of massive amounts of structured and unstructured health data. In developing countries, the use of Electronic Medical Records (EMR) is necessary to address the need for efficient delivery of services and informed decision-making, especially at the local level where health facilities and practitioners may be lacking. Text mining is a variation of data mining that tries to extract non-trivial information and knowledge from unstructured text. This study aims to determine the feasibility of integrating an intelligent agent within EMRs for automatic diagnosis prediction based …

Towards An Infodemiological Algorithm For Classification Of Filipino Health Tweets, Ma. Regina Justina E. Estuar, Kennedy E. Espina, Delfin Jay Sabido Ix, Raymond Josef Edward Lara, Vikki Car De Los Reyes

Department of Information Systems & Computer Science Faculty Publications

Finding innovative ICT solutions to enhance the Philippines’ health sector is part and parcel of the Philippine eHealth Strategic Framework and Plan 2020 program. This study sees the opportunity of using collected Twitter data to create a model that processes tweets to produce a dataset that may be relevant in the field of epidemiology and infodemiology. Through the collection of relevant tweets, future studies may make use of the output of this research for various purposes, such as the improvement of epidemiological systems of the Department of Health in support of the eHealth strategy. In this study, we …

Evaluating Distributed Word Representations For Predicting Missing Words In Sentences, Saniya Saifee

Dissertations and Theses

In recent years, the distributed representation of words in vector space or word embeddings have become very popular as they have shown significant improvements in many statistical natural language processing (NLP) tasks as compared to traditional language models like Ngram. In this thesis, we explored various state-of-the-art methods like Latent Semantic Analysis, word2vec, and GloVe to learn the distributed representation of words. Their performance was compared based on the accuracy achieved when tasked with selecting the right missing word in the sentence, given five possible options. For this NLP task we trained each of these methods using a training corpus …

Web Scraping The Easy Way, Yolande Neil

Honors College Theses

Web scraping refers to a software program that mimics human web surfing behavior by pointing to a website and collecting large amounts of data that would otherwise be difficult for a human to extract. A typical program will extract both unstructured and semi-structured data, as well as images, and convert the data into a structured format. Web scraping is commonly used to facilitate online price comparisons, aggregate contact information, extract online product catalog data, extract economic/demographic/statistical data, and create web mashups, among other uses. Additionally, in the era of big data, semantic analysis, and business intelligence, web scraping is the …

An Experimental Study On The Role Of Password Strength And Cognitive Load On Employee Productivity, Stephen Mujeye

CCAC Theses and Dissertations

The proliferation of information systems (IS) over the past decades has increased the demand for system authentication. While the majority of system authentications are password-based, it is well documented that passwords have significant limitations. To address this issue, companies have been placing increased requirements on the user to ensure their passwords are more complex and consequently stronger. In addition to meeting a certain complexity threshold, the password must also be changed on a regular basis. As the cognitive load increases on the employees using complex passwords and changing them often, they may have difficulty recalling their passwords. As such, the …

An Investigation Of Socio-Technical Components Of Knowledge Management System (Kms) Usage, Noel Wint Jr.

CCAC Theses and Dissertations

Existing literature indicates that although both academics and practitioners recognize knowledge management (KM) as a source of competitive advantage, users are not always willing to use a knowledge management system (KMS). Because of the social nature of knowledge transfer, a KMS can be considered a socio-technical system. Many explanations have been presented for this failure to utilize the KMS. These explanations include a number of the socio-technical factors relating to people, processes, and technologies. While these factors may have significant explanatory power when examined independently, existing studies have not sufficiently addressed the interactions among all three socio-technical factors or their …

Understanding The Impact Of Hacker Innovation Upon Is Security Countermeasures, Sean M. Zadig

CCAC Theses and Dissertations

Hackers external to the organization continue to wreak havoc upon the information systems infrastructure of firms through breaches of security defenses, despite constant development of and continual investment in new IS security countermeasures by security professionals and vendors. These breaches are exceedingly costly and damaging to the affected organizations. The continued success of hackers in the face of massive amounts of security investments suggests that the defenders are losing and that the hackers can innovate at a much faster pace.

Underground hacker communities have been shown to be an environment where attackers can learn new techniques and share tools pertaining …

An Empirical Investigation Of The Willingness Of Us Intelligence Community Analysts To Contribute Knowledge To A Knowledge Management System (Kms) In A Highly Classified And Sensitive Environment, Robert Hambly

CCAC Theses and Dissertations

Since September 11, 2001, the United States Government (USG) has possessed unparalleled capability in terms of dedicated intelligence and information collection assets supporting the analysts of the Intelligence Community (IC). The USG IC has sponsored, developed, and borne witness to extraordinary advances in technology, techniques, and procedures focused on knowledge harvesting, knowledge sharing, and collaboration. Knowledge, within successful (effective & productive) organizations, exists as a commodity; a commodity that can be created, captured, imparted, shared, and leveraged. The research problem that this study addressed is the challenge of maintaining strong organizational effectiveness and productivity through the use of an information …

Harnessing The Power Of Text Mining For The Detection Of Abusive Content In Social Media, Hao Chen, Susan Mckeever, Sarah Jane Delany

Conference papers

Abstract The issues of cyberbullying and online harassment have gained considerable coverage in the last number of years. Social media providers need to be able to detect abusive content both accurately and efficiently in order to protect their users. Our aim is to investigate the application of core text mining techniques for the automatic detection of abusive content across a range of social media sources include blogs, forums, media-sharing, Q&A and chat - using datasets from Twitter, YouTube, MySpace, Kongregate, Formspring and Slashdot. Using supervised machine learning, we compare alternative text representations and dimension reduction approaches, including feature selection and …

Cognitive Neuroscience In Information Systems Research, Keng Siau, Yeli Zhao

Research Collection School Of Computing and Information Systems

This paper reviews cognitive neuroscience and several neurophysiological tools e.g., fMRI, PET, EEG, MEG, and eye tracking. The strengths and weaknesses of such tools for information systems research are presented. The paper provides examples of existing cognitive neuroscience studies in varies areas, such as neuroeconomics, neuromarketing, and eye tracking. In addition, this paper provides an overview of brain areas that response to various mental processes, and discusses the localization and functionality of each brain area. Because of the popularity of eye-tracking research in information systems, measurements and metrics related and derived from eye-tracking technique e.g., fixation, saccades and scanpath are …

Towards A Science Of Security Games, Thanh Hong Nguyen, Debarun Kar, Matthew Brown, Arunesh Sinha, Albert Xin Jiang, Milind Tambe

Research Collection School Of Computing and Information Systems

Security is a critical concern around the world. In many domains from counter-terrorism to sustainability, limited security resources prevent full security coverage at all times; instead, these limited resources must be scheduled, while simultaneously taking into account different target priorities, the responses of the adversaries to the security posture and potential uncertainty over adversary types.Computational game theory can help design such security schedules. Indeed, casting the problem as a Bayesian Stackelberg game, we have developed new algorithms that are now deployed over multiple years in multiple applications for security scheduling. These applications are leading to real-world use-inspired research in the …

A Study On Singapore Haze, Bingtian Dai, Kasthuri Jayarajah, Ee-Peng Lim, Archan Misra, Shriguru Nayak

Research Collection School Of Computing and Information Systems

In 2015, Singaporean have experienced one of the worse air pollution crises in history. With datasets from a well-known photo sharing social network, we analyze how this haze affects Singaporean's daily life. We will share our preliminary results in this paper.

Posting Topics ≠ Reading Topics: On Discovering Posting And Reading Topics In Social Media, Wei Gong, Ee-Peng Lim, Feida Zhu

Research Collection School Of Computing and Information Systems

Social media users make decisions about what content to post and read. As posted content is often visible to others, users are likely to impose self-censorship when deciding what content to post. On the other hand, such a concern may not apply to reading social media content. As a result, the topics of content that a user posted and read can be different and this has major implications to the applications that require personalization. To better determine and profile social media users’ topic interests, we conduct a user survey in Twitter. In this survey, participants chose the topics they like …

A Comparison Of Fundamental Network Formation Principles Between Offline And Online Friends On Twitter, Felicia Natali, Feida Zhu

Research Collection School Of Computing and Information Systems

We investigate the differences between how some of the fundamental principles of network formation apply among offline friends and how they apply among online friends on Twitter. We consider three fundamental principles of network formation proposed by Schaefer et al.: reciprocity, popularity, and triadic closure. Overall, we discover that these principles mainly apply to offline friends on Twitter. Based on how these principles apply to offline versus online friends, we formulate rules to predict offline friendship on Twitter. We compare our algorithm with popular machine learning algorithms and Xiewei’s random walk algorithm. Our algorithm beats the machine learning algorithms on …

Information Source Detection Via Maximum A Posteriori Estimation, Biao Chang, Feida Zhu, Enhong Chen, Qi. Liu

Research Collection School Of Computing and Information Systems

The problem of information source detection, whose goal is to identify the source of a piece of information from a diffusion process (e.g., computer virus, rumor, epidemic, and so on), has attracted ever-increasing attention from research community in recent years. Although various methods have been proposed, such as those based on centrality, spectral and belief propagation, the existing solutions still suffer from high time complexity and inadequate effectiveness. To this end, we revisit this problem in the paper and present a comprehensive study from the perspective of likelihood approximation. Different from many previous works, we consider both infected and uninfected …

Friendship Maintenance And Prediction In Multiple Social Networks, Roy Ka-Wei Lee, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Due to the proliferation of online social networks (OSNs), users find themselves participating in multiple OSNs. These users leave their activity traces as they maintain friendships and interact with other users in these OSNs. In this work, we analyze how users maintain friendship in multiple OSNs by studying users who have accounts in both Twitter and Instagram. Specifically, we study the similarity of a user's friendship and the evenness of friendship distribution in multiple OSNs. Our study shows that most users in Twitter and Instagram prefer to maintain different friendships in the two OSNs, keeping only a small clique of …

Smart Ambient Sound Analysis Via Structured Statistical Modeling, Jialie Shen, Liqiang Nie, Tat Seng Chua

Research Collection School Of Computing and Information Systems

In this paper, we introduce a novel framework called SASA (Smart Ambient Sound Analyser) to support different ambient audio mining tasks (e.g., audio classification and location estimation). To gain comprehensive ambient sound modelling, SASA extracts a variety of acoustic features from different sound components (e.g., music, voice and background), and translates them into structured information. This significantly enhances quality of audio content representation. Further, distinguished from existing approaches, SASA’s multilayered architecture seamlessly integrates mixture models and aPEGASOS (adaptive PEGASOS) SVM algorithm into a unified classification framework. The approach can leverage complimentary strengths of both models. Experimental results based on three …